Compare commits

..

22 Commits

Author SHA1 Message Date
blob42
84d7ad397d langchain-docker readme 2023-03-03 22:55:44 +01:00
blob42
de551d62a8 linting in docker and parallel make jobs
- linting can be run in docker in parallel with `make -j4 docker.lint`
2023-03-03 22:55:44 +01:00
blob42
d8fd0e790c enable test + lint on docker 2023-03-03 22:55:44 +01:00
blob42
97c2b31cc5 added all extra dependencies to dev image + customized builds
- downgraded to python 3.10 to accomadate installing all dependencies
- by default installs all dev + extra dependencies
- option to install only dev dependencies by customizing .env file
2023-03-03 22:55:44 +01:00
blob42
f1dc03d0cc docker development image and helper makefile
separate makefile and build env:

- separate makefile for docker
- only show docker commands when docker detected in system
- only rebuild container on change
- use an unpriviliged user

builder image and base dev image:

- fully isolated environment inside container.
- all venv installed inside container shell and available as commands.
    - ex: `docker run IMG jupyter notebook` to launch notebook.
- pure python based container without poetry.
- custom motd to add a message displayed to users when they connect to
container.
- print environment versions (git, package, python) on login
- display help message when starting container
2023-03-03 22:55:44 +01:00
Harrison Chase
f76e9eaab1 bump version (#1342) 2023-03-03 22:55:44 +01:00
Harrison Chase
db2e9c2b0d partial variables (#1308) 2023-03-03 22:55:44 +01:00
Tim Asp
d22651d82a Add new iFixit document loader (#1333)
iFixit is a wikipedia-like site that has a huge amount of open content
on how to fix things, questions/answers for common troubleshooting and
"things" related content that is more technical in nature. All content
is licensed under CC-BY-SA-NC 3.0

Adding docs from iFixit as context for user questions like "I dropped my
phone in water, what do I do?" or "My macbook pro is making a whining
noise, what's wrong with it?" can yield significantly better responses
than context free response from LLMs.
2023-03-03 22:55:44 +01:00
Matt Robinson
c46478d70e feat: document loader for image files (#1330)
### Summary

Adds a document loader for image files such as `.jpg` and `.png` files.

### Testing

Run the following using the example document from the [`unstructured`
repo](https://github.com/Unstructured-IO/unstructured/tree/main/example-docs).

```python
from langchain.document_loaders.image import UnstructuredImageLoader

loader = UnstructuredImageLoader("layout-parser-paper-fast.jpg")
loader.load()
```
2023-03-03 22:55:44 +01:00
Eugene Yurtsev
e3fcc72879 Documentation: Minor typo fixes (#1327)
Fixing a few minor typos in the documentation (and likely introducing
other
ones in the process).
2023-03-03 22:55:44 +01:00
blob42
2fdb1d842b refactoring into submodules 2023-03-03 22:55:15 +01:00
blob42
c30ef7dbc4 drop network capabilities by default, example on using networking 2023-03-03 21:59:22 +01:00
blob42
8a7871ece3 add exec_attached: attach to running container and exec cmd 2023-03-03 21:22:45 +01:00
blob42
201ecdc9ee fix run and exec_run default commands, actually use gVisor
- run and exec_run need a separate default command. Run usually executes
  a script while exec_run simulates an interactive session. The image
  templates and run funcs have been upgraded to handle both
  types of commands.

- test: make docker tests run when docker is installed and docker lib
  avaialble.
  - test that runsc runtime is used by default when gVisor is installed.
    (manually removing gVisor skips the test)
2023-03-02 22:33:17 +01:00
blob42
149fe0055e exec_run fixes to keep stdin open 2023-03-02 20:39:48 +01:00
blob42
096b82f2a1 update notebook for utility 2023-03-02 20:32:10 +01:00
blob42
87b5a84cfb update tests and docstrings 2023-03-02 19:33:48 +01:00
blob42
ed97aa65af exec_run: add timeout and delay params
- use `delay` to wait for sent payload to finish
- use `timeout` to control how long to wait for output
2023-03-02 19:11:58 +01:00
blob42
c9e6baf60d image templates, enhanced wrapper building with custom prameters
- quickly run or exec_run commands with sane defaults
- wip image templates with parameters for common docker images
- shell escaping logic
- capture stdout+stderr for exec commands
- added minimal testing
2023-03-02 04:23:59 +01:00
blob42
7cde1cbfc3 docker: attach to container's stdin
- wip image helper for optimized params with common images
- gVisor runtime checker
- make tests skipped if docker installed
2023-02-27 18:31:06 +01:00
blob42
17213209e0 stream stdin and stdout to container through docker API's socket 2023-02-27 18:31:06 +01:00
blob42
895f862662 docker wrapper tool for untrusted execution 2023-02-27 18:31:06 +01:00
679 changed files with 11057 additions and 43992 deletions

144
.dockerignore Normal file
View File

@ -0,0 +1,144 @@
.vscode/
.idea/
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# Jupyter Notebook
.ipynb_checkpoints
notebooks/
# IPython
profile_default/
ipython_config.py
# pyenv
.python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
.venvs
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# macOS display setting files
.DS_Store
# docker
docker/
!docker/assets/
.dockerignore
docker.build

9
.gitignore vendored
View File

@ -106,7 +106,7 @@ celerybeat.pid
# Environments
.env
.envrc
!docker/.env
.venv
.venvs
env/
@ -135,9 +135,4 @@ dmypy.json
# macOS display setting files
.DS_Store
# Wandb directory
wandb/
# asdf tool versions
.tool-versions
docker.build

View File

@ -73,8 +73,6 @@ poetry install -E all
This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. Note the `-E all` flag will install all optional dependencies necessary for integration testing.
❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
Now, you should be able to run the common tasks in the following section.
## ✅Common Tasks
@ -153,6 +151,10 @@ poetry run jupyter notebook
When you run `poetry install`, the `langchain` package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.
## Using Docker
Refer to [DOCKER.md](docker/DOCKER.md) for more information.
## Documentation
### Contribute Documentation

View File

@ -1,5 +1,8 @@
.PHONY: all clean format lint test tests test_watch integration_tests help
GIT_HASH ?= $(shell git rev-parse --short HEAD)
LANGCHAIN_VERSION := $(shell grep '^version' pyproject.toml | cut -d '=' -f2 | tr -d '"')
all: help
coverage:
@ -31,8 +34,7 @@ lint:
test:
poetry run pytest tests/unit_tests
tests:
poetry run pytest tests/unit_tests
tests: test
test_watch:
poetry run ptw --now . -- tests/unit_tests
@ -46,8 +48,26 @@ help:
@echo 'docs_build - build the documentation'
@echo 'docs_clean - clean the documentation build artifacts'
@echo 'docs_linkcheck - run linkchecker on the documentation'
ifneq ($(shell command -v docker 2> /dev/null),)
@echo 'docker - build and run the docker dev image'
@echo 'docker.run - run the docker dev image'
@echo 'docker.jupyter - start a jupyter notebook inside container'
@echo 'docker.build - build the docker dev image'
@echo 'docker.force_build - force a rebuild'
@echo 'docker.test - run the unit tests in docker'
@echo 'docker.lint - run the linters in docker'
@echo 'docker.clean - remove the docker dev image'
endif
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'test_watch - run unit tests in watch mode'
@echo 'integration_tests - run integration tests'
# include the following makefile if the docker executable is available
ifeq ($(shell command -v docker 2> /dev/null),)
$(info Docker not found, skipping docker-related targets)
else
include docker/Makefile
endif

View File

@ -1,11 +1,15 @@
# 🦜️🔗 LangChain
# 🦜️🔗 LangChain - Docker
⚡ Building applications with LLMs through composability ⚡
WIP: This is a fork of langchain focused on implementing a docker warpper and
toolchain. The goal is to make it easy to use LLM chains running inside a
container, build custom docker based tools and let agents run arbitrary
untrusted code inside.
[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
Currently exploring the following:
**Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set up a dedicated support Slack channel.
- Docker wrapper for LLMs and chains
- Creating a toolchain for building docker based LLM tools.
- Building agents that can run arbitrary untrusted code inside a container.
## Quick Install
@ -32,7 +36,7 @@ This library is aimed at assisting in the development of those types of applicat
**🤖 Agents**
- [Documentation](https://langchain.readthedocs.io/en/latest/modules/agents.html)
- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/agents.html)
- End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
## 📖 Documentation
@ -42,7 +46,7 @@ Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documen
- Getting started (installation, setting up the environment, simple examples)
- How-To examples (demos, integrations, helper functions)
- Reference (full API docs)
- Resources (high-level explanation of core concepts)
Resources (high-level explanation of core concepts)
## 🚀 What can this help with?
@ -79,4 +83,4 @@ For more information on these concepts, please see our [full documentation](http
As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
For detailed information on how to contribute, see [here](CONTRIBUTING.md).

13
docker/.env Normal file
View File

@ -0,0 +1,13 @@
# python env
PYTHON_VERSION=3.10
# -E flag is required
# comment the following line to only install dev dependencies
POETRY_EXTRA_PACKAGES="-E all"
# at least one group needed
POETRY_DEPENDENCIES="dev,test,lint,typing"
# langchain env. warning: these variables will be baked into the docker image !
OPENAI_API_KEY=${OPENAI_API_KEY:-}
SERPAPI_API_KEY=${SERPAPI_API_KEY:-}

53
docker/DOCKER.md Normal file
View File

@ -0,0 +1,53 @@
# Using Docker
To quickly get started, run the command `make docker`.
If docker is installed the Makefile will export extra targets in the fomrat `docker.*` to build and run the docker image. Type `make` for a list of available tasks.
There is a basic `docker-compose.yml` in the docker directory.
## Building the development image
Using `make docker` will build the dev image if it does not exist, then drops
you inside the container with the langchain environment available in the shell.
### Customizing the image and installed dependencies
The image is built with a default python version and all extras and dev
dependencies. It can be customized by changing the variables in the [.env](/docker/.env)
file.
If you don't need all the `extra` dependencies a slimmer image can be obtained by
commenting out `POETRY_EXTRA_PACKAGES` in the [.env](docker/.env) file.
### Image caching
The Dockerfile is optimized to cache the poetry install step. A rebuild is triggered when there a change to the source code.
## Example Usage
All commands from langchain's python environment are available by default in the container.
A few examples:
```bash
# run jupyter notebook
docker run --rm -it IMG jupyter notebook
# run ipython
docker run --rm -it IMG ipython
# start web server
docker run --rm -p 8888:8888 IMG python -m http.server 8888
```
## Testing / Linting
Tests and lints are run using your local source directory that is mounted on the volume /src.
Run unit tests in the container with `make docker.test`.
Run the linting and formatting checks with `make docker.lint`.
Note: this task can run in parallel using `make -j4 docker.lint`.

104
docker/Dockerfile Normal file
View File

@ -0,0 +1,104 @@
# vim: ft=dockerfile
#
# see also: https://github.com/python-poetry/poetry/discussions/1879
# - with https://github.com/bneijt/poetry-lock-docker
# see https://github.com/thehale/docker-python-poetry
# see https://github.com/max-pfeiffer/uvicorn-poetry
# use by default the slim version of python
ARG PYTHON_IMAGE_TAG=slim
ARG PYTHON_VERSION=${PYTHON_VERSION:-3.11.2}
####################
# Base Environment
####################
FROM python:$PYTHON_VERSION-$PYTHON_IMAGE_TAG AS lchain-base
ARG UID=1000
ARG USERNAME=lchain
ENV USERNAME=$USERNAME
RUN groupadd -g ${UID} $USERNAME
RUN useradd -l -m -u ${UID} -g ${UID} $USERNAME
# used for mounting source code
RUN mkdir /src
VOLUME /src
#######################
## Poetry Builder Image
#######################
FROM lchain-base AS lchain-base-builder
ARG POETRY_EXTRA_PACKAGES=$POETRY_EXTRA_PACKAGES
ARG POETRY_DEPENDENCIES=$POETRY_DEPENDENCIES
ENV HOME=/root
ENV POETRY_HOME=/root/.poetry
ENV POETRY_VIRTUALENVS_IN_PROJECT=false
ENV POETRY_NO_INTERACTION=1
ENV CACHE_DIR=$HOME/.cache
ENV POETRY_CACHE_DIR=$CACHE_DIR/pypoetry
ENV PATH="$POETRY_HOME/bin:$PATH"
WORKDIR /root
RUN apt-get update && \
apt-get install -y \
build-essential \
git \
curl
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
RUN mkdir -p $CACHE_DIR
## setup poetry
RUN curl -sSL -o $CACHE_DIR/pypoetry-installer.py https://install.python-poetry.org/
RUN python3 $CACHE_DIR/pypoetry-installer.py
# # Copy poetry files
COPY poetry.* pyproject.toml ./
RUN mkdir /pip-prefix
RUN poetry export $POETRY_EXTRA_PACKAGES --with $POETRY_DEPENDENCIES -f requirements.txt --output requirements.txt --without-hashes && \
pip install --no-cache-dir --disable-pip-version-check --prefix /pip-prefix -r requirements.txt
# add custom motd message
COPY docker/assets/etc/motd /tmp/motd
RUN cat /tmp/motd > /etc/motd
RUN printf "\n%s\n%s\n" "$(poetry version)" "$(python --version)" >> /etc/motd
###################
## Runtime Image
###################
FROM lchain-base AS lchain
#jupyter port
EXPOSE 8888
COPY docker/assets/entry.sh /entry
RUN chmod +x /entry
COPY --from=lchain-base-builder /etc/motd /etc/motd
COPY --from=lchain-base-builder /usr/bin/git /usr/bin/git
USER ${USERNAME:-lchain}
ENV HOME /home/$USERNAME
WORKDIR /home/$USERNAME
COPY --chown=lchain:lchain --from=lchain-base-builder /pip-prefix $HOME/.local/
COPY . .
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
RUN pip install --no-deps --disable-pip-version-check --no-cache-dir -e .
entrypoint ["/entry"]

84
docker/Makefile Normal file
View File

@ -0,0 +1,84 @@
#do not call this makefile it is included in the main Makefile
.PHONY: docker docker.jupyter docker.run docker.force_build docker.clean \
docker.test docker.lint docker.lint.mypy docker.lint.black \
docker.lint.isort docker.lint.flake
# read python version from .env file ignoring comments
PYTHON_VERSION := $(shell grep PYTHON_VERSION docker/.env | cut -d '=' -f2)
POETRY_EXTRA_PACKAGES := $(shell grep '^[^#]*POETRY_EXTRA_PACKAGES' docker/.env | cut -d '=' -f2)
POETRY_DEPENDENCIES := $(shell grep 'POETRY_DEPENDENCIES' docker/.env | cut -d '=' -f2)
DOCKER_SRC := $(shell find docker -type f)
DOCKER_IMAGE_NAME = langchain/dev
# SRC is all files matched by the git ls-files command
SRC := $(shell git ls-files -- '*' ':!:docker/*')
# set DOCKER_BUILD_PROGRESS=plain to see detailed build progress
DOCKER_BUILD_PROGRESS ?= auto
# extra message to show when entering the docker container
DOCKER_MOTD := docker/assets/etc/motd
ROOTDIR := $(shell git rev-parse --show-toplevel)
DOCKER_LINT_CMD = docker run --rm -i -u lchain -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH)
docker: docker.run
docker.run: docker.build
@echo "Docker image: $(DOCKER_IMAGE_NAME):$(GIT_HASH)"
docker run --rm -it -u lchain -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH)
docker.jupyter: docker.build
docker run --rm -it -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH) jupyter notebook
docker.build: $(SRC) $(DOCKER_SRC) $(DOCKER_MOTD)
ifdef $(DOCKER_BUILDKIT)
docker buildx build --build-arg PYTHON_VERSION=$(PYTHON_VERSION) \
--build-arg POETRY_EXTRA_PACKAGES=$(POETRY_EXTRA_PACKAGES) \
--build-arg POETRY_DEPENDENCIES=$(POETRY_DEPENDENCIES) \
--progress=$(DOCKER_BUILD_PROGRESS) \
$(BUILD_FLAGS) -f docker/Dockerfile -t $(DOCKER_IMAGE_NAME):$(GIT_HASH) .
else
docker build --build-arg PYTHON_VERSION=$(PYTHON_VERSION) \
--build-arg POETRY_EXTRA_PACKAGES=$(POETRY_EXTRA_PACKAGES) \
--build-arg POETRY_DEPENDENCIES=$(POETRY_DEPENDENCIES) \
$(BUILD_FLAGS) -f docker/Dockerfile -t $(DOCKER_IMAGE_NAME):$(GIT_HASH) .
endif
docker tag $(DOCKER_IMAGE_NAME):$(GIT_HASH) $(DOCKER_IMAGE_NAME):latest
@touch $@ # this prevents docker from rebuilding dependencies that have not
@ # changed. Remove the file `docker/docker.build` to force a rebuild.
docker.force_build: $(DOCKER_SRC)
@rm -f docker.build
@$(MAKE) docker.build BUILD_FLAGS=--no-cache
docker.clean:
docker rmi $(DOCKER_IMAGE_NAME):$(GIT_HASH) $(DOCKER_IMAGE_NAME):latest
docker.test: docker.build
docker run --rm -it -u lchain -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH) \
pytest /src/tests/unit_tests
# this assumes that the docker image has been built
docker.lint: docker.lint.mypy docker.lint.black docker.lint.isort \
docker.lint.flake
# these can run in parallel with -j[njobs]
docker.lint.mypy:
@$(DOCKER_LINT_CMD) mypy /src
@printf "\t%s\n" "mypy ... "
docker.lint.black:
@$(DOCKER_LINT_CMD) black /src --check
@printf "\t%s\n" "black ... "
docker.lint.isort:
@$(DOCKER_LINT_CMD) isort /src --check
@printf "\t%s\n" "isort ... "
docker.lint.flake:
@$(DOCKER_LINT_CMD) flake8 /src
@printf "\t%s\n" "flake8 ... "

10
docker/assets/entry.sh Normal file
View File

@ -0,0 +1,10 @@
#!/usr/bin/env bash
export PATH=$HOME/.local/bin:$PATH
if [ -z "$1" ]; then
cat /etc/motd
exec /bin/bash
fi
exec "$@"

8
docker/assets/etc/motd Normal file
View File

@ -0,0 +1,8 @@
All dependencies have been installed in the current shell. There is no
virtualenv or a need for `poetry` inside the container.
Running the command `make docker.run` at the root directory of the project will
build the container the first time. On the next runs it will use the cached
image. A rebuild will happen when changes are made to the source code.
You local source directory has been mounted to the /src directory.

17
docker/docker-compose.yml Normal file
View File

@ -0,0 +1,17 @@
version: "3.7"
services:
langchain:
hostname: langchain
image: langchain/dev:latest
build:
context: ../
dockerfile: docker/Dockerfile
args:
PYTHON_VERSION: ${PYTHON_VERSION}
POETRY_EXTRA_PACKAGES: ${POETRY_EXTRA_PACKAGES}
POETRY_DEPENDENCIES: ${POETRY_DEPENDENCIES}
restart: unless-stopped
ports:
- 127.0.0.1:8888:8888

View File

@ -23,14 +23,13 @@ with open("../pyproject.toml") as f:
# -- Project information -----------------------------------------------------
project = "🦜🔗 LangChain"
copyright = "2023, Harrison Chase"
copyright = "2022, Harrison Chase"
author = "Harrison Chase"
version = data["tool"]["poetry"]["version"]
release = version
html_title = project + " " + version
html_last_updated_fmt = "%b %d, %Y"
# -- General configuration ---------------------------------------------------
@ -46,7 +45,6 @@ extensions = [
"sphinx.ext.viewcode",
"sphinxcontrib.autodoc_pydantic",
"myst_nb",
"sphinx_copybutton",
"sphinx_panels",
"IPython.sphinxext.ipython_console_highlighting",
]

View File

@ -37,6 +37,3 @@ A minimal example on how to run LangChain on Vercel using Flask.
## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.
This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.
## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
This repository allows users to serve local chains and agents as RESTful, gRPC, or Websocket APIs thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.

View File

@ -1,21 +1,19 @@
# AtlasDB
This page covers how to use Nomic's Atlas ecosystem within LangChain.
This page covers how to Nomic's Atlas ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
## Installation and Setup
- Install the Python package with `pip install nomic`
- Nomic is also included in langchains poetry extras `poetry install -E all`
-
## Wrappers
### VectorStore
There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore.
This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling.
Please see [the Atlas docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
Please see [the Nomic docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
@ -24,4 +22,4 @@ To import this vectorstore:
from langchain.vectorstores import AtlasDB
```
For a more detailed walkthrough of the AtlasDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/atlas.ipynb)
For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

View File

@ -5,7 +5,7 @@ It is broken into two parts: installation and setup, and then references to spec
## Installation and Setup
- Install with `pip install banana-dev`
- Install with `pip3 install banana-dev`
- Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
## Define your Banana Template

View File

@ -17,4 +17,4 @@ To import this vectorstore:
from langchain.vectorstores import Chroma
```
For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)
For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

View File

@ -22,4 +22,4 @@ There exists an Cohere Embeddings wrapper, which you can access with
```python
from langchain.embeddings import CohereEmbeddings
```
For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/cohere.ipynb)
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)

View File

@ -22,4 +22,4 @@ from langchain.vectorstores import DeepLake
```
For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](../modules/indexes/vectorstores/examples/deeplake.ipynb)
For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](../modules/indexes/vectorstore_examples/deeplake.ipynb)

View File

@ -18,7 +18,7 @@ There exists a GoogleSearchAPIWrapper utility which wraps this API. To import th
from langchain.utilities import GoogleSearchAPIWrapper
```
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_search.ipynb).
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_search.ipynb).
### Tool
@ -29,4 +29,4 @@ from langchain.agents import load_tools
tools = load_tools(["google-search"])
```
For more information on this, see [this page](../modules/agents/tools/getting_started.md)
For more information on this, see [this page](../modules/agents/tools.md)

View File

@ -34,8 +34,7 @@ search = GoogleSerperAPIWrapper()
tools = [
Tool(
name="Intermediate Answer",
func=search.run,
description="useful for when you need to ask with search"
func=search.run
)
]
@ -58,7 +57,7 @@ So the final answer is: El Palmar, Spain
'El Palmar, Spain'
```
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_serper.ipynb).
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_serper.ipynb).
### Tool
@ -69,4 +68,4 @@ from langchain.agents import load_tools
tools = load_tools(["google-serper"])
```
For more information on this, see [this page](../modules/agents/tools/getting_started.md)
For more information on this, see [this page](../modules/agents/tools.md)

View File

@ -1,6 +1,6 @@
# Graphsignal
This page covers how to use the Graphsignal ecosystem to trace and monitor LangChain.
This page covers how to use the Graphsignal to trace and monitor LangChain.
## Installation and Setup

View File

@ -1,6 +1,6 @@
# Helicone
This page covers how to use the [Helicone](https://helicone.ai) ecosystem within LangChain.
This page covers how to use the [Helicone](https://helicone.ai) within LangChain.
## What is Helicone?

View File

@ -30,7 +30,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
```python
from langchain.llms import HuggingFaceHub
```
For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](../modules/models/llms/integrations/huggingface_hub.ipynb)
For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](../modules/llms/integrations/huggingface_hub.ipynb)
### Embeddings
@ -47,7 +47,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
```python
from langchain.embeddings import HuggingFaceHubEmbeddings
```
For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingfacehub.ipynb)
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
### Tokenizer
@ -59,7 +59,7 @@ You can also use it to count tokens when splitting documents with
from langchain.text_splitter import CharacterTextSplitter
CharacterTextSplitter.from_huggingface_tokenizer(...)
```
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/huggingface_length_function.ipynb)
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
### Datasets

View File

@ -1,18 +0,0 @@
# Jina
This page covers how to use the Jina ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Jina wrappers.
## Installation and Setup
- Install the Python SDK with `pip install jina`
- Get a Jina AI Cloud auth token from [here](https://cloud.jina.ai/settings/tokens) and set it as an environment variable (`JINA_AUTH_TOKEN`)
## Wrappers
### Embeddings
There exists a Jina Embeddings wrapper, which you can access with
```python
from langchain.embeddings import JinaEmbeddings
```
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)

View File

@ -1,20 +0,0 @@
# Milvus
This page covers how to use the Milvus ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Milvus wrappers.
## Installation and Setup
- Install the Python SDK with `pip install pymilvus`
## Wrappers
### VectorStore
There exists a wrapper around Milvus indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.
To import this vectorstore:
```python
from langchain.vectorstores import Milvus
```
For a more detailed walkthrough of the Miluvs wrapper, see [this notebook](../modules/indexes/vectorstores/examples/milvus.ipynb)

View File

@ -21,7 +21,7 @@ If you are using a model hosted on Azure, you should use different wrapper for t
```python
from langchain.llms import AzureOpenAI
```
For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/models/llms/integrations/azure_openai_example.ipynb)
For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/llms/integrations/azure_openai_example.ipynb)
@ -31,7 +31,7 @@ There exists an OpenAI Embeddings wrapper, which you can access with
```python
from langchain.embeddings import OpenAIEmbeddings
```
For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/openai.ipynb)
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
### Tokenizer
@ -44,7 +44,7 @@ You can also use it to count tokens when splitting documents with
from langchain.text_splitter import CharacterTextSplitter
CharacterTextSplitter.from_tiktoken_encoder(...)
```
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/tiktoken.ipynb)
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
### Moderation
You can also access the OpenAI content moderation endpoint with

View File

@ -18,4 +18,4 @@ To import this vectorstore:
from langchain.vectorstores import OpenSearchVectorSearch
```
For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstores/examples/opensearch.ipynb)
For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstore_examples/opensearch.ipynb)

View File

@ -1,29 +0,0 @@
# PGVector
This page covers how to use the Postgres [PGVector](https://github.com/pgvector/pgvector) ecosystem within LangChain
It is broken into two parts: installation and setup, and then references to specific PGVector wrappers.
## Installation
- Install the Python package with `pip install pgvector`
## Setup
1. The first step is to create a database with the `pgvector` extension installed.
Follow the steps at [PGVector Installation Steps](https://github.com/pgvector/pgvector#installation) to install the database and the extension. The docker image is the easiest way to get started.
## Wrappers
### VectorStore
There exists a wrapper around Postgres vector databases, allowing you to use it as a vectorstore,
whether for semantic search or example selection.
To import this vectorstore:
```python
from langchain.vectorstores.pgvector import PGVector
```
### Usage
For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](../modules/indexes/vectorstores/examples/pgvector.ipynb)

View File

@ -17,4 +17,4 @@ To import this vectorstore:
from langchain.vectorstores import Pinecone
```
For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/vectorstores/examples/pinecone.ipynb)
For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

View File

@ -25,25 +25,7 @@ from langchain.llms import PromptLayerOpenAI
llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
```
To get the PromptLayer request id, use the argument `return_pl_id` when instanializing the LLM
```python
from langchain.llms import PromptLayerOpenAI
llm = PromptLayerOpenAI(return_pl_id=True)
```
This will add the PromptLayer request ID in the `generation_info` field of the `Generation` returned when using `.generate` or `.agenerate`
For example:
```python
llm_results = llm.generate(["hello world"])
for res in llm_results.generations:
print("pl request id: ", res[0].generation_info["pl_request_id"])
```
You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. [Read more about it here](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
This LLM is identical to the [OpenAI LLM](./openai.md), except that
This LLM is identical to the [OpenAI LLM](./openai), except that
- all your requests will be logged to your PromptLayer account
- you can add `pl_tags` when instantializing to tag your requests on PromptLayer
- you can add `return_pl_id` when instantializing to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9).
PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](../modules/models/chat/integrations/promptlayer_chatopenai.ipynb) and `PromptLayerOpenAIChat`

View File

@ -1,20 +0,0 @@
# Qdrant
This page covers how to use the Qdrant ecosystem within LangChain.
It is broken into two parts: installation and setup, and then references to specific Qdrant wrappers.
## Installation and Setup
- Install the Python SDK with `pip install qdrant-client`
## Wrappers
### VectorStore
There exists a wrapper around Qdrant indexes, allowing you to use it as a vectorstore,
whether for semantic search or example selection.
To import this vectorstore:
```python
from langchain.vectorstores import Qdrant
```
For a more detailed walkthrough of the Qdrant wrapper, see [this notebook](../modules/indexes/vectorstores/examples/qdrant.ipynb)

View File

@ -1,47 +0,0 @@
# Replicate
This page covers how to run models on Replicate within LangChain.
## Installation and Setup
- Create a [Replicate](https://replicate.com) account. Get your API key and set it as an environment variable (`REPLICATE_API_TOKEN`)
- Install the [Replicate python client](https://github.com/replicate/replicate-python) with `pip install replicate`
## Calling a model
Find a model on the [Replicate explore page](https://replicate.com/explore), and then paste in the model name and version in this format: `owner-name/model-name:version`
For example, for this [flan-t5 model](https://replicate.com/daanelson/flan-t5), click on the API tab. The model name/version would be: `daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8`
Only the `model` param is required, but any other model parameters can also be passed in with the format `input={model_param: value, ...}`
For example, if we were running stable diffusion and wanted to change the image dimensions:
```
Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf", input={'image_dimensions': '512x512'})
```
*Note that only the first output of a model will be returned.*
From here, we can initialize our model:
```python
llm = Replicate(model="daanelson/flan-t5:04e422a9b85baed86a4f24981d7f9953e20c5fd82f6103b74ebc431588e1cec8")
```
And run it:
```python
prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
llm(prompt)
```
We can call any Replicate model (not just LLMs) using this syntax. For example, we can call [Stable Diffusion](https://replicate.com/stability-ai/stable-diffusion):
```python
text2image = Replicate(model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
input={'image_dimensions'='512x512'}
image_output = text2image("A cat riding a motorcycle by Picasso")
```

View File

@ -15,7 +15,7 @@ custom LLMs, you can use the `SelfHostedPipeline` parent class.
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
```
For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/self_hosted_examples.ipynb)
For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/llms/integrations/self_hosted_examples.ipynb)
## Self-hosted Embeddings
There are several ways to use self-hosted embeddings with LangChain via Runhouse.
@ -26,4 +26,6 @@ the `SelfHostedEmbedding` class.
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
```
For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/models/text_embedding/examples/self-hosted.ipynb)
For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
##

View File

@ -5,66 +5,31 @@ It is broken into two parts: installation and setup, and then references to the
## Installation and Setup
While it is possible to utilize the wrapper in conjunction with [public searx
instances](https://searx.space/) these instances frequently do not permit API
access (see note on output format below) and have limitations on the frequency
of requests. It is recommended to opt for a self-hosted instance instead.
### Self Hosted Instance:
See [this page](https://searxng.github.io/searxng/admin/installation.html) for installation instructions.
When you install SearxNG, the only active output format by default is the HTML format.
You need to activate the `json` format to use the API. This can be done by adding the following line to the `settings.yml` file:
```yaml
search:
formats:
- html
- json
```
You can make sure that the API is working by issuing a curl request to the API endpoint:
`curl -kLX GET --data-urlencode q='langchain' -d format=json http://localhost:8888`
This should return a JSON object with the results.
- You can find a list of public SearxNG instances [here](https://searx.space/).
- It recommended to use a self-hosted instance to avoid abuse on the public instances. Also note that public instances often have a limit on the number of requests.
- To run a self-hosted instance see [this page](https://searxng.github.io/searxng/admin/installation.html) for more information.
- To use the tool you need to provide the searx host url by:
1. passing the named parameter `searx_host` when creating the instance.
2. exporting the environment variable `SEARXNG_HOST`.
## Wrappers
### Utility
To use the wrapper we need to pass the host of the SearxNG instance to the wrapper with:
1. the named parameter `searx_host` when creating the instance.
2. exporting the environment variable `SEARXNG_HOST`.
You can use the wrapper to get results from a SearxNG instance.
```python
from langchain.utilities import SearxSearchWrapper
s = SearxSearchWrapper(searx_host="http://localhost:8888")
s.run("what is a large language model?")
```
### Tool
You can also load this wrapper as a Tool (to use with an Agent).
You can also easily load this wrapper as a Tool (to use with an Agent).
You can do this with:
```python
from langchain.agents import load_tools
tools = load_tools(["searx-search"],
searx_host="http://localhost:8888",
engines=["github"])
tools = load_tools(["searx-search"], searx_host="https://searx.example.com")
```
Note that we could _optionally_ pass custom engines to use.
If you want to obtain results with metadata as *json* you can use:
```python
tools = load_tools(["searx-search-results-json"],
searx_host="http://localhost:8888",
num_results=5)
```
For more information on tools, see [this page](../modules/agents/tools/getting_started.md)
For more information on this, see [this page](../modules/agents/tools.md)

View File

@ -17,7 +17,7 @@ There exists a SerpAPI utility which wraps this API. To import this utility:
from langchain.utilities import SerpAPIWrapper
```
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/serpapi.ipynb).
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/serpapi.ipynb).
### Tool
@ -28,4 +28,4 @@ from langchain.agents import load_tools
tools = load_tools(["serpapi"])
```
For more information on this, see [this page](../modules/agents/tools/getting_started.md)
For more information on this, see [this page](../modules/agents/tools.md)

View File

@ -17,12 +17,9 @@ This page is broken into two parts: installation and setup, and then references
- `poppler-utils`
- `tesseract-ocr`
- `libreoffice`
- If you are parsing PDFs using the `"hi_res"` strategy, run the following to install the `detectron2` model, which
- If you are parsing PDFs, run the following to install the `detectron2` model, which
`unstructured` uses for layout detection:
- `pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2"`
- If `detectron2` is not installed, `unstructured` will fallback to processing PDFs
using the `"fast"` strategy, which uses `pdfminer` directly and doesn't require
`detectron2`.
## Wrappers

View File

@ -1,625 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Weights & Biases\n",
"\n",
"This notebook goes over how to track your LangChain experiments into one centralized Weights and Biases dashboard. To learn more about prompt engineering and the callback please refer to this Report which explains both alongside the resultant dashboards you can expect to see.\n",
"\n",
"Run in Colab: https://colab.research.google.com/drive/1DXH4beT4HFaRKy_Vm4PoxhXVDRf7Ym8L?usp=sharing\n",
"\n",
"View Report: https://wandb.ai/a-sh0ts/langchain_callback_demo/reports/Prompt-Engineering-LLMs-with-LangChain-and-W-B--VmlldzozNjk1NTUw#👋-how-to-build-a-callback-in-langchain-for-better-prompt-engineering"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install wandb\n",
"!pip install pandas\n",
"!pip install textstat\n",
"!pip install spacy\n",
"!python -m spacy download en_core_web_sm"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "T1bSmKd6V2If"
},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"WANDB_API_KEY\"] = \"\"\n",
"# os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
"# os.environ[\"SERPAPI_API_KEY\"] = \"\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "8WAGnTWpUUnD"
},
"outputs": [],
"source": [
"from datetime import datetime\n",
"from langchain.callbacks import WandbCallbackHandler, StdOutCallbackHandler\n",
"from langchain.callbacks.base import CallbackManager\n",
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"Callback Handler that logs to Weights and Biases.\n",
"\n",
"Parameters:\n",
" job_type (str): The type of job.\n",
" project (str): The project to log to.\n",
" entity (str): The entity to log to.\n",
" tags (list): The tags to log.\n",
" group (str): The group to log to.\n",
" name (str): The name of the run.\n",
" notes (str): The notes to log.\n",
" visualize (bool): Whether to visualize the run.\n",
" complexity_metrics (bool): Whether to log complexity metrics.\n",
" stream_logs (bool): Whether to stream callback actions to W&B\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cxBFfZR8d9FC"
},
"source": [
"```\n",
"Default values for WandbCallbackHandler(...)\n",
"\n",
"visualize: bool = False,\n",
"complexity_metrics: bool = False,\n",
"stream_logs: bool = False,\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NOTE: For beta workflows we have made the default analysis based on textstat and the visualizations based on spacy"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "KAz8weWuUeXF"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mharrison-chase\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
]
},
{
"data": {
"text/html": [
"Tracking run with wandb version 0.14.0"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150408-e47j1914</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">llm</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The wandb callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/wandb/wandb/issues with the tag `langchain`.\n"
]
}
],
"source": [
"\"\"\"Main function.\n",
"\n",
"This function is used to try the callback handler.\n",
"Scenarios:\n",
"1. OpenAI LLM\n",
"2. Chain with multiple SubChains on multiple generations\n",
"3. Agent with Tools\n",
"\"\"\"\n",
"session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
"wandb_callback = WandbCallbackHandler(\n",
" job_type=\"inference\",\n",
" project=\"langchain_callback_demo\",\n",
" group=f\"minimal_{session_group}\",\n",
" name=\"llm\",\n",
" tags=[\"test\"],\n",
")\n",
"manager = CallbackManager([StdOutCallbackHandler(), wandb_callback])\n",
"llm = OpenAI(temperature=0, callback_manager=manager, verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q-65jwrDeK6w"
},
"source": [
"\n",
"\n",
"```\n",
"# Defaults for WandbCallbackHandler.flush_tracker(...)\n",
"\n",
"reset: bool = True,\n",
"finish: bool = False,\n",
"```\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `flush_tracker` function is used to log LangChain sessions to Weights & Biases. It takes in the LangChain module or agent, and logs at minimum the prompts and generations alongside the serialized form of the LangChain module to the specified Weights & Biases project. By default we reset the session as opposed to concluding the session outright."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "o_VmneyIUyx8"
},
"outputs": [
{
"data": {
"text/html": [
"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View run <strong style=\"color:#cdcd00\">llm</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/e47j1914</a><br/>Synced 5 W&B file(s), 2 media file(s), 5 artifact file(s) and 0 other file(s)"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Find logs at: <code>./wandb/run-20230318_150408-e47j1914/logs</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0d7b4307ccdb450ea631497174fca2d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016745895149999985, max=1.0…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Tracking run with wandb version 0.14.0"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150534-jyxma7hu</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">simple_sequential</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# SCENARIO 1 - LLM\n",
"llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
"wandb_callback.flush_tracker(llm, name=\"simple_sequential\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "trxslyb1U28Y"
},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
"from langchain.chains import LLMChain"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "uauQk10SUzF6"
},
"outputs": [
{
"data": {
"text/html": [
"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View run <strong style=\"color:#cdcd00\">simple_sequential</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/jyxma7hu</a><br/>Synced 4 W&B file(s), 2 media file(s), 6 artifact file(s) and 0 other file(s)"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Find logs at: <code>./wandb/run-20230318_150534-jyxma7hu/logs</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dbdbf28fb8ed40a3a60218d2e6d1a987",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.016736786816666675, max=1.0…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Tracking run with wandb version 0.14.0"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Run data is saved locally in <code>/Users/harrisonchase/workplace/langchain/docs/ecosystem/wandb/run-20230318_150550-wzy59zjq</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Syncing run <strong><a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">agent</a></strong> to <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View project at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View run at <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# SCENARIO 2 - Chain\n",
"template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
"Title: {title}\n",
"Playwright: This is a synopsis for the above play:\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
"synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callback_manager=manager)\n",
"\n",
"test_prompts = [\n",
" {\n",
" \"title\": \"documentary about good video games that push the boundary of game design\"\n",
" },\n",
" {\"title\": \"cocaine bear vs heroin wolf\"},\n",
" {\"title\": \"the best in class mlops tooling\"},\n",
"]\n",
"synopsis_chain.apply(test_prompts)\n",
"wandb_callback.flush_tracker(synopsis_chain, name=\"agent\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "_jN73xcPVEpI"
},
"outputs": [],
"source": [
"from langchain.agents import initialize_agent, load_tools"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "Gpq4rk6VT9cu"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
"Action: Search\n",
"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mDiCaprio had a steady girlfriend in Camila Morrone. He had been with the model turned actress for nearly five years, as they were first said to be dating at the end of 2017. And the now 26-year-old Morrone is no stranger to Hollywood.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to calculate her age raised to the 0.43 power.\n",
"Action: Calculator\n",
"Action Input: 26^0.43\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: Leo DiCaprio's girlfriend is Camila Morrone and her current age raised to the 0.43 power is 4.059182145592686.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/html": [
"Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
" View run <strong style=\"color:#cdcd00\">agent</strong> at: <a href='https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq' target=\"_blank\">https://wandb.ai/harrison-chase/langchain_callback_demo/runs/wzy59zjq</a><br/>Synced 5 W&B file(s), 2 media file(s), 7 artifact file(s) and 0 other file(s)"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"Find logs at: <code>./wandb/run-20230318_150550-wzy59zjq/logs</code>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# SCENARIO 3 - Agent with Tools\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callback_manager=manager)\n",
"agent = initialize_agent(\n",
" tools,\n",
" llm,\n",
" agent=\"zero-shot-react-description\",\n",
" callback_manager=manager,\n",
" verbose=True,\n",
")\n",
"agent.run(\n",
" \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
")\n",
"wandb_callback.flush_tracker(agent, reset=False, finish=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}

View File

@ -30,4 +30,4 @@ To import this vectorstore:
from langchain.vectorstores import Weaviate
```
For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)
For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

View File

@ -20,7 +20,7 @@ There exists a WolframAlphaAPIWrapper utility which wraps this API. To import th
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
```
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/wolfram_alpha.ipynb).
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/wolfram_alpha.ipynb).
### Tool
@ -31,4 +31,4 @@ from langchain.agents import load_tools
tools = load_tools(["wolfram-alpha"])
```
For more information on this, see [this page](../modules/agents/tools/getting_started.md)
For more information on this, see [this page](../modules/agents/tools.md)

View File

@ -158,14 +158,14 @@ Open Source
---
.. link-button:: https://github.com/jerryjliu/llama_index
.. link-button:: https://github.com/jerryjliu/gpt_index
:type: url
:text: LlamaIndex
:text: GPT Index
:classes: stretched-link btn-lg
+++
LlamaIndex (formerly GPT Index) is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
GPT Index is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
---
@ -322,14 +322,5 @@ Proprietary
By Zahid Khawaja, this demo utilizes question answering to answer questions about a given website. A followup added this for `YouTube videos <https://twitter.com/chillzaza_/status/1593739682013220865?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_, and then another followup added it for `Wikipedia <https://twitter.com/chillzaza_/status/1594847151238037505?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_.
---
.. link-button:: https://mynd.so
:type: url
:text: Mynd
:classes: stretched-link btn-lg
+++
A journaling app for self-care that uses AI to uncover insights and patterns over time.

View File

@ -36,7 +36,7 @@ os.environ["OPENAI_API_KEY"] = "..."
```
## Building a Language Model Application: LLMs
## Building a Language Model Application
Now that we have installed LangChain and set up our environment, we can start building our language model application.
@ -66,7 +66,7 @@ llm = OpenAI(temperature=0.9)
We can now call it on some input!
```python
text = "What would be a good company name for a company that makes colorful socks?"
text = "What would be a good company name a company that makes colorful socks?"
print(llm(text))
```
@ -74,7 +74,7 @@ print(llm(text))
Feetful of Fun
```
For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/models/llms/getting_started.ipynb).
For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/llms/getting_started.ipynb).
`````
@ -111,7 +111,7 @@ What is a good name for a company that makes colorful socks?
```
[For more details, check out the getting started guide for prompts.](../modules/prompts/chat_prompt_template.ipynb)
[For more details, check out the getting started guide for prompts.](../modules/prompts/getting_started.ipynb)
`````
@ -160,7 +160,7 @@ This is one of the simpler types of chains, but understanding how it works will
`````
`````{dropdown} Agents: Dynamically Call Chains Based on User Input
`````{dropdown} Agents: Dynamically call chains based on user input
So far the chains we've looked at run in a predetermined order.
@ -210,31 +210,35 @@ tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
# Now let's test it out!
agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
```
```pycon
> Entering new AgentExecutor chain...
I need to find the temperature first, then use the calculator to raise it to the .023 power.
Entering new AgentExecutor chain...
I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.
Action: Search
Action Input: "High temperature in SF yesterday"
Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ...
Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power.
Action Input: "Olivia Wilde boyfriend"
Observation: Jason Sudeikis
Thought: I need to find out Jason Sudeikis' age
Action: Search
Action Input: "Jason Sudeikis age"
Observation: 47 years
Thought: I need to calculate 47 raised to the 0.23 power
Action: Calculator
Action Input: 57^.023
Observation: Answer: 1.0974509573251117
Action Input: 47^0.23
Observation: Answer: 2.4242784855673896
Thought: I now know the final answer
Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.
> Finished chain.
Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.
> Finished AgentExecutor chain.
"Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896."
```
`````
`````{dropdown} Memory: Add State to Chains and Agents
`````{dropdown} Memory: Add state to chains and agents
So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).
@ -283,218 +287,4 @@ AI:
> Finished chain.
" That's great! What would you like to talk about?"
```
`````
## Building a Language Model Application: Chat Models
Similarly, you can use chat models instead of LLMs. Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
`````{dropdown} Get Message Completions from a Chat Model
You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
```python
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatOpenAI(temperature=0)
```
You can get completions by passing in a single message.
```python
chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
```
You can also pass in multiple messages for OpenAI's gpt-3.5-turbo and gpt-4 models.
```python
messages = [
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
]
chat(messages)
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
```
You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter:
```python
batch_messages = [
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love programming.")
],
[
SystemMessage(content="You are a helpful assistant that translates English to French."),
HumanMessage(content="Translate this sentence from English to French. I love artificial intelligence.")
],
]
result = chat.generate(batch_messages)
result
# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}})
```
You can recover things like token usage from this LLMResult:
```
result.llm_output['token_usage']
# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
```
`````
`````{dropdown} Chat Prompt Templates
Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
For convience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
```
`````
`````{dropdown} Chains with Chat Models
The `LLMChain` discussed in the above section can be used with chat models as well:
```python
from langchain.chat_models import ChatOpenAI
from langchain import LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(llm=chat, prompt=chat_prompt)
chain.run(input_language="English", output_language="French", text="I love programming.")
# -> "J'aime programmer."
```
`````
`````{dropdown} Agents with Chat Models
Agents can also be used with chat models, you can initialize one using `"chat-zero-shot-react-description"` as the agent type.
```python
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
chat = ChatOpenAI(temperature=0)
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, chat, agent="chat-zero-shot-react-description", verbose=True)
# Now let's test it out!
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
```
```pycon
> Entering new AgentExecutor chain...
Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
Action:
{
"action": "Search",
"action_input": "Olivia Wilde boyfriend"
}
Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
Thought:I need to use a search engine to find Harry Styles' current age.
Action:
{
"action": "Search",
"action_input": "Harry Styles age"
}
Observation: 29 years
Thought:Now I need to calculate 29 raised to the 0.23 power.
Action:
{
"action": "Calculator",
"action_input": "29^0.23"
}
Observation: Answer: 2.169459462491557
Thought:I now know the final answer.
Final Answer: 2.169459462491557
> Finished chain.
'2.169459462491557'
```
`````
`````{dropdown} Memory: Add State to Chains and Agents
You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
```python
from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
prompt = ChatPromptTemplate.from_messages([
SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."),
MessagesPlaceholder(variable_name="history"),
HumanMessagePromptTemplate.from_template("{input}")
])
llm = ChatOpenAI(temperature=0)
memory = ConversationBufferMemory(return_messages=True)
conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
conversation.predict(input="Hi there!")
# -> 'Hello! How can I assist you today?'
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
# -> "That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?"
conversation.predict(input="Tell me about yourself.")
# -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
```
`````
```

View File

@ -32,7 +32,7 @@ This induces the to model to think about what action to take, then take it.
Resources:
- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
- [LangChain Example](modules/agents/agents/examples/react.ipynb)
- [LangChain Example](./modules/agents/implementations/react.ipynb)
## Self-ask
@ -42,7 +42,7 @@ In this method, the model explicitly asks itself follow-up questions, which are
Resources:
- [Paper](https://ofir.io/self-ask.pdf)
- [LangChain Example](modules/agents/agents/examples/self_ask_with_search.ipynb)
- [LangChain Example](./modules/agents/implementations/self_ask_with_search.ipynb)
## Prompt Chaining

View File

@ -1,14 +1,28 @@
Welcome to LangChain
==========================
LangChain is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model via an API, but will also:
Large language models (LLMs) are emerging as a transformative technology, enabling
developers to build applications that they previously could not.
But using these LLMs in isolation is often not enough to
create a truly powerful app - the real power comes when you are able to
combine them with other sources of computation or knowledge.
- *Be data-aware*: connect a language model to other sources of data
- *Be agentic*: allow a language model to interact with its environment
This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
The LangChain framework is designed with the above principles in mind.
**❓ Question Answering over specific documents**
This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see `here <https://docs.langchain.com/docs/>`_. For the JavaScript documentation, see `here <https://js.langchain.com/docs/>`_.
- `Documentation <./use_cases/question_answering.html>`_
- End-to-end Example: `Question Answering over Notion Database <https://github.com/hwchase17/notion-qa>`_
**💬 Chatbots**
- `Documentation <./use_cases/chatbots.html>`_
- End-to-end Example: `Chat-LangChain <https://github.com/hwchase17/chat-langchain>`_
**🤖 Agents**
- `Documentation <./use_cases/agents.html>`_
- End-to-end Example: `GPT+WolframAlpha <https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain>`_
Getting Started
----------------
@ -32,18 +46,23 @@ There are several main modules that LangChain provides support for.
For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
These modules are, in increasing order of complexity:
- `Models <./modules/models.html>`_: The various model types and model integrations LangChain supports.
- `Prompts <./modules/prompts.html>`_: This includes prompt management, prompt optimization, and prompt serialization.
- `Memory <./modules/memory.html>`_: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
- `LLMs <./modules/llms.html>`_: This includes a generic interface for all LLMs, and common utilities for working with LLMs.
- `Indexes <./modules/indexes.html>`_: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.
- `Document Loaders <./modules/document_loaders.html>`_: This includes a standard interface for loading documents, as well as specific integrations to all types of text data sources.
- `Utils <./modules/utils.html>`_: Language models are often more powerful when interacting with other sources of knowledge or computation. This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.
- `Chains <./modules/chains.html>`_: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
- `Indexes <./modules/indexes.html>`_: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.
- `Agents <./modules/agents.html>`_: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
- `Memory <./modules/memory.html>`_: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
.. toctree::
:maxdepth: 1
@ -51,34 +70,37 @@ These modules are, in increasing order of complexity:
:name: modules
:hidden:
./modules/models.rst
./modules/prompts.rst
./modules/prompts.md
./modules/llms.md
./modules/document_loaders.md
./modules/utils.md
./modules/indexes.md
./modules/memory.md
./modules/chains.md
./modules/agents.md
./modules/memory.md
Use Cases
----------
The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.
- `Personal Assistants <./use_cases/personal_assistants.html>`_: The main LangChain use case. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
- `Question Answering <./use_cases/question_answering.html>`_: The second big LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
- `Agents <./use_cases/agents.html>`_: Agents are systems that use a language model to interact with other tools. These can be used to do more grounded question/answering, interact with APIs, or even take actions.
- `Chatbots <./use_cases/chatbots.html>`_: Since language models are good at producing text, that makes them ideal for creating chatbots.
- `Querying Tabular Data <./use_cases/tabular.html>`_: If you want to understand how to use LLMs to query data that is stored in a tabular format (csvs, SQL, dataframes, etc) you should read this page.
- `Data Augmented Generation <./use_cases/combine_docs.html>`_: Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
- `Interacting with APIs <./use_cases/apis.html>`_: Enabling LLMs to interact with APIs is extremely powerful in order to give them more up-to-date information and allow them to take actions.
- `Extraction <./use_cases/extraction.html>`_: Extract structured information from text.
- `Question Answering <./use_cases/question_answering.html>`_: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation.
- `Summarization <./use_cases/summarization.html>`_: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.
- `Evaluation <./use_cases/evaluation.html>`_: Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
- `Generate similar examples <./use_cases/generate_examples.html>`_: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.
- `Compare models <./use_cases/model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
.. toctree::
:maxdepth: 1
@ -86,14 +108,14 @@ The above modules can be used in a variety of ways. LangChain also provides guid
:name: use_cases
:hidden:
./use_cases/personal_assistants.md
./use_cases/question_answering.md
./use_cases/agents.md
./use_cases/chatbots.md
./use_cases/tabular.rst
./use_cases/apis.md
./use_cases/generate_examples.ipynb
./use_cases/combine_docs.md
./use_cases/question_answering.md
./use_cases/summarization.md
./use_cases/extraction.md
./use_cases/evaluation.rst
./use_cases/model_laboratory.ipynb
Reference Docs
@ -144,12 +166,10 @@ Additional collection of resources we think may be useful as you develop your ap
- `Deployments <./deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
- `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
- `Model Laboratory <./model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
- `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
- `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
- `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
@ -164,6 +184,5 @@ Additional collection of resources we think may be useful as you develop your ap
./gallery.rst
./deployments.md
./tracing.md
./use_cases/model_laboratory.ipynb
Discord <https://discord.gg/6adMQxSpJS>
Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

View File

@ -1,52 +1,30 @@
Agents
==========================
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents>`_
Some applications will require not just a predetermined chain of calls to LLMs/other tools,
but potentially an unknown chain that depends on the user's input.
In these types of chains, there is a “agent” which has access to a suite of tools.
Depending on the user input, the agent can then decide which, if any, of these tools to call.
In this section of documentation, we first start with a Getting Started notebook to over over how to use all things related to agents in an end-to-end manner.
The following sections of documentation are provided:
- `Getting Started <./agents/getting_started.html>`_: A notebook to help you get started working with agents as quickly as possible.
- `Key Concepts <./agents/key_concepts.html>`_: A conceptual guide going over the various concepts related to agents.
- `How-To Guides <./agents/how_to_guides.html>`_: A collection of how-to guides. These highlight how to integrate various types of tools, how to work with different types of agents, and how to customize agents.
- `Reference <../reference/modules/agents.html>`_: API reference documentation for all Agent classes.
.. toctree::
:maxdepth: 1
:caption: Agents
:name: Agents
:hidden:
./agents/getting_started.ipynb
We then split the documentation into the following sections:
**Tools**
An overview of the various tools LangChain supports.
**Agents**
An overview of the different agent types.
**Toolkits**
An overview of toolkits, and examples of the different ones LangChain supports.
**Agent Executor**
An overview of the Agent Executor class and examples of how to use it.
Go Deeper
---------
.. toctree::
:maxdepth: 1
./agents/tools.rst
./agents/agents.rst
./agents/toolkits.rst
./agents/agent_executors.rst
./agents/key_concepts.md
./agents/how_to_guides.rst
Reference<../reference/modules/agents.rst>

View File

@ -1,17 +0,0 @@
Agent Executors
===============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent-executor>`_
Agent executors take an agent and tools and use the agent to decide which tools to call and in what order.
In this part of the documentation we cover other related functionality to agent executors
.. toctree::
:maxdepth: 1
:glob:
./agent_executors/examples/*

View File

@ -1,548 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fa6802ac",
"metadata": {},
"source": [
"# How to add SharedMemory to an Agent and its Tools\n",
"\n",
"This notebook goes over adding memory to **both** of an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n",
"\n",
"- [Adding memory to an LLM Chain](../../memory/examples/adding_memory.ipynb)\n",
"- [Custom Agents](custom_agent.ipynb)\n",
"\n",
"We are going to create a custom Agent. The agent has access to a conversation memory, search tool, and a summarization tool. And, the summarization tool also needs access to the conversation memory."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "8db95912",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import ZeroShotAgent, Tool, AgentExecutor\n",
"from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n",
"from langchain import OpenAI, LLMChain, PromptTemplate\n",
"from langchain.utilities import GoogleSearchAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "06b7187b",
"metadata": {},
"outputs": [],
"source": [
"template = \"\"\"This is a conversation between a human and a bot:\n",
"\n",
"{chat_history}\n",
"\n",
"Write a summary of the conversation for {input}:\n",
"\"\"\"\n",
"\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"input\", \"chat_history\"], \n",
" template=template\n",
")\n",
"memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
"readonlymemory = ReadOnlySharedMemory(memory=memory)\n",
"summry_chain = LLMChain(\n",
" llm=OpenAI(), \n",
" prompt=prompt, \n",
" verbose=True, \n",
" memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "97ad8467",
"metadata": {},
"outputs": [],
"source": [
"search = GoogleSearchAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" ),\n",
" Tool(\n",
" name = \"Summary\",\n",
" func=summry_chain.run,\n",
" description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e3439cd6",
"metadata": {},
"outputs": [],
"source": [
"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
"suffix = \"\"\"Begin!\"\n",
"\n",
"{chat_history}\n",
"Question: {input}\n",
"{agent_scratchpad}\"\"\"\n",
"\n",
"prompt = ZeroShotAgent.create_prompt(\n",
" tools, \n",
" prefix=prefix, \n",
" suffix=suffix, \n",
" input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "0021675b",
"metadata": {},
"source": [
"We can now construct the LLMChain, with the Memory object, and then create the agent."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c56a0e73",
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
"agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
"agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ca4bc1fb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I should research ChatGPT to answer this question.\n",
"Action: Search\n",
"Action Input: \"ChatGPT\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mNov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... ChatGPT. We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... Feb 2, 2023 ... ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after ... 2 days ago ... ChatGPT recently launched a new version of its own plagiarism detection tool, with hopes that it will squelch some of the criticism around how ... An API for accessing new AI models developed by OpenAI. Feb 19, 2023 ... ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You ... ChatGPT is fine-tuned from GPT-3.5, a language model trained to produce text. ChatGPT was optimized for dialogue by using Reinforcement Learning with Human ... 3 days ago ... Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. Dec 1, 2022 ... ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"What is ChatGPT?\")"
]
},
{
"cell_type": "markdown",
"id": "45627664",
"metadata": {},
"source": [
"To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "eecc0462",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to find out who developed ChatGPT\n",
"Action: Search\n",
"Action Input: Who developed ChatGPT\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. The organization is headquartered in San ... Feb 8, 2023 ... ChatGPT is an AI chatbot developed by San Francisco-based startup OpenAI. OpenAI was co-founded in 2015 by Elon Musk and Sam Altman and is ... Dec 7, 2022 ... ChatGPT is an AI chatbot designed and developed by OpenAI. The bot works by generating text responses based on human-user input, like questions ... Jan 12, 2023 ... In 2019, Microsoft invested $1 billion in OpenAI, the tiny San Francisco company that designed ChatGPT. And in the years since, it has quietly ... Jan 25, 2023 ... The inside story of ChatGPT: How OpenAI founder Sam Altman built the world's hottest technology with billions from Microsoft. Dec 3, 2022 ... ChatGPT went viral on social media for its ability to do anything from code to write essays. · The company that created the AI chatbot has a ... Jan 17, 2023 ... While many Americans were nursing hangovers on New Year's Day, 22-year-old Edward Tian was working feverishly on a new app to combat misuse ... ChatGPT is a language model created by OpenAI, an artificial intelligence research laboratory consisting of a team of researchers and engineers focused on ... 1 day ago ... Everyone is talking about ChatGPT, developed by OpenAI. This is such a great tool that has helped to make AI more accessible to a wider ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: ChatGPT was developed by OpenAI.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'ChatGPT was developed by OpenAI.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"Who developed it?\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c34424cf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to simplify the conversation for a 5 year old.\n",
"Action: Summary\n",
"Action Input: My daughter 5 years old\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mThis is a conversation between a human and a bot:\n",
"\n",
"Human: What is ChatGPT?\n",
"AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
"Human: Who developed it?\n",
"AI: ChatGPT was developed by OpenAI.\n",
"\n",
"Write a summary of the conversation for My daughter 5 years old:\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"The conversation was about ChatGPT, an artificial intelligence chatbot. It was created by OpenAI and can send and receive images while chatting.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
]
},
{
"cell_type": "markdown",
"id": "4ebd8326",
"metadata": {},
"source": [
"Confirm that the memory was correctly updated."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b91f8c85",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Human: What is ChatGPT?\n",
"AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
"Human: Who developed it?\n",
"AI: ChatGPT was developed by OpenAI.\n",
"Human: Thanks. Summarize the conversation, for my daughter 5 years old.\n",
"AI: ChatGPT is an artificial intelligence chatbot created by OpenAI that can send and receive images while chatting.\n"
]
}
],
"source": [
"print(agent_chain.memory.buffer)"
]
},
{
"cell_type": "markdown",
"id": "cc3d0aa4",
"metadata": {},
"source": [
"For comparison, below is a bad example that uses the same memory for both the Agent and the tool."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3359d043",
"metadata": {},
"outputs": [],
"source": [
"## This is a bad practice for using the memory.\n",
"## Use the ReadOnlySharedMemory class, as shown above.\n",
"\n",
"template = \"\"\"This is a conversation between a human and a bot:\n",
"\n",
"{chat_history}\n",
"\n",
"Write a summary of the conversation for {input}:\n",
"\"\"\"\n",
"\n",
"prompt = PromptTemplate(\n",
" input_variables=[\"input\", \"chat_history\"], \n",
" template=template\n",
")\n",
"memory = ConversationBufferMemory(memory_key=\"chat_history\")\n",
"summry_chain = LLMChain(\n",
" llm=OpenAI(), \n",
" prompt=prompt, \n",
" verbose=True, \n",
" memory=memory, # <--- this is the only change\n",
")\n",
"\n",
"search = GoogleSearchAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" ),\n",
" Tool(\n",
" name = \"Summary\",\n",
" func=summry_chain.run,\n",
" description=\"useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.\"\n",
" )\n",
"]\n",
"\n",
"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
"suffix = \"\"\"Begin!\"\n",
"\n",
"{chat_history}\n",
"Question: {input}\n",
"{agent_scratchpad}\"\"\"\n",
"\n",
"prompt = ZeroShotAgent.create_prompt(\n",
" tools, \n",
" prefix=prefix, \n",
" suffix=suffix, \n",
" input_variables=[\"input\", \"chat_history\", \"agent_scratchpad\"]\n",
")\n",
"\n",
"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
"agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)\n",
"agent_chain = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "970d23df",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I should research ChatGPT to answer this question.\n",
"Action: Search\n",
"Action Input: \"ChatGPT\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mNov 30, 2022 ... We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... ChatGPT. We've trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer ... Feb 2, 2023 ... ChatGPT, the popular chatbot from OpenAI, is estimated to have reached 100 million monthly active users in January, just two months after ... 2 days ago ... ChatGPT recently launched a new version of its own plagiarism detection tool, with hopes that it will squelch some of the criticism around how ... An API for accessing new AI models developed by OpenAI. Feb 19, 2023 ... ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You ... ChatGPT is fine-tuned from GPT-3.5, a language model trained to produce text. ChatGPT was optimized for dialogue by using Reinforcement Learning with Human ... 3 days ago ... Visual ChatGPT connects ChatGPT and a series of Visual Foundation Models to enable sending and receiving images during chatting. Dec 1, 2022 ... ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"What is ChatGPT?\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d9ea82f0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to find out who developed ChatGPT\n",
"Action: Search\n",
"Action Input: Who developed ChatGPT\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large ... Feb 15, 2023 ... Who owns Chat GPT? Chat GPT is owned and developed by AI research and deployment company, OpenAI. The organization is headquartered in San ... Feb 8, 2023 ... ChatGPT is an AI chatbot developed by San Francisco-based startup OpenAI. OpenAI was co-founded in 2015 by Elon Musk and Sam Altman and is ... Dec 7, 2022 ... ChatGPT is an AI chatbot designed and developed by OpenAI. The bot works by generating text responses based on human-user input, like questions ... Jan 12, 2023 ... In 2019, Microsoft invested $1 billion in OpenAI, the tiny San Francisco company that designed ChatGPT. And in the years since, it has quietly ... Jan 25, 2023 ... The inside story of ChatGPT: How OpenAI founder Sam Altman built the world's hottest technology with billions from Microsoft. Dec 3, 2022 ... ChatGPT went viral on social media for its ability to do anything from code to write essays. · The company that created the AI chatbot has a ... Jan 17, 2023 ... While many Americans were nursing hangovers on New Year's Day, 22-year-old Edward Tian was working feverishly on a new app to combat misuse ... ChatGPT is a language model created by OpenAI, an artificial intelligence research laboratory consisting of a team of researchers and engineers focused on ... 1 day ago ... Everyone is talking about ChatGPT, developed by OpenAI. This is such a great tool that has helped to make AI more accessible to a wider ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: ChatGPT was developed by OpenAI.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'ChatGPT was developed by OpenAI.'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"Who developed it?\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5b1f9223",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to simplify the conversation for a 5 year old.\n",
"Action: Summary\n",
"Action Input: My daughter 5 years old\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001b[32;1m\u001b[1;3mThis is a conversation between a human and a bot:\n",
"\n",
"Human: What is ChatGPT?\n",
"AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
"Human: Who developed it?\n",
"AI: ChatGPT was developed by OpenAI.\n",
"\n",
"Write a summary of the conversation for My daughter 5 years old:\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"The conversation was about ChatGPT, an artificial intelligence chatbot developed by OpenAI. It is designed to have conversations with humans and can also send and receive images.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"Thanks. Summarize the conversation, for my daughter 5 years old.\")"
]
},
{
"cell_type": "markdown",
"id": "d07415da",
"metadata": {},
"source": [
"The final answer is not wrong, but we see the 3rd Human input is actually from the agent in the memory because the memory was modified by the summary tool."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "32f97b21",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Human: What is ChatGPT?\n",
"AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and is optimized for dialogue by using Reinforcement Learning with Human-in-the-Loop. It is also capable of sending and receiving images during chatting.\n",
"Human: Who developed it?\n",
"AI: ChatGPT was developed by OpenAI.\n",
"Human: My daughter 5 years old\n",
"AI: \n",
"The conversation was about ChatGPT, an artificial intelligence chatbot developed by OpenAI. It is designed to have conversations with humans and can also send and receive images.\n",
"Human: Thanks. Summarize the conversation, for my daughter 5 years old.\n",
"AI: ChatGPT is an artificial intelligence chatbot developed by OpenAI that can have conversations with humans and send and receive images.\n"
]
}
],
"source": [
"print(agent_chain.memory.buffer)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,9 +1,12 @@
# Agent Types
# Agents
Agents use an LLM to determine which actions to take and in what order.
An action can either be using a tool and observing its output, or returning a response to the user.
For a list of easily loadable tools, see [here](tools.md).
Here are the agents available in LangChain.
For a tutorial on how to load agents, see [here](getting_started.ipynb).
## `zero-shot-react-description`
This agent uses the ReAct framework to determine which tool to use

View File

@ -1,35 +0,0 @@
Agents
=============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/agent>`_
In this part of the documentation we cover the different types of agents, disregarding which specific tools they are used with.
For a high level overview of the different types of agents, see the below documentation.
.. toctree::
:maxdepth: 1
:glob:
./agents/agent_types.md
For documentation on how to create a custom agent, see the below.
We also have documentation for an in-depth dive into each agent type.
.. toctree::
:maxdepth: 1
:glob:
./agents/custom_agent.ipynb
We also have documentation for an in-depth dive into each agent type.
.. toctree::
:maxdepth: 1
:glob:
./agents/examples/*

View File

@ -1,309 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4658d71a",
"metadata": {},
"source": [
"# Conversation Agent (for Chat Models)\n",
"\n",
"This notebook walks through using an agent optimized for conversation, using ChatModels. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.\n",
"\n",
"This is accomplished with a specific type of agent (`chat-conversational-react-description`) which expects to be used with a memory component."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f4f5d1a8",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"LANGCHAIN_HANDLER\"] = \"langchain\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "f65308ab",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import Tool\n",
"from langchain.memory import ConversationBufferMemory\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.utilities import SerpAPIWrapper\n",
"from langchain.agents import initialize_agent"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5fb14d6d",
"metadata": {},
"outputs": [],
"source": [
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"Current Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events or the current state of the world. the input to this should be a single search term.\"\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dddc34c4",
"metadata": {},
"outputs": [],
"source": [
"memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cafe9bc1",
"metadata": {},
"outputs": [],
"source": [
"llm=ChatOpenAI(temperature=0)\n",
"agent_chain = initialize_agent(tools, llm, agent=\"chat-conversational-react-description\", verbose=True, memory=memory)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "dc70b454",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
"}\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Hello Bob! How can I assist you today?'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"hi, i am bob\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "3dcf7953",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Your name is Bob.\"\n",
"}\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Your name is Bob.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"what's my name?\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "aa05f566",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Current Search\",\n",
" \"action_input\": \"Thai food dinner recipes\"\n",
"}\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m59 easy Thai recipes for any night of the week · Marion Grasby's Thai spicy chilli and basil fried rice · Thai curry noodle soup · Marion Grasby's ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\"\n",
"}\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Here are some Thai food dinner recipes you can make this week: Thai spicy chilli and basil fried rice, Thai curry noodle soup, and many more. You can find 59 easy Thai recipes for any night of the week on Marion Grasby's website.\""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "c5d8b7ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m```json\n",
"{\n",
" \"action\": \"Current Search\",\n",
" \"action_input\": \"who won the world cup in 1978\"\n",
"}\n",
"```\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mThe Argentina national football team represents Argentina in men's international football and is administered by the Argentine Football Association, the governing body for football in Argentina. Nicknamed La Albiceleste, they are the reigning world champions, having won the most recent World Cup in 2022.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m```json\n",
"{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\"\n",
"}\n",
"```\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"The last letter in your name is 'b'. The Argentina national football team won the World Cup in 1978.\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f608889b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Current Search\",\n",
" \"action_input\": \"weather in pomfret\"\n",
"}\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mMostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers possible. High near 40F. Winds NNW at 20 to 30 mph.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.\"\n",
"}\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The weather in Pomfret is mostly cloudy with gusty winds developing during the afternoon. A few flurries or snow showers are possible. High near 40F. Winds NNW at 20 to 30 mph.'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_chain.run(input=\"whats the weather like in pomfret?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0084efd6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,253 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "f1390152",
"metadata": {},
"source": [
"# MRKL Chat\n",
"\n",
"This notebook showcases using an agent to replicate the MRKL chain using an agent optimized for chat models."
]
},
{
"cell_type": "markdown",
"id": "39ea3638",
"metadata": {},
"source": [
"This uses the example Chinook database.\n",
"To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ac561cc4",
"metadata": {},
"outputs": [],
"source": [
"from langchain import OpenAI, LLMMathChain, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
"from langchain.agents import initialize_agent, Tool\n",
"from langchain.chat_models import ChatOpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "07e96d99",
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(temperature=0)\n",
"llm1 = OpenAI(temperature=0)\n",
"search = SerpAPIWrapper()\n",
"llm_math_chain = LLMMathChain(llm=llm1, verbose=True)\n",
"db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
"db_chain = SQLDatabaseChain(llm=llm1, database=db, verbose=True)\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
" ),\n",
" Tool(\n",
" name=\"Calculator\",\n",
" func=llm_math_chain.run,\n",
" description=\"useful for when you need to answer questions about math\"\n",
" ),\n",
" Tool(\n",
" name=\"FooBar DB\",\n",
" func=db_chain.run,\n",
" description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\n",
" )\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a069c4b6",
"metadata": {},
"outputs": [],
"source": [
"mrkl = initialize_agent(tools, llm, agent=\"chat-zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e603cd7d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: The first question requires a search, while the second question requires a calculator.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Search\",\n",
" \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mFor the second question, I need to use the calculator tool to raise her current age to the 0.43 power.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Calculator\",\n",
" \"action_input\": \"22.0^(0.43)\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"22.0^(0.43)\u001b[32;1m\u001b[1;3m\n",
"```python\n",
"import math\n",
"print(math.pow(22.0, 0.43))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m3.777824273683966\n",
"\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.777824273683966\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
"Final Answer: Camila Morrone, 3.777824273683966.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Camila Morrone, 3.777824273683966.'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mrkl.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a5c07010",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mQuestion: What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\n",
"Thought: I should use the Search tool to find the answer to the first part of the question and then use the FooBar DB tool to find the answer to the second part of the question.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Search\",\n",
" \"action_input\": \"Who recently released an album called 'The Storm Before the Calm'\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mAlanis Morissette\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mNow that I have the name of the artist, I can use the FooBar DB tool to find their albums in the database.\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"FooBar DB\",\n",
" \"action_input\": \"What albums does Alanis Morissette have in the database?\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
"What albums does Alanis Morissette have in the database? \n",
"SQLQuery:"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/harrisonchase/workplace/langchain/langchain/sql_database.py:141: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
" sample_rows = connection.execute(command)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[32;1m\u001b[1;3m SELECT Title FROM Album WHERE ArtistId IN (SELECT ArtistId FROM Artist WHERE Name = 'Alanis Morissette') LIMIT 5;\u001b[0m\n",
"SQLResult: \u001b[33;1m\u001b[1;3m[('Jagged Little Pill',)]\u001b[0m\n",
"Answer:\u001b[32;1m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[38;5;200m\u001b[1;3m Alanis Morissette has the album 'Jagged Little Pill' in the database.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mI have found the answer to both parts of the question.\n",
"Final Answer: The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"The artist who recently released an album called 'The Storm Before the Calm' is Alanis Morissette. The album 'Jagged Little Pill' is in the FooBar database.\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mrkl.run(\"What is the full name of the artist who recently released an album called 'The Storm Before the Calm' and are they in the FooBar database? If so, what albums of theirs are in the FooBar database?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "af016a70",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -5,7 +5,7 @@
"id": "68b24990",
"metadata": {},
"source": [
"# How to combine agents and vectorstores\n",
"# Agents and Vectorstores\n",
"\n",
"This notebook covers how to combine agents and vectorstores. The use case for this is that you've ingested your data into a vectorstore and want to interact with it in an agentic manner.\n",
"\n",
@ -22,7 +22,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 20,
"id": "2e87c10a",
"metadata": {},
"outputs": [],
@ -30,30 +30,13 @@
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.llms import OpenAI\n",
"from langchain.chains import RetrievalQA\n",
"from langchain import OpenAI, VectorDBQA\n",
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "0b7b772b",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"relevant_parts = []\n",
"for p in Path(\".\").absolute().parts:\n",
" relevant_parts.append(p)\n",
" if relevant_parts[-3:] == [\"langchain\", \"docs\", \"modules\"]:\n",
" break\n",
"doc_path = str(Path(*relevant_parts) / \"state_of_the_union.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 37,
"id": "f2675861",
"metadata": {},
"outputs": [
@ -68,7 +51,7 @@
],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader(doc_path)\n",
"loader = TextLoader('../../state_of_the_union.txt')\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",
@ -79,17 +62,17 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 38,
"id": "bc5403d4",
"metadata": {},
"outputs": [],
"source": [
"state_of_union = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever())"
"state_of_union = VectorDBQA.from_chain_type(llm=llm, chain_type=\"stuff\", vectorstore=docsearch)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 39,
"id": "1431cded",
"metadata": {},
"outputs": [],
@ -99,7 +82,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 40,
"id": "915d3ff3",
"metadata": {},
"outputs": [],
@ -109,7 +92,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 41,
"id": "96a2edf8",
"metadata": {},
"outputs": [
@ -126,7 +109,7 @@
"docs = loader.load()\n",
"ruff_texts = text_splitter.split_documents(docs)\n",
"ruff_db = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")\n",
"ruff = RetrievalQA.from_chain_type(llm=llm, chain_type=\"stuff\", retriever=ruff_db.as_retriever())"
"ruff = VectorDBQA.from_chain_type(llm=llm, chain_type=\"stuff\", vectorstore=ruff_db)"
]
},
{
@ -281,9 +264,9 @@
"id": "9161ba91",
"metadata": {},
"source": [
"You can also set `return_direct=True` if you intend to use the agent as a router and just want to directly return the result of the RetrievalQAChain.\n",
"You can also set `return_direct=True` if you intend to use the agent as a router and just want to directly return the result of the VectorDBQaChain.\n",
"\n",
"Notice that in the above examples the agent did some extra work after querying the RetrievalQAChain. You can avoid that and just return the result directly."
"Notice that in the above examples the agent did some extra work after querying the VectorDBQAChain. You can avoid that and just return the result directly."
]
},
{

View File

@ -5,7 +5,7 @@
"id": "6fb92deb-d89e-439b-855d-c7f2607d794b",
"metadata": {},
"source": [
"# How to use the async API for Agents\n",
"# Async API for Agent\n",
"\n",
"LangChain provides async support for Agents by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n",
"\n",

View File

@ -5,7 +5,7 @@
"id": "5436020b",
"metadata": {},
"source": [
"# How to access intermediate steps\n",
"# Intermediate Steps\n",
"\n",
"In order to get more visibility into what an agent is doing, we can also return intermediate steps. This comes in the form of an extra key in the return value, which is a list of (action, observation) tuples."
]

View File

@ -0,0 +1,130 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "991b1cc1",
"metadata": {},
"source": [
"# Loading from LangChainHub\n",
"\n",
"This notebook covers how to load agents from [LangChainHub](https://github.com/hwchase17/langchain-hub)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bd4450a2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No `_type` key found, defaulting to `prompt`.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m Yes.\n",
"Follow up: Who is the reigning men's U.S. Open champion?\u001b[0m\n",
"Intermediate answer: \u001b[36;1m\u001b[1;3m2016 · SUI · Stan Wawrinka ; 2017 · ESP · Rafael Nadal ; 2018 · SRB · Novak Djokovic ; 2019 · ESP · Rafael Nadal.\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mSo the reigning men's U.S. Open champion is Rafael Nadal.\n",
"Follow up: What is Rafael Nadal's hometown?\u001b[0m\n",
"Intermediate answer: \u001b[36;1m\u001b[1;3mIn 2016, he once again showed his deep ties to Mallorca and opened the Rafa Nadal Academy in his hometown of Manacor.\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mSo the final answer is: Manacor, Mallorca, Spain.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Manacor, Mallorca, Spain.'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain import OpenAI, SerpAPIWrapper\n",
"from langchain.agents import initialize_agent, Tool\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name=\"Intermediate Answer\",\n",
" func=search.run\n",
" )\n",
"]\n",
"\n",
"self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc://agents/self-ask-with-search/agent.json\", verbose=True)\n",
"self_ask_with_search.run(\"What is the hometown of the reigning men's U.S. Open champion?\")"
]
},
{
"cell_type": "markdown",
"id": "3aede965",
"metadata": {},
"source": [
"# Pinning Dependencies\n",
"\n",
"Specific versions of LangChainHub agents can be pinned with the `lc@<ref>://` syntax."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e679f7b6",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No `_type` key found, defaulting to `prompt`.\n"
]
}
],
"source": [
"self_ask_with_search = initialize_agent(tools, llm, agent_path=\"lc@2826ef9e8acdf88465e1e5fc8a7bf59e0f9d0a85://agents/self-ask-with-search/agent.json\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d3d6697",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -5,7 +5,7 @@
"id": "75c041b7",
"metadata": {},
"source": [
"# How to cap the max number of iterations\n",
"# Max Iterations\n",
"\n",
"This notebook walks through how to cap an agent at taking a certain number of steps. This can be useful to ensure that they do not go haywire and take too many steps."
]

View File

@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"id": "e6860c2d",
"metadata": {
"pycharm": {
@ -28,7 +28,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"id": "dadbcfcd",
"metadata": {},
"outputs": [],
@ -238,92 +238,6 @@
"source": [
"agent.run(\"What is the weather in Pomfret?\")"
]
},
{
"cell_type": "markdown",
"id": "eabad3af",
"metadata": {},
"source": [
"## SearxNG Meta Search Engine\n",
"\n",
"Here we will be using a self hosted SearxNG meta search engine."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b196c704",
"metadata": {},
"outputs": [],
"source": [
"tools = load_tools([\"searx-search\"], searx_host=\"http://localhost:8888\", llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9023eeaa",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3aad92c1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I should look up the current weather\n",
"Action: SearX Search\n",
"Action Input: \"weather in Pomfret\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mMainly cloudy with snow showers around in the morning. High around 40F. Winds NNW at 5 to 10 mph. Chance of snow 40%. Snow accumulations less than one inch.\n",
"\n",
"10 Day Weather - Pomfret, MD As of 1:37 pm EST Today 49°/ 41° 52% Mon 27 | Day 49° 52% SE 14 mph Cloudy with occasional rain showers. High 49F. Winds SE at 10 to 20 mph. Chance of rain 50%....\n",
"\n",
"10 Day Weather - Pomfret, VT As of 3:51 am EST Special Weather Statement Today 39°/ 32° 37% Wed 01 | Day 39° 37% NE 4 mph Cloudy with snow showers developing for the afternoon. High 39F....\n",
"\n",
"Pomfret, CT ; Current Weather. 1:06 AM. 35°F · RealFeel® 32° ; TODAY'S WEATHER FORECAST. 3/3. 44°Hi. RealFeel® 50° ; TONIGHT'S WEATHER FORECAST. 3/3. 32°Lo.\n",
"\n",
"Pomfret, MD Forecast Today Hourly Daily Morning 41° 1% Afternoon 43° 0% Evening 35° 3% Overnight 34° 2% Don't Miss Finally, Heres Why We Get More Colds and Flu When Its Cold Coast-To-Coast...\n",
"\n",
"Pomfret, MD Weather Forecast | AccuWeather Current Weather 5:35 PM 35° F RealFeel® 36° RealFeel Shade™ 36° Air Quality Excellent Wind E 3 mph Wind Gusts 5 mph Cloudy More Details WinterCast...\n",
"\n",
"Pomfret, VT Weather Forecast | AccuWeather Current Weather 11:21 AM 23° F RealFeel® 27° RealFeel Shade™ 25° Air Quality Fair Wind ESE 3 mph Wind Gusts 7 mph Cloudy More Details WinterCast...\n",
"\n",
"Pomfret Center, CT Weather Forecast | AccuWeather Daily Current Weather 6:50 PM 39° F RealFeel® 36° Air Quality Fair Wind NW 6 mph Wind Gusts 16 mph Mostly clear More Details WinterCast...\n",
"\n",
"12:00 pm · Feels Like36° · WindN 5 mph · Humidity43% · UV Index3 of 10 · Cloud Cover65% · Rain Amount0 in ...\n",
"\n",
"Pomfret Center, CT Weather Conditions | Weather Underground star Popular Cities San Francisco, CA 49 °F Clear Manhattan, NY 37 °F Fair Schiller Park, IL (60176) warning39 °F Mostly Cloudy...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The current weather in Pomfret is mainly cloudy with snow showers around in the morning. The temperature is around 40F with winds NNW at 5 to 10 mph. Chance of snow is 40%.'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"What is the weather in Pomfret\")"
]
}
],
"metadata": {
@ -342,7 +256,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.11"
"version": "3.9.1"
},
"vscode": {
"interpreter": {

View File

@ -0,0 +1,154 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "bfe18e28",
"metadata": {},
"source": [
"# Serialization\n",
"\n",
"This notebook goes over how to serialize agents. For this notebook, it is important to understand the distinction we draw between `agents` and `tools`. An agent is the LLM powered decision maker that decides which actions to take and in which order. Tools are various instruments (functions) an agent has access to, through which an agent can interact with the outside world. When people generally use agents, they primarily talk about using an agent WITH tools. However, when we talk about serialization of agents, we are talking about the agent by itself. We plan to add support for serializing an agent WITH tools sometime in the future.\n",
"\n",
"Let's start by creating an agent with tools as we normally do:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "eb729f16",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import load_tools\n",
"from langchain.agents import initialize_agent\n",
"from langchain.llms import OpenAI\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n",
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "markdown",
"id": "0578f566",
"metadata": {},
"source": [
"Let's now serialize the agent. To be explicit that we are serializing ONLY the agent, we will call the `save_agent` method."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dc544de6",
"metadata": {},
"outputs": [],
"source": [
"agent.save_agent('agent.json')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "62dd45bf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\r\n",
" \"llm_chain\": {\r\n",
" \"memory\": null,\r\n",
" \"verbose\": false,\r\n",
" \"prompt\": {\r\n",
" \"input_variables\": [\r\n",
" \"input\",\r\n",
" \"agent_scratchpad\"\r\n",
" ],\r\n",
" \"output_parser\": null,\r\n",
" \"template\": \"Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\r\n",
" \"template_format\": \"f-string\",\r\n",
" \"validate_template\": true,\r\n",
" \"_type\": \"prompt\"\r\n",
" },\r\n",
" \"llm\": {\r\n",
" \"model_name\": \"text-davinci-003\",\r\n",
" \"temperature\": 0.0,\r\n",
" \"max_tokens\": 256,\r\n",
" \"top_p\": 1,\r\n",
" \"frequency_penalty\": 0,\r\n",
" \"presence_penalty\": 0,\r\n",
" \"n\": 1,\r\n",
" \"best_of\": 1,\r\n",
" \"request_timeout\": null,\r\n",
" \"logit_bias\": {},\r\n",
" \"_type\": \"openai\"\r\n",
" },\r\n",
" \"output_key\": \"text\",\r\n",
" \"_type\": \"llm_chain\"\r\n",
" },\r\n",
" \"allowed_tools\": [\r\n",
" \"Search\",\r\n",
" \"Calculator\"\r\n",
" ],\r\n",
" \"return_values\": [\r\n",
" \"output\"\r\n",
" ],\r\n",
" \"_type\": \"zero-shot-react-description\"\r\n",
"}"
]
}
],
"source": [
"!cat agent.json"
]
},
{
"cell_type": "markdown",
"id": "0eb72510",
"metadata": {},
"source": [
"We can now load the agent back in"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "eb660b76",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(tools, llm, agent_path=\"agent.json\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa624ea5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,15 +1,6 @@
How-To Guides
=============
There are three types of examples in this section:
1. Agent Overview: how-to-guides for generic agent functionality
2. Agent Toolkits: how-to-guides for specific agent toolkits (agents optimized for interacting with a certain resource)
3. Agent Types: how-to-guides for working with the different agent types
Agent Overview
---------------
The first category of how-to guides here cover specific parts of working with agents.
`Load From Hub <./examples/load_from_hub.html>`_: This notebook covers how to load agents from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.
@ -30,48 +21,7 @@ The first category of how-to guides here cover specific parts of working with ag
`Asynchronous <./examples/async_agent.html>`_: Covering asynchronous functionality.
.. toctree::
:maxdepth: 1
:glob:
:hidden:
./examples/*
Agent Toolkits
---------------
The next set of examples covers agents with toolkits.
As opposed to the examples above, these examples are not intended to show off an agent `type`,
but rather to show off an agent applied to particular use case.
`SQLDatabase Agent <./agent_toolkits/sql_database.html>`_: This notebook covers how to interact with an arbitrary SQL database using an agent.
`JSON Agent <./agent_toolkits/json.html>`_: This notebook covers how to interact with a JSON dictionary using an agent.
`OpenAPI Agent <./agent_toolkits/openapi.html>`_: This notebook covers how to interact with an arbitrary OpenAPI endpoint using an agent.
`VectorStore Agent <./agent_toolkits/vectorstore.html>`_: This notebook covers how to interact with VectorStores using an agent.
`Python Agent <./agent_toolkits/python.html>`_: This notebook covers how to produce and execute python code using an agent.
`Pandas DataFrame Agent <./agent_toolkits/pandas.html>`_: This notebook covers how to do question answering over a pandas dataframe using an agent. Under the hood this calls the Python agent..
`CSV Agent <./agent_toolkits/csv.html>`_: This notebook covers how to do question answering over a csv file. Under the hood this calls the Pandas DataFrame agent.
.. toctree::
:maxdepth: 1
:glob:
:hidden:
./agent_toolkits/*
Agent Types
---------------
The final set of examples are all end-to-end example of different agent types.
The next set of examples are all end-to-end agents for specific applications.
In all examples there is an Agent with a particular set of tools.
- Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <../chains.html>`_ documentation. LangChain also provides a list of easily loadable tools. For detailed information on those, please see `this documentation <./tools.html>`_
@ -101,13 +51,16 @@ In all examples there is an Agent with a particular set of tools.
.. toctree::
:maxdepth: 1
:glob:
:hidden:
./examples/*
.. toctree::
:maxdepth: 1
:glob:
:hidden:
./implementations/*
./implementations/*

View File

@ -0,0 +1,87 @@
"""Run NatBot."""
import time
from langchain.chains.natbot.base import NatBotChain
from langchain.chains.natbot.crawler import Crawler
def run_cmd(cmd: str, _crawler: Crawler) -> None:
"""Run command."""
cmd = cmd.split("\n")[0]
if cmd.startswith("SCROLL UP"):
_crawler.scroll("up")
elif cmd.startswith("SCROLL DOWN"):
_crawler.scroll("down")
elif cmd.startswith("CLICK"):
commasplit = cmd.split(",")
id = commasplit[0].split(" ")[1]
_crawler.click(id)
elif cmd.startswith("TYPE"):
spacesplit = cmd.split(" ")
id = spacesplit[1]
text_pieces = spacesplit[2:]
text = " ".join(text_pieces)
# Strip leading and trailing double quotes
text = text[1:-1]
if cmd.startswith("TYPESUBMIT"):
text += "\n"
_crawler.type(id, text)
time.sleep(2)
if __name__ == "__main__":
objective = "Make a reservation for 2 at 7pm at bistro vida in menlo park"
print("\nWelcome to natbot! What is your objective?")
i = input()
if len(i) > 0:
objective = i
quiet = False
nat_bot_chain = NatBotChain.from_default(objective)
_crawler = Crawler()
_crawler.go_to_page("google.com")
try:
while True:
browser_content = "\n".join(_crawler.crawl())
llm_command = nat_bot_chain.execute(_crawler.page.url, browser_content)
if not quiet:
print("URL: " + _crawler.page.url)
print("Objective: " + objective)
print("----------------\n" + browser_content + "\n----------------\n")
if len(llm_command) > 0:
print("Suggested command: " + llm_command)
command = input()
if command == "r" or command == "":
run_cmd(llm_command, _crawler)
elif command == "g":
url = input("URL:")
_crawler.go_to_page(url)
elif command == "u":
_crawler.scroll("up")
time.sleep(1)
elif command == "d":
_crawler.scroll("down")
time.sleep(1)
elif command == "c":
id = input("id:")
_crawler.click(id)
time.sleep(1)
elif command == "t":
id = input("id:")
text = input("text:")
_crawler.type(id, text)
time.sleep(1)
elif command == "o":
objective = input("Objective:")
else:
print(
"(g) to visit url\n(u) scroll up\n(d) scroll down\n(c) to click"
"\n(t) to type\n(h) to view commands again"
"\n(r/enter) to run suggested command\n(o) change objective"
)
except KeyboardInterrupt:
print("\n[!] Ctrl+C detected, exiting gracefully.")
exit(0)

View File

@ -12,7 +12,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"id": "4e272b47",
"metadata": {},
"outputs": [],
@ -24,13 +24,11 @@
"tools = [\n",
" Tool(\n",
" name=\"Search\",\n",
" func=docstore.search,\n",
" description=\"useful for when you need to ask with search\"\n",
" func=docstore.search\n",
" ),\n",
" Tool(\n",
" name=\"Lookup\",\n",
" func=docstore.lookup,\n",
" description=\"useful for when you need to ask with lookup\"\n",
" func=docstore.lookup\n",
" )\n",
"]\n",
"\n",
@ -40,7 +38,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 5,
"id": "8078c8f1",
"metadata": {},
"outputs": [
@ -61,8 +59,7 @@
"Observation 2: \u001b[36;1m\u001b[1;3mWilliam James Crowe Jr. (January 2, 1925 October 18, 2007) was a United States Navy admiral and diplomat who served as the 11th chairman of the Joint Chiefs of Staff under Presidents Ronald Reagan and George H. W. Bush, and as the ambassador to the United Kingdom and Chair of the Intelligence Oversight Board under President Bill Clinton.\u001b[0m\n",
"Thought 3:\u001b[32;1m\u001b[1;3m The President William J. Crowe served as the ambassador to the United Kingdom under is Bill Clinton.\n",
"Action 3: Finish[Bill Clinton]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
"\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
]
},
{
@ -71,7 +68,7 @@
"'Bill Clinton'"
]
},
"execution_count": 2,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@ -80,19 +77,11 @@
"question = \"Author David Chanoff has collaborated with a U.S. Navy admiral who served as the ambassador to the United Kingdom under which President?\"\n",
"react.run(question)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc4a6efe",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.0 64-bit ('llm-env')",
"language": "python",
"name": "python3"
},
@ -106,7 +95,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.9.0"
},
"vscode": {
"interpreter": {

View File

@ -52,8 +52,7 @@
"tools = [\n",
" Tool(\n",
" name=\"Intermediate Answer\",\n",
" func=search.run,\n",
" description=\"useful for when you need to ask with search\"\n",
" func=search.run\n",
" )\n",
"]\n",
"\n",
@ -64,7 +63,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3.9.0 64-bit ('llm-env')",
"language": "python",
"name": "python3"
},
@ -78,7 +77,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
"version": "3.9.0"
},
"vscode": {
"interpreter": {

View File

@ -0,0 +1,10 @@
# Key Concepts
## Agents
Agents use an LLM to determine which actions to take and in what order.
For more detailed information on agents, and different types of agents in LangChain, see [this documentation](agents.md).
## Tools
Tools are functions that agents can use to interact with the world.
These tools can be generic utilities (e.g. search), other chains, or even other agents.
For more detailed information on tools, and different types of tools in LangChain, see [this documentation](tools.md).

View File

@ -1,18 +0,0 @@
Toolkits
==============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/toolkit>`_
This section of documentation covers agents with toolkits - eg an agent applied to a particular use case.
See below for a full list of agent toolkits
.. toctree::
:maxdepth: 1
:glob:
./toolkits/examples/*

View File

@ -1,202 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "7094e328",
"metadata": {},
"source": [
"# CSV Agent\n",
"\n",
"This notebook shows how to use agents to interact with a csv. It is mostly optimized for question answering.\n",
"\n",
"**NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "827982c7",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import create_csv_agent"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "caae0bec",
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "16c4dc59",
"metadata": {},
"outputs": [],
"source": [
"agent = create_csv_agent(OpenAI(temperature=0), 'titanic.csv', verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "46b9489d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
"Action: python_repl_ast\n",
"Action Input: len(df)\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'There are 891 rows in the dataframe.'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"how many rows are there?\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a96309be",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
"Action: python_repl_ast\n",
"Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'30 people have more than 3 siblings.'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"how many people have more than 3 sibligngs\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "964a09f7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
"Action: python_repl_ast\n",
"Action Input: df['Age'].mean()\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
"Action: python_repl_ast\n",
"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
"Action: python_repl_ast\n",
"Action Input: import math\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
"Action: python_repl_ast\n",
"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 5.449689683556195\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'5.449689683556195'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"whats the square root of the average age?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "551de2be",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,190 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "85fb2c03-ab88-4c8c-97e3-a7f2954555ab",
"metadata": {},
"source": [
"# JSON Agent\n",
"\n",
"This notebook showcases an agent designed to interact with large JSON/dict objects. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. The agent is able to iteratively explore the blob to find what it needs to answer the user's question.\n",
"\n",
"In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find [here](https://github.com/openai/openai-openapi/blob/master/openapi.yaml).\n",
"\n",
"We will use the JSON agent to answer some questions about the API spec."
]
},
{
"cell_type": "markdown",
"id": "893f90fd-f8f6-470a-a76d-1f200ba02e2f",
"metadata": {},
"source": [
"## Initialization"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ff988466-c389-4ec6-b6ac-14364a537fd5",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"import yaml\n",
"\n",
"from langchain.agents import (\n",
" create_json_agent,\n",
" AgentExecutor\n",
")\n",
"from langchain.agents.agent_toolkits import JsonToolkit\n",
"from langchain.chains import LLMChain\n",
"from langchain.llms.openai import OpenAI\n",
"from langchain.requests import RequestsWrapper\n",
"from langchain.tools.json.tool import JsonSpec"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9ecd1ba0-3937-4359-a41e-68605f0596a1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"with open(\"openai_openapi.yml\") as f:\n",
" data = yaml.load(f, Loader=yaml.FullLoader)\n",
"json_spec = JsonSpec(dict_=data, max_value_length=4000)\n",
"json_toolkit = JsonToolkit(spec=json_spec)\n",
"\n",
"json_agent_executor = create_json_agent(\n",
" llm=OpenAI(temperature=0),\n",
" toolkit=json_toolkit,\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "05cfcb24-4389-4b8f-ad9e-466e3fca8db0",
"metadata": {},
"source": [
"## Example: getting the required POST parameters for a request"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "faf13702-50f0-4d1b-b91f-48c750ccfd98",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
"Action Input: data\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the /completions endpoint to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['post']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the post key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the requestBody key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['required', 'content']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
"Action: json_spec_get_value\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"required\"]\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3mTrue\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the content key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['application/json']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the application/json key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['schema']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the schema key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['$ref']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to see what parameters are required\n",
"Action: json_spec_get_value\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m#/components/schemas/CreateCompletionRequest\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the CreateCompletionRequest schema to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['type', 'properties', 'required']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
"Action: json_spec_get_value\n",
"Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"][\"required\"]\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m['model']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The required parameters in the request body to the /completions endpoint are 'model'.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"The required parameters in the request body to the /completions endpoint are 'model'.\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"json_agent_executor.run(\"What are the required parameters in the request body to the /completions endpoint?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ba9c9d30",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

File diff suppressed because it is too large Load Diff

View File

@ -1,242 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "85fb2c03-ab88-4c8c-97e3-a7f2954555ab",
"metadata": {},
"source": [
"# OpenAPI Agent\n",
"\n",
"This notebook showcases an agent designed to interact with an OpenAPI spec and make a correct API request based on the information it has gathered from the spec.\n",
"\n",
"In the below example, we are using the OpenAPI spec for the OpenAI API, which you can find [here](https://github.com/openai/openai-openapi/blob/master/openapi.yaml)."
]
},
{
"cell_type": "markdown",
"id": "893f90fd-f8f6-470a-a76d-1f200ba02e2f",
"metadata": {},
"source": [
"## Initialization"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ff988466-c389-4ec6-b6ac-14364a537fd5",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"import yaml\n",
"\n",
"from langchain.agents import create_openapi_agent\n",
"from langchain.agents.agent_toolkits import OpenAPIToolkit\n",
"from langchain.llms.openai import OpenAI\n",
"from langchain.requests import RequestsWrapper\n",
"from langchain.tools.json.tool import JsonSpec"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9ecd1ba0-3937-4359-a41e-68605f0596a1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"with open(\"openai_openapi.yml\") as f:\n",
" data = yaml.load(f, Loader=yaml.FullLoader)\n",
"json_spec=JsonSpec(dict_=data, max_value_length=4000)\n",
"headers = {\n",
" \"Authorization\": f\"Bearer {os.getenv('OPENAI_API_KEY')}\"\n",
"}\n",
"requests_wrapper=RequestsWrapper(headers=headers)\n",
"openapi_toolkit = OpenAPIToolkit.from_llm(OpenAI(temperature=0), json_spec, requests_wrapper, verbose=True)\n",
"openapi_agent_executor = create_openapi_agent(\n",
" llm=OpenAI(temperature=0),\n",
" toolkit=openapi_toolkit,\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "f111879d-ae84-41f9-ad82-d3e6b72c41ba",
"metadata": {},
"source": [
"## Example: agent capable of analyzing OpenAPI spec and making requests"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "548db7f7-337b-4ba8-905c-e7fd58c01799",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: json_explorer\n",
"Action Input: What is the base url for the API?\u001b[0m\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
"Action Input: data\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the servers key to see what the base url is\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"servers\"][0]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mValueError('Value at path `data[\"servers\"][0]` is not a dict, get the value directly.')\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should get the value of the servers key\n",
"Action: json_spec_get_value\n",
"Action Input: data[\"servers\"][0]\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m{'url': 'https://api.openai.com/v1'}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the base url for the API\n",
"Final Answer: The base url for the API is https://api.openai.com/v1\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mThe base url for the API is https://api.openai.com/v1\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should find the path for the /completions endpoint.\n",
"Action: json_explorer\n",
"Action Input: What is the path for the /completions endpoint?\u001b[0m\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
"Action Input: data\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the path for the /completions endpoint\n",
"Final Answer: data[\"paths\"][2]\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mdata[\"paths\"][2]\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should find the required parameters for the POST request.\n",
"Action: json_explorer\n",
"Action Input: What are the required parameters for a POST request to the /completions endpoint?\u001b[0m\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: json_spec_list_keys\n",
"Action Input: data\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['openapi', 'info', 'servers', 'tags', 'paths', 'components', 'x-oaiMeta']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the paths key to see what endpoints exist\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['/engines', '/engines/{engine_id}', '/completions', '/edits', '/images/generations', '/images/edits', '/images/variations', '/embeddings', '/engines/{engine_id}/search', '/files', '/files/{file_id}', '/files/{file_id}/content', '/answers', '/classifications', '/fine-tunes', '/fine-tunes/{fine_tune_id}', '/fine-tunes/{fine_tune_id}/cancel', '/fine-tunes/{fine_tune_id}/events', '/models', '/models/{model}', '/moderations']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the /completions endpoint to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['post']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the post key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['operationId', 'tags', 'summary', 'requestBody', 'responses', 'x-oaiMeta']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the requestBody key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['required', 'content']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the content key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['application/json']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the application/json key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['schema']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the schema key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['$ref']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mValueError('Value at path `data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]` is not a dict, get the value directly.')\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the $ref key to get the value directly\n",
"Action: json_spec_get_value\n",
"Action Input: data[\"paths\"][\"/completions\"][\"post\"][\"requestBody\"][\"content\"][\"application/json\"][\"schema\"][\"$ref\"]\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m#/components/schemas/CreateCompletionRequest\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the CreateCompletionRequest schema to see what parameters are required\n",
"Action: json_spec_list_keys\n",
"Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m['type', 'properties', 'required']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the required key to see what parameters are required\n",
"Action: json_spec_get_value\n",
"Action Input: data[\"components\"][\"schemas\"][\"CreateCompletionRequest\"][\"required\"]\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m['model']\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The required parameters for a POST request to the /completions endpoint are 'model'.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mThe required parameters for a POST request to the /completions endpoint are 'model'.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the parameters needed to make the request.\n",
"Action: requests_post\n",
"Action Input: { \"url\": \"https://api.openai.com/v1/completions\", \"data\": { \"model\": \"davinci\", \"prompt\": \"tell me a joke\" } }\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m{\"id\":\"cmpl-6oeEcNETfq8TOuIUQvAct6NrBXihs\",\"object\":\"text_completion\",\"created\":1677529082,\"model\":\"davinci\",\"choices\":[{\"text\":\"\\n\\n\\n\\nLove is a battlefield\\n\\n\\n\\nIt's me...And some\",\"index\":0,\"logprobs\":null,\"finish_reason\":\"length\"}],\"usage\":{\"prompt_tokens\":4,\"completion_tokens\":16,\"total_tokens\":20}}\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: Love is a battlefield. It's me...And some.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Love is a battlefield. It's me...And some.\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"openapi_agent_executor.run(\"Make a post request to openai /completions. The prompt should be 'tell me a joke.'\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ec9582b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,204 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "c81da886",
"metadata": {},
"source": [
"# Pandas Dataframe Agent\n",
"\n",
"This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.\n",
"\n",
"**NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "0cdd9bf5",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import create_pandas_dataframe_agent"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "051ebe84",
"metadata": {},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv('titanic.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4185ff46",
"metadata": {},
"outputs": [],
"source": [
"agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a9207a2e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
"Action: python_repl_ast\n",
"Action Input: len(df)\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: There are 891 rows in the dataframe.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'There are 891 rows in the dataframe.'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"how many rows are there?\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bd43617c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
"Action: python_repl_ast\n",
"Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'30 people have more than 3 siblings.'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"how many people have more than 3 sibligngs\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "94e64b58",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
"Action: python_repl_ast\n",
"Action Input: df['Age'].mean()\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
"Action: python_repl_ast\n",
"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mname 'math' is not defined\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
"Action: python_repl_ast\n",
"Action Input: import math\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mNone\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I can now calculate the square root\n",
"Action: python_repl_ast\n",
"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 5.449689683556195\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'5.449689683556195'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"whats the square root of the average age?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eba13b4d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,228 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "82a4c2cc-20ea-4b20-a565-63e905dee8ff",
"metadata": {},
"source": [
"# Python Agent\n",
"\n",
"This notebook showcases an agent designed to write and execute python code to answer a question."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f98e9c90-5c37-4fb9-af3e-d09693af8543",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.agents.agent_toolkits import create_python_agent\n",
"from langchain.tools.python.tool import PythonREPLTool\n",
"from langchain.python import PythonREPL\n",
"from langchain.llms.openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cc422f53-c51c-4694-a834-72ecd1e68363",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"agent_executor = create_python_agent(\n",
" llm=OpenAI(temperature=0, max_tokens=1000),\n",
" tool=PythonREPLTool(),\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c16161de",
"metadata": {},
"source": [
"## Fibonacci Example\n",
"This example was created by [John Wiseman](https://twitter.com/lemonodor/status/1628270074074398720?s=20)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "25cd4f92-ea9b-4fe6-9838-a4f85f81eebe",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to calculate the 10th fibonacci number\n",
"Action: Python REPL\n",
"Action Input: def fibonacci(n):\n",
" if n == 0:\n",
" return 0\n",
" elif n == 1:\n",
" return 1\n",
" else:\n",
" return fibonacci(n-1) + fibonacci(n-2)\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to call the function with 10 as the argument\n",
"Action: Python REPL\n",
"Action Input: fibonacci(10)\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 55\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'55'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What is the 10th fibonacci number?\")"
]
},
{
"cell_type": "markdown",
"id": "7caa30de",
"metadata": {},
"source": [
"## Training neural net\n",
"This example was created by [Samee Ur Rehman](https://twitter.com/sameeurehman/status/1630130518133207046?s=20)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4b9f60e7-eb6a-4f14-8604-498d863d4482",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to write a neural network in PyTorch and train it on the given data.\n",
"Action: Python REPL\n",
"Action Input: \n",
"import torch\n",
"\n",
"# Define the model\n",
"model = torch.nn.Sequential(\n",
" torch.nn.Linear(1, 1)\n",
")\n",
"\n",
"# Define the loss\n",
"loss_fn = torch.nn.MSELoss()\n",
"\n",
"# Define the optimizer\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
"\n",
"# Define the data\n",
"x_data = torch.tensor([[1.0], [2.0], [3.0], [4.0]])\n",
"y_data = torch.tensor([[2.0], [4.0], [6.0], [8.0]])\n",
"\n",
"# Train the model\n",
"for epoch in range(1000):\n",
" # Forward pass\n",
" y_pred = model(x_data)\n",
"\n",
" # Compute and print loss\n",
" loss = loss_fn(y_pred, y_data)\n",
" if (epoch+1) % 100 == 0:\n",
" print(f'Epoch {epoch+1}: loss = {loss.item():.4f}')\n",
"\n",
" # Zero the gradients\n",
" optimizer.zero_grad()\n",
"\n",
" # Backward pass\n",
" loss.backward()\n",
"\n",
" # Update the weights\n",
" optimizer.step()\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mEpoch 100: loss = 0.0013\n",
"Epoch 200: loss = 0.0007\n",
"Epoch 300: loss = 0.0004\n",
"Epoch 400: loss = 0.0002\n",
"Epoch 500: loss = 0.0001\n",
"Epoch 600: loss = 0.0001\n",
"Epoch 700: loss = 0.0000\n",
"Epoch 800: loss = 0.0000\n",
"Epoch 900: loss = 0.0000\n",
"Epoch 1000: loss = 0.0000\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The prediction for x = 5 is 10.0.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The prediction for x = 5 is 10.0.'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"\"\"Understand, write a single neuron neural network in PyTorch.\n",
"Take synthetic data for y=2x. Train for 1000 epochs and print every 100 epochs.\n",
"Return prediction for x = 5\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb654671",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,527 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "0e499e90-7a6d-4fab-8aab-31a4df417601",
"metadata": {},
"source": [
"# SQL Database Agent\n",
"\n",
"This notebook showcases an agent designed to interact with a sql databases. The agent builds off of [SQLDatabaseChain](https://langchain.readthedocs.io/en/latest/modules/chains/examples/sqlite.html) and is designed to answer more general questions about a database, as well as recover from errors.\n",
"\n",
"Note that, as this agent is in active development, all answers might not be correct. Additionally, it is not guaranteed that the agent won't perform DML statements on your database given certain questions. Be careful running it on sensitive data!\n",
"\n",
"This uses the example Chinook database. To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file in a notebooks folder at the root of this repository."
]
},
{
"cell_type": "markdown",
"id": "ec927ac6-9b2a-4e8a-9a6e-3e429191875c",
"metadata": {
"tags": []
},
"source": [
"## Initialization"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "53422913-967b-4f2a-8022-00269c1be1b1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.agents import create_sql_agent\n",
"from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
"from langchain.sql_database import SQLDatabase\n",
"from langchain.llms.openai import OpenAI\n",
"from langchain.agents import AgentExecutor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "090f3699-79c6-4ce1-ab96-a94f0121fd64",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"db = SQLDatabase.from_uri(\"sqlite:///../../../../notebooks/Chinook.db\")\n",
"toolkit = SQLDatabaseToolkit(db=db)\n",
"\n",
"agent_executor = create_sql_agent(\n",
" llm=OpenAI(temperature=0),\n",
" toolkit=toolkit,\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "36ae48c7-cb08-4fef-977e-c7d4b96a464b",
"metadata": {},
"source": [
"## Example: describing a table"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ff70e83d-5ad0-4fc7-bb96-27d82ac166d7",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
"Action Input: \"\"\u001b[0m\n",
"Observation: \u001b[38;5;200m\u001b[1;3mArtist, Invoice, Playlist, Genre, Album, PlaylistTrack, Track, InvoiceLine, MediaType, Employee, Customer\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the playlisttrack table\n",
"Action: schema_sql_db\n",
"Action Input: \"PlaylistTrack\"\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"CREATE TABLE \"PlaylistTrack\" (\n",
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
"\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
"\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
")\n",
"\n",
"SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
"PlaylistId TrackId\n",
"1 3402\n",
"1 3389\n",
"1 3390\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The PlaylistTrack table has two columns, PlaylistId and TrackId, and is linked to the Playlist and Track tables.'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"Describe the playlisttrack table\")"
]
},
{
"cell_type": "markdown",
"id": "9abcfe8e-1868-42a4-8345-ad2d9b44c681",
"metadata": {},
"source": [
"## Example: describing a table, recovering from an error\n",
"\n",
"In this example, the agent tries to search for a table that doesn't exist, but finds the next best result"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "bea76658-a65b-47e2-b294-6d52c5556246",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
"Action Input: \"\"\u001b[0m\n",
"Observation: \u001b[38;5;200m\u001b[1;3mGenre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the PlaylistSong table\n",
"Action: schema_sql_db\n",
"Action Input: \"PlaylistSong\"\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3mError: table_names {'PlaylistSong'} not found in database\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should check the spelling of the table\n",
"Action: list_tables_sql_db\n",
"Action Input: \"\"\u001b[0m\n",
"Observation: \u001b[38;5;200m\u001b[1;3mGenre, PlaylistTrack, MediaType, Invoice, InvoiceLine, Track, Playlist, Customer, Album, Employee, Artist\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m The table is called PlaylistTrack\n",
"Action: schema_sql_db\n",
"Action Input: \"PlaylistTrack\"\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"CREATE TABLE \"PlaylistTrack\" (\n",
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
"\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
"\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
")\n",
"\n",
"SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
"PlaylistId TrackId\n",
"1 3402\n",
"1 3389\n",
"1 3390\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The PlaylistTrack table contains two columns, PlaylistId and TrackId, which are both integers and are used to link Playlist and Track tables.'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"Describe the playlistsong table\")"
]
},
{
"cell_type": "markdown",
"id": "6fbc26af-97e4-4a21-82aa-48bdc992da26",
"metadata": {},
"source": [
"## Example: running queries"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "17bea710-4a23-4de0-b48e-21d57be48293",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
"Action Input: \"\"\u001b[0m\n",
"Observation: \u001b[38;5;200m\u001b[1;3mInvoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the relevant tables to see what columns I can use.\n",
"Action: schema_sql_db\n",
"Action Input: \"Invoice, Customer\"\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"CREATE TABLE \"Customer\" (\n",
"\t\"CustomerId\" INTEGER NOT NULL, \n",
"\t\"FirstName\" NVARCHAR(40) NOT NULL, \n",
"\t\"LastName\" NVARCHAR(20) NOT NULL, \n",
"\t\"Company\" NVARCHAR(80), \n",
"\t\"Address\" NVARCHAR(70), \n",
"\t\"City\" NVARCHAR(40), \n",
"\t\"State\" NVARCHAR(40), \n",
"\t\"Country\" NVARCHAR(40), \n",
"\t\"PostalCode\" NVARCHAR(10), \n",
"\t\"Phone\" NVARCHAR(24), \n",
"\t\"Fax\" NVARCHAR(24), \n",
"\t\"Email\" NVARCHAR(60) NOT NULL, \n",
"\t\"SupportRepId\" INTEGER, \n",
"\tPRIMARY KEY (\"CustomerId\"), \n",
"\tFOREIGN KEY(\"SupportRepId\") REFERENCES \"Employee\" (\"EmployeeId\")\n",
")\n",
"\n",
"SELECT * FROM 'Customer' LIMIT 3;\n",
"CustomerId FirstName LastName Company Address City State Country PostalCode Phone Fax Email SupportRepId\n",
"1 Luís Gonçalves Embraer - Empresa Brasileira de Aeronáutica S.A. Av. Brigadeiro Faria Lima, 2170 São José dos Campos SP Brazil 12227-000 +55 (12) 3923-5555 +55 (12) 3923-5566 luisg@embraer.com.br 3\n",
"2 Leonie Köhler None Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 +49 0711 2842222 None leonekohler@surfeu.de 5\n",
"3 François Tremblay None 1498 rue Bélanger Montréal QC Canada H2G 1A7 +1 (514) 721-4711 None ftremblay@gmail.com 3\n",
"\n",
"\n",
"CREATE TABLE \"Invoice\" (\n",
"\t\"InvoiceId\" INTEGER NOT NULL, \n",
"\t\"CustomerId\" INTEGER NOT NULL, \n",
"\t\"InvoiceDate\" DATETIME NOT NULL, \n",
"\t\"BillingAddress\" NVARCHAR(70), \n",
"\t\"BillingCity\" NVARCHAR(40), \n",
"\t\"BillingState\" NVARCHAR(40), \n",
"\t\"BillingCountry\" NVARCHAR(40), \n",
"\t\"BillingPostalCode\" NVARCHAR(10), \n",
"\t\"Total\" NUMERIC(10, 2) NOT NULL, \n",
"\tPRIMARY KEY (\"InvoiceId\"), \n",
"\tFOREIGN KEY(\"CustomerId\") REFERENCES \"Customer\" (\"CustomerId\")\n",
")\n",
"\n",
"SELECT * FROM 'Invoice' LIMIT 3;\n",
"InvoiceId CustomerId InvoiceDate BillingAddress BillingCity BillingState BillingCountry BillingPostalCode Total\n",
"1 2 2009-01-01 00:00:00 Theodor-Heuss-Straße 34 Stuttgart None Germany 70174 1.98\n",
"2 4 2009-01-02 00:00:00 Ullevålsveien 14 Oslo None Norway 0171 3.96\n",
"3 8 2009-01-03 00:00:00 Grétrystraat 63 Brussels None Belgium 1000 5.94\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should query the Invoice and Customer tables to get the total sales per country.\n",
"Action: query_sql_db\n",
"Action Input: SELECT c.Country, SUM(i.Total) AS TotalSales FROM Invoice i INNER JOIN Customer c ON i.CustomerId = c.CustomerId GROUP BY c.Country ORDER BY TotalSales DESC LIMIT 10\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m[('USA', 523.0600000000003), ('Canada', 303.9599999999999), ('France', 195.09999999999994), ('Brazil', 190.09999999999997), ('Germany', 156.48), ('United Kingdom', 112.85999999999999), ('Czech Republic', 90.24000000000001), ('Portugal', 77.23999999999998), ('India', 75.25999999999999), ('Chile', 46.62)]\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: The customers from the USA spent the most, with a total of $523.06.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The customers from the USA spent the most, with a total of $523.06.'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"List the total sales per country. Which country's customers spent the most?\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "474dddda-c067-4eeb-98b1-e763ee78b18c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
"Action Input: \"\"\u001b[0m\n",
"Observation: \u001b[38;5;200m\u001b[1;3mInvoice, MediaType, Artist, InvoiceLine, Genre, Playlist, Employee, Album, PlaylistTrack, Track, Customer\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the Playlist and PlaylistTrack tables to see what columns I can use.\n",
"Action: schema_sql_db\n",
"Action Input: \"Playlist, PlaylistTrack\"\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"CREATE TABLE \"Playlist\" (\n",
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120), \n",
"\tPRIMARY KEY (\"PlaylistId\")\n",
")\n",
"\n",
"SELECT * FROM 'Playlist' LIMIT 3;\n",
"PlaylistId Name\n",
"1 Music\n",
"2 Movies\n",
"3 TV Shows\n",
"\n",
"\n",
"CREATE TABLE \"PlaylistTrack\" (\n",
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"PlaylistId\", \"TrackId\"), \n",
"\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
"\tFOREIGN KEY(\"PlaylistId\") REFERENCES \"Playlist\" (\"PlaylistId\")\n",
")\n",
"\n",
"SELECT * FROM 'PlaylistTrack' LIMIT 3;\n",
"PlaylistId TrackId\n",
"1 3402\n",
"1 3389\n",
"1 3390\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I can use a SELECT statement to get the total number of tracks in each playlist.\n",
"Action: query_checker_sql_db\n",
"Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name\u001b[0m\n",
"Observation: \u001b[31;1m\u001b[1;3m\n",
"\n",
"SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m The query looks correct, I can now execute it.\n",
"Action: query_sql_db\n",
"Action Input: SELECT Playlist.Name, COUNT(PlaylistTrack.TrackId) AS TotalTracks FROM Playlist INNER JOIN PlaylistTrack ON Playlist.PlaylistId = PlaylistTrack.PlaylistId GROUP BY Playlist.Name LIMIT 10\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m[('90s Music', 1477), ('Brazilian Music', 39), ('Classical', 75), ('Classical 101 - Deep Cuts', 25), ('Classical 101 - Next Steps', 25), ('Classical 101 - The Basics', 25), ('Grunge', 15), ('Heavy Metal Classic', 26), ('Music', 6580), ('Music Videos', 1)]\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: The total number of tracks in each playlist are: '90s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1).\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"The total number of tracks in each playlist are: '90s Music' (1477), 'Brazilian Music' (39), 'Classical' (75), 'Classical 101 - Deep Cuts' (25), 'Classical 101 - Next Steps' (25), 'Classical 101 - The Basics' (25), 'Grunge' (15), 'Heavy Metal Classic' (26), 'Music' (6580), 'Music Videos' (1).\""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"Show the total number of tracks in each playlist. The Playlist name should be included in the result.\")"
]
},
{
"cell_type": "markdown",
"id": "7c7503b5-d9d9-4faa-b064-29fcdb5ff213",
"metadata": {},
"source": [
"## Recovering from an error\n",
"\n",
"In this example, the agent is able to recover from an error after initially trying to access an attribute (`Track.ArtistId`) which doesn't exist."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9fe4901e-f9e1-4022-b6bc-80e2b2d6a3a4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
"Action Input: \"\"\u001b[0m\n",
"Observation: \u001b[38;5;200m\u001b[1;3mMediaType, Track, Invoice, Album, Playlist, Customer, Employee, InvoiceLine, PlaylistTrack, Genre, Artist\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should look at the schema of the Artist, InvoiceLine, and Track tables to see what columns I can use.\n",
"Action: schema_sql_db\n",
"Action Input: \"Artist, InvoiceLine, Track\"\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m\n",
"CREATE TABLE \"Artist\" (\n",
"\t\"ArtistId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120), \n",
"\tPRIMARY KEY (\"ArtistId\")\n",
")\n",
"\n",
"SELECT * FROM 'Artist' LIMIT 3;\n",
"ArtistId Name\n",
"1 AC/DC\n",
"2 Accept\n",
"3 Aerosmith\n",
"\n",
"\n",
"CREATE TABLE \"Track\" (\n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(200) NOT NULL, \n",
"\t\"AlbumId\" INTEGER, \n",
"\t\"MediaTypeId\" INTEGER NOT NULL, \n",
"\t\"GenreId\" INTEGER, \n",
"\t\"Composer\" NVARCHAR(220), \n",
"\t\"Milliseconds\" INTEGER NOT NULL, \n",
"\t\"Bytes\" INTEGER, \n",
"\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
"\tPRIMARY KEY (\"TrackId\"), \n",
"\tFOREIGN KEY(\"MediaTypeId\") REFERENCES \"MediaType\" (\"MediaTypeId\"), \n",
"\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
"\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
")\n",
"\n",
"SELECT * FROM 'Track' LIMIT 3;\n",
"TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice\n",
"1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99\n",
"2 Balls to the Wall 2 2 1 None 342562 5510424 0.99\n",
"3 Fast As a Shark 3 2 1 F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619 3990994 0.99\n",
"\n",
"\n",
"CREATE TABLE \"InvoiceLine\" (\n",
"\t\"InvoiceLineId\" INTEGER NOT NULL, \n",
"\t\"InvoiceId\" INTEGER NOT NULL, \n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\t\"UnitPrice\" NUMERIC(10, 2) NOT NULL, \n",
"\t\"Quantity\" INTEGER NOT NULL, \n",
"\tPRIMARY KEY (\"InvoiceLineId\"), \n",
"\tFOREIGN KEY(\"TrackId\") REFERENCES \"Track\" (\"TrackId\"), \n",
"\tFOREIGN KEY(\"InvoiceId\") REFERENCES \"Invoice\" (\"InvoiceId\")\n",
")\n",
"\n",
"SELECT * FROM 'InvoiceLine' LIMIT 3;\n",
"InvoiceLineId InvoiceId TrackId UnitPrice Quantity\n",
"1 1 2 0.99 1\n",
"2 1 4 0.99 1\n",
"3 2 6 0.99 1\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should query the database to get the top 3 best selling artists.\n",
"Action: query_sql_db\n",
"Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mError: (sqlite3.OperationalError) no such column: Track.ArtistId\n",
"[SQL: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3]\n",
"(Background on this error at: https://sqlalche.me/e/14/e3q8)\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I should double check my query before executing it.\n",
"Action: query_checker_sql_db\n",
"Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Track ON Artist.ArtistId = Track.ArtistId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
"Observation: \u001b[31;1m\u001b[1;3m\n",
"\n",
"SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity \n",
"FROM Artist \n",
"INNER JOIN Track ON Artist.ArtistId = Track.ArtistId \n",
"INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId \n",
"GROUP BY Artist.Name \n",
"ORDER BY TotalQuantity DESC \n",
"LIMIT 3;\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Action: query_sql_db\n",
"Action Input: SELECT Artist.Name, SUM(InvoiceLine.Quantity) AS TotalQuantity FROM Artist INNER JOIN Album ON Artist.ArtistId = Album.ArtistId INNER JOIN Track ON Album.AlbumId = Track.AlbumId INNER JOIN InvoiceLine ON Track.TrackId = InvoiceLine.TrackId GROUP BY Artist.Name ORDER BY TotalQuantity DESC LIMIT 3\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m[('Iron Maiden', 140), ('U2', 107), ('Metallica', 91)]\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: The top 3 best selling artists are Iron Maiden, U2, and Metallica.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'The top 3 best selling artists are Iron Maiden, U2, and Metallica.'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"Who are the top 3 best selling artists?\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,892 +0,0 @@
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
220,0,2,"Harris, Mr. Walter",male,30,0,0,W/C 14208,10.5,,S
221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
230,0,3,"Lefebre, Miss. Mathilde",female,,3,1,4133,25.4667,,S
231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
232,0,3,"Larsson, Mr. Bengt Edvin",male,29,0,0,347067,7.775,,S
233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
236,0,3,"Harknett, Miss. Alice Phoebe",female,,0,0,W./C. 6609,7.55,,S
237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
238,1,2,"Collyer, Miss. Marjorie ""Lottie""",female,8,0,2,C.A. 31921,26.25,,S
239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
250,0,2,"Carter, Rev. Ernest Courtenay",male,54,1,0,244252,26,,S
251,0,3,"Reed, Mr. James George",male,,0,0,362316,7.25,,S
252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
253,0,1,"Stead, Mr. William Thomas",male,62,0,0,113514,26.55,C87,S
254,0,3,"Lobb, Mr. William Arthur",male,30,1,0,A/5. 3336,16.1,,S
255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
275,1,3,"Healy, Miss. Hanora ""Nora""",female,,0,0,370375,7.75,,Q
276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
290,1,3,"Connolly, Miss. Kate",female,22,0,0,370373,7.75,,Q
291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
310,1,1,"Francatelli, Miss. Laura Mabel",female,30,0,0,PC 17485,56.9292,E36,C
311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
331,1,3,"McCoy, Miss. Agnes",female,,2,0,367226,23.25,,Q
332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
333,0,1,"Graham, Mr. George Edward",male,38,0,1,PC 17582,153.4625,C91,S
334,0,3,"Vander Planke, Mr. Leo Edmondus",male,16,2,0,345764,18,,S
335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
341,1,2,"Navratil, Master. Edmond Roger",male,2,1,1,230080,26,F2,S
342,1,1,"Fortune, Miss. Alice Elizabeth",female,24,3,2,19950,263,C23 C25 C27,S
343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
345,0,2,"Fox, Mr. Stanley Hubert",male,36,0,0,229236,13,,S
346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
347,1,2,"Smith, Miss. Marion Elsie",female,40,0,0,31418,13,,S
348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
367,1,1,"Warren, Mrs. Frank Manley (Anna Sophia Atkinson)",female,60,1,0,110813,75.25,D37,C
368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
370,1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3,B35,C
371,1,1,"Harder, Mr. George Achilles",male,25,1,0,11765,55.4417,E50,C
372,0,3,"Wiklund, Mr. Jakob Alfred",male,18,1,0,3101267,6.4958,,S
373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
375,0,3,"Palsson, Miss. Stina Viola",female,3,3,1,349909,21.075,,S
376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
392,1,3,"Jansson, Mr. Carl Olof",male,21,0,0,350034,7.7958,,S
393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
394,1,1,"Newell, Miss. Marjorie",female,23,1,0,35273,113.275,D36,C
395,1,3,"Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson)",female,24,0,2,PP 9549,16.7,G6,S
396,0,3,"Johansson, Mr. Erik",male,22,0,0,350052,7.7958,,S
397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
403,0,3,"Jussila, Miss. Mari Aina",female,21,1,0,4137,9.825,,S
404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
405,0,3,"Oreskovic, Miss. Marija",female,20,0,0,315096,8.6625,,S
406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
407,0,3,"Widegren, Mr. Carl/Charles Peter",male,51,0,0,347064,7.75,,S
408,1,2,"Richards, Master. William Rowe",male,3,1,1,29106,18.75,,S
409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
414,0,2,"Cunningham, Mr. Alfred Fleming",male,,0,0,239853,0,,S
415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
418,1,2,"Silven, Miss. Lyyli Karoliina",female,18,0,2,250652,13,,S
419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
420,0,3,"Van Impe, Miss. Catharina",female,10,0,2,345773,24.15,,S
421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
423,0,3,"Zimmerman, Mr. Leo",male,29,0,0,315082,7.875,,S
424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
425,0,3,"Rosblom, Mr. Viktor Richard",male,18,1,1,370129,20.2125,,S
426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
427,1,2,"Clarke, Mrs. Charles V (Ada Maria Winfield)",female,28,1,0,2003,26,,S
428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
429,0,3,"Flynn, Mr. James",male,,0,0,364851,7.75,,Q
430,1,3,"Pickard, Mr. Berk (Berk Trembisky)",male,32,0,0,SOTON/O.Q. 392078,8.05,E10,S
431,1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.55,C52,S
432,1,3,"Thorneycroft, Mrs. Percival (Florence Kate White)",female,,1,0,376564,16.1,,S
433,1,2,"Louch, Mrs. Charles Alexander (Alice Adelaide Slow)",female,42,1,0,SC/AH 3085,26,,S
434,0,3,"Kallio, Mr. Nikolai Erland",male,17,0,0,STON/O 2. 3101274,7.125,,S
435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
436,1,1,"Carter, Miss. Lucile Polk",female,14,1,2,113760,120,B96 B98,S
437,0,3,"Ford, Miss. Doolina Margaret ""Daisy""",female,21,2,2,W./C. 6608,34.375,,S
438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
439,0,1,"Fortune, Mr. Mark",male,64,1,4,19950,263,C23 C25 C27,S
440,0,2,"Kvillner, Mr. Johan Henrik Johannesson",male,31,0,0,C.A. 18723,10.5,,S
441,1,2,"Hart, Mrs. Benjamin (Esther Ada Bloomfield)",female,45,1,1,F.C.C. 13529,26.25,,S
442,0,3,"Hampe, Mr. Leon",male,20,0,0,345769,9.5,,S
443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
444,1,2,"Reynaldo, Ms. Encarnacion",female,28,0,0,230434,13,,S
445,1,3,"Johannesen-Bratthammer, Mr. Bernt",male,,0,0,65306,8.1125,,S
446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
447,1,2,"Mellinger, Miss. Madeleine Violet",female,13,0,1,250644,19.5,,S
448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
449,1,3,"Baclini, Miss. Marie Catherine",female,5,2,1,2666,19.2583,,C
450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
451,0,2,"West, Mr. Edwy Arthur",male,36,1,2,C.A. 34651,27.75,,S
452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
454,1,1,"Goldenberg, Mr. Samuel L",male,49,1,0,17453,89.1042,C92,C
455,0,3,"Peduzzi, Mr. Joseph",male,,0,0,A/5 2817,8.05,,S
456,1,3,"Jalsevac, Mr. Ivan",male,29,0,0,349240,7.8958,,C
457,0,1,"Millet, Mr. Francis Davis",male,65,0,0,13509,26.55,E38,S
458,1,1,"Kenyon, Mrs. Frederick R (Marion)",female,,1,0,17464,51.8625,D21,S
459,1,2,"Toomey, Miss. Ellen",female,50,0,0,F.C.C. 13531,10.5,,S
460,0,3,"O'Connor, Mr. Maurice",male,,0,0,371060,7.75,,Q
461,1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.55,E12,S
462,0,3,"Morley, Mr. William",male,34,0,0,364506,8.05,,S
463,0,1,"Gee, Mr. Arthur H",male,47,0,0,111320,38.5,E63,S
464,0,2,"Milling, Mr. Jacob Christian",male,48,0,0,234360,13,,S
465,0,3,"Maisner, Mr. Simon",male,,0,0,A/S 2816,8.05,,S
466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
467,0,2,"Campbell, Mr. William",male,,0,0,239853,0,,S
468,0,1,"Smart, Mr. John Montgomery",male,56,0,0,113792,26.55,,S
469,0,3,"Scanlan, Mr. James",male,,0,0,36209,7.725,,Q
470,1,3,"Baclini, Miss. Helene Barbara",female,0.75,2,1,2666,19.2583,,C
471,0,3,"Keefe, Mr. Arthur",male,,0,0,323592,7.25,,S
472,0,3,"Cacic, Mr. Luka",male,38,0,0,315089,8.6625,,S
473,1,2,"West, Mrs. Edwy Arthur (Ada Mary Worth)",female,33,1,2,C.A. 34651,27.75,,S
474,1,2,"Jerwan, Mrs. Amin S (Marie Marthe Thuillard)",female,23,0,0,SC/AH Basle 541,13.7917,D,C
475,0,3,"Strandberg, Miss. Ida Sofia",female,22,0,0,7553,9.8375,,S
476,0,1,"Clifford, Mr. George Quincy",male,,0,0,110465,52,A14,S
477,0,2,"Renouf, Mr. Peter Henry",male,34,1,0,31027,21,,S
478,0,3,"Braund, Mr. Lewis Richard",male,29,1,0,3460,7.0458,,S
479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
481,0,3,"Goodwin, Master. Harold Victor",male,9,5,2,CA 2144,46.9,,S
482,0,2,"Frost, Mr. Anthony Wood ""Archie""",male,,0,0,239854,0,,S
483,0,3,"Rouse, Mr. Richard Henry",male,50,0,0,A/5 3594,8.05,,S
484,1,3,"Turkula, Mrs. (Hedwig)",female,63,0,0,4134,9.5875,,S
485,1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C
486,0,3,"Lefebre, Miss. Jeannie",female,,3,1,4133,25.4667,,S
487,1,1,"Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby)",female,35,1,0,19943,90,C93,S
488,0,1,"Kent, Mr. Edward Austin",male,58,0,0,11771,29.7,B37,C
489,0,3,"Somerton, Mr. Francis William",male,30,0,0,A.5. 18509,8.05,,S
490,1,3,"Coutts, Master. Eden Leslie ""Neville""",male,9,1,1,C.A. 37671,15.9,,S
491,0,3,"Hagland, Mr. Konrad Mathias Reiersen",male,,1,0,65304,19.9667,,S
492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
493,0,1,"Molson, Mr. Harry Markland",male,55,0,0,113787,30.5,C30,S
494,0,1,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C
495,0,3,"Stanley, Mr. Edward Roland",male,21,0,0,A/4 45380,8.05,,S
496,0,3,"Yousseff, Mr. Gerious",male,,0,0,2627,14.4583,,C
497,1,1,"Eustis, Miss. Elizabeth Mussey",female,54,1,0,36947,78.2667,D20,C
498,0,3,"Shellard, Mr. Frederick William",male,,0,0,C.A. 6212,15.1,,S
499,0,1,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.55,C22 C26,S
500,0,3,"Svensson, Mr. Olof",male,24,0,0,350035,7.7958,,S
501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
502,0,3,"Canavan, Miss. Mary",female,21,0,0,364846,7.75,,Q
503,0,3,"O'Sullivan, Miss. Bridget Mary",female,,0,0,330909,7.6292,,Q
504,0,3,"Laitinen, Miss. Kristina Sofia",female,37,0,0,4135,9.5875,,S
505,1,1,"Maioni, Miss. Roberta",female,16,0,0,110152,86.5,B79,S
506,0,1,"Penasco y Castellana, Mr. Victor de Satode",male,18,1,0,PC 17758,108.9,C65,C
507,1,2,"Quick, Mrs. Frederick Charles (Jane Richards)",female,33,0,2,26360,26,,S
508,1,1,"Bradley, Mr. George (""George Arthur Brayton"")",male,,0,0,111427,26.55,,S
509,0,3,"Olsen, Mr. Henry Margido",male,28,0,0,C 4001,22.525,,S
510,1,3,"Lang, Mr. Fang",male,26,0,0,1601,56.4958,,S
511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
512,0,3,"Webber, Mr. James",male,,0,0,SOTON/OQ 3101316,8.05,,S
513,1,1,"McGough, Mr. James Robert",male,36,0,0,PC 17473,26.2875,E25,S
514,1,1,"Rothschild, Mrs. Martin (Elizabeth L. Barrett)",female,54,1,0,PC 17603,59.4,,C
515,0,3,"Coleff, Mr. Satio",male,24,0,0,349209,7.4958,,S
516,0,1,"Walker, Mr. William Anderson",male,47,0,0,36967,34.0208,D46,S
517,1,2,"Lemore, Mrs. (Amelia Milley)",female,34,0,0,C.A. 34260,10.5,F33,S
518,0,3,"Ryan, Mr. Patrick",male,,0,0,371110,24.15,,Q
519,1,2,"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)",female,36,1,0,226875,26,,S
520,0,3,"Pavlovic, Mr. Stefo",male,32,0,0,349242,7.8958,,S
521,1,1,"Perreault, Miss. Anne",female,30,0,0,12749,93.5,B73,S
522,0,3,"Vovk, Mr. Janko",male,22,0,0,349252,7.8958,,S
523,0,3,"Lahoud, Mr. Sarkis",male,,0,0,2624,7.225,,C
524,1,1,"Hippach, Mrs. Louis Albert (Ida Sophia Fischer)",female,44,0,1,111361,57.9792,B18,C
525,0,3,"Kassem, Mr. Fared",male,,0,0,2700,7.2292,,C
526,0,3,"Farrell, Mr. James",male,40.5,0,0,367232,7.75,,Q
527,1,2,"Ridsdale, Miss. Lucy",female,50,0,0,W./C. 14258,10.5,,S
528,0,1,"Farthing, Mr. John",male,,0,0,PC 17483,221.7792,C95,S
529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
530,0,2,"Hocking, Mr. Richard George",male,23,2,1,29104,11.5,,S
531,1,2,"Quick, Miss. Phyllis May",female,2,1,1,26360,26,,S
532,0,3,"Toufik, Mr. Nakli",male,,0,0,2641,7.2292,,C
533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
534,1,3,"Peter, Mrs. Catherine (Catherine Rizk)",female,,0,2,2668,22.3583,,C
535,0,3,"Cacic, Miss. Marija",female,30,0,0,315084,8.6625,,S
536,1,2,"Hart, Miss. Eva Miriam",female,7,0,2,F.C.C. 13529,26.25,,S
537,0,1,"Butt, Major. Archibald Willingham",male,45,0,0,113050,26.55,B38,S
538,1,1,"LeRoy, Miss. Bertha",female,30,0,0,PC 17761,106.425,,C
539,0,3,"Risien, Mr. Samuel Beard",male,,0,0,364498,14.5,,S
540,1,1,"Frolicher, Miss. Hedwig Margaritha",female,22,0,2,13568,49.5,B39,C
541,1,1,"Crosby, Miss. Harriet R",female,36,0,2,WE/P 5735,71,B22,S
542,0,3,"Andersson, Miss. Ingeborg Constanzia",female,9,4,2,347082,31.275,,S
543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
545,0,1,"Douglas, Mr. Walter Donald",male,50,1,0,PC 17761,106.425,C86,C
546,0,1,"Nicholson, Mr. Arthur Ernest",male,64,0,0,693,26,,S
547,1,2,"Beane, Mrs. Edward (Ethel Clarke)",female,19,1,0,2908,26,,S
548,1,2,"Padro y Manent, Mr. Julian",male,,0,0,SC/PARIS 2146,13.8625,,C
549,0,3,"Goldsmith, Mr. Frank John",male,33,1,1,363291,20.525,,S
550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
551,1,1,"Thayer, Mr. John Borland Jr",male,17,0,2,17421,110.8833,C70,C
552,0,2,"Sharp, Mr. Percival James R",male,27,0,0,244358,26,,S
553,0,3,"O'Brien, Mr. Timothy",male,,0,0,330979,7.8292,,Q
554,1,3,"Leeni, Mr. Fahim (""Philip Zenni"")",male,22,0,0,2620,7.225,,C
555,1,3,"Ohman, Miss. Velin",female,22,0,0,347085,7.775,,S
556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
557,1,1,"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")",female,48,1,0,11755,39.6,A16,C
558,0,1,"Robbins, Mr. Victor",male,,0,0,PC 17757,227.525,,C
559,1,1,"Taussig, Mrs. Emil (Tillie Mandelbaum)",female,39,1,1,110413,79.65,E67,S
560,1,3,"de Messemaeker, Mrs. Guillaume Joseph (Emma)",female,36,1,0,345572,17.4,,S
561,0,3,"Morrow, Mr. Thomas Rowan",male,,0,0,372622,7.75,,Q
562,0,3,"Sivic, Mr. Husein",male,40,0,0,349251,7.8958,,S
563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
565,0,3,"Meanwell, Miss. (Marion Ogden)",female,,0,0,SOTON/O.Q. 392087,8.05,,S
566,0,3,"Davies, Mr. Alfred J",male,24,2,0,A/4 48871,24.15,,S
567,0,3,"Stoytcheff, Mr. Ilia",male,19,0,0,349205,7.8958,,S
568,0,3,"Palsson, Mrs. Nils (Alma Cornelia Berglund)",female,29,0,4,349909,21.075,,S
569,0,3,"Doharr, Mr. Tannous",male,,0,0,2686,7.2292,,C
570,1,3,"Jonsson, Mr. Carl",male,32,0,0,350417,7.8542,,S
571,1,2,"Harris, Mr. George",male,62,0,0,S.W./PP 752,10.5,,S
572,1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S
573,1,1,"Flynn, Mr. John Irwin (""Irving"")",male,36,0,0,PC 17474,26.3875,E25,S
574,1,3,"Kelly, Miss. Mary",female,,0,0,14312,7.75,,Q
575,0,3,"Rush, Mr. Alfred George John",male,16,0,0,A/4. 20589,8.05,,S
576,0,3,"Patchett, Mr. George",male,19,0,0,358585,14.5,,S
577,1,2,"Garside, Miss. Ethel",female,34,0,0,243880,13,,S
578,1,1,"Silvey, Mrs. William Baird (Alice Munger)",female,39,1,0,13507,55.9,E44,S
579,0,3,"Caram, Mrs. Joseph (Maria Elias)",female,,1,0,2689,14.4583,,C
580,1,3,"Jussila, Mr. Eiriik",male,32,0,0,STON/O 2. 3101286,7.925,,S
581,1,2,"Christy, Miss. Julie Rachel",female,25,1,1,237789,30,,S
582,1,1,"Thayer, Mrs. John Borland (Marian Longstreth Morris)",female,39,1,1,17421,110.8833,C68,C
583,0,2,"Downton, Mr. William James",male,54,0,0,28403,26,,S
584,0,1,"Ross, Mr. John Hugo",male,36,0,0,13049,40.125,A10,C
585,0,3,"Paulner, Mr. Uscher",male,,0,0,3411,8.7125,,C
586,1,1,"Taussig, Miss. Ruth",female,18,0,2,110413,79.65,E68,S
587,0,2,"Jarvis, Mr. John Denzil",male,47,0,0,237565,15,,S
588,1,1,"Frolicher-Stehli, Mr. Maxmillian",male,60,1,1,13567,79.2,B41,C
589,0,3,"Gilinski, Mr. Eliezer",male,22,0,0,14973,8.05,,S
590,0,3,"Murdlin, Mr. Joseph",male,,0,0,A./5. 3235,8.05,,S
591,0,3,"Rintamaki, Mr. Matti",male,35,0,0,STON/O 2. 3101273,7.125,,S
592,1,1,"Stephenson, Mrs. Walter Bertram (Martha Eustis)",female,52,1,0,36947,78.2667,D20,C
593,0,3,"Elsbury, Mr. William James",male,47,0,0,A/5 3902,7.25,,S
594,0,3,"Bourke, Miss. Mary",female,,0,2,364848,7.75,,Q
595,0,2,"Chapman, Mr. John Henry",male,37,1,0,SC/AH 29037,26,,S
596,0,3,"Van Impe, Mr. Jean Baptiste",male,36,1,1,345773,24.15,,S
597,1,2,"Leitch, Miss. Jessie Wills",female,,0,0,248727,33,,S
598,0,3,"Johnson, Mr. Alfred",male,49,0,0,LINE,0,,S
599,0,3,"Boulos, Mr. Hanna",male,,0,0,2664,7.225,,C
600,1,1,"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")",male,49,1,0,PC 17485,56.9292,A20,C
601,1,2,"Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)",female,24,2,1,243847,27,,S
602,0,3,"Slabenoff, Mr. Petco",male,,0,0,349214,7.8958,,S
603,0,1,"Harrington, Mr. Charles H",male,,0,0,113796,42.4,,S
604,0,3,"Torber, Mr. Ernst William",male,44,0,0,364511,8.05,,S
605,1,1,"Homer, Mr. Harry (""Mr E Haven"")",male,35,0,0,111426,26.55,,C
606,0,3,"Lindell, Mr. Edvard Bengtsson",male,36,1,0,349910,15.55,,S
607,0,3,"Karaic, Mr. Milan",male,30,0,0,349246,7.8958,,S
608,1,1,"Daniel, Mr. Robert Williams",male,27,0,0,113804,30.5,,S
609,1,2,"Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue)",female,22,1,2,SC/Paris 2123,41.5792,,C
610,1,1,"Shutes, Miss. Elizabeth W",female,40,0,0,PC 17582,153.4625,C125,S
611,0,3,"Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)",female,39,1,5,347082,31.275,,S
612,0,3,"Jardin, Mr. Jose Neto",male,,0,0,SOTON/O.Q. 3101305,7.05,,S
613,1,3,"Murphy, Miss. Margaret Jane",female,,1,0,367230,15.5,,Q
614,0,3,"Horgan, Mr. John",male,,0,0,370377,7.75,,Q
615,0,3,"Brocklebank, Mr. William Alfred",male,35,0,0,364512,8.05,,S
616,1,2,"Herman, Miss. Alice",female,24,1,2,220845,65,,S
617,0,3,"Danbom, Mr. Ernst Gilbert",male,34,1,1,347080,14.4,,S
618,0,3,"Lobb, Mrs. William Arthur (Cordelia K Stanlick)",female,26,1,0,A/5. 3336,16.1,,S
619,1,2,"Becker, Miss. Marion Louise",female,4,2,1,230136,39,F4,S
620,0,2,"Gavey, Mr. Lawrence",male,26,0,0,31028,10.5,,S
621,0,3,"Yasbeck, Mr. Antoni",male,27,1,0,2659,14.4542,,C
622,1,1,"Kimball, Mr. Edwin Nelson Jr",male,42,1,0,11753,52.5542,D19,S
623,1,3,"Nakid, Mr. Sahid",male,20,1,1,2653,15.7417,,C
624,0,3,"Hansen, Mr. Henry Damsgaard",male,21,0,0,350029,7.8542,,S
625,0,3,"Bowen, Mr. David John ""Dai""",male,21,0,0,54636,16.1,,S
626,0,1,"Sutton, Mr. Frederick",male,61,0,0,36963,32.3208,D50,S
627,0,2,"Kirkland, Rev. Charles Leonard",male,57,0,0,219533,12.35,,Q
628,1,1,"Longley, Miss. Gretchen Fiske",female,21,0,0,13502,77.9583,D9,S
629,0,3,"Bostandyeff, Mr. Guentcho",male,26,0,0,349224,7.8958,,S
630,0,3,"O'Connell, Mr. Patrick D",male,,0,0,334912,7.7333,,Q
631,1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30,A23,S
632,0,3,"Lundahl, Mr. Johan Svensson",male,51,0,0,347743,7.0542,,S
633,1,1,"Stahelin-Maeglin, Dr. Max",male,32,0,0,13214,30.5,B50,C
634,0,1,"Parr, Mr. William Henry Marsh",male,,0,0,112052,0,,S
635,0,3,"Skoog, Miss. Mabel",female,9,3,2,347088,27.9,,S
636,1,2,"Davis, Miss. Mary",female,28,0,0,237668,13,,S
637,0,3,"Leinonen, Mr. Antti Gustaf",male,32,0,0,STON/O 2. 3101292,7.925,,S
638,0,2,"Collyer, Mr. Harvey",male,31,1,1,C.A. 31921,26.25,,S
639,0,3,"Panula, Mrs. Juha (Maria Emilia Ojala)",female,41,0,5,3101295,39.6875,,S
640,0,3,"Thorneycroft, Mr. Percival",male,,1,0,376564,16.1,,S
641,0,3,"Jensen, Mr. Hans Peder",male,20,0,0,350050,7.8542,,S
642,1,1,"Sagesser, Mlle. Emma",female,24,0,0,PC 17477,69.3,B35,C
643,0,3,"Skoog, Miss. Margit Elizabeth",female,2,3,2,347088,27.9,,S
644,1,3,"Foo, Mr. Choong",male,,0,0,1601,56.4958,,S
645,1,3,"Baclini, Miss. Eugenie",female,0.75,2,1,2666,19.2583,,C
646,1,1,"Harper, Mr. Henry Sleeper",male,48,1,0,PC 17572,76.7292,D33,C
647,0,3,"Cor, Mr. Liudevit",male,19,0,0,349231,7.8958,,S
648,1,1,"Simonius-Blumer, Col. Oberst Alfons",male,56,0,0,13213,35.5,A26,C
649,0,3,"Willey, Mr. Edward",male,,0,0,S.O./P.P. 751,7.55,,S
650,1,3,"Stanley, Miss. Amy Zillah Elsie",female,23,0,0,CA. 2314,7.55,,S
651,0,3,"Mitkoff, Mr. Mito",male,,0,0,349221,7.8958,,S
652,1,2,"Doling, Miss. Elsie",female,18,0,1,231919,23,,S
653,0,3,"Kalvik, Mr. Johannes Halvorsen",male,21,0,0,8475,8.4333,,S
654,1,3,"O'Leary, Miss. Hanora ""Norah""",female,,0,0,330919,7.8292,,Q
655,0,3,"Hegarty, Miss. Hanora ""Nora""",female,18,0,0,365226,6.75,,Q
656,0,2,"Hickman, Mr. Leonard Mark",male,24,2,0,S.O.C. 14879,73.5,,S
657,0,3,"Radeff, Mr. Alexander",male,,0,0,349223,7.8958,,S
658,0,3,"Bourke, Mrs. John (Catherine)",female,32,1,1,364849,15.5,,Q
659,0,2,"Eitemiller, Mr. George Floyd",male,23,0,0,29751,13,,S
660,0,1,"Newell, Mr. Arthur Webster",male,58,0,2,35273,113.275,D48,C
661,1,1,"Frauenthal, Dr. Henry William",male,50,2,0,PC 17611,133.65,,S
662,0,3,"Badt, Mr. Mohamed",male,40,0,0,2623,7.225,,C
663,0,1,"Colley, Mr. Edward Pomeroy",male,47,0,0,5727,25.5875,E58,S
664,0,3,"Coleff, Mr. Peju",male,36,0,0,349210,7.4958,,S
665,1,3,"Lindqvist, Mr. Eino William",male,20,1,0,STON/O 2. 3101285,7.925,,S
666,0,2,"Hickman, Mr. Lewis",male,32,2,0,S.O.C. 14879,73.5,,S
667,0,2,"Butler, Mr. Reginald Fenton",male,25,0,0,234686,13,,S
668,0,3,"Rommetvedt, Mr. Knud Paust",male,,0,0,312993,7.775,,S
669,0,3,"Cook, Mr. Jacob",male,43,0,0,A/5 3536,8.05,,S
670,1,1,"Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright)",female,,1,0,19996,52,C126,S
671,1,2,"Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)",female,40,1,1,29750,39,,S
672,0,1,"Davidson, Mr. Thornton",male,31,1,0,F.C. 12750,52,B71,S
673,0,2,"Mitchell, Mr. Henry Michael",male,70,0,0,C.A. 24580,10.5,,S
674,1,2,"Wilhelms, Mr. Charles",male,31,0,0,244270,13,,S
675,0,2,"Watson, Mr. Ennis Hastings",male,,0,0,239856,0,,S
676,0,3,"Edvardsson, Mr. Gustaf Hjalmar",male,18,0,0,349912,7.775,,S
677,0,3,"Sawyer, Mr. Frederick Charles",male,24.5,0,0,342826,8.05,,S
678,1,3,"Turja, Miss. Anna Sofia",female,18,0,0,4138,9.8417,,S
679,0,3,"Goodwin, Mrs. Frederick (Augusta Tyler)",female,43,1,6,CA 2144,46.9,,S
680,1,1,"Cardeza, Mr. Thomas Drake Martinez",male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
681,0,3,"Peters, Miss. Katie",female,,0,0,330935,8.1375,,Q
682,1,1,"Hassab, Mr. Hammad",male,27,0,0,PC 17572,76.7292,D49,C
683,0,3,"Olsvigen, Mr. Thor Anderson",male,20,0,0,6563,9.225,,S
684,0,3,"Goodwin, Mr. Charles Edward",male,14,5,2,CA 2144,46.9,,S
685,0,2,"Brown, Mr. Thomas William Solomon",male,60,1,1,29750,39,,S
686,0,2,"Laroche, Mr. Joseph Philippe Lemercier",male,25,1,2,SC/Paris 2123,41.5792,,C
687,0,3,"Panula, Mr. Jaako Arnold",male,14,4,1,3101295,39.6875,,S
688,0,3,"Dakic, Mr. Branko",male,19,0,0,349228,10.1708,,S
689,0,3,"Fischer, Mr. Eberhard Thelander",male,18,0,0,350036,7.7958,,S
690,1,1,"Madill, Miss. Georgette Alexandra",female,15,0,1,24160,211.3375,B5,S
691,1,1,"Dick, Mr. Albert Adrian",male,31,1,0,17474,57,B20,S
692,1,3,"Karun, Miss. Manca",female,4,0,1,349256,13.4167,,C
693,1,3,"Lam, Mr. Ali",male,,0,0,1601,56.4958,,S
694,0,3,"Saad, Mr. Khalil",male,25,0,0,2672,7.225,,C
695,0,1,"Weir, Col. John",male,60,0,0,113800,26.55,,S
696,0,2,"Chapman, Mr. Charles Henry",male,52,0,0,248731,13.5,,S
697,0,3,"Kelly, Mr. James",male,44,0,0,363592,8.05,,S
698,1,3,"Mullens, Miss. Katherine ""Katie""",female,,0,0,35852,7.7333,,Q
699,0,1,"Thayer, Mr. John Borland",male,49,1,1,17421,110.8833,C68,C
700,0,3,"Humblen, Mr. Adolf Mathias Nicolai Olsen",male,42,0,0,348121,7.65,F G63,S
701,1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.525,C62 C64,C
702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
703,0,3,"Barbara, Miss. Saiide",female,18,0,1,2691,14.4542,,C
704,0,3,"Gallagher, Mr. Martin",male,25,0,0,36864,7.7417,,Q
705,0,3,"Hansen, Mr. Henrik Juul",male,26,1,0,350025,7.8542,,S
706,0,2,"Morley, Mr. Henry Samuel (""Mr Henry Marshall"")",male,39,0,0,250655,26,,S
707,1,2,"Kelly, Mrs. Florence ""Fannie""",female,45,0,0,223596,13.5,,S
708,1,1,"Calderhead, Mr. Edward Pennington",male,42,0,0,PC 17476,26.2875,E24,S
709,1,1,"Cleaver, Miss. Alice",female,22,0,0,113781,151.55,,S
710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
711,1,1,"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")",female,24,0,0,PC 17482,49.5042,C90,C
712,0,1,"Klaber, Mr. Herman",male,,0,0,113028,26.55,C124,S
713,1,1,"Taylor, Mr. Elmer Zebley",male,48,1,0,19996,52,C126,S
714,0,3,"Larsson, Mr. August Viktor",male,29,0,0,7545,9.4833,,S
715,0,2,"Greenberg, Mr. Samuel",male,52,0,0,250647,13,,S
716,0,3,"Soholt, Mr. Peter Andreas Lauritz Andersen",male,19,0,0,348124,7.65,F G73,S
717,1,1,"Endres, Miss. Caroline Louise",female,38,0,0,PC 17757,227.525,C45,C
718,1,2,"Troutt, Miss. Edwina Celia ""Winnie""",female,27,0,0,34218,10.5,E101,S
719,0,3,"McEvoy, Mr. Michael",male,,0,0,36568,15.5,,Q
720,0,3,"Johnson, Mr. Malkolm Joackim",male,33,0,0,347062,7.775,,S
721,1,2,"Harper, Miss. Annie Jessie ""Nina""",female,6,0,1,248727,33,,S
722,0,3,"Jensen, Mr. Svend Lauritz",male,17,1,0,350048,7.0542,,S
723,0,2,"Gillespie, Mr. William Henry",male,34,0,0,12233,13,,S
724,0,2,"Hodges, Mr. Henry Price",male,50,0,0,250643,13,,S
725,1,1,"Chambers, Mr. Norman Campbell",male,27,1,0,113806,53.1,E8,S
726,0,3,"Oreskovic, Mr. Luka",male,20,0,0,315094,8.6625,,S
727,1,2,"Renouf, Mrs. Peter Henry (Lillian Jefferys)",female,30,3,0,31027,21,,S
728,1,3,"Mannion, Miss. Margareth",female,,0,0,36866,7.7375,,Q
729,0,2,"Bryhl, Mr. Kurt Arnold Gottfrid",male,25,1,0,236853,26,,S
730,0,3,"Ilmakangas, Miss. Pieta Sofia",female,25,1,0,STON/O2. 3101271,7.925,,S
731,1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S
732,0,3,"Hassan, Mr. Houssein G N",male,11,0,0,2699,18.7875,,C
733,0,2,"Knight, Mr. Robert J",male,,0,0,239855,0,,S
734,0,2,"Berriman, Mr. William John",male,23,0,0,28425,13,,S
735,0,2,"Troupiansky, Mr. Moses Aaron",male,23,0,0,233639,13,,S
736,0,3,"Williams, Mr. Leslie",male,28.5,0,0,54636,16.1,,S
737,0,3,"Ford, Mrs. Edward (Margaret Ann Watson)",female,48,1,3,W./C. 6608,34.375,,S
738,1,1,"Lesurer, Mr. Gustave J",male,35,0,0,PC 17755,512.3292,B101,C
739,0,3,"Ivanoff, Mr. Kanio",male,,0,0,349201,7.8958,,S
740,0,3,"Nankoff, Mr. Minko",male,,0,0,349218,7.8958,,S
741,1,1,"Hawksford, Mr. Walter James",male,,0,0,16988,30,D45,S
742,0,1,"Cavendish, Mr. Tyrell William",male,36,1,0,19877,78.85,C46,S
743,1,1,"Ryerson, Miss. Susan Parker ""Suzette""",female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
744,0,3,"McNamee, Mr. Neal",male,24,1,0,376566,16.1,,S
745,1,3,"Stranden, Mr. Juho",male,31,0,0,STON/O 2. 3101288,7.925,,S
746,0,1,"Crosby, Capt. Edward Gifford",male,70,1,1,WE/P 5735,71,B22,S
747,0,3,"Abbott, Mr. Rossmore Edward",male,16,1,1,C.A. 2673,20.25,,S
748,1,2,"Sinkkonen, Miss. Anna",female,30,0,0,250648,13,,S
749,0,1,"Marvin, Mr. Daniel Warner",male,19,1,0,113773,53.1,D30,S
750,0,3,"Connaghton, Mr. Michael",male,31,0,0,335097,7.75,,Q
751,1,2,"Wells, Miss. Joan",female,4,1,1,29103,23,,S
752,1,3,"Moor, Master. Meier",male,6,0,1,392096,12.475,E121,S
753,0,3,"Vande Velde, Mr. Johannes Joseph",male,33,0,0,345780,9.5,,S
754,0,3,"Jonkoff, Mr. Lalio",male,23,0,0,349204,7.8958,,S
755,1,2,"Herman, Mrs. Samuel (Jane Laver)",female,48,1,2,220845,65,,S
756,1,2,"Hamalainen, Master. Viljo",male,0.67,1,1,250649,14.5,,S
757,0,3,"Carlsson, Mr. August Sigfrid",male,28,0,0,350042,7.7958,,S
758,0,2,"Bailey, Mr. Percy Andrew",male,18,0,0,29108,11.5,,S
759,0,3,"Theobald, Mr. Thomas Leonard",male,34,0,0,363294,8.05,,S
760,1,1,"Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)",female,33,0,0,110152,86.5,B77,S
761,0,3,"Garfirth, Mr. John",male,,0,0,358585,14.5,,S
762,0,3,"Nirva, Mr. Iisakki Antino Aijo",male,41,0,0,SOTON/O2 3101272,7.125,,S
763,1,3,"Barah, Mr. Hanna Assi",male,20,0,0,2663,7.2292,,C
764,1,1,"Carter, Mrs. William Ernest (Lucile Polk)",female,36,1,2,113760,120,B96 B98,S
765,0,3,"Eklund, Mr. Hans Linus",male,16,0,0,347074,7.775,,S
766,1,1,"Hogeboom, Mrs. John C (Anna Andrews)",female,51,1,0,13502,77.9583,D11,S
767,0,1,"Brewe, Dr. Arthur Jackson",male,,0,0,112379,39.6,,C
768,0,3,"Mangan, Miss. Mary",female,30.5,0,0,364850,7.75,,Q
769,0,3,"Moran, Mr. Daniel J",male,,1,0,371110,24.15,,Q
770,0,3,"Gronnestad, Mr. Daniel Danielsen",male,32,0,0,8471,8.3625,,S
771,0,3,"Lievens, Mr. Rene Aime",male,24,0,0,345781,9.5,,S
772,0,3,"Jensen, Mr. Niels Peder",male,48,0,0,350047,7.8542,,S
773,0,2,"Mack, Mrs. (Mary)",female,57,0,0,S.O./P.P. 3,10.5,E77,S
774,0,3,"Elias, Mr. Dibo",male,,0,0,2674,7.225,,C
775,1,2,"Hocking, Mrs. Elizabeth (Eliza Needs)",female,54,1,3,29105,23,,S
776,0,3,"Myhrman, Mr. Pehr Fabian Oliver Malkolm",male,18,0,0,347078,7.75,,S
777,0,3,"Tobin, Mr. Roger",male,,0,0,383121,7.75,F38,Q
778,1,3,"Emanuel, Miss. Virginia Ethel",female,5,0,0,364516,12.475,,S
779,0,3,"Kilgannon, Mr. Thomas J",male,,0,0,36865,7.7375,,Q
780,1,1,"Robert, Mrs. Edward Scott (Elisabeth Walton McMillan)",female,43,0,1,24160,211.3375,B3,S
781,1,3,"Ayoub, Miss. Banoura",female,13,0,0,2687,7.2292,,C
782,1,1,"Dick, Mrs. Albert Adrian (Vera Gillespie)",female,17,1,0,17474,57,B20,S
783,0,1,"Long, Mr. Milton Clyde",male,29,0,0,113501,30,D6,S
784,0,3,"Johnston, Mr. Andrew G",male,,1,2,W./C. 6607,23.45,,S
785,0,3,"Ali, Mr. William",male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
786,0,3,"Harmer, Mr. Abraham (David Lishin)",male,25,0,0,374887,7.25,,S
787,1,3,"Sjoblom, Miss. Anna Sofia",female,18,0,0,3101265,7.4958,,S
788,0,3,"Rice, Master. George Hugh",male,8,4,1,382652,29.125,,Q
789,1,3,"Dean, Master. Bertram Vere",male,1,1,2,C.A. 2315,20.575,,S
790,0,1,"Guggenheim, Mr. Benjamin",male,46,0,0,PC 17593,79.2,B82 B84,C
791,0,3,"Keane, Mr. Andrew ""Andy""",male,,0,0,12460,7.75,,Q
792,0,2,"Gaskell, Mr. Alfred",male,16,0,0,239865,26,,S
793,0,3,"Sage, Miss. Stella Anna",female,,8,2,CA. 2343,69.55,,S
794,0,1,"Hoyt, Mr. William Fisher",male,,0,0,PC 17600,30.6958,,C
795,0,3,"Dantcheff, Mr. Ristiu",male,25,0,0,349203,7.8958,,S
796,0,2,"Otter, Mr. Richard",male,39,0,0,28213,13,,S
797,1,1,"Leader, Dr. Alice (Farnham)",female,49,0,0,17465,25.9292,D17,S
798,1,3,"Osman, Mrs. Mara",female,31,0,0,349244,8.6833,,S
799,0,3,"Ibrahim Shawah, Mr. Yousseff",male,30,0,0,2685,7.2292,,C
800,0,3,"Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)",female,30,1,1,345773,24.15,,S
801,0,2,"Ponesell, Mr. Martin",male,34,0,0,250647,13,,S
802,1,2,"Collyer, Mrs. Harvey (Charlotte Annie Tate)",female,31,1,1,C.A. 31921,26.25,,S
803,1,1,"Carter, Master. William Thornton II",male,11,1,2,113760,120,B96 B98,S
804,1,3,"Thomas, Master. Assad Alexander",male,0.42,0,1,2625,8.5167,,C
805,1,3,"Hedman, Mr. Oskar Arvid",male,27,0,0,347089,6.975,,S
806,0,3,"Johansson, Mr. Karl Johan",male,31,0,0,347063,7.775,,S
807,0,1,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0,A36,S
808,0,3,"Pettersson, Miss. Ellen Natalia",female,18,0,0,347087,7.775,,S
809,0,2,"Meyer, Mr. August",male,39,0,0,248723,13,,S
810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33,1,0,113806,53.1,E8,S
811,0,3,"Alexander, Mr. William",male,26,0,0,3474,7.8875,,S
812,0,3,"Lester, Mr. James",male,39,0,0,A/4 48871,24.15,,S
813,0,2,"Slemen, Mr. Richard James",male,35,0,0,28206,10.5,,S
814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6,4,2,347082,31.275,,S
815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,0,0,364499,8.05,,S
816,0,1,"Fry, Mr. Richard",male,,0,0,112058,0,B102,S
817,0,3,"Heininen, Miss. Wendla Maria",female,23,0,0,STON/O2. 3101290,7.925,,S
818,0,2,"Mallet, Mr. Albert",male,31,1,1,S.C./PARIS 2079,37.0042,,C
819,0,3,"Holm, Mr. John Fredrik Alexander",male,43,0,0,C 7075,6.45,,S
820,0,3,"Skoog, Master. Karl Thorsten",male,10,3,2,347088,27.9,,S
821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52,1,1,12749,93.5,B69,S
822,1,3,"Lulic, Mr. Nikola",male,27,0,0,315098,8.6625,,S
823,0,1,"Reuchlin, Jonkheer. John George",male,38,0,0,19972,0,,S
824,1,3,"Moor, Mrs. (Beila)",female,27,0,1,392096,12.475,E121,S
825,0,3,"Panula, Master. Urho Abraham",male,2,4,1,3101295,39.6875,,S
826,0,3,"Flynn, Mr. John",male,,0,0,368323,6.95,,Q
827,0,3,"Lam, Mr. Len",male,,0,0,1601,56.4958,,S
828,1,2,"Mallet, Master. Andre",male,1,0,2,S.C./PARIS 2079,37.0042,,C
829,1,3,"McCormack, Mr. Thomas Joseph",male,,0,0,367228,7.75,,Q
830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62,0,0,113572,80,B28,
831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15,1,0,2659,14.4542,,C
832,1,2,"Richards, Master. George Sibley",male,0.83,1,1,29106,18.75,,S
833,0,3,"Saad, Mr. Amin",male,,0,0,2671,7.2292,,C
834,0,3,"Augustsson, Mr. Albert",male,23,0,0,347468,7.8542,,S
835,0,3,"Allum, Mr. Owen George",male,18,0,0,2223,8.3,,S
836,1,1,"Compton, Miss. Sara Rebecca",female,39,1,1,PC 17756,83.1583,E49,C
837,0,3,"Pasic, Mr. Jakob",male,21,0,0,315097,8.6625,,S
838,0,3,"Sirota, Mr. Maurice",male,,0,0,392092,8.05,,S
839,1,3,"Chip, Mr. Chang",male,32,0,0,1601,56.4958,,S
840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20,0,0,SOTON/O2 3101287,7.925,,S
842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
843,1,1,"Serepeca, Miss. Augusta",female,30,0,0,113798,31,,C
844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,0,0,2683,6.4375,,C
845,0,3,"Culumovic, Mr. Jeso",male,17,0,0,315090,8.6625,,S
846,0,3,"Abbing, Mr. Anthony",male,42,0,0,C.A. 5547,7.55,,S
847,0,3,"Sage, Mr. Douglas Bullen",male,,8,2,CA. 2343,69.55,,S
848,0,3,"Markoff, Mr. Marin",male,35,0,0,349213,7.8958,,C
849,0,2,"Harper, Rev. John",male,28,0,1,248727,33,,S
850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,1,0,17453,89.1042,C92,C
851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4,4,2,347082,31.275,,S
852,0,3,"Svensson, Mr. Johan",male,74,0,0,347060,7.775,,S
853,0,3,"Boulos, Miss. Nourelain",female,9,1,1,2678,15.2458,,C
854,1,1,"Lines, Miss. Mary Conover",female,16,0,1,PC 17592,39.4,D28,S
855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44,1,0,244252,26,,S
856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18,0,1,392091,9.35,,S
857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24,0,3,2666,19.2583,,C
860,0,3,"Razi, Mr. Raihed",male,,0,0,2629,7.2292,,C
861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
862,0,2,"Giles, Mr. Frederick Edward",male,21,1,0,28134,11.5,,S
863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48,0,0,17466,25.9292,D17,S
864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,8,2,CA. 2343,69.55,,S
865,0,2,"Gill, Mr. John William",male,24,0,0,233866,13,,S
866,1,2,"Bystrom, Mrs. (Karolina)",female,42,0,0,236852,13,,S
867,1,2,"Duran y More, Miss. Asuncion",female,27,1,0,SC/PARIS 2149,13.8583,,C
868,0,1,"Roebling, Mr. Washington Augustus II",male,31,0,0,PC 17590,50.4958,A24,S
869,0,3,"van Melkebeke, Mr. Philemon",male,,0,0,345777,9.5,,S
870,1,3,"Johnson, Master. Harold Theodor",male,4,1,1,347742,11.1333,,S
871,0,3,"Balkic, Mr. Cerin",male,26,0,0,349248,7.8958,,S
872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S
873,0,1,"Carlsson, Mr. Frans Olof",male,33,0,0,695,5,B51 B53 B55,S
874,0,3,"Vander Cruyssen, Mr. Victor",male,47,0,0,345765,9,,S
875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28,1,0,P/PP 3381,24,,C
876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15,0,0,2667,7.225,,C
877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20,0,0,7534,9.8458,,S
878,0,3,"Petroff, Mr. Nedelio",male,19,0,0,349212,7.8958,,S
879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q
1 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
2 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
3 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C
4 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.925 S
5 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S
6 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 S
7 6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q
8 7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.8625 E46 S
9 8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.075 S
10 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1333 S
11 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.0708 C
12 11 1 3 Sandstrom, Miss. Marguerite Rut female 4 1 1 PP 9549 16.7 G6 S
13 12 1 1 Bonnell, Miss. Elizabeth female 58 0 0 113783 26.55 C103 S
14 13 0 3 Saundercock, Mr. William Henry male 20 0 0 A/5. 2151 8.05 S
15 14 0 3 Andersson, Mr. Anders Johan male 39 1 5 347082 31.275 S
16 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 0 350406 7.8542 S
17 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55 0 0 248706 16 S
18 17 0 3 Rice, Master. Eugene male 2 4 1 382652 29.125 Q
19 18 1 2 Williams, Mr. Charles Eugene male 0 0 244373 13 S
20 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 18 S
21 20 1 3 Masselmani, Mrs. Fatima female 0 0 2649 7.225 C
22 21 0 2 Fynney, Mr. Joseph J male 35 0 0 239865 26 S
23 22 1 2 Beesley, Mr. Lawrence male 34 0 0 248698 13 D56 S
24 23 1 3 McGowan, Miss. Anna "Annie" female 15 0 0 330923 8.0292 Q
25 24 1 1 Sloper, Mr. William Thompson male 28 0 0 113788 35.5 A6 S
26 25 0 3 Palsson, Miss. Torborg Danira female 8 3 1 349909 21.075 S
27 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38 1 5 347077 31.3875 S
28 27 0 3 Emir, Mr. Farred Chehab male 0 0 2631 7.225 C
29 28 0 1 Fortune, Mr. Charles Alexander male 19 3 2 19950 263 C23 C25 C27 S
30 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female 0 0 330959 7.8792 Q
31 30 0 3 Todoroff, Mr. Lalio male 0 0 349216 7.8958 S
32 31 0 1 Uruchurtu, Don. Manuel E male 40 0 0 PC 17601 27.7208 C
33 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female 1 0 PC 17569 146.5208 B78 C
34 33 1 3 Glynn, Miss. Mary Agatha female 0 0 335677 7.75 Q
35 34 0 2 Wheadon, Mr. Edward H male 66 0 0 C.A. 24579 10.5 S
36 35 0 1 Meyer, Mr. Edgar Joseph male 28 1 0 PC 17604 82.1708 C
37 36 0 1 Holverson, Mr. Alexander Oskar male 42 1 0 113789 52 S
38 37 1 3 Mamee, Mr. Hanna male 0 0 2677 7.2292 C
39 38 0 3 Cann, Mr. Ernest Charles male 21 0 0 A./5. 2152 8.05 S
40 39 0 3 Vander Planke, Miss. Augusta Maria female 18 2 0 345764 18 S
41 40 1 3 Nicola-Yarred, Miss. Jamila female 14 1 0 2651 11.2417 C
42 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40 1 0 7546 9.475 S
43 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27 1 0 11668 21 S
44 43 0 3 Kraeff, Mr. Theodor male 0 0 349253 7.8958 C
45 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3 1 2 SC/Paris 2123 41.5792 C
46 45 1 3 Devaney, Miss. Margaret Delia female 19 0 0 330958 7.8792 Q
47 46 0 3 Rogers, Mr. William John male 0 0 S.C./A.4. 23567 8.05 S
48 47 0 3 Lennon, Mr. Denis male 1 0 370371 15.5 Q
49 48 1 3 O'Driscoll, Miss. Bridget female 0 0 14311 7.75 Q
50 49 0 3 Samaan, Mr. Youssef male 2 0 2662 21.6792 C
51 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18 1 0 349237 17.8 S
52 51 0 3 Panula, Master. Juha Niilo male 7 4 1 3101295 39.6875 S
53 52 0 3 Nosworthy, Mr. Richard Cater male 21 0 0 A/4. 39886 7.8 S
54 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49 1 0 PC 17572 76.7292 D33 C
55 54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29 1 0 2926 26 S
56 55 0 1 Ostby, Mr. Engelhart Cornelius male 65 0 1 113509 61.9792 B30 C
57 56 1 1 Woolner, Mr. Hugh male 0 0 19947 35.5 C52 S
58 57 1 2 Rugg, Miss. Emily female 21 0 0 C.A. 31026 10.5 S
59 58 0 3 Novel, Mr. Mansouer male 28.5 0 0 2697 7.2292 C
60 59 1 2 West, Miss. Constance Mirium female 5 1 2 C.A. 34651 27.75 S
61 60 0 3 Goodwin, Master. William Frederick male 11 5 2 CA 2144 46.9 S
62 61 0 3 Sirayanian, Mr. Orsen male 22 0 0 2669 7.2292 C
63 62 1 1 Icard, Miss. Amelie female 38 0 0 113572 80 B28
64 63 0 1 Harris, Mr. Henry Birkhardt male 45 1 0 36973 83.475 C83 S
65 64 0 3 Skoog, Master. Harald male 4 3 2 347088 27.9 S
66 65 0 1 Stewart, Mr. Albert A male 0 0 PC 17605 27.7208 C
67 66 1 3 Moubarek, Master. Gerios male 1 1 2661 15.2458 C
68 67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29 0 0 C.A. 29395 10.5 F33 S
69 68 0 3 Crease, Mr. Ernest James male 19 0 0 S.P. 3464 8.1583 S
70 69 1 3 Andersson, Miss. Erna Alexandra female 17 4 2 3101281 7.925 S
71 70 0 3 Kink, Mr. Vincenz male 26 2 0 315151 8.6625 S
72 71 0 2 Jenkin, Mr. Stephen Curnow male 32 0 0 C.A. 33111 10.5 S
73 72 0 3 Goodwin, Miss. Lillian Amy female 16 5 2 CA 2144 46.9 S
74 73 0 2 Hood, Mr. Ambrose Jr male 21 0 0 S.O.C. 14879 73.5 S
75 74 0 3 Chronopoulos, Mr. Apostolos male 26 1 0 2680 14.4542 C
76 75 1 3 Bing, Mr. Lee male 32 0 0 1601 56.4958 S
77 76 0 3 Moen, Mr. Sigurd Hansen male 25 0 0 348123 7.65 F G73 S
78 77 0 3 Staneff, Mr. Ivan male 0 0 349208 7.8958 S
79 78 0 3 Moutal, Mr. Rahamin Haim male 0 0 374746 8.05 S
80 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29 S
81 80 1 3 Dowdell, Miss. Elizabeth female 30 0 0 364516 12.475 S
82 81 0 3 Waelens, Mr. Achille male 22 0 0 345767 9 S
83 82 1 3 Sheerlinck, Mr. Jan Baptist male 29 0 0 345779 9.5 S
84 83 1 3 McDermott, Miss. Brigdet Delia female 0 0 330932 7.7875 Q
85 84 0 1 Carrau, Mr. Francisco M male 28 0 0 113059 47.1 S
86 85 1 2 Ilett, Miss. Bertha female 17 0 0 SO/C 14885 10.5 S
87 86 1 3 Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson) female 33 3 0 3101278 15.85 S
88 87 0 3 Ford, Mr. William Neal male 16 1 3 W./C. 6608 34.375 S
89 88 0 3 Slocovski, Mr. Selman Francis male 0 0 SOTON/OQ 392086 8.05 S
90 89 1 1 Fortune, Miss. Mabel Helen female 23 3 2 19950 263 C23 C25 C27 S
91 90 0 3 Celotti, Mr. Francesco male 24 0 0 343275 8.05 S
92 91 0 3 Christmann, Mr. Emil male 29 0 0 343276 8.05 S
93 92 0 3 Andreasson, Mr. Paul Edvin male 20 0 0 347466 7.8542 S
94 93 0 1 Chaffee, Mr. Herbert Fuller male 46 1 0 W.E.P. 5734 61.175 E31 S
95 94 0 3 Dean, Mr. Bertram Frank male 26 1 2 C.A. 2315 20.575 S
96 95 0 3 Coxon, Mr. Daniel male 59 0 0 364500 7.25 S
97 96 0 3 Shorney, Mr. Charles Joseph male 0 0 374910 8.05 S
98 97 0 1 Goldschmidt, Mr. George B male 71 0 0 PC 17754 34.6542 A5 C
99 98 1 1 Greenfield, Mr. William Bertram male 23 0 1 PC 17759 63.3583 D10 D12 C
100 99 1 2 Doling, Mrs. John T (Ada Julia Bone) female 34 0 1 231919 23 S
101 100 0 2 Kantor, Mr. Sinai male 34 1 0 244367 26 S
102 101 0 3 Petranec, Miss. Matilda female 28 0 0 349245 7.8958 S
103 102 0 3 Petroff, Mr. Pastcho ("Pentcho") male 0 0 349215 7.8958 S
104 103 0 1 White, Mr. Richard Frasar male 21 0 1 35281 77.2875 D26 S
105 104 0 3 Johansson, Mr. Gustaf Joel male 33 0 0 7540 8.6542 S
106 105 0 3 Gustafsson, Mr. Anders Vilhelm male 37 2 0 3101276 7.925 S
107 106 0 3 Mionoff, Mr. Stoytcho male 28 0 0 349207 7.8958 S
108 107 1 3 Salkjelsvik, Miss. Anna Kristine female 21 0 0 343120 7.65 S
109 108 1 3 Moss, Mr. Albert Johan male 0 0 312991 7.775 S
110 109 0 3 Rekic, Mr. Tido male 38 0 0 349249 7.8958 S
111 110 1 3 Moran, Miss. Bertha female 1 0 371110 24.15 Q
112 111 0 1 Porter, Mr. Walter Chamberlain male 47 0 0 110465 52 C110 S
113 112 0 3 Zabour, Miss. Hileni female 14.5 1 0 2665 14.4542 C
114 113 0 3 Barton, Mr. David John male 22 0 0 324669 8.05 S
115 114 0 3 Jussila, Miss. Katriina female 20 1 0 4136 9.825 S
116 115 0 3 Attalah, Miss. Malake female 17 0 0 2627 14.4583 C
117 116 0 3 Pekoniemi, Mr. Edvard male 21 0 0 STON/O 2. 3101294 7.925 S
118 117 0 3 Connors, Mr. Patrick male 70.5 0 0 370369 7.75 Q
119 118 0 2 Turpin, Mr. William John Robert male 29 1 0 11668 21 S
120 119 0 1 Baxter, Mr. Quigg Edmond male 24 0 1 PC 17558 247.5208 B58 B60 C
121 120 0 3 Andersson, Miss. Ellis Anna Maria female 2 4 2 347082 31.275 S
122 121 0 2 Hickman, Mr. Stanley George male 21 2 0 S.O.C. 14879 73.5 S
123 122 0 3 Moore, Mr. Leonard Charles male 0 0 A4. 54510 8.05 S
124 123 0 2 Nasser, Mr. Nicholas male 32.5 1 0 237736 30.0708 C
125 124 1 2 Webber, Miss. Susan female 32.5 0 0 27267 13 E101 S
126 125 0 1 White, Mr. Percival Wayland male 54 0 1 35281 77.2875 D26 S
127 126 1 3 Nicola-Yarred, Master. Elias male 12 1 0 2651 11.2417 C
128 127 0 3 McMahon, Mr. Martin male 0 0 370372 7.75 Q
129 128 1 3 Madsen, Mr. Fridtjof Arne male 24 0 0 C 17369 7.1417 S
130 129 1 3 Peter, Miss. Anna female 1 1 2668 22.3583 F E69 C
131 130 0 3 Ekstrom, Mr. Johan male 45 0 0 347061 6.975 S
132 131 0 3 Drazenoic, Mr. Jozef male 33 0 0 349241 7.8958 C
133 132 0 3 Coelho, Mr. Domingos Fernandeo male 20 0 0 SOTON/O.Q. 3101307 7.05 S
134 133 0 3 Robins, Mrs. Alexander A (Grace Charity Laury) female 47 1 0 A/5. 3337 14.5 S
135 134 1 2 Weisz, Mrs. Leopold (Mathilde Francoise Pede) female 29 1 0 228414 26 S
136 135 0 2 Sobey, Mr. Samuel James Hayden male 25 0 0 C.A. 29178 13 S
137 136 0 2 Richard, Mr. Emile male 23 0 0 SC/PARIS 2133 15.0458 C
138 137 1 1 Newsom, Miss. Helen Monypeny female 19 0 2 11752 26.2833 D47 S
139 138 0 1 Futrelle, Mr. Jacques Heath male 37 1 0 113803 53.1 C123 S
140 139 0 3 Osen, Mr. Olaf Elon male 16 0 0 7534 9.2167 S
141 140 0 1 Giglio, Mr. Victor male 24 0 0 PC 17593 79.2 B86 C
142 141 0 3 Boulos, Mrs. Joseph (Sultana) female 0 2 2678 15.2458 C
143 142 1 3 Nysten, Miss. Anna Sofia female 22 0 0 347081 7.75 S
144 143 1 3 Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck) female 24 1 0 STON/O2. 3101279 15.85 S
145 144 0 3 Burke, Mr. Jeremiah male 19 0 0 365222 6.75 Q
146 145 0 2 Andrew, Mr. Edgardo Samuel male 18 0 0 231945 11.5 S
147 146 0 2 Nicholls, Mr. Joseph Charles male 19 1 1 C.A. 33112 36.75 S
148 147 1 3 Andersson, Mr. August Edvard ("Wennerstrom") male 27 0 0 350043 7.7958 S
149 148 0 3 Ford, Miss. Robina Maggie "Ruby" female 9 2 2 W./C. 6608 34.375 S
150 149 0 2 Navratil, Mr. Michel ("Louis M Hoffman") male 36.5 0 2 230080 26 F2 S
151 150 0 2 Byles, Rev. Thomas Roussel Davids male 42 0 0 244310 13 S
152 151 0 2 Bateman, Rev. Robert James male 51 0 0 S.O.P. 1166 12.525 S
153 152 1 1 Pears, Mrs. Thomas (Edith Wearne) female 22 1 0 113776 66.6 C2 S
154 153 0 3 Meo, Mr. Alfonzo male 55.5 0 0 A.5. 11206 8.05 S
155 154 0 3 van Billiard, Mr. Austin Blyler male 40.5 0 2 A/5. 851 14.5 S
156 155 0 3 Olsen, Mr. Ole Martin male 0 0 Fa 265302 7.3125 S
157 156 0 1 Williams, Mr. Charles Duane male 51 0 1 PC 17597 61.3792 C
158 157 1 3 Gilnagh, Miss. Katherine "Katie" female 16 0 0 35851 7.7333 Q
159 158 0 3 Corn, Mr. Harry male 30 0 0 SOTON/OQ 392090 8.05 S
160 159 0 3 Smiljanic, Mr. Mile male 0 0 315037 8.6625 S
161 160 0 3 Sage, Master. Thomas Henry male 8 2 CA. 2343 69.55 S
162 161 0 3 Cribb, Mr. John Hatfield male 44 0 1 371362 16.1 S
163 162 1 2 Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne) female 40 0 0 C.A. 33595 15.75 S
164 163 0 3 Bengtsson, Mr. John Viktor male 26 0 0 347068 7.775 S
165 164 0 3 Calic, Mr. Jovo male 17 0 0 315093 8.6625 S
166 165 0 3 Panula, Master. Eino Viljami male 1 4 1 3101295 39.6875 S
167 166 1 3 Goldsmith, Master. Frank John William "Frankie" male 9 0 2 363291 20.525 S
168 167 1 1 Chibnall, Mrs. (Edith Martha Bowerman) female 0 1 113505 55 E33 S
169 168 0 3 Skoog, Mrs. William (Anna Bernhardina Karlsson) female 45 1 4 347088 27.9 S
170 169 0 1 Baumann, Mr. John D male 0 0 PC 17318 25.925 S
171 170 0 3 Ling, Mr. Lee male 28 0 0 1601 56.4958 S
172 171 0 1 Van der hoef, Mr. Wyckoff male 61 0 0 111240 33.5 B19 S
173 172 0 3 Rice, Master. Arthur male 4 4 1 382652 29.125 Q
174 173 1 3 Johnson, Miss. Eleanor Ileen female 1 1 1 347742 11.1333 S
175 174 0 3 Sivola, Mr. Antti Wilhelm male 21 0 0 STON/O 2. 3101280 7.925 S
176 175 0 1 Smith, Mr. James Clinch male 56 0 0 17764 30.6958 A7 C
177 176 0 3 Klasen, Mr. Klas Albin male 18 1 1 350404 7.8542 S
178 177 0 3 Lefebre, Master. Henry Forbes male 3 1 4133 25.4667 S
179 178 0 1 Isham, Miss. Ann Elizabeth female 50 0 0 PC 17595 28.7125 C49 C
180 179 0 2 Hale, Mr. Reginald male 30 0 0 250653 13 S
181 180 0 3 Leonard, Mr. Lionel male 36 0 0 LINE 0 S
182 181 0 3 Sage, Miss. Constance Gladys female 8 2 CA. 2343 69.55 S
183 182 0 2 Pernot, Mr. Rene male 0 0 SC/PARIS 2131 15.05 C
184 183 0 3 Asplund, Master. Clarence Gustaf Hugo male 9 4 2 347077 31.3875 S
185 184 1 2 Becker, Master. Richard F male 1 2 1 230136 39 F4 S
186 185 1 3 Kink-Heilmann, Miss. Luise Gretchen female 4 0 2 315153 22.025 S
187 186 0 1 Rood, Mr. Hugh Roscoe male 0 0 113767 50 A32 S
188 187 1 3 O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey) female 1 0 370365 15.5 Q
189 188 1 1 Romaine, Mr. Charles Hallace ("Mr C Rolmane") male 45 0 0 111428 26.55 S
190 189 0 3 Bourke, Mr. John male 40 1 1 364849 15.5 Q
191 190 0 3 Turcin, Mr. Stjepan male 36 0 0 349247 7.8958 S
192 191 1 2 Pinsky, Mrs. (Rosa) female 32 0 0 234604 13 S
193 192 0 2 Carbines, Mr. William male 19 0 0 28424 13 S
194 193 1 3 Andersen-Jensen, Miss. Carla Christine Nielsine female 19 1 0 350046 7.8542 S
195 194 1 2 Navratil, Master. Michel M male 3 1 1 230080 26 F2 S
196 195 1 1 Brown, Mrs. James Joseph (Margaret Tobin) female 44 0 0 PC 17610 27.7208 B4 C
197 196 1 1 Lurette, Miss. Elise female 58 0 0 PC 17569 146.5208 B80 C
198 197 0 3 Mernagh, Mr. Robert male 0 0 368703 7.75 Q
199 198 0 3 Olsen, Mr. Karl Siegwart Andreas male 42 0 1 4579 8.4042 S
200 199 1 3 Madigan, Miss. Margaret "Maggie" female 0 0 370370 7.75 Q
201 200 0 2 Yrois, Miss. Henriette ("Mrs Harbeck") female 24 0 0 248747 13 S
202 201 0 3 Vande Walle, Mr. Nestor Cyriel male 28 0 0 345770 9.5 S
203 202 0 3 Sage, Mr. Frederick male 8 2 CA. 2343 69.55 S
204 203 0 3 Johanson, Mr. Jakob Alfred male 34 0 0 3101264 6.4958 S
205 204 0 3 Youseff, Mr. Gerious male 45.5 0 0 2628 7.225 C
206 205 1 3 Cohen, Mr. Gurshon "Gus" male 18 0 0 A/5 3540 8.05 S
207 206 0 3 Strom, Miss. Telma Matilda female 2 0 1 347054 10.4625 G6 S
208 207 0 3 Backstrom, Mr. Karl Alfred male 32 1 0 3101278 15.85 S
209 208 1 3 Albimona, Mr. Nassef Cassem male 26 0 0 2699 18.7875 C
210 209 1 3 Carr, Miss. Helen "Ellen" female 16 0 0 367231 7.75 Q
211 210 1 1 Blank, Mr. Henry male 40 0 0 112277 31 A31 C
212 211 0 3 Ali, Mr. Ahmed male 24 0 0 SOTON/O.Q. 3101311 7.05 S
213 212 1 2 Cameron, Miss. Clear Annie female 35 0 0 F.C.C. 13528 21 S
214 213 0 3 Perkin, Mr. John Henry male 22 0 0 A/5 21174 7.25 S
215 214 0 2 Givard, Mr. Hans Kristensen male 30 0 0 250646 13 S
216 215 0 3 Kiernan, Mr. Philip male 1 0 367229 7.75 Q
217 216 1 1 Newell, Miss. Madeleine female 31 1 0 35273 113.275 D36 C
218 217 1 3 Honkanen, Miss. Eliina female 27 0 0 STON/O2. 3101283 7.925 S
219 218 0 2 Jacobsohn, Mr. Sidney Samuel male 42 1 0 243847 27 S
220 219 1 1 Bazzani, Miss. Albina female 32 0 0 11813 76.2917 D15 C
221 220 0 2 Harris, Mr. Walter male 30 0 0 W/C 14208 10.5 S
222 221 1 3 Sunderland, Mr. Victor Francis male 16 0 0 SOTON/OQ 392089 8.05 S
223 222 0 2 Bracken, Mr. James H male 27 0 0 220367 13 S
224 223 0 3 Green, Mr. George Henry male 51 0 0 21440 8.05 S
225 224 0 3 Nenkoff, Mr. Christo male 0 0 349234 7.8958 S
226 225 1 1 Hoyt, Mr. Frederick Maxfield male 38 1 0 19943 90 C93 S
227 226 0 3 Berglund, Mr. Karl Ivar Sven male 22 0 0 PP 4348 9.35 S
228 227 1 2 Mellors, Mr. William John male 19 0 0 SW/PP 751 10.5 S
229 228 0 3 Lovell, Mr. John Hall ("Henry") male 20.5 0 0 A/5 21173 7.25 S
230 229 0 2 Fahlstrom, Mr. Arne Jonas male 18 0 0 236171 13 S
231 230 0 3 Lefebre, Miss. Mathilde female 3 1 4133 25.4667 S
232 231 1 1 Harris, Mrs. Henry Birkhardt (Irene Wallach) female 35 1 0 36973 83.475 C83 S
233 232 0 3 Larsson, Mr. Bengt Edvin male 29 0 0 347067 7.775 S
234 233 0 2 Sjostedt, Mr. Ernst Adolf male 59 0 0 237442 13.5 S
235 234 1 3 Asplund, Miss. Lillian Gertrud female 5 4 2 347077 31.3875 S
236 235 0 2 Leyson, Mr. Robert William Norman male 24 0 0 C.A. 29566 10.5 S
237 236 0 3 Harknett, Miss. Alice Phoebe female 0 0 W./C. 6609 7.55 S
238 237 0 2 Hold, Mr. Stephen male 44 1 0 26707 26 S
239 238 1 2 Collyer, Miss. Marjorie "Lottie" female 8 0 2 C.A. 31921 26.25 S
240 239 0 2 Pengelly, Mr. Frederick William male 19 0 0 28665 10.5 S
241 240 0 2 Hunt, Mr. George Henry male 33 0 0 SCO/W 1585 12.275 S
242 241 0 3 Zabour, Miss. Thamine female 1 0 2665 14.4542 C
243 242 1 3 Murphy, Miss. Katherine "Kate" female 1 0 367230 15.5 Q
244 243 0 2 Coleridge, Mr. Reginald Charles male 29 0 0 W./C. 14263 10.5 S
245 244 0 3 Maenpaa, Mr. Matti Alexanteri male 22 0 0 STON/O 2. 3101275 7.125 S
246 245 0 3 Attalah, Mr. Sleiman male 30 0 0 2694 7.225 C
247 246 0 1 Minahan, Dr. William Edward male 44 2 0 19928 90 C78 Q
248 247 0 3 Lindahl, Miss. Agda Thorilda Viktoria female 25 0 0 347071 7.775 S
249 248 1 2 Hamalainen, Mrs. William (Anna) female 24 0 2 250649 14.5 S
250 249 1 1 Beckwith, Mr. Richard Leonard male 37 1 1 11751 52.5542 D35 S
251 250 0 2 Carter, Rev. Ernest Courtenay male 54 1 0 244252 26 S
252 251 0 3 Reed, Mr. James George male 0 0 362316 7.25 S
253 252 0 3 Strom, Mrs. Wilhelm (Elna Matilda Persson) female 29 1 1 347054 10.4625 G6 S
254 253 0 1 Stead, Mr. William Thomas male 62 0 0 113514 26.55 C87 S
255 254 0 3 Lobb, Mr. William Arthur male 30 1 0 A/5. 3336 16.1 S
256 255 0 3 Rosblom, Mrs. Viktor (Helena Wilhelmina) female 41 0 2 370129 20.2125 S
257 256 1 3 Touma, Mrs. Darwis (Hanne Youssef Razi) female 29 0 2 2650 15.2458 C
258 257 1 1 Thorne, Mrs. Gertrude Maybelle female 0 0 PC 17585 79.2 C
259 258 1 1 Cherry, Miss. Gladys female 30 0 0 110152 86.5 B77 S
260 259 1 1 Ward, Miss. Anna female 35 0 0 PC 17755 512.3292 C
261 260 1 2 Parrish, Mrs. (Lutie Davis) female 50 0 1 230433 26 S
262 261 0 3 Smith, Mr. Thomas male 0 0 384461 7.75 Q
263 262 1 3 Asplund, Master. Edvin Rojj Felix male 3 4 2 347077 31.3875 S
264 263 0 1 Taussig, Mr. Emil male 52 1 1 110413 79.65 E67 S
265 264 0 1 Harrison, Mr. William male 40 0 0 112059 0 B94 S
266 265 0 3 Henry, Miss. Delia female 0 0 382649 7.75 Q
267 266 0 2 Reeves, Mr. David male 36 0 0 C.A. 17248 10.5 S
268 267 0 3 Panula, Mr. Ernesti Arvid male 16 4 1 3101295 39.6875 S
269 268 1 3 Persson, Mr. Ernst Ulrik male 25 1 0 347083 7.775 S
270 269 1 1 Graham, Mrs. William Thompson (Edith Junkins) female 58 0 1 PC 17582 153.4625 C125 S
271 270 1 1 Bissette, Miss. Amelia female 35 0 0 PC 17760 135.6333 C99 S
272 271 0 1 Cairns, Mr. Alexander male 0 0 113798 31 S
273 272 1 3 Tornquist, Mr. William Henry male 25 0 0 LINE 0 S
274 273 1 2 Mellinger, Mrs. (Elizabeth Anne Maidment) female 41 0 1 250644 19.5 S
275 274 0 1 Natsch, Mr. Charles H male 37 0 1 PC 17596 29.7 C118 C
276 275 1 3 Healy, Miss. Hanora "Nora" female 0 0 370375 7.75 Q
277 276 1 1 Andrews, Miss. Kornelia Theodosia female 63 1 0 13502 77.9583 D7 S
278 277 0 3 Lindblom, Miss. Augusta Charlotta female 45 0 0 347073 7.75 S
279 278 0 2 Parkes, Mr. Francis "Frank" male 0 0 239853 0 S
280 279 0 3 Rice, Master. Eric male 7 4 1 382652 29.125 Q
281 280 1 3 Abbott, Mrs. Stanton (Rosa Hunt) female 35 1 1 C.A. 2673 20.25 S
282 281 0 3 Duane, Mr. Frank male 65 0 0 336439 7.75 Q
283 282 0 3 Olsson, Mr. Nils Johan Goransson male 28 0 0 347464 7.8542 S
284 283 0 3 de Pelsmaeker, Mr. Alfons male 16 0 0 345778 9.5 S
285 284 1 3 Dorking, Mr. Edward Arthur male 19 0 0 A/5. 10482 8.05 S
286 285 0 1 Smith, Mr. Richard William male 0 0 113056 26 A19 S
287 286 0 3 Stankovic, Mr. Ivan male 33 0 0 349239 8.6625 C
288 287 1 3 de Mulder, Mr. Theodore male 30 0 0 345774 9.5 S
289 288 0 3 Naidenoff, Mr. Penko male 22 0 0 349206 7.8958 S
290 289 1 2 Hosono, Mr. Masabumi male 42 0 0 237798 13 S
291 290 1 3 Connolly, Miss. Kate female 22 0 0 370373 7.75 Q
292 291 1 1 Barber, Miss. Ellen "Nellie" female 26 0 0 19877 78.85 S
293 292 1 1 Bishop, Mrs. Dickinson H (Helen Walton) female 19 1 0 11967 91.0792 B49 C
294 293 0 2 Levy, Mr. Rene Jacques male 36 0 0 SC/Paris 2163 12.875 D C
295 294 0 3 Haas, Miss. Aloisia female 24 0 0 349236 8.85 S
296 295 0 3 Mineff, Mr. Ivan male 24 0 0 349233 7.8958 S
297 296 0 1 Lewy, Mr. Ervin G male 0 0 PC 17612 27.7208 C
298 297 0 3 Hanna, Mr. Mansour male 23.5 0 0 2693 7.2292 C
299 298 0 1 Allison, Miss. Helen Loraine female 2 1 2 113781 151.55 C22 C26 S
300 299 1 1 Saalfeld, Mr. Adolphe male 0 0 19988 30.5 C106 S
301 300 1 1 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50 0 1 PC 17558 247.5208 B58 B60 C
302 301 1 3 Kelly, Miss. Anna Katherine "Annie Kate" female 0 0 9234 7.75 Q
303 302 1 3 McCoy, Mr. Bernard male 2 0 367226 23.25 Q
304 303 0 3 Johnson, Mr. William Cahoone Jr male 19 0 0 LINE 0 S
305 304 1 2 Keane, Miss. Nora A female 0 0 226593 12.35 E101 Q
306 305 0 3 Williams, Mr. Howard Hugh "Harry" male 0 0 A/5 2466 8.05 S
307 306 1 1 Allison, Master. Hudson Trevor male 0.92 1 2 113781 151.55 C22 C26 S
308 307 1 1 Fleming, Miss. Margaret female 0 0 17421 110.8833 C
309 308 1 1 Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo) female 17 1 0 PC 17758 108.9 C65 C
310 309 0 2 Abelson, Mr. Samuel male 30 1 0 P/PP 3381 24 C
311 310 1 1 Francatelli, Miss. Laura Mabel female 30 0 0 PC 17485 56.9292 E36 C
312 311 1 1 Hays, Miss. Margaret Bechstein female 24 0 0 11767 83.1583 C54 C
313 312 1 1 Ryerson, Miss. Emily Borie female 18 2 2 PC 17608 262.375 B57 B59 B63 B66 C
314 313 0 2 Lahtinen, Mrs. William (Anna Sylfven) female 26 1 1 250651 26 S
315 314 0 3 Hendekovic, Mr. Ignjac male 28 0 0 349243 7.8958 S
316 315 0 2 Hart, Mr. Benjamin male 43 1 1 F.C.C. 13529 26.25 S
317 316 1 3 Nilsson, Miss. Helmina Josefina female 26 0 0 347470 7.8542 S
318 317 1 2 Kantor, Mrs. Sinai (Miriam Sternin) female 24 1 0 244367 26 S
319 318 0 2 Moraweck, Dr. Ernest male 54 0 0 29011 14 S
320 319 1 1 Wick, Miss. Mary Natalie female 31 0 2 36928 164.8667 C7 S
321 320 1 1 Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone) female 40 1 1 16966 134.5 E34 C
322 321 0 3 Dennis, Mr. Samuel male 22 0 0 A/5 21172 7.25 S
323 322 0 3 Danoff, Mr. Yoto male 27 0 0 349219 7.8958 S
324 323 1 2 Slayter, Miss. Hilda Mary female 30 0 0 234818 12.35 Q
325 324 1 2 Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh) female 22 1 1 248738 29 S
326 325 0 3 Sage, Mr. George John Jr male 8 2 CA. 2343 69.55 S
327 326 1 1 Young, Miss. Marie Grice female 36 0 0 PC 17760 135.6333 C32 C
328 327 0 3 Nysveen, Mr. Johan Hansen male 61 0 0 345364 6.2375 S
329 328 1 2 Ball, Mrs. (Ada E Hall) female 36 0 0 28551 13 D S
330 329 1 3 Goldsmith, Mrs. Frank John (Emily Alice Brown) female 31 1 1 363291 20.525 S
331 330 1 1 Hippach, Miss. Jean Gertrude female 16 0 1 111361 57.9792 B18 C
332 331 1 3 McCoy, Miss. Agnes female 2 0 367226 23.25 Q
333 332 0 1 Partner, Mr. Austen male 45.5 0 0 113043 28.5 C124 S
334 333 0 1 Graham, Mr. George Edward male 38 0 1 PC 17582 153.4625 C91 S
335 334 0 3 Vander Planke, Mr. Leo Edmondus male 16 2 0 345764 18 S
336 335 1 1 Frauenthal, Mrs. Henry William (Clara Heinsheimer) female 1 0 PC 17611 133.65 S
337 336 0 3 Denkoff, Mr. Mitto male 0 0 349225 7.8958 S
338 337 0 1 Pears, Mr. Thomas Clinton male 29 1 0 113776 66.6 C2 S
339 338 1 1 Burns, Miss. Elizabeth Margaret female 41 0 0 16966 134.5 E40 C
340 339 1 3 Dahl, Mr. Karl Edwart male 45 0 0 7598 8.05 S
341 340 0 1 Blackwell, Mr. Stephen Weart male 45 0 0 113784 35.5 T S
342 341 1 2 Navratil, Master. Edmond Roger male 2 1 1 230080 26 F2 S
343 342 1 1 Fortune, Miss. Alice Elizabeth female 24 3 2 19950 263 C23 C25 C27 S
344 343 0 2 Collander, Mr. Erik Gustaf male 28 0 0 248740 13 S
345 344 0 2 Sedgwick, Mr. Charles Frederick Waddington male 25 0 0 244361 13 S
346 345 0 2 Fox, Mr. Stanley Hubert male 36 0 0 229236 13 S
347 346 1 2 Brown, Miss. Amelia "Mildred" female 24 0 0 248733 13 F33 S
348 347 1 2 Smith, Miss. Marion Elsie female 40 0 0 31418 13 S
349 348 1 3 Davison, Mrs. Thomas Henry (Mary E Finck) female 1 0 386525 16.1 S
350 349 1 3 Coutts, Master. William Loch "William" male 3 1 1 C.A. 37671 15.9 S
351 350 0 3 Dimic, Mr. Jovan male 42 0 0 315088 8.6625 S
352 351 0 3 Odahl, Mr. Nils Martin male 23 0 0 7267 9.225 S
353 352 0 1 Williams-Lambert, Mr. Fletcher Fellows male 0 0 113510 35 C128 S
354 353 0 3 Elias, Mr. Tannous male 15 1 1 2695 7.2292 C
355 354 0 3 Arnold-Franchi, Mr. Josef male 25 1 0 349237 17.8 S
356 355 0 3 Yousif, Mr. Wazli male 0 0 2647 7.225 C
357 356 0 3 Vanden Steen, Mr. Leo Peter male 28 0 0 345783 9.5 S
358 357 1 1 Bowerman, Miss. Elsie Edith female 22 0 1 113505 55 E33 S
359 358 0 2 Funk, Miss. Annie Clemmer female 38 0 0 237671 13 S
360 359 1 3 McGovern, Miss. Mary female 0 0 330931 7.8792 Q
361 360 1 3 Mockler, Miss. Helen Mary "Ellie" female 0 0 330980 7.8792 Q
362 361 0 3 Skoog, Mr. Wilhelm male 40 1 4 347088 27.9 S
363 362 0 2 del Carlo, Mr. Sebastiano male 29 1 0 SC/PARIS 2167 27.7208 C
364 363 0 3 Barbara, Mrs. (Catherine David) female 45 0 1 2691 14.4542 C
365 364 0 3 Asim, Mr. Adola male 35 0 0 SOTON/O.Q. 3101310 7.05 S
366 365 0 3 O'Brien, Mr. Thomas male 1 0 370365 15.5 Q
367 366 0 3 Adahl, Mr. Mauritz Nils Martin male 30 0 0 C 7076 7.25 S
368 367 1 1 Warren, Mrs. Frank Manley (Anna Sophia Atkinson) female 60 1 0 110813 75.25 D37 C
369 368 1 3 Moussa, Mrs. (Mantoura Boulos) female 0 0 2626 7.2292 C
370 369 1 3 Jermyn, Miss. Annie female 0 0 14313 7.75 Q
371 370 1 1 Aubart, Mme. Leontine Pauline female 24 0 0 PC 17477 69.3 B35 C
372 371 1 1 Harder, Mr. George Achilles male 25 1 0 11765 55.4417 E50 C
373 372 0 3 Wiklund, Mr. Jakob Alfred male 18 1 0 3101267 6.4958 S
374 373 0 3 Beavan, Mr. William Thomas male 19 0 0 323951 8.05 S
375 374 0 1 Ringhini, Mr. Sante male 22 0 0 PC 17760 135.6333 C
376 375 0 3 Palsson, Miss. Stina Viola female 3 3 1 349909 21.075 S
377 376 1 1 Meyer, Mrs. Edgar Joseph (Leila Saks) female 1 0 PC 17604 82.1708 C
378 377 1 3 Landergren, Miss. Aurora Adelia female 22 0 0 C 7077 7.25 S
379 378 0 1 Widener, Mr. Harry Elkins male 27 0 2 113503 211.5 C82 C
380 379 0 3 Betros, Mr. Tannous male 20 0 0 2648 4.0125 C
381 380 0 3 Gustafsson, Mr. Karl Gideon male 19 0 0 347069 7.775 S
382 381 1 1 Bidois, Miss. Rosalie female 42 0 0 PC 17757 227.525 C
383 382 1 3 Nakid, Miss. Maria ("Mary") female 1 0 2 2653 15.7417 C
384 383 0 3 Tikkanen, Mr. Juho male 32 0 0 STON/O 2. 3101293 7.925 S
385 384 1 1 Holverson, Mrs. Alexander Oskar (Mary Aline Towner) female 35 1 0 113789 52 S
386 385 0 3 Plotcharsky, Mr. Vasil male 0 0 349227 7.8958 S
387 386 0 2 Davies, Mr. Charles Henry male 18 0 0 S.O.C. 14879 73.5 S
388 387 0 3 Goodwin, Master. Sidney Leonard male 1 5 2 CA 2144 46.9 S
389 388 1 2 Buss, Miss. Kate female 36 0 0 27849 13 S
390 389 0 3 Sadlier, Mr. Matthew male 0 0 367655 7.7292 Q
391 390 1 2 Lehmann, Miss. Bertha female 17 0 0 SC 1748 12 C
392 391 1 1 Carter, Mr. William Ernest male 36 1 2 113760 120 B96 B98 S
393 392 1 3 Jansson, Mr. Carl Olof male 21 0 0 350034 7.7958 S
394 393 0 3 Gustafsson, Mr. Johan Birger male 28 2 0 3101277 7.925 S
395 394 1 1 Newell, Miss. Marjorie female 23 1 0 35273 113.275 D36 C
396 395 1 3 Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson) female 24 0 2 PP 9549 16.7 G6 S
397 396 0 3 Johansson, Mr. Erik male 22 0 0 350052 7.7958 S
398 397 0 3 Olsson, Miss. Elina female 31 0 0 350407 7.8542 S
399 398 0 2 McKane, Mr. Peter David male 46 0 0 28403 26 S
400 399 0 2 Pain, Dr. Alfred male 23 0 0 244278 10.5 S
401 400 1 2 Trout, Mrs. William H (Jessie L) female 28 0 0 240929 12.65 S
402 401 1 3 Niskanen, Mr. Juha male 39 0 0 STON/O 2. 3101289 7.925 S
403 402 0 3 Adams, Mr. John male 26 0 0 341826 8.05 S
404 403 0 3 Jussila, Miss. Mari Aina female 21 1 0 4137 9.825 S
405 404 0 3 Hakkarainen, Mr. Pekka Pietari male 28 1 0 STON/O2. 3101279 15.85 S
406 405 0 3 Oreskovic, Miss. Marija female 20 0 0 315096 8.6625 S
407 406 0 2 Gale, Mr. Shadrach male 34 1 0 28664 21 S
408 407 0 3 Widegren, Mr. Carl/Charles Peter male 51 0 0 347064 7.75 S
409 408 1 2 Richards, Master. William Rowe male 3 1 1 29106 18.75 S
410 409 0 3 Birkeland, Mr. Hans Martin Monsen male 21 0 0 312992 7.775 S
411 410 0 3 Lefebre, Miss. Ida female 3 1 4133 25.4667 S
412 411 0 3 Sdycoff, Mr. Todor male 0 0 349222 7.8958 S
413 412 0 3 Hart, Mr. Henry male 0 0 394140 6.8583 Q
414 413 1 1 Minahan, Miss. Daisy E female 33 1 0 19928 90 C78 Q
415 414 0 2 Cunningham, Mr. Alfred Fleming male 0 0 239853 0 S
416 415 1 3 Sundman, Mr. Johan Julian male 44 0 0 STON/O 2. 3101269 7.925 S
417 416 0 3 Meek, Mrs. Thomas (Annie Louise Rowley) female 0 0 343095 8.05 S
418 417 1 2 Drew, Mrs. James Vivian (Lulu Thorne Christian) female 34 1 1 28220 32.5 S
419 418 1 2 Silven, Miss. Lyyli Karoliina female 18 0 2 250652 13 S
420 419 0 2 Matthews, Mr. William John male 30 0 0 28228 13 S
421 420 0 3 Van Impe, Miss. Catharina female 10 0 2 345773 24.15 S
422 421 0 3 Gheorgheff, Mr. Stanio male 0 0 349254 7.8958 C
423 422 0 3 Charters, Mr. David male 21 0 0 A/5. 13032 7.7333 Q
424 423 0 3 Zimmerman, Mr. Leo male 29 0 0 315082 7.875 S
425 424 0 3 Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren) female 28 1 1 347080 14.4 S
426 425 0 3 Rosblom, Mr. Viktor Richard male 18 1 1 370129 20.2125 S
427 426 0 3 Wiseman, Mr. Phillippe male 0 0 A/4. 34244 7.25 S
428 427 1 2 Clarke, Mrs. Charles V (Ada Maria Winfield) female 28 1 0 2003 26 S
429 428 1 2 Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall") female 19 0 0 250655 26 S
430 429 0 3 Flynn, Mr. James male 0 0 364851 7.75 Q
431 430 1 3 Pickard, Mr. Berk (Berk Trembisky) male 32 0 0 SOTON/O.Q. 392078 8.05 E10 S
432 431 1 1 Bjornstrom-Steffansson, Mr. Mauritz Hakan male 28 0 0 110564 26.55 C52 S
433 432 1 3 Thorneycroft, Mrs. Percival (Florence Kate White) female 1 0 376564 16.1 S
434 433 1 2 Louch, Mrs. Charles Alexander (Alice Adelaide Slow) female 42 1 0 SC/AH 3085 26 S
435 434 0 3 Kallio, Mr. Nikolai Erland male 17 0 0 STON/O 2. 3101274 7.125 S
436 435 0 1 Silvey, Mr. William Baird male 50 1 0 13507 55.9 E44 S
437 436 1 1 Carter, Miss. Lucile Polk female 14 1 2 113760 120 B96 B98 S
438 437 0 3 Ford, Miss. Doolina Margaret "Daisy" female 21 2 2 W./C. 6608 34.375 S
439 438 1 2 Richards, Mrs. Sidney (Emily Hocking) female 24 2 3 29106 18.75 S
440 439 0 1 Fortune, Mr. Mark male 64 1 4 19950 263 C23 C25 C27 S
441 440 0 2 Kvillner, Mr. Johan Henrik Johannesson male 31 0 0 C.A. 18723 10.5 S
442 441 1 2 Hart, Mrs. Benjamin (Esther Ada Bloomfield) female 45 1 1 F.C.C. 13529 26.25 S
443 442 0 3 Hampe, Mr. Leon male 20 0 0 345769 9.5 S
444 443 0 3 Petterson, Mr. Johan Emil male 25 1 0 347076 7.775 S
445 444 1 2 Reynaldo, Ms. Encarnacion female 28 0 0 230434 13 S
446 445 1 3 Johannesen-Bratthammer, Mr. Bernt male 0 0 65306 8.1125 S
447 446 1 1 Dodge, Master. Washington male 4 0 2 33638 81.8583 A34 S
448 447 1 2 Mellinger, Miss. Madeleine Violet female 13 0 1 250644 19.5 S
449 448 1 1 Seward, Mr. Frederic Kimber male 34 0 0 113794 26.55 S
450 449 1 3 Baclini, Miss. Marie Catherine female 5 2 1 2666 19.2583 C
451 450 1 1 Peuchen, Major. Arthur Godfrey male 52 0 0 113786 30.5 C104 S
452 451 0 2 West, Mr. Edwy Arthur male 36 1 2 C.A. 34651 27.75 S
453 452 0 3 Hagland, Mr. Ingvald Olai Olsen male 1 0 65303 19.9667 S
454 453 0 1 Foreman, Mr. Benjamin Laventall male 30 0 0 113051 27.75 C111 C
455 454 1 1 Goldenberg, Mr. Samuel L male 49 1 0 17453 89.1042 C92 C
456 455 0 3 Peduzzi, Mr. Joseph male 0 0 A/5 2817 8.05 S
457 456 1 3 Jalsevac, Mr. Ivan male 29 0 0 349240 7.8958 C
458 457 0 1 Millet, Mr. Francis Davis male 65 0 0 13509 26.55 E38 S
459 458 1 1 Kenyon, Mrs. Frederick R (Marion) female 1 0 17464 51.8625 D21 S
460 459 1 2 Toomey, Miss. Ellen female 50 0 0 F.C.C. 13531 10.5 S
461 460 0 3 O'Connor, Mr. Maurice male 0 0 371060 7.75 Q
462 461 1 1 Anderson, Mr. Harry male 48 0 0 19952 26.55 E12 S
463 462 0 3 Morley, Mr. William male 34 0 0 364506 8.05 S
464 463 0 1 Gee, Mr. Arthur H male 47 0 0 111320 38.5 E63 S
465 464 0 2 Milling, Mr. Jacob Christian male 48 0 0 234360 13 S
466 465 0 3 Maisner, Mr. Simon male 0 0 A/S 2816 8.05 S
467 466 0 3 Goncalves, Mr. Manuel Estanslas male 38 0 0 SOTON/O.Q. 3101306 7.05 S
468 467 0 2 Campbell, Mr. William male 0 0 239853 0 S
469 468 0 1 Smart, Mr. John Montgomery male 56 0 0 113792 26.55 S
470 469 0 3 Scanlan, Mr. James male 0 0 36209 7.725 Q
471 470 1 3 Baclini, Miss. Helene Barbara female 0.75 2 1 2666 19.2583 C
472 471 0 3 Keefe, Mr. Arthur male 0 0 323592 7.25 S
473 472 0 3 Cacic, Mr. Luka male 38 0 0 315089 8.6625 S
474 473 1 2 West, Mrs. Edwy Arthur (Ada Mary Worth) female 33 1 2 C.A. 34651 27.75 S
475 474 1 2 Jerwan, Mrs. Amin S (Marie Marthe Thuillard) female 23 0 0 SC/AH Basle 541 13.7917 D C
476 475 0 3 Strandberg, Miss. Ida Sofia female 22 0 0 7553 9.8375 S
477 476 0 1 Clifford, Mr. George Quincy male 0 0 110465 52 A14 S
478 477 0 2 Renouf, Mr. Peter Henry male 34 1 0 31027 21 S
479 478 0 3 Braund, Mr. Lewis Richard male 29 1 0 3460 7.0458 S
480 479 0 3 Karlsson, Mr. Nils August male 22 0 0 350060 7.5208 S
481 480 1 3 Hirvonen, Miss. Hildur E female 2 0 1 3101298 12.2875 S
482 481 0 3 Goodwin, Master. Harold Victor male 9 5 2 CA 2144 46.9 S
483 482 0 2 Frost, Mr. Anthony Wood "Archie" male 0 0 239854 0 S
484 483 0 3 Rouse, Mr. Richard Henry male 50 0 0 A/5 3594 8.05 S
485 484 1 3 Turkula, Mrs. (Hedwig) female 63 0 0 4134 9.5875 S
486 485 1 1 Bishop, Mr. Dickinson H male 25 1 0 11967 91.0792 B49 C
487 486 0 3 Lefebre, Miss. Jeannie female 3 1 4133 25.4667 S
488 487 1 1 Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby) female 35 1 0 19943 90 C93 S
489 488 0 1 Kent, Mr. Edward Austin male 58 0 0 11771 29.7 B37 C
490 489 0 3 Somerton, Mr. Francis William male 30 0 0 A.5. 18509 8.05 S
491 490 1 3 Coutts, Master. Eden Leslie "Neville" male 9 1 1 C.A. 37671 15.9 S
492 491 0 3 Hagland, Mr. Konrad Mathias Reiersen male 1 0 65304 19.9667 S
493 492 0 3 Windelov, Mr. Einar male 21 0 0 SOTON/OQ 3101317 7.25 S
494 493 0 1 Molson, Mr. Harry Markland male 55 0 0 113787 30.5 C30 S
495 494 0 1 Artagaveytia, Mr. Ramon male 71 0 0 PC 17609 49.5042 C
496 495 0 3 Stanley, Mr. Edward Roland male 21 0 0 A/4 45380 8.05 S
497 496 0 3 Yousseff, Mr. Gerious male 0 0 2627 14.4583 C
498 497 1 1 Eustis, Miss. Elizabeth Mussey female 54 1 0 36947 78.2667 D20 C
499 498 0 3 Shellard, Mr. Frederick William male 0 0 C.A. 6212 15.1 S
500 499 0 1 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25 1 2 113781 151.55 C22 C26 S
501 500 0 3 Svensson, Mr. Olof male 24 0 0 350035 7.7958 S
502 501 0 3 Calic, Mr. Petar male 17 0 0 315086 8.6625 S
503 502 0 3 Canavan, Miss. Mary female 21 0 0 364846 7.75 Q
504 503 0 3 O'Sullivan, Miss. Bridget Mary female 0 0 330909 7.6292 Q
505 504 0 3 Laitinen, Miss. Kristina Sofia female 37 0 0 4135 9.5875 S
506 505 1 1 Maioni, Miss. Roberta female 16 0 0 110152 86.5 B79 S
507 506 0 1 Penasco y Castellana, Mr. Victor de Satode male 18 1 0 PC 17758 108.9 C65 C
508 507 1 2 Quick, Mrs. Frederick Charles (Jane Richards) female 33 0 2 26360 26 S
509 508 1 1 Bradley, Mr. George ("George Arthur Brayton") male 0 0 111427 26.55 S
510 509 0 3 Olsen, Mr. Henry Margido male 28 0 0 C 4001 22.525 S
511 510 1 3 Lang, Mr. Fang male 26 0 0 1601 56.4958 S
512 511 1 3 Daly, Mr. Eugene Patrick male 29 0 0 382651 7.75 Q
513 512 0 3 Webber, Mr. James male 0 0 SOTON/OQ 3101316 8.05 S
514 513 1 1 McGough, Mr. James Robert male 36 0 0 PC 17473 26.2875 E25 S
515 514 1 1 Rothschild, Mrs. Martin (Elizabeth L. Barrett) female 54 1 0 PC 17603 59.4 C
516 515 0 3 Coleff, Mr. Satio male 24 0 0 349209 7.4958 S
517 516 0 1 Walker, Mr. William Anderson male 47 0 0 36967 34.0208 D46 S
518 517 1 2 Lemore, Mrs. (Amelia Milley) female 34 0 0 C.A. 34260 10.5 F33 S
519 518 0 3 Ryan, Mr. Patrick male 0 0 371110 24.15 Q
520 519 1 2 Angle, Mrs. William A (Florence "Mary" Agnes Hughes) female 36 1 0 226875 26 S
521 520 0 3 Pavlovic, Mr. Stefo male 32 0 0 349242 7.8958 S
522 521 1 1 Perreault, Miss. Anne female 30 0 0 12749 93.5 B73 S
523 522 0 3 Vovk, Mr. Janko male 22 0 0 349252 7.8958 S
524 523 0 3 Lahoud, Mr. Sarkis male 0 0 2624 7.225 C
525 524 1 1 Hippach, Mrs. Louis Albert (Ida Sophia Fischer) female 44 0 1 111361 57.9792 B18 C
526 525 0 3 Kassem, Mr. Fared male 0 0 2700 7.2292 C
527 526 0 3 Farrell, Mr. James male 40.5 0 0 367232 7.75 Q
528 527 1 2 Ridsdale, Miss. Lucy female 50 0 0 W./C. 14258 10.5 S
529 528 0 1 Farthing, Mr. John male 0 0 PC 17483 221.7792 C95 S
530 529 0 3 Salonen, Mr. Johan Werner male 39 0 0 3101296 7.925 S
531 530 0 2 Hocking, Mr. Richard George male 23 2 1 29104 11.5 S
532 531 1 2 Quick, Miss. Phyllis May female 2 1 1 26360 26 S
533 532 0 3 Toufik, Mr. Nakli male 0 0 2641 7.2292 C
534 533 0 3 Elias, Mr. Joseph Jr male 17 1 1 2690 7.2292 C
535 534 1 3 Peter, Mrs. Catherine (Catherine Rizk) female 0 2 2668 22.3583 C
536 535 0 3 Cacic, Miss. Marija female 30 0 0 315084 8.6625 S
537 536 1 2 Hart, Miss. Eva Miriam female 7 0 2 F.C.C. 13529 26.25 S
538 537 0 1 Butt, Major. Archibald Willingham male 45 0 0 113050 26.55 B38 S
539 538 1 1 LeRoy, Miss. Bertha female 30 0 0 PC 17761 106.425 C
540 539 0 3 Risien, Mr. Samuel Beard male 0 0 364498 14.5 S
541 540 1 1 Frolicher, Miss. Hedwig Margaritha female 22 0 2 13568 49.5 B39 C
542 541 1 1 Crosby, Miss. Harriet R female 36 0 2 WE/P 5735 71 B22 S
543 542 0 3 Andersson, Miss. Ingeborg Constanzia female 9 4 2 347082 31.275 S
544 543 0 3 Andersson, Miss. Sigrid Elisabeth female 11 4 2 347082 31.275 S
545 544 1 2 Beane, Mr. Edward male 32 1 0 2908 26 S
546 545 0 1 Douglas, Mr. Walter Donald male 50 1 0 PC 17761 106.425 C86 C
547 546 0 1 Nicholson, Mr. Arthur Ernest male 64 0 0 693 26 S
548 547 1 2 Beane, Mrs. Edward (Ethel Clarke) female 19 1 0 2908 26 S
549 548 1 2 Padro y Manent, Mr. Julian male 0 0 SC/PARIS 2146 13.8625 C
550 549 0 3 Goldsmith, Mr. Frank John male 33 1 1 363291 20.525 S
551 550 1 2 Davies, Master. John Morgan Jr male 8 1 1 C.A. 33112 36.75 S
552 551 1 1 Thayer, Mr. John Borland Jr male 17 0 2 17421 110.8833 C70 C
553 552 0 2 Sharp, Mr. Percival James R male 27 0 0 244358 26 S
554 553 0 3 O'Brien, Mr. Timothy male 0 0 330979 7.8292 Q
555 554 1 3 Leeni, Mr. Fahim ("Philip Zenni") male 22 0 0 2620 7.225 C
556 555 1 3 Ohman, Miss. Velin female 22 0 0 347085 7.775 S
557 556 0 1 Wright, Mr. George male 62 0 0 113807 26.55 S
558 557 1 1 Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan") female 48 1 0 11755 39.6 A16 C
559 558 0 1 Robbins, Mr. Victor male 0 0 PC 17757 227.525 C
560 559 1 1 Taussig, Mrs. Emil (Tillie Mandelbaum) female 39 1 1 110413 79.65 E67 S
561 560 1 3 de Messemaeker, Mrs. Guillaume Joseph (Emma) female 36 1 0 345572 17.4 S
562 561 0 3 Morrow, Mr. Thomas Rowan male 0 0 372622 7.75 Q
563 562 0 3 Sivic, Mr. Husein male 40 0 0 349251 7.8958 S
564 563 0 2 Norman, Mr. Robert Douglas male 28 0 0 218629 13.5 S
565 564 0 3 Simmons, Mr. John male 0 0 SOTON/OQ 392082 8.05 S
566 565 0 3 Meanwell, Miss. (Marion Ogden) female 0 0 SOTON/O.Q. 392087 8.05 S
567 566 0 3 Davies, Mr. Alfred J male 24 2 0 A/4 48871 24.15 S
568 567 0 3 Stoytcheff, Mr. Ilia male 19 0 0 349205 7.8958 S
569 568 0 3 Palsson, Mrs. Nils (Alma Cornelia Berglund) female 29 0 4 349909 21.075 S
570 569 0 3 Doharr, Mr. Tannous male 0 0 2686 7.2292 C
571 570 1 3 Jonsson, Mr. Carl male 32 0 0 350417 7.8542 S
572 571 1 2 Harris, Mr. George male 62 0 0 S.W./PP 752 10.5 S
573 572 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female 53 2 0 11769 51.4792 C101 S
574 573 1 1 Flynn, Mr. John Irwin ("Irving") male 36 0 0 PC 17474 26.3875 E25 S
575 574 1 3 Kelly, Miss. Mary female 0 0 14312 7.75 Q
576 575 0 3 Rush, Mr. Alfred George John male 16 0 0 A/4. 20589 8.05 S
577 576 0 3 Patchett, Mr. George male 19 0 0 358585 14.5 S
578 577 1 2 Garside, Miss. Ethel female 34 0 0 243880 13 S
579 578 1 1 Silvey, Mrs. William Baird (Alice Munger) female 39 1 0 13507 55.9 E44 S
580 579 0 3 Caram, Mrs. Joseph (Maria Elias) female 1 0 2689 14.4583 C
581 580 1 3 Jussila, Mr. Eiriik male 32 0 0 STON/O 2. 3101286 7.925 S
582 581 1 2 Christy, Miss. Julie Rachel female 25 1 1 237789 30 S
583 582 1 1 Thayer, Mrs. John Borland (Marian Longstreth Morris) female 39 1 1 17421 110.8833 C68 C
584 583 0 2 Downton, Mr. William James male 54 0 0 28403 26 S
585 584 0 1 Ross, Mr. John Hugo male 36 0 0 13049 40.125 A10 C
586 585 0 3 Paulner, Mr. Uscher male 0 0 3411 8.7125 C
587 586 1 1 Taussig, Miss. Ruth female 18 0 2 110413 79.65 E68 S
588 587 0 2 Jarvis, Mr. John Denzil male 47 0 0 237565 15 S
589 588 1 1 Frolicher-Stehli, Mr. Maxmillian male 60 1 1 13567 79.2 B41 C
590 589 0 3 Gilinski, Mr. Eliezer male 22 0 0 14973 8.05 S
591 590 0 3 Murdlin, Mr. Joseph male 0 0 A./5. 3235 8.05 S
592 591 0 3 Rintamaki, Mr. Matti male 35 0 0 STON/O 2. 3101273 7.125 S
593 592 1 1 Stephenson, Mrs. Walter Bertram (Martha Eustis) female 52 1 0 36947 78.2667 D20 C
594 593 0 3 Elsbury, Mr. William James male 47 0 0 A/5 3902 7.25 S
595 594 0 3 Bourke, Miss. Mary female 0 2 364848 7.75 Q
596 595 0 2 Chapman, Mr. John Henry male 37 1 0 SC/AH 29037 26 S
597 596 0 3 Van Impe, Mr. Jean Baptiste male 36 1 1 345773 24.15 S
598 597 1 2 Leitch, Miss. Jessie Wills female 0 0 248727 33 S
599 598 0 3 Johnson, Mr. Alfred male 49 0 0 LINE 0 S
600 599 0 3 Boulos, Mr. Hanna male 0 0 2664 7.225 C
601 600 1 1 Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan") male 49 1 0 PC 17485 56.9292 A20 C
602 601 1 2 Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy) female 24 2 1 243847 27 S
603 602 0 3 Slabenoff, Mr. Petco male 0 0 349214 7.8958 S
604 603 0 1 Harrington, Mr. Charles H male 0 0 113796 42.4 S
605 604 0 3 Torber, Mr. Ernst William male 44 0 0 364511 8.05 S
606 605 1 1 Homer, Mr. Harry ("Mr E Haven") male 35 0 0 111426 26.55 C
607 606 0 3 Lindell, Mr. Edvard Bengtsson male 36 1 0 349910 15.55 S
608 607 0 3 Karaic, Mr. Milan male 30 0 0 349246 7.8958 S
609 608 1 1 Daniel, Mr. Robert Williams male 27 0 0 113804 30.5 S
610 609 1 2 Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue) female 22 1 2 SC/Paris 2123 41.5792 C
611 610 1 1 Shutes, Miss. Elizabeth W female 40 0 0 PC 17582 153.4625 C125 S
612 611 0 3 Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren) female 39 1 5 347082 31.275 S
613 612 0 3 Jardin, Mr. Jose Neto male 0 0 SOTON/O.Q. 3101305 7.05 S
614 613 1 3 Murphy, Miss. Margaret Jane female 1 0 367230 15.5 Q
615 614 0 3 Horgan, Mr. John male 0 0 370377 7.75 Q
616 615 0 3 Brocklebank, Mr. William Alfred male 35 0 0 364512 8.05 S
617 616 1 2 Herman, Miss. Alice female 24 1 2 220845 65 S
618 617 0 3 Danbom, Mr. Ernst Gilbert male 34 1 1 347080 14.4 S
619 618 0 3 Lobb, Mrs. William Arthur (Cordelia K Stanlick) female 26 1 0 A/5. 3336 16.1 S
620 619 1 2 Becker, Miss. Marion Louise female 4 2 1 230136 39 F4 S
621 620 0 2 Gavey, Mr. Lawrence male 26 0 0 31028 10.5 S
622 621 0 3 Yasbeck, Mr. Antoni male 27 1 0 2659 14.4542 C
623 622 1 1 Kimball, Mr. Edwin Nelson Jr male 42 1 0 11753 52.5542 D19 S
624 623 1 3 Nakid, Mr. Sahid male 20 1 1 2653 15.7417 C
625 624 0 3 Hansen, Mr. Henry Damsgaard male 21 0 0 350029 7.8542 S
626 625 0 3 Bowen, Mr. David John "Dai" male 21 0 0 54636 16.1 S
627 626 0 1 Sutton, Mr. Frederick male 61 0 0 36963 32.3208 D50 S
628 627 0 2 Kirkland, Rev. Charles Leonard male 57 0 0 219533 12.35 Q
629 628 1 1 Longley, Miss. Gretchen Fiske female 21 0 0 13502 77.9583 D9 S
630 629 0 3 Bostandyeff, Mr. Guentcho male 26 0 0 349224 7.8958 S
631 630 0 3 O'Connell, Mr. Patrick D male 0 0 334912 7.7333 Q
632 631 1 1 Barkworth, Mr. Algernon Henry Wilson male 80 0 0 27042 30 A23 S
633 632 0 3 Lundahl, Mr. Johan Svensson male 51 0 0 347743 7.0542 S
634 633 1 1 Stahelin-Maeglin, Dr. Max male 32 0 0 13214 30.5 B50 C
635 634 0 1 Parr, Mr. William Henry Marsh male 0 0 112052 0 S
636 635 0 3 Skoog, Miss. Mabel female 9 3 2 347088 27.9 S
637 636 1 2 Davis, Miss. Mary female 28 0 0 237668 13 S
638 637 0 3 Leinonen, Mr. Antti Gustaf male 32 0 0 STON/O 2. 3101292 7.925 S
639 638 0 2 Collyer, Mr. Harvey male 31 1 1 C.A. 31921 26.25 S
640 639 0 3 Panula, Mrs. Juha (Maria Emilia Ojala) female 41 0 5 3101295 39.6875 S
641 640 0 3 Thorneycroft, Mr. Percival male 1 0 376564 16.1 S
642 641 0 3 Jensen, Mr. Hans Peder male 20 0 0 350050 7.8542 S
643 642 1 1 Sagesser, Mlle. Emma female 24 0 0 PC 17477 69.3 B35 C
644 643 0 3 Skoog, Miss. Margit Elizabeth female 2 3 2 347088 27.9 S
645 644 1 3 Foo, Mr. Choong male 0 0 1601 56.4958 S
646 645 1 3 Baclini, Miss. Eugenie female 0.75 2 1 2666 19.2583 C
647 646 1 1 Harper, Mr. Henry Sleeper male 48 1 0 PC 17572 76.7292 D33 C
648 647 0 3 Cor, Mr. Liudevit male 19 0 0 349231 7.8958 S
649 648 1 1 Simonius-Blumer, Col. Oberst Alfons male 56 0 0 13213 35.5 A26 C
650 649 0 3 Willey, Mr. Edward male 0 0 S.O./P.P. 751 7.55 S
651 650 1 3 Stanley, Miss. Amy Zillah Elsie female 23 0 0 CA. 2314 7.55 S
652 651 0 3 Mitkoff, Mr. Mito male 0 0 349221 7.8958 S
653 652 1 2 Doling, Miss. Elsie female 18 0 1 231919 23 S
654 653 0 3 Kalvik, Mr. Johannes Halvorsen male 21 0 0 8475 8.4333 S
655 654 1 3 O'Leary, Miss. Hanora "Norah" female 0 0 330919 7.8292 Q
656 655 0 3 Hegarty, Miss. Hanora "Nora" female 18 0 0 365226 6.75 Q
657 656 0 2 Hickman, Mr. Leonard Mark male 24 2 0 S.O.C. 14879 73.5 S
658 657 0 3 Radeff, Mr. Alexander male 0 0 349223 7.8958 S
659 658 0 3 Bourke, Mrs. John (Catherine) female 32 1 1 364849 15.5 Q
660 659 0 2 Eitemiller, Mr. George Floyd male 23 0 0 29751 13 S
661 660 0 1 Newell, Mr. Arthur Webster male 58 0 2 35273 113.275 D48 C
662 661 1 1 Frauenthal, Dr. Henry William male 50 2 0 PC 17611 133.65 S
663 662 0 3 Badt, Mr. Mohamed male 40 0 0 2623 7.225 C
664 663 0 1 Colley, Mr. Edward Pomeroy male 47 0 0 5727 25.5875 E58 S
665 664 0 3 Coleff, Mr. Peju male 36 0 0 349210 7.4958 S
666 665 1 3 Lindqvist, Mr. Eino William male 20 1 0 STON/O 2. 3101285 7.925 S
667 666 0 2 Hickman, Mr. Lewis male 32 2 0 S.O.C. 14879 73.5 S
668 667 0 2 Butler, Mr. Reginald Fenton male 25 0 0 234686 13 S
669 668 0 3 Rommetvedt, Mr. Knud Paust male 0 0 312993 7.775 S
670 669 0 3 Cook, Mr. Jacob male 43 0 0 A/5 3536 8.05 S
671 670 1 1 Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) female 1 0 19996 52 C126 S
672 671 1 2 Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) female 40 1 1 29750 39 S
673 672 0 1 Davidson, Mr. Thornton male 31 1 0 F.C. 12750 52 B71 S
674 673 0 2 Mitchell, Mr. Henry Michael male 70 0 0 C.A. 24580 10.5 S
675 674 1 2 Wilhelms, Mr. Charles male 31 0 0 244270 13 S
676 675 0 2 Watson, Mr. Ennis Hastings male 0 0 239856 0 S
677 676 0 3 Edvardsson, Mr. Gustaf Hjalmar male 18 0 0 349912 7.775 S
678 677 0 3 Sawyer, Mr. Frederick Charles male 24.5 0 0 342826 8.05 S
679 678 1 3 Turja, Miss. Anna Sofia female 18 0 0 4138 9.8417 S
680 679 0 3 Goodwin, Mrs. Frederick (Augusta Tyler) female 43 1 6 CA 2144 46.9 S
681 680 1 1 Cardeza, Mr. Thomas Drake Martinez male 36 0 1 PC 17755 512.3292 B51 B53 B55 C
682 681 0 3 Peters, Miss. Katie female 0 0 330935 8.1375 Q
683 682 1 1 Hassab, Mr. Hammad male 27 0 0 PC 17572 76.7292 D49 C
684 683 0 3 Olsvigen, Mr. Thor Anderson male 20 0 0 6563 9.225 S
685 684 0 3 Goodwin, Mr. Charles Edward male 14 5 2 CA 2144 46.9 S
686 685 0 2 Brown, Mr. Thomas William Solomon male 60 1 1 29750 39 S
687 686 0 2 Laroche, Mr. Joseph Philippe Lemercier male 25 1 2 SC/Paris 2123 41.5792 C
688 687 0 3 Panula, Mr. Jaako Arnold male 14 4 1 3101295 39.6875 S
689 688 0 3 Dakic, Mr. Branko male 19 0 0 349228 10.1708 S
690 689 0 3 Fischer, Mr. Eberhard Thelander male 18 0 0 350036 7.7958 S
691 690 1 1 Madill, Miss. Georgette Alexandra female 15 0 1 24160 211.3375 B5 S
692 691 1 1 Dick, Mr. Albert Adrian male 31 1 0 17474 57 B20 S
693 692 1 3 Karun, Miss. Manca female 4 0 1 349256 13.4167 C
694 693 1 3 Lam, Mr. Ali male 0 0 1601 56.4958 S
695 694 0 3 Saad, Mr. Khalil male 25 0 0 2672 7.225 C
696 695 0 1 Weir, Col. John male 60 0 0 113800 26.55 S
697 696 0 2 Chapman, Mr. Charles Henry male 52 0 0 248731 13.5 S
698 697 0 3 Kelly, Mr. James male 44 0 0 363592 8.05 S
699 698 1 3 Mullens, Miss. Katherine "Katie" female 0 0 35852 7.7333 Q
700 699 0 1 Thayer, Mr. John Borland male 49 1 1 17421 110.8833 C68 C
701 700 0 3 Humblen, Mr. Adolf Mathias Nicolai Olsen male 42 0 0 348121 7.65 F G63 S
702 701 1 1 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18 1 0 PC 17757 227.525 C62 C64 C
703 702 1 1 Silverthorne, Mr. Spencer Victor male 35 0 0 PC 17475 26.2875 E24 S
704 703 0 3 Barbara, Miss. Saiide female 18 0 1 2691 14.4542 C
705 704 0 3 Gallagher, Mr. Martin male 25 0 0 36864 7.7417 Q
706 705 0 3 Hansen, Mr. Henrik Juul male 26 1 0 350025 7.8542 S
707 706 0 2 Morley, Mr. Henry Samuel ("Mr Henry Marshall") male 39 0 0 250655 26 S
708 707 1 2 Kelly, Mrs. Florence "Fannie" female 45 0 0 223596 13.5 S
709 708 1 1 Calderhead, Mr. Edward Pennington male 42 0 0 PC 17476 26.2875 E24 S
710 709 1 1 Cleaver, Miss. Alice female 22 0 0 113781 151.55 S
711 710 1 3 Moubarek, Master. Halim Gonios ("William George") male 1 1 2661 15.2458 C
712 711 1 1 Mayne, Mlle. Berthe Antonine ("Mrs de Villiers") female 24 0 0 PC 17482 49.5042 C90 C
713 712 0 1 Klaber, Mr. Herman male 0 0 113028 26.55 C124 S
714 713 1 1 Taylor, Mr. Elmer Zebley male 48 1 0 19996 52 C126 S
715 714 0 3 Larsson, Mr. August Viktor male 29 0 0 7545 9.4833 S
716 715 0 2 Greenberg, Mr. Samuel male 52 0 0 250647 13 S
717 716 0 3 Soholt, Mr. Peter Andreas Lauritz Andersen male 19 0 0 348124 7.65 F G73 S
718 717 1 1 Endres, Miss. Caroline Louise female 38 0 0 PC 17757 227.525 C45 C
719 718 1 2 Troutt, Miss. Edwina Celia "Winnie" female 27 0 0 34218 10.5 E101 S
720 719 0 3 McEvoy, Mr. Michael male 0 0 36568 15.5 Q
721 720 0 3 Johnson, Mr. Malkolm Joackim male 33 0 0 347062 7.775 S
722 721 1 2 Harper, Miss. Annie Jessie "Nina" female 6 0 1 248727 33 S
723 722 0 3 Jensen, Mr. Svend Lauritz male 17 1 0 350048 7.0542 S
724 723 0 2 Gillespie, Mr. William Henry male 34 0 0 12233 13 S
725 724 0 2 Hodges, Mr. Henry Price male 50 0 0 250643 13 S
726 725 1 1 Chambers, Mr. Norman Campbell male 27 1 0 113806 53.1 E8 S
727 726 0 3 Oreskovic, Mr. Luka male 20 0 0 315094 8.6625 S
728 727 1 2 Renouf, Mrs. Peter Henry (Lillian Jefferys) female 30 3 0 31027 21 S
729 728 1 3 Mannion, Miss. Margareth female 0 0 36866 7.7375 Q
730 729 0 2 Bryhl, Mr. Kurt Arnold Gottfrid male 25 1 0 236853 26 S
731 730 0 3 Ilmakangas, Miss. Pieta Sofia female 25 1 0 STON/O2. 3101271 7.925 S
732 731 1 1 Allen, Miss. Elisabeth Walton female 29 0 0 24160 211.3375 B5 S
733 732 0 3 Hassan, Mr. Houssein G N male 11 0 0 2699 18.7875 C
734 733 0 2 Knight, Mr. Robert J male 0 0 239855 0 S
735 734 0 2 Berriman, Mr. William John male 23 0 0 28425 13 S
736 735 0 2 Troupiansky, Mr. Moses Aaron male 23 0 0 233639 13 S
737 736 0 3 Williams, Mr. Leslie male 28.5 0 0 54636 16.1 S
738 737 0 3 Ford, Mrs. Edward (Margaret Ann Watson) female 48 1 3 W./C. 6608 34.375 S
739 738 1 1 Lesurer, Mr. Gustave J male 35 0 0 PC 17755 512.3292 B101 C
740 739 0 3 Ivanoff, Mr. Kanio male 0 0 349201 7.8958 S
741 740 0 3 Nankoff, Mr. Minko male 0 0 349218 7.8958 S
742 741 1 1 Hawksford, Mr. Walter James male 0 0 16988 30 D45 S
743 742 0 1 Cavendish, Mr. Tyrell William male 36 1 0 19877 78.85 C46 S
744 743 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21 2 2 PC 17608 262.375 B57 B59 B63 B66 C
745 744 0 3 McNamee, Mr. Neal male 24 1 0 376566 16.1 S
746 745 1 3 Stranden, Mr. Juho male 31 0 0 STON/O 2. 3101288 7.925 S
747 746 0 1 Crosby, Capt. Edward Gifford male 70 1 1 WE/P 5735 71 B22 S
748 747 0 3 Abbott, Mr. Rossmore Edward male 16 1 1 C.A. 2673 20.25 S
749 748 1 2 Sinkkonen, Miss. Anna female 30 0 0 250648 13 S
750 749 0 1 Marvin, Mr. Daniel Warner male 19 1 0 113773 53.1 D30 S
751 750 0 3 Connaghton, Mr. Michael male 31 0 0 335097 7.75 Q
752 751 1 2 Wells, Miss. Joan female 4 1 1 29103 23 S
753 752 1 3 Moor, Master. Meier male 6 0 1 392096 12.475 E121 S
754 753 0 3 Vande Velde, Mr. Johannes Joseph male 33 0 0 345780 9.5 S
755 754 0 3 Jonkoff, Mr. Lalio male 23 0 0 349204 7.8958 S
756 755 1 2 Herman, Mrs. Samuel (Jane Laver) female 48 1 2 220845 65 S
757 756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5 S
758 757 0 3 Carlsson, Mr. August Sigfrid male 28 0 0 350042 7.7958 S
759 758 0 2 Bailey, Mr. Percy Andrew male 18 0 0 29108 11.5 S
760 759 0 3 Theobald, Mr. Thomas Leonard male 34 0 0 363294 8.05 S
761 760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33 0 0 110152 86.5 B77 S
762 761 0 3 Garfirth, Mr. John male 0 0 358585 14.5 S
763 762 0 3 Nirva, Mr. Iisakki Antino Aijo male 41 0 0 SOTON/O2 3101272 7.125 S
764 763 1 3 Barah, Mr. Hanna Assi male 20 0 0 2663 7.2292 C
765 764 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36 1 2 113760 120 B96 B98 S
766 765 0 3 Eklund, Mr. Hans Linus male 16 0 0 347074 7.775 S
767 766 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51 1 0 13502 77.9583 D11 S
768 767 0 1 Brewe, Dr. Arthur Jackson male 0 0 112379 39.6 C
769 768 0 3 Mangan, Miss. Mary female 30.5 0 0 364850 7.75 Q
770 769 0 3 Moran, Mr. Daniel J male 1 0 371110 24.15 Q
771 770 0 3 Gronnestad, Mr. Daniel Danielsen male 32 0 0 8471 8.3625 S
772 771 0 3 Lievens, Mr. Rene Aime male 24 0 0 345781 9.5 S
773 772 0 3 Jensen, Mr. Niels Peder male 48 0 0 350047 7.8542 S
774 773 0 2 Mack, Mrs. (Mary) female 57 0 0 S.O./P.P. 3 10.5 E77 S
775 774 0 3 Elias, Mr. Dibo male 0 0 2674 7.225 C
776 775 1 2 Hocking, Mrs. Elizabeth (Eliza Needs) female 54 1 3 29105 23 S
777 776 0 3 Myhrman, Mr. Pehr Fabian Oliver Malkolm male 18 0 0 347078 7.75 S
778 777 0 3 Tobin, Mr. Roger male 0 0 383121 7.75 F38 Q
779 778 1 3 Emanuel, Miss. Virginia Ethel female 5 0 0 364516 12.475 S
780 779 0 3 Kilgannon, Mr. Thomas J male 0 0 36865 7.7375 Q
781 780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43 0 1 24160 211.3375 B3 S
782 781 1 3 Ayoub, Miss. Banoura female 13 0 0 2687 7.2292 C
783 782 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17 1 0 17474 57 B20 S
784 783 0 1 Long, Mr. Milton Clyde male 29 0 0 113501 30 D6 S
785 784 0 3 Johnston, Mr. Andrew G male 1 2 W./C. 6607 23.45 S
786 785 0 3 Ali, Mr. William male 25 0 0 SOTON/O.Q. 3101312 7.05 S
787 786 0 3 Harmer, Mr. Abraham (David Lishin) male 25 0 0 374887 7.25 S
788 787 1 3 Sjoblom, Miss. Anna Sofia female 18 0 0 3101265 7.4958 S
789 788 0 3 Rice, Master. George Hugh male 8 4 1 382652 29.125 Q
790 789 1 3 Dean, Master. Bertram Vere male 1 1 2 C.A. 2315 20.575 S
791 790 0 1 Guggenheim, Mr. Benjamin male 46 0 0 PC 17593 79.2 B82 B84 C
792 791 0 3 Keane, Mr. Andrew "Andy" male 0 0 12460 7.75 Q
793 792 0 2 Gaskell, Mr. Alfred male 16 0 0 239865 26 S
794 793 0 3 Sage, Miss. Stella Anna female 8 2 CA. 2343 69.55 S
795 794 0 1 Hoyt, Mr. William Fisher male 0 0 PC 17600 30.6958 C
796 795 0 3 Dantcheff, Mr. Ristiu male 25 0 0 349203 7.8958 S
797 796 0 2 Otter, Mr. Richard male 39 0 0 28213 13 S
798 797 1 1 Leader, Dr. Alice (Farnham) female 49 0 0 17465 25.9292 D17 S
799 798 1 3 Osman, Mrs. Mara female 31 0 0 349244 8.6833 S
800 799 0 3 Ibrahim Shawah, Mr. Yousseff male 30 0 0 2685 7.2292 C
801 800 0 3 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 24.15 S
802 801 0 2 Ponesell, Mr. Martin male 34 0 0 250647 13 S
803 802 1 2 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31 1 1 C.A. 31921 26.25 S
804 803 1 1 Carter, Master. William Thornton II male 11 1 2 113760 120 B96 B98 S
805 804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167 C
806 805 1 3 Hedman, Mr. Oskar Arvid male 27 0 0 347089 6.975 S
807 806 0 3 Johansson, Mr. Karl Johan male 31 0 0 347063 7.775 S
808 807 0 1 Andrews, Mr. Thomas Jr male 39 0 0 112050 0 A36 S
809 808 0 3 Pettersson, Miss. Ellen Natalia female 18 0 0 347087 7.775 S
810 809 0 2 Meyer, Mr. August male 39 0 0 248723 13 S
811 810 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33 1 0 113806 53.1 E8 S
812 811 0 3 Alexander, Mr. William male 26 0 0 3474 7.8875 S
813 812 0 3 Lester, Mr. James male 39 0 0 A/4 48871 24.15 S
814 813 0 2 Slemen, Mr. Richard James male 35 0 0 28206 10.5 S
815 814 0 3 Andersson, Miss. Ebba Iris Alfrida female 6 4 2 347082 31.275 S
816 815 0 3 Tomlin, Mr. Ernest Portage male 30.5 0 0 364499 8.05 S
817 816 0 1 Fry, Mr. Richard male 0 0 112058 0 B102 S
818 817 0 3 Heininen, Miss. Wendla Maria female 23 0 0 STON/O2. 3101290 7.925 S
819 818 0 2 Mallet, Mr. Albert male 31 1 1 S.C./PARIS 2079 37.0042 C
820 819 0 3 Holm, Mr. John Fredrik Alexander male 43 0 0 C 7075 6.45 S
821 820 0 3 Skoog, Master. Karl Thorsten male 10 3 2 347088 27.9 S
822 821 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52 1 1 12749 93.5 B69 S
823 822 1 3 Lulic, Mr. Nikola male 27 0 0 315098 8.6625 S
824 823 0 1 Reuchlin, Jonkheer. John George male 38 0 0 19972 0 S
825 824 1 3 Moor, Mrs. (Beila) female 27 0 1 392096 12.475 E121 S
826 825 0 3 Panula, Master. Urho Abraham male 2 4 1 3101295 39.6875 S
827 826 0 3 Flynn, Mr. John male 0 0 368323 6.95 Q
828 827 0 3 Lam, Mr. Len male 0 0 1601 56.4958 S
829 828 1 2 Mallet, Master. Andre male 1 0 2 S.C./PARIS 2079 37.0042 C
830 829 1 3 McCormack, Mr. Thomas Joseph male 0 0 367228 7.75 Q
831 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 0 0 113572 80 B28
832 831 1 3 Yasbeck, Mrs. Antoni (Selini Alexander) female 15 1 0 2659 14.4542 C
833 832 1 2 Richards, Master. George Sibley male 0.83 1 1 29106 18.75 S
834 833 0 3 Saad, Mr. Amin male 0 0 2671 7.2292 C
835 834 0 3 Augustsson, Mr. Albert male 23 0 0 347468 7.8542 S
836 835 0 3 Allum, Mr. Owen George male 18 0 0 2223 8.3 S
837 836 1 1 Compton, Miss. Sara Rebecca female 39 1 1 PC 17756 83.1583 E49 C
838 837 0 3 Pasic, Mr. Jakob male 21 0 0 315097 8.6625 S
839 838 0 3 Sirota, Mr. Maurice male 0 0 392092 8.05 S
840 839 1 3 Chip, Mr. Chang male 32 0 0 1601 56.4958 S
841 840 1 1 Marechal, Mr. Pierre male 0 0 11774 29.7 C47 C
842 841 0 3 Alhomaki, Mr. Ilmari Rudolf male 20 0 0 SOTON/O2 3101287 7.925 S
843 842 0 2 Mudd, Mr. Thomas Charles male 16 0 0 S.O./P.P. 3 10.5 S
844 843 1 1 Serepeca, Miss. Augusta female 30 0 0 113798 31 C
845 844 0 3 Lemberopolous, Mr. Peter L male 34.5 0 0 2683 6.4375 C
846 845 0 3 Culumovic, Mr. Jeso male 17 0 0 315090 8.6625 S
847 846 0 3 Abbing, Mr. Anthony male 42 0 0 C.A. 5547 7.55 S
848 847 0 3 Sage, Mr. Douglas Bullen male 8 2 CA. 2343 69.55 S
849 848 0 3 Markoff, Mr. Marin male 35 0 0 349213 7.8958 C
850 849 0 2 Harper, Rev. John male 28 0 1 248727 33 S
851 850 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female 1 0 17453 89.1042 C92 C
852 851 0 3 Andersson, Master. Sigvard Harald Elias male 4 4 2 347082 31.275 S
853 852 0 3 Svensson, Mr. Johan male 74 0 0 347060 7.775 S
854 853 0 3 Boulos, Miss. Nourelain female 9 1 1 2678 15.2458 C
855 854 1 1 Lines, Miss. Mary Conover female 16 0 1 PC 17592 39.4 D28 S
856 855 0 2 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44 1 0 244252 26 S
857 856 1 3 Aks, Mrs. Sam (Leah Rosen) female 18 0 1 392091 9.35 S
858 857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45 1 1 36928 164.8667 S
859 858 1 1 Daly, Mr. Peter Denis male 51 0 0 113055 26.55 E17 S
860 859 1 3 Baclini, Mrs. Solomon (Latifa Qurban) female 24 0 3 2666 19.2583 C
861 860 0 3 Razi, Mr. Raihed male 0 0 2629 7.2292 C
862 861 0 3 Hansen, Mr. Claus Peter male 41 2 0 350026 14.1083 S
863 862 0 2 Giles, Mr. Frederick Edward male 21 1 0 28134 11.5 S
864 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48 0 0 17466 25.9292 D17 S
865 864 0 3 Sage, Miss. Dorothy Edith "Dolly" female 8 2 CA. 2343 69.55 S
866 865 0 2 Gill, Mr. John William male 24 0 0 233866 13 S
867 866 1 2 Bystrom, Mrs. (Karolina) female 42 0 0 236852 13 S
868 867 1 2 Duran y More, Miss. Asuncion female 27 1 0 SC/PARIS 2149 13.8583 C
869 868 0 1 Roebling, Mr. Washington Augustus II male 31 0 0 PC 17590 50.4958 A24 S
870 869 0 3 van Melkebeke, Mr. Philemon male 0 0 345777 9.5 S
871 870 1 3 Johnson, Master. Harold Theodor male 4 1 1 347742 11.1333 S
872 871 0 3 Balkic, Mr. Cerin male 26 0 0 349248 7.8958 S
873 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 1 11751 52.5542 D35 S
874 873 0 1 Carlsson, Mr. Frans Olof male 33 0 0 695 5 B51 B53 B55 S
875 874 0 3 Vander Cruyssen, Mr. Victor male 47 0 0 345765 9 S
876 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 0 P/PP 3381 24 C
877 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15 0 0 2667 7.225 C
878 877 0 3 Gustafsson, Mr. Alfred Ossian male 20 0 0 7534 9.8458 S
879 878 0 3 Petroff, Mr. Nedelio male 19 0 0 349212 7.8958 S
880 879 0 3 Laleff, Mr. Kristo male 0 0 349217 7.8958 S
881 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 1 11767 83.1583 C50 C
882 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 1 230433 26 S
883 882 0 3 Markun, Mr. Johann male 33 0 0 349257 7.8958 S
884 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22 0 0 7552 10.5167 S
885 884 0 2 Banfield, Mr. Frederick James male 28 0 0 C.A./SOTON 34068 10.5 S
886 885 0 3 Sutehall, Mr. Henry Jr male 25 0 0 SOTON/OQ 392076 7.05 S
887 886 0 3 Rice, Mrs. William (Margaret Norton) female 39 0 5 382652 29.125 Q
888 887 0 2 Montvila, Rev. Juozas male 27 0 0 211536 13 S
889 888 1 1 Graham, Miss. Margaret Edith female 19 0 0 112053 30 B42 S
890 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female 1 2 W./C. 6607 23.45 S
891 890 1 1 Behr, Mr. Karl Howell male 26 0 0 111369 30 C148 C
892 891 0 3 Dooley, Mr. Patrick male 32 0 0 370376 7.75 Q

View File

@ -1,417 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "18ada398-dce6-4049-9b56-fc0ede63da9c",
"metadata": {},
"source": [
"# Vectorstore Agent\n",
"\n",
"This notebook showcases an agent designed to retrieve information from one or more vectorstores, either with or without sources."
]
},
{
"cell_type": "markdown",
"id": "eecb683b-3a46-4b9d-81a3-7caefbfec1a1",
"metadata": {},
"source": [
"## Create the Vectorstores"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9bfd0ed8-a5eb-443e-8e92-90be8cabb0a7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain import OpenAI, VectorDBQA\n",
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "345bb078-4ec1-4e3a-827b-cd238c49054d",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Chroma using direct local API.\n",
"Using DuckDB in-memory for database. Data will be transient.\n"
]
}
],
"source": [
"from langchain.document_loaders import TextLoader\n",
"loader = TextLoader('../../../state_of_the_union.txt')\n",
"documents = loader.load()\n",
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
"texts = text_splitter.split_documents(documents)\n",
"\n",
"embeddings = OpenAIEmbeddings()\n",
"state_of_union_store = Chroma.from_documents(texts, embeddings, collection_name=\"state-of-union\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5f50eb82-e1a5-4252-8306-8ec1b478d9b4",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running Chroma using direct local API.\n",
"Using DuckDB in-memory for database. Data will be transient.\n"
]
}
],
"source": [
"from langchain.document_loaders import WebBaseLoader\n",
"loader = WebBaseLoader(\"https://beta.ruff.rs/docs/faq/\")\n",
"docs = loader.load()\n",
"ruff_texts = text_splitter.split_documents(docs)\n",
"ruff_store = Chroma.from_documents(ruff_texts, embeddings, collection_name=\"ruff\")"
]
},
{
"cell_type": "markdown",
"id": "f4814175-964d-42f1-aa9d-22801ce1e912",
"metadata": {},
"source": [
"## Initialize Toolkit and Agent\n",
"\n",
"First, we'll create an agent with a single vectorstore."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5b3b3206",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents.agent_toolkits import (\n",
" create_vectorstore_agent,\n",
" VectorStoreToolkit,\n",
" VectorStoreInfo,\n",
")\n",
"vectorstore_info = VectorStoreInfo(\n",
" name=\"state_of_union_address\",\n",
" description=\"the most recent state of the Union adress\",\n",
" vectorstore=state_of_union_store\n",
")\n",
"toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)\n",
"agent_executor = create_vectorstore_agent(\n",
" llm=llm,\n",
" toolkit=toolkit,\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "8a38ad10",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3f2f455c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find the answer in the state of the union address\n",
"Action: state_of_union_address\n",
"Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d61e1e63",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to use the state_of_union_address_with_sources tool to answer this question.\n",
"Action: state_of_union_address_with_sources\n",
"Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m{\"answer\": \" Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence.\\n\", \"sources\": \"../../state_of_the_union.txt\"}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Biden said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to the United States Supreme Court, and that she is one of the nation's top legal minds who will continue Justice Breyer's legacy of excellence. Sources: ../../state_of_the_union.txt\""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address? List the source.\")"
]
},
{
"cell_type": "markdown",
"id": "7ca07707",
"metadata": {},
"source": [
"## Multiple Vectorstores\n",
"We can also easily use this initialize an agent with multiple vectorstores and use the agent to route between them. To do this. This agent is optimized for routing, so it is a different toolkit and initializer."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c3209fd3",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents.agent_toolkits import (\n",
" create_vectorstore_router_agent,\n",
" VectorStoreRouterToolkit,\n",
" VectorStoreInfo,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "815c4f39-308d-4949-b992-1361036e6e09",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ruff_vectorstore_info = VectorStoreInfo(\n",
" name=\"ruff\",\n",
" description=\"Information about the Ruff python linting library\",\n",
" vectorstore=ruff_store\n",
")\n",
"router_toolkit = VectorStoreRouterToolkit(\n",
" vectorstores=[vectorstore_info, ruff_vectorstore_info],\n",
" llm=llm\n",
")\n",
"agent_executor = create_vectorstore_agent(\n",
" llm=llm,\n",
" toolkit=router_toolkit,\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "markdown",
"id": "71680984-edaf-4a63-90f5-94edbd263550",
"metadata": {},
"source": [
"## Examples"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3cd1bf3e-e3df-4e69-bbe1-71c64b1af947",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to use the state_of_union_address tool to answer this question.\n",
"Action: state_of_union_address\n",
"Action Input: What did biden say about ketanji brown jackson\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Biden said that Ketanji Brown Jackson is one of the nation's top legal minds and that she will continue Justice Breyer's legacy of excellence.\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What did biden say about ketanji brown jackson is the state of the union address?\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c5998b8d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what tool ruff uses to run over Jupyter Notebooks\n",
"Action: ruff\n",
"Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What tool does ruff use to run over Jupyter Notebooks?\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "744e9b51-fbd9-4778-b594-ea957d0f3467",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out what tool ruff uses and if the president mentioned it in the state of the union.\n",
"Action: ruff\n",
"Action Input: What tool does ruff use to run over Jupyter Notebooks?\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3m Ruff is integrated into nbQA, a tool for running linters and code formatters over Jupyter Notebooks. After installing ruff and nbqa, you can run Ruff over a notebook like so: > nbqa ruff Untitled.ipynb\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find out if the president mentioned nbQA in the state of the union.\n",
"Action: state_of_union_address\n",
"Action Input: Did the president mention nbQA in the state of the union?\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: No, the president did not mention nbQA in the state of the union.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'No, the president did not mention nbQA in the state of the union.'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What tool does ruff use to run over Jupyter Notebooks? Did the president mention that tool in the state of the union?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92203aa9-f63a-4ce1-b562-fadf4474ad9d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,4 +1,4 @@
# Getting Started
# Tools
Tools are functions that agents can use to interact with the world.
These tools can be generic utilities (e.g. search), other chains, or even other agents.
@ -118,7 +118,7 @@ Below is a list of all supported tools and relevant information:
- Notes: Uses the Google Custom Search API
- Requires LLM: No
- Extra Parameters: `google_api_key`, `google_cse_id`
- For more information on this, see [this page](../../../ecosystem/google_search.md)
- For more information on this, see [this page](../../ecosystem/google_search.md)
**searx-search**
@ -135,28 +135,4 @@ Below is a list of all supported tools and relevant information:
- Notes: Calls the [serper.dev](https://serper.dev) Google Search API and then parses results.
- Requires LLM: No
- Extra Parameters: `serper_api_key`
- For more information on this, see [this page](../../../ecosystem/google_serper.md)
**wikipedia**
- Tool Name: Wikipedia
- Tool Description: A wrapper around Wikipedia. Useful for when you need to answer general questions about people, places, companies, historical events, or other subjects. Input should be a search query.
- Notes: Uses the [wikipedia](https://pypi.org/project/wikipedia/) Python package to call the MediaWiki API and then parses results.
- Requires LLM: No
- Extra Parameters: `top_k_results`
**podcast-api**
- Tool Name: Podcast API
- Tool Description: Use the Listen Notes Podcast API to search all podcasts or episodes. The input should be a question in natural language that this API can answer.
- Notes: A natural language connection to the Listen Notes Podcast API (`https://www.PodcastAPI.com`), specifically the `/search/` endpoint.
- Requires LLM: Yes
- Extra Parameters: `listen_api_key` (your api key to access this endpoint)
**openweathermap-api**
- Tool Name: OpenWeatherMap
- Tool Description: A wrapper around OpenWeatherMap API. Useful for fetching current weather information for a specified location. Input should be a location string (e.g. 'London,GB').
- Notes: A connection to the OpenWeatherMap API (https://api.openweathermap.org), specifically the `/data/2.5/weather` endpoint.
- Requires LLM: No
- Extra Parameters: `openweathermap_api_key` (your API key to access this endpoint)
- For more information on this, see [this page](../../ecosystem/google_serper.md)

View File

@ -1,38 +0,0 @@
Tools
=============
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/agents/tool>`_
Tools are ways that an agent can use to interact with the outside world.
For an overview of what a tool is, how to use them, and a full list of examples, please see the getting started documentation
.. toctree::
:maxdepth: 1
:glob:
./tools/getting_started.md
Next, we have some examples of customizing and generically working with tools
.. toctree::
:maxdepth: 1
:glob:
./tools/custom_tools.ipynb
./tools/multi_input_tool.ipynb
In this documentation we cover generic tooling functionality (eg how to create your own)
as well as examples of tools and how to use them.
.. toctree::
:maxdepth: 1
:glob:
./tools/examples/*

View File

@ -1,120 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "3f34700b",
"metadata": {},
"source": [
"# ChatGPT Plugins\n",
"\n",
"This example shows how to use ChatGPT Plugins within LangChain abstractions.\n",
"\n",
"Note 1: This currently only works for plugins with no auth.\n",
"\n",
"Note 2: There are almost certainly other ways to do this, this is just a first pass. If you have better ideas, please open a PR!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d41405b5",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.agents import load_tools, initialize_agent\n",
"from langchain.tools import AIPluginTool"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d9e61df5",
"metadata": {},
"outputs": [],
"source": [
"tool = AIPluginTool.from_plugin_url(\"https://www.klarna.com/.well-known/ai-plugin.json\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "edc0ea0e",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mI need to check the Klarna Shopping API to see if it has information on available t shirts.\n",
"Action: KlarnaProducts\n",
"Action Input: None\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3mUsage Guide: Use the Klarna plugin to get relevant product suggestions for any shopping or researching purpose. The query to be sent should not include stopwords like articles, prepositions and determinants. The api works best when searching for words that are related to products, like their name, brand, model or category. Links will always be returned and should be shown to the user.\n",
"\n",
"OpenAPI Spec: {'openapi': '3.0.1', 'info': {'version': 'v0', 'title': 'Open AI Klarna product Api'}, 'servers': [{'url': 'https://www.klarna.com/us/shopping'}], 'tags': [{'name': 'open-ai-product-endpoint', 'description': 'Open AI Product Endpoint. Query for products.'}], 'paths': {'/public/openai/v0/products': {'get': {'tags': ['open-ai-product-endpoint'], 'summary': 'API for fetching Klarna product information', 'operationId': 'productsUsingGET', 'parameters': [{'name': 'q', 'in': 'query', 'description': 'query, must be between 2 and 100 characters', 'required': True, 'schema': {'type': 'string'}}, {'name': 'size', 'in': 'query', 'description': 'number of products returned', 'required': False, 'schema': {'type': 'integer'}}, {'name': 'budget', 'in': 'query', 'description': 'maximum price of the matching product in local currency, filters results', 'required': False, 'schema': {'type': 'integer'}}], 'responses': {'200': {'description': 'Products found', 'content': {'application/json': {'schema': {'$ref': '#/components/schemas/ProductResponse'}}}}, '503': {'description': 'one or more services are unavailable'}}, 'deprecated': False}}}, 'components': {'schemas': {'Product': {'type': 'object', 'properties': {'attributes': {'type': 'array', 'items': {'type': 'string'}}, 'name': {'type': 'string'}, 'price': {'type': 'string'}, 'url': {'type': 'string'}}, 'title': 'Product'}, 'ProductResponse': {'type': 'object', 'properties': {'products': {'type': 'array', 'items': {'$ref': '#/components/schemas/Product'}}}, 'title': 'ProductResponse'}}}}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mI need to use the Klarna Shopping API to search for t shirts.\n",
"Action: requests_get\n",
"Action Input: https://www.klarna.com/us/shopping/public/openai/v0/products?q=t%20shirts\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m{\"products\":[{\"name\":\"Lacoste Men's Pack of Plain T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202043025/Clothing/Lacoste-Men-s-Pack-of-Plain-T-Shirts/?utm_source=openai\",\"price\":\"$26.60\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\"]},{\"name\":\"Hanes Men's Ultimate 6pk. Crewneck T-Shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3201808270/Clothing/Hanes-Men-s-Ultimate-6pk.-Crewneck-T-Shirts/?utm_source=openai\",\"price\":\"$13.82\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White\"]},{\"name\":\"Nike Boy's Jordan Stretch T-shirts\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl359/3201863202/Children-s-Clothing/Nike-Boy-s-Jordan-Stretch-T-shirts/?utm_source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Material:Cotton\",\"Color:White,Green\",\"Model:Boy\",\"Size (Small-Large):S,XL,L,M\"]},{\"name\":\"Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3203028500/Clothing/Polo-Classic-Fit-Cotton-V-Neck-T-Shirts-3-Pack/?utm_source=openai\",\"price\":\"$29.95\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Blue,Black\"]},{\"name\":\"adidas Comfort T-shirts Men's 3-pack\",\"url\":\"https://www.klarna.com/us/shopping/pl/cl10001/3202640533/Clothing/adidas-Comfort-T-shirts-Men-s-3-pack/?utm_source=openai\",\"price\":\"$14.99\",\"attributes\":[\"Material:Cotton\",\"Target Group:Man\",\"Color:White,Black\",\"Neckline:Round\"]}]}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\n",
"Final Answer: The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"The available t shirts in Klarna are Lacoste Men's Pack of Plain T-Shirts, Hanes Men's Ultimate 6pk. Crewneck T-Shirts, Nike Boy's Jordan Stretch T-shirts, Polo Classic Fit Cotton V-Neck T-Shirts 3-Pack, and adidas Comfort T-shirts Men's 3-pack.\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"llm = ChatOpenAI(temperature=0,)\n",
"tools = load_tools([\"requests\"] )\n",
"tools += [tool]\n",
"\n",
"agent_chain = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)\n",
"agent_chain.run(\"what t shirts are available in klarna?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e49318a4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,132 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Human as a tool\n",
"\n",
"Human are AGI so they can certainly be used as a tool to help out AI agent \n",
"when it is confused."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.llms import OpenAI\n",
"from langchain.agents import load_tools, initialize_agent\n",
"\n",
"llm = ChatOpenAI(temperature=0.0)\n",
"math_llm = OpenAI(temperature=0.0)\n",
"tools = load_tools(\n",
" [\"human\", \"llm-math\"], \n",
" llm=math_llm,\n",
")\n",
"\n",
"agent_chain = initialize_agent(\n",
" tools,\n",
" llm,\n",
" agent=\"zero-shot-react-description\",\n",
" verbose=True,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above code you can see the tool takes input directly from command line.\n",
"You can customize `prompt_func` and `input_func` according to your need."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mI don't know Eric Zhu, so I should ask a human for guidance.\n",
"Action: Human\n",
"Action Input: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
"\n",
"Do you know when Eric Zhu's birthday is?\n",
"last week\n",
"\n",
"Observation: \u001b[36;1m\u001b[1;3mlast week\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThat's not very helpful. I should ask for more information.\n",
"Action: Human\n",
"Action Input: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
"\n",
"Do you know the specific date of Eric Zhu's birthday?\n",
"august 1st\n",
"\n",
"Observation: \u001b[36;1m\u001b[1;3maugust 1st\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mNow that I have the date, I can check if it's a leap year or not.\n",
"Action: Calculator\n",
"Action Input: \"Is 2021 a leap year?\"\u001b[0m\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: False\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mI have all the information I need to answer the original question.\n",
"Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
"# Answer with \"last week\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

View File

@ -1,128 +0,0 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "245a954a",
"metadata": {},
"source": [
"# OpenWeatherMap API\n",
"\n",
"This notebook goes over how to use the OpenWeatherMap component to fetch weather information.\n",
"\n",
"First, you need to sign up for an OpenWeatherMap API key:\n",
"\n",
"1. Go to OpenWeatherMap and sign up for an API key [here](https://openweathermap.org/api/)\n",
"2. pip install pyowm\n",
"\n",
"Then we will need to set some environment variables:\n",
"1. Save your API KEY into OPENWEATHERMAP_API_KEY env variable"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "961b3689",
"metadata": {
"vscode": {
"languageId": "shellscript"
}
},
"outputs": [],
"source": [
"pip install pyowm"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "34bb5968",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"OPENWEATHERMAP_API_KEY\"] = \"\""
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "ac4910f8",
"metadata": {},
"outputs": [],
"source": [
"from langchain.utilities import OpenWeatherMapAPIWrapper"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "84b8f773",
"metadata": {},
"outputs": [],
"source": [
"weather = OpenWeatherMapAPIWrapper()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "9651f324-e74a-4f08-a28a-89db029f66f8",
"metadata": {},
"outputs": [],
"source": [
"weather_data = weather.run(\"London,GB\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "028f4cba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In London,GB, the current weather is as follows:\n",
"Detailed status: overcast clouds\n",
"Wind speed: 4.63 m/s, direction: 150°\n",
"Humidity: 67%\n",
"Temperature: \n",
" - Current: 5.35°C\n",
" - High: 6.26°C\n",
" - Low: 3.49°C\n",
" - Feels like: 1.95°C\n",
"Rain: {}\n",
"Heat index: None\n",
"Cloud cover: 100%\n"
]
}
],
"source": [
"print(weather_data)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

File diff suppressed because one or more lines are too long

View File

@ -1,326 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "16763ed3",
"metadata": {},
"source": [
"# Zapier Natural Language Actions API\n",
"\\\n",
"Full docs here: https://nla.zapier.com/api/v1/docs\n",
"\n",
"**Zapier Natural Language Actions** gives you access to the 5k+ apps, 20k+ actions on Zapier's platform through a natural language API interface.\n",
"\n",
"NLA supports apps like Gmail, Salesforce, Trello, Slack, Asana, HubSpot, Google Sheets, Microsoft Teams, and thousands more apps: https://zapier.com/apps\n",
"\n",
"Zapier NLA handles ALL the underlying API auth and translation from natural language --> underlying API call --> return simplified output for LLMs. The key idea is you, or your users, expose a set of actions via an oauth-like setup window, which you can then query and execute via a REST API.\n",
"\n",
"NLA offers both API Key and OAuth for signing NLA API requests.\n",
"\n",
"1. Server-side (API Key): for quickly getting started, testing, and production scenarios where LangChain will only use actions exposed in the developer's Zapier account (and will use the developer's connected accounts on Zapier.com)\n",
"\n",
"2. User-facing (Oauth): for production scenarios where you are deploying an end-user facing application and LangChain needs access to end-user's exposed actions and connected accounts on Zapier.com\n",
"\n",
"This quick start will focus on the server-side use case for brevity. Review [full docs](https://nla.zapier.com/api/v1/docs) or reach out to nla@zapier.com for user-facing oauth developer support.\n",
"\n",
"This example goes over how to use the Zapier integration with a `SimpleSequentialChain`, then an `Agent`.\n",
"In code, below:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a363309c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5cf33377",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# get from https://platform.openai.com/\n",
"os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\", \"\")\n",
"\n",
"# get from https://nla.zapier.com/demo/provider/debug (under User Information, after logging in): \n",
"os.environ[\"ZAPIER_NLA_API_KEY\"] = os.environ.get(\"ZAPIER_NLA_API_KEY\", \"\")"
]
},
{
"cell_type": "markdown",
"id": "4881b484-1b97-478f-b206-aec407ceff66",
"metadata": {},
"source": [
"## Example with Agent\n",
"Zapier tools can be used with an agent. See the example below."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b2044b17-c941-4ffb-8a03-027a35e2df81",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.agents import initialize_agent\n",
"from langchain.agents.agent_toolkits import ZapierToolkit\n",
"from langchain.utilities.zapier import ZapierNLAWrapper"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7b505eeb",
"metadata": {},
"outputs": [],
"source": [
"## step 0. expose gmail 'find email' and slack 'send channel message' actions\n",
"\n",
"# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields \"Have AI guess\"\n",
"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cab18227-c232-4214-9256-bb8dd352266c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)\n",
"zapier = ZapierNLAWrapper()\n",
"toolkit = ZapierToolkit.from_zapier_nla_wrapper(zapier)\n",
"agent = initialize_agent(toolkit.get_tools(), llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f94713de-b64d-465f-a087-00288b5f80ec",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find the email and summarize it.\n",
"Action: Gmail: Find Email\n",
"Action Input: Find the latest email from Silicon Valley Bank\u001b[0m\n",
"Observation: \u001b[31;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to summarize the email and send it to the #test-zapier channel in Slack.\n",
"Action: Slack: Send Channel Message\n",
"Action Input: Send a slack message to the #test-zapier channel with the text \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m{\"message__text\": \"Silicon Valley Bank has announced that Tim Mayopoulos is the new CEO. FDIC is fully insuring all deposits and they have an ask for clients and partners as they rebuild.\", \"message__permalink\": \"https://langchain.slack.com/archives/C04TSGU0RA7/p1678859932375259\", \"channel\": \"C04TSGU0RA7\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:58:52Z\", \"message__bot_profile__icons__image_36\": \"https://avatars.slack-edge.com/2022-08-02/3888649620612_f864dc1bb794cf7d82b0_36.png\", \"message__blocks[]block_id\": \"kdZZ\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'I have sent a summary of the last email from Silicon Valley Bank to the #test-zapier channel in Slack.'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Summarize the last email I received regarding Silicon Valley Bank. Send the summary to the #test-zapier channel in slack.\")"
]
},
{
"cell_type": "markdown",
"id": "bcdea831",
"metadata": {},
"source": [
"# Example with SimpleSequentialChain\n",
"If you need more explicit control, use a chain, like below."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "10a46e7e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
"from langchain.chains import LLMChain, TransformChain, SimpleSequentialChain\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.tools.zapier.tool import ZapierNLARunAction\n",
"from langchain.utilities.zapier import ZapierNLAWrapper"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b9358048",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"## step 0. expose gmail 'find email' and slack 'send direct message' actions\n",
"\n",
"# first go here, log in, expose (enable) the two actions: https://nla.zapier.com/demo/start -- for this example, can leave all fields \"Have AI guess\"\n",
"# in an oauth scenario, you'd get your own <provider> id (instead of 'demo') which you route your users through first\n",
"\n",
"actions = ZapierNLAWrapper().list()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4e80f461",
"metadata": {},
"outputs": [],
"source": [
"## step 1. gmail find email\n",
"\n",
"GMAIL_SEARCH_INSTRUCTIONS = \"Grab the latest email from Silicon Valley Bank\"\n",
"\n",
"def nla_gmail(inputs):\n",
" action = next((a for a in actions if a[\"description\"].startswith(\"Gmail: Find Email\")), None)\n",
" return {\"email_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(inputs[\"instructions\"])}\n",
"gmail_chain = TransformChain(input_variables=[\"instructions\"], output_variables=[\"email_data\"], transform=nla_gmail)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "46893233",
"metadata": {},
"outputs": [],
"source": [
"## step 2. generate draft reply\n",
"\n",
"template = \"\"\"You are an assisstant who drafts replies to an incoming email. Output draft reply in plain text (not JSON).\n",
"\n",
"Incoming email:\n",
"{email_data}\n",
"\n",
"Draft email reply:\"\"\"\n",
"\n",
"prompt_template = PromptTemplate(input_variables=[\"email_data\"], template=template)\n",
"reply_chain = LLMChain(llm=OpenAI(temperature=.7), prompt=prompt_template)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "cd85c4f8",
"metadata": {},
"outputs": [],
"source": [
"## step 3. send draft reply via a slack direct message\n",
"\n",
"SLACK_HANDLE = \"@Ankush Gola\"\n",
"\n",
"def nla_slack(inputs):\n",
" action = next((a for a in actions if a[\"description\"].startswith(\"Slack: Send Direct Message\")), None)\n",
" instructions = f'Send this to {SLACK_HANDLE} in Slack: {inputs[\"draft_reply\"]}'\n",
" return {\"slack_data\": ZapierNLARunAction(action_id=action[\"id\"], zapier_description=action[\"description\"], params_schema=action[\"params\"]).run(instructions)}\n",
"slack_chain = TransformChain(input_variables=[\"draft_reply\"], output_variables=[\"slack_data\"], transform=nla_slack)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4829cab4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3m{\"from__name\": \"Silicon Valley Bridge Bank, N.A.\", \"from__email\": \"sreply@svb.com\", \"body_plain\": \"Dear Clients, After chaotic, tumultuous & stressful days, we have clarity on path for SVB, FDIC is fully insuring all deposits & have an ask for clients & partners as we rebuild. Tim Mayopoulos <https://eml.svb.com/NjEwLUtBSy0yNjYAAAGKgoxUeBCLAyF_NxON97X4rKEaNBLG\", \"reply_to__email\": \"sreply@svb.com\", \"subject\": \"Meet the new CEO Tim Mayopoulos\", \"date\": \"Tue, 14 Mar 2023 23:42:29 -0500 (CDT)\", \"message_url\": \"https://mail.google.com/mail/u/0/#inbox/186e393b13cfdf0a\", \"attachment_count\": \"0\", \"to__emails\": \"ankush@langchain.dev\", \"message_id\": \"186e393b13cfdf0a\", \"labels\": \"IMPORTANT, CATEGORY_UPDATES, INBOX\"}\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m\n",
"Dear Silicon Valley Bridge Bank, \n",
"\n",
"Thank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \n",
"\n",
"Best regards, \n",
"[Your Name]\u001b[0m\n",
"\u001b[38;5;200m\u001b[1;3m{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\n\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\n\\nBest regards, \\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[['text']]\", \"message__blocks[]elements[]type\": \"['rich_text_section']\"}\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'{\"message__text\": \"Dear Silicon Valley Bridge Bank, \\\\n\\\\nThank you for your email and the update regarding your new CEO Tim Mayopoulos. We appreciate your dedication to keeping your clients and partners informed and we look forward to continuing our relationship with you. \\\\n\\\\nBest regards, \\\\n[Your Name]\", \"message__permalink\": \"https://langchain.slack.com/archives/D04TKF5BBHU/p1678859968241629\", \"channel\": \"D04TKF5BBHU\", \"message__bot_profile__name\": \"Zapier\", \"message__team\": \"T04F8K3FZB5\", \"message__bot_id\": \"B04TRV4R74K\", \"message__bot_profile__deleted\": \"false\", \"message__bot_profile__app_id\": \"A024R9PQM\", \"ts_time\": \"2023-03-15T05:59:28Z\", \"message__blocks[]block_id\": \"p7i\", \"message__blocks[]elements[]elements[]type\": \"[[\\'text\\']]\", \"message__blocks[]elements[]type\": \"[\\'rich_text_section\\']\"}'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## finally, execute\n",
"\n",
"overall_chain = SimpleSequentialChain(chains=[gmail_chain, reply_chain, slack_chain], verbose=True)\n",
"overall_chain.run(GMAIL_SEARCH_INSTRUCTIONS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "09ff954e-45f2-4595-92ea-91627abde4a0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

View File

@ -1,10 +1,6 @@
Chains
==========================
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/chains>`_
Using an LLM in isolation is fine for some simple applications,
but many more complex ones require chaining LLMs - either with each other or with other experts.
LangChain provides a standard interface for Chains, as well as some common implementations of chains for ease of use.
@ -13,6 +9,8 @@ The following sections of documentation are provided:
- `Getting Started <./chains/getting_started.html>`_: A getting started guide for chains, to get you up and running quickly.
- `Key Concepts <./chains/key_concepts.html>`_: A conceptual guide going over the various concepts related to chains.
- `How-To Guides <./chains/how_to_guides.html>`_: A collection of how-to guides. These highlight how to use various types of chains.
- `Reference <../reference/modules/chains.html>`_: API reference documentation for all Chain classes.
@ -27,4 +25,5 @@ The following sections of documentation are provided:
./chains/getting_started.ipynb
./chains/how_to_guides.rst
./chains/key_concepts.rst
Reference<../reference/modules/chains.rst>

View File

@ -39,7 +39,7 @@
"\n",
"\n",
"SparkleSmile Toothpaste\n",
"\u001B[1mConcurrent executed in 1.54 seconds.\u001B[0m\n",
"\u001b[1mConcurrent executed in 1.54 seconds.\u001b[0m\n",
"\n",
"\n",
"BrightSmile Toothpaste Co.\n",
@ -55,7 +55,7 @@
"\n",
"\n",
"BrightSmile Toothpaste.\n",
"\u001B[1mSerial executed in 6.38 seconds.\u001B[0m\n"
"\u001b[1mSerial executed in 6.38 seconds.\u001b[0m\n"
]
}
],

View File

@ -149,33 +149,6 @@
"chain.run(\"Search for 'Avatar'\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Listen API Example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from langchain.llms import OpenAI\n",
"from langchain.chains.api import podcast_docs\n",
"from langchain.chains import APIChain\n",
"\n",
"# Get api key here: https://www.listennotes.com/api/pricing/\n",
"listen_api_key = 'xxx'\n",
"\n",
"llm = OpenAI(temperature=0)\n",
"headers = {\"X-ListenAPI-Key\": listen_api_key}\n",
"chain = APIChain.from_llm_and_api_docs(llm, podcast_docs.PODCAST_DOCS, headers=headers, verbose=True)\n",
"chain.run(\"Search for 'silicon valley bank' podcast episodes, audio length is more than 30 minutes, return only 1 results\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -200,7 +173,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.9"
}
},
"nbformat": 4,

File diff suppressed because it is too large Load Diff

View File

@ -71,17 +71,17 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3mdef solution():\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mdef solution():\n",
" \"\"\"Jan has three times the number of pets as Marcia. Marcia has two more pets than Cindy. If Cindy has four pets, how many total pets do the three have?\"\"\"\n",
" cindy_pets = 4\n",
" marcia_pets = cindy_pets + 2\n",
" jan_pets = marcia_pets * 3\n",
" total_pets = cindy_pets + marcia_pets + jan_pets\n",
" result = total_pets\n",
" return result\u001B[0m\n",
" return result\u001b[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
@ -139,8 +139,8 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
"objects = []\n",
"objects += [('booklet', 'blue')] * 2\n",
"objects += [('booklet', 'purple')] * 2\n",
@ -151,9 +151,9 @@
"\n",
"# Count number of purple objects\n",
"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
"answer = num_purple\u001B[0m\n",
"answer = num_purple\u001b[0m\n",
"\n",
"\u001B[1m> Finished PALChain chain.\u001B[0m\n"
"\u001b[1m> Finished PALChain chain.\u001b[0m\n"
]
},
{
@ -212,8 +212,8 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new PALChain chain...\u001B[0m\n",
"\u001B[32;1m\u001B[1;3m# Put objects into a list to record ordering\n",
"\u001b[1m> Entering new PALChain chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m# Put objects into a list to record ordering\n",
"objects = []\n",
"objects += [('booklet', 'blue')] * 2\n",
"objects += [('booklet', 'purple')] * 2\n",
@ -224,9 +224,9 @@
"\n",
"# Count number of purple objects\n",
"num_purple = len([object for object in objects if object[1] == 'purple'])\n",
"answer = num_purple\u001B[0m\n",
"answer = num_purple\u001b[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
"\u001b[1m> Finished chain.\u001b[0m\n"
]
}
],

View File

@ -377,20 +377,11 @@
"\tFOREIGN KEY(\"GenreId\") REFERENCES \"Genre\" (\"GenreId\"), \n",
"\tFOREIGN KEY(\"AlbumId\") REFERENCES \"Album\" (\"AlbumId\")\n",
")\n",
"/*\n",
"2 rows from Track table:\n",
"TrackId\tName\tAlbumId\tMediaTypeId\tGenreId\tComposer\tMilliseconds\tBytes\tUnitPrice\n",
"1\tFor Those About To Rock (We Salute You)\t1\t1\t1\tAngus Young, Malcolm Young, Brian Johnson\t343719\t11170334\t0.99\n",
"2\tBalls to the Wall\t2\t2\t1\tNone\t342562\t5510424\t0.99\n",
"*/\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jon/projects/langchain/langchain/sql_database.py:135: SAWarning: Dialect sqlite+pysqlite does *not* support Decimal objects natively, and SQLAlchemy must convert from floating point - rounding errors and other issues may occur. Please consider storing Decimal numbers as strings or integers on this platform for lossless storage.\n",
" sample_rows = connection.execute(command)\n"
"\n",
"SELECT * FROM 'Track' LIMIT 2;\n",
"TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice\n",
"1 For Those About To Rock (We Salute You) 1 1 1 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 0.99\n",
"2 Balls to the Wall 2 2 1 None 342562 5510424 0.99\n"
]
}
],
@ -443,134 +434,6 @@
"db_chain.run(\"What are some example tracks by Bach?\")"
]
},
{
"cell_type": "markdown",
"id": "ef94e948",
"metadata": {},
"source": [
"### Custom Table Info\n",
"In some cases, it can be useful to provide custom table information instead of using the automatically generated table definitions and the first `sample_rows_in_table_info` sample rows. For example, if you know that the first few rows of a table are uninformative, it could help to manually provide example rows that are more diverse or provide more information to the model. It is also possible to limit the columns that will be visible to the model if there are unnecessary columns. \n",
"\n",
"This information can be provided as a dictionary with table names as the keys and table information as the values. For example, let's provide a custom definition and sample rows for the Track table with only a few columns:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2ad33ab1",
"metadata": {},
"outputs": [],
"source": [
"custom_table_info = {\n",
" \"Track\": \"\"\"CREATE TABLE Track (\n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(200) NOT NULL,\n",
"\t\"Composer\" NVARCHAR(220),\n",
"\tPRIMARY KEY (\"TrackId\")\n",
")\n",
"/*\n",
"3 rows from Track table:\n",
"TrackId\tName\tComposer\n",
"1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n",
"2\tBalls to the Wall\tNone\n",
"3\tMy favorite song ever\tThe coolest composer of all time\n",
"*/\"\"\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "db144352",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"CREATE TABLE \"Playlist\" (\n",
"\t\"PlaylistId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(120), \n",
"\tPRIMARY KEY (\"PlaylistId\")\n",
")\n",
"/*\n",
"2 rows from Playlist table:\n",
"PlaylistId\tName\n",
"1\tMusic\n",
"2\tMovies\n",
"*/\n",
"\n",
"CREATE TABLE Track (\n",
"\t\"TrackId\" INTEGER NOT NULL, \n",
"\t\"Name\" NVARCHAR(200) NOT NULL,\n",
"\t\"Composer\" NVARCHAR(220),\n",
"\tPRIMARY KEY (\"TrackId\")\n",
")\n",
"/*\n",
"3 rows from Track table:\n",
"TrackId\tName\tComposer\n",
"1\tFor Those About To Rock (We Salute You)\tAngus Young, Malcolm Young, Brian Johnson\n",
"2\tBalls to the Wall\tNone\n",
"3\tMy favorite song ever\tThe coolest composer of all time\n",
"*/\n"
]
}
],
"source": [
"db = SQLDatabase.from_uri(\n",
" \"sqlite:///../../../../notebooks/Chinook.db\",\n",
" include_tables=['Track', 'Playlist'],\n",
" sample_rows_in_table_info=2,\n",
" custom_table_info=custom_table_info)\n",
"\n",
"print(db.table_info)"
]
},
{
"cell_type": "markdown",
"id": "5fc6f507",
"metadata": {},
"source": [
"Note how our custom table definition and sample rows for `Track` overrides the `sample_rows_in_table_info` parameter. Tables that are not overridden by `custom_table_info`, in this example `Playlist`, will have their table info gathered automatically as usual."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "dfbda4e6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
"What are some example tracks by Bach? \n",
"SQLQuery:\u001b[32;1m\u001b[1;3m SELECT Name, Composer FROM Track WHERE Composer LIKE '%Bach%' LIMIT 5;\u001b[0m\n",
"SQLResult: \u001b[33;1m\u001b[1;3m[('American Woman', 'B. Cummings/G. Peterson/M.J. Kale/R. Bachman'), ('Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Johann Sebastian Bach'), ('Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Johann Sebastian Bach'), ('Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', 'Johann Sebastian Bach'), ('Toccata and Fugue in D Minor, BWV 565: I. Toccata', 'Johann Sebastian Bach')]\u001b[0m\n",
"Answer:\u001b[32;1m\u001b[1;3m Some example tracks by Bach are 'American Woman', 'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace', 'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria', 'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude', and 'Toccata and Fugue in D Minor, BWV 565: I. Toccata'.\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"' Some example tracks by Bach are \\'American Woman\\', \\'Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\\', \\'Aria Mit 30 Veränderungen, BWV 988 \"Goldberg Variations\": Aria\\', \\'Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\\', and \\'Toccata and Fugue in D Minor, BWV 565: I. Toccata\\'.'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)\n",
"db_chain.run(\"What are some example tracks by Bach?\")"
]
},
{
"cell_type": "markdown",
"id": "c12ae15a",

View File

@ -42,13 +42,13 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001B[32;1m\u001B[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
"\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
"\n",
"Answer: Let's think step by step.\u001B[0m\n",
"Answer: Let's think step by step.\u001b[0m\n",
"\n",
"\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
]
},
{
@ -95,11 +95,11 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001B[32;1m\u001B[1;3mWrite a sad poem about ducks.\u001B[0m\n",
"\u001b[32;1m\u001b[1;3mWrite a sad poem about ducks.\u001b[0m\n",
"\n",
"\u001B[1m> Finished LLMChain chain.\u001B[0m\n"
"\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
]
},
{

View File

@ -36,25 +36,6 @@
{
"cell_type": "code",
"execution_count": 1,
"id": "7a886879",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cannot find .env file\n"
]
}
],
"source": [
"%load_ext dotenv\n",
"%dotenv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3f2f9b8c",
"metadata": {},
"outputs": [],
@ -66,7 +47,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 2,
"id": "b8237d1a",
"metadata": {},
"outputs": [],
@ -83,7 +64,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 3,
"id": "4a391730",
"metadata": {},
"outputs": [],
@ -101,7 +82,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 4,
"id": "9368bd63",
"metadata": {},
"outputs": [],
@ -113,7 +94,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 5,
"id": "d39e15f5",
"metadata": {},
"outputs": [
@ -126,20 +107,22 @@
"\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
"\u001b[36;1m\u001b[1;3m\n",
"\n",
"Tragedy at Sunset on the Beach is a story of a young couple, Jack and Sarah, who are in love and looking forward to their future together. On the night of their anniversary, they decide to take a walk on the beach at sunset. As they are walking, they come across a mysterious figure, who tells them that their love will be tested in the near future. \n",
"Tragedy at Sunset on the Beach follows the story of a young couple, Jack and Annie, who have just started to explore the possibility of a relationship together. After a day spent in the sun and sand, they decide to take a romantic stroll down the beach as the sun sets. \n",
"\n",
"The figure then tells the couple that the sun will soon set, and with it, a tragedy will strike. If Jack and Sarah can stay together and pass the test, they will be granted everlasting love. However, if they fail, their love will be lost forever.\n",
"However, their romantic evening quickly turns tragic when they stumble upon a body lying in the sand. As they approach to investigate, they are shocked to discover that it is Jack's long-lost brother, who has been missing for several years. \n",
"\n",
"The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succumb to the tragedy of the sunset.\u001b[0m\n",
"The story follows Jack and Annie as they navigate their way through the tragedy and their newfound relationship. With the help of their friends, family, and the beach's inhabitants, Jack and Annie must come to terms with their deep-seated emotions and the reality of the situation. \n",
"\n",
"Ultimately, the play explores themes of family, love, and loss, as Jack and Annie's story unfolds against the beautiful backdrop of the beach at sunset.\u001b[0m\n",
"\u001b[33;1m\u001b[1;3m\n",
"\n",
"Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. \n",
"Tragedy at Sunset on the Beach is an emotionally complex tale of family, love, and loss. Told against the beautiful backdrop of a beach at sunset, the story follows Jack and Annie, a young couple just beginning to explore a relationship together. When they stumble upon the body of Jack's long-lost brother on the beach, they must face the reality of the tragedy and come to terms with their deep-seated emotions. \n",
"\n",
"The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. \n",
"The playwright has crafted a heartfelt and thought-provoking story, one that probes into the depths of the human experience. The cast of characters is well-rounded and fully realized, and the dialogue is natural and emotional. The direction and choreography are top-notch, and the scenic design is breathtaking. \n",
"\n",
"The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.\u001b[0m\n",
"Overall, Tragedy at Sunset on the Beach is a powerful and moving story about the fragility of life and the strength of love. It is sure to tug at your heartstrings and leave you with a newfound appreciation of life's precious moments. Highly recommended.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
"\u001b[1m> Finished SimpleSequentialChain chain.\u001b[0m\n"
]
}
],
@ -149,7 +132,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 6,
"id": "c6649a01",
"metadata": {},
"outputs": [
@ -159,11 +142,11 @@
"text": [
"\n",
"\n",
"Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. \n",
"Tragedy at Sunset on the Beach is an emotionally complex tale of family, love, and loss. Told against the beautiful backdrop of a beach at sunset, the story follows Jack and Annie, a young couple just beginning to explore a relationship together. When they stumble upon the body of Jack's long-lost brother on the beach, they must face the reality of the tragedy and come to terms with their deep-seated emotions. \n",
"\n",
"The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. \n",
"The playwright has crafted a heartfelt and thought-provoking story, one that probes into the depths of the human experience. The cast of characters is well-rounded and fully realized, and the dialogue is natural and emotional. The direction and choreography are top-notch, and the scenic design is breathtaking. \n",
"\n",
"The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.\n"
"Overall, Tragedy at Sunset on the Beach is a powerful and moving story about the fragility of life and the strength of love. It is sure to tug at your heartstrings and leave you with a newfound appreciation of life's precious moments. Highly recommended.\n"
]
}
],
@ -184,7 +167,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 7,
"id": "02016a51",
"metadata": {},
"outputs": [],
@ -202,7 +185,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 8,
"id": "8bd38cc2",
"metadata": {},
"outputs": [],
@ -220,7 +203,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 9,
"id": "524523af",
"metadata": {},
"outputs": [],
@ -237,7 +220,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 10,
"id": "3fd3a7be",
"metadata": {},
"outputs": [
@ -248,8 +231,14 @@
"\n",
"\n",
"\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
"\u001b[1mChain 0\u001b[0m:\n",
"{'synopsis': \" \\n\\nTragedy at Sunset on the Beach is a dark and gripping drama set in Victorian England. The play follows the story of two lovers, Emma and Edward, whose passionate relationship is threatened by the strict rules and regulations of the time.\\n\\nThe two are deeply in love, but Edward is from a wealthy family and Emma is from a lower class background. Despite the obstacles, the two are determined to be together and decide to elope.\\n\\nOn the night of their planned escape, Emma and Edward meet at the beach at sunset to declare their love for one another and begin a new life together. However, their plans are disrupted when Emma's father discovers their plan and appears on the beach with a gun.\\n\\nIn a heartbreaking scene, Emma's father orders Edward to leave, but Edward refuses and fights for their love. In a fit of rage, Emma's father shoots Edward, killing him instantly. \\n\\nThe tragedy of the play lies in the fact that Emma and Edward are denied their chance at a happy ending due to the rigid social conventions of Victorian England. The audience is left with a heavy heart as the play ends with Emma standing alone on the beach, mourning the loss of her beloved.\"}\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
"\u001b[1mChain 1\u001b[0m:\n",
"{'review': \"\\n\\nTragedy at Sunset on the Beach is an emotionally charged production that will leave audiences heartsick. The play follows the ill-fated love story of Emma and Edward, two star-crossed lovers whose passionate relationship is tragically thwarted by Victorian England's societal conventions. The performance is captivating from start to finish, as the audience is taken on an emotional rollercoaster of love, loss, and heartbreak.\\n\\nThe acting is powerful and sincere, and the performances of the two leads are particularly stirring. Emma and Edward are both portrayed with such tenderness and emotion that it's hard not to feel their pain as they fight for their forbidden love. The climactic scene, in which Edward is shot by Emma's father, is especially heartbreaking and will leave audience members on the edge of their seats.\\n\\nOverall, Tragedy at Sunset on the Beach is a powerful and moving work of theatre. It is a tragedy of impossible love, and a vivid reminder of the devastating consequences of social injustice. The play is sure to leave a lasting impression on anyone who experiences it.\"}\n",
"\n",
"\n",
"\u001b[1m> Finished SequentialChain chain.\u001b[0m\n"
]
}
],
@ -257,91 +246,10 @@
"review = overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
]
},
{
"cell_type": "markdown",
"id": "d2fac817",
"metadata": {},
"source": [
"### Memory in Sequential Chains\n",
"Sometimes you may want to pass along some context to use in each step of the chain or in a later part of the chain, but maintaining and chaining together the input/output variables can quickly get messy. Using `SimpleMemory` is a convenient way to do manage this and clean up your chains.\n",
"\n",
"For example, using the previous playwright SequentialChain, lets say you wanted to include some context about date, time and location of the play, and using the generated synopsis and review, create some social media post text. You could add these new context variables as `input_variables`, or we can add a `SimpleMemory` to the chain to manage this context:"
]
},
{
"cell_type": "markdown",
"id": "b2cf3098",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6b7b3a7a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"{'title': 'Tragedy at sunset on the beach',\n",
" 'era': 'Victorian England',\n",
" 'time': 'December 25th, 8pm PST',\n",
" 'location': 'Theater in the Park',\n",
" 'social_post_text': \"\\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love is tragically cut short. Don't miss this emotional and thought-provoking production that is sure to leave you in tears. #AWalkOnTheBeach #LoveAndLoss #TheaterInThePark #VictorianEngland\"}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.chains import SequentialChain\n",
"from langchain.memory import SimpleMemory\n",
"\n",
"llm = OpenAI(temperature=.7)\n",
"template = \"\"\"You are a social media manager for a theater company. Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.\n",
"\n",
"Here is some context about the time and location of the play:\n",
"Date and Time: {time}\n",
"Location: {location}\n",
"\n",
"Play Synopsis:\n",
"{synopsis}\n",
"Review from a New York Times play critic of the above play:\n",
"{review}\n",
"\n",
"Social Media Post:\n",
"\"\"\"\n",
"prompt_template = PromptTemplate(input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template)\n",
"social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"social_post_text\")\n",
"\n",
"overall_chain = SequentialChain(\n",
" memory=SimpleMemory(memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}),\n",
" chains=[synopsis_chain, review_chain, social_chain],\n",
" input_variables=[\"era\", \"title\"],\n",
" # Here we return multiple variables\n",
" output_variables=[\"social_post_text\"],\n",
" verbose=True)\n",
"\n",
"overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee9bc09c",
"id": "6be70d27",
"metadata": {},
"outputs": [],
"source": []
@ -363,7 +271,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.9"
}
},
"nbformat": 4,

View File

@ -136,13 +136,13 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
"\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
"\n",
"Answer: Let's think step by step.\u001B[0m\n",
"Answer: Let's think step by step.\u001b[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
@ -319,13 +319,13 @@
"text": [
"\n",
"\n",
"\u001B[1m> Entering new LLMChain chain...\u001B[0m\n",
"\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
"Prompt after formatting:\n",
"\u001B[32;1m\u001B[1;3mQuestion: whats 2 + 2\n",
"\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n",
"\n",
"Answer: Let's think step by step.\u001B[0m\n",
"Answer: Let's think step by step.\u001b[0m\n",
"\n",
"\u001B[1m> Finished chain.\u001B[0m\n"
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{

View File

@ -31,7 +31,7 @@
"metadata": {},
"outputs": [],
"source": [
"with open(\"../../state_of_the_union.txt\") as f:\n",
"with open('../../state_of_the_union.txt') as f:\n",
" state_of_the_union = f.read()"
]
},
@ -122,7 +122,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.10.9"
}
},
"nbformat": 4,

View File

@ -0,0 +1,33 @@
Generic Chains
--------------
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
The examples here are all generic end-to-end chains that are meant to be used to construct other chains rather than serving a specific purpose.
**LLMChain**
- **Links Used**: PromptTemplate, LLM
- **Notes**: This chain is the simplest chain, and is widely used by almost every other chain. This chain takes arbitrary user input, creates a prompt with it from the PromptTemplate, passes that to the LLM, and then returns the output of the LLM as the final output.
- `Example Notebook <./generic/llm_chain.html>`_
**Transformation Chain**
- **Links Used**: TransformationChain
- **Notes**: This notebook shows how to use the Transformation Chain, which takes an arbitrary python function and applies it to inputs/outputs of other chains.
- `Example Notebook <./generic/transformation.html>`_
**Sequential Chain**
- **Links Used**: Sequential
- **Notes**: This notebook shows how to combine calling multiple other chains in sequence.
- `Example Notebook <./generic/sequential_chains.html>`_
.. toctree::
:maxdepth: 1
:glob:
:caption: Generic Chains
:name: generic
:hidden:
./generic/*

View File

@ -32,9 +32,7 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [],
"source": [
"from langchain.prompts import PromptTemplate\n",
@ -57,9 +55,7 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"metadata": {},
"outputs": [
{
"name": "stdout",
@ -67,7 +63,7 @@
"text": [
"\n",
"\n",
"Rainbow Socks Co.\n"
"Vibrancy Socks.\n"
]
}
],
@ -79,48 +75,6 @@
"print(chain.run(\"colorful socks\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can use a chat model in an `LLMChain` as well:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Rainbow Threads\n"
]
}
],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.prompts.chat import (\n",
" ChatPromptTemplate,\n",
" HumanMessagePromptTemplate,\n",
")\n",
"human_message_prompt = HumanMessagePromptTemplate(\n",
" prompt=PromptTemplate(\n",
" template=\"What is a good name for a company that makes {product}?\",\n",
" input_variables=[\"product\"],\n",
" )\n",
" )\n",
"chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])\n",
"chat = ChatOpenAI(temperature=0.9)\n",
"chain = LLMChain(llm=chat, prompt=chat_prompt_template)\n",
"print(chain.run(\"colorful socks\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
@ -320,5 +274,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 4
"nbformat_minor": 2
}

View File

@ -2,37 +2,23 @@ How-To Guides
=============
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either `prompts <../prompts.html>`_, `models <../models.html>`_, arbitrary functions, or other chains.
The examples here are broken up into three sections:
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
The examples here are all end-to-end chains for specific applications.
They are broken up into three categories:
**Generic Functionality**
Covers both generic chains (that are useful in a wide variety of applications) as well as generic functionality related to those chains.
1. `Generic Chains <./generic_how_to.html>`_: Generic chains, that are meant to help build other chains rather than serve a particular purpose.
2. `Utility Chains <./utility_how_to.html>`_: Chains consisting of an LLMChain interacting with a specific util.
3. `Asynchronous <./async_chain.html>`_: Covering asynchronous functionality.
.. toctree::
:maxdepth: 1
:glob:
:hidden:
./generic/*
./generic_how_to.rst
./utility_how_to.rst
./async_chain.ipynb
**Index-related Chains**
Chains related to working with indexes.
.. toctree::
:maxdepth: 1
:glob:
./index_examples/*
**All other chains**
All other types of chains!
.. toctree::
:maxdepth: 1
:glob:
./examples/*
In addition to different types of chains, we also have the following how-to guides for working with chains in general:
`Load From Hub <./generic/from_hub.html>`_: This notebook covers how to load chains from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.

View File

@ -0,0 +1,11 @@
# Key Concepts
## Chains
A chain is made up of links, which can be either primitives or other chains.
They vary greatly in complexity and are combination of generic, highly configurable pipelines and more narrow (but usually more complex) pipelines.
## Sequential Chain
This is a specific type of chain where multiple other chains are run in sequence, with the outputs being added as inputs
to the next. A subtype of this type of chain is the [`SimpleSequentialChain`](./generic/sequential_chains.html#simplesequentialchain), where all subchains have only one input and one output,
and the output of one is therefore used as sole input to the next chain.

View File

@ -0,0 +1,65 @@
Utility Chains
--------------
A chain is made up of links, which can be either primitives or other chains.
Primitives can be either `prompts <../prompts.html>`_, `llms <../llms.html>`_, `utils <../utils.html>`_, or other chains.
The examples here are all end-to-end chains for specific applications, focused on interacting an LLMChain with a specific utility.
**LLMMath**
- **Links Used**: Python REPL, LLMChain
- **Notes**: This chain takes user input (a math question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
- `Example Notebook <./examples/llm_math.html>`_
**PAL**
- **Links Used**: Python REPL, LLMChain
- **Notes**: This chain takes user input (a reasoning question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
- `Paper <https://arxiv.org/abs/2211.10435>`_
- `Example Notebook <./examples/pal.html>`_
**SQLDatabase Chain**
- **Links Used**: SQLDatabase, LLMChain
- **Notes**: This chain takes user input (a question), uses a first LLM chain to construct a SQL query to run against the SQL database, and then uses another LLMChain to take the results of that query and use it to answer the original question.
- `Example Notebook <./examples/sqlite.html>`_
**API Chain**
- **Links Used**: LLMChain, Requests
- **Notes**: This chain first uses a LLM to construct the url to hit, then makes that request with the Requests wrapper, and finally runs that result through the language model again in order to product a natural language response.
- `Example Notebook <./examples/api.html>`_
**LLMBash Chain**
- **Links Used**: BashProcess, LLMChain
- **Notes**: This chain takes user input (a question), uses an LLM chain to convert it to a bash command to run in the terminal, and then returns that as the result.
- `Example Notebook <./examples/llm_bash.html>`_
**LLMChecker Chain**
- **Links Used**: LLMChain
- **Notes**: This chain takes user input (a question), uses an LLM chain to answer that question, and then uses other LLMChains to self-check that answer.
- `Example Notebook <./examples/llm_checker.html>`_
**LLMRequests Chain**
- **Links Used**: Requests, LLMChain
- **Notes**: This chain takes a URL and other inputs, uses Requests to get the data at that URL, and then passes that along with the other inputs into an LLMChain to generate a response. The example included shows how to ask a question to Google - it firsts constructs a Google url, then fetches the data there, then passes that data + the original question into an LLMChain to get an answer.
- `Example Notebook <./examples/llm_requests.html>`_
**Moderation Chain**
- **Links Used**: LLMChain, ModerationChain
- **Notes**: This chain shows how to use OpenAI's content moderation endpoint to screen output, and shows how to connect this to an LLMChain.
- `Example Notebook <./examples/moderation.html>`_
.. toctree::
:maxdepth: 1
:glob:
:caption: Generic Chains
:name: generic
:hidden:
./examples/*

View File

@ -1,10 +1,6 @@
Document Loaders
==========================
.. note::
`Conceptual Guide <https://docs.langchain.com/docs/components/indexing/document-loaders>`_
Combining language models with your own text data is a powerful way to differentiate them.
The first step in doing this is to load the data into "documents" - a fancy way of say some pieces of text.
This module is aimed at making this easy.
@ -14,11 +10,20 @@ This package is a great way to transform all types of files - text, powerpoint,
For detailed instructions on how to get set up with Unstructured, see installation guidelines `here <https://github.com/Unstructured-IO/unstructured#coffee-getting-started>`_.
The following document loaders are provided:
The following sections of documentation are provided:
- `Key Concepts <./document_loaders/key_concepts.html>`_: A conceptual guide going over the various concepts related to loading documents.
- `How-To Guides <./document_loaders/how_to_guides.html>`_: A collection of how-to guides. These highlight different types of loaders.
.. toctree::
:maxdepth: 1
:glob:
:caption: Document Loaders
:name: Document Loaders
:hidden:
./document_loaders/examples/*
./document_loaders/key_concepts.md
./document_loaders/how_to_guides.rst

Some files were not shown because too many files have changed in this diff Show More