- **Description:** Fixes the Mathpix PDF loader API integration.
Specifically, ensures that Mathpix auth headers are provided for every
request, and ensures that we recognize all errors that can occur during
a request. Also, the option to provide API keys as kwargs never actually
worked before, but now that's fixed too.
- **Issue:** #11249
- **Dependencies:** None
- **Description:**
This PR introduces the Slack toolkit to LangChain, which allows users to
read and write to Slack using the Slack API. Specifically, we've added
the following tools.
1. get_channel: Provides a summary of all the channels in a workspace.
2. get_message: Gets the message history of a channel.
3. send_message: Sends a message to a channel.
4. schedule_message: Sends a message to a channel at a specific time and
date.
- **Issue:** This pull request addresses [Add Slack Toolkit
#11747](https://github.com/langchain-ai/langchain/issues/11747)
- **Dependencies:** package`slack_sdk`
Note: For this toolkit to function you will need to add a Slack app to
your workspace. Additional info can be found
[here](https://slack.com/help/articles/202035138-Add-apps-to-your-Slack-workspace).
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ArianneLavada <ariannelavada@gmail.com>
Co-authored-by: ArianneLavada <84357335+ArianneLavada@users.noreply.github.com>
Co-authored-by: ariannelavada@gmail.com <you@example.com>
Unnecessarily overridden methods:
- Give the idea the subclass is doing something special (when it isn't)
- Block CTRL-click to the actual method
This PR removes some unnecessarily overridden methods in
`StdOutCallbackHandler`
Supercedes https://github.com/langchain-ai/langchain/pull/12858
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Hi,
There is some unintended behavior in Html2TextTransformer.
The current code is **directly modifying the original documents that are
passed as arguments to the function.**
Therefore, not only the return of the function but also the input
variables are being modified simultaneously.
**To resolve this, I added unit test code as well.**
reference link: [Shallow vs Deep Copying of Python
Objects](https://realpython.com/copying-python-objects/)
Thanks! ☺️
Before, we need to use `params` to pass extra parameters:
```python
from langchain.llms import Databricks
Databricks(..., params={"temperature": 0.0})
```
Now, we can directly specify extra params:
```python
from langchain.llms import Databricks
Databricks(..., temperature=0.0)
```
This PR adds an "Azure AI data" document loader, which allows Azure AI
users to load their registered data assets as a document object in
langchain.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
See PR title.
From what I can see, `poetry` will auto-include this. Please let me know
if I am missing something here.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
… properly
Fixed a bug that was causing the streaming transfer to not work
properly.
- **Description:
1、The on_llm_new_token method in the streaming callback can now be
called properly in streaming transfer mode.
2、In streaming transfer mode, LLM can now correctly output the complete
response instead of just the first token.
- **Tag maintainer: @wangxuqi
- **Twitter handle: @kGX7XJjuYxzX9Km
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
* Add support for passing a specific file to the file system blob loader
* Allow specifying a class parameter for the parser for the generic
loader
```python
class AudioLoader(GenericLoader):
@staticmethod
def get_parser(**kwargs):
return MyAudioParser(**kwargs):
```
The intent of the GenericLoader is to provide on-ramps from different
sources (e.g., web, s3, file system).
An alternative is to use pipelining syntax or creating a Pipeline
```
FileSystemBlobLoader(...) | MyAudioParser
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** just a little change of ErnieChatBot class
description, sugguesting user to use more suitable class
- **Issue:** none,
- **Dependencies:** none,
- **Tag maintainer:** @baskaryan ,
- **Twitter handle:** none
**Description**
`embed_with_retry` is for sync operations and not for async operations.
Use `async_embed_with_retry` for appropriate async operations.
I'm using `OpenAIEmbedding(http_client=httpx.AsyncClient())` with only
async operations.
However, I got an error when I use `embedding.aembed_documents` because
`embed_with_retry` uses sync OpenAI client with async http client.
Description
when the desc of arg in python docstring contains ":", the
`_parse_python_function_docstring` will raise **ValueError: too many
values to unpack (expected 2)**.
A sample desc would be:
"""
Args:
error_arg: this is an arg with an additional ":" symbol
"""
So, set `maxsplit` parameter to fix it.
The number of times I try to format a string (especially in lcel) is
embarrassingly high. Think this may be more actionable than the default
error message. Now I get nice helpful errors
```
KeyError: "Input to ChatPromptTemplate is missing variable 'input'. Expected: ['input'] Received: ['dialogue']"
```
**Description:** By combining the document timestamp refresh within a
single call to update(), this enables batching of multiple documents in
a single SQL statement. This is important for non-local databases where
tens of milliseconds has a huge impact on performance when doing
document-by-document SQL statements.
**Issue:** #11935
**Dependencies:** None
**Tag maintainer:** @eyurtsev
CC @baskaryan @hwchase17 @jmorganca
Having a bit of trouble importing `langchain_experimental` from a
notebook, will figure it out tomorrow
~Ah and also is blocked by #13226~
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Related to https://github.com/mlflow/mlflow/pull/10420. MLflow AI
gateway will be deprecated and replaced by the `mlflow.deployments`
module. Happy to split this PR if it's too large.
```
pip install git+https://github.com/langchain-ai/langchain.git@refs/pull/13699/merge#subdirectory=libs/langchain
```
## Dependencies
Install mlflow from https://github.com/mlflow/mlflow/pull/10420:
```
pip install git+https://github.com/mlflow/mlflow.git@refs/pull/10420/merge
```
## Testing plan
The following code works fine on local and databricks:
<details><summary>Click</summary>
<p>
```python
"""
Setup
-----
mlflow deployments start-server --config-path examples/gateway/openai/config.yaml
databricks secrets create-scope <scope>
databricks secrets put-secret <scope> openai-api-key --string-value $OPENAI_API_KEY
Run
---
python /path/to/this/file.py secrets/<scope>/openai-api-key
"""
from langchain.chat_models import ChatMlflow, ChatDatabricks
from langchain.embeddings import MlflowEmbeddings, DatabricksEmbeddings
from langchain.llms import Databricks, Mlflow
from langchain.schema.messages import HumanMessage
from langchain.chains.loading import load_chain
from mlflow.deployments import get_deploy_client
import uuid
import sys
import tempfile
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
###############################
# MLflow
###############################
chat = ChatMlflow(
target_uri="http://127.0.0.1:5000", endpoint="chat", params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
embeddings = MlflowEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
llm = Mlflow(
target_uri="http://127.0.0.1:5000",
endpoint="completions",
params={"temperature": 0.1},
)
print(llm("I am"))
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke",
),
)
print(llm_chain.run(adjective="funny"))
# serialization/deserialization
with tempfile.TemporaryDirectory() as tmpdir:
print(tmpdir)
path = f"{tmpdir}/llm.yaml"
llm_chain.save(path)
loaded_chain = load_chain(path)
print(loaded_chain("funny"))
###############################
# Databricks
###############################
secret = sys.argv[1]
client = get_deploy_client("databricks")
# External - chat
name = f"chat-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "gpt-4",
"provider": "openai",
"task": "llm/v1/chat",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
chat = ChatDatabricks(
target_uri="databricks", endpoint=name, params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
finally:
client.delete_endpoint(endpoint=name)
# External - embeddings
name = f"embeddings-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "text-embedding-ada-002",
"provider": "openai",
"task": "llm/v1/embeddings",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
embeddings = DatabricksEmbeddings(target_uri="databricks", endpoint=name)
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
finally:
client.delete_endpoint(endpoint=name)
# External - completions
name = f"completions-{uuid.uuid4()}"
client.create_endpoint(
name=name,
config={
"served_entities": [
{
"name": "test",
"external_model": {
"name": "gpt-3.5-turbo-instruct",
"provider": "openai",
"task": "llm/v1/completions",
"openai_config": {
"openai_api_key": "{{" + secret + "}}",
},
},
}
],
},
)
try:
llm = Databricks(
endpoint_name=name,
model_kwargs={"temperature": 0.1},
)
print(llm("I am"))
finally:
client.delete_endpoint(endpoint=name)
# Foundation model - chat
chat = ChatDatabricks(
endpoint="databricks-llama-2-70b-chat", params={"temperature": 0.1}
)
print(chat([HumanMessage(content="hello")]))
# Foundation model - embeddings
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
print(embeddings.embed_query("hello")[:3])
# Foundation model - completions
llm = Databricks(
endpoint_name="databricks-mpt-7b-instruct", model_kwargs={"temperature": 0.1}
)
print(llm("hello"))
llm_chain = LLMChain(
llm=llm,
prompt=PromptTemplate(
input_variables=["adjective"],
template="Tell me a {adjective} joke",
),
)
print(llm_chain.run(adjective="funny"))
# serialization/deserialization
with tempfile.TemporaryDirectory() as tmpdir:
print(tmpdir)
path = f"{tmpdir}/llm.yaml"
llm_chain.save(path)
loaded_chain = load_chain(path)
print(loaded_chain("funny"))
```
Output:
```
content='Hello! How can I assist you today?'
[-0.025058426, -0.01938856, -0.027781019]
[-0.025058426, -0.01938856, -0.027781019]
sorry, but I cannot continue the sentence as it is incomplete. Can you please provide more information or context?
Sure, here's a classic one for you:
Why don't scientists trust atoms?
Because they make up everything!
/var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmpx_4no6ad
{'adjective': 'funny', 'text': "Sure, here's a classic one for you:\n\nWhy don't scientists trust atoms?\n\nBecause they make up everything!"}
content='Hello! How can I assist you today?'
[-0.025058426, -0.01938856, -0.027781019]
[-0.025058426, -0.01938856, -0.027781019]
a 23 year old female and I am currently studying for my master's degree
content="\nHello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?"
[0.051055908203125, 0.007221221923828125, 0.003879547119140625]
[0.051055908203125, 0.007221221923828125, 0.003879547119140625]
hello back
Well, I don't really know many jokes, but I do know this funny story...
/var/folders/dz/cd_nvlf14g9g__n3ph0d_0pm0000gp/T/tmp7_ds72ex
{'adjective': 'funny', 'text': " Well, I don't really know many jokes, but I do know this funny story..."}
```
</p>
</details>
The existing workflow doesn't break:
<details><summary>click</summary>
<p>
```python
import uuid
import mlflow
from mlflow.models import ModelSignature
from mlflow.types.schema import ColSpec, Schema
class MyModel(mlflow.pyfunc.PythonModel):
def predict(self, context, model_input):
return str(uuid.uuid4())
with mlflow.start_run():
mlflow.pyfunc.log_model(
"model",
python_model=MyModel(),
pip_requirements=["mlflow==2.8.1", "cloudpickle<3"],
signature=ModelSignature(
inputs=Schema(
[
ColSpec("string", "prompt"),
ColSpec("string", "stop"),
]
),
outputs=Schema(
[
ColSpec(name=None, type="string"),
]
),
),
registered_model_name=f"lang-{uuid.uuid4()}",
)
# Manually create a serving endpoint with the registered model and run
from langchain.llms import Databricks
llm = Databricks(endpoint_name="<name>")
llm("hello") # 9d0b2491-3d13-487c-bc02-1287f06ecae7
```
</p>
</details>
## Follow-up tasks
(This PR is too large. I'll file a separate one for follow-up tasks.)
- Update `docs/docs/integrations/providers/mlflow_ai_gateway.mdx` and
`docs/docs/integrations/providers/databricks.md`.
---------
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
…parameters.
In Langchain's `dumps()` function, I've added a `**kwargs` parameter.
This allows users to pass additional parameters to the underlying
`json.dumps()` function, providing greater flexibility and control over
JSON serialization.
Many parameters available in `json.dumps()` can be useful or even
necessary in specific situations. For example, when using an Agent with
return_intermediate_steps set to true, the output is a list of
AgentAction objects. These objects can't be serialized without using
Langchain's `dumps()` function.
The issue arises when using the Agent with a language other than
English, which may contain non-ASCII characters like 'é'. The default
behavior of `json.dumps()` sets ensure_ascii to true, converting
`{"name": "José"}` into `{"name": "Jos\u00e9"}`. This can make the
output hard to read, especially in the case of intermediate steps in
agent logs.
By allowing users to pass additional parameters to `json.dumps()` via
Langchain's dumps(), we can solve this problem. For instance, users can
set `ensure_ascii=False` to maintain the original characters.
This update also enables users to pass other useful `json.dumps()`
parameters like `sort_keys`, providing even more flexibility.
The implementation takes into account edge cases where a user might pass
a "default" parameter, which is already defined by `dumps()`, or an
"indent" parameter, which is also predefined if `pretty=True` is set.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
<!-- Thank you for contributing to LangChain!
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@baskaryan, @eyurtsev, @hwchase17.
-->
### Description
Hello,
The [integration_test
README](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests)
was indicating incorrect paths for the `.env.example` and `.env` files.
`tests/.env.example` ->`tests/integration_tests/.env.example`
While it’s a minor error, it could **potentially lead to confusion** for
the document’s readers, so I’ve made the necessary corrections.
Thank you! ☺️
### Related Issue
- https://github.com/langchain-ai/langchain/pull/2806
**Description:**
Added support for a Pandas DataFrame OutputParser with format
instructions, along with unit tests and a demo notebook. Namely, we've
added the ability to request data from a DataFrame, have the LLM parse
the request, and then use that request to retrieve a well-formatted
response.
Within LangChain, it seamlessly integrates with language models like
OpenAI's `text-davinci-003`, facilitating streamlined interaction using
the format instructions (just like the other output parsers).
This parser structures its requests as
`<operation/column/row>[<optional_array_params>]`. The instructions
detail permissible operations, valid columns, and array formats,
ensuring clarity and adherence to the required format.
For example:
- When the LLM receives the input: "Retrieve the mean of `num_legs` from
rows 1 to 3."
- The provided format instructions guide the LLM to structure the
request as: "mean:num_legs[1..3]".
The parser processes this formatted request, leveraging the LLM's
understanding to extract the mean of `num_legs` from rows 1 to 3 within
the Pandas DataFrame.
This integration allows users to communicate requests naturally, with
the LLM transforming these instructions into structured commands
understood by the `PandasDataFrameOutputParser`. The format instructions
act as a bridge between natural language queries and precise DataFrame
operations, optimizing communication and data retrieval.
**Issue:**
- https://github.com/langchain-ai/langchain/issues/11532
**Dependencies:**
No additional dependencies :)
**Tag maintainer:**
@baskaryan
**Twitter handle:**
No need. :)
---------
Co-authored-by: Wasee Alam <waseealam@protonmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
When using Vald, only insecure grpc connection was supported, so secure
connection is now supported.
In addition, grpc metadata can be added to Vald requests to enable
authentication with a token.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
Response_if_no_docs_found is not implemented in
ConversationalRetrievalChain for async code paths. Implemented it and
added test cases
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Description
This PR implements Self-Query Retriever for MongoDB Atlas vector store.
I've implemented the comparators and operators that are supported by
MongoDB Atlas vector store according to the section titled "Atlas Vector
Search Pre-Filter" from
https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/.
Namely:
```
allowed_comparators = [
Comparator.EQ,
Comparator.NE,
Comparator.GT,
Comparator.GTE,
Comparator.LT,
Comparator.LTE,
Comparator.IN,
Comparator.NIN,
]
"""Subset of allowed logical operators."""
allowed_operators = [
Operator.AND,
Operator.OR
]
```
Translations from comparators/operators to MongoDB Atlas filter
operators(you can find the syntax in the "Atlas Vector Search
Pre-Filter" section from the previous link) are done using the following
dictionary:
```
map_dict = {
Operator.AND: "$and",
Operator.OR: "$or",
Comparator.EQ: "$eq",
Comparator.NE: "$ne",
Comparator.GTE: "$gte",
Comparator.LTE: "$lte",
Comparator.LT: "$lt",
Comparator.GT: "$gt",
Comparator.IN: "$in",
Comparator.NIN: "$nin",
}
```
In visit_structured_query() the filters are passed as "pre_filter" and
not "filter" as in the MongoDB link above since langchain's
implementation of MongoDB atlas vector
store(libs\langchain\langchain\vectorstores\mongodb_atlas.py) in
_similarity_search_with_score() sets the "filter" key to have the value
of the "pre_filter" argument.
```
params["filter"] = pre_filter
```
Test cases and documentation have also been added.
# Issue
#11616
# Dependencies
No new dependencies have been added.
# Documentation
I have created the notebook mongodb_atlas_self_query.ipynb outlining the
steps to get the self-query mechanism working.
I worked closely with [@Farhan-Faisal](https://github.com/Farhan-Faisal)
on this PR.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Description
We implemented a simple tool for accessing the Merriam-Webster
Collegiate Dictionary API
(https://dictionaryapi.com/products/api-collegiate-dictionary).
Here's a simple usage example:
```py
from langchain.llms import OpenAI
from langchain.agents import load_tools, initialize_agent, AgentType
llm = OpenAI()
tools = load_tools(["serpapi", "merriam-webster"], llm=llm) # Serp API gives our agent access to Google
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is the english word for the german word Himbeere? Define that word.")
```
Sample output:
```
> Entering new AgentExecutor chain...
I need to find the english word for Himbeere and then get the definition of that word.
Action: Search
Action Input: "English word for Himbeere"
Observation: {'type': 'translation_result'}
Thought: Now I have the english word, I can look up the definition.
Action: MerriamWebster
Action Input: raspberry
Observation: Definitions of 'raspberry':
1. rasp-ber-ry, noun: any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries
2. rasp-ber-ry, noun: a perennial plant (genus Rubus) of the rose family that bears raspberries
3. rasp-ber-ry, noun: a sound of contempt made by protruding the tongue between the lips and expelling air forcibly to produce a vibration; broadly : an expression of disapproval or contempt
4. black raspberry, noun: a raspberry (Rubus occidentalis) of eastern North America that has a purplish-black fruit and is the source of several cultivated varieties —called also blackcap
Thought: I now know the final answer.
Final Answer: Raspberry is an english word for Himbeere and it is defined as any of various usually black or red edible berries that are aggregate fruits consisting of numerous small drupes on a fleshy receptacle and that are usually rounder and smaller than the closely related blackberries.
> Finished chain.
```
# Issue
This closes#12039.
# Dependencies
We added no extra dependencies.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Lara <63805048+larkgz@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Update the document for drop box loader + made the
messages more verbose when loading pdf file since people were getting
confused
- **Issue:** #13952
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17,
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Added a tool called RedditSearchRun and an
accompanying API wrapper, which searches Reddit for posts with support
for time filtering, post sorting, query string and subreddit filtering.
- **Issue:** #13891
- **Dependencies:** `praw` module is used to search Reddit
- **Tag maintainer:** @baskaryan , and any of the other maintainers if
needed
- **Twitter handle:** None.
Hello,
This is our first PR and we hope that our changes will be helpful to the
community. We have run `make format`, `make lint` and `make test`
locally before submitting the PR. To our knowledge, our changes do not
introduce any new errors.
Our PR integrates the `praw` package which is already used by
RedditPostsLoader in LangChain. Nonetheless, we have added integration
tests and edited unit tests to test our changes. An example notebook is
also provided. These changes were put together by me, @Anika2000,
@CharlesXu123, and @Jeremy-Cheng-stack
Thank you in advance to the maintainers for their time.
---------
Co-authored-by: What-Is-A-Username <49571870+What-Is-A-Username@users.noreply.github.com>
Co-authored-by: Anika2000 <anika.sultana@mail.utoronto.ca>
Co-authored-by: Jeremy Cheng <81793294+Jeremy-Cheng-stack@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Added some of the more endpoints supported by serpapi
that are not suported on langchain at the moment, like google trends,
google finance, google jobs, and google lens
- **Issue:** [Add support for many of the querying endpoints with
serpapi #11811](https://github.com/langchain-ai/langchain/issues/11811)
---------
Co-authored-by: zushenglu <58179949+zushenglu@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Ian Xu <ian.xu@mail.utoronto.ca>
Co-authored-by: zushenglu <zushenglu1809@gmail.com>
Co-authored-by: KevinT928 <96837880+KevinT928@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Volc Engine MaaS serves as an enterprise-grade,
large-model service platform designed for developers. You can visit its
homepage at https://www.volcengine.com/docs/82379/1099455 for details.
This change will facilitate developers to integrate quickly with the
platform.
- **Issue:** None
- **Dependencies:** volcengine
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @he1v3tica
---------
Co-authored-by: lvzhong <lvzhong@bytedance.com>
- **Description:** use post field validation for `CohereRerank`
- **Issue:** #12899 and #13058
- **Dependencies:**
- **Tag maintainer:** @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Update 5 pdf document loaders in
`langchain.document_loaders.pdf`, to store a url in the metadata
(instead of a temporary, local file path) if the user provides a web
path to a pdf: `PyPDFium2Loader`, `PDFMinerLoader`,
`PDFMinerPDFasHTMLLoader`, `PyMuPDFLoader`, and `PDFPlumberLoader` were
updated.
- The updates follow the approach used to update `PyPDFLoader` for the
same behavior in #12092
- The `PyMuPDFLoader` changes required additional work in updating
`langchain.document_loaders.parsers.pdf.PyMuPDFParser` to be able to
process either an `io.BufferedReader` (from local pdf) or `io.BytesIO`
(from online pdf)
- The `PDFMinerPDFasHTMLLoader` change used a simpler approach since the
metadata is assigned by the loader and not the parser
- **Issue:** Fixes#7034
- **Dependencies:** None
```python
# PyPDFium2Loader example:
# old behavior
>>> from langchain.document_loaders import PyPDFium2Loader
>>> loader = PyPDFium2Loader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': '/var/folders/7z/d5dt407n673drh1f5cm8spj40000gn/T/tmpm5oqa92f/tmp.pdf', 'page': 0}
# new behavior
>>> from langchain.document_loaders import PyPDFium2Loader
>>> loader = PyPDFium2Loader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': 'https://arxiv.org/pdf/1706.03762.pdf', 'page': 0}
```
- **Description:** Updated to remove deprecated parameter penalty_alpha,
and use string variation of prompt rather than json object for better
flexibility. - **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** N/A
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** @symbldotai
---------
Co-authored-by: toshishjawale <toshish@symbl.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Instead of using JSON-like syntax to describe node and relationship
properties we changed to a shorter and more concise schema description
Old:
```
Node properties are the following:
[{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}]
Relationship properties are the following:
[]
The relationships are the following:
['(:Actor)-[:ACTED_IN]->(:Movie)']
```
New:
```
Node properties are the following:
Movie {name: STRING},Actor {name: STRING}
Relationship properties are the following:
The relationships are the following:
(:Actor)-[:ACTED_IN]->(:Movie)
```
Implements
[#12115](https://github.com/langchain-ai/langchain/issues/12115)
Who can review?
@baskaryan , @eyurtsev , @hwchase17
Integrated Stack Exchange API into Langchain, enabling access to diverse
communities within the platform. This addition enhances Langchain's
capabilities by allowing users to query Stack Exchange for specialized
information and engage in discussions. The integration provides seamless
interaction with Stack Exchange content, offering content from varied
knowledge repositories.
A notebook example and test cases were included to demonstrate the
functionality and reliability of this integration.
- Add StackExchange as a tool.
- Add unit test for the StackExchange wrapper and tool.
- Add documentation for the StackExchange wrapper and tool.
If you have time, could you please review the code and provide any
feedback as necessary! My team is welcome to any suggestions.
---------
Co-authored-by: Yuval Kamani <yuvalkamani@gmail.com>
Co-authored-by: Aryan Thakur <aryanthakur@Aryans-MacBook-Pro.local>
Co-authored-by: Manas1818 <79381912+manas1818@users.noreply.github.com>
Co-authored-by: aryan-thakur <61063777+aryan-thakur@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** The class allows to only select between a few
predefined prompts from the paper. That is not ideal, since other use
cases might need a custom prompt. The changes made allow for this. To be
able to monitor those, I also added functionality to supply a custom
run_manager.
- **Issue:** no issue, but a new feature,
- **Dependencies:** none,
- **Tag maintainer:** @hwchase17,
- **Twitter handle:** @yvesloy
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Support providing whatever extra parameters you want
to the Mathpix PDF loader API request.
- **Issue:** #12773
- **Dependencies:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Adds a tqdm progress bar to GooglePalmEmbeddings when
embedding a list.
- **Issue:** #13637
- **Dependencies:** TQDM as a main dependency (instead of extra)
Signed-off-by: ugm2 <unaigaraymaestre@gmail.com>
---------
Signed-off-by: ugm2 <unaigaraymaestre@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR is fixing an attributeError: object endpoint has no attribute
"_public_match_client" when using gcp matching engine with private VPC
network.
@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** As of OpenAI's Python package 1.0, the existing
DallEAPIWrapper does not work correctly, so the example in the LangChain
Documentation link below does not work either.
https://python.langchain.com/docs/integrations/tools/dalle_image_generator
Also, since OpenAI only supports DALL-E version 2 or version 3, I
modified the DallEAPIWrapper to support it.
- **Issue:** #13825
- **Twitter handle:** ggeutzzang
- **Description:** According to the document
https://cloud.baidu.com/doc/WENXINWORKSHOP/s/6lp69is2a, add ERNIE-Bot-8K
model support for ErnieBotChat.
- **Dependencies:** Before using the ERNIE-Bot-8K, you should have the
model's access authority.
Replace this entire comment with:
- **Description:** updates `create_llm_result` function within
`openai.py` to consider latest `params`,
- **Issue:** #8928
- **Dependencies:** -,
- **Tag maintainer:** -
- **Twitter handle:** [burkomr](https://twitter.com/burkomr)
<!-- If no one reviews your PR within a few days, please @-mention one
of @baskaryan, @eyurtsev, @hwchase17. -->
---------
Co-authored-by: Burak Ömür <burakomur@retorio.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Replace this entire comment with:
- **Description:** VertexAI models are now GA, moved away from using
preview ones from the SDK
- **Issue:** #13606
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
**Description:**
Repair Wikipedia document loader `load_max_docs` and improve test
coverage.
**Issue:**
The Wikipedia document loader was not respecting the `load_max_docs`
paramater (not reported) and would always return a maximum of 10
documents. This is because the API wrapper (in `utilities/wikipedia.py`)
wasn't passing `top_k_results` to the underlying [Wikipedia
library](https://wikipedia.readthedocs.io/en/latest/code.html#module-wikipedia).
By default this library returns 10 results.
The default number of results for the document loader has been reduced
from 100 to 25. This is because loading 100 results takes a very long
time and is an inconvenient default. It should possibly be 10.
In addition, the documentation for the loader reported that there was a
hard limit (300) on the number of documents returned. In actuality 300
is the maximum Wikipedia query character length set by the API wrapper.
Tests have been added for the document loader (previously missing) and
to test the correct numbers of documents are being returned by each
class, both by default, and when overridden. Also repaired is the
`assert_docs` test which has been updated to correctly test for the
default metadata (which includes `source` in recent releases).
**Dependencies:**
nil
**Tag maintainer:**
@leo-gan
**Twitter handle:**
@queenvictoria
### **Description:**
Previously `python_repl` was a built-in tool, but now it has been moved
to `langchain_experimental`.
When I use `load_tools` I get an error:
```python
In [1]: from langchain.agents import load_tools
In [2]: load_tools(["python_repl"])
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[2], line 1
----> 1 load_tools(["python_repl"])
File ~/workspace/langchain/libs/langchain/langchain/agents/load_tools.py:530, in load_tools(tool_names, llm, callbacks, **kwargs)
528 tool_names.extend(requests_method_tools)
529 elif name in _BASE_TOOLS:
--> 530 tools.append(_BASE_TOOLS[name]())
531 elif name in _LLM_TOOLS:
532 if llm is None:
File ~/workspace/langchain/libs/langchain/langchain/agents/load_tools.py:84, in _get_python_repl()
83 def _get_python_repl() -> BaseTool:
---> 84 raise ImportError(
85 "This tool has been moved to langchain experiment. "
86 "This tool has access to a python REPL. "
87 "For best practices make sure to sandbox this tool. "
88 "Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md "
89 "To keep using this code as is, install langchain experimental and "
90 "update relevant imports replacing 'langchain' with 'langchain_experimental'"
91 )
ImportError: This tool has been moved to langchain experiment. This tool has access to a python REPL. For best practices make sure to sandbox this tool. Read https://github.com/langchain-ai/langchain/blob/master/SECURITY.md To keep using this code as is, install langchain experimental and update relevant imports replacing 'langchain' with 'langchain_experimental'
```
In this case, it will be very confusing. I think it is no longer a
built-in tool now, so it can be removed from `_BASE_TOOLS`
### **Issue:**
https://github.com/langchain-ai/langchain/issues/13858,
https://github.com/langchain-ai/langchain/issues/13859,
https://github.com/langchain-ai/langchain/issues/13856
### **Twitter handle:**
[lin_bob57617](https://twitter.com/lin_bob57617)
The `integrations/vectorstores/matchingengine.ipynb` example has the
"Google Vertex AI Vector Search" title. This place this Title in the
wrong order in the ToC (it is sorted by the file name).
- Renamed `integrations/vectorstores/matchingengine.ipynb` into
`integrations/vectorstores/google_vertex_ai_vector_search.ipynb`.
- Updated a correspondent comment in docstring
- Rerouted old URL to a new URL
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
It was :
`from langchain.schema.prompts import BasePromptTemplate`
but because of the breaking change in the ns, it is now
`from langchain.schema.prompt_template import BasePromptTemplate`
This bug prevents building the API Reference for the langchain_experimental
There are the following main changes in this PR:
1. Rewrite of the DocugamiLoader to not do any XML parsing of the DGML
format internally, and instead use the `dgml-utils` library we are
separately working on. This is a very lightweight dependency.
2. Added MMR search type as an option to multi-vector retriever, similar
to other retrievers. MMR is especially useful when using Docugami for
RAG since we deal with large sets of documents within which a few might
be duplicates and straight similarity based search doesn't give great
results in many cases.
We are @docugami on twitter, and I am @tjaffri
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
- **Description:** Adds a retriever implementation for [Knowledge Bases
for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/), a
new service announced at AWS re:Invent, shortly before this PR was
opened. This depends on the `bedrock-agent-runtime` service, which will
be included in a future version of `boto3` and of `botocore`. We will
open a follow-up PR documenting the minimum required versions of `boto3`
and `botocore` after that information is available.
- **Issue:** N/A
- **Dependencies:** `boto3>=1.33.2, botocore>=1.33.2`
- **Tag maintainer:** @baskaryan
- **Twitter handles:** `@pjain7` `@dead_letter_q`
This PR includes a documentation notebook under
`docs/docs/integrations/retrievers`, which I (@dlqqq) have verified
independently.
EDIT: `bedrock-agent-runtime` service is now included in
`boto3>=1.33.2`:
5cf793f493
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Addressing incorrect order being sent to callbacks / tracers, due to the
nature of threading
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Add arg to omit streamed_output list, in cases where final_output is
enough this saves bandwidth
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
This PR rearranges the docstring for the `AstraDB` vector store class so
as to have all useful information in the _class_ docstring for ease of
reading.
(incidentally, due to an oversight, the docstring that was in the
constructor ended up buried below some lines of code, thereby
disappearing altogether from accessibility. Apologies.)
- **Description:** Updates to `AnthropicFunctions` to be compatible with
the OpenAI `function_call` functionality.
- **Issue:** The functionality to indicate `auto`, `none` and a forced
function_call was not completely implemented in the existing code.
- **Dependencies:** None
- **Tag maintainer:** @baskaryan , and any of the other maintainers if
needed.
- **Twitter handle:** None
I have specifically tested this functionality via AWS Bedrock with the
Claude-2 and Claude-Instant models.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** We are adding functionality to extract message
content from the `attributedBody` field of the database, in case the
content is not in the `text` field.
- **Issue:** Closes#13326 and #10680
- **Dependencies:** None.
- **Tag maintainer:** @eyurtsev, @hwchase17
---------
Co-authored-by: onotate <johnp.pham@mail.utoronto.ca>
- **Description:** Previously `MarkdownHeaderTextSplitter` did not
consider tilde-fenced code blocks
(https://spec.commonmark.org/0.30/#fenced-code-blocks). This PR fixes
that.
````md
# Bug caused by previous implementation:
~~~py
foo()
# This is a comment that would be considered header
bar()
~~~
````
- **Tag maintainer:** @baskaryan
Several bug fixes:
- emails: instead of `bcc` the `cc` is used.
- errors in the truncation descriptions
- no truncation of the `message_search`
Several updates:
- generalized UTC format
- truncation limit can be changed now in _call()
Fixes#13407.
This workaround consists in letting the RunnableLambda create its
self.afunc from its self.func when self.afunc is not provided; the
change has no dependency.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Nuno Campos <nuno@langchain.dev>
```
---- chunk 1
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]}
---- chunk 2
{'messages': [FunctionMessage(content="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”", name='Search')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”")]}
---- chunk 3
{'actions': [AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]}
---- chunk 4
{'messages': [FunctionMessage(content='25 years', name='Search')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]), observation='25 years')]}
---- chunk 5
{'actions': [AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]}
---- chunk 6
{'messages': [FunctionMessage(content='Answer: 3.991298452658078', name='Calculator')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')]}
---- chunk 7
{'messages': [AIMessage(content="Leonardo DiCaprio's current girlfriend is the Italian model Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 power is approximately 3.99.")],
'output': "Leonardo DiCaprio's current girlfriend is the Italian model "
'Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 '
'power is approximately 3.99.'}
---- final
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]),
AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]),
AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}}),
FunctionMessage(content="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”", name='Search'),
AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}}),
FunctionMessage(content='25 years', name='Search'),
AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}}),
FunctionMessage(content='Answer: 3.991298452658078', name='Calculator'),
AIMessage(content="Leonardo DiCaprio's current girlfriend is the Italian model Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 power is approximately 3.99.")],
'output': "Leonardo DiCaprio's current girlfriend is the Italian model "
'Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 '
'power is approximately 3.99.',
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”"),
AgentStep(action=AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]), observation='25 years'),
AgentStep(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')]}
```
- **Description:** Existing model used for Prompt Injection is quite
outdated but we fine-tuned and open-source a new model based on the same
model deberta-v3-base from Microsoft -
[laiyer/deberta-v3-base-prompt-injection](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection).
It supports more up-to-date injections and less prone to
false-positives.
- **Dependencies:** No
- **Tag maintainer:** -
- **Twitter handle:** @alex_yaremchuk
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** The experimental package needs to be compatible with
the usage of importing agents
For example, if i use `from langchain.agents import
create_pandas_dataframe_agent`, running the program will prompt the
following information:
```
Traceback (most recent call last):
File "/Users/dongwm/test/main.py", line 1, in <module>
from langchain.agents import create_pandas_dataframe_agent
File "/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 87, in __getattr__
raise ImportError(
ImportError: create_pandas_dataframe_agent has been moved to langchain experimental. See https://github.com/langchain-ai/langchain/discussions/11680 for more information.
Please update your import statement from: `langchain.agents.create_pandas_dataframe_agent` to `langchain_experimental.agents.create_pandas_dataframe_agent`.
```
But when I changed to `from langchain_experimental.agents import
create_pandas_dataframe_agent`, it was actually wrong:
```python
Traceback (most recent call last):
File "/Users/dongwm/test/main.py", line 2, in <module>
from langchain_experimental.agents import create_pandas_dataframe_agent
ImportError: cannot import name 'create_pandas_dataframe_agent' from 'langchain_experimental.agents' (/Users/dongwm/test/venv/lib/python3.11/site-packages/langchain_experimental/agents/__init__.py)
```
I should use `from langchain_experimental.agents.agent_toolkits import
create_pandas_dataframe_agent`. In order to solve the problem and make
it compatible, I added additional import code to the
langchain_experimental package. Now it can be like this Used `from
langchain_experimental.agents import create_pandas_dataframe_agent`
- **Twitter handle:** [lin_bob57617](https://twitter.com/lin_bob57617)
- **Description:** Adds a tqdm progress bar to OllamaEmbeddings when
embedding a list.
- **Issue:** Related to #13637, but extended to Ollama.
- **Dependencies:** `tqdm` made a necessary dependency.
Thanks to @ugm2 for helping identify a common problem. Embeddings take a
very long time to finish on local machines, and require a progress bar
to help identify if one should even attempt the workload.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Added a line to pass the tenant parameter to
add_data_object
- **Issue:** An extra line added from the fix for #9956
- **Dependencies:** n/a
- **Tag maintainer:** @baskaryan
Tested locally, works as expected with the line change.
---------
Co-authored-by: Simon Dai <simon6752@gmail.com>
Description: Some Elastic indexes do not return a 'metadata' field in
'_source'. However, prior to this PR, the code assumed there always is a
'metadata' field. This PR adds support for cases where the field is
missing by adding it manually.
Issue: #13869
**Description:**
This PR adds Databricks Vector Search as a new vector store in
LangChain.
- [x] Add `DatabricksVectorSearch` in `langchain/vectorstores/`
- [x] Unit tests
- [x] Add
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
as a new optional dependency
We ran the following checks:
- `make format` passed ✅
- `make lint` failed but the failures were caused by other files
+ Files touched by this PR passed the linter ✅
- `make test` passed ✅
- `make coverage` failed but the failures were caused by other files.
Tests added by or related to this PR all passed
+ langchain/vectorstores/databricks_vector_search.py test coverage 94% ✅
- `make spell_check` passed ✅
The example notebook and updates to the [provider's documentation
page](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/providers/databricks.md)
will be added later in a separate PR.
**Dependencies:**
Optional dependency:
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This pull request addresses an issue found in the example code within
the docstring of `libs/core/langchain_core/runnables/passthrough.py`
The original code snippet caused a `NameError` due to the missing import
of `RunnableLambda`. The error was as follows:
```
12 return "completion"
13
---> 14 chain = RunnableLambda(fake_llm) | {
15 'original': RunnablePassthrough(), # Original LLM output
16 'parsed': lambda text: text[::-1] # Parsing logic
NameError: name 'RunnableLambda' is not defined
```
To resolve this, I have modified the example code to include the
necessary import statement for `RunnableLambda`. Additionally, I have
adjusted the indentation in the code snippet to ensure consistency and
readability.
The modified code now successfully defines and utilizes
`RunnableLambda`, ensuring that users referencing the docstring will
have a functional and clear example to follow.
There are no related GitHub issues for this particular change.
Modified Code:
```python
from langchain_core.runnables import RunnablePassthrough, RunnableParallel
from langchain_core.runnables import RunnableLambda
runnable = RunnableParallel(
origin=RunnablePassthrough(),
modified=lambda x: x+1
)
runnable.invoke(1) # {'origin': 1, 'modified': 2}
def fake_llm(prompt: str) -> str: # Fake LLM for the example
return "completion"
chain = RunnableLambda(fake_llm) | {
'original': RunnablePassthrough(), # Original LLM output
'parsed': lambda text: text[::-1] # Parsing logic
}
chain.invoke('hello') # {'original': 'completion', 'parsed': 'noitelpmoc'}
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Added a retriever for the Outline API to ask
questions on knowledge base
- **Issue:** resolves#11814
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
- **Description:** Simple change, I just added title metadata to
GoogleDriveLoader for optional File Loaders
- **Dependencies:** no dependencies
- **Tag maintainer:** @hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR provides idiomatic implementations for the exact-match and the
semantic LLM caches using Astra DB as backend through the database's
HTTP JSON API. These caches require the `astrapy` library as dependency.
Comes with integration tests and example usage in the `llm_cache.ipynb`
in the docs.
@baskaryan this is the Astra DB counterpart for the Cassandra classes
you merged some time ago, tagging you for your familiarity with the
topic. Thank you!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds a chat message history component that uses Astra DB for
persistence through the JSON API.
The `astrapy` package is required for this class to work.
I have added tests and a small notebook, and updated the relevant
references in the other docs pages.
(@rlancemartin this is the counterpart of the Cassandra equivalent class
you so helpfully reviewed back at the end of June)
Thank you!
- **Description:** This commit fixed the problem that Redis vector store
will change the value of a metadata from 0 to empty when saving the
document, which should be an un-intended behavior.
- **Issue:** N/A
- **Dependencies:** N/A
**Description:** Currently, if we pass in a ToolMessage back to the
chain, it crashes with error
`Got unsupported message type: `
This fixes it.
Tested locally
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** BaseStringMessagePromptTemplate.from_template was
passing the value of partial_variables into cls(...) via **kwargs,
rather than passing it to PromptTemplate.from_template. Which resulted
in those *partial_variables being* lost and becoming required
*input_variables*.
Co-authored-by: Josep Pon Farreny <josep.pon-farreny@siemens.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fix some circular deps:
- move PromptValue into top level module bc both PromptTemplates and
OutputParsers import
- move tracer context vars to `tracers.context` and import them in
functions in `callbacks.manager`
- add core import tests
- **Description:** We need to update the Dockerfile for templates to
also copy your README.md. This is because poetry requires that a readme
exists if it is specified in the pyproject.toml
Changes:
- remove langchain_core/schema since no clear distinction b/n schema and
non-schema modules
- make every module that doesn't end in -y plural
- where easy have 1-2 classes per file
- no more than one level of nesting in directories
- only import from top level core modules in langchain
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** fix a bug that prevented as_retriever() in Vectara to
use the desired input arguments
- **Issue:** as_retriever did not pass the arguments properly
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ofermend
I encountered this during summarization with VertexAI. I was receiving
an INVALID_ARGUMENT error, as it was trying to send a list of about
17000 single characters.
The [count_tokens
method](https://github.com/googleapis/python-aiplatform/blob/main/vertexai/language_models/_language_models.py#L658)
made available by Google takes in a list of prompts. It does not fail
for small texts, but it does for longer documents because the argument
list will be exceeding Googles allowed limit. Enforcing the list type
makes it work successfully.
This change will cast the input text to count to a list of that single
text so that the input format is always correct.
[Twitter](https://www.x.com/stijn_tratsaert)
- **Description:** ERNIE-Bot-Chat-4 Large Language Model adds the
ability of `Function Calling` by passing parameters through the
`functions` parameter in the request. To simplify function calling for
ERNIE-Bot-Chat-4, the `create_ernie_fn_chain()` function has been added.
The definition and usage of the `create_ernie_fn_chain()` function is
similar to that of the `create_openai_fn_chain()` function.
Examples as the follows:
```
import json
from langchain.chains.ernie_functions import (
create_ernie_fn_chain,
)
from langchain.chat_models import ErnieBotChat
from langchain.prompts import ChatPromptTemplate
def get_current_news(location: str) -> str:
"""Get the current news based on the location.'
Args:
location (str): The location to query.
Returs:
str: Current news based on the location.
"""
news_info = {
"location": location,
"news": [
"I have a Book.",
"It's a nice day, today."
]
}
return json.dumps(news_info)
def get_current_weather(location: str, unit: str="celsius") -> str:
"""Get the current weather in a given location
Args:
location (str): location of the weather.
unit (str): unit of the tempuature.
Returns:
str: weather in the given location.
"""
weather_info = {
"location": location,
"temperature": "27",
"unit": unit,
"forecast": ["sunny", "windy"],
}
return json.dumps(weather_info)
llm = ErnieBotChat(model_name="ERNIE-Bot-4")
prompt = ChatPromptTemplate.from_messages(
[
("human", "{query}"),
]
)
chain = create_ernie_fn_chain([get_current_weather, get_current_news], llm, prompt, verbose=True)
res = chain.run("北京今天的新闻是什么？")
print(res)
```
The running results of the above program are shown below：
```
> Entering new LLMChain chain...
Prompt after formatting:
Human: 北京今天的新闻是什么？
> Finished chain.
{'name': 'get_current_news', 'thoughts': '用户想要知道北京今天的新闻。我可以使用get_current_news工具来获取这些信息。', 'arguments': {'location': '北京'}}
```
- **Description:** during search with DeepLake some people are facing
backwards compatibility issues, this PR fixes it by making search
accessible for the older datasets
---------
Co-authored-by: adolkhan <adilkhan.sarsen@alumni.nu.edu.kz>
- **Description:**
- Fixes a `key_prefix` bug where passing it in on
`Redis.from_existing(...)` did not work properly. Updates doc strings
accordingly.
- Updates Redis filter classes logic with best practices on typing,
string formatting, and handling "empty" filters.
- Fixes a bug that would prevent multiple tag filters from being applied
together in some scenarios.
- Added a whole new filter unit testing module. Also updated code
formatting for a number of modules that were failing the `make`
commands.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @tchutch94
In the `FORMAT_INSTRUCTIONS` template, 4 curly braces (escaping) are
used to get single curly brace after formatting:
```
"{{{ ... }}}}" -> format_instructions.format() -> "{{ ... }}" -> template.format() -> "{ ... }".
```
Tool's `args_schema` string contains single braces `{ ... }`, and is
also transformed to `{{{{ ... }}}}` form. But this is not really correct
since there is only one `format()` call:
```
"{{{{ ... }}}}" -> template.format() -> "{{ ... }}".
```
As a result we get double curly braces in the prompt:
````
Respond to the human as helpfully and accurately as possible. You have access to the following tools:
foo: Test tool FOO, args: {{'tool_input': {{'type': 'string'}}}} # <--- !!!
...
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
````
This PR fixes curly braces escaping in the `args_schema` to have single
braces in the final prompt:
````
Respond to the human as helpfully and accurately as possible. You have access to the following tools:
foo: Test tool FOO, args: {'tool_input': {'type': 'string'}} # <--- !!!
...
Provide only ONE action per $JSON_BLOB, as shown:
```
{
"action": $TOOL_NAME,
"action_input": $INPUT
}
```
````
---------
Co-authored-by: Sergey Kozlov <sergey.kozlov@ludditelabs.io>
Hi 👋 We are working with Llama2 on Bedrock, and would like to add it to
Langchain. We saw a [pull
request](https://github.com/langchain-ai/langchain/pull/13322) to add it
to the `llm.Bedrock` class, but since it concerns a chat model, we would
like to add it to `BedrockChat` as well.
- **Description:** Add support for Llama2 to `BedrockChat` in
`chat_models`
- **Issue:** the issue # it fixes (if applicable)
[#13316](https://github.com/langchain-ai/langchain/issues/13316)
- **Dependencies:** any dependencies required for this change `None`
- **Tag maintainer:** /
- **Twitter handle:** `@SimonBockaert @WouterDurnez`
---------
Co-authored-by: wouter.durnez <wouter.durnez@showpad.com>
Co-authored-by: Simon Bockaert <simon.bockaert@showpad.com>
- **Description:** This change adds an agent to the Azure Cognitive
Services toolkit for identifying healthcare entities
- **Dependencies:** azure-ai-textanalytics (Optional)
---------
Co-authored-by: James Beck <James.Beck@sa.gov.au>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Hi!
This short PR aims at:
* Fixing `OpenAIEmbeddings`' check on `chunk_size` when used with Azure
OpenAI (thus with openai < 1.0). Azure OpenAI embeddings support at most
16 chunks per batch, I believe we are supposed to take the min between
the passed value/default value and 16, not the max - which, I suppose,
was introduced by accident while refactoring the previous version of
this check from this other PR of mine: #10707
* Porting this fix to the newest class (`AzureOpenAIEmbeddings`) for
openai >= 1.0
This fixes#13539 (closed but the issue persists).
@baskaryan @hwchase17
- **Description:** There are several mistakes in the sample code in the
doc-string of `DashVector` class, and this pull request aims to correct
them.
The correction code has been tested against latest version (at the time
of creation of this pull request) of: `langchain==0.0.336`
`dashvector==1.0.6` .
- **Issue:** No issue is created for this.
- **Dependencies:** No dependency is required for this change,
<!-- - **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below), -->
- **Twitter handle:** `zeyanglin`
<!-- Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** AstraDB is going to deprecate the `$similarity`
projection property in favor of the ´includeSimilarity´ option flag. I
moved all the queries to the new format.
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
Added a `search_kwargs` field to BingSearchAPIWrapper in
`bing_search.py,` enabling users to include extra keyword arguments in
Bing search queries. This update, like specifying language preferences,
adds more customization to searches. The `search_kwargs` seamlessly
merge with standard parameters in `_bing_search_results` method.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Fix Astra integration tests that are failing. The
`delete` always return True as the deletion is successful if no errors
are thrown. I aligned the test to verify this behaviour
- **Tag maintainer:** @hemidactylus
- **Twitter handle:** nicoloboschi
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
The issue was accuring because of `openai` update in Completions. its
not accepting `api_key` and 'api_base' args.
The fix is we check for the openai version and if ats v1 then remove
these keys from args before passing them to `Compilation.create(...)`
when sending from `VLLMOpenAI`
Fixed: #13507
@eyu
@efriis
@hwchase17
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** In this pull request, we address an issue related to
assigning a schema to the SQLDatabase class when utilizing an Oracle
database. The current implementation encounters a bug where, upon
attempting to execute a query, the alter session parse is not
appropriately defined for Oracle, leading to an error,
- **Issue:** #7928,
- **Dependencies:** No dependencies,
- **Tag maintainer:** @baskaryan,
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This change allows for the `MWDumpLoader` to load all
namespaces including custom by default instead of only loading the
[default
namespaces](https://www.mediawiki.org/wiki/Help:Namespaces#Localisation).
- **Tag maintainer:** @hwchase17
**Description:**
This commit adds embedchain retriever along with tests and docs.
Embedchain is a RAG framework to create data pipelines.
**Twitter handle:**
- [Taranjeet's twitter](https://twitter.com/taranjeetio) and
[Embedchain's twitter](https://twitter.com/embedchain)
**Reviewer**
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
Enhance the functionality of YoutubeLoader to enable the translation of
available transcripts by refining the existing logic.
**Issue:**
Encountering a problem with YoutubeLoader (#13523) where the translation
feature is not functioning as expected.
Tag maintainers/contributors who might be interested:
@eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
BUG: langchain.agents.openai_assistant has a reference as
`from langchain_experimental.openai_assistant.base import
OpenAIAssistantRunnable`
should be
`from langchain.agents.openai_assistant.base import
OpenAIAssistantRunnable`
This prevents building of the API Reference docs
## Update 2023-09-08
This PR now supports further models in addition to Lllama-2 chat models.
See [this comment](#issuecomment-1668988543) for further details. The
title of this PR has been updated accordingly.
## Original PR description
This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to
serve Llama-2 chat models (like `LlamaCPP`,
`HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`,
converts a list of chat messages into the [required Llama-2 chat prompt
format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and
forwards the formatted prompt as `str` to the wrapped `LLM`. Usage
example:
```python
# uses a locally hosted Llama2 chat model
llm = HuggingFaceTextGenInference(
inference_server_url="http://127.0.0.1:8080/",
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
)
# Wrap llm to support Llama2 chat prompt format.
# Resulting model is a chat model
model = Llama2Chat(llm=llm)
messages = [
SystemMessage(content="You are a helpful assistant."),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{text}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt, memory=memory)
# use chat model in a conversation
# ...
```
Also part of this PR are tests and a demo notebook.
- Tag maintainer: @hwchase17
- Twitter handle: `@mrt1nz`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Added a method `fetch_valid_documents` to
`WebResearchRetriever` class that will test the connection for every url
in `new_urls` and remove those that raise a `ConnectionError`.
- **Issue:** [Previous
PR](https://github.com/langchain-ai/langchain/pull/13353),
- **Dependencies:** None,
- **Tag maintainer:** @efriis
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
## Description
This PR adds an option to allow unsigned requests to the Neptune
database when using the `NeptuneGraph` class.
```python
graph = NeptuneGraph(
host='<my-cluster>',
port=8182,
sign=False
)
```
Also, added is an option in the `NeptuneOpenCypherQAChain` to provide
additional domain instructions to the graph query generation prompt.
This will be injected in the prompt as-is, so you should include any
provider specific tags, for example `<instructions>` or `<INSTR>`.
```python
chain = NeptuneOpenCypherQAChain.from_llm(
llm=llm,
graph=graph,
extra_instructions="""
Follow these instructions to build the query:
1. Countries contain airports, not the other way around
2. Use the airport code for identifying airports
"""
)
```
**Description**
MongoDB drivers are used in various flavors and languages. Making sure
we exercise our due diligence in identifying the "origin" of the library
calls makes it best to understand how our Atlas servers get accessed.
**Description/Issue:**
When OpenAI calls a function with no args, the args are `""` rather than
`"{}"`. Then `json.loads("")` blows up. This PR handles it correctly.
**Dependencies:** None
This PR brings a few minor improvements to the docs, namely class/method
docstrings and the demo notebook.
- A note on how to control concurrency levels to tune performance in
bulk inserts, both in the class docstring and the demo notebook;
- Slightly increased concurrency defaults after careful experimentation
(still on the conservative side even for clients running on
less-than-typical network/hardware specs)
- renamed the DB token variable to the standardized
`ASTRA_DB_APPLICATION_TOKEN` name (used elsewhere, e.g. in the Astra DB
docs)
- added a note and a reference (add_text docstring, demo notebook) on
allowed metadata field names.
Thank you!
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fix#13356
Add supports following properties for metadata to NotionDBLoader.
- `checkbox`
- `email`
- `number`
- `select`
There are no relevant tests for this code to be updated.
- **Description:** Adds `limit_to_domains` param to the APIChain based
tools (open_meteo, TMDB, podcast_docs, and news_api)
- **Issue:** I didn't open an issue, but after upgrading to 0.0.328
using these tools would throw an error.
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
**Note**: I included the trailing / simply because the docs here did
fc886cc303/docs/docs/use_cases/apis.ipynb (L246)
, but I checked the code and it is using `urlparse`. SoI followed the
docs since it comes down to stylee.
Hi,
this PR adds support for OpenAI API v1 for Azure OpenAI completion API.
@baskaryan @hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Bumps [pyarrow](https://github.com/apache/arrow) from 13.0.0 to 14.0.1.
<details>
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MINOR: [Release] Update .deb/.rpm changelogs for 14.0.1</li>
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<a
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<a
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href="https://redirect.github.com/apache/arrow/issues/38337">#38337</a>)</li>
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<a
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Hey @rlancemartin, @eyurtsev ,
I did some minimal changes to the `ElasticVectorSearch` client so that
it plays better with existing ES indices.
Main changes are as follows:
1. You can pass the dense vector field name into `_default_script_query`
2. You can pass a custom script query implementation and the respective
parameters to `similarity_search_with_score`
3. You can pass functions for building page content and metadata for the
resulting `Document`
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hi!
This is pretty straight-forward: The sdist package does not contain the
license file (which is needed by e.g. conda) because the package is
built from the subdir and can't see the license.
I _copied_ the license but since I'm unfamiliar with the projects
direction, I'm not sure that's correct.
thanks!
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Fixes: #8207
Description:
Pinecone returns scores (not distances) with cosine similarity. The
values according to the docs are [-1, 1], although I could never
reproduce negative values.
This PR ensures that the score returned from Pinecone is preserved,
rather than inverted, so the most relevant documents can be filtered (eg
when using similarity thresholds)
I'll leave this as a draft PR as I couldn't run the tests (my pinecone
account might not be enough - some errors were being thrown around
namespaces) so hopefully someone who _can_ will pick this up.
Maintainers:
@rlancemartin, @eyurtsev
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Fixed a serialization issue in the add_texts method
of the Matching Engine Vector Store caused by a typo, leading to an
attempt to serialize the json module itself.
- **Issue:** #12154
- **Dependencies:** ./.
- **Tag maintainer:**
Due to the possibility of external inputs including UUIDs, there may be
additional values in **kwargs, while Weaviate's `__init__` method does
not support passing extra **kwarg parameters.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
When calling max_marginal_relevance_search from PGVector the filter
param is not carried over to max_marginal_relevance_search_by_vector
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Uses `endpoint_url` if provided with a boto3 session.
When running dynamodb locally, credentials are required even if invalid.
With this change, it will be possible to pass a boto3 session with
credentials and specify an endpoint_url
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
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Replace this entire comment with:
- **Description:** Add MyScaleWithoutJSON which allows user to wrap
columns into Document's Metadata
- **Tag maintainer:** @baskaryan
**Description**
Bumps the Momento dependency to the latest version and refactors the
usage of `SearchHit` in the Momento Vector Index (MVI) vector store
integration. This change is a one liner where we use the preferred
attribute `score` to read the query-document similarity instead of
`distance`. The latest versions of Momento clients will use this
attribute going forward.
**Dependencies**
Updated the Momento dependency to latest version.
**Tests**
💚 I re-ran the existing MVI integration tests
(`tests/integration_tests/vectorstores/test_momento_vector_index.py`)
and they pass.
**Review**
cc @baskaryan @eyurtsev
**Description**
the ollama api now supports passing system prompt and template directly
instead of modifying the model file , but the ollama integration in
langchain did not have this change updated . The update just adds these
two parameters to it ( there are 2 more parameters that are pending to
be updated, I was not sure about their utility wrt to langchain )
Refer :
8713ac23a8
**Issue** : None Applicable
**Dependencies** : None Changed
**Twitter handle** : https://twitter.com/violetto96
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
`_get_kwarg_value` function is useless, one can rely on python builtin
functionalities to do the exact same thing.
- **Description:** Removed `_get_kwarg_value`. Helps with code
readability.
- **Issue:** the issue # it fixes (if applicable),
- **Twitter handle:** @Guillem_96
Improve CSV reader which can't call .strip() on NoneType if there are
less cells in the row compared to the header
<!-- Thank you for contributing to LangChain!
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- **Description:**
I have a CSV file as followed
```
headerA,headerB,headerC
v1A,v1B,v1C,
v2A,v2B
v3A,v3B,v3C
```
In this case, row 2 is missing a value, which results in reading a None
type. The strip() method can not be called on None, hence raising. In
this PR I am making the change to only call strip if the value if not
None.
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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Added a Docusaurus Loader
Issue: #6353
I had to implement this for working with the Ionic documentation, and
wanted to open this up as a draft to get some guidance on building this
out further. I wasn't sure if having it be a light extension of the
SitemapLoader was in the spirit of a proper feature for the library --
but I'm grateful for the opportunities Langchain has given me and I'd
love to build this out properly for the sake of the community.
Any feedback welcome!
- **Description:** `AzureMLChatOnlineEndpoint` object from
langchain/chat_models/azureml_endpoint.py safe to print
without having any secrets included in raw format in the string
representation.
- **Issue:** #12165,
- **Tag maintainer:** @eyurtsev
---------
Co-authored-by: Faysal Bougamale <faysal.bougamale@horiba.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Adding documentation to the runnable.
- Documentation is not organized in the best way for the runnable; i.e.,
in
terms of LCEL vs. other standard methods, will follow up with more
edits.
**Description:** Removing the single quote wrapper around the table
names in the SQL agent toolkit.py file as it misleads the LLM into
querying against tables with single quotes around their names.
**Issue:** #7457
**Dependencies:** None
**Tag maintainer:** @hwchase17
**Twitter handle:** None
- Implement config_specs to include session_id
- Remove Runnable method and update notebook
- Add more details to notebook, eg. show input schema and config schema
before and after adding message history
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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This commit fixes the issue that langchain.llms OpenAI completion
stopped working since the V1 openai client update.
Replace this entire comment with:
- **Description:** This PR fixes the issue [AttributeError: module
'openai' has no attribute
'Completion'](https://github.com/langchain-ai/langchain/issues/12967)
similar to
8e0cb2eb84
and https://github.com/langchain-ai/langchain/pull/12969,
- **Issue:** https://github.com/langchain-ai/langchain/issues/12967,
- **Dependencies:** `openai` v1.x.x client,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** @dosuken123
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@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This adds the response message as a document to the rag retriever so
users can choose to use this. Also drops document limit.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
We need to centralize the API we use to get the project name for our
tracers. This PR makes it so we always get this from a shared function
in the langsmith sdk.
## Dependencies
Upgraded langsmith from 0.52 to 0.62 to include the new API
`get_tracer_project`
- **Description:**
Recently Chroma rolled out a breaking change on the way we handle
embedding functions, in order to support multi-modal collections.
This broke the way LangChain's `Chroma` objects get created, because we
were passing the EF down into the Chroma collection:
https://docs.trychroma.com/migration#migration-to-0416---november-7-2023
However, internally, we are never actually using embeddings on the
chroma collection - LangChain's `Chroma` object calls it instead. Thus
we just don't pass an `embedding_function` to Chroma itself, which fixes
the issue.
- **Description:** The issue was not listing the proper import error for
amazon textract loader.
- **Issue:** Time wasted trying to figure out what to install...
(langchain docs don't list the dependency either)
- **Dependencies:** N/A
- **Tag maintainer:** @sbusso
- **Twitter handle:** @h9ste
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Astra DB Vector store integration
- **Description:** This PR adds a `VectorStore` implementation for
DataStax Astra DB using its HTTP API
- **Issue:** (no related issue)
- **Dependencies:** A new required dependency is `astrapy` (`>=0.5.3`)
which was added to pyptoject.toml, optional, as per guidelines
- **Tag maintainer:** I recently mentioned to @baskaryan this
integration was coming
- **Twitter handle:** `@rsprrs` if you want to mention me
This PR introduces the `AstraDB` vector store class, extensive
integration test coverage, a reworking of the documentation which
conflates Cassandra and Astra DB on a single "provider" page and a new,
completely reworked vector-store example notebook (common to the
Cassandra store, since parts of the flow is shared by the two APIs). I
also took care in ensuring docs (and redirects therein) are behaving
correctly.
All style, linting, typechecks and tests pass as far as the `AstraDB`
integration is concerned.
I could build the documentation and check it all right (but ran into
trouble with the `api_docs_build` makefile target which I could not
verify: `Error: Unable to import module
'plan_and_execute.agent_executor' with error: No module named
'langchain_experimental'` was the first of many similar errors)
Thank you for a review!
Stefano
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Cohere released the new embedding API (Embed v3:
https://txt.cohere.com/introducing-embed-v3/) that treats document and
query embeddings differently. This PR updated the `CohereEmbeddings` to
use them appropriately. It also works with the old models.
Description: This PR masks API key secrets for the Nebula model from
Symbl.ai
Issue: #12165
Maintainer: @eyurtsev
---------
Co-authored-by: Praveen Venkateswaran <praveen.venkateswaran@ibm.com>
* ChatAnyscale was missing coercion to SecretStr for anyscale api key
* The model inherits from ChatOpenAI so it should not force the openai
api key to be secret str until openai model has the same changes
https://github.com/langchain-ai/langchain/issues/12841
Qdrant was incorrectly calculating the cosine similarity and returning
`0.0` for the best match, instead of `1.0`. Internally Qdrant returns a
cosine score from `-1.0` (worst match) to `1.0` (best match), and the
current formula reflects it.
Possibility to pass on_artifacts to a conversation. It can be then
achieved by adding this way:
```python
result = agent.run(
input=message.text,
metadata={
"on_artifact": CALLBACK_FUNCTION
},
)
```
Calls uvicorn directly from cli:
Reload works if you define app by import string instead of object.
(was doing subprocess in order to get reloading)
Version bump to 0.0.14
Remove the need for [serve] for simplicity.
Readmes are updated in #12847 to avoid cluttering this PR
Previously we treated trace_on_chain_group as a command to always start
tracing. This is unintuitive (makes the function do 2 things), and makes
it harder to toggle tracing
When you use a MultiQuery it might be useful to use the original query
as well as the newly generated ones to maximise the changes to retriever
the correct document. I haven't created an issue, it seems a very small
and easy thing.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Correct number of elements in config list in
`batch()` and `abatch()` of `BaseLLM` in case `max_concurrency` is not
None.
- **Issue:** #12643
- **Twitter handle:** @akionux
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Zep now has the ability to search over chat history summaries. This PR
adds support for doing so. More here: https://blog.getzep.com/zep-v0-17/
@baskaryan @eyurtsev
…s present
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### Enabling `device_map` in HuggingFacePipeline
For multi-gpu settings with large models, the
[accelerate](https://huggingface.co/docs/accelerate/usage_guides/big_modeling#using--accelerate)
library provides the `device_map` parameter to automatically distribute
the model across GPUs / disk.
The [Transformers
pipeline](3520e37e86/src/transformers/pipelines/__init__.py (L543))
enables users to specify `device` (or) `device_map`, and handles cases
(with warnings) when both are specified.
However, Langchain's HuggingFacePipeline only supports specifying
`device` when calling transformers which limits large models and
multi-gpu use-cases.
Additionally, the [default
value](8bd3ce59cd/libs/langchain/langchain/llms/huggingface_pipeline.py (L72))
of `device` is initialized to `-1` , which is incompatible with the
transformers pipeline when `device_map` is specified.
This PR addresses the addition of `device_map` as a parameter , and
solves the incompatibility of `device = -1` when `device_map` is also
specified.
An additional test has been added for this feature.
Additionally, some existing tests no longer work since
1. `max_new_tokens` has to be specified under `pipeline_kwargs` and not
`model_kwargs`
2. The GPT2 tokenizer raises a `ValueError: Pipeline with tokenizer
without pad_token cannot do batching`, since the `tokenizer.pad_token`
is `None` ([related
issue](https://github.com/huggingface/transformers/issues/19853) on the
transformers repo).
This PR handles fixing these tests as well.
Co-authored-by: Praveen Venkateswaran <praveen.venkateswaran@ibm.com>
[The python
spec](https://docs.python.org/3/reference/datamodel.html#object.__getattr__)
requires that `__getattr__` throw `AttributeError` for missing
attributes but there are several places throwing `ImportError` in the
current code base. This causes a specific problem with `hasattr` since
it calls `__getattr__` then looks only for `AttributeError` exceptions.
At present, calling `hasattr` on any of these modules will raise an
unexpected exception that most code will not handle as `hasattr`
throwing exceptions is not expected.
In our case this is triggered by an exception tracker (Airbrake) that
attempts to collect the version of all installed modules with code that
looks like: `if hasattr(mod, "__version__"):`. With `HEAD` this is
causing our exception tracker to fail on all exceptions.
I only changed instances of unknown attributes raising `ImportError` and
left instances of known attributes raising `ImportError`. It feels a
little weird but doesn't seem to break anything.
- **Description:** Use all Google search results data in SerpApi.com
wrapper instead of the first one only
- **Tag maintainer:** @hwchase17
_P.S. `libs/langchain/tests/integration_tests/utilities/test_serpapi.py`
are not executed during the `make test`._
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It was passing in message instead of generation
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* Restrict the chain to specific domains by default
* This is a breaking change, but it will fail loudly upon object
instantiation -- so there should be no silent errors for users
* Resolves CVE-2023-32786
* This is an opt-in feature, so users should be aware of risks if using
jinja2.
* Regardless we'll add sandboxing by default to jinja2 templates -- this
sandboxing is a best effort basis.
* Best strategy is still to make sure that jinja2 templates are only
loaded from trusted sources.
**Description:** Update `langchain.document_loaders.pdf.PyPDFLoader` to
store url in metadata (instead of a temporary file path) if user
provides a web path to a pdf
- **Issue:** Related to #7034; the reporter on that issue submitted a PR
updating `PyMuPDFParser` for this behavior, but it has unresolved merge
issues as of 20 Oct 2023 #7077
- In addition to `PyPDFLoader` and `PyMuPDFParser`, these other classes
in `langchain.document_loaders.pdf` exhibit similar behavior and could
benefit from an update: `PyPDFium2Loader`, `PDFMinerLoader`,
`PDFMinerPDFasHTMLLoader`, `PDFPlumberLoader` (I'm happy to contribute
to some/all of that, including assisting with `PyMuPDFParser`, if my
work is agreeable)
- The root cause is that the underlying pdf parser classes, e.g.
`langchain.document_loaders.parsers.pdf.PyPDFParser`, never receive
information about the url; the parsers receive a
`langchain.document_loaders.blob_loaders.blob`, which contains the pdf
contents and local file path, but not the url
- This update passes the web path directly to the parser since it's
minimally invasive and doesn't require further changes to maintain
existing behavior for local files... bigger picture, I'd consider
extending `blob` so that extra information like this can be
communicated, but that has much bigger implications on the codebase
which I think warrants maintainer input
- **Dependencies:** None
```python
# old behavior
>>> from langchain.document_loaders import PyPDFLoader
>>> loader = PyPDFLoader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': '/var/folders/w2/zx77z1cs01s1thx5dhshkd58h3jtrv/T/tmpfgrorsi5/tmp.pdf', 'page': 0}
# new behavior
>>> from langchain.document_loaders import PyPDFLoader
>>> loader = PyPDFLoader('https://arxiv.org/pdf/1706.03762.pdf')
>>> docs = loader.load()
>>> docs[0].metadata
{'source': 'https://arxiv.org/pdf/1706.03762.pdf', 'page': 0}
```
- **Description:** #12273 's suggestion PR
Like other PDFLoader, loading pdf per each page and giving page
metadata.
- **Issue:** #12273
- **Twitter handle:** @blue0_0hope
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This will allow you create the schema beforehand. The check was failing
and preventing importing into existing classes.
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- **Description:** implement [quip](https://quip.com) loader
- **Issue:** https://github.com/langchain-ai/langchain/issues/10352
- **Dependencies:** No
- pass make format, make lint, make test
---------
Co-authored-by: Hao Fan <h_fan@apple.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
latest release broken, this fixes it
---------
Co-authored-by: Roman Vasilyev <rvasilyev@mozilla.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Prior to this PR, `ruff` was used only for linting and not for
formatting, despite the names of the commands. This PR makes it be used
for both linting code and autoformatting it.
This input key was missed in the last update PR:
https://github.com/langchain-ai/langchain/pull/7391
The input/output formats are intended to be like this:
```
{"inputs": [<prompt>]}
{"outputs": [<output_text>]}
```
## Description
This PR adds support for
[lm-format-enforcer](https://github.com/noamgat/lm-format-enforcer) to
LangChain.
The library is similar to jsonformer / RELLM which are supported in
Langchain, but has several advantages such as
- Batching and Beam search support
- More complete JSON Schema support
- LLM has control over whitespace, improving quality
- Better runtime performance due to only calling the LLM's generate()
function once per generate() call.
The integration is loosely based on the jsonformer integration in terms
of project structure.
## Dependencies
No compile-time dependency was added, but if `lm-format-enforcer` is not
installed, a runtime error will occur if it is trying to be used.
## Tests
Due to the integration modifying the internal parameters of the
underlying huggingface transformer LLM, it is not possible to test
without building a real LM, which requires internet access. So, similar
to the jsonformer and RELLM integrations, the testing is via the
notebook.
## Twitter Handle
[@noamgat](https://twitter.com/noamgat)
Looking forward to hearing feedback!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Best to review one commit at a time, since two of the commits are 100%
autogenerated changes from running `ruff format`:
- Install and use `ruff format` instead of black for code formatting.
- Output of `ruff format .` in the `langchain` package.
- Use `ruff format` in experimental package.
- Format changes in experimental package by `ruff format`.
- Manual formatting fixes to make `ruff .` pass.
I always take 20-30 seconds to re-discover where the
`convert_to_openai_function` wrapper lives in our codebase. Chat
langchain [has no
clue](https://smith.langchain.com/public/3989d687-18c7-4108-958e-96e88803da86/r)
what to do either. There's the older `create_openai_fn_chain` , but we
haven't been recommending it in LCEL. The example we show in the
[cookbook](https://python.langchain.com/docs/expression_language/how_to/binding#attaching-openai-functions)
is really verbose.
General function calling should be as simple as possible to do, so this
seems a bit more ergonomic to me (feel free to disagree). Another option
would be to directly coerce directly in the class's init (or when
calling invoke), if provided. I'm not 100% set against that. That
approach may be too easy but not simple. This PR feels like a decent
compromise between simple and easy.
```
from enum import Enum
from typing import Optional
from pydantic import BaseModel, Field
class Category(str, Enum):
"""The category of the issue."""
bug = "bug"
nit = "nit"
improvement = "improvement"
other = "other"
class IssueClassification(BaseModel):
"""Classify an issue."""
category: Category
other_description: Optional[str] = Field(
description="If classified as 'other', the suggested other category"
)
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI().bind_functions([IssueClassification])
llm.invoke("This PR adds a convenience wrapper to the bind argument")
# AIMessage(content='', additional_kwargs={'function_call': {'name': 'IssueClassification', 'arguments': '{\n "category": "improvement"\n}'}})
```
- Prefer lambda type annotations over inferred dict schema
- For sequences that start with RunnableAssign infer seq input type as
"input type of 2nd item in sequence - output type of runnable assign"
Replace this entire comment with:
-Add MultiOn close function and update key value and add async
functionality
- solved the key value TabId not found.. (updated to use latest key
value)
@hwchase17
- **Description:** This pull request removes secrets present in raw
format,
- **Issue:** Fireworks api key was exposed when printing out the
langchain object
[#12165](https://github.com/langchain-ai/langchain/issues/12165)
- **Maintainer:** @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Textract PDF Loader generating linearized output,
meaning it will replicate the structure of the source document as close
as possible based on the features passed into the call (e. g. LAYOUT,
FORMS, TABLES). With LAYOUT reading order for multi-column documents or
identification of lists and figures is supported and with TABLES it will
generate the table structure as well. FORMS will indicate "key: value"
with columms.
- **Issue:** the issue fixes#12068
- **Dependencies:** amazon-textract-textractor is added, which provides
the linearization
- **Tag maintainer:** @3coins
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
can get the correct token count instead of using gpt-2 model
**Description:**
Implement get_num_tokens within VertexLLM to use google's count_tokens
function.
(https://cloud.google.com/vertex-ai/docs/generative-ai/get-token-count).
So we don't need to download gpt-2 model from huggingface, also when we
do the mapreduce chain we can get correct token count.
**Tag maintainer:**
@lkuligin
**Twitter handle:**
My twitter: @abehsu1992626
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Following this tutoral about using OpenAI Embeddings with FAISS
https://python.langchain.com/docs/integrations/vectorstores/faiss
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader("../../../extras/modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
```
This works fine
```python
db = FAISS.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
```
But the async version is not
```python
db = await FAISS.afrom_documents(docs, embeddings) # NotImplementedError
query = "What did the president say about Ketanji Brown Jackson"
docs = await db.asimilarity_search(query) # this will use await asyncio.get_event_loop().run_in_executor under the hood and will not call OpenAIEmbeddings.aembed_query but call OpenAIEmbeddings.embed_query
```
So this PR add async/await supports for FAISS
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Description: adding support to Activeloop's DeepMemory feature that
boosts recall up to 25%. Added Jupyter notebook showcasing the feature
and also made index params explicit.
- Twitter handle: will really appreciate if we could announce this on
twitter.
---------
Co-authored-by: adolkhan <adilkhan.sarsen@alumni.nu.edu.kz>
Hey, we're looking to invest more in adding cohere integrations to
langchain so would love to get more of an idea for how it's used.
Hopefully this pr is acceptable. This week I'm also going to be looking
into adding our new [retrieval augmented generation
product](https://txt.cohere.com/chat-with-rag/) to langchain.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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## **Description:**
When building our own readthedocs.io scraper, we noticed a couple
interesting things:
1. Text lines with a lot of nested <span> tags would give unclean text
with a bunch of newlines. For example, for [Langchain's
documentation](https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.readthedocs.ReadTheDocsLoader.html#langchain.document_loaders.readthedocs.ReadTheDocsLoader),
a single line is represented in a complicated nested HTML structure, and
the naive `soup.get_text()` call currently being made will create a
newline for each nested HTML element. Therefore, the document loader
would give a messy, newline-separated blob of text. This would be true
in a lot of cases.
<img width="945" alt="Screenshot 2023-10-26 at 6 15 39 PM"
src="https://github.com/langchain-ai/langchain/assets/44193474/eca85d1f-d2bf-4487-a18a-e1e732fadf19">
<img width="1031" alt="Screenshot 2023-10-26 at 6 16 00 PM"
src="https://github.com/langchain-ai/langchain/assets/44193474/035938a0-9892-4f6a-83cd-0d7b409b00a3">
Additionally, content from iframes, code from scripts, css from styles,
etc. will be gotten if it's a subclass of the selector (which happens
more often than you'd think). For example, [this
page](https://pydeck.gl/gallery/contour_layer.html#) will scrape 1.5
million characters of content that looks like this:
<img width="1372" alt="Screenshot 2023-10-26 at 6 32 55 PM"
src="https://github.com/langchain-ai/langchain/assets/44193474/dbd89e39-9478-4a18-9e84-f0eb91954eac">
Therefore, I wrote a recursive _get_clean_text(soup) class function that
1. skips all irrelevant elements, and 2. only adds newlines when
necessary.
2. Index pages (like [this
one](https://api.python.langchain.com/en/latest/api_reference.html))
would be loaded, chunked, and eventually embedded. This is really bad
not just because the user will be embedding irrelevant information - but
because index pages are very likely to show up in retrieved content,
making retrieval less effective (in our tests). Therefore, I added a
bool parameter `exclude_index_pages` defaulted to False (which is the
current behavior — although I'd petition to default this to True) that
will skip all pages where links take up 50%+ of the page. Through manual
testing, this seems to be the best threshold.
## Other Information:
- **Issue:** n/a
- **Dependencies:** n/a
- **Tag maintainer:** n/a
- **Twitter handle:** @andrewthezhou
---------
Co-authored-by: Andrew Zhou <andrew@heykona.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
* Add unit tests for document_transformers/beautiful_soup_transformer.py
* Basic functionality is tested (extract tags, remove tags, drop lines)
* add a FIXME comment about the order of tags that is not preserved
(and a passing test, but with the expected tags now out-of-order)
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @rlancemartin
- **Twitter handle:** `peter_v`
Please make sure your PR is passing linting and testing before
submitting.
=> OK: I ran `make format`, `make test` (passing after install of
beautifulsoup4) and `make lint`.
- **Description:** Added masking of the API Key for AI21 LLM when
printed and improved the docstring for AI21 LLM.
- Updated the AI21 LLM to utilize SecretStr from pydantic to securely
manage API key.
- Made improvements in the docstring of AI21 LLM. It now mentions that
the API key can also be passed as a named parameter to the constructor.
- Added unit tests.
- **Issue:** #12165
- **Tag maintainer:** @eyurtsev
---------
Co-authored-by: Anirudh Gautam <anirudh@Anirudhs-Mac-mini.local>
Currently this gives a bug:
```
from langchain.schema.runnable import RunnableLambda
bound = RunnableLambda(lambda x: x).with_config({"callbacks": []})
# ConfigError: field "callbacks" not yet prepared so type is still a ForwardRef, you might need to call RunnableConfig.update_forward_refs().
```
Rather than deal with cyclic imports and extra load time, etc., I think
it makes sense to just have a separate Callbacks definition here that is
a relaxed typehint.
1. Allow run evaluators to return {"results": [list of evaluation
results]} in the evaluator callback.
2. Allows run evaluators to pick the target run ID to provide feedback
to
(1) means you could do something like a function call that populates a
full rubric in one go (not sure how reliable that is in general though)
rather than splitting off into separate LLM calls - cheaper and less
code to write
(2) means you can provide feedback to runs on subsequent calls.
Immediate use case is if you wanted to add an evaluator to a chat bot
and assign to assign to previous conversation turns
have a corresponding one in the SDK
