- **Description:** Adds a RAG template that uses NVIDIA AI playground
and embedding models, along with Milvus vector store
- **Dependencies:** This template depends on the AI playground service
in NVIDIA NGC. API keys with a significant trial compute are available
(10k queries at the time of writing). This template also depends on the
Milvus Vector store which is publicly available.
Note: [A quick link to get a
key](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/codellama-13b/api)
when you have an NGC account. Generate Key button at the top right of
the code window.
---------
Co-authored-by: Sagar B Manjunath <sbogadimanju@nvidia.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
…tch]: import models from community
ran
```bash
git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g"
git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g"
git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g"
git checkout master libs/langchain/tests/unit_tests/llms
git checkout master libs/langchain/tests/unit_tests/chat_models
git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py
make format
cd libs/langchain; make format
cd ../experimental; make format
cd ../core; make format
```
# Description: _python-lint_
This agent writes Python code that is formatted and linted using
`black`, `ruff`, and `mypy`, but does not execute the code. It writes
the code to a temporary file and then runs the linters. Once these
checks pass, the code is returned.
# Dependencies
- black
- ruff
- mypy
# Demo
The functionality can be seen here:
https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo
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If you're adding a new integration, please include:
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Description: Adding Summarization to Vectara, to reflect it provides not
only vector-store type functionality but also can return a summary.
Also added:
MMR capability (in the Vectara platform side)
Updated templates
Updated documentation and IPYNB examples
Tag maintainer: @baskaryan
Twitter handle: @ofermend
---------
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
This PR adds a simple LangChain template that uses [Anthropic's Claude
on Amazon Bedrock ⛰️](https://aws.amazon.com/bedrock/claude/) to behave
like JCVD.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
Fixes to rag-semi-structured template.
- Added required libraries
- pdfminer was causing issues when installing with pip. pdfminer.six
works best
- Changed the pdf name for demo from llama2 to llava
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- **Description:** Change instances of RunnableMap to RunnableParallel,
as that should be the one used going forward. This makes it consistent
across the codebase.
This is a template demonstrating how to utilize Google Sensitive Data
Protection in conjunction with ChatVertexAI(). Tagging you @efriis as
you reviewed my last template. :) Thanks!
Proof of successful execution:
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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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.
-->
Adding rag-opensearch template.
---------
Signed-off-by: kalyanr <kalyan.ben10@live.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
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submitting. Run `make format`, `make lint` and `make test` to check this
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tests, lint, etc:
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If you're adding a new integration, please include:
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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.
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
…rnative LLMs until used
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submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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tests, lint, etc:
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If you're adding a new integration, please include:
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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.
-->
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submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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tests, lint, etc:
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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: Harrison Chase <hw.chase.17@gmail.com>
The `langchain` repo was being flagged for using vulnerable
dependencies, some of which were in this template's lockfile. Updating
to newer versions should fix that.
Just `poetry lock` and moving `langchain` to the latest version, in case
folks copy this template.
This resolves some vulnerable dependency alerts GitHub code scanning was
flagging.
**Description:** This is like the rag-conversation template in many
ways. What's different is:
- support for a timescale vector store.
- support for time-based filters.
- support for metadata filters.
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-->
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
# 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>
- **Description:** Correct naming for package in README
- **Issue:** README wasn't aligned with pyproject.toml, resulting in not
being able to install the rag-supabase package.
- **Tag maintainer:** @gregnr
This PR adds a self-querying template using Qdrant as a vector store.
The template uses an artificial dataset and was implemented in a way
that simplifies passing different components and choosing LLM and
embedding providers.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
# Description
Add a RAG template showcasing Momento Vector Index as a vector store.
Includes a project directory and README.
# **Twitter handle**
Tag the company @momentohq for a mention and @mlonml for the
contribution.
This change adds a new template for simple RAG using the SingleStoreDB
vectorstore.
Twitter: @alexjpeng
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
