Thank you for contributing to LangChain!
- **AI Agent Built With LangChain and FireWorksAI**: "community
notebook"
- **Description:** Added a new AI agent in the cookbook folder that
integrates prompt compression using LLMLingua and arXiv retrieval tools.
The agent is designed to optimize the efficiency and performance of
research tasks by compressing lengthy prompts and retrieving relevant
academic papers. The agent also makes uses of MongoDB to store
conversational history and as it's knowledge base using MongoDB vector
store
- **Twitter handle:** https://x.com/richmondalake
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Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
- This PR exposes some functions in VDMS vectorstore, updates VDMS
related notebooks, updates tests, and upgrade version of VDMS (>=0.0.20)
**Issue:** N/A
**Dependencies:**
- Update vdms>=0.0.20
- **Description:** Adding notebook to demonstrate visual RAG which uses
both video scene description generated by open source vision models (ex.
video-llama, video-llava etc.) as text embeddings and frames as image
embeddings to perform vector similarity search using VDMS.
- **Issue:** N/A
- **Dependencies:** N/A
This cookbook guides user to implement RAG locally on CPU using
langchain tools and open source models. It enables Llama2 model to
answer queries about Intel Q1 2024 earning release using RAG pipeline.
Main libraries are langchain, llama-cpp-python and gpt4all.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
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Co-authored-by: Sriragavi <sriragavi.r@intel.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- Oracle AI Vector Search
Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
- Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
This Pull Requests Adds the following functionalities
Oracle AI Vector Search : Vector Store
Oracle AI Vector Search : Document Loader
Oracle AI Vector Search : Document Splitter
Oracle AI Vector Search : Summary
Oracle AI Vector Search : Oracle Embeddings
- We have added unit tests and have our own local unit test suite which
verifies all the code is correct. We have made sure to add guides for
each of the components and one end to end guide that shows how the
entire thing runs.
- We have made sure that make format and make lint run clean.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
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Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com>
Co-authored-by: hroyofc <harichandan.roy@oracle.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR is a successor to this PR -
https://github.com/langchain-ai/langchain/pull/17436
This PR updates the cookbook README with the notebook so that it is
available on langchain docs for discoverability.
cc: @baskaryan, @3coins
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Co-authored-by: Bagatur <baskaryan@gmail.com>
added Parallel Function Calling for Structured Data Extraction notebook
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Co-authored-by: Erick Friis <erick@langchain.dev>
Cookbook showing how to incoporate RAG search within a postgreSQL
database using pgvector.
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
