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Working with Langchain

Requirements

To work with those examples you will need a Hugging Face Text Generation Inference server deployed, and a model served.

You can either install Langchain and its dependencies in your workbench (pip install langchain), or you can directly use a pre-built custom Workbench image that comes with everything needed: quay.io/opendatahub-contrib/workbench-images:cuda-jupyter-langchain-c9s-py311_2023c_latest.

Ref: How to import a custom notebook image

If you want to create your own custom image, with a different IDE like VSCode for example, you will find instructions here.

Content

All the Notebooks in this folder use Langchain to interact with different types of LLM Inference Servers, as well as different types of Vector Databases.

The name of the notebook indicates which elements are used, and the purpose of the notebook. For example:

  • Langchain-Milvus-Ingest.ipynb shows how to ingest documents into a Milvus vector database.
  • RAG_with_sources_Langchain-vLLM-Milvus.ipynb shows how to create a Retrieval Augmented Generation (RAG) pipeline with Langchain, a Milvus database, and a vLLM Inference Server.