LangChain

This section details KDB.AI and the integration with LangChain.

KDB.AI can integrate with LangChain, which utilizes LLMs to offer a more human-like interaction.

LangChain is a framework designed to simplify the creation of applications using large language models. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis. - Wikipedia

Check out the official LangChain repository on GitHub.

Getting Started

Prerequisites

• Python 3
• Pip
• Git

Install

#run
pip install langchain
git clone -b KDBAI_v1.4 https://github.com/KxSystems/langchain.git
cd langchain/libs/community
pip install .

#or
pip install ipython jupyter pyarrow openai pypdf tiktoken kdbai-client langchain

Start Jupyter

jupyter notebook

If you need help with the integration, feel free to reach out to the KDB.AI Slack community or email support@kdb.ai.

Examples

• Check out Building applications with LLMs through composability for KDB.AI 1.4.0. Run a Notebook example to use KDB.AI as a LangChain vector store. You need a KDB.AI endpoint and API keys for KDB.AI and OpenAI.
• Open the RAG sample and the RAG evaluation samplein our GitHub repo.
• Run the RAG and RAG evaluation notebooks directly in Google Colab.
• Watch this YouTube tutorial demonstrating Retrieval Augmented Generation with LangChain and Vector Databases.