LangChain

LangChain is a popular framework for working with AI, Vectors, and embeddings. Used to simplify building a variety of AI applications.

Elasticsearch can be used with LangChain in three ways:

• Use the LangChain ElasticsearchStore to store and retrieve documents from Elasticsearch.

• Use the LangChain self-query retriever, with the help of an LLM like OpenAI, to transform a user's query into a query + filter to retrieve relevant documents from Elasticsearch.

• Use the LangChain ElasticsearchRetriever for the most flexible way to retrieve documents from Elasticsearch.

Blogs to get started with Elasticsearch and LangChain

• Elasticsearch and LangChain: unlocking LLMs

• Privacy first AI search with Elasticsearch and LangChain

• Build a financial AI search workflow using LangGraph.js and Elasticsearch

Notebooks

• Question Answering with LangChain and Elasticsearch

• Chatbot with LangChain and Elasticsearch

• Self Query Retriever Example

• Self Query Retriever for Question Answering

• Self Query Retriever with BM25 Retrieval

LangServe Templates

• Self Query Retriever

• RAG with Elasticsearch

LangChain Powered RAG Reference App

This reference app demonstrates how to use LangChain to power a RAG (Retrieval Augmented Generation) model. The app uses the ElasticsearchStore to store and retrieve documents from Elasticsearch. This is a quick way to get started with Langchain and Elasticsearch.

https://github.com/elastic/elasticsearch-labs/tree/main/example-apps/chatbot-rag-app

LangChain in Elastic AI Assistants

Security AI Assistant