11 июн. 2024 г. · Explore the LangChain and Elasticsearch integration and how it enables you to easily build RAG solutions and leverage retrievers.

Elasticsearch is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. Setup · Initialization · Manage vector store

4 янв. 2024 г. · Explore the use of Elasticsearch for RAG to achieve a fast and efficient way of data retrieval and accurate response generation.

This template performs RAG using Elasticsearch. It relies on sentence transformer MiniLM-L6-v2 for embedding passages and questions. Environment Setup​.

Продолжительность: 24:05
Опубликовано: 3 июн. 2024 г.

This reference app demonstrates how to use LangChain to power a RAG (Retrieval Augmented Generation) model. The app uses the ElasticsearchStore to store and ...

20 сент. 2024 г. · RAG (Retrieval Augmented Generation) is an architectural approach designed to enhance the capability of LLMs by enabling them to answer queries ...

Build context-aware reasoning applications. Contribute to langchain-ai/langchain development by creating an account on GitHub.

Example of Langchain-Elasticsearch integrations & RAG. 9 stars 5 forks Branches Tags Activity Star Notifications Additional navigation options

This guide has walked through the basic steps of setting up RAG by using LangChain to load data from a pdf, create a VectorStore in Elsticsearch, query ...