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Project makes use of LangChain and FastAPI - Focus and Async integration with Vectorstore

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Coding-Crashkurse/Advanced-LangChain-with-FastAPI

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Langchain-FastAPI Integration

Overview

This project integrates Langchain with FastAPI, providing a framework for document indexing and retrieval, as well as chat functionality, using PostgreSQL and pgvector. It is designed to support both synchronous and asynchronous operations.

Features

  • Document Management: Methods for adding, retrieving, and deleting documents.
  • Vector Store: Utilizes Langchain's vector store for efficient document retrieval.
  • Chat Functionality: Includes a chat endpoint that leverages Langchain for response generation.
  • Asynchronous Support: Offers async operations for enhanced performance.

Configuration

Ensure to set environment variables such as POSTGRES_DB, POSTGRES_USER, POSTGRES_PASSWORD, DB_HOST, DB_PORT, and OPENAI_API_KEY. Configure the database connection string appropriately and initialize Langchain and OpenAI embeddings according to project needs.

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Project makes use of LangChain and FastAPI - Focus and Async integration with Vectorstore

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