mrankitvish/rag-chatbot

A FastAPI application that uses Retrieval-Augmented Generation (RAG) with a large language model (LLM) to create an interactive chatbot. This chatbot leverages PostgreSQL vector store for efficient document retrieval and supports text and CSV data sources for initialization.

RAG Chatbot using LangChain, Ollama (LLM), PG Vector (vector store db) and FastAPI

This FastAPI application leverages LangChain to provide chat functionalities powered by HuggingFace embeddings and Ollama language models. It supports initializing a PostgreSQL vector database with text or CSV data.

Features

• Initialize database with text or CSV data
• Perform chat queries using retrieval-augmented generation (RAG)
• Conversation history management

Prerequisites

• Python 3.10+
• PostgreSQL database with PGVector extention

  docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 5432:5432 -d pgvector/pgvector:pg16

• Ollama server for LLM

Installation

1. Clone the repository:

   git clone https://github.com/mrankitvish/rag-chatbot.git
   cd rag-chatbot

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Create a .env file or rename .env_example in the root directory and set the following environment variables:

   EMBEDDING_MODEL=your_huggingface_embedding_model
   COLLECTION_NAME=your_pgvector_collection_name
   DB_NAME=your_database_name
   DB_IPADDR=your_database_ip
   DB_PORT=your_database_port
   DB_USER=your_database_user
   DB_PASSWD=your_database_password
   DATA=path_to_your_text_data_file
   DATA_CSV=path_to_your_csv_data_file
   LLM_MODEL=your_Ollama_model_name
   LLM_BASEURL=your_Ollama_server_url
   TOP_K=5

   Note Here LLM is used via Ollama

Running the Application

1. Start the FastAPI application:

   fastapi dev app.py --host 0.0.0.0

2. The application will be accessible at http://0.0.0.0:8000.

API Endpoints

Root Endpoint

• GET /

  Returns a greeting message.

  Response:

  {
      "message": "RAG ChatBot built for you to chat with you data."
  }

Database Initialization

• POST /db_init/{txt_or_csv}

  Initializes the database with text or CSV data.

  Path Parameters:

  • txt_or_csv: Specifies whether to initialize with text (txt) or CSV (csv) data.

  Responses:

  {
      "message": "Database initialized successfully"
  }

  {
      "message": "Invalid input"
  }

Chat Endpoint

• POST /chat

  Performs a chat query using the RAG chain.

  Request Body:

  {
      "question": "Your question here"
  }

  Response:

  {
      "answer": "Generated response based on the question and context"
  }

Example Usage

Initializing the Database

To initialize the database with text data:

curl -X POST http://127.0.0.1:8000/db_init/txt

To access SwaggerUI

http://127.0.0.1:8000/docs