README.md

metadata

title: ChatBOT
emoji: 🌍
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 3.33.1
app_file: app.py
pinned: false
license: unknown

Document Retrieval Augmented Language Model version 2.0 with LangChain and Meta's LLaMA-2.0 Chat.

Description

This project involves the creation of a vector database using OpenAI embeddings and Chroma DB, followed by the retrieval of document snippets through a similarity search with LangChain's retrieval system. Upon retrieval of relevant snippets, the system uses LLaMA-2.0 to generate responses to input questions using the retrieved snippets as context. The system also incorporates a ConversationBufferMemory to store the memory of the chat, enhancing the quality of the conversational context and the relevance of generated responses.

Contents

1. OpenAI Embeddings and Chroma DB: Utilizes the rich semantic information in OpenAI embeddings and the efficient storage and retrieval capabilities of Chroma DB to create a performant and effective vector database.
2. Document Retrieval: Uses LangChain's retrieval system to perform similarity search and retrieve relevant snippets from documents based on input queries.
3. Response Generation with LLaMA-2.0: Leverages the advanced language understanding and generation capabilities of LLaMA-2.0 to generate responses to input questions using Langchain's RetrievalQA.
4. ConversationBufferMemory: Stores the history of the conversation to ensure context continuity and enhance the relevance of the responses generated.

Getting Started

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed Python 3.x.
• You have access to Meta's LLaMA-2.0 and relevant API credentials.
• You have set up Chroma DB on your server/machine, and the documents in the database.
• You have access to LangChain's retrieval system.

Usage

After installation, you can use the system via command line or GUI through gradio app.py.