LangChain

I've created a comprehensive implementation of a RAG-based chatbot using LangChain. Here's a breakdown of the key components:

Document Loading:

• Supports PDF, CSV, and DOCX files
• Uses appropriate loaders from LangChain community

Text Splitting:

• Uses RecursiveCharacterTextSplitter
• Configurable chunk size and overlap
• Default chunk size of 1000 with 200 overlap

Embeddings:

• Uses OpenAI's text-embedding-3-small model
• Generates embeddings for document chunks

Vectorstore:

• Uses Pinecone as the vector database
• Automatically creates index if it doesn't exist
• Stores embeddings with metadata

LCEL Chain:

• Implements a Retrieval-Augmented Generation (RAG) chain
• Uses ChatGPT (gpt-3.5-turbo) as the base model
• Custom prompt template for answer generation
• Includes context retrieval and answer generation

To use this chatbot, you'll need to:

Install required packages:

pip install langchain langchain-openai pinecone-client python-docx pypdf

Set up your API keys:

• OpenAI API key
• Pinecone API key and environment