RAG (Retrieval Augmented Generation) Pipeline

Description:

Unlock the full potential of Large Language Models (LLMs) with our innovative RAG Pipeline! This cutting-edge technology enables LLMs to answer questions based on specific documents, revolutionizing information retrieval and generation. Our pipeline seamlessly integrates loading, splitting, storing, retrieving, and generating text, empowering you to:

Load documents from various sources (webpages, PDFs, CSVs, etc.) using LangChain's document loaders

Split text into manageable chunks with LangChain's text splitters

Store and embed data in a vector store (Pinecone)

Retrieve relevant information using similarity search with LangChain's retrievers

Generate answers and engage in conversations using LLMs (OpenAI) with LangChain's chat models

Key Features:

Document loading and splitting with LangChain

Vector store integration (Pinecone)

Retrieval and generation using LLMs (OpenAI) and LangChain

Conversational capabilities with memory storage

Customizable prompts and templates

Benefits:

Enhance LLM capabilities with document-specific knowledge

Streamline information retrieval and generation

Engage in conversational AI with context-aware memory

Unlock new possibilities for AI applications

Frameworks Used:

LangChain (document loading, splitting, retrieval, and generation)

Pinecone (vector store and embedding)

Collaboration Opportunities:

Developers and researchers seeking to advance LLM technology

Businesses looking to integrate AI into their document management

Innovators exploring new AI applications