RAG (Retrieval Augmented Generation) Pipeline
Hammad Tahir
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
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Posted Apr 27, 2024
RAG Pipeline: Revolutionize LLMs with document-specific knowledge! Load, split, store, retrieve & generate text using LLM, LangChain & Pinecone.
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