🧠📚 Modular RAG Pipeline — Powered by FastAPI + LangChain

Welcome to the RAG (Retrieval-Augmented Generation) project!

This is a modular and scalable system built to handle document ingestion, vector search, memory-powered interactions, and custom prompts — all wrapped in a FastAPI interface with a user-friendly frontend.

⚙️ Built for speed, flexibility, and production-grade applications.

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🚀 Features

• 📄 Upload and process PDF documents
• ✂️ Chunk documents and embed them using HuggingFace models
• 🧠 Vector store powered by FAISS
• 🔁 Conversational memory with LangChain's ConversationBufferMemory
• ⚙️ Dynamic RAG pipeline powered by Groq + LLaMA 3 (blazing fast inference)
• 🌐 REST API and simple HTML frontend via FastAPI
• 📬 Query your documents and get relevant, context-aware answers
• 💬 Customizable system prompts (coming soon)

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✅ Current Progress

The core system includes:

• ✅ LangChain with Groq’s LLaMA 3 LLM
• ✅ HuggingFace sentence-transformers (all-MiniLM-L6-v2) for document embeddings
• ✅ FAISS for in-memory vector storage
• ✅ Conversational memory (chat history preserved across queries)
• ✅ Document upload and question-answering from PDFs
• ✅ FastAPI backend with a basic but functional HTML UI

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🧪 Example Usage

from pathlib import Path
from rag_pipeline import RagPipeline

# Initialize the pipeline
pipeline = RagPipeline()

# Process a PDF and prepare the QA chain
qa_chain = pipeline.run_pipeline(Path("example.pdf"))

# Ask a question
response = qa_chain.run("What is this document about?")
print(response)