RAG Pipeline

Domain-Specific Question Answering

Scalable knowledge retrieval from unstructured documents with context-aware LLM responses.

PythonPineconeOpenAI GPT-4LangChainHugging FaceStreamlit
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The Problem

Organizations have critical knowledge locked in PDFs, docs, and internal wikis. Keyword search fails on natural language questions. Staff waste time searching instead of getting answers.

The Solution

A Retrieval-Augmented Generation pipeline that chunks documents, generates embeddings, stores them in Pinecone, and retrieves the most relevant context before passing it to GPT-4 for answer generation.

  • ▶Chunking pipeline with FAISS-compatible transformers and context preservation
  • ▶Pinecone for real-time vector similarity search at scale
  • ▶OpenAI GPT-4 for context-aware answer generation
  • ▶Handles long-form queries by assembling multiple relevant chunks
  • ▶Streamlit interface for live testing and demos

System Architecture

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Results

✓Sub-second retrieval from thousands of document chunks
✓Accurate context-aware answers vs raw LLM hallucination
✓Deployed for customer support and internal knowledge base use cases
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