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RAG Pipeline

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RAG Pipeline

Custom RAG implementation supporting document ingestion, chunking, embedding, and retrieval with LLM-powered response generation. Supports multiple vector store backends (Pinecone, FAISS) and LLM providers. Served as a learning and experimentation ground that directly informed the Cognity-AI library.

Tech Stack

PythonLangChainPineconeOpenAIFAISSLlamaIndex

Highlights

  • Multi-backend vector store support
  • Pluggable LLM provider integration
  • Custom chunking strategies

Challenges

  • Choosing optimal chunk size and overlap to balance retrieval precision and context coverage
  • Reducing hallucination rate by grounding the LLM strictly within retrieved context