RAG systems for large codebases or documents

    Retrieval pipelines tuned for large code repos and document stores, smart chunking, hybrid search, and context packing at scale.

    Muhammad Zeeshan

    Technologies Used

    Python
    RAG
    Vector DBs
    LangChain
    Semantic Search

    Key Features

    1

    Hierarchy-aware chunking for code and docs

    2

    Hybrid dense + keyword retrieval

    3

    Context budgeting for long corpora

    Implementation

    Built ingestion pipelines with metadata filters, re-ranking, and citation spans so answers stay grounded in massive sources.

    Results & Impact

    Improved answer precision on enterprise-scale knowledge bases without blowing token budgets.