Tensorlake + Qdrant: Fast, filtered retrieval for structured and unstructured documents

Just dropped a new integration for developers working with real-world documents and vector search:

Tensorlake + Qdrant = structure-aware retrieval at scale.

Instead of flat blobs, you can:

• Parse PDFs into labeled fields extracting metadata like names, dates, balances, etc

• Embed with semantic structure

• Store and query with Qdrant filters that actually match your use case

Built for RAG, document agents, contract intelligence, and anyone tired of lossy parsing.

Open to feedback or ideas on what else we should integrate next 👇

3d ago

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