RAG Pipelines with Schema Chunks
Many Retrieval-Augmented Generation (RAG) pipelines fail because of inconsistent or noisy context. Tensorlake gives you fine-grained control by parsing documents into structured schema fields and clean, layout-aware chunks.
This enables precise, filterable context for your retrieval system—no hallucinated blobs, no irrelevant footers.
Why This Matters
- LLMs hallucinate when given long, unstructured content
- Chunking by paragraph or page often misses document semantics
- Structured data helps ground LLMs in fact-based retrieval
How Tensorlake Helps
- Extracts schema-defined fields (e.g., coverage limits, terms, roles)
- Produces clean text chunks with metadata (page #, section, bounding box)
- Includes detection of tables, forms, and layout structure
- Integrates with LlamaIndex, Weaviate, Pinecone, or your vector store of choice
Want help connecting Tensorlake output to your vector store? Join the Slack Community.