How to Improve RAG with VECTOR FEED

Retrieval-Augmented Generation (RAG) quality depends critically on the cleanliness and structural integrity of the source documents fed into the embedding pipeline. VECTOR FEED provides five optimization layers that directly improve retrieval accuracy and reduce LLM hallucinations.

1. Layout Preservation

Problem: Standard parsers split text at page boundaries, margins, and inline graphics, producing fragments that lose semantic continuity. Embedding models receive broken sentences, producing poor vectors.

Solution: VECTOR FEED preserves reading order across page boundaries. Paragraphs that span pages are merged, multi-column layouts are reconstructed in correct sequence, and inline images are handled without breaking surrounding text flow.

2. Data Rinsing™ (Metadata Removal)

Problem: Headers, footers, page numbers, watermarks, and administrative notes contaminate embeddings with repeated tokens, inflating vector size and diluting semantic signal.

Solution: The proprietary Data Rinsing™ pass classifies and strips recurring metadata elements, condensing the document to its core narrative before vectorization.

3. Table Normalization

Problem: Tables are typically flattened into unstructured text, losing relationships between cells and making numerical retrieval unreliable.

Solution: Tables are extracted as structured HTML/JSON with preserved cell relationships, column headers, and row hierarchies. This enables targeted retrieval of specific table cells or rows.

4. Formula-to-LaTeX Translation

Problem: Mathematical expressions are either lost entirely or rendered as garbled text, making technical RAG unreliable for STEM domains.

Solution: MFD/MFR models convert formulas to clean LaTeX notation, enabling semantic search over mathematical content.

5. Semantic Chunking

Problem: Fixed-length chunking splits mid-sentence or mid-paragraph, breaking context windows.

Solution: VECTOR FEED outputs clean Markdown with natural paragraph and section boundaries, enabling chunking strategies aligned with document semantics rather than byte offsets.