Role of Document Extraction in a RAG Pipeline
Document extraction serves as the foundational step in Retrieval-Augmented Generation pipelines. The accuracy of information retrieved from vector databases depends directly on how source documents were parsed, segmented, and structured before ingestion. Poor extraction—such as missing tables, broken text flow, or flattened visual layouts—degrades retrieval precision and generates inaccurate LLM responses downstream.
LandingAI ADE Prepares Documents for RAG with Structured Outputs
LandingAI Agentic Document Extraction preserves document structure using visual-first parsing and semantic chunking. Each page is divided into coherent units such as text blocks, tables, figures, or form fields. Outputs include structured Markdown and JSON with visual metadata, including page numbers and bounding boxes. These structured chunks can be directly ingested into vector databases, enabling semantic search, precise retrieval, and traceable LLM responses across large document collections.
ADE Output and Relevance for Vector Databases
ADE returns structured JSON with top-level fields:
| Field | Description | RAG Relevance |
|---|---|---|
| markdown | Complete Markdown representation of document | Direct LLM input or embedding source |
| chunks | Array of typed segments (text, table, figure, marginalia, logo, card, attestation, scan_code) | Primary vector indexing units |
| splits | Page/section organization (populated when split="page") | Page-level chunking and retrieval |
| grounding | Bounding boxes and page numbers mapped by chunk ID | Source attribution and verification |
| metadata | Processing info (credit usage, duration, filename, job_id, page_count, version) | Provenance tracking and debugging |
Each chunk includes:
- id: Unique chunk identifier
- type: Semantic chunk type
- markdown: Chunk content in Markdown
- grounding: Page number + bounding box coordinates ({page, box: {left, top, right, bottom}})
Key features for RAG:
- Semantic chunking preserves document structure and meaning
- Grounding enables verification by linking extractions to source locations
- Layout-agnostic parsing handles complex documents without templates
- Supports multi-format input (PDFs, images, spreadsheets, presentations)
RAG Pipeline Integration
Standard workflow: Parse → Embed → Index → Retrieve+Generate
Parse: Call ADE Parse API with your document. Get back a chunks array where each chunk is already semantically segmented.
Embed: Iterate through chunks. Pass each chunk's markdown field to your embedding model. You get vectors plus metadata (id, type, page, bounding box).
Index: Store vectors in your database (Pinecone, Weaviate, Qdrant, ChromaDB, Snowflake). Attach grounding metadata to enable:
- Filtered retrieval (search only tables, specific pages, specific chunk types)
- Source citation (show users exact page and location in PDF)
Retrieve + Generate: When users query, the retriever returns complete semantic units (entire tables with all rows/columns, full sections with context). The LLM sees structured data and relationships, not fragments.
Why ADE Solves RAG Ingestion Problems
Traditional ingestion fails because:
- Documents flattened to plain text (structure lost)
- Tables become linear strings (row/column relationships destroyed)
- Sections merge (hierarchy disappears)
- Images, charts, handwriting ignored or poorly recognized
ADE's visual-first approach:
- Semantic chunking: Groups content by meaning (complete tables, full sections, logical units) instead of arbitrary length
- Layout preservation: Maintains document structure, headings, and spatial relationships
- Visual grounding: Links every chunk to exact page + coordinates in source document
- Format flexibility: Handles PDFs, images, spreadsheets, presentations
Result:
- Retrieval systems find relevant, precise information
- Language models receive complete context (entire tables, structured sections)
- Every answer traceable back to source location
- Reduced hallucinations, improved accuracy, full auditability
Large Document Processing
ADE provides two API options based on document size:
Parse API (Synchronous)
- Maximum: 100 pages
- Use for: Real-time processing, small documents
- Returns: Immediate response with parsed results
Parse Jobs API (Asynchronous)
- Maximum: 6,000 pages or 1 GB
- Use for: Large files, batch processing
- Benefit: Avoids timeout issues common with large-file pipelines
Enterprise benefits:
- Compliance-ready with complete audit trails
- Supports filtered retrieval by chunk type, page, or region
- Enables source citation directly in PDF viewers
- Scales across diverse document collections
Frequently Asked Questions
What is the best way to chunk documents for RAG using LandingAI ADE?
ADE's Parse API returns chunks in a typed JSON structure. These chunks reflect semantic document boundaries, such as text, tables, and figures. For most RAG use cases, the chunks from the ADE JSON response can be used directly as the indexing units.
How do I implement document extraction for a RAG system using ADE?
Parse your document with the ADE Parse API. Use the returned chunks array to generate embeddings for each chunk's Markdown content. Store the vectors in a vector database with the chunk's id, type, page number, and bounding box as metadata. For field-specific retrieval, chain the ADE Extract API to produce schema-validated key-value outputs alongside the parsed chunks.
Does ADE work with LangChain, LlamaIndex, or other RAG frameworks?
Yes. ADE's JSON and Markdown outputs integrate with RAG frameworks. The chunks array can be easily converted to the document node structures used by LangChain, LlamaIndex, and similar libraries. Each chunk's content, metadata (type, page, bounding box), and unique ID provide all the information needed for framework-specific document loaders and node parsers.
Can ADE extract specific clauses or fields from legal documents for a RAG system?
Yes. The ADE Extract API accepts a JSON schema defining the fields to extract, such as party names, governing law clauses, or termination conditions. The API returns structured values for each field, grounded to their source location in the document. These extracted fields can be stored as structured metadata in a vector database to support filtered retrieval by clause type.