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Document Processing

Document Processing Pipeline

Build robust document processing pipelines that transform raw documents into searchable knowledge. From PDF extraction to intelligent chunking and embedding, we optimize every step for retrieval quality.

Build PipelineView Projects

50+

Document Formats

95%

Extraction Accuracy

Millions

Documents Processed

Supported Formats

Documents we process

PDFs

Reports, manuals, contracts, research papers

Office Documents

Word, Excel, PowerPoint

Web Content

HTML, Markdown, wikis

Structured Data

JSON, XML, CSV

Email

MSG, EML, MBOX

Code

Source files, documentation

Images

Scanned documents with OCR

Audio/Video

Transcripts from media

Pipeline

Processing pipeline stages

Document Ingestion

Load documents from various sources including file systems, cloud storage, databases, and APIs.

  • S3, GCS, Azure Blob support
  • Database connectors
  • API integrations
  • Real-time streaming

Content Extraction

Extract text, tables, images, and metadata from documents while preserving structure.

  • OCR for scanned docs
  • Table extraction
  • Image captioning
  • Metadata preservation

Intelligent Chunking

Split documents into semantically meaningful chunks optimized for retrieval.

  • Semantic boundaries
  • Overlap configuration
  • Size optimization
  • Context preservation

Embedding Generation

Generate dense vector embeddings using state-of-the-art embedding models.

  • Multiple model options
  • Batch processing
  • Dimension optimization
  • Quality validation

Vector Storage

Store embeddings with metadata in vector databases for efficient retrieval.

  • Metadata indexing
  • Namespace organization
  • Version tracking
  • Deduplication

Incremental Updates

Keep your knowledge base current with efficient incremental processing.

  • Change detection
  • Delta processing
  • Version control
  • Rollback support

Chunking

Intelligent chunking strategies

Fixed Size

Split by character or token count with overlap

Best for: Simple documents, consistent formatting

Predictable chunksEasy to implement

Semantic

Split at natural boundaries (paragraphs, sections)

Best for: Well-structured documents

Preserves meaningBetter retrieval

Recursive

Try multiple separators in priority order

Best for: Mixed content types

FlexibleAdapts to content

Document-Aware

Use document structure (headings, chapters)

Best for: Technical documentation, books

Maintains hierarchyContext-aware

Embeddings

Embedding model options

ModelDimensionsQuality
OpenAI text-embedding-31536/3072Excellent
Cohere Embed v31024Excellent
BGE-Large1024Very Good
E5-Large1024Very Good
all-MiniLM-L6384Good
Instructor768Very Good

Quality

Quality enhancements

Duplicate detection and removal
Language detection and filtering
PII detection and redaction
Quality scoring and filtering
Metadata enrichment with NER
Auto-summarization for long docs
Cross-reference linking
Citation extraction

Ready to process your documents?

Let's build a document processing pipeline optimized for your content and retrieval needs.

Start Document Pipeline Project