#atom

End-to-end workflow for transforming raw documents into searchable, AI-accessible knowledge

Core Idea: A document processing pipeline transforms unstructured documents into structured, searchable data through a series of steps including parsing, OCR, embedding generation, and storage in vector databases to enable retrieval-augmented generation.

Key Elements

Pipeline Components

Ingestion Layer

• Document Parsing: Extracting text and structure from various document formats
  • Tools: deepdoc, NV-Ingest
  • Handles PDFs, Word documents, HTML, and other formats
• OCR Processing: Converting image-based text to machine-readable content
  • Tools: OLMOCR, PaddleOCR, YOLOX
  • Critical for scanned documents and images containing text

Processing Layer

• Chunking: Breaking documents into manageable segments
• Embedding Generation: Converting text into vector representations
  • Common models: BGE Embeddings, SentenceTransformers
• Metadata Extraction: Capturing document attributes, creation dates, authors, etc.
• Quality Control: Verification of extracted content quality

Storage Layer

• Document Storage: Raw document preservation (e.g., MinIO)
• Vector Database: Storing embeddings for similarity search (e.g., Milvus)
• Metadata Database: Organizing document information (e.g., PostgreSQL)
• Queue Management: Coordinating processing workflow (e.g., Redis)

Retrieval Process

1. Vector Search: Finding relevant document chunks via embedding similarity
2. Reranking: Refining search results (e.g., BGE Reranker)
3. Context Assembly: Preparing retrieved information for LLM consumption
4. LLM Processing: Generating responses based on retrieved context

Optimization Techniques

• Hybrid Search: Combining vector similarity with keyword-based methods (BM25)
• Hallucination Detection: Verifying LLM outputs against source material
• Context Verification: Ensuring retrieved information is relevant
• Scalability Approaches: Distributed processing for large document collections

Connections

• Related Concepts: RAG Systems (enabled by this pipeline), Vector Databases (critical storage component), Embedding Models (vector generation)
• Broader Context: Knowledge Management Systems (enterprise application), Information Retrieval (theoretical foundation)
• Applications: Enterprise Search (common use case), Chatbots (consumer interface)
• Components: OCR Technology (text extraction), Document Storage (persistence layer)

References

1. GitHub: ragflow/deepdoc
2. GitHub: allenai/olmocr
3. GitHub: nvidia/nv-ingest

#document-processing #rag #vector-search #data-pipeline #nlp

Connections:

Sources:

• From: 2025-03-16 REDDIT Set up n8n + Ollama RAG — disappointed with local LLMs. Anyone else