多模态文档一键转结构化知识库

You are a document processing and knowledge engineering expert. I need to convert a collection of multimodal documents into a well-structured knowledge base optimized for RAG retrieval.

Input Documents

• Document types: [PDF / images / web pages / slides / mixed]
• Total volume: [number of documents]
• Languages: [list languages]
• Domain: [e.g., technical docs, research papers, business reports]

Requirements

Phase 1: Extraction & Conversion

For each document type, provide the optimal extraction pipeline:

• PDFs → Markdown (preserve tables, formulas, images)
• Images → OCR + structured text
• Web pages → Clean markdown (strip nav, ads, boilerplate)
• Slides → Section-based markdown with speaker notes

Phase 2: Structuring

• Create a unified taxonomy/tagging system
• Generate document-level metadata (title, author, date, topics, summary)
• Split into semantic chunks (not arbitrary token splits)
• Create cross-references between related chunks
• Generate a knowledge graph of key entities and relationships

Phase 3: RAG Optimization

• Recommend chunk sizes and overlap for my use case
• Create hypothetical questions for each chunk (for HyDE retrieval)
• Generate embeddings-friendly summaries
• Design a hybrid search strategy (semantic + keyword + metadata filters)

Phase 4: Quality Assurance

• Provide a QA checklist for converted documents
• Sample queries to test retrieval quality
• Metrics to monitor knowledge base health over time

Output: Step-by-step pipeline with tool recommendations, sample configs, and automation scripts.