RAG Knowledge Base Builder

I have analyzed your request for industrial boiler manual ingestion. The following strategy focuses on preserving technical specifications and ensuring high-precision retrieval for support engineers.

[RAG Knowledge-Base Builder Report]

📄 Document Assessment & Chunking Strategy:

• Document Type: PDF Technical Manuals (High density of specs and tables)
• Recommended Strategy: Recursive Character Chunking with Markdown Conversion
• Settings: Chunk size: 800 tokens, Overlap: 100 tokens (12.5%)

🏷️ Metadata Schema:

• source_file: [String] Original manual filename
• equipment_model: [String] Specific boiler model number
• section_type: [String] e.g., Troubleshooting, Installation, Wiring
• page_number: [Integer] For source attribution
• last_updated: [ISO8601 Date] To prioritize current documentation

🧹 Pre-processing Checklist:

• Convert all PDF tables to Markdown tables to preserve relational data
• Use OCR for wiring diagrams and store descriptions as text
• Strip recurring page headers and company addresses from every page
• Normalize unit measurements (e.g., PSI, Bar) to a consistent format

🔍 Initial Golden Queries (For Testing):

• Simple: "What is the maximum operating pressure for the B-200 series boiler?"
• Complex: "If the error code E04 appears on a 2022 model, what are the primary check steps for the gas valve?"
• Negative: "Who is the CEO of the boiler manufacturing company?"

[Self-Check]

• Chunking strategy explicitly recommended based on document type
• Metadata schema defined for hybrid search
• Pre-processing checklist provided
• Initial golden queries drafted for testing

Next steps

1. Implement a Python script using PyMuPDF or Unstructured to extract text and convert tables to Markdown.
2. Initialize your Pinecone index with the defined metadata fields as indexed dimensions.
3. Run the Golden Queries after the first 10 documents are ingested to verify chunk relevance.