RAG Knowledge Base Builder

Problems It Addresses

Direct LLM answers can produce hallucinations, rely on outdated information, and miss private enterprise documents. For internal policies, contract review, customer-support FAQs, product manuals, and equipment operations, teams need answers grounded in trusted documents.

How It Works

The skill provides an engineering path from environment setup to the question-answering chain:

• Core components: use Milvus for vector storage, Ollama for local model serving, and MinIO for document object storage.
• Document parsing: supports PDF, Word, Excel, Markdown, TXT, and image OCR, with notes on tables, multi-sheet workbooks, encoding, and heading hierarchy.
• Chunking: choose chunk_size and chunk_overlap by scenario; policy documents favor smaller chunks, technical docs use standard chunks, and FAQs are split by question-answer pairs.
• Retrieval and generation: recommends BM25 + vector search + Rerank for better handling of proper nouns, abbreviations, and precision-critical answers; GraphRAG can be added for complex entity relationships.
• Permissions and operations: includes Milvus Partition Key guidance and notes on indexing, model downloads, and production deployment.

Boundaries And Notes

Small knowledge bases usually only need hybrid retrieval; GraphRAG is more useful for large, complex knowledge systems. Scanned documents depend on OCR, first runs may need model downloads, indexing grows with document volume, and production use should consider clustering and GPU inference cost.