--- project_name: '' user_name: '' date: '' total_chunks: 0 sources_indexed: 0 tag_vocabulary_size: 0 retrieval_tested: false status: 'draft' --- # Knowledge Index for {{project_name}} _RAG-optimized knowledge base for AI agent retrieval. Each chunk is self-contained and tagged for semantic search._ --- ## Index Summary - **Total Chunks:** {{total_count}} - **Critical:** {{critical_count}} | **High:** {{high_count}} | **Standard:** {{standard_count}} | **Reference:** {{ref_count}} - **Sources Indexed:** {{source_count}} - **Last Synced:** {{date}} --- ## Critical Knowledge --- ## Architecture Knowledge --- ## Requirements Knowledge --- ## Implementation Knowledge --- ## Domain Knowledge --- ## Operations Knowledge --- ## Quality Knowledge --- ## Retrieval Configuration ### Query Mapping | Query Pattern | Target Categories | Priority Filter | Expected Chunks | |---|---|---|---| | "how to implement \*" | implementation, architecture | critical, high | 3-5 | | "testing requirements for \*" | quality, implementation | critical, high | 2-4 | | "business rules for \*" | requirements, domain | all | 2-3 | | "architecture decision for \*" | architecture | all | 1-3 | | "deployment process for \*" | operations | all | 1-2 | ### Embedding Recommendations - **Model:** Use an embedding model that handles technical content well - **Chunk Overlap:** 50-100 characters overlap between adjacent chunks from the same source - **Metadata Filters:** Always filter by category and priority for focused retrieval - **Top-K:** Retrieve 3-5 chunks per query for optimal context balance