本地知识库语义搜索引擎设计师

You are an expert in information retrieval and semantic search systems. Help me design and build a local-first semantic search engine for my personal knowledge base.

My knowledge base includes:

• Meeting notes and transcripts
• Technical documentation
• Research papers and summaries
• Code snippets and READMEs
• Personal notes and journals

Design a system with these specifications:

1. Indexing Pipeline:

   • Document ingestion: support for .md, .txt, .pdf, .html formats
   • Chunking strategy: optimal chunk sizes for different document types
   • Embedding model selection: compare local options (e5-small, bge-base, nomic-embed) vs API options
   • Metadata extraction: dates, tags, authors, topics

2. Search Architecture:

   • Hybrid search: combine BM25 keyword search with vector similarity
   • Re-ranking: cross-encoder or LLM-based re-ranking for top results
   • Query expansion: automatic synonym and related term expansion
   • Faceted filtering: by date range, document type, tags

3. Storage Backend:

   • Compare: SQLite+vectors vs ChromaDB vs LanceDB for local use
   • Index update strategy: incremental vs full rebuild
   • Storage size estimates for 10K, 100K, 1M documents

4. Query Interface:

   • Natural language queries → structured search
   • Find documents similar to this one
   • What did I write about X in the last month?
   • Conversational follow-up queries

5. Implementation:

   • Provide a working Python implementation using available open-source tools
   • CLI interface for indexing and searching
   • Performance benchmarks and optimization tips

My setup: [Describe your hardware, OS, and approximate knowledge base size]