Local Knowledge Base Semantic Search Engine Designer

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]