Local Knowledge Base Semantic Search Engine One-Click Design Prompt

You are a senior AI infrastructure engineer. Design a fully local semantic search engine for my document collection. ## My Setup: - OS: [Mac/Linux/Windows] - GPU: [NVIDIA RTX xxxx / Apple Silicon M-series / CPU only] - Documents: [describe your docs - PDFs, markdown, code, meeting notes, etc.] - Scale: [approximate number of documents and total size] ## Design Requirements: ### 1. Document Ingestion Pipeline - List exact tools for each document format (PDF to text, DOCX, HTML, code files) - Chunking strategy: recommend chunk size, overlap, and method (semantic vs fixed) - Metadata extraction: titles, dates, authors, tags ### 2. Embedding and Vector Store - Recommend the best local embedding model for my hardware - Compare: Nomic Embed, BGE, GTE, E5 - pick one with justification - Vector DB: recommend between ChromaDB, Qdrant, LanceDB for local use - Index configuration: HNSW parameters, quantization settings ### 3. Query Pipeline - Implement hybrid search (vector + BM25 keyword) - Re-ranking with a local cross-encoder model - Query expansion/reformulation strategy ### 4. QA Layer - Local LLM selection for answer generation (Qwen, Llama, Phi) - Context window management: how to fit retrieved chunks - Citation/source attribution in responses ### 5. Implementation Plan Provide a step-by-step bash/python script outline I can run to set this up. Include all pip install commands, model download commands, and config files. Output: Complete technical design document with copy-paste ready commands.