53295444de
Features: - Fixed Ollama configuration for native (localhost) instead of Docker - Increased LLM timeout from 30s to 120s for quality answers - Implemented KB slug system for clean MCP endpoint URLs - Added multiple knowledge base support with type metadata - Integrated MCP server with FastAPI (port 8051) - React frontend with KB management UI - PostgreSQL + pgvector for vector embeddings Performance: - 33-56% faster than Docker setup with native Ollama - Direct Metal GPU acceleration on Apple Silicon - Optimized RAG engine with caching - ~40s response time for comprehensive answers Documentation: - Complete native setup guide (README_NATIVE_SETUP.md) - Performance benchmarking and comparison - MCP endpoint documentation - Slug implementation details - Decision rationale for native vs Docker Cleanup: - Removed old Streamlit setup files - Consolidated setup scripts to single setup_native.sh - Consolidated service management scripts - Removed test artifacts and old analysis docs - Ready for sharing with team 🚀 Production-ready for local team deployment 🤖 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
97 lines
3.2 KiB
PL/PgSQL
97 lines
3.2 KiB
PL/PgSQL
-- Enable pgvector extension
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CREATE EXTENSION IF NOT EXISTS vector;
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-- Knowledge Bases (Collections)
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CREATE TABLE IF NOT EXISTS knowledge_bases (
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id SERIAL PRIMARY KEY,
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name VARCHAR(255) UNIQUE NOT NULL,
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slug VARCHAR(255) UNIQUE NOT NULL,
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kb_type VARCHAR(50) NOT NULL DEFAULT 'generic',
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description TEXT,
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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tags TEXT,
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metadata JSONB DEFAULT '{}'::jsonb
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);
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CREATE INDEX idx_kb_name ON knowledge_bases(name);
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CREATE INDEX idx_kb_slug ON knowledge_bases(slug);
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CREATE INDEX idx_kb_type ON knowledge_bases(kb_type);
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CREATE INDEX idx_kb_created_at ON knowledge_bases(created_at);
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-- Documents
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CREATE TABLE IF NOT EXISTS documents (
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id SERIAL PRIMARY KEY,
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kb_id INTEGER NOT NULL REFERENCES knowledge_bases(id) ON DELETE CASCADE,
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doc_id VARCHAR(255) NOT NULL,
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content TEXT NOT NULL,
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embedding vector(768), -- nomic-embed-text produces 768-dim vectors
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metadata JSONB DEFAULT '{}'::jsonb,
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(kb_id, doc_id)
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);
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CREATE INDEX idx_doc_kb_id ON documents(kb_id);
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CREATE INDEX idx_doc_doc_id ON documents(doc_id);
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CREATE INDEX idx_doc_metadata ON documents USING GIN(metadata);
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CREATE INDEX idx_doc_embedding ON documents USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);
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-- Agent OS specific content types
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CREATE TABLE IF NOT EXISTS agent_os_content (
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id SERIAL PRIMARY KEY,
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kb_id INTEGER NOT NULL REFERENCES knowledge_bases(id) ON DELETE CASCADE,
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content_type VARCHAR(50) NOT NULL, -- standard, agent, workflow, command, product, spec
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name VARCHAR(255) NOT NULL,
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content TEXT NOT NULL,
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file_path TEXT,
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metadata JSONB DEFAULT '{}'::jsonb,
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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UNIQUE(kb_id, content_type, name)
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);
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CREATE INDEX idx_aos_kb_id ON agent_os_content(kb_id);
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CREATE INDEX idx_aos_content_type ON agent_os_content(content_type);
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CREATE INDEX idx_aos_name ON agent_os_content(name);
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CREATE INDEX idx_aos_metadata ON agent_os_content USING GIN(metadata);
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-- Function to update updated_at timestamp
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CREATE OR REPLACE FUNCTION update_updated_at_column()
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RETURNS TRIGGER AS $$
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BEGIN
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NEW.updated_at = CURRENT_TIMESTAMP;
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RETURN NEW;
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END;
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$$ language 'plpgsql';
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-- Trigger to auto-update updated_at
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CREATE TRIGGER update_kb_updated_at BEFORE UPDATE ON knowledge_bases
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FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();
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-- Helper function for cosine similarity search
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CREATE OR REPLACE FUNCTION search_documents(
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p_kb_id INTEGER,
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p_query_embedding vector(768),
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p_limit INTEGER DEFAULT 10,
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p_metadata_filter JSONB DEFAULT NULL
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)
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RETURNS TABLE (
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doc_id VARCHAR,
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content TEXT,
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similarity FLOAT,
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metadata JSONB
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) AS $$
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BEGIN
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RETURN QUERY
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SELECT
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d.doc_id,
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d.content,
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1 - (d.embedding <=> p_query_embedding) AS similarity,
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d.metadata
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FROM documents d
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WHERE d.kb_id = p_kb_id
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AND (p_metadata_filter IS NULL OR d.metadata @> p_metadata_filter)
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ORDER BY d.embedding <=> p_query_embedding
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LIMIT p_limit;
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END;
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$$ LANGUAGE plpgsql;
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