e90ea1591b
Node.js/TypeScript server with MCP endpoint for AI assistant integration, Discord bot for thought capture and slash commands, PostgreSQL + pgvector for semantic search, and OpenRouter for embeddings/metadata extraction. Dockerized for deployment behind Caddy. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
59 lines
1.7 KiB
PL/PgSQL
59 lines
1.7 KiB
PL/PgSQL
CREATE EXTENSION IF NOT EXISTS vector;
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CREATE TABLE IF NOT EXISTS thoughts (
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id UUID DEFAULT gen_random_uuid() PRIMARY KEY,
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user_id TEXT NOT NULL DEFAULT 'default',
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content TEXT NOT NULL,
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embedding VECTOR(1536),
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metadata JSONB DEFAULT '{}'::jsonb,
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source TEXT NOT NULL DEFAULT 'mcp',
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created_at TIMESTAMPTZ DEFAULT now(),
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updated_at TIMESTAMPTZ DEFAULT now()
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);
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CREATE INDEX IF NOT EXISTS thoughts_embedding_idx ON thoughts USING hnsw (embedding vector_cosine_ops);
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CREATE INDEX IF NOT EXISTS thoughts_metadata_idx ON thoughts USING gin (metadata);
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CREATE INDEX IF NOT EXISTS thoughts_created_at_idx ON thoughts (created_at DESC);
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CREATE INDEX IF NOT EXISTS thoughts_user_id_idx ON thoughts (user_id);
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-- Auto-update trigger for updated_at
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CREATE OR REPLACE FUNCTION update_updated_at() RETURNS TRIGGER AS $$
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BEGIN
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NEW.updated_at = now();
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RETURN NEW;
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END;
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$$ LANGUAGE plpgsql;
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DROP TRIGGER IF EXISTS thoughts_updated_at ON thoughts;
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CREATE TRIGGER thoughts_updated_at
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BEFORE UPDATE ON thoughts
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FOR EACH ROW EXECUTE FUNCTION update_updated_at();
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-- Semantic search function
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CREATE OR REPLACE FUNCTION match_thoughts(
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query_embedding VECTOR(1536),
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match_threshold FLOAT DEFAULT 0.7,
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match_count INT DEFAULT 10,
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filter JSONB DEFAULT '{}'::jsonb
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) RETURNS TABLE (
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id UUID,
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content TEXT,
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metadata JSONB,
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similarity FLOAT,
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created_at TIMESTAMPTZ
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) LANGUAGE plpgsql AS $$
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BEGIN
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RETURN QUERY
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SELECT
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t.id,
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t.content,
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t.metadata,
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(1 - (t.embedding <=> query_embedding))::FLOAT AS similarity,
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t.created_at
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FROM thoughts t
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WHERE 1 - (t.embedding <=> query_embedding) > match_threshold
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ORDER BY t.embedding <=> query_embedding
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LIMIT match_count;
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END;
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$$;
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