42 lines
1.4 KiB
SQL
42 lines
1.4 KiB
SQL
-- RAG vector DB schema for Postgres (pgvector).
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-- GigaChat Embeddings = 1024; for OpenAI use vector(1536). Usage: psql "$RAG_DB_DSN" -f scripts/schema.sql
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CREATE EXTENSION IF NOT EXISTS vector;
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CREATE TABLE IF NOT EXISTS stories (
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id SERIAL PRIMARY KEY,
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slug TEXT UNIQUE NOT NULL,
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created_at TIMESTAMPTZ NOT NULL DEFAULT (NOW() AT TIME ZONE 'utc'),
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indexed_base_ref TEXT,
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indexed_head_ref TEXT,
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indexed_at TIMESTAMPTZ
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);
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CREATE TABLE IF NOT EXISTS documents (
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id SERIAL PRIMARY KEY,
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story_id INTEGER NOT NULL REFERENCES stories(id) ON DELETE CASCADE,
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path TEXT NOT NULL,
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version TEXT NOT NULL,
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updated_at TIMESTAMPTZ NOT NULL,
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UNIQUE(story_id, path)
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);
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CREATE TABLE IF NOT EXISTS chunks (
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id SERIAL PRIMARY KEY,
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document_id INTEGER NOT NULL REFERENCES documents(id) ON DELETE CASCADE,
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chunk_index INTEGER NOT NULL,
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hash TEXT NOT NULL,
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content TEXT NOT NULL,
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embedding vector(1024) NOT NULL,
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start_line INTEGER,
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end_line INTEGER,
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change_type TEXT NOT NULL DEFAULT 'added'
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CHECK (change_type IN ('added', 'modified', 'unchanged')),
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previous_content TEXT
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);
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CREATE INDEX IF NOT EXISTS idx_documents_story_id ON documents(story_id);
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CREATE INDEX IF NOT EXISTS idx_chunks_document_id ON chunks(document_id);
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CREATE INDEX IF NOT EXISTS idx_chunks_embedding ON chunks USING ivfflat (embedding vector_cosine_ops);
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CREATE INDEX IF NOT EXISTS idx_chunks_change_type ON chunks(change_type);
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