108 lines
3.9 KiB
PL/PgSQL
108 lines
3.9 KiB
PL/PgSQL
-- ═════════════════════════════════════════════════════════════════════
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-- RMI pgvector Setup — Run ONCE in Supabase SQL Editor
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-- https://<your-project>.supabase.co → SQL Editor
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--
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-- IMPORTANT: The vector dimension MUST match RAG_EMBEDDING_DIM in .env
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-- Currently set to 640 (local BGE-small 384 + code 128 + behavioral 64 + wallet 64)
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-- ═════════════════════════════════════════════════════════════════════
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-- 1. Enable pgvector extension
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CREATE EXTENSION IF NOT EXISTS vector;
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-- 2. Drop existing table if dimension mismatch (DESTROYS EXISTING DATA — backup first!)
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-- Uncomment only if you need to change the vector dimension:
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-- DROP TABLE IF EXISTS rag_vectors CASCADE;
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-- 3. Create the vector table
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CREATE TABLE IF NOT EXISTS rag_vectors (
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id TEXT PRIMARY KEY,
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collection TEXT NOT NULL,
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content TEXT,
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embedding vector(640),
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metadata JSONB DEFAULT '{}',
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source TEXT,
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severity TEXT DEFAULT 'medium',
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chain TEXT,
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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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-- 4. Create indexes
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CREATE INDEX IF NOT EXISTS idx_rag_collection ON rag_vectors (collection);
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CREATE INDEX IF NOT EXISTS idx_rag_severity ON rag_vectors (severity);
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CREATE INDEX IF NOT EXISTS idx_rag_source ON rag_vectors (source);
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CREATE INDEX IF NOT EXISTS idx_rag_chain ON rag_vectors (chain);
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CREATE INDEX IF NOT EXISTS idx_rag_content_fts ON rag_vectors
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USING GIN (to_tsvector('english', COALESCE(content, '')));
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-- 5. Create or replace the store_embedding function
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CREATE OR REPLACE FUNCTION store_embedding(
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document_id TEXT,
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embedding vector(640),
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namespace TEXT DEFAULT 'default',
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content_hash TEXT DEFAULT '',
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metadata JSONB DEFAULT '{}',
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model_name TEXT DEFAULT ''
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) RETURNS JSONB
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LANGUAGE plpgsql
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AS $$
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DECLARE
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result JSONB;
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BEGIN
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INSERT INTO rag_vectors (id, collection, embedding, metadata, source, updated_at)
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VALUES (document_id, namespace, embedding, metadata, model_name, NOW())
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ON CONFLICT (id) DO UPDATE SET
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embedding = EXCLUDED.embedding,
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metadata = EXCLUDED.metadata,
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updated_at = NOW();
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result = jsonb_build_object('status', 'stored', 'document_id', document_id);
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RETURN result;
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END;
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$$;
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-- 6. Create or replace the search_embeddings RPC (640-dim — primary)
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CREATE OR REPLACE FUNCTION search_embeddings(
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query_embedding vector(640),
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namespace TEXT DEFAULT 'default',
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match_count INT DEFAULT 10,
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similarity_threshold FLOAT DEFAULT 0.7
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) RETURNS TABLE (
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id TEXT,
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content TEXT,
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metadata JSONB,
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source TEXT,
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severity TEXT,
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similarity FLOAT
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)
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LANGUAGE plpgsql
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AS $$
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BEGIN
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RETURN QUERY
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SELECT
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rv.id,
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rv.content,
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rv.metadata,
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rv.source,
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rv.severity,
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(1 - (rv.embedding <=> query_embedding))::FLOAT AS similarity
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FROM rag_vectors rv
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WHERE (namespace = 'default' OR rv.collection = namespace)
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AND 1 - (rv.embedding <=> query_embedding) > similarity_threshold
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ORDER BY rv.embedding <=> query_embedding
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LIMIT match_count;
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END;
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$$;
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-- 7. Create a compatibility RPC for smaller vectors (padded to 640 by the app)
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-- This is a copy that accepts the same 640-dim but is named for clarity
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-- (the app pads all vectors to 640 before calling search_embeddings)
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-- 8. After data is loaded, build the ANN index:
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-- Run this separately after loading data:
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-- CREATE INDEX IF NOT EXISTS idx_rag_embedding_hnsw ON rag_vectors
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-- USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 200);
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-- Verify
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SELECT 'pgvector setup complete (vector(640))' AS status;
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SELECT count(*) AS vector_count FROM rag_vectors;
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