merge: chore/cleanup-remove-bloat-and-secrets into main

2026-07-02 01:24:22 +07:00 · 2026-07-02 01:24:22 +07:00 · bde2f3a97d
commit bde2f3a97d
1173 changed files with 437609 additions and 0 deletions
--- a/supabase_pgvector_setup.sql
+++ b/supabase_pgvector_setup.sql
@ -0,0 +1,108 @@
+-- ═════════════════════════════════════════════════════════════════════
+-- RMI pgvector Setup — Run ONCE in Supabase SQL Editor
+-- https://<your-project>.supabase.co → SQL Editor
+--
+-- IMPORTANT: The vector dimension MUST match RAG_EMBEDDING_DIM in .env
+-- Currently set to 640 (local BGE-small 384 + code 128 + behavioral 64 + wallet 64)
+-- ═════════════════════════════════════════════════════════════════════
+
+-- 1. Enable pgvector extension
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- 2. Drop existing table if dimension mismatch (DESTROYS EXISTING DATA — backup first!)
+-- Uncomment only if you need to change the vector dimension:
+-- DROP TABLE IF EXISTS rag_vectors CASCADE;
+
+-- 3. Create the vector table
+CREATE TABLE IF NOT EXISTS rag_vectors (
+    id TEXT PRIMARY KEY,
+    collection TEXT NOT NULL,
+    content TEXT,
+    embedding vector(640),
+    metadata JSONB DEFAULT '{}',
+    source TEXT,
+    severity TEXT DEFAULT 'medium',
+    chain TEXT,
+    created_at TIMESTAMPTZ DEFAULT NOW(),
+    updated_at TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- 4. Create indexes
+CREATE INDEX IF NOT EXISTS idx_rag_collection ON rag_vectors (collection);
+CREATE INDEX IF NOT EXISTS idx_rag_severity ON rag_vectors (severity);
+CREATE INDEX IF NOT EXISTS idx_rag_source ON rag_vectors (source);
+CREATE INDEX IF NOT EXISTS idx_rag_chain ON rag_vectors (chain);
+CREATE INDEX IF NOT EXISTS idx_rag_content_fts ON rag_vectors
+    USING GIN (to_tsvector('english', COALESCE(content, '')));
+
+-- 5. Create or replace the store_embedding function
+CREATE OR REPLACE FUNCTION store_embedding(
+    document_id TEXT,
+    embedding vector(640),
+    namespace TEXT DEFAULT 'default',
+    content_hash TEXT DEFAULT '',
+    metadata JSONB DEFAULT '{}',
+    model_name TEXT DEFAULT ''
+) RETURNS JSONB
+LANGUAGE plpgsql
+AS $$
+DECLARE
+    result JSONB;
+BEGIN
+    INSERT INTO rag_vectors (id, collection, embedding, metadata, source, updated_at)
+    VALUES (document_id, namespace, embedding, metadata, model_name, NOW())
+    ON CONFLICT (id) DO UPDATE SET
+        embedding = EXCLUDED.embedding,
+        metadata = EXCLUDED.metadata,
+        updated_at = NOW();
+
+    result = jsonb_build_object('status', 'stored', 'document_id', document_id);
+    RETURN result;
+END;
+$$;
+
+-- 6. Create or replace the search_embeddings RPC (640-dim — primary)
+CREATE OR REPLACE FUNCTION search_embeddings(
+    query_embedding vector(640),
+    namespace TEXT DEFAULT 'default',
+    match_count INT DEFAULT 10,
+    similarity_threshold FLOAT DEFAULT 0.7
+) RETURNS TABLE (
+    id TEXT,
+    content TEXT,
+    metadata JSONB,
+    source TEXT,
+    severity TEXT,
+    similarity FLOAT
+)
+LANGUAGE plpgsql
+AS $$
+BEGIN
+    RETURN QUERY
+    SELECT
+        rv.id,
+        rv.content,
+        rv.metadata,
+        rv.source,
+        rv.severity,
+        (1 - (rv.embedding <=> query_embedding))::FLOAT AS similarity
+    FROM rag_vectors rv
+    WHERE (namespace = 'default' OR rv.collection = namespace)
+      AND 1 - (rv.embedding <=> query_embedding) > similarity_threshold
+    ORDER BY rv.embedding <=> query_embedding
+    LIMIT match_count;
+END;
+$$;
+
+-- 7. Create a compatibility RPC for smaller vectors (padded to 640 by the app)
+-- This is a copy that accepts the same 640-dim but is named for clarity
+-- (the app pads all vectors to 640 before calling search_embeddings)
+
+-- 8. After data is loaded, build the ANN index:
+-- Run this separately after loading data:
+-- CREATE INDEX IF NOT EXISTS idx_rag_embedding_hnsw ON rag_vectors
+--     USING hnsw (embedding vector_cosine_ops) WITH (m = 16, ef_construction = 200);
+
+-- Verify
+SELECT 'pgvector setup complete (vector(640))' AS status;
+SELECT count(*) AS vector_count FROM rag_vectors;