temp: firebase deployment progress

backend/supabase_setup.sql

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+-- Enable the pgvector extension
+CREATE EXTENSION IF NOT EXISTS vector;
+
+-- Create document_chunks table with vector support
+CREATE TABLE IF NOT EXISTS document_chunks (
+  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+  document_id VARCHAR(255) NOT NULL,
+  chunk_index INTEGER NOT NULL,
+  content TEXT NOT NULL,
+  embedding vector(1536), -- OpenAI embeddings are 1536 dimensions
+  metadata JSONB DEFAULT '{}',
+  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+  updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+);
+
+-- Create indexes for better performance
+CREATE INDEX IF NOT EXISTS document_chunks_document_id_idx ON document_chunks(document_id);
+CREATE INDEX IF NOT EXISTS document_chunks_embedding_idx ON document_chunks USING ivfflat (embedding vector_cosine_ops);
+
+-- Create function to enable pgvector (for RPC calls)
+CREATE OR REPLACE FUNCTION enable_pgvector()
+RETURNS VOID AS $$
+BEGIN
+  CREATE EXTENSION IF NOT EXISTS vector;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Create function to create document_chunks table (for RPC calls)
+CREATE OR REPLACE FUNCTION create_document_chunks_table()
+RETURNS VOID AS $$
+BEGIN
+  CREATE TABLE IF NOT EXISTS document_chunks (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    document_id VARCHAR(255) NOT NULL,
+    chunk_index INTEGER NOT NULL,
+    content TEXT NOT NULL,
+    embedding vector(1536),
+    metadata JSONB DEFAULT '{}',
+    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
+  );
+  
+  CREATE INDEX IF NOT EXISTS document_chunks_document_id_idx ON document_chunks(document_id);
+  CREATE INDEX IF NOT EXISTS document_chunks_embedding_idx ON document_chunks USING ivfflat (embedding vector_cosine_ops);
+END;
+$$ LANGUAGE plpgsql;
+
+-- Create function to match documents based on vector similarity
+CREATE OR REPLACE FUNCTION match_documents(
+  query_embedding vector(1536),
+  match_threshold float DEFAULT 0.7,
+  match_count int DEFAULT 10
+)
+RETURNS TABLE(
+  id UUID,
+  content TEXT,
+  metadata JSONB,
+  document_id VARCHAR(255),
+  similarity FLOAT
+) AS $$
+BEGIN
+  RETURN QUERY
+  SELECT
+    document_chunks.id,
+    document_chunks.content,
+    document_chunks.metadata,
+    document_chunks.document_id,
+    1 - (document_chunks.embedding <=> query_embedding) AS similarity
+  FROM document_chunks
+  WHERE 1 - (document_chunks.embedding <=> query_embedding) > match_threshold
+  ORDER BY document_chunks.embedding <=> query_embedding
+  LIMIT match_count;
+END;
+$$ LANGUAGE plpgsql;
+
+-- Enable Row Level Security (RLS) if needed
+-- ALTER TABLE document_chunks ENABLE ROW LEVEL SECURITY;
+
+-- Create policies for RLS (adjust as needed for your auth requirements)
+-- CREATE POLICY "Users can view all document chunks" ON document_chunks FOR SELECT USING (true);
+-- CREATE POLICY "Users can insert document chunks" ON document_chunks FOR INSERT WITH CHECK (true);
+-- CREATE POLICY "Users can update document chunks" ON document_chunks FOR UPDATE USING (true);
+-- CREATE POLICY "Users can delete document chunks" ON document_chunks FOR DELETE USING (true);
+
+-- Grant necessary permissions
+GRANT ALL ON document_chunks TO authenticated;
+GRANT ALL ON document_chunks TO anon;
+GRANT EXECUTE ON FUNCTION match_documents TO authenticated;
+GRANT EXECUTE ON FUNCTION match_documents TO anon;