🌐 Cloud-Native Architecture: - Firebase Functions deployment (no Docker) - Supabase database (replacing local PostgreSQL) - Google Cloud Storage integration - Document AI + Agentic RAG processing pipeline - Claude-3.5-Sonnet LLM integration ✅ Full BPCP CIM Review Template (7 sections): - Deal Overview - Business Description - Market & Industry Analysis - Financial Summary (with historical financials table) - Management Team Overview - Preliminary Investment Thesis - Key Questions & Next Steps 🔧 Cloud Migration Improvements: - PostgreSQL → Supabase migration complete - Local storage → Google Cloud Storage - Docker deployment → Firebase Functions - Schema mapping fixes (camelCase/snake_case) - Enhanced error handling and logging - Vector database with fallback mechanisms 📄 Complete End-to-End Cloud Workflow: 1. Upload PDF → Document AI extraction 2. Agentic RAG processing → Structured CIM data 3. Store in Supabase → Vector embeddings 4. Auto-generate PDF → Full BPCP template 5. Download complete CIM review 🚀 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
104 lines
3.2 KiB
JavaScript
104 lines
3.2 KiB
JavaScript
const { createClient } = require('@supabase/supabase-js');
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const fs = require('fs');
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// Load environment variables
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require('dotenv').config();
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const supabaseUrl = process.env.SUPABASE_URL;
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const supabaseServiceKey = process.env.SUPABASE_SERVICE_KEY;
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const supabase = createClient(supabaseUrl, supabaseServiceKey);
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async function tryCreateFunction() {
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console.log('🚀 Attempting to create vector search function...');
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const functionSQL = `
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CREATE OR REPLACE FUNCTION match_document_chunks(
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query_embedding VECTOR(1536),
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match_threshold FLOAT DEFAULT 0.7,
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match_count INTEGER DEFAULT 10
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)
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RETURNS TABLE (
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id UUID,
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document_id TEXT,
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content TEXT,
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metadata JSONB,
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chunk_index INTEGER,
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similarity FLOAT
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)
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LANGUAGE SQL STABLE
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AS $$
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SELECT
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document_chunks.id,
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document_chunks.document_id,
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document_chunks.content,
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document_chunks.metadata,
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document_chunks.chunk_index,
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1 - (document_chunks.embedding <=> query_embedding) AS similarity
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FROM document_chunks
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WHERE document_chunks.embedding IS NOT NULL
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AND 1 - (document_chunks.embedding <=> query_embedding) > match_threshold
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ORDER BY document_chunks.embedding <=> query_embedding
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LIMIT match_count;
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$$;`;
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// Try direct SQL execution
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try {
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const { data, error } = await supabase.rpc('query', {
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query: functionSQL
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});
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if (error) {
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console.log('❌ Direct query failed:', error.message);
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} else {
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console.log('✅ Function created via direct query!');
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}
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} catch (e) {
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console.log('❌ Direct query method not available');
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}
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// Alternative: Try creating via Edge Functions (if available)
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try {
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const response = await fetch(`${supabaseUrl}/rest/v1/rpc/sql`, {
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method: 'POST',
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headers: {
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'apikey': supabaseServiceKey,
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'Authorization': `Bearer ${supabaseServiceKey}`,
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'Content-Type': 'application/json'
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},
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body: JSON.stringify({ query: functionSQL })
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});
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if (response.ok) {
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console.log('✅ Function created via REST API!');
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} else {
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console.log('❌ REST API method failed:', response.status);
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}
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} catch (e) {
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console.log('❌ REST API method not available');
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}
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// Test if function exists now
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console.log('🧪 Testing if function exists...');
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const testEmbedding = new Array(1536).fill(0.1);
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const { data, error } = await supabase.rpc('match_document_chunks', {
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query_embedding: testEmbedding,
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match_threshold: 0.5,
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match_count: 5
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});
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if (error) {
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console.log('❌ Function still not available:', error.message);
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console.log('');
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console.log('📋 Manual steps required:');
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console.log('1. Go to https://supabase.com/dashboard/project/gzoclmbqmgmpuhufbnhy/sql');
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console.log('2. Run the SQL from vector_function.sql');
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console.log('3. Then test with: node test-vector-search.js');
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} else {
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console.log('✅ Function is working!');
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console.log('Found', data ? data.length : 0, 'results');
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}
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}
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tryCreateFunction(); |