feat: Production release v2.0.0 - Simple Document Processor

Major release with significant performance improvements and new processing strategy.

## Core Changes
- Implemented simple_full_document processing strategy (default)
- Full document → LLM approach: 1-2 passes, ~5-6 minutes processing time
- Achieved 100% completeness with 2 API calls (down from 5+)
- Removed redundant Document AI passes for faster processing

## Financial Data Extraction
- Enhanced deterministic financial table parser
- Improved FY3/FY2/FY1/LTM identification from varying CIM formats
- Automatic merging of parser results with LLM extraction

## Code Quality & Infrastructure
- Cleaned up debug logging (removed emoji markers from production code)
- Fixed Firebase Secrets configuration (using modern defineSecret approach)
- Updated OpenAI API key
- Resolved deployment conflicts (secrets vs environment variables)
- Added .env files to Firebase ignore list

## Deployment
- Firebase Functions v2 deployment successful
- All 7 required secrets verified and configured
- Function URL: https://api-y56ccs6wva-uc.a.run.app

## Performance Improvements
- Processing time: ~5-6 minutes (down from 23+ minutes)
- API calls: 1-2 (down from 5+)
- Completeness: 100% achievable
- LLM Model: claude-3-7-sonnet-latest

## Breaking Changes
- Default processing strategy changed to 'simple_full_document'
- RAG processor available as alternative strategy 'document_ai_agentic_rag'

## Files Changed
- 36 files changed, 5642 insertions(+), 4451 deletions(-)
- Removed deprecated documentation files
- Cleaned up unused services and models

This release represents a major refactoring focused on speed, accuracy, and maintainability.

backend/sql/fix_vector_search_timeout.sql

@@ -0,0 +1,43 @@
+-- Fix vector search timeout by adding document_id filtering and optimizing the query
+-- This prevents searching across all documents and only searches within a specific document
+
+-- Drop the old function (handle all possible signatures)
+DROP FUNCTION IF EXISTS match_document_chunks(vector(1536), float, int);
+DROP FUNCTION IF EXISTS match_document_chunks(vector(1536), float, int, text);
+
+-- Create optimized function with document_id filtering
+-- document_id is TEXT (varchar) in the actual schema
+CREATE OR REPLACE FUNCTION match_document_chunks (
+  query_embedding vector(1536),
+  match_threshold float,
+  match_count int,
+  filter_document_id text DEFAULT NULL
+)
+RETURNS TABLE (
+  id UUID,
+  document_id TEXT,
+  content text,
+  metadata JSONB,
+  chunk_index INT,
+  similarity float
+)
+LANGUAGE sql STABLE
+AS $$
+  SELECT
+    document_chunks.id,
+    document_chunks.document_id,
+    document_chunks.content,
+    document_chunks.metadata,
+    document_chunks.chunk_index,
+    1 - (document_chunks.embedding <=> query_embedding) AS similarity
+  FROM document_chunks
+  WHERE document_chunks.embedding IS NOT NULL
+    AND (filter_document_id IS NULL OR document_chunks.document_id = filter_document_id)
+    AND 1 - (document_chunks.embedding <=> query_embedding) > match_threshold
+  ORDER BY document_chunks.embedding <=> query_embedding
+  LIMIT match_count;
+$$;
+
+-- Add comment explaining the optimization
+COMMENT ON FUNCTION match_document_chunks IS 'Optimized vector search that filters by document_id first to prevent timeouts. Always pass filter_document_id when searching within a specific document.';
+