leeworks-agents/SPARC
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Code Issues 10 Pull Requests 9 Activity
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a4aa968434be047bcf0bd193f9c8a4e59d17c603
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6 Commits

Author SHA1 Message Date
agent-company fbb72fe2a5 ci: add pytest and ruff linting to CI, fix all lint errors 
- Add test job to build.yaml that runs pytest and ruff before building images
- Add standalone test.yaml workflow for PRs
- Add ruff.toml with E/F/I rules configured
- Fix all ruff lint errors: sort imports, remove unused imports, fix re-exports
- Build jobs now depend on test job passing (needs: test)

Closes leeworks-agents/SPARC#18
Closes leeworks-agents/SPARC#19

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-26 07:04:00 +00:00
agent-company d366443b38 refactor(db): use shared pooled DatabaseClient singleton instead of per-call instances 
- Replace get_db_client() creating new DatabaseClient on every call with a
  module-level singleton initialized once at startup via init_db_client()
- Add init_db_client() and close_db_client() lifecycle functions called
  from FastAPI lifespan handler
- Migrate all DatabaseClient methods from legacy self.connect()/self.conn
  to pooled self.get_conn() context manager for thread-safe connection reuse
- Pool is properly torn down on application shutdown

Closes leeworks-agents/SPARC#7

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-26 06:03:56 +00:00
agent-company 96d5d27b17 feat(jobs): persist async batch job state in PostgreSQL 
- Add jobs table to database schema (job_id, status, progress, result_json, etc.)
- Add DatabaseClient methods: create_job, update_job, get_job, list_jobs
- Add mark_stale_jobs_failed() called at startup to handle interrupted jobs
- Refactor _run_batch_job and job endpoints to read/write from PostgreSQL
- Remove in-memory _jobs dict; job state now survives API restarts
- Update init_database.py to list all tables in output

Closes leeworks-agents/SPARC#8

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-26 04:22:57 +00:00
0xWheatyz 3154f6b732 feat(database): add patent/serp caching tables and connection pooling 
- Add patents table (patent_id PK, raw_sections JSONB, minimized_content)
- Add serp_queries table (query_hash unique, result_patent_ids, expires_at)
- Add cache methods: get/store_patent, get/store_serp_query
- Replace single connection with ThreadedConnectionPool (min=2, max=10)
- Add get_conn() context manager for thread-safe connection checkout
- Legacy single-connection path preserved for backwards compatibility
 2026-03-24 14:34:33 -04:00
0xWheatyz af52107ed8 feat(backend): add response caching and user management 
Replace USE_DATABASE toggle with USE_CACHE for smarter LLM response handling:
- Add prompt hashing for efficient cache lookups
- Cache API responses in database to reduce token usage
- Always store responses for analytics (cache or fresh)

Add user authentication infrastructure:
- User table with bcrypt password hashing
- CRUD operations for user management
- Role-based access control (admin/user)

Dependencies: add bcrypt and PyJWT for auth

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-03-14 13:40:34 -04:00
0xWheatyz 44456cb073 feat: add database mode for LLM message storage and analytics 
Implements a database mode that stores LLM prompts and responses in PostgreSQL
instead of making API calls. This enables:
- Testing without consuming API credits
- Collecting analytics on usage patterns
- Development and debugging workflows

Changes:
- Added DatabaseClient class for PostgreSQL operations
- Modified LLMAnalyzer to support database/API mode toggle
- Added USE_DATABASE config flag to switch between modes
- Included Docker Compose setup for PostgreSQL
- Added utility scripts for database init and analytics viewing
- Comprehensive documentation in DATABASE_MODE.md

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-03-10 21:13:13 -04:00