This commit introduces a novelty checking system that uses embeddings and LLM-based judging to ensure programs added to the database are meaningfully different from existing ones in their island.
Changes:
- Added EmbeddingClient class for computing code embeddings using OpenAI/Azure models
- Added novelty judge prompts for LLM-based similarity assessment
- Modified ProgramDatabase to support novelty checking before adding programs
- Added embedding vector field to Program dataclass
- Added configuration options for embedding model, novelty LLM, and similarity threshold
- Integrated novelty LLM ensemble in controller setup
- Updated example config with novelty checking parameters
The novelty check uses cosine similarity of embeddings as a first pass, then uses an LLM judge to determine if programs exceeding the similarity threshold are truly novel or just trivial variations.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>

- Fix novelty check in database.py to pass program.id instead of program object
- Fix missing self parameter in embedding.py _get_client_model method
- Fix serialization error by clearing novelty_llm before config deepcopy
- Update function_minimization config for testing with different models
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>