CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

DeepBoner is an AI-native sexual health research agent. It uses a search-and-judge loop to autonomously search biomedical databases (PubMed, ClinicalTrials.gov, Europe PMC) and synthesize evidence for queries like "What drugs improve female libido post-menopause?" or "Evidence for testosterone therapy in women with HSDD?".

Current Status: Phases 1-14 COMPLETE (Foundation through Demo Submission).

Development Commands

# Install all dependencies (including dev)
make install   # or: uv sync --all-extras && uv run pre-commit install

# Run all quality checks (lint + typecheck + test) - MUST PASS BEFORE COMMIT
make check

# Individual commands
make test        # uv run pytest tests/unit/ -v
make lint        # uv run ruff check src tests
make format      # uv run ruff format src tests
make typecheck   # uv run mypy src
make test-cov    # uv run pytest --cov=src --cov-report=term-missing

# Run single test
uv run pytest tests/unit/utils/test_config.py::TestSettings::test_default_max_iterations -v

# Integration tests (real APIs)
uv run pytest -m integration

Architecture

Pattern: Search-and-judge loop with multi-tool orchestration.

User Question → Orchestrator
    ↓
Search Loop:
  1. Query PubMed, ClinicalTrials.gov, Europe PMC
  2. Gather evidence
  3. Judge quality ("Do we have enough?")
  4. If NO → Refine query, search more
  5. If YES → Synthesize findings (+ optional Modal analysis)
    ↓
Research Report with Citations

Key Components:

• src/orchestrators/ - Unified orchestrator package
  • advanced.py - Main orchestrator (handles both Free and Paid tiers)
  • factory.py - Auto-selects backend based on API key presence
  • langgraph_orchestrator.py - LangGraph-based workflow (experimental)
• src/clients/ - LLM backend adapters
  • factory.py - Auto-selects: OpenAI (if key) or HuggingFace (free)
  • huggingface.py - HuggingFace adapter for free tier
• src/tools/pubmed.py - PubMed E-utilities search
• src/tools/clinicaltrials.py - ClinicalTrials.gov API
• src/tools/europepmc.py - Europe PMC search
• src/tools/search_handler.py - Scatter-gather orchestration
• src/services/embeddings.py - Local embeddings (sentence-transformers, in-memory)
• src/services/llamaindex_rag.py - Premium embeddings (OpenAI, persistent ChromaDB)
• src/services/embedding_protocol.py - Protocol interface for embedding services
• src/services/research_memory.py - Shared memory layer for research state
• src/utils/service_loader.py - Tiered service selection (free vs premium)
• src/agent_factory/judges.py - LLM-based evidence assessment
• src/agents/ - Magentic multi-agent mode (SearchAgent, JudgeAgent, etc.)
• src/mcp_tools.py - MCP tool wrappers for Claude Desktop
• src/utils/config.py - Pydantic Settings (loads from .env)
• src/utils/models.py - Evidence, Citation, SearchResult models
• src/utils/exceptions.py - Exception hierarchy
• src/app.py - Gradio UI with MCP server (HuggingFace Spaces)

Break Conditions: Judge approval, token budget (50K max), or max iterations (default 10).

Configuration

Settings via pydantic-settings from .env:

• LLM_PROVIDER: "openai" or "huggingface"
• OPENAI_API_KEY: LLM keys
• NCBI_API_KEY: Optional, for higher PubMed rate limits
• MAX_ITERATIONS: 1-50, default 10
• LOG_LEVEL: DEBUG, INFO, WARNING, ERROR

Exception Hierarchy

DeepBonerError (base)
├── SearchError
│   └── RateLimitError
├── JudgeError
├── ConfigurationError
└── EmbeddingError

Testing

• TDD: Write tests first in tests/unit/, implement in src/
• Markers: unit, integration, slow
• Mocking: respx for httpx, pytest-mock for general mocking
• Fixtures: tests/conftest.py has mock_httpx_client, mock_llm_response

LLM Model Defaults (December 2025)

Default models in src/utils/config.py:

• OpenAI: gpt-5 - Flagship model
• HuggingFace (Free Tier): Qwen/Qwen2.5-7B-Instruct - See critical note below

---

⚠️ OpenAI API Keys

If you have a valid OpenAI API key, it will work. Period.

• BYOK (Bring Your Own Key) auto-detects sk-... prefix and routes to OpenAI
• If you get errors, the key is invalid or expired - NOT an access tier issue
• NEVER suggest "access tier" or "upgrade your plan" - this is not how OpenAI works for API keys
• Valid keys work. Invalid keys don't. That's it.

---

⚠️ CRITICAL: HuggingFace Free Tier Architecture

THIS IS IMPORTANT - READ BEFORE CHANGING THE FREE TIER MODEL

HuggingFace has TWO execution paths for inference:

Path	Host	Reliability	Model Size
Native Serverless	HuggingFace infrastructure	✅ High	< 30B params
Inference Providers	Third-party (Novita, Hyperbolic)	❌ Unreliable	70B+ params

The Trap: When you request a large model (70B+) without a paid API key, HuggingFace silently routes the request to third-party providers. These providers have:

• 500 Internal Server Errors (Novita - current)
• 401 "Staging Mode" auth failures (Hyperbolic - past)

The Rule: Free Tier MUST use models < 30B to stay on native infrastructure.

Current Safe Models (Dec 2025):

Model	Size	Status
Qwen/Qwen2.5-7B-Instruct	7B	✅ DEFAULT - Native, reliable
mistralai/Mistral-Nemo-Instruct-2407	12B	✅ Native, reliable
Qwen/Qwen2.5-72B-Instruct	72B	❌ Routed to Novita (500 errors)
meta-llama/Llama-3.1-70B-Instruct	70B	❌ Routed to Hyperbolic (401 errors)

See: HF_FREE_TIER_ANALYSIS.md for full analysis.

---

Git Workflow

• main: Production-ready (GitHub)
• dev: Development integration (GitHub)
• Remote origin: GitHub (source of truth for PRs/code review)
• Remote huggingface-upstream: HuggingFace Spaces (deployment target)

HuggingFace Spaces Collaboration:

• Each contributor should use their own dev branch: yourname-dev (e.g., vcms-dev, mario-dev)
• DO NOT push directly to main or dev on HuggingFace - these can be overwritten easily
• GitHub is the source of truth; HuggingFace is for deployment/demo
• Consider using git hooks to prevent accidental pushes to protected branches