GEMINI.md

DeepBoner Context

Project Overview

DeepBoner is an AI-native Sexual Health Research Agent. Goal: To accelerate research into sexual health, wellness, and reproductive medicine by intelligently searching biomedical literature (PubMed, ClinicalTrials.gov, Europe PMC), evaluating evidence, and synthesizing findings.

Architecture: The project follows a Vertical Slice Architecture (Search -> Judge -> Orchestrator) and adheres to Strict TDD (Test-Driven Development).

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

Tech Stack & Tooling

• Language: Python 3.11 (Pinned)
• Package Manager: uv (Rust-based, extremely fast)
• Frameworks: pydantic, pydantic-ai, httpx, gradio[mcp]
• Vector DB: chromadb with sentence-transformers for semantic search
• Code Execution: modal for secure sandboxed Python execution
• Testing: pytest, pytest-asyncio, respx (for mocking)
• Quality: ruff (linting/formatting), mypy (strict type checking), pre-commit

Building & Running

Command	Description
make install	Install dependencies and pre-commit hooks.
make test	Run unit tests.
make lint	Run Ruff linter.
make format	Run Ruff formatter.
make typecheck	Run Mypy static type checker.
make check	The Golden Gate: Runs lint, typecheck, and test. Must pass before committing.
make clean	Clean up cache and artifacts.

Directory Structure

• src/: Source code
  • utils/: Shared utilities (config.py, exceptions.py, models.py)
  • tools/: Search tools (pubmed.py, clinicaltrials.py, europepmc.py, code_execution.py)
  • services/: Services (embeddings.py, statistical_analyzer.py)
  • agents/: Magentic multi-agent mode agents
  • agent_factory/: Agent definitions (judges, prompts)
  • mcp_tools.py: MCP tool wrappers for Claude Desktop integration
  • app.py: Gradio UI with MCP server
• tests/: Test suite
  • unit/: Isolated unit tests (Mocked)
  • integration/: Real API tests (Marked as slow/integration)
• docs/: Documentation and Implementation Specs
• examples/: Working demos for each phase

Key Components

• src/orchestrators/ - Orchestrator package (simple, advanced, langgraph modes)
  • simple.py - Main search-and-judge loop
  • advanced.py - Multi-agent Magentic mode
  • langgraph_orchestrator.py - LangGraph-based workflow
• src/tools/pubmed.py - PubMed E-utilities search
• src/tools/clinicaltrials.py - ClinicalTrials.gov API
• src/tools/europepmc.py - Europe PMC search
• src/tools/code_execution.py - Modal sandbox execution
• 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/services/statistical_analyzer.py - Statistical analysis via Modal
• src/utils/service_loader.py - Tiered service selection (free vs premium)
• src/mcp_tools.py - MCP tool wrappers
• src/app.py - Gradio UI (HuggingFace Spaces) with MCP server

Configuration

Settings via pydantic-settings from .env:

• LLM_PROVIDER: "openai" or "anthropic"
• OPENAI_API_KEY / ANTHROPIC_API_KEY: LLM keys
• NCBI_API_KEY: Optional, for higher PubMed rate limits
• MODAL_TOKEN_ID / MODAL_TOKEN_SECRET: For Modal sandbox (optional)
• MAX_ITERATIONS: 1-50, default 10
• LOG_LEVEL: DEBUG, INFO, WARNING, ERROR

LLM Model Defaults (November 2025)

Given the rapid advancements, as of November 29, 2025, the DeepBoner project uses the following default LLM models in its configuration (src/utils/config.py):

• OpenAI: gpt-5
  • Current flagship model (November 2025). Requires Tier 5 access.
• Anthropic: claude-sonnet-4-5-20250929
  • This is the mid-range Claude 4.5 model, released on September 29, 2025.
  • The flagship Claude Opus 4.5 (released November 24, 2025) is also available and can be configured by advanced users for enhanced capabilities.
• HuggingFace (Free Tier): meta-llama/Llama-3.1-70B-Instruct
  • This remains the default for the free tier, subject to quota limits.

It is crucial to keep these defaults updated as the LLM landscape evolves.

Development Conventions

1. Strict TDD: Write failing tests in tests/unit/ before implementing logic in src/.
2. Type Safety: All code must pass mypy --strict. Use Pydantic models for data exchange.
3. Linting: Zero tolerance for Ruff errors.
4. Mocking: Use respx or unittest.mock for all external API calls in unit tests.
5. Vertical Slices: Implement features end-to-end rather than layer-by-layer.

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