Hugging Face's logo

Spaces:
DataQuests
/
DeepCritical
Running

App Files Community
4
DeepCritical
File size: 3,071 Bytes 
5e7604a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
 
# CLAUDE.md

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

## Project Overview

DeepCritical is an AI-native drug repurposing research agent for a HuggingFace hackathon. It uses a search-and-judge loop to autonomously search biomedical databases (PubMed) and synthesize evidence for queries like "What existing drugs might help treat long COVID fatigue?".

## Development Commands

```bash
# 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
  2. Gather evidence
  3. Judge quality ("Do we have enough?")
  4. If NO → Refine query, search more
  5. If YES → Synthesize findings
    ↓
Research Report with Citations
```

**Key Components**:
- `src/orchestrator.py` - Main agent loop
- `src/tools/pubmed.py` - PubMed E-utilities search
- `src/tools/search_handler.py` - Scatter-gather orchestration
- `src/services/embeddings.py` - Semantic search & deduplication (ChromaDB)
- `src/agent_factory/judges.py` - LLM-based evidence assessment
- `src/agents/` - Magentic multi-agent mode (SearchAgent, JudgeAgent, etc.)
- `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 (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 "anthropic"
- `OPENAI_API_KEY` / `ANTHROPIC_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

```
DeepCriticalError (base)
├── SearchError
│   └── RateLimitError
├── JudgeError
└── ConfigurationError
```

## 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`

## Git Workflow

- `main`: Production-ready
- `dev`: Development
- `vcms-dev`: HuggingFace Spaces sandbox
- Remote `origin`: GitHub
- Remote `huggingface-upstream`: HuggingFace Spaces