| # CLAUDE.md |
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| This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. |
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| ## Project Overview |
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| A benchmark for evaluating vision-capable LLMs on Indian competitive exam questions (JEE Main, JEE Advanced, NEET). Questions are images sent to models via the OpenRouter API; responses are parsed from `<answer>...</answer>` tags and scored using exam-specific marking schemes. |
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| ## Running the Benchmark |
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| ```bash |
| # Setup |
| uv sync |
| echo "OPENROUTER_API_KEY=your_key" > .env |
| |
| # Must run from project root (paths are resolved relative to cwd) |
| uv run python src/benchmark_runner.py --model "google/gemini-2.5-pro-preview-03-25" --exam_name JEE_ADVANCED --exam_year 2025 |
| |
| # Filter by question IDs |
| uv run python src/benchmark_runner.py --model "openai/o3" --question_ids "N24T3001,N24T3002" |
| ``` |
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| CLI args: `--model` (required), `--exam_name` (all/NEET/JEE_ADVANCED/JEE_MAIN), `--exam_year` (all/2024/2025), `--question_ids`, `--output_dir`, `--config`, `--resume`. |
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| ## Testing |
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| ```bash |
| # Run the full pytest suite (68 tests) |
| uv run pytest tests/ -v |
| |
| # Run individual module self-tests |
| uv run python src/utils.py # answer parsing logic |
| uv run python src/evaluation.py # scoring logic |
| uv run python src/llm_interface.py # API calls (requires .env and network) |
| ``` |
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| ## Architecture |
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| ``` |
| benchmark_runner.py ─── orchestrator / entry point |
| ├── loads config from configs/benchmark_config.yaml |
| ├── loads dataset directly from metadata.jsonl (JSONL → HuggingFace Dataset) |
| │ ├── metadata.jsonl (question metadata, 578 questions) |
| │ └── images/ (question PNGs, stored in Git LFS) |
| ├── calls llm_interface.py for each question |
| │ ├── prompts.py (prompt templates) |
| │ └── utils.py (parse_llm_answer extracts from <answer> tags) |
| ├── scores via evaluation.py (exam-specific marking schemes) |
| └── writes results incrementally to results/{model}_{exam}_{year}_{timestamp}/ |
| ├── predictions.jsonl (raw API responses) |
| ├── summary.jsonl (scored per-question results) |
| └── summary.md (human-readable report) |
| ``` |
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| ### Key data flow |
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| 1. Dataset loaded directly from `metadata.jsonl` into a HuggingFace `Dataset` object, filtered by exam/year |
| 2. Each question image is base64-encoded and sent to OpenRouter with a structured prompt |
| 3. If the response can't be parsed, a re-prompt is sent (text-only, with the bad response) |
| 4. If the API call fails, the question is queued for retry (up to 3 attempts, exponential backoff via `tenacity`) |
| 5. Answers are parsed from `<answer>...</answer>` tags by `utils.parse_llm_answer()` |
| 6. `evaluation.py` scores using JEE/NEET marking schemes (partial credit for MCQ_MULTIPLE_CORRECT in JEE Advanced) |
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| ## Answer Format Conventions |
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| - `MCQ_SINGLE_CORRECT`: `<answer>A</answer>` → `["A"]` |
| - `MCQ_MULTIPLE_CORRECT`: `<answer>A,C</answer>` → `["A", "C"]` (sorted, deduplicated) |
| - `INTEGER`: `<answer>42</answer>` → `["42"]` |
| - `SKIP`: `<answer>SKIP</answer>` → no penalty |
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| ## Important Notes |
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| - **Git LFS**: Images and `metadata.jsonl` are in LFS. Run `git lfs pull` after cloning. |
| - **Working directory**: Scripts must be run from project root — config, data, and image paths are resolved relative to cwd. |
| - **Python 3.10+**: Uses union type syntax (`list[str] | str | None`). |
| - **Models**: Configured in `configs/benchmark_config.yaml` under `openrouter_models`. All must support vision input. |
| - **Result directory naming**: `results/{provider}_{model}_{exam}_{year}_{YYYYMMDD_HHMMSS}/` (slashes in model IDs replaced with underscores). |
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