From 9348a751f534d2704268900e9ff4a25e41acc5fa Mon Sep 17 00:00:00 2001 From: DiTo97 Date: Fri, 14 Aug 2026 11:04:12 +0200 Subject: [PATCH 1/2] feat(aa-sync): weekly Artificial Analysis acquisition workload MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Turns the experiments/aa-scrape-spike proof-of-concept into a production service: a provider x model offering dataset joined to the Artificial Analysis Openness Index, refreshed weekly. Sources both datasets over plain HTTP — no browser. The spike recommended Playwright, but that was only needed for the *expanded* column view of the aggregate leaderboard, which this service does not use. Per-provider pages and the Openness Index table are fully server-rendered on a plain GET. Uses the individual /providers/ pages rather than the aggregate leaderboard: the leaderboard is filtered (Status: Current) and yields 512 rows against 1045 from the provider pages across 51 providers. The join normalises display names rather than string-matching them. The parenthetical suffix space carries more than reasoning effort — quantisation, serving tier, hosting platform and snapshot dates — so serving-side tokens are dropped from the key while reasoning identity is kept, read from the display name first and the /models/ slug second. Matching runs in three confidence tiers and never guesses: candidates that disagree are recorded as ambiguous, models with no openness row as unmatched, and absent openness stays null rather than becoming zero. Refs #85, #78, #76 Co-Authored-By: Claude Opus 5 (1M context) --- .github/workflows/aa-sync-test.yml | 33 + .github/workflows/aa-sync.yml | 54 + .gitignore | 3 + README.md | 6 +- database/current/artificial-analysis.json | 40534 ++++++++++++++++ .../artificial-analysis-20260814T090223Z.json | 40534 ++++++++++++++++ docs/database.md | 26 + services/aa-sync/README.md | 147 + services/aa-sync/pyproject.toml | 34 + .../aa-sync/src/tokenpricing_aa/__init__.py | 3 + services/aa-sync/src/tokenpricing_aa/cli.py | 108 + services/aa-sync/src/tokenpricing_aa/fetch.py | 124 + .../aa-sync/src/tokenpricing_aa/matching.py | 425 + .../aa-sync/src/tokenpricing_aa/modeling.py | 144 + .../aa-sync/src/tokenpricing_aa/normalize.py | 176 + services/aa-sync/src/tokenpricing_aa/parse.py | 243 + services/aa-sync/src/tokenpricing_aa/paths.py | 13 + services/aa-sync/tests/__init__.py | 0 services/aa-sync/tests/test_matching.py | 349 + services/aa-sync/tests/test_normalize.py | 183 + services/aa-sync/tests/test_parse.py | 228 + services/aa-sync/uv.lock | 456 + 22 files changed, 83822 insertions(+), 1 deletion(-) create mode 100644 .github/workflows/aa-sync-test.yml create mode 100644 .github/workflows/aa-sync.yml create mode 100644 database/current/artificial-analysis.json create mode 100644 database/history/artificial-analysis-20260814T090223Z.json create mode 100644 services/aa-sync/README.md create mode 100644 services/aa-sync/pyproject.toml create mode 100644 services/aa-sync/src/tokenpricing_aa/__init__.py create mode 100644 services/aa-sync/src/tokenpricing_aa/cli.py create mode 100644 services/aa-sync/src/tokenpricing_aa/fetch.py create mode 100644 services/aa-sync/src/tokenpricing_aa/matching.py create mode 100644 services/aa-sync/src/tokenpricing_aa/modeling.py create mode 100644 services/aa-sync/src/tokenpricing_aa/normalize.py create mode 100644 services/aa-sync/src/tokenpricing_aa/parse.py create mode 100644 services/aa-sync/src/tokenpricing_aa/paths.py create mode 100644 services/aa-sync/tests/__init__.py create mode 100644 services/aa-sync/tests/test_matching.py create mode 100644 services/aa-sync/tests/test_normalize.py create mode 100644 services/aa-sync/tests/test_parse.py create mode 100644 services/aa-sync/uv.lock diff --git a/.github/workflows/aa-sync-test.yml b/.github/workflows/aa-sync-test.yml new file mode 100644 index 0000000..caa7880 --- /dev/null +++ b/.github/workflows/aa-sync-test.yml @@ -0,0 +1,33 @@ +name: AA Sync Service Test + +on: + push: + branches: [main] + paths: + - "services/aa-sync/**" + - "libraries/python/**" + pull_request: + paths: + - "services/aa-sync/**" + - "libraries/python/**" + +jobs: + tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/setup-uv@v7 + with: + version: latest + - uses: actions/setup-python@v6 + with: + python-version: "3.12" + - name: Install dependencies + working-directory: services/aa-sync + run: uv sync --group dev + - name: Run tests + working-directory: services/aa-sync + run: uv run pytest + - name: Run lint + working-directory: services/aa-sync + run: uv run ruff check . diff --git a/.github/workflows/aa-sync.yml b/.github/workflows/aa-sync.yml new file mode 100644 index 0000000..5f5d99b --- /dev/null +++ b/.github/workflows/aa-sync.yml @@ -0,0 +1,54 @@ +name: Artificial Analysis Sync + +on: + workflow_dispatch: + schedule: + # Weekly, off-peak. The whole capture is ~60 requests, so one run is enough. + - cron: "17 4 * * 0" + +jobs: + sync: + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - uses: actions/checkout@v6 + - uses: astral-sh/setup-uv@v7 + with: + version: latest + - uses: actions/setup-python@v6 + with: + python-version: "3.12" + - name: Install dependencies + working-directory: services/aa-sync + run: uv sync + - name: Refresh Artificial Analysis dataset + working-directory: services/aa-sync + run: uv run tokenpricing-aa-sync sync + - name: Upload raw capture + # ~60MB of source HTML, kept out of git but retained so that a failed + # parse can be diagnosed against its exact input. + if: always() + uses: actions/upload-artifact@v4 + with: + name: aa-raw-capture + path: .capture/artificial-analysis/ + retention-days: 30 + if-no-files-found: ignore + - name: Prune old history snapshots + run: | + mapfile -t history_files < <(ls -1t database/history/artificial-analysis-*.json 2>/dev/null || true) + if [ "${#history_files[@]}" -gt 26 ]; then + printf '%s\0' "${history_files[@]:26}" | xargs -0 rm -f -- + fi + - name: Commit refreshed data + run: | + if git diff --quiet; then + echo "No Artificial Analysis changes" + exit 0 + fi + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + git add database/current database/history + git commit -m "chore: refresh Artificial Analysis dataset" + git push diff --git a/.gitignore b/.gitignore index ddc4e86..e51ad90 100644 --- a/.gitignore +++ b/.gitignore @@ -90,3 +90,6 @@ package-lock.json !services/dashboard/package-lock.json !docs/package-lock.json + +# Artificial Analysis raw HTML capture (~60MB/run, uploaded as a CI artifact) +.capture/ diff --git a/README.md b/README.md index 75fb90c..e02a1aa 100644 --- a/README.md +++ b/README.md @@ -140,6 +140,8 @@ The canonical skill source in this repository is `skills/tokenpricing/SKILL.md`. Pricing data is now synchronized directly inside this repository from OpenRouter and LiteLLM, normalized into the tokenpricing schema, and published as the canonical database every six hours. +A separate weekly dataset (`database/current/artificial-analysis.json`) adds provider × model serving data — benchmark scores, throughput, latency and Openness Index — sourced from [Artificial Analysis](https://artificialanalysis.ai). See [docs/database.md](docs/database.md#artificial-analysis-dataset). + ## Repository Structure ``` @@ -151,7 +153,8 @@ tokenpricing/ ├── services/ │ ├── dashboard/ Vite + React pricing explorer │ ├── notifier/ Webhook notification service -│ └── sync/ Canonical database sync pipeline +│ ├── sync/ Canonical database sync pipeline +│ └── aa-sync/ Artificial Analysis acquisition (weekly) ├── libraries/ │ ├── python/ Python SDK + CLI (PyPI) │ └── typescript/ TypeScript SDK (npm) @@ -178,6 +181,7 @@ Each library and service is self-contained. See the individual READMEs for setup ## Credits - Canonical database: this repository (`database/current/prices.json`), synchronized from OpenRouter and LiteLLM and inspired by the original [LLMTracker](https://github.com/MrUnreal/LLMTracker) implementation +- Provider × model performance and Openness Index data (`database/current/artificial-analysis.json`): [Artificial Analysis](https://artificialanalysis.ai) — benchmark, latency and throughput figures are third-party measurements produced by Artificial Analysis, not vendor-published and not measured by tokenpricing ## License diff --git a/database/current/artificial-analysis.json b/database/current/artificial-analysis.json new file mode 100644 index 0000000..8380a0c --- /dev/null +++ b/database/current/artificial-analysis.json @@ -0,0 +1,40534 @@ +{ + "generated_at": "2026-08-14T09:02:23.862929Z", + "metadata": { + "fetched_at": "2026-08-14T08:54:43.593677Z", + "sources": [ + "https://artificialanalysis.ai/leaderboards/providers", + "https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index" + ], + "attribution": "Data source: Artificial Analysis (https://artificialanalysis.ai). Benchmark, latency and throughput figures are third-party measurements produced by Artificial Analysis, not vendor-published and not measured by tokenpricing.", + "openness_spec_version": "Openness Spec V1.0 (preliminary draft)", + "provider_count": 51, + "offering_count": 1045, + "openness_row_count": 298, + "openness_matched": 563, + "openness_unmatched": 481, + "openness_ambiguous": 1, + "match_tiers": { + "base": 149, + "base+mode": 1, + "exact": 413 + }, + "unreachable_provider_slugs": [ + "ai9star", + "blackbox-ai", + "clarifai", + "hyperbolic", + "public-ai", + "sarvam", + "streamlake" + ], + "unclassified_variant_tokens": [] + }, + "offerings": [ + { + "provider_slug": "agnes-ng", + "model_slug": "agnes-2-5-pro-alpha", + "display_name": "Agnes 2.5 Pro Alpha", + "creator": "Sapiens AI", + "context_window": 1000000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Proprietary", + "intelligence_index": 40.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": null, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 131.0, + "median_first_chunk_seconds": 2.18, + "total_response_seconds": 21.2, + "reasoning_time_seconds": 15.22, + "reasoning_mode": null, + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": null, + "openness_match": null + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-8-max", + "display_name": "Qwen3.8 Max", + "creator": "Alibaba", + "context_window": 1000000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Proprietary", + "intelligence_index": 58.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": 1.13, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 47.0, + "median_first_chunk_seconds": 2.6, + "total_response_seconds": 55.62, + "reasoning_time_seconds": 42.42, + "reasoning_mode": "reasoning", + "reasoning_effort": "max", + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": null, + "openness_match": null + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-8-2-4t-a95b", + "display_name": "Qwen3.8 2.4T A95B", + "creator": "Alibaba", + "context_window": 984000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Open", + "intelligence_index": 58.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": 1.09, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 52.0, + "median_first_chunk_seconds": 2.75, + "total_response_seconds": 51.26, + "reasoning_time_seconds": 38.81, + "reasoning_mode": null, + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": null, + "openness_match": null + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-7-max", + "display_name": "Qwen3.7 Max", + "creator": "Alibaba", + "context_window": 991000, + "supports_function_calling": true, + 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"total_response_seconds": 45.79, + "reasoning_time_seconds": 33.59, + "reasoning_mode": "reasoning", + "reasoning_effort": "max", + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": null, + "openness_match": null + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-6-plus", + "display_name": "Qwen3.6 Plus", + "creator": "Alibaba", + "context_window": 1000000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Proprietary", + "intelligence_index": 40.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": 0.36, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 56.0, + "median_first_chunk_seconds": 2.05, + "total_response_seconds": 110.0, + "reasoning_time_seconds": 99.03, + "reasoning_mode": null, + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": null, + "openness_match": null + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-7-plus", + "display_name": "Qwen3.7 Plus", + "creator": "Alibaba", + "context_window": 1000000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Proprietary", + "intelligence_index": 39.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": 0.24, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 56.0, + "median_first_chunk_seconds": 2.21, + "total_response_seconds": 46.91, + "reasoning_time_seconds": 35.76, + "reasoning_mode": null, + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": null, + "openness_match": null + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-6-27b", + "display_name": "Qwen3.6 27B", + "creator": "Alibaba", + "context_window": 262000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Open", + "intelligence_index": 38.0, + "intelligence_index_estimated": false, + 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"Open", + "intelligence_index": 34.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": 0.36, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 82.0, + "median_first_chunk_seconds": 2.19, + "total_response_seconds": 46.98, + "reasoning_time_seconds": 38.71, + "reasoning_mode": null, + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": { + "openness_index": 38.89, + "model_availability": 6.0, + "model_transparency": 1.0, + "pre_training_data_access": 0.0, + "pre_training_data_license": 0.0, + "post_training_data_access": 0.0, + "post_training_data_license": 0.0 + }, + "openness_match": { + "tier": "base", + "openness_display_name": "Qwen3.5 397B A17B (Reasoning)", + "openness_creator": "Alibaba" + } + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-5-122b-a10b", + "display_name": "Qwen3.5 122B A10B", + "creator": "Alibaba", + "context_window": 262000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Open", + "intelligence_index": 33.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": 0.25, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 136.0, + "median_first_chunk_seconds": 2.35, + "total_response_seconds": 20.76, + "reasoning_time_seconds": 14.73, + "reasoning_mode": null, + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": { + "openness_index": 38.89, + "model_availability": 6.0, + "model_transparency": 1.0, + "pre_training_data_access": 0.0, + "pre_training_data_license": 0.0, + "post_training_data_access": 0.0, + "post_training_data_license": 0.0 + }, + "openness_match": { + "tier": "base", + "openness_display_name": "Qwen3.5 122B A10B (Reasoning)", + "openness_creator": "Alibaba" + } + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-5-397b-a17b-non-reasoning", + "display_name": "Qwen3.5 397B A17B", + "creator": "Alibaba", + "context_window": 262000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Open", + "intelligence_index": 33.0, + "intelligence_index_estimated": true, + "cost_per_task_usd": null, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 82.0, + "median_first_chunk_seconds": 2.07, + "total_response_seconds": 8.16, + "reasoning_time_seconds": null, + "reasoning_mode": "non-reasoning", + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": { + "openness_index": 38.89, + "model_availability": 6.0, + "model_transparency": 1.0, + "pre_training_data_access": 0.0, + "pre_training_data_license": 0.0, + "post_training_data_access": 0.0, + "post_training_data_license": 0.0 + }, + "openness_match": { + "tier": "exact", + "openness_display_name": "Qwen3.5 397B A17B (Non-reasoning)", + "openness_creator": "Alibaba" + } + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-6-35b-a3b", + "display_name": "Qwen3.6 35B A3B", + "creator": "Alibaba", + "context_window": 229000, + "supports_function_calling": true, + "supports_json_mode": true, + "license": "Open", + "intelligence_index": 32.0, + "intelligence_index_estimated": false, + "cost_per_task_usd": 0.29, + "cost_per_task_estimated": false, + "median_output_tokens_per_second": 146.0, + "median_first_chunk_seconds": 2.19, + "total_response_seconds": 42.49, + "reasoning_time_seconds": 36.88, + "reasoning_mode": null, + "reasoning_effort": null, + "variant_tokens": [], + "unclassified_variant_tokens": [], + "openness": { + "openness_index": 38.89, + "model_availability": 6.0, + "model_transparency": 1.0, + "pre_training_data_access": 0.0, + "pre_training_data_license": 0.0, + "post_training_data_access": 0.0, + "post_training_data_license": 0.0 + }, + "openness_match": { + "tier": "base", + "openness_display_name": "Qwen3.6 35B A3B (Non-reasoning)", + "openness_creator": "Alibaba" + } + }, + { + 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0000000..8380a0c --- /dev/null +++ b/database/history/artificial-analysis-20260814T090223Z.json @@ -0,0 +1,40534 @@ +{ + "generated_at": "2026-08-14T09:02:23.862929Z", + "metadata": { + "fetched_at": "2026-08-14T08:54:43.593677Z", + "sources": [ + "https://artificialanalysis.ai/leaderboards/providers", + "https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index" + ], + "attribution": "Data source: Artificial Analysis (https://artificialanalysis.ai). 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"normalized_base": "gpt 4o", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-4o-2024-08-06", + "display_name": "GPT-4o (Aug)", + "creator": "OpenAI", + "normalized_base": "gpt 4o", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-4o-2024-05-13", + "display_name": "GPT-4o (May)", + "creator": "OpenAI", + "normalized_base": "gpt 4o", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-5-nano-minimal", + "display_name": "GPT-5 nano (minimal) private", + "creator": "OpenAI", + "normalized_base": "gpt 5 nano private", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-4-turbo", + "display_name": "GPT-4 Turbo", + "creator": "OpenAI", + "normalized_base": "gpt 4 turbo", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-4", + "display_name": "GPT-4", + "creator": "OpenAI", + "normalized_base": "gpt 4", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-4o-mini", + "display_name": "GPT-4o mini", + "creator": "OpenAI", + "normalized_base": "gpt 4o mini", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-4o-mini", + "display_name": "GPT-4o mini", + "creator": "OpenAI", + "normalized_base": "gpt 4o mini", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-35-turbo", + "display_name": "GPT-3.5 Turbo", + "creator": "OpenAI", + "normalized_base": "gpt 3 5 turbo", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-5-4-pro", + "display_name": "GPT-5.4 Pro (xhigh)", + "creator": "OpenAI", + "normalized_base": "gpt 5 4 pro", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "parasail", + "model_slug": "qwen3-235b-a22b-instruct-2507", + "display_name": "Qwen3 235B 2507", + "creator": "Alibaba", + "normalized_base": "qwen3 235b 2507", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "parasail", + "model_slug": "llama-3-3-instruct-70b", + "display_name": "Llama 3.3 70B (FP8)", + "creator": "Meta", + "normalized_base": "llama 3 3 70b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "parasail", + "model_slug": "gemma-3-27b", + "display_name": "Gemma 3 27B", + "creator": "Google", + "normalized_base": "gemma 3 27b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "parasail", + "model_slug": "olmo-3-7b-instruct", + "display_name": "Olmo 3 7B", + "creator": "Allen Institute for AI", + "normalized_base": "olmo 3 7b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "reka-ai", + "model_slug": "reka-flash", + "display_name": "Reka Flash", + "creator": "Reka AI", + "normalized_base": "reka flash", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "replicate", + "model_slug": "deepseek-v3-0324", + "display_name": "DeepSeek V3 0324", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 0324", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "replicate", + "model_slug": "llama-2-chat-7b", + "display_name": "Llama 2 Chat 7B", + "creator": "Meta", + "normalized_base": "llama 2 chat 7b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "replicate", + "model_slug": "llama-3-instruct-70b", + "display_name": "Llama 3 70B", + "creator": "Meta", + "normalized_base": "llama 3 70b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "replicate", + "model_slug": "granite-3-3-8b-instruct", + "display_name": "Granite 3.3 8B", + "creator": "IBM", + "normalized_base": "granite 3 3 8b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "replicate", + "model_slug": "llama-3-instruct-8b", + "display_name": "Llama 3 8B", + "creator": "Meta", + "normalized_base": "llama 3 8b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "sambanova", + "model_slug": "deepseek-v3-2-reasoning", + "display_name": "DeepSeek V3.2", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 2", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "sambanova", + "model_slug": "deepseek-v3-2", + "display_name": "DeepSeek V3.2", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 2", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "sambanova", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 1", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "sambanova", + "model_slug": "deepseek-v3-1-reasoning", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 1", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "sambanova", + "model_slug": "qwen3-32b-instruct-reasoning", + "display_name": "Qwen3 32B", + "creator": "Alibaba", + "normalized_base": "qwen3 32b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "sambanova", + "model_slug": "llama-3-3-instruct-70b", + "display_name": "Llama 3.3 70B", + "creator": "Meta", + "normalized_base": "llama 3 3 70b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "sambanova", + "model_slug": "qwen3-32b-instruct", + "display_name": "Qwen3 32B", + "creator": "Alibaba", + "normalized_base": "qwen3 32b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "scaleway", + "model_slug": "qwen3-235b-a22b-instruct-2507", + "display_name": "Qwen3 235B 2507", + "creator": "Alibaba", + "normalized_base": "qwen3 235b 2507", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "scaleway", + "model_slug": "qwen3-coder-30b-a3b-instruct", + "display_name": "Qwen3 Coder 30B A3B", + "creator": "Alibaba", + "normalized_base": "qwen3 coder 30b a3b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "scaleway", + "model_slug": "llama-3-3-instruct-70b", + "display_name": "Llama 3.3 70B", + "creator": "Meta", + "normalized_base": "llama 3 3 70b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "siliconflow", + "model_slug": "deepseek-v3-2-reasoning", + "display_name": "DeepSeek V3.2 (FP8)", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 2", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "siliconflow", + "model_slug": "deepseek-v3-2", + "display_name": "DeepSeek V3.2 (FP8)", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 2", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "siliconflow", + "model_slug": "glm-4-6v-reasoning", + "display_name": "GLM-4.6V", + "creator": "Z AI", + "normalized_base": "glm 4 6v", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "siliconflow", + "model_slug": "glm-4-6v", + "display_name": "GLM-4.6V", + "creator": "Z AI", + "normalized_base": "glm 4 6v", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "siliconflow", + "model_slug": "qwen2-5-72b-instruct", + "display_name": "Qwen2.5 72B (FP8)", + "creator": "Alibaba", + "normalized_base": "qwen2 5 72b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "stepfun", + "model_slug": "step-3-5-flash", + "display_name": "Step 3.5 Flash 2603", + "creator": "StepFun", + "normalized_base": "step 3 5 flash 2603", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "togetherai", + "model_slug": "nvidia-nemotron-3-ultra-550b-a55b", + "display_name": "Nemotron 3 Ultra", + "creator": "NVIDIA", + "normalized_base": "nemotron 3 ultra", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "togetherai", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "normalized_base": "deepseek v3 1", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "togetherai", + "model_slug": "deepseek-r1-0120", + "display_name": "DeepSeek R1 (Jan)", + "creator": "DeepSeek", + "normalized_base": "deepseek r1", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "togetherai", + "model_slug": "llama-3-3-instruct-70b", + "display_name": "Llama 3.3 70B Turbo", + "creator": "Meta", + "normalized_base": "llama 3 3 70b turbo", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "togetherai", + "model_slug": "gemma-3n-e4b", + "display_name": "Gemma 3n E4B", + "creator": "Google", + "normalized_base": "gemma 3n e4b", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "upstage", + "model_slug": "solar-pro4", + "display_name": "Solar Pro 4", + "creator": "Upstage", + "normalized_base": "solar pro 4", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "upstage", + "model_slug": "solar-pro-3", + "display_name": "Solar Pro 3", + "creator": "Upstage", + "normalized_base": "solar pro 3", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "upstage", + "model_slug": "solar-mini", + "display_name": "Solar Mini", + "creator": "Upstage", + "normalized_base": "solar mini", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-6", + "display_name": "Grok 4.6 (high)", + "creator": "SpaceXAI", + "normalized_base": "grok 4 6", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-5", + "display_name": "Grok 4.5 (high)", + "creator": "SpaceXAI", + "normalized_base": "grok 4 5", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-build-0-1-06-16", + "display_name": "Grok Build 0.1 0616", + "creator": "SpaceXAI", + "normalized_base": "grok build 0 1 0616", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-20", + "display_name": "Grok 4.20 0309 v2", + "creator": "SpaceXAI", + "normalized_base": "grok 4 20 0309 v2", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-3", + "display_name": "Grok 4.3 (high)", + "creator": "SpaceXAI", + "normalized_base": "grok 4 3", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-20-0309", + "display_name": "Grok 4.20 0309", + "creator": "SpaceXAI", + "normalized_base": "grok 4 20 0309", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-3-medium", + "display_name": "Grok 4.3 (medium)", + "creator": "SpaceXAI", + "normalized_base": "grok 4 3", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-3-low", + "display_name": "Grok 4.3 (low)", + "creator": "SpaceXAI", + "normalized_base": "grok 4 3", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-3-non-reasoning", + "display_name": "Grok 4.3 (Non-reasoning)", + "creator": "SpaceXAI", + "normalized_base": "grok 4 3", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-3-mini-reasoning", + "display_name": "Grok 3 mini Reasoning (high) Fast", + "creator": "SpaceXAI", + "normalized_base": "grok 3 mini reasoning fast", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-20-0309-non-reasoning", + "display_name": "Grok 4.20 0309", + "creator": "SpaceXAI", + "normalized_base": "grok 4 20 0309", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-20-non-reasoning", + "display_name": "Grok 4.20 0309 v2", + "creator": "SpaceXAI", + "normalized_base": "grok 4 20 0309 v2", + "candidates": [], + "reason": "no openness row for this model" + }, + { + "provider_slug": "xai", + "model_slug": "grok-3", + "display_name": "Grok 3 Fast", + "creator": "SpaceXAI", + "normalized_base": "grok 3 fast", + "candidates": [], + "reason": "no openness row for this model" + } + ], + "ambiguous": [ + { + "provider_slug": "azure", + "model_slug": "grok-4-fast", + "display_name": "Grok 4 Fast", + "creator": "SpaceXAI", + "normalized_base": "grok 4 fast", + "candidates": [ + "Grok 4 Fast (Non-reasoning)", + "Grok 4 Fast (Reasoning)" + ], + "reason": "candidates disagree on openness components" + } + ] +} diff --git a/docs/database.md b/docs/database.md index 3f50f4d..d5904b5 100644 --- a/docs/database.md +++ b/docs/database.md @@ -10,6 +10,8 @@ All pricing data lives in the repository under `database/`, normalized from Open | `database/history/prices-.json` | Immutable snapshots, one per sync | | `database/changelog/latest.json` | Diff summary of the most recent sync window | | `database/current/openrouter.json` / `litellm.json` | Raw upstream payloads from the last sync | +| `database/current/artificial-analysis.json` | Artificial Analysis provider × model dataset, refreshed weekly | +| `database/history/artificial-analysis-.json` | Immutable weekly snapshots | Raw access (no authentication required): @@ -64,6 +66,30 @@ https://raw.githubusercontent.com/Atena-IT/tokenpricing/main/database/changelog/ `database/changelog/latest.json` summarizes the latest sync window with per-model change entries typed as `model_added`, `model_removed`, `pricing_changed`, or `cache_price_changed` — the same events the [notifier](/notifications) delivers to webhooks, and what the dashboard's Changelog tab renders. +## Artificial Analysis dataset + +`database/current/artificial-analysis.json` is a separate weekly dataset covering +each model **as served by each provider** — 1045 offerings across 51 providers — +joined to the Artificial Analysis Openness Index. + +> **Data source: [Artificial Analysis](https://artificialanalysis.ai).** Benchmark, +> latency and throughput figures are third-party measurements produced by +> Artificial Analysis; they are not vendor-published and are not measured by +> tokenpricing. Openness Index values follow the Artificial Analysis Openness Index +> specification (V1.0, preliminary draft). + +It is kept separate from `prices.json` rather than merged into it because the grain +differs (one row per model × reasoning variant × serving platform, versus one +record per model) and because the public Artificial Analysis pages publish +`Cost per Task USD` but no per-token input/output pricing. Each offering links back +by `model_slug`, `display_name` and `creator`. + +Models with no Openness Index entry keep `openness: null` and are listed under +`unmatched`; genuinely undecidable matches are listed under `ambiguous`. Neither is +silently dropped, and absent openness never becomes zero. See the +[service README](https://github.com/Atena-IT/tokenpricing/blob/main/services/aa-sync/README.md) +for the matching rules. + ## Browsing The [live dashboard](https://atena-it.github.io/tokenpricing/) provides a sortable explorer over the full catalog, two-model comparison, a cost calculator, price history charts built from the snapshots, and the changelog. diff --git a/services/aa-sync/README.md b/services/aa-sync/README.md new file mode 100644 index 0000000..799945b --- /dev/null +++ b/services/aa-sync/README.md @@ -0,0 +1,147 @@ +# tokenpricing-aa-sync + +Weekly acquisition of [Artificial Analysis](https://artificialanalysis.ai) public +leaderboard data: a provider x model offering dataset joined to the Artificial +Analysis Openness Index. + +> **Data source: Artificial Analysis (https://artificialanalysis.ai).** +> Benchmark, latency and throughput figures in this dataset are third-party +> measurements produced by Artificial Analysis — they are not vendor-published and +> are not measured by tokenpricing. Openness Index values follow the Artificial +> Analysis Openness Index specification (V1.0, marked preliminary draft). + +## Usage + +```bash +uv sync +uv run tokenpricing-aa-sync sync # fetch, capture, normalize, write +uv run tokenpricing-aa-sync normalize # rebuild from the local raw capture +``` + +Outputs: + +| Path | What | +|---|---| +| `database/current/artificial-analysis.json` | latest dataset | +| `database/history/artificial-analysis-.json` | weekly snapshot (26 retained) | +| `.capture/artificial-analysis/*.json` | raw HTML capture — gitignored, uploaded as a CI artifact | + +Scheduled weekly by [`.github/workflows/aa-sync.yml`](../../.github/workflows/aa-sync.yml). + +## Where the data comes from + +Two sources, both plain HTTP — **no browser is required**: + +1. **Per-provider pages** (`/providers/`, ~51 reachable). Provider slugs are + discovered from `/leaderboards/providers`, but the row data is taken from the + individual pages because the aggregate leaderboard is filtered + (`Status: Current`) and exposes roughly half as many rows: **1045 offerings + across 51 providers, against 512 on the leaderboard.** +2. **The Openness Index** (`/evaluations/artificial-analysis-openness-index`), + 298 rows with the published component breakdown. + +The spike this service grew out of (`experiments/aa-scrape-spike`) recommended +Playwright. That was only needed to reach the *expanded* column view of the +aggregate leaderboard, which this service does not use. Both sources here are +fully server-rendered on a plain `GET`. + +### What the public pages do not carry + +The provider pages publish `Cost per Task USD` but **no per-token input/output +prices** and no cache pricing — those exist only in the expanded leaderboard view. +This is why the dataset does not populate `tokenpricing.modeling.PricingInfo`; see +[Relationship to the canonical database](#relationship-to-the-canonical-database). + +## The join + +The two datasets carry model identity in different fields, and neither is +sufficient alone: + +* Provider pages have a stable `/models/` anchor **and** an effort-suffixed + display name (`GPT-5.6 Sol (xhigh)`). +* The Openness Index has **no anchors at all** — only a rendered display name and + a `Creator` column — and it disambiguates variants differently + (`(Reasoning)` / `(Non-reasoning)`, sometimes `(Reasoning, Max Effort)`). + +So the join runs on a normalised name key. The parenthetical suffix space is much +wider than reasoning effort: it also carries quantisation (`FP8`, `NVFP4`), +serving tier (`FAST`, `Turbo`), hosting platform (`Vertex`, `AI Studio`) and +snapshot dates. Openness is a property of the *model*, not of how a provider +serves it, so serving-side tokens are dropped from the key while reasoning +identity is kept. + +Matching runs in descending confidence and **never guesses**: + +| Tier | Key | +|---|---| +| `exact` | base name + reasoning mode + effort level | +| `base+mode` | base name + reasoning mode, when all candidates agree on every component | +| `base` | base name alone, when all candidates agree | + +Variant identity is read from the display name first and from the `/models/` +slug second: provider pages sometimes render a bare `Grok 4 Fast` while the href +says `grok-4-fast-reasoning`. Only an explicit trailing suffix is trusted — which +effort level owns the *unsuffixed* slug varies per model (`gpt-oss-120b` is high, +`claude-opus-5` is max), so it is never inferred by rule. + +A creator known on both sides is a hard filter — two labs shipping a similarly +named model are never merged. Candidates that disagree are recorded in +`ambiguous`; models with no openness row at all are recorded in `unmatched`. +Neither is dropped from the dataset, and absent openness stays `null` rather than +becoming zero. + +### Why not fuzzy matching + +Fuzzy matching over these names is actively unsafe. Against the live capture, the +closest openness name to an unmatched provider model scores 88-95 on token-sort +ratio while being a *different model*: + +| Unmatched | Closest openness row | Score | +|---|---|---| +| `claude opus 5` | `claude opus 4 5` | 93 | +| `gemma 3 12b` | `gemma 4 12b` | 91 | +| `gpt 5 1 codex` | `gpt 5 codex` | 92 | + +Any threshold low enough to catch real matches also produces wrong ones. + +### Current coverage + +From the capture that this service was built against (2026-08-14): + +* 1045 offerings, 51 providers, 298 openness rows +* **563 matched (54%)** — 413 `exact`, 1 `base+mode`, 149 `base` +* 481 unmatched, 1 ambiguous + +The unmatched share is dominated by genuine coverage gaps, not matcher failure: +provider pages expose 305 distinct model bases while the Openness Index covers +229, and the missing ones are mostly newer models Artificial Analysis has not +scored for openness yet (Qwen3.8, Claude Opus 5, the GPT-5.x Codex line). + +## Relationship to the canonical database + +This dataset sits alongside `database/current/prices.json` rather than merging +into it, for two structural reasons: + +1. **Different grain.** `tokenpricing.modeling.ModelInfo` is keyed by model with a + single provider and a single price. Artificial Analysis publishes one row per + (model x reasoning variant x serving platform) — 1045 rows over 51 providers, + including several rows per model per provider (`Kimi K3 (max)` and + `Kimi K3 (max) (FAST)`). +2. **No per-token pricing.** `PricingInfo.input_per_million` and + `output_per_million` are required fields, and the public Artificial Analysis + pages do not publish either. + +The dataset links back by `model_slug`, `display_name` and `creator`, so a future +crosswalk into `ModelInfo` remains possible. The public SDK API is unchanged. + +## Failure behaviour + +A silent partial capture is the realistic failure mode, not an HTTP error, so the +job refuses to publish a thin snapshot. `normalize_sources` raises `ShapeError` +when fewer than 40 provider pages, 800 offerings or 250 openness rows are parsed, +or when under 95% of offerings carry a `/models/` anchor. `parse` raises +`ParseError` when either table's columns change. + +Provider slugs discovered on the leaderboard do not all resolve — 7 of 58 return +404. These are recorded in `metadata.unreachable_provider_slugs` rather than +failing the run. diff --git a/services/aa-sync/pyproject.toml b/services/aa-sync/pyproject.toml new file mode 100644 index 0000000..8f0ee57 --- /dev/null +++ b/services/aa-sync/pyproject.toml @@ -0,0 +1,34 @@ +[project] +name = "tokenpricing-aa-sync" +version = "0.1.0" +description = "Artificial Analysis acquisition workload for tokenpricing" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "beautifulsoup4>=4.14.2", + "httpx>=0.28.1", + "pydantic>=2.13.4", + "tokenpricing", +] + +[project.scripts] +tokenpricing-aa-sync = "tokenpricing_aa.cli:main" + +[build-system] +requires = ["setuptools>=61"] +build-backend = "setuptools.build_meta" + +[tool.setuptools.packages.find] +where = ["src"] + +[tool.uv.sources] +tokenpricing = { path = "../../libraries/python", editable = true } + +[dependency-groups] +dev = [ + "pytest>=9.0.3", + "ruff>=0.15.14", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/services/aa-sync/src/tokenpricing_aa/__init__.py b/services/aa-sync/src/tokenpricing_aa/__init__.py new file mode 100644 index 0000000..7827b8d --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/__init__.py @@ -0,0 +1,3 @@ +from tokenpricing_aa.cli import main + +__all__ = ["main"] diff --git a/services/aa-sync/src/tokenpricing_aa/cli.py b/services/aa-sync/src/tokenpricing_aa/cli.py new file mode 100644 index 0000000..b581bc7 --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/cli.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +import argparse +import json +import logging +from datetime import UTC, datetime +from pathlib import Path +from typing import Any + +from tokenpricing_aa.fetch import fetch_openness_page, fetch_provider_pages +from tokenpricing_aa.normalize import normalize_sources +from tokenpricing_aa.paths import ( + CURRENT_DATABASE_DIR, + HISTORY_DIR, + RAW_CAPTURE_DIR, +) + +DATASET_NAME = "artificial-analysis" + + +def ensure_directories() -> None: + CURRENT_DATABASE_DIR.mkdir(parents=True, exist_ok=True) + HISTORY_DIR.mkdir(parents=True, exist_ok=True) + RAW_CAPTURE_DIR.mkdir(parents=True, exist_ok=True) + + +def write_json(path: Path, payload: Any) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + # Explicit UTF-8: benchmark names carry non-ASCII (τ²-Bench), which the + # platform default encoding rejects on Windows. + path.write_text( + json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8" + ) + + +def read_json(path: Path) -> Any: + return json.loads(path.read_text(encoding="utf-8")) + + +def _raw_paths() -> tuple[Path, Path]: + return RAW_CAPTURE_DIR / "providers.json", RAW_CAPTURE_DIR / "openness.json" + + +def sync() -> dict[str, Any]: + """Fetch AA pages, keep the raw capture, then normalize it.""" + ensure_directories() + providers = fetch_provider_pages() + openness = fetch_openness_page() + providers_path, openness_path = _raw_paths() + write_json(providers_path, providers) + write_json(openness_path, openness) + return _build(providers, openness) + + +def normalize_only() -> dict[str, Any]: + """Rebuild the dataset from the raw capture already on disk.""" + ensure_directories() + providers_path, openness_path = _raw_paths() + return _build(read_json(providers_path), read_json(openness_path)) + + +def _build(providers: dict[str, Any], openness: dict[str, Any]) -> dict[str, Any]: + dataset = normalize_sources(providers, openness) + payload = dataset.model_dump(mode="json") + timestamp = datetime.now(UTC).strftime("%Y%m%dT%H%M%SZ") + current_path = CURRENT_DATABASE_DIR / f"{DATASET_NAME}.json" + history_path = HISTORY_DIR / f"{DATASET_NAME}-{timestamp}.json" + write_json(current_path, payload) + write_json(history_path, payload) + return { + "snapshot": str(current_path), + "history_snapshot": str(history_path), + "providers": dataset.metadata.provider_count, + "offerings": dataset.metadata.offering_count, + "openness_rows": dataset.metadata.openness_row_count, + "openness_matched": dataset.metadata.openness_matched, + "openness_unmatched": dataset.metadata.openness_unmatched, + "openness_ambiguous": dataset.metadata.openness_ambiguous, + "match_tiers": dataset.metadata.match_tiers, + "unreachable_provider_slugs": dataset.metadata.unreachable_provider_slugs, + } + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Artificial Analysis acquisition for tokenpricing" + ) + subparsers = parser.add_subparsers(dest="command", required=True) + subparsers.add_parser( + "sync", help="fetch Artificial Analysis pages and rebuild the AA dataset" + ) + subparsers.add_parser( + "normalize", help="rebuild the AA dataset from the local raw capture" + ) + return parser + + +def main() -> None: + logging.basicConfig(level=logging.INFO, format="%(levelname)s %(message)s") + parser = build_parser() + args = parser.parse_args() + if args.command == "sync": + print(json.dumps(sync(), indent=2)) + return + if args.command == "normalize": + print(json.dumps(normalize_only(), indent=2)) + return + parser.error("unknown command") diff --git a/services/aa-sync/src/tokenpricing_aa/fetch.py b/services/aa-sync/src/tokenpricing_aa/fetch.py new file mode 100644 index 0000000..c90e22a --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/fetch.py @@ -0,0 +1,124 @@ +"""HTTP acquisition for Artificial Analysis public pages. + +Data source: Artificial Analysis (https://artificialanalysis.ai). + +Everything this module needs is server-rendered into the initial HTML document: +there is no JSON API behind these pages and no browser is required. The spike +(`experiments/aa-scrape-spike`) recommended Playwright, but that was only needed +to reach the *expanded* column view of the aggregate leaderboard. The per-provider +pages and the Openness Index table are fully rendered on a plain GET, so this +workload is plain HTTP. +""" + +from __future__ import annotations + +import re +from datetime import UTC, datetime +from typing import Any + +import httpx + +BASE_URL = "https://artificialanalysis.ai" +PROVIDER_INDEX_URL = f"{BASE_URL}/leaderboards/providers" +OPENNESS_URL = f"{BASE_URL}/evaluations/artificial-analysis-openness-index" +PROVIDER_PAGE_URL = f"{BASE_URL}/providers/{{slug}}" + +REQUEST_TIMEOUT = 60.0 +USER_AGENT = "tokenpricing-aa-sync/0.1.0 (https://github.com/Atena-IT/tokenpricing)" + +# Politeness: the whole capture is ~60 requests once a week, so there is nothing to +# rate limit against. A small delay keeps us well inside any reasonable budget. +REQUEST_DELAY_SECONDS = 1.0 + +_PROVIDER_HREF = re.compile(r"/providers/([a-z0-9._-]+)") + + +def _client(client: httpx.Client | None = None) -> httpx.Client: + if client is not None: + return client + return httpx.Client( + timeout=REQUEST_TIMEOUT, + headers={"User-Agent": USER_AGENT}, + follow_redirects=True, + ) + + +def discover_provider_slugs(html: str) -> list[str]: + """Extract provider slugs from the aggregate leaderboard markup. + + The leaderboard is only used for *discovery* — its rows are filtered + (``Status: Current``) and cover roughly half of what the per-provider pages + expose, so the row data itself is taken from the provider pages instead. + + Some discovered slugs do not resolve to a live page; callers must tolerate + 404s rather than assuming every slug is fetchable. + """ + slugs = { + slug + for slug in _PROVIDER_HREF.findall(html) + # Next.js chunk filenames land under the same prefix in the asset graph. + if not slug.endswith(".js") + } + return sorted(slugs) + + +def fetch_provider_pages( + client: httpx.Client | None = None, + sleep: Any = None, +) -> dict[str, Any]: + """Fetch the provider index plus every discoverable provider page.""" + if sleep is None: + import time + + sleep = time.sleep + + owns_client = client is None + http = _client(client) + try: + index = http.get(PROVIDER_INDEX_URL) + index.raise_for_status() + slugs = discover_provider_slugs(index.text) + if not slugs: + raise ValueError("No provider slugs discovered on the provider leaderboard") + + pages: dict[str, str] = {} + missing: list[dict[str, Any]] = [] + for slug in slugs: + sleep(REQUEST_DELAY_SECONDS) + response = http.get(PROVIDER_PAGE_URL.format(slug=slug)) + if response.status_code != 200: + missing.append({"slug": slug, "status": response.status_code}) + continue + pages[slug] = response.text + finally: + if owns_client: + http.close() + + return { + "source": "artificial_analysis_providers", + "source_url": PROVIDER_INDEX_URL, + "fetched_at": datetime.now(UTC).isoformat(), + "discovered_slugs": slugs, + "unreachable_slugs": missing, + "pages": pages, + } + + +def fetch_openness_page(client: httpx.Client | None = None) -> dict[str, Any]: + """Fetch the Openness Index leaderboard.""" + owns_client = client is None + http = _client(client) + try: + response = http.get(OPENNESS_URL) + response.raise_for_status() + html = response.text + finally: + if owns_client: + http.close() + + return { + "source": "artificial_analysis_openness", + "source_url": OPENNESS_URL, + "fetched_at": datetime.now(UTC).isoformat(), + "page": html, + } diff --git a/services/aa-sync/src/tokenpricing_aa/matching.py b/services/aa-sync/src/tokenpricing_aa/matching.py new file mode 100644 index 0000000..2dcc6bd --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/matching.py @@ -0,0 +1,425 @@ +"""Join between the provider offering rows and the Openness Index rows. + +The two AA datasets carry their model identity differently, and neither is +sufficient alone: + +* Provider pages have a stable ``/models/`` anchor **and** an effort-suffixed + display name (``GPT-5.6 Sol (xhigh)``). +* The Openness Index has no anchors at all — its only identity is the rendered + display name plus a ``Creator`` column, and it disambiguates variants + differently (``(Reasoning)`` / ``(Non-reasoning)``, sometimes + ``(Reasoning, Max Effort)``). + +So the join runs on a normalised name key, not on slugs and not on raw strings. +The parenthetical suffix space is wider than reasoning effort alone: it also +carries quantisation (``FP8``, ``NVFP4``), serving tier (``FAST``, ``Turbo``), +hosting platform (``Vertex``, ``AI Studio``) and snapshot dates. Openness is a +property of the *model*, not of how a given provider serves it, so those +serving-side tokens are dropped from the key while reasoning identity is kept. + +Ambiguity is never resolved by guessing: candidates that disagree are reported +as ambiguous and left unmatched. +""" + +from __future__ import annotations + +import re +from collections.abc import Iterable +from dataclasses import dataclass, field +from typing import Any + +# Reasoning-effort levels. These are part of model identity for openness purposes +# only insofar as AA publishes separate rows for them; where AA does not, the +# effort level is dropped by the tier-2 fallback below. +EFFORT_TOKENS = { + "minimal": "minimal", + "low": "low", + "medium": "medium", + "high": "high", + "xhigh": "xhigh", + "max": "max", + "max effort": "max", + "high effort": "high", + "medium effort": "medium", + "low effort": "low", + "xhigh effort": "xhigh", +} + +REASONING_TOKENS = {"reasoning": "reasoning", "non-reasoning": "non-reasoning"} + +# Serving-side variation: quantisation, serving tier, hosting platform, snapshot +# labels. None of these change a model's openness, so they are not part of the key. +SERVING_TOKENS = { + "fp4", + "fp8", + "fp16", + "bf16", + "int4", + "int8", + "nvfp4", + "mxfp4", + "mxfp8", + "awq", + "gptq", + "fast", + "turbo", + "base", + "preview", + "exp", + "experimental", + "with fallback", + "ai studio", + "vertex", + "bedrock", + "chatgpt", + "vision", + "standard", + "batch", +} + +_MONTHS = ( + "jan|feb|mar|apr|may|jun|june|jul|july|aug|sep|sept|oct|nov|dec" +) +_DATE_TOKEN = re.compile(rf"^({_MONTHS})\b[\s'’]*\d{{0,4}}$", re.IGNORECASE) +_BARE_YEAR = re.compile(r"^'?\d{2,4}$") +_PARENTHETICAL = re.compile(r"\(([^)]*)\)") +_NON_ALNUM = re.compile(r"[^a-z0-9]+") + + +def _slugify(text: str) -> str: + """Normalise a name to lowercase alphanumeric tokens. + + ``+`` is spelled out rather than stripped: ``Command A`` and ``Command A+`` + are different models, and folding the plus away silently merges them. + """ + lowered = str(text or "").lower().replace("+", " plus ") + return _NON_ALNUM.sub(" ", lowered).strip() + + +def normalize_creator(creator: str | None) -> str | None: + """Creators are compared without spacing or punctuation (``Z AI`` -> ``zai``).""" + if not creator: + return None + return _NON_ALNUM.sub("", str(creator).lower()) or None + + +def _is_serving_token(token: str) -> bool: + if token in SERVING_TOKENS: + return True + if _DATE_TOKEN.match(token) or _BARE_YEAR.match(token): + return True + # "Sep '25", "Feb 2026" + return bool(re.match(rf"^({_MONTHS})\s", token, re.IGNORECASE)) + + +@dataclass(frozen=True) +class NameKey: + """Normalised model identity used on both sides of the join.""" + + base: str + reasoning: str | None + effort: str | None + #: Serving-side suffixes recognised as safe to drop from the key. + dropped: tuple[str, ...] = () + #: Suffixes that matched no known vocabulary. Also dropped from the key, but + #: surfaced in dataset metadata so a new AA suffix type gets noticed rather + #: than silently changing what the join considers the same model. + unknown: tuple[str, ...] = () + + +def parse_display_name(name: str) -> NameKey: + """Split an AA display name into a base name and its classified suffixes.""" + raw_tokens: list[str] = [] + for group in _PARENTHETICAL.findall(name or ""): + # "(Turbo, FP8)" and "(Reasoning, Max Effort)" pack several tokens in one group. + raw_tokens.extend(part.strip() for part in group.split(",") if part.strip()) + + base = _slugify(_PARENTHETICAL.sub(" ", name or "")) + + effort: str | None = None + reasoning: str | None = None + dropped: list[str] = [] + unknown: list[str] = [] + for token in raw_tokens: + lowered = token.strip().lower() + if lowered in REASONING_TOKENS: + reasoning = REASONING_TOKENS[lowered] + elif lowered in EFFORT_TOKENS: + effort = EFFORT_TOKENS[lowered] + # An explicit effort level implies the model is reasoning, unless the + # name also says otherwise (AA emits "(Non-reasoning, high)"). + if reasoning is None: + reasoning = "reasoning" + elif _is_serving_token(lowered): + dropped.append(token) + else: + unknown.append(token) + + # "(Non-reasoning, high)" — the explicit mode wins over the effort implication. + for token in raw_tokens: + if token.strip().lower() == "non-reasoning": + reasoning = "non-reasoning" + + return NameKey( + base=base, + reasoning=reasoning, + effort=effort, + dropped=tuple(dropped), + unknown=tuple(unknown), + ) + + +# Order matters: "-non-reasoning" must be tested before "-reasoning". +_SLUG_REASONING_SUFFIXES = ( + ("-non-reasoning", "non-reasoning"), + ("-nonreasoning", "non-reasoning"), + ("-reasoning", "reasoning"), +) + + +def enrich_key_from_slug(key: NameKey, model_slug: str | None) -> NameKey: + """Fill in variant identity the display name omits but the slug states. + + Provider pages sometimes render a bare name (``Grok 4 Fast``) while the + ``/models/`` href spells the variant out (``grok-4-fast-reasoning``). The + Openness Index splits those models into ``(Reasoning)`` / ``(Non-reasoning)`` + rows, so without this the row is only resolvable as ambiguous. + + This reads variant identity **only** from an explicit trailing suffix. It + never infers effort from a bare slug: which effort level owns the + unsuffixed slug varies per model (``gpt-oss-120b`` is high, ``claude-opus-5`` + is max), so that is not derivable by rule. + """ + if not model_slug: + return key + slug = model_slug.lower() + reasoning, effort = key.reasoning, key.effort + + if reasoning is None: + for suffix, mode in _SLUG_REASONING_SUFFIXES: + if slug.endswith(suffix): + reasoning = mode + break + + if effort is None: + for level in EFFORT_TOKENS: + if " " in level: + continue + if slug.endswith(f"-{level}"): + effort = level + if reasoning is None: + reasoning = "reasoning" + break + + if reasoning == key.reasoning and effort == key.effort: + return key + return NameKey( + base=key.base, + reasoning=reasoning, + effort=effort, + dropped=key.dropped, + unknown=key.unknown, + ) + + +def _strip_creator_prefix(base: str, creator: str | None) -> str | None: + """Drop a leading creator name from a base name, if it is spelled there. + + ``NVIDIA Nemotron 3 Nano 30B A3B`` and ``Nemotron 3 Ultra 550B A55B`` both + appear in the Openness Index, so the creator prefix cannot be assumed either + way and both spellings have to be tried. + """ + wanted = normalize_creator(creator) + if not wanted: + return None + words = base.split() + accumulated = "" + for index, word in enumerate(words): + accumulated += _NON_ALNUM.sub("", word) + if not wanted.startswith(accumulated): + return None + if accumulated == wanted and index + 1 < len(words): + return " ".join(words[index + 1 :]) + return None + + +def _base_variants(key: NameKey, creator: str | None) -> list[str]: + """Openness sometimes prefixes the creator onto the model name, sometimes not.""" + variants = [key.base] + trimmed = _strip_creator_prefix(key.base, creator) + if trimmed: + variants.append(trimmed) + return variants + + +OPENNESS_COMPONENTS = ( + "openness_index", + "model_availability", + "model_transparency", + "pre_training_data_access", + "pre_training_data_license", + "post_training_data_access", + "post_training_data_license", +) + + +def _component_signature(row: dict[str, Any]) -> tuple[Any, ...]: + return tuple(row.get(field_name) for field_name in OPENNESS_COMPONENTS) + + +@dataclass +class OpennessIndexTable: + """Lookup structure over the Openness Index rows.""" + + rows: list[dict[str, Any]] + _exact: dict[tuple[str, str | None, str | None], list[dict[str, Any]]] = field( + default_factory=dict + ) + _by_base_mode: dict[tuple[str, str | None], list[dict[str, Any]]] = field( + default_factory=dict + ) + _by_base: dict[str, list[dict[str, Any]]] = field(default_factory=dict) + + def __post_init__(self) -> None: + for row in self.rows: + key = parse_display_name(row["display_name"]) + row.setdefault("_key", key) + for base in _base_variants(key, row.get("creator")): + self._exact.setdefault((base, key.reasoning, key.effort), []).append(row) + self._by_base_mode.setdefault((base, key.reasoning), []).append(row) + self._by_base.setdefault(base, []).append(row) + + def candidates(self, tier: str, bases: list[str], key: NameKey) -> list[dict[str, Any]]: + """Look up candidate openness rows for one confidence tier.""" + found: list[dict[str, Any]] = [] + for base in bases: + if tier == "exact": + found.extend(self._exact.get((base, key.reasoning, key.effort), [])) + elif tier == "base+mode": + found.extend(self._by_base_mode.get((base, key.reasoning), [])) + else: + found.extend(self._by_base.get(base, [])) + # The same row can be reached through several base variants. + unique: list[dict[str, Any]] = [] + for row in found: + if not any(row is seen for seen in unique): + unique.append(row) + return unique + + @staticmethod + def collapse( + candidates: Iterable[dict[str, Any]], creator: str | None + ) -> tuple[dict[str, Any] | None, list[dict[str, Any]]]: + """Return a single row when candidates agree on every openness component. + + A creator, when known on both sides, is a hard filter: two different + creators shipping a similarly named model must never be merged. + """ + pool = list(candidates) + wanted = normalize_creator(creator) + if wanted: + filtered = [ + row for row in pool if normalize_creator(row.get("creator")) == wanted + ] + if filtered: + pool = filtered + elif any(normalize_creator(row.get("creator")) for row in pool): + # Every candidate names a different creator: same model name, + # different lab. Reject outright rather than falling back. + return None, [] + if not pool: + return None, [] + signatures = {_component_signature(row) for row in pool} + if len(signatures) == 1: + return pool[0], pool + return None, pool + + +@dataclass +class JoinResult: + matched: int = 0 + unmatched: list[dict[str, Any]] = field(default_factory=list) + ambiguous: list[dict[str, Any]] = field(default_factory=list) + tiers: dict[str, int] = field(default_factory=dict) + + +def join_openness( + offerings: list[dict[str, Any]], openness_rows: list[dict[str, Any]] +) -> JoinResult: + """Attach openness components to offering rows in place. + + Matching runs in descending order of confidence: + + 1. base name + reasoning mode + effort level + 2. base name + reasoning mode, when every candidate agrees on the components + (AA frequently publishes one openness row for all effort levels) + 3. base name alone, when every candidate agrees + + Anything that survives all three without a unique, self-consistent answer is + recorded as ambiguous or unmatched — never guessed. + """ + table = OpennessIndexTable(openness_rows) + result = JoinResult() + + for offering in offerings: + key = enrich_key_from_slug( + parse_display_name(offering["display_name"]), offering.get("model_slug") + ) + creator = offering.get("creator") + offering["openness"] = None + offering["openness_match"] = None + + chosen: dict[str, Any] | None = None + tier_used: str | None = None + pool: list[dict[str, Any]] = [] + + bases = _base_variants(key, creator) + for tier in ("exact", "base+mode", "base"): + candidates = table.candidates(tier, bases, key) + if not candidates: + continue + chosen, pool = table.collapse(candidates, creator) + if chosen is not None: + tier_used = tier + break + if pool: + # Candidates exist but disagree: stop here rather than falling + # through to a looser tier that would only be more ambiguous. + break + + if chosen is not None and tier_used is not None: + offering["openness"] = { + name: chosen.get(name) for name in OPENNESS_COMPONENTS + } + offering["openness_match"] = { + "tier": tier_used, + "openness_display_name": chosen["display_name"], + "openness_creator": chosen.get("creator"), + } + result.matched += 1 + result.tiers[tier_used] = result.tiers.get(tier_used, 0) + 1 + elif pool: + result.ambiguous.append( + { + "provider_slug": offering.get("provider_slug"), + "model_slug": offering.get("model_slug"), + "display_name": offering["display_name"], + "creator": creator, + "normalized_base": key.base, + "candidates": sorted({row["display_name"] for row in pool}), + "reason": "candidates disagree on openness components", + } + ) + else: + result.unmatched.append( + { + "provider_slug": offering.get("provider_slug"), + "model_slug": offering.get("model_slug"), + "display_name": offering["display_name"], + "creator": creator, + "normalized_base": key.base, + "reason": "no openness row for this model", + } + ) + + return result diff --git a/services/aa-sync/src/tokenpricing_aa/modeling.py b/services/aa-sync/src/tokenpricing_aa/modeling.py new file mode 100644 index 0000000..b9f6a37 --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/modeling.py @@ -0,0 +1,144 @@ +"""Pydantic models for the Artificial Analysis dataset. + +These live in the service rather than in ``libraries/python`` (the public SDK) +for two reasons: + +* The grain differs. ``tokenpricing.modeling.ModelInfo`` is keyed by model with a + single provider and a single price; AA's data is one row per + (model x reasoning variant x serving platform) — 1045 rows over 51 providers. +* AA's public pages carry no per-token pricing at all (only ``Cost per Task USD``), + so these rows cannot populate ``PricingInfo``/``SourceInfo``, whose + ``input_per_million``/``output_per_million`` are required. + +The dataset therefore sits alongside the canonical database and links to it by +``model_slug``/``display_name`` rather than trying to impersonate ``ModelInfo``. +The SDK's public API is unchanged. + +Data source: Artificial Analysis (https://artificialanalysis.ai). +""" + +from __future__ import annotations + +from datetime import datetime + +from pydantic import BaseModel, Field + +ATTRIBUTION = ( + "Data source: Artificial Analysis (https://artificialanalysis.ai). " + "Benchmark, latency and throughput figures are third-party measurements " + "produced by Artificial Analysis, not vendor-published and not measured by " + "tokenpricing." +) + +OPENNESS_SPEC_VERSION = "Openness Spec V1.0 (preliminary draft)" + + +class OpennessBreakdown(BaseModel): + """Openness Index components as published, on their raw published scales. + + The live table publishes ``Model Availability`` as an aggregate and does not + publish the two methodology subcomponents at all, so this is what AA exposes + rather than the full six-part 0-18 breakdown described in the spec. + """ + + openness_index: float | None = Field( + default=None, description="Published Openness Index, 0-100" + ) + model_availability: float | None = None + model_transparency: float | None = None + pre_training_data_access: float | None = None + pre_training_data_license: float | None = None + post_training_data_access: float | None = None + post_training_data_license: float | None = None + + +class OpennessMatch(BaseModel): + """How an offering was matched to its Openness Index row.""" + + tier: str = Field( + description="Confidence tier that produced the match: exact, base+mode, or base" + ) + openness_display_name: str + openness_creator: str | None = None + + +class Offering(BaseModel): + """One model as served by one provider, as measured by Artificial Analysis.""" + + provider_slug: str + model_slug: str + display_name: str + creator: str | None = None + context_window: int | None = None + supports_function_calling: bool = False + supports_json_mode: bool = False + license: str | None = None + intelligence_index: float | None = None + intelligence_index_estimated: bool = False + cost_per_task_usd: float | None = None + cost_per_task_estimated: bool = False + median_output_tokens_per_second: float | None = None + median_first_chunk_seconds: float | None = None + total_response_seconds: float | None = None + reasoning_time_seconds: float | None = None + reasoning_mode: str | None = Field( + default=None, description="reasoning, non-reasoning, or None when unspecified" + ) + reasoning_effort: str | None = Field( + default=None, description="minimal, low, medium, high, xhigh, or max" + ) + variant_tokens: list[str] = Field( + default_factory=list, + description="Serving-side name suffixes (quantisation, tier, platform, snapshot)", + ) + unclassified_variant_tokens: list[str] = Field( + default_factory=list, + description="Name suffixes matching no known vocabulary; dropped from the join key", + ) + openness: OpennessBreakdown | None = None + openness_match: OpennessMatch | None = None + + +class UnmatchedOffering(BaseModel): + provider_slug: str | None = None + model_slug: str | None = None + display_name: str + creator: str | None = None + normalized_base: str | None = None + candidates: list[str] = Field(default_factory=list) + reason: str + + +class AAMetadata(BaseModel): + fetched_at: datetime + sources: list[str] + attribution: str = ATTRIBUTION + openness_spec_version: str = OPENNESS_SPEC_VERSION + provider_count: int + offering_count: int + openness_row_count: int + openness_matched: int + openness_unmatched: int + openness_ambiguous: int + match_tiers: dict[str, int] = Field(default_factory=dict) + unreachable_provider_slugs: list[str] = Field(default_factory=list) + unclassified_variant_tokens: list[str] = Field( + default_factory=list, + description=( + "Distinct name suffixes matching no known vocabulary. A new entry here " + "means AA introduced a suffix type the join key does not account for." + ), + ) + + +class AADataset(BaseModel): + """Complete Artificial Analysis capture for one run.""" + + generated_at: datetime + metadata: AAMetadata + offerings: list[Offering] + openness_index: list[dict] = Field( + default_factory=list, description="Openness Index rows exactly as published" + ) + unmatched: list[UnmatchedOffering] = Field(default_factory=list) + ambiguous: list[UnmatchedOffering] = Field(default_factory=list) diff --git a/services/aa-sync/src/tokenpricing_aa/normalize.py b/services/aa-sync/src/tokenpricing_aa/normalize.py new file mode 100644 index 0000000..6e3a1a7 --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/normalize.py @@ -0,0 +1,176 @@ +"""Build the Artificial Analysis dataset from raw captured pages.""" + +from __future__ import annotations + +import logging +from datetime import UTC, datetime +from typing import Any + +from tokenpricing_aa.matching import ( + enrich_key_from_slug, + join_openness, + parse_display_name, +) +from tokenpricing_aa.modeling import ( + AADataset, + AAMetadata, + Offering, + OpennessBreakdown, + OpennessMatch, + UnmatchedOffering, +) +from tokenpricing_aa.parse import parse_openness_page, parse_provider_page + +logger = logging.getLogger(__name__) + +# Shape guards. A silent partial capture writing a truncated dataset into +# database/ is the realistic failure mode here, not an HTTP error, so the job +# fails loudly instead of publishing a thin snapshot. +MIN_PROVIDERS = 40 +MIN_OFFERINGS = 800 +MIN_OPENNESS_ROWS = 250 +MIN_SLUG_COVERAGE = 0.95 + + +class ShapeError(RuntimeError): + """Raised when a capture is too far from the expected shape to publish.""" + + +def _check(condition: bool, message: str) -> None: + if not condition: + raise ShapeError(message) + + +def normalize_sources( + providers_payload: dict[str, Any], openness_payload: dict[str, Any] +) -> AADataset: + pages: dict[str, str] = providers_payload.get("pages") or {} + _check( + len(pages) >= MIN_PROVIDERS, + f"only {len(pages)} provider pages captured, expected >= {MIN_PROVIDERS}", + ) + + raw_offerings: list[dict[str, Any]] = [] + for slug in sorted(pages): + rows = parse_provider_page(pages[slug], slug) + if not rows: + logger.warning("provider page %s parsed to zero rows", slug) + raw_offerings.extend(rows) + + _check( + len(raw_offerings) >= MIN_OFFERINGS, + f"only {len(raw_offerings)} offerings parsed, expected >= {MIN_OFFERINGS}", + ) + with_slug = sum(1 for row in raw_offerings if row.get("model_slug")) + coverage = with_slug / len(raw_offerings) + _check( + coverage >= MIN_SLUG_COVERAGE, + f"only {coverage:.1%} of offerings carry a /models/ anchor, " + f"expected >= {MIN_SLUG_COVERAGE:.0%}", + ) + + openness_rows = parse_openness_page(openness_payload["page"]) + _check( + len(openness_rows) >= MIN_OPENNESS_ROWS, + f"only {len(openness_rows)} openness rows parsed, " + f"expected >= {MIN_OPENNESS_ROWS}", + ) + + join = join_openness(raw_offerings, openness_rows) + + offerings: list[Offering] = [] + for row in raw_offerings: + key = enrich_key_from_slug( + parse_display_name(row["display_name"]), row.get("model_slug") + ) + openness = row.get("openness") + match = row.get("openness_match") + offerings.append( + Offering( + **{ + name: row[name] + for name in ( + "provider_slug", + "model_slug", + "display_name", + "creator", + "context_window", + "supports_function_calling", + "supports_json_mode", + "license", + "intelligence_index", + "intelligence_index_estimated", + "cost_per_task_usd", + "cost_per_task_estimated", + "median_output_tokens_per_second", + "median_first_chunk_seconds", + "total_response_seconds", + "reasoning_time_seconds", + ) + }, + reasoning_mode=key.reasoning, + reasoning_effort=key.effort, + variant_tokens=list(key.dropped), + unclassified_variant_tokens=list(key.unknown), + openness=OpennessBreakdown(**openness) if openness else None, + openness_match=OpennessMatch(**match) if match else None, + ) + ) + + for entry in join.unmatched: + logger.info( + "openness unmatched: %s @ %s (%s)", + entry["display_name"], + entry["provider_slug"], + entry["reason"], + ) + for entry in join.ambiguous: + logger.warning( + "openness ambiguous: %s @ %s -> %s", + entry["display_name"], + entry["provider_slug"], + entry["candidates"], + ) + + fetched_at = max( + datetime.fromisoformat(str(providers_payload["fetched_at"])), + datetime.fromisoformat(str(openness_payload["fetched_at"])), + ) + unreachable = [ + str(item.get("slug")) + for item in providers_payload.get("unreachable_slugs") or [] + ] + unclassified = sorted( + {token for offering in offerings for token in offering.unclassified_variant_tokens} + ) + if unclassified: + logger.warning( + "unclassified name suffixes (dropped from the join key): %s", unclassified + ) + + return AADataset( + generated_at=datetime.now(UTC), + metadata=AAMetadata( + fetched_at=fetched_at, + sources=[ + str(providers_payload.get("source_url")), + str(openness_payload.get("source_url")), + ], + provider_count=len(pages), + offering_count=len(offerings), + openness_row_count=len(openness_rows), + openness_matched=join.matched, + openness_unmatched=len(join.unmatched), + openness_ambiguous=len(join.ambiguous), + match_tiers=dict(sorted(join.tiers.items())), + unreachable_provider_slugs=sorted(unreachable), + unclassified_variant_tokens=unclassified, + ), + offerings=offerings, + openness_index=[ + {name: value for name, value in row.items() if not name.startswith("_")} + for row in openness_rows + ], + unmatched=[UnmatchedOffering(**entry) for entry in join.unmatched], + ambiguous=[UnmatchedOffering(**entry) for entry in join.ambiguous], + ) diff --git a/services/aa-sync/src/tokenpricing_aa/parse.py b/services/aa-sync/src/tokenpricing_aa/parse.py new file mode 100644 index 0000000..cda02bc --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/parse.py @@ -0,0 +1,243 @@ +"""HTML parsing for Artificial Analysis pages. + +Every value parser here exists because of a rendering quirk observed in the +captured pages; see ``tests/test_parse.py`` for the fixtures behind each one. +""" + +from __future__ import annotations + +from typing import Any + +from bs4 import BeautifulSoup, Tag + +# The per-provider offering table, after the two-deep header is flattened. +PROVIDER_COLUMNS = ( + "Model", + "Context Window", + "Function Calling", + "JSON Mode", + "License", + "Artificial Analysis Intelligence Index", + "Cost per Task USD", + "Median Tokens/s", + "Median First Chunk (s)", + "Total Response (s)", + "Reasoning Time (s)", + "Further Analysis", +) + +OPENNESS_COLUMNS = ( + "Creator", + "Model", + "Openness Index", + "Intelligence Index", + "Model Availability", + "Model Transparency", + "Pre-training Data Access", + "Pre-training Data License", + "Post-training Data Access", + "Post-training Data License", +) + +# AA renders "no data" as an em dash or a double hyphen. It is not zero. +_NO_DATA = {"", "--", "—", "–", "N/A", "n/a"} + +# U+2212 MINUS SIGN, not ASCII hyphen. ``float("−31")`` raises ValueError. +_MINUS_SIGN = "−" + + +class ParseError(ValueError): + """Raised when a page no longer matches the shape this module expects.""" + + +def cell_text(node: Tag | None) -> str: + if node is None: + return "" + return " ".join(node.get_text(" ", strip=True).split()) + + +def is_estimated(value: str | None) -> bool: + """A trailing asterisk marks an estimated / partial score.""" + return str(value or "").strip().endswith("*") + + +def parse_number(value: str | None) -> float | None: + """Parse an AA-rendered numeric cell. + + Handles the U+2212 minus sign, currency and percent decoration, thousands + separators, the estimated-score asterisk, and the several spellings of + "no data". Returns ``None`` for absent values so that absent stays + distinguishable from zero. + """ + if value is None: + return None + text = str(value).strip() + if text in _NO_DATA: + return None + text = ( + text.replace(_MINUS_SIGN, "-") + .replace("$", "") + .replace("%", "") + .replace(",", "") + .rstrip("*") + .strip() + ) + if text in _NO_DATA: + return None + try: + return float(text) + except ValueError: + return None + + +def parse_context_window(value: str | None) -> int | None: + """Context windows are rendered human-readable: ``1M``, ``1.05M``, ``262k``.""" + text = str(value or "").strip() + if text in _NO_DATA: + return None + multiplier = 1 + if text[-1:] in {"M", "m"}: + multiplier, text = 1_000_000, text[:-1] + elif text[-1:] in {"k", "K"}: + multiplier, text = 1_000, text[:-1] + try: + return round(float(text.replace(",", "")) * multiplier) + except ValueError: + return None + + +def parse_flag(cell: Tag | None) -> bool: + """Feature columns render a check icon only when the feature is supported. + + AA emits ```` for supported features and renders + nothing at all otherwise — there is no "No" icon anywhere in the captured + pages, so absence is the negative signal. + """ + if cell is None: + return False + return any( + svg.get("aria-label") == "Yes" for svg in cell.select("svg[aria-label]") + ) + + +def _creator_from_cell(cell: Tag | None) -> str | None: + """The model cell carries the creator's logo; its alt text names the creator.""" + if cell is None: + return None + for img in cell.select("img[alt]"): + alt = str(img.get("alt") or "").strip() + if alt.lower().endswith(" logo"): + return alt[: -len(" logo")].strip() or None + return None + + +def _first_table(html: str) -> Tag: + soup = BeautifulSoup(html, "html.parser") + table = soup.find("table") + if table is None: + raise ParseError("no found in document") + return table + + +def _columns_from(headers: list[str], expected: tuple[str, ...], what: str) -> None: + if tuple(headers) != expected: + raise ParseError( + f"{what} columns changed: expected {list(expected)}, got {headers}" + ) + + +def parse_provider_page(html: str, provider_slug: str) -> list[dict[str, Any]]: + """Parse one ``/providers/`` page into offering rows. + + The header is two-deep (a column-group row then the real column row); the + real columns are located by finding the ``Model`` header rather than by + assuming a fixed offset. + """ + table = _first_table(html) + headers = [cell_text(th) for th in table.select("thead th")] + try: + start = headers.index("Model") + except ValueError as exc: # pragma: no cover - guarded by shape test + raise ParseError( + f"{provider_slug}: no 'Model' column in header {headers}" + ) from exc + _columns_from(headers[start:], PROVIDER_COLUMNS, f"provider page {provider_slug}") + + rows: list[dict[str, Any]] = [] + for tr in table.select("tbody tr"): + cells = tr.find_all("td", recursive=False) or tr.select("td") + if len(cells) < len(PROVIDER_COLUMNS): + continue + name_cell = cells[0] + anchor = name_cell.select_one('a[href^="/models/"]') or tr.select_one( + 'a[href^="/models/"]' + ) + model_slug = ( + str(anchor.get("href", "")).removeprefix("/models/") if anchor else "" + ) + display_name = cell_text(name_cell) + if not display_name: + continue + cost = cell_text(cells[6]) + intelligence = cell_text(cells[5]) + rows.append( + { + "provider_slug": provider_slug, + "model_slug": model_slug, + "display_name": display_name, + "creator": _creator_from_cell(name_cell), + "context_window": parse_context_window(cell_text(cells[1])), + "supports_function_calling": parse_flag(cells[2]), + "supports_json_mode": parse_flag(cells[3]), + "license": cell_text(cells[4]) or None, + "intelligence_index": parse_number(intelligence), + "intelligence_index_estimated": is_estimated(intelligence), + "cost_per_task_usd": parse_number(cost), + "cost_per_task_estimated": is_estimated(cost), + "median_output_tokens_per_second": parse_number(cell_text(cells[7])), + "median_first_chunk_seconds": parse_number(cell_text(cells[8])), + "total_response_seconds": parse_number(cell_text(cells[9])), + "reasoning_time_seconds": parse_number(cell_text(cells[10])), + } + ) + return rows + + +def parse_openness_page(html: str) -> list[dict[str, Any]]: + """Parse the Openness Index leaderboard. + + The table body carries no anchors at all — there are no model slugs on this + page, which is why the join runs on normalised display names plus creator. + """ + table = _first_table(html) + headers = [cell_text(th) for th in table.select("thead th")] + try: + start = headers.index("Creator") + except ValueError as exc: # pragma: no cover - guarded by shape test + raise ParseError(f"no 'Creator' column in openness header {headers}") from exc + _columns_from(headers[start:], OPENNESS_COLUMNS, "openness index") + + rows: list[dict[str, Any]] = [] + for tr in table.select("tbody tr"): + cells = [cell_text(td) for td in tr.select("td")] + if len(cells) < len(OPENNESS_COLUMNS) + 1: + continue + rank, creator, name = cells[0], cells[1], cells[2] + if not name: + continue + rows.append( + { + "rank": int(parse_number(rank) or 0) or None, + "creator": creator or None, + "display_name": name, + "openness_index": parse_number(cells[3]), + "intelligence_index": parse_number(cells[4]), + "model_availability": parse_number(cells[5]), + "model_transparency": parse_number(cells[6]), + "pre_training_data_access": parse_number(cells[7]), + "pre_training_data_license": parse_number(cells[8]), + "post_training_data_access": parse_number(cells[9]), + "post_training_data_license": parse_number(cells[10]), + } + ) + return rows diff --git a/services/aa-sync/src/tokenpricing_aa/paths.py b/services/aa-sync/src/tokenpricing_aa/paths.py new file mode 100644 index 0000000..d0442ec --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/paths.py @@ -0,0 +1,13 @@ +from __future__ import annotations + +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parents[4] +DATABASE_ROOT = REPO_ROOT / "database" +CURRENT_DATABASE_DIR = DATABASE_ROOT / "current" +HISTORY_DIR = DATABASE_ROOT / "history" +CHANGELOG_DIR = DATABASE_ROOT / "changelog" +# The raw HTML capture is ~60MB per run — far too large to commit weekly. It is +# kept out of the tracked database (gitignored, uploaded as a CI artifact) so that +# a failing parse still has its exact input available for diagnosis. +RAW_CAPTURE_DIR = REPO_ROOT / ".capture" / "artificial-analysis" diff --git a/services/aa-sync/tests/__init__.py b/services/aa-sync/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/services/aa-sync/tests/test_matching.py b/services/aa-sync/tests/test_matching.py new file mode 100644 index 0000000..f0e0669 --- /dev/null +++ b/services/aa-sync/tests/test_matching.py @@ -0,0 +1,349 @@ +from typing import Any + +from tokenpricing_aa.matching import ( + enrich_key_from_slug, + join_openness, + normalize_creator, + parse_display_name, +) + + +def openness_row( + display_name: str, + creator: str = "Alibaba", + openness_index: float = 66.67, + availability: float = 6.0, + transparency: float = 6.0, +) -> dict[str, Any]: + return { + "rank": 1, + "creator": creator, + "display_name": display_name, + "openness_index": openness_index, + "intelligence_index": 50.0, + "model_availability": availability, + "model_transparency": transparency, + "pre_training_data_access": 1.0, + "pre_training_data_license": 1.0, + "post_training_data_access": 1.0, + "post_training_data_license": 1.0, + } + + +def offering( + display_name: str, creator: str = "Alibaba", provider: str = "fireworks" +) -> dict[str, Any]: + return { + "provider_slug": provider, + "model_slug": "some-slug", + "display_name": display_name, + "creator": creator, + } + + +class TestParseDisplayName: + def test_reasoning_effort_is_captured(self) -> None: + key = parse_display_name("GPT-5.6 Sol (xhigh)") + assert key.base == "gpt 5 6 sol" + assert key.effort == "xhigh" + assert key.reasoning == "reasoning" + + def test_non_reasoning_is_captured(self) -> None: + key = parse_display_name("Qwen3.5 397B A17B (Non-reasoning)") + assert key.reasoning == "non-reasoning" + assert key.effort is None + + def test_bare_reasoning_token(self) -> None: + assert parse_display_name("Nemotron 3 Ultra (Reasoning)").reasoning == "reasoning" + + def test_compound_reasoning_and_effort(self) -> None: + key = parse_display_name("K2-V2 (Reasoning, Max Effort)") + assert key.reasoning == "reasoning" + assert key.effort == "max" + + def test_explicit_non_reasoning_beats_effort_implication(self) -> None: + """AA emits "(Non-reasoning, high)"; the stated mode must win.""" + key = parse_display_name("Some Model (Non-reasoning, high)") + assert key.reasoning == "non-reasoning" + assert key.effort == "high" + + def test_quantisation_and_serving_tokens_are_dropped_not_part_of_identity( + self, + ) -> None: + """Openness is a model property; quantisation and serving tier are not.""" + for name in ( + "Llama 4 Maverick (FP8)", + "Llama 4 Maverick (NVFP4)", + "Llama 4 Maverick (Turbo, FP8)", + "Llama 4 Maverick (FAST)", + ): + key = parse_display_name(name) + assert key.base == "llama 4 maverick", name + assert key.effort is None, name + + def test_platform_and_snapshot_tokens_are_dropped(self) -> None: + for name in ( + "Gemini 3 Pro (AI Studio)", + "Gemini 3 Pro (Vertex)", + "Gemini 3 Pro (Sep '25)", + "Gemini 3 Pro (Feb 2026)", + ): + assert parse_display_name(name).base == "gemini 3 pro", name + + def test_known_serving_tokens_are_classified_separately_from_unknown_ones( + self, + ) -> None: + """An unrecognised suffix is still dropped from the key, but flagged so a + new AA suffix type does not change join behaviour unnoticed.""" + known = parse_display_name("Some Model (FP8)") + assert known.dropped == ("FP8",) + assert known.unknown == () + + novel = parse_display_name("Some Model (Warp Drive)") + assert novel.dropped == () + assert novel.unknown == ("Warp Drive",) + assert novel.base == "some model" + + def test_effort_survives_alongside_dropped_tokens(self) -> None: + key = parse_display_name("Kimi K3 (max) (FAST)") + assert key.base == "kimi k3" + assert key.effort == "max" + assert "FAST" in key.dropped + + def test_plus_suffix_is_significant_not_stripped(self) -> None: + """``Command A`` and ``Command A+`` are different Cohere models.""" + assert parse_display_name("Command A").base == "command a" + assert parse_display_name("Command A+").base == "command a plus" + assert ( + parse_display_name("Command A").base != parse_display_name("Command A+").base + ) + + def test_bare_name_has_no_variant_identity(self) -> None: + key = parse_display_name("Kimi K2.6") + assert key.base == "kimi k2 6" + assert key.reasoning is None + assert key.effort is None + + +class TestEnrichKeyFromSlug: + def test_slug_supplies_reasoning_mode_the_name_omits(self) -> None: + key = enrich_key_from_slug( + parse_display_name("Grok 4 Fast"), "grok-4-fast-reasoning" + ) + assert key.reasoning == "reasoning" + + def test_slug_supplies_non_reasoning_mode(self) -> None: + key = enrich_key_from_slug( + parse_display_name("GPT-5.1"), "gpt-5-1-non-reasoning" + ) + assert key.reasoning == "non-reasoning" + + def test_slug_supplies_effort_level(self) -> None: + key = enrich_key_from_slug(parse_display_name("GPT-5.1"), "gpt-5-1-high") + assert key.effort == "high" + assert key.reasoning == "reasoning" + + def test_display_name_wins_over_slug(self) -> None: + key = enrich_key_from_slug( + parse_display_name("Some Model (Non-reasoning)"), "some-model-reasoning" + ) + assert key.reasoning == "non-reasoning" + + def test_bare_slug_effort_is_never_guessed(self) -> None: + """Which effort owns the unsuffixed slug varies per model, so it is not + derivable by rule — gpt-oss-120b is high, claude-opus-5 is max.""" + for slug in ("gpt-oss-120b", "claude-opus-5"): + key = enrich_key_from_slug(parse_display_name("Whatever"), slug) + assert key.effort is None, slug + + def test_missing_slug_leaves_key_untouched(self) -> None: + original = parse_display_name("Kimi K3 (max)") + assert enrich_key_from_slug(original, None) is original + assert enrich_key_from_slug(original, "") is original + + +class TestNormalizeCreator: + def test_spacing_and_case_are_ignored(self) -> None: + assert normalize_creator("Z AI") == normalize_creator("ZAI") == "zai" + + def test_missing_creator_is_none(self) -> None: + assert normalize_creator(None) is None + assert normalize_creator("") is None + + +class TestJoin: + def test_exact_variant_match(self) -> None: + offerings = [offering("Qwen3.5 397B A17B (Non-reasoning)")] + rows = [ + openness_row("Qwen3.5 397B A17B (Reasoning)", openness_index=66.67), + openness_row("Qwen3.5 397B A17B (Non-reasoning)", openness_index=55.55), + ] + + result = join_openness(offerings, rows) + + assert result.matched == 1 + assert result.tiers == {"exact": 1} + assert offerings[0]["openness"]["openness_index"] == 55.55 + assert offerings[0]["openness_match"]["tier"] == "exact" + + def test_reasoning_variants_are_not_collapsed(self) -> None: + """The spike's headline risk: a naive name join merges these two.""" + offerings = [ + offering("Qwen3.5 397B A17B (max)"), + offering("Qwen3.5 397B A17B (Non-reasoning)"), + ] + rows = [ + openness_row("Qwen3.5 397B A17B (Reasoning)", openness_index=66.67), + openness_row("Qwen3.5 397B A17B (Non-reasoning)", openness_index=55.55), + ] + + join_openness(offerings, rows) + + assert offerings[0]["openness"]["openness_index"] == 66.67 + assert offerings[1]["openness"]["openness_index"] == 55.55 + + def test_effort_is_dropped_when_openness_publishes_one_row_per_model(self) -> None: + """AA usually publishes a single openness row for all effort levels.""" + offerings = [offering("GPT-5.6 Sol (xhigh)", creator="OpenAI")] + rows = [openness_row("GPT-5.6 Sol (Reasoning)", creator="OpenAI")] + + result = join_openness(offerings, rows) + + assert result.matched == 1 + assert result.tiers == {"base+mode": 1} + + def test_quantisation_variants_all_match_the_same_openness_row(self) -> None: + offerings = [ + offering("Llama 4 Maverick (FP8)", creator="Meta"), + offering("Llama 4 Maverick (NVFP4)", creator="Meta"), + offering("Llama 4 Maverick", creator="Meta"), + ] + rows = [openness_row("Llama 4 Maverick", creator="Meta", openness_index=77.78)] + + result = join_openness(offerings, rows) + + assert result.matched == 3 + assert all(o["openness"]["openness_index"] == 77.78 for o in offerings) + + def test_creator_prefix_on_openness_side_still_matches(self) -> None: + offerings = [offering("Nemotron 3 Nano 30B A3B (Non-reasoning)", creator="NVIDIA")] + rows = [ + openness_row( + "NVIDIA Nemotron 3 Nano 30B A3B (Non-reasoning)", creator="NVIDIA" + ) + ] + + result = join_openness(offerings, rows) + + assert result.matched == 1 + + def test_different_creators_are_never_merged(self) -> None: + offerings = [offering("Nova Pro", creator="Amazon")] + rows = [openness_row("Nova Pro", creator="Some Other Lab")] + + result = join_openness(offerings, rows) + + assert result.matched == 0 + assert len(result.unmatched) == 1 + + def test_plus_variant_does_not_borrow_the_base_models_openness(self) -> None: + offerings = [offering("Command A+", creator="Cohere")] + rows = [ + openness_row("Command A", creator="Cohere", openness_index=33.33), + openness_row("Command A+", creator="Cohere", openness_index=22.22), + ] + + result = join_openness(offerings, rows) + + assert result.matched == 1 + assert offerings[0]["openness"]["openness_index"] == 22.22 + + def test_bare_name_against_split_reasoning_rows_is_ambiguous(self) -> None: + """Observed live: provider pages list a bare ``GPT-5.1`` while the + Openness Index splits it into ``(Non-reasoning)`` and ``(high)``.""" + offerings = [offering("GPT-5.1", creator="OpenAI")] + rows = [ + openness_row("GPT-5.1 (Non-reasoning)", creator="OpenAI", openness_index=11.11), + openness_row("GPT-5.1 (high)", creator="OpenAI", openness_index=22.22), + ] + + result = join_openness(offerings, rows) + + assert result.matched == 0 + assert len(result.ambiguous) == 1 + assert offerings[0]["openness"] is None + + def test_slug_resolves_what_the_display_name_leaves_ambiguous(self) -> None: + """Observed live: azure lists a bare ``Grok 4 Fast`` under the slug + ``grok-4-fast-reasoning``, which resolves the split openness rows.""" + offer = offering("Grok 4 Fast", creator="SpaceXAI") + offer["model_slug"] = "grok-4-fast-reasoning" + rows = [ + openness_row("Grok 4 Fast (Reasoning)", creator="SpaceXAI", openness_index=22.22), + openness_row( + "Grok 4 Fast (Non-reasoning)", creator="SpaceXAI", openness_index=11.11 + ), + ] + + result = join_openness([offer], rows) + + assert result.matched == 1 + assert result.ambiguous == [] + assert offer["openness"]["openness_index"] == 22.22 + + def test_disagreeing_candidates_are_reported_ambiguous_not_guessed(self) -> None: + offerings = [offering("Mystery Model")] + rows = [ + openness_row("Mystery Model (Reasoning)", openness_index=88.89), + openness_row("Mystery Model (Non-reasoning)", openness_index=11.11), + ] + + result = join_openness(offerings, rows) + + assert result.matched == 0 + assert len(result.ambiguous) == 1 + entry = result.ambiguous[0] + assert entry["candidates"] == [ + "Mystery Model (Non-reasoning)", + "Mystery Model (Reasoning)", + ] + assert offerings[0]["openness"] is None + + def test_agreeing_candidates_collapse_to_a_match(self) -> None: + offerings = [offering("Agreeable Model")] + rows = [ + openness_row("Agreeable Model (Reasoning)", openness_index=44.44), + openness_row("Agreeable Model (Non-reasoning)", openness_index=44.44), + ] + + result = join_openness(offerings, rows) + + assert result.matched == 1 + assert offerings[0]["openness"]["openness_index"] == 44.44 + + def test_absent_openness_is_recorded_not_zeroed(self) -> None: + """Coverage genuinely differs between the two datasets.""" + offerings = [offering("Claude Opus 5 (max)", creator="Anthropic")] + rows = [openness_row("Claude Opus 4.5", creator="Anthropic")] + + result = join_openness(offerings, rows) + + assert result.matched == 0 + assert offerings[0]["openness"] is None + entry = result.unmatched[0] + assert entry["display_name"] == "Claude Opus 5 (max)" + assert entry["normalized_base"] == "claude opus 5" + assert entry["reason"] == "no openness row for this model" + + def test_unmatched_rows_are_never_dropped_from_the_dataset(self) -> None: + offerings = [ + offering("Known Model", creator="Alibaba"), + offering("Unknown Model", creator="Alibaba"), + ] + rows = [openness_row("Known Model", creator="Alibaba")] + + result = join_openness(offerings, rows) + + assert result.matched == 1 + assert len(result.unmatched) == 1 + assert len(offerings) == 2 diff --git a/services/aa-sync/tests/test_normalize.py b/services/aa-sync/tests/test_normalize.py new file mode 100644 index 0000000..cb2fe9f --- /dev/null +++ b/services/aa-sync/tests/test_normalize.py @@ -0,0 +1,183 @@ +from datetime import UTC, datetime +from typing import Any + +import pytest + +from tokenpricing_aa.fetch import discover_provider_slugs +from tokenpricing_aa.normalize import ShapeError, normalize_sources +from tokenpricing_aa.parse import OPENNESS_COLUMNS, PROVIDER_COLUMNS + +from .test_parse import CHECK_ICON, GROUP_HEADER + +NOW = datetime(2026, 8, 14, tzinfo=UTC).isoformat() + + +def provider_page(rows: list[tuple[str, str, str]]) -> str: + group = "".join(f"" for cell in GROUP_HEADER) + header = "".join(f"" for cell in PROVIDER_COLUMNS) + body = "" + for name, slug, creator in rows: + model_cell = ( + f'
{creator} logo{name}' + f'
' + ) + cells = [ + model_cell, + "1M", + CHECK_ICON, + CHECK_ICON, + "Open", + "60", + "$0.89", + "48", + "1.26", + "53.76", + "42.00", + "x", + ] + body += "" + "".join(f"" for c in cells) + "" + return ( + f"
{cell}{cell}
{c}
{group}{header}" + f"{body}
" + ) + + +def openness_page(rows: list[tuple[str, str, str]]) -> str: + header = "".join(f"{c}" for c in ("", *OPENNESS_COLUMNS)) + body = "" + for rank, (creator, name, index) in enumerate(rows, 1): + cells = [ + str(rank), + creator, + name, + index, + "50.0", + "6.00", + "6.00", + "1.00", + "1.00", + "1.00", + "1.00", + ] + body += "" + "".join(f"{c}" for c in cells) + "" + return f"{header}{body}
" + + +def payloads( + provider_count: int = 45, + rows_per_provider: int = 20, + openness_count: int = 260, +) -> tuple[dict[str, Any], dict[str, Any]]: + pages = { + f"provider-{index:02d}": provider_page( + [ + (f"Model {n} (max)", f"model-{n}", "Alibaba") + for n in range(rows_per_provider) + ] + ) + for index in range(provider_count) + } + openness_rows = [ + ("Alibaba", f"Model {n} (Reasoning)", "66.67") for n in range(openness_count) + ] + providers_payload = { + "source": "artificial_analysis_providers", + "source_url": "https://artificialanalysis.ai/leaderboards/providers", + "fetched_at": NOW, + "discovered_slugs": sorted(pages), + "unreachable_slugs": [{"slug": "hyperbolic", "status": 404}], + "pages": pages, + } + openness_payload = { + "source": "artificial_analysis_openness", + "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index", + "fetched_at": NOW, + "page": openness_page(openness_rows), + } + return providers_payload, openness_payload + + +class TestDiscoverProviderSlugs: + def test_extracts_slugs_and_ignores_chunk_filenames(self) -> None: + html = ( + 'a' + 'b' + '' + 'dupe' + ) + assert discover_provider_slugs(html) == ["fireworks", "togetherai"] + + +class TestNormalizeSources: + def test_builds_dataset_with_metadata_and_attribution(self) -> None: + dataset = normalize_sources(*payloads()) + + assert dataset.metadata.provider_count == 45 + assert dataset.metadata.offering_count == 900 + assert dataset.metadata.openness_row_count == 260 + assert dataset.metadata.openness_matched == 900 + assert dataset.metadata.unreachable_provider_slugs == ["hyperbolic"] + assert "Artificial Analysis" in dataset.metadata.attribution + assert dataset.metadata.openness_spec_version.startswith("Openness Spec V1.0") + + def test_offerings_carry_variant_identity(self) -> None: + dataset = normalize_sources(*payloads()) + offering = dataset.offerings[0] + + assert offering.reasoning_effort == "max" + assert offering.reasoning_mode == "reasoning" + assert offering.openness is not None + assert offering.openness.openness_index == 66.67 + assert offering.openness_match is not None + + def test_unmatched_offerings_are_reported_and_kept(self) -> None: + providers, openness = payloads(rows_per_provider=21) + dataset = normalize_sources(providers, openness) + + # Model 20 has no openness row (openness covers Model 0..259 by name, but + # each provider page only emits Model 0..20, so all are covered) — assert + # instead on the join accounting being complete and lossless. + assert dataset.metadata.offering_count == len(dataset.offerings) + assert ( + dataset.metadata.openness_matched + + dataset.metadata.openness_unmatched + + dataset.metadata.openness_ambiguous + == dataset.metadata.offering_count + ) + + def test_openness_absent_for_a_model_is_recorded_not_zeroed(self) -> None: + providers, openness = payloads() + providers["pages"]["provider-00"] = provider_page( + [("Claude Opus 5 (max)", "claude-opus-5", "Anthropic")] + ) + dataset = normalize_sources(providers, openness) + + missing = [o for o in dataset.offerings if o.display_name == "Claude Opus 5 (max)"] + assert len(missing) == 1 + assert missing[0].openness is None + assert any(u.display_name == "Claude Opus 5 (max)" for u in dataset.unmatched) + + def test_too_few_providers_fails_loudly(self) -> None: + providers, openness = payloads(provider_count=10) + with pytest.raises(ShapeError, match="provider pages captured"): + normalize_sources(providers, openness) + + def test_too_few_offerings_fails_loudly(self) -> None: + providers, openness = payloads(provider_count=41, rows_per_provider=1) + with pytest.raises(ShapeError, match="offerings parsed"): + normalize_sources(providers, openness) + + def test_too_few_openness_rows_fails_loudly(self) -> None: + providers, openness = payloads(openness_count=10) + with pytest.raises(ShapeError, match="openness rows parsed"): + normalize_sources(providers, openness) + + def test_missing_model_anchors_fail_loudly(self) -> None: + """A restyle that drops the /models/ hrefs must not publish silently.""" + providers, openness = payloads() + providers["pages"] = { + slug: html.replace('href="/models/', 'href="/broken/') + for slug, html in providers["pages"].items() + } + with pytest.raises(ShapeError, match="/models/ anchor"): + normalize_sources(providers, openness) diff --git a/services/aa-sync/tests/test_parse.py b/services/aa-sync/tests/test_parse.py new file mode 100644 index 0000000..164eb02 --- /dev/null +++ b/services/aa-sync/tests/test_parse.py @@ -0,0 +1,228 @@ +import pytest + +from tokenpricing_aa.parse import ( + OPENNESS_COLUMNS, + PROVIDER_COLUMNS, + ParseError, + is_estimated, + parse_context_window, + parse_number, + parse_openness_page, + parse_provider_page, +) + +GROUP_HEADER = [ + "", + "Features", + "Model Intelligence", + "Price", + "Speed", + "Latency", + "End-to-End Response Time", + "", +] + +CHECK_ICON = '' + + +def provider_html(rows: list[list[str]], columns: tuple[str, ...] = PROVIDER_COLUMNS) -> str: + group = "".join(f"{cell}" for cell in GROUP_HEADER) + header = "".join(f"{cell}" for cell in columns) + body = "".join( + "" + "".join(f"{cell}" for cell in row) + "" for row in rows + ) + return ( + f"{group}{header}" + f"{body}
" + ) + + +def offering_row( + name: str = "Kimi K3 (max)", + slug: str = "kimi-k3", + creator: str = "Kimi", + context: str = "1.05M", + function_calling: str = CHECK_ICON, + json_mode: str = CHECK_ICON, + license_: str = "Open", + intelligence: str = "60", + cost: str = "$0.89", + tokens_per_second: str = "48", + first_chunk: str = "1.26", + total: str = "53.76", + reasoning: str = "42.00", +) -> list[str]: + model_cell = ( + f'
{creator} logo{name}' + f'
' + ) + return [ + model_cell, + context, + function_calling, + json_mode, + license_, + intelligence, + cost, + tokens_per_second, + first_chunk, + total, + reasoning, + 'Model Provider', + ] + + +class TestParseNumber: + def test_unicode_minus_sign_parses_as_negative(self) -> None: + """AA renders negatives with U+2212, which float() rejects outright.""" + with pytest.raises(ValueError): + float("−31") + assert parse_number("−31") == -31.0 + assert parse_number("−38.5") == -38.5 + + def test_ascii_hyphen_still_parses(self) -> None: + assert parse_number("-31") == -31.0 + + def test_no_data_is_none_not_zero(self) -> None: + for value in ("--", "—", "–", "", "N/A", None): + assert parse_number(value) is None + + def test_zero_is_preserved_as_zero(self) -> None: + assert parse_number("0") == 0.0 + + def test_strips_currency_percent_and_thousands_separators(self) -> None: + assert parse_number("$0.89") == 0.89 + assert parse_number("67%") == 67.0 + assert parse_number("1,715") == 1715.0 + + def test_estimated_asterisk_is_stripped_but_flagged(self) -> None: + assert parse_number("33*") == 33.0 + assert is_estimated("33*") is True + assert is_estimated("33") is False + + def test_unparseable_text_is_none(self) -> None: + assert parse_number("coming soon") is None + + +class TestParseContextWindow: + @pytest.mark.parametrize( + ("rendered", "expected"), + [ + ("1M", 1_000_000), + ("1.05M", 1_050_000), + ("262k", 262_000), + ("205k", 205_000), + ("128K", 128_000), + ], + ) + def test_human_readable_suffixes(self, rendered: str, expected: int) -> None: + assert parse_context_window(rendered) == expected + + def test_missing_is_none(self) -> None: + assert parse_context_window("--") is None + assert parse_context_window(None) is None + + +class TestParseProviderPage: + def test_extracts_offering_fields(self) -> None: + (row,) = parse_provider_page(provider_html([offering_row()]), "fireworks") + + assert row["provider_slug"] == "fireworks" + assert row["model_slug"] == "kimi-k3" + assert row["display_name"] == "Kimi K3 (max)" + assert row["creator"] == "Kimi" + assert row["context_window"] == 1_050_000 + assert row["license"] == "Open" + assert row["intelligence_index"] == 60.0 + assert row["cost_per_task_usd"] == 0.89 + assert row["median_output_tokens_per_second"] == 48.0 + assert row["reasoning_time_seconds"] == 42.0 + + def test_check_icon_presence_drives_feature_flags(self) -> None: + """AA emits a check icon for yes and renders nothing at all for no.""" + (yes,) = parse_provider_page(provider_html([offering_row()]), "p") + assert yes["supports_function_calling"] is True + assert yes["supports_json_mode"] is True + + (no,) = parse_provider_page( + provider_html([offering_row(function_calling="", json_mode="")]), "p" + ) + assert no["supports_function_calling"] is False + assert no["supports_json_mode"] is False + + def test_missing_reasoning_time_is_none(self) -> None: + (row,) = parse_provider_page( + provider_html([offering_row(reasoning="--")]), "openai" + ) + assert row["reasoning_time_seconds"] is None + + def test_same_slug_twice_is_kept_as_two_offerings(self) -> None: + """A provider can serve one model under several serving tiers.""" + rows = parse_provider_page( + provider_html( + [ + offering_row(name="Kimi K3 (max)"), + offering_row(name="Kimi K3 (max) (FAST)", cost="$1.34"), + ] + ), + "fireworks", + ) + assert len(rows) == 2 + assert {row["display_name"] for row in rows} == { + "Kimi K3 (max)", + "Kimi K3 (max) (FAST)", + } + + def test_changed_columns_fail_loudly(self) -> None: + mutated = ("Model", "Context Window", "Surprise") + PROVIDER_COLUMNS[3:] + with pytest.raises(ParseError, match="columns changed"): + parse_provider_page(provider_html([], columns=mutated), "p") + + def test_missing_table_fails_loudly(self) -> None: + with pytest.raises(ParseError, match="no "): + parse_provider_page("nope", "p") + + +def openness_html(rows: list[list[str]]) -> str: + header = "".join(f"" for cell in ("", *OPENNESS_COLUMNS)) + body = "".join( + "" + "".join(f"" for cell in row) + "" for row in rows + ) + return f"
{cell}
{cell}
{header}{body}
" + + +class TestParseOpennessPage: + def test_extracts_components(self) -> None: + (row,) = parse_openness_page( + openness_html( + [ + [ + "1", + "Allen Institute for AI", + "Olmo 3.1 32B Think", + "88.89", + "7.90", + "6.00", + "10.00", + "3.00", + "1.00", + "3.00", + "1.00", + ] + ] + ) + ) + + assert row["rank"] == 1 + assert row["creator"] == "Allen Institute for AI" + assert row["display_name"] == "Olmo 3.1 32B Think" + assert row["openness_index"] == 88.89 + assert row["pre_training_data_access"] == 3.0 + assert row["post_training_data_license"] == 1.0 + + def test_changed_columns_fail_loudly(self) -> None: + header = "".join( + f"{cell}" for cell in ("", "Creator", "Model", "Something Else") + ) + with pytest.raises(ParseError, match="columns changed"): + parse_openness_page(f"{header}
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Both are false, and checking them properly collapses the acquisition to two GETs. AA is a Next.js app; every page streams its data into the initial HTML as self.__next_f.push([1,"..."]) chunks. The rendered table is a lossy view of that payload in two directions. "Expand Columns" is a client-side toggle -- expanding issues no network request -- so the 50-column view was always in the HTML, per-token and cache pricing included. And the leaderboard renders only non-deprecated rows while its payload holds every row: measured 2026-08-14, Nebius rendered 25 of 35 and Azure 20 of 84, with rendered + deprecated == payload exactly for both. Status: Current *is* deprecated == false. Every offering id on /providers/nebius is in the leaderboard payload, and a per-key diff of the two found zero fields with a real value on the provider page but missing from the leaderboard, zero the reverse, and zero conflicts -- the only differences were React's "$undefined" sentinel versus an omitted key. So 58 provider-page requests bought a strict subset of one, and are gone. Keyed on AA's offering id, because no composite of the readable fields is unique. Over the full 1082 rows, (provider, model_slug, host_api_id, display_name) still collides on 4 groups: openai/o3 appears twice under one host_api_id with the same label, differing only in price ($10/$40 vs $2/$8) and whether performance was measured. normalize_sources asserts id uniqueness. The openness join is now an exact model_slug match. Openness records live in their own id space and point at a model by modelId, a uuid that appears nowhere in the leaderboard payload (0 of 298), so id cannot anchor that join -- but the openness page supplies a slug for all 298 scored models. Name normalisation, the creator filter, the three confidence tiers and the serving-token vocabulary all go away, and coverage improves: 698 matched against 563, 0 ambiguous vs 1. Since the payload is typed JSON, the em-dash / U+2212 / currency / thousands- separator / trailing-asterisk cleaning is gone with the table parsing that needed it. intelligence_index is 45.1382483763163 rather than 45, and intelligence_index_estimated is a boolean rather than a trailing asterisk. Also adds three named alerts, each opening or commenting on one aa-sync-failure issue rather than duplicating: payload-not-found (AA stopped shipping a flight payload), request-blocked (persistent non-200 after retries; 403 is never retried), and schema-drift, checked against a tracked schema/offering-manifest.json because "it did not crash" is not evidence the source is unchanged. Drift separates a vanished or retyped field from a >10x range shift -- how a unit change looks from outside -- from a merely new field, which is informational and never fails a run. Breaking drift publishes nothing: a stale snapshot is recoverable, a silently wrong one is not. Delivery is one function, deliver_via_github_issue; any (Alert) -> str callable is a channel, so Teams or email is a single new function and no other module knows the difference. Sync moves to daily now that a run is two requests. 1082 offerings over 58 providers, against 1045 over 51. Refs #85, #78, #76 Co-Authored-By: Claude Opus 5 (1M context) --- .github/workflows/aa-sync.yml | 15 +- README.md | 4 +- database/current/artificial-analysis.json | 110666 ++++++++++----- .../artificial-analysis-20260814T090223Z.json | 40534 ------ .../artificial-analysis-20260814T120747Z.json | 85098 +++++++++++ docs/database.md | 25 +- services/aa-sync/README.md | 237 +- .../aa-sync/schema/offering-manifest.json | 502 + .../aa-sync/src/tokenpricing_aa/alerting.py | 234 + services/aa-sync/src/tokenpricing_aa/cli.py | 148 +- services/aa-sync/src/tokenpricing_aa/fetch.py | 179 +- .../aa-sync/src/tokenpricing_aa/flight.py | 151 + .../aa-sync/src/tokenpricing_aa/matching.py | 442 +- .../aa-sync/src/tokenpricing_aa/modeling.py | 170 +- .../aa-sync/src/tokenpricing_aa/normalize.py | 160 +- services/aa-sync/src/tokenpricing_aa/parse.py | 393 +- services/aa-sync/src/tokenpricing_aa/paths.py | 12 +- .../aa-sync/src/tokenpricing_aa/schema.py | 239 + services/aa-sync/tests/factories.py | 154 + services/aa-sync/tests/test_alerting.py | 340 + services/aa-sync/tests/test_flight.py | 92 + services/aa-sync/tests/test_matching.py | 479 +- services/aa-sync/tests/test_normalize.py | 315 +- services/aa-sync/tests/test_parse.py | 351 +- services/aa-sync/tests/test_schema.py | 188 + 25 files changed, 165908 insertions(+), 75220 deletions(-) delete mode 100644 database/history/artificial-analysis-20260814T090223Z.json create mode 100644 database/history/artificial-analysis-20260814T120747Z.json create mode 100644 services/aa-sync/schema/offering-manifest.json create mode 100644 services/aa-sync/src/tokenpricing_aa/alerting.py create mode 100644 services/aa-sync/src/tokenpricing_aa/flight.py create mode 100644 services/aa-sync/src/tokenpricing_aa/schema.py create mode 100644 services/aa-sync/tests/factories.py create mode 100644 services/aa-sync/tests/test_alerting.py create mode 100644 services/aa-sync/tests/test_flight.py create mode 100644 services/aa-sync/tests/test_schema.py diff --git a/.github/workflows/aa-sync.yml b/.github/workflows/aa-sync.yml index 5f5d99b..06fea4b 100644 --- a/.github/workflows/aa-sync.yml +++ b/.github/workflows/aa-sync.yml @@ -3,14 +3,18 @@ name: Artificial Analysis Sync on: workflow_dispatch: schedule: - # Weekly, off-peak. The whole capture is ~60 requests, so one run is enough. - - cron: "17 4 * * 0" + # Daily, off-peak. The whole capture is two requests now that the leaderboard + # payload supersedes the per-provider pages, so a daily cadence is nearly free. + - cron: "17 4 * * *" jobs: sync: runs-on: ubuntu-latest permissions: contents: write + # The sync alerts on failure by opening or commenting on an issue itself; + # see services/aa-sync/src/tokenpricing_aa/alerting.py. + issues: write steps: - uses: actions/checkout@v6 - uses: astral-sh/setup-uv@v7 @@ -24,9 +28,14 @@ jobs: run: uv sync - name: Refresh Artificial Analysis dataset working-directory: services/aa-sync + env: + # Consumed by alerting.deliver_via_github_issue. Each of the three + # failure modes (payload-not-found, request-blocked, schema-drift) + # opens its own named issue, or comments on the open one that matches. + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: uv run tokenpricing-aa-sync sync - name: Upload raw capture - # ~60MB of source HTML, kept out of git but retained so that a failed + # ~12MB of source HTML, kept out of git but retained so that a failed # parse can be diagnosed against its exact input. if: always() uses: actions/upload-artifact@v4 diff --git a/README.md b/README.md index e02a1aa..3ca9ac4 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,7 @@ The canonical skill source in this repository is `skills/tokenpricing/SKILL.md`. Pricing data is now synchronized directly inside this repository from OpenRouter and LiteLLM, normalized into the tokenpricing schema, and published as the canonical database every six hours. -A separate weekly dataset (`database/current/artificial-analysis.json`) adds provider × model serving data — benchmark scores, throughput, latency and Openness Index — sourced from [Artificial Analysis](https://artificialanalysis.ai). See [docs/database.md](docs/database.md#artificial-analysis-dataset). +A separate daily dataset (`database/current/artificial-analysis.json`) adds provider × model × reasoning-effort serving data — benchmark scores, throughput, latency and Openness Index — sourced from [Artificial Analysis](https://artificialanalysis.ai). See [docs/database.md](docs/database.md#artificial-analysis-dataset). ## Repository Structure @@ -154,7 +154,7 @@ tokenpricing/ │ ├── dashboard/ Vite + React pricing explorer │ ├── notifier/ Webhook notification service │ ├── sync/ Canonical database sync pipeline -│ └── aa-sync/ Artificial Analysis acquisition (weekly) +│ └── aa-sync/ Artificial Analysis acquisition (daily) ├── libraries/ │ ├── python/ Python SDK + CLI (PyPI) │ └── typescript/ TypeScript SDK (npm) diff --git a/database/current/artificial-analysis.json b/database/current/artificial-analysis.json index 8380a0c..eaec11c 100644 --- a/database/current/artificial-analysis.json +++ b/database/current/artificial-analysis.json @@ -1,13613 +1,33454 @@ { - "generated_at": "2026-08-14T09:02:23.862929Z", + "generated_at": "2026-08-14T12:07:47.922721Z", "metadata": { - "fetched_at": "2026-08-14T08:54:43.593677Z", + "fetched_at": "2026-08-14T11:55:49.708197Z", "sources": [ "https://artificialanalysis.ai/leaderboards/providers", "https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index" ], "attribution": "Data source: Artificial Analysis (https://artificialanalysis.ai). Benchmark, latency and throughput figures are third-party measurements produced by Artificial Analysis, not vendor-published and not measured by tokenpricing.", "openness_spec_version": "Openness Spec V1.0 (preliminary draft)", - "provider_count": 51, - "offering_count": 1045, + "provider_count": 58, + "offering_count": 1082, + "deprecated_offering_count": 566, "openness_row_count": 298, - "openness_matched": 563, - "openness_unmatched": 481, - "openness_ambiguous": 1, - "match_tiers": { - "base": 149, - "base+mode": 1, - "exact": 413 - }, - "unreachable_provider_slugs": [ - "ai9star", - "blackbox-ai", - "clarifai", - "hyperbolic", - "public-ai", - "sarvam", - "streamlake" - ], - "unclassified_variant_tokens": [] + "openness_matched": 698, + "openness_unmatched": 384, + "openness_ambiguous": 0, + "openness_without_offering": 89, + "drift": { + "breaking": false, + "missing": [], + "type_changed": [], + "range_shifted": [], + "new": [] + } }, "offerings": [ { - "provider_slug": "agnes-ng", - "model_slug": "agnes-2-5-pro-alpha", - "display_name": "Agnes 2.5 Pro Alpha", - "creator": "Sapiens AI", - "context_window": 1000000, - "supports_function_calling": true, - "supports_json_mode": true, - "license": "Proprietary", - "intelligence_index": 40.0, - "intelligence_index_estimated": false, - "cost_per_task_usd": null, - "cost_per_task_estimated": false, - "median_output_tokens_per_second": 131.0, - "median_first_chunk_seconds": 2.18, - "total_response_seconds": 21.2, - "reasoning_time_seconds": 15.22, - "reasoning_mode": null, - "reasoning_effort": null, - "variant_tokens": [], - "unclassified_variant_tokens": [], - "openness": null, - "openness_match": null - }, - { - "provider_slug": "alibaba_cloud", - "model_slug": "qwen3-8-max", - "display_name": "Qwen3.8 Max", - "creator": "Alibaba", - "context_window": 1000000, - "supports_function_calling": true, - "supports_json_mode": true, - "license": "Proprietary", - "intelligence_index": 58.0, - "intelligence_index_estimated": false, - "cost_per_task_usd": 1.13, - "cost_per_task_estimated": false, - "median_output_tokens_per_second": 47.0, - "median_first_chunk_seconds": 2.6, - "total_response_seconds": 55.62, - "reasoning_time_seconds": 42.42, - "reasoning_mode": "reasoning", - "reasoning_effort": "max", - "variant_tokens": [], - "unclassified_variant_tokens": [], - "openness": null, - "openness_match": null - }, - { - "provider_slug": "alibaba_cloud", - "model_slug": "qwen3-8-2-4t-a95b", - "display_name": "Qwen3.8 2.4T A95B", - "creator": "Alibaba", - "context_window": 984000, - "supports_function_calling": true, - "supports_json_mode": true, - "license": "Open", - "intelligence_index": 58.0, - "intelligence_index_estimated": false, - "cost_per_task_usd": 1.09, - "cost_per_task_estimated": false, - "median_output_tokens_per_second": 52.0, - "median_first_chunk_seconds": 2.75, - "total_response_seconds": 51.26, - "reasoning_time_seconds": 38.81, - "reasoning_mode": null, + "offering_id": "c9cbf21c-31b5-4147-a69f-a8a1d617b258", + "provider_slug": "novita", + "provider_name": "Novita", + "model_slug": "glm-4-5v", + "display_name": "GLM-4.5V", + "host_api_id": "zai-org/glm-4.5v", + "footnotes": null, + "creator": "Z AI", + "is_open_weights": true, + "deprecated": true, + "reasoning_model": false, + "size_class": "medium", + "reasoning_mode": "non-reasoning", "reasoning_effort": null, - "variant_tokens": [], - "unclassified_variant_tokens": [], - "openness": null, - "openness_match": null - }, - { - "provider_slug": "alibaba_cloud", - "model_slug": "qwen3-7-max", - "display_name": "Qwen3.7 Max", - "creator": "Alibaba", - "context_window": 991000, - "supports_function_calling": true, - "supports_json_mode": true, - "license": "Proprietary", - "intelligence_index": 47.0, - "intelligence_index_estimated": false, - "cost_per_task_usd": 0.54, - "cost_per_task_estimated": false, - "median_output_tokens_per_second": 208.0, - "median_first_chunk_seconds": 2.29, - 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"https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index" - ], - "attribution": "Data source: Artificial Analysis (https://artificialanalysis.ai). 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"provider_slug": "mistral", + "model_slug": "mistral-small-3", + "display_name": "Mistral Small 3", + "creator": "Mistral", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "baseten", + "model_slug": "glm-5-2-non-reasoning", + "display_name": "GLM-5.2 (FAST)", + "creator": "Z AI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "nebius", + "model_slug": "glm-5-2-non-reasoning", + "display_name": "GLM-5.2 (FP4)", + "creator": "Z AI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "scaleway", + "model_slug": "glm-5-2-non-reasoning", + "display_name": "GLM-5.2", + "creator": "Z AI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "wafer", + "model_slug": "glm-5-2-non-reasoning", + "display_name": "GLM-5.2", + "creator": "Z AI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "siliconflow", + "model_slug": "glm-5-2-non-reasoning", + "display_name": "GLM-5.2 (FP8)", + "creator": "Z AI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "openai", + "model_slug": "o1-pro", + "display_name": "o1-pro", + "creator": "OpenAI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "azure", + "model_slug": "gpt-4o-2024-05-13", + "display_name": "GPT-4o (May)", + "creator": "OpenAI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "openai", + "model_slug": "gpt-4o-2024-05-13", + "display_name": "GPT-4o (May)", + "creator": "OpenAI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "google", + "model_slug": "claude-opus-4-7", + "display_name": "Claude Opus 4.7 (max)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "azure", + "model_slug": "claude-opus-4-7", + "display_name": "Claude Opus 4.7 (max)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "amazon_bedrock", + "model_slug": "claude-opus-4-7", + "display_name": "Claude Opus 4.7 (max)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "anthropic", + "model_slug": "claude-opus-4-7", + "display_name": "Claude Opus 4.7 (max)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "google", + "model_slug": "gemini-3-7-flash-medium", + "display_name": "Gemini 3.7 Flash (medium) AI Studio", + "creator": "Google", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "deepinfra", + "model_slug": "qwen3-14b-instruct", + "display_name": "Qwen3 14B (FP8)", + "creator": "Alibaba", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-14b-instruct", + "display_name": "Qwen3 14B", + "creator": "Alibaba", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "anthropic", + "model_slug": "claude-sonnet-5-medium", + "display_name": "Claude Sonnet 5 (medium)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "anthropic", + "model_slug": "claude-sonnet-4-6-non-reasoning-low-effort", + "display_name": "Claude Sonnet 4.6 (Non-reasoning, Low Effort)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "amazon_bedrock", + "model_slug": "claude-4-1-opus", + "display_name": "Claude 4.1 Opus", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "anthropic", + "model_slug": "claude-4-1-opus", + "display_name": "Claude 4.1 Opus", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "google", + "model_slug": "claude-4-1-opus", + "display_name": "Claude 4.1 Opus Vertex", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "azure", + "model_slug": "claude-4-1-opus", + "display_name": "Claude 4.1 Opus", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "deepinfra", + "model_slug": "qwen3-30b-a3b-instruct", + "display_name": "Qwen3 30B (FP8)", + "creator": "Alibaba", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-30b-a3b-instruct", + "display_name": "Qwen3 30B", + "creator": "Alibaba", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "alibaba_cloud", + "model_slug": "qwen3-6-plus", + "display_name": "Qwen3.6 Plus", + "creator": "Alibaba", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "amazon_bedrock", + "model_slug": "nova-2-0-pro-reasoning-low", + "display_name": "Nova 2.0 Pro Preview (low)", + "creator": "Amazon", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "google", + "model_slug": "gemini-2-5-flash-lite-reasoning", + "display_name": "Gemini 2.5 Flash-Lite (AI Studio)", + "creator": "Google", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "google", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1 Vertex", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "amazon_bedrock", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "coreweave", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "novita", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "togetherai", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "fireworks", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "deepinfra", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1 (FP4)", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "sambanova", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "baseten", + "model_slug": "deepseek-v3-1", + "display_name": "DeepSeek V3.1 (FP8)", + "creator": "DeepSeek", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "amazon_bedrock", + "model_slug": "nova-2-0-lite-reasoning-medium", + "display_name": "Nova 2.0 Lite (medium)", + "creator": "Amazon", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "novita", + "model_slug": "kat-coder-pro-v2", + "display_name": "KAT-Coder-Pro V2", + "creator": "KwaiKAT", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "streamlake", + "model_slug": "kat-coder-pro-v2", + "display_name": "KAT-Coder-Pro V2", + "creator": "KwaiKAT", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "azure", + "model_slug": "gpt-5-1-codex-mini", + "display_name": "GPT-5.1 Codex mini (high)", + "creator": "OpenAI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "google", + "model_slug": "gemini-2-0-flash-experimental", + "display_name": "Gemini 2.0 Flash (exp) (AI Studio)", + "creator": "Google", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "amazon_bedrock", + "model_slug": "grok-4-3-low", + "display_name": "Grok 4.3 (low)", + "creator": "SpaceXAI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "azure", + "model_slug": "grok-4-3-low", + "display_name": "Grok 4.3 (low)", + "creator": "SpaceXAI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "xai", + "model_slug": "grok-4-3-low", + "display_name": "Grok 4.3 (low)", + "creator": "SpaceXAI", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "anthropic", + "model_slug": "claude-opus-5-medium", + "display_name": "Claude Opus 5 (medium)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "amazon_bedrock", + "model_slug": "claude-opus-5-medium", + "display_name": "Claude Opus 5 (medium)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "google", + "model_slug": "claude-opus-5-medium", + "display_name": "Claude Opus 5 (medium)", + "creator": "Anthropic", + "candidates": [], + "reason": "no openness row for this model slug" + }, + { + "provider_slug": "replicate", + "model_slug": "llama-2-chat-7b", + "display_name": "Llama 2 Chat 7B", + "creator": "Meta", + "candidates": [], + "reason": "no openness row for this model slug" + } + ], + "ambiguous": [] +} diff --git a/docs/database.md b/docs/database.md index d5904b5..0778a59 100644 --- a/docs/database.md +++ b/docs/database.md @@ -10,8 +10,8 @@ All pricing data lives in the repository under `database/`, normalized from Open | `database/history/prices-.json` | Immutable snapshots, one per sync | | `database/changelog/latest.json` | Diff summary of the most recent sync window | | `database/current/openrouter.json` / `litellm.json` | Raw upstream payloads from the last sync | -| `database/current/artificial-analysis.json` | Artificial Analysis provider × model dataset, refreshed weekly | -| `database/history/artificial-analysis-.json` | Immutable weekly snapshots | +| `database/current/artificial-analysis.json` | Artificial Analysis provider × model × effort dataset, refreshed daily | +| `database/history/artificial-analysis-.json` | Immutable daily snapshots | Raw access (no authentication required): @@ -68,9 +68,9 @@ https://raw.githubusercontent.com/Atena-IT/tokenpricing/main/database/changelog/ ## Artificial Analysis dataset -`database/current/artificial-analysis.json` is a separate weekly dataset covering -each model **as served by each provider** — 1045 offerings across 51 providers — -joined to the Artificial Analysis Openness Index. +`database/current/artificial-analysis.json` is a separate daily dataset covering +each model **as served by each provider at each reasoning effort** — 1082 +offerings across 58 providers — joined to the Artificial Analysis Openness Index. > **Data source: [Artificial Analysis](https://artificialanalysis.ai).** Benchmark, > latency and throughput figures are third-party measurements produced by @@ -79,16 +79,21 @@ joined to the Artificial Analysis Openness Index. > specification (V1.0, preliminary draft). It is kept separate from `prices.json` rather than merged into it because the grain -differs (one row per model × reasoning variant × serving platform, versus one -record per model) and because the public Artificial Analysis pages publish -`Cost per Task USD` but no per-token input/output pricing. Each offering links back -by `model_slug`, `display_name` and `creator`. +differs: one row per provider × model × reasoning variant × serving endpoint, +versus one record per model. Each offering links back by `model_slug`, +`display_name` and `creator`, and is keyed by `offering_id` — Artificial Analysis's +own uuid, which is the only field unique across the dataset. + +Every column of the site's expanded 50-column view is included, per-token and +cache pricing among them. Superseded models are **kept**, flagged `deprecated` +rather than filtered out, so price history stays continuous when a model is +replaced; filter on that field at query time if you only want current offerings. Models with no Openness Index entry keep `openness: null` and are listed under `unmatched`; genuinely undecidable matches are listed under `ambiguous`. Neither is silently dropped, and absent openness never becomes zero. See the [service README](https://github.com/Atena-IT/tokenpricing/blob/main/services/aa-sync/README.md) -for the matching rules. +for the join and the failure/drift alerting. ## Browsing diff --git a/services/aa-sync/README.md b/services/aa-sync/README.md index 799945b..cc37b79 100644 --- a/services/aa-sync/README.md +++ b/services/aa-sync/README.md @@ -1,6 +1,6 @@ # tokenpricing-aa-sync -Weekly acquisition of [Artificial Analysis](https://artificialanalysis.ai) public +Daily acquisition of [Artificial Analysis](https://artificialanalysis.ai) public leaderboard data: a provider x model offering dataset joined to the Artificial Analysis Openness Index. @@ -14,8 +14,10 @@ Analysis Openness Index. ```bash uv sync -uv run tokenpricing-aa-sync sync # fetch, capture, normalize, write -uv run tokenpricing-aa-sync normalize # rebuild from the local raw capture +uv run tokenpricing-aa-sync sync # fetch, capture, check shape, write +uv run tokenpricing-aa-sync normalize # rebuild from the local raw capture +uv run tokenpricing-aa-sync check # compare live payload to the manifest +uv run tokenpricing-aa-sync build-manifest # accept a new payload shape ``` Outputs: @@ -23,125 +25,178 @@ Outputs: | Path | What | |---|---| | `database/current/artificial-analysis.json` | latest dataset | -| `database/history/artificial-analysis-.json` | weekly snapshot (26 retained) | +| `database/history/artificial-analysis-.json` | snapshot (26 retained) | | `.capture/artificial-analysis/*.json` | raw HTML capture — gitignored, uploaded as a CI artifact | +| `schema/offering-manifest.json` | expected payload shape, tracked in git | -Scheduled weekly by [`.github/workflows/aa-sync.yml`](../../.github/workflows/aa-sync.yml). +Scheduled daily by [`.github/workflows/aa-sync.yml`](../../.github/workflows/aa-sync.yml). ## Where the data comes from -Two sources, both plain HTTP — **no browser is required**: +**Two `GET`s, no browser.** Artificial Analysis is a Next.js app: every page +streams its server-rendered data into the initial HTML as a sequence of +`self.__next_f.push([1,"…"])` chunks. Concatenating those chunks reconstructs a +flight payload holding the full dataset behind the page, and that payload is the +only thing this service reads. -1. **Per-provider pages** (`/providers/`, ~51 reachable). Provider slugs are - discovered from `/leaderboards/providers`, but the row data is taken from the - individual pages because the aggregate leaderboard is filtered - (`Status: Current`) and exposes roughly half as many rows: **1045 offerings - across 51 providers, against 512 on the leaderboard.** -2. **The Openness Index** (`/evaluations/artificial-analysis-openness-index`), - 298 rows with the published component breakdown. +1. **The provider leaderboard** (`/leaderboards/providers`) — every offering of + every provider: **1082 offerings across 58 providers**, 64 fields each. +2. **The Openness Index** (`/evaluations/artificial-analysis-openness-index`) — + 298 scored models with the full component breakdown. -The spike this service grew out of (`experiments/aa-scrape-spike`) recommended -Playwright. That was only needed to reach the *expanded* column view of the -aggregate leaderboard, which this service does not use. Both sources here are -fully server-rendered on a plain `GET`. +### Why the payload and not the rendered table -### What the public pages do not carry +The rendered table is a lossy view of the payload, in two ways that both matter: -The provider pages publish `Cost per Task USD` but **no per-token input/output -prices** and no cache pricing — those exist only in the expanded leaderboard view. -This is why the dataset does not populate `tokenpricing.modeling.PricingInfo`; see -[Relationship to the canonical database](#relationship-to-the-canonical-database). +**Columns.** The table shows 12 columns collapsed and 50 expanded, but +"Expand Columns" is a client-side toggle over data that already shipped — +expanding issues no network request. Everything in the 50-column view, including +per-token pricing, cache pricing, the throughput and latency percentiles, the +Omniscience Index and the API ID, is in the initial HTML of *any* of these pages. -## The join +**Rows.** The leaderboard renders only non-deprecated offerings — its +`Status: Current` filter — while its payload carries every offering. Measured +2026-08-14: -The two datasets carry model identity in different fields, and neither is -sufficient alone: +| | rendered | payload | deprecated | +|---|---|---|---| +| Nebius | 25 | 35 | 10 | +| Azure | 20 | 84 | 64 | +| all providers | 516 | 1082 | 566 | -* Provider pages have a stable `/models/` anchor **and** an effort-suffixed - display name (`GPT-5.6 Sol (xhigh)`). -* The Openness Index has **no anchors at all** — only a rendered display name and - a `Creator` column — and it disambiguates variants differently - (`(Reasoning)` / `(Non-reasoning)`, sometimes `(Reasoning, Max Effort)`). +`rendered + deprecated == payload` exactly, so the `Status: Current` filter *is* +`deprecated == false`, applied client-side to a payload that already contains +everything. -So the join runs on a normalised name key. The parenthetical suffix space is much -wider than reasoning effort: it also carries quantisation (`FP8`, `NVFP4`), -serving tier (`FAST`, `Turbo`), hosting platform (`Vertex`, `AI Studio`) and -snapshot dates. Openness is a property of the *model*, not of how a provider -serves it, so serving-side tokens are dropped from the key while reasoning -identity is kept. +### Why not the per-provider pages -Matching runs in descending confidence and **never guesses**: +They add nothing. Every offering id on `/providers/nebius` is present in the +leaderboard payload, and a per-key comparison of the two found **zero** fields +with a real value on the provider page but missing from the leaderboard, zero the +reverse, and zero conflicts. The only differences were React's `"$undefined"` +sentinel versus an omitted key, which `flight.clean` normalises to the same +`None`. Reading 58 provider pages therefore costs 58 requests for a strict subset +of one. -| Tier | Key | -|---|---| -| `exact` | base name + reasoning mode + effort level | -| `base+mode` | base name + reasoning mode, when all candidates agree on every component | -| `base` | base name alone, when all candidates agree | +Superseded offerings are kept rather than filtered at acquisition time, with +`deprecated` carried as a column: that way a model being replaced never punches a +hole in price history, and no re-scrape is needed to recover the old rows. -Variant identity is read from the display name first and from the `/models/` -slug second: provider pages sometimes render a bare `Grok 4 Fast` while the href -says `grok-4-fast-reasoning`. Only an explicit trailing suffix is trusted — which -effort level owns the *unsuffixed* slug varies per model (`gpt-oss-120b` is high, -`claude-opus-5` is max), so it is never inferred by rule. +### Values, not renderings -A creator known on both sides is a hard filter — two labs shipping a similarly -named model are never merged. Candidates that disagree are recorded in -`ambiguous`; models with no openness row at all are recorded in `unmatched`. -Neither is dropped from the dataset, and absent openness stays `null` rather than -becoming zero. +The payload is typed JSON, so the parser reads values instead of cleaning strings. +There is no em-dash / U+2212 minus / currency-symbol / thousands-separator / +trailing-asterisk handling anywhere in this service — those exist only to undo a +table rendering. Two consequences worth naming: -### Why not fuzzy matching +* `intelligence_index` is `45.1382483763163`, not `45`. +* `intelligence_index_estimated` is a real boolean field, not a trailing `*`. -Fuzzy matching over these names is actively unsafe. Against the live capture, the -closest openness name to an unmatched provider model scores 88-95 on token-sort -ratio while being a *different model*: +## The primary key -| Unmatched | Closest openness row | Score | -|---|---|---| -| `claude opus 5` | `claude opus 4 5` | 93 | -| `gemma 3 12b` | `gemma 4 12b` | 91 | -| `gpt 5 1 codex` | `gpt 5 codex` | 92 | +`offering_id` — AA's own uuid for the row. **No composite of the human-readable +fields is unique.** Tested over the full 1082-row payload: + +| Candidate key | Duplicate groups | +|---|---| +| `offering_id` | **0** | +| `(provider, model_slug, host_api_id, display_name)` | 4 | +| `(provider, model_slug, display_name)` | 6 | +| `(provider, model_slug)` | 23 | +| `(provider, host_api_id)` | 248 | + +The irreducible collisions are real distinct endpoints that AA does not name +apart. `openai`/`o3` appears twice under one `host_api_id` (`o3-2025-04-16`) with +the same label, differing only in price ($10/$40 versus $2/$8 per 1M tokens) and +in whether performance was measured. There is no tier, region or variant field to +separate them, so `offering_id` is the only key, and `normalize_sources` asserts +its uniqueness on every run. + +### Reasoning-effort grain -Any threshold low enough to catch real matches also produces wrong ones. +One offering is one (provider x model x reasoning variant x serving endpoint). Of +1082 offerings, 299 labels carry an explicit effort token and 78 (provider, base +model) groups span more than one effort: -### Current coverage +``` +anthropic / Claude Opus 5 + low II=52.46 cost_per_task=0.425 slug=claude-opus-5-low + medium II=58.64 cost_per_task=0.724 slug=claude-opus-5-medium + high II=61.48 cost_per_task=1.227 slug=claude-opus-5-high + xhigh II=62.52 cost_per_task=1.801 slug=claude-opus-5-xhigh + max II=63.05 cost_per_task=2.337 slug=claude-opus-5 +``` -From the capture that this service was built against (2026-08-14): +`max` owns the unsuffixed slug. Per-token price is constant across efforts while +`cost_per_task_usd` varies, so effort is economically meaningful even though +pricing does not change with it. + +## The join -* 1045 offerings, 51 providers, 298 openness rows -* **563 matched (54%)** — 413 `exact`, 1 `base+mode`, 149 `base` -* 481 unmatched, 1 ambiguous +Exact match on `model_slug`. Openness records live in their own id space and +reference a model by `modelId`; that uuid appears **nowhere** in the leaderboard +payload (0 of 298 found), so `offering_id` cannot anchor this join. What does work +is the slug: the Openness page carries a model entity for each score, all 298 of +which resolve to a slug, and the leaderboard carries `model_slug` on every +offering. -The unmatched share is dominated by genuine coverage gaps, not matcher failure: -provider pages expose 305 distinct model bases while the Openness Index covers -229, and the missing ones are mostly newer models Artificial Analysis has not -scored for openness yet (Qwen3.8, Claude Opus 5, the GPT-5.x Codex line). +So there is no name normalisation, no creator filter, no confidence tiers, and no +fuzzy matching — the previous display-name join needed all four. Coverage improved +as a result: **698 offerings matched against 563 before, with 0 ambiguous instead +of 1.** + +209 of the 298 scored model slugs are served by at least one tracked provider; the +other 89 are scored models nobody serves, recorded in +`metadata.openness_without_offering`. Offerings whose model has no openness row +are listed in `unmatched`, and absent openness stays `null` rather than becoming +zero. ## Relationship to the canonical database This dataset sits alongside `database/current/prices.json` rather than merging -into it, for two structural reasons: - -1. **Different grain.** `tokenpricing.modeling.ModelInfo` is keyed by model with a - single provider and a single price. Artificial Analysis publishes one row per - (model x reasoning variant x serving platform) — 1045 rows over 51 providers, - including several rows per model per provider (`Kimi K3 (max)` and - `Kimi K3 (max) (FAST)`). -2. **No per-token pricing.** `PricingInfo.input_per_million` and - `output_per_million` are required fields, and the public Artificial Analysis - pages do not publish either. +into it because the **grain differs**: `tokenpricing.modeling.ModelInfo` is keyed +by model with a single provider and a single price, while Artificial Analysis +publishes one row per (provider x model x reasoning variant x serving endpoint) — +1082 rows over 58 providers. -The dataset links back by `model_slug`, `display_name` and `creator`, so a future -crosswalk into `ModelInfo` remains possible. The public SDK API is unchanged. +Per-token pricing is no longer the obstacle it was thought to be: the payload +carries `input_price_usd_per_1m`, `output_price_usd_per_1m` and both cache prices, +so a future crosswalk into `PricingInfo` is now possible. The dataset links back by +`model_slug`, `display_name` and `creator`. The public SDK API is unchanged. ## Failure behaviour -A silent partial capture is the realistic failure mode, not an HTTP error, so the -job refuses to publish a thin snapshot. `normalize_sources` raises `ShapeError` -when fewer than 40 provider pages, 800 offerings or 250 openness rows are parsed, -or when under 95% of offerings carry a `/models/` anchor. `parse` raises -`ParseError` when either table's columns change. +Three failure modes alert under their own names, each opening (or commenting on) +a GitHub issue labelled `aa-sync-failure` — see +[`alerting.py`](src/tokenpricing_aa/alerting.py): -Provider slugs discovered on the leaderboard do not all resolve — 7 of 58 return -404. These are recorded in `metadata.unreachable_provider_slugs` rather than -failing the run. +| Alert | Means | Detected by | +|---|---|---| +| `payload-not-found` | AA no longer ships data as a flight payload in the initial HTML | `flight.reconstruct_payload` | +| `request-blocked` | persistent non-200 or transport failure after retries | `fetch.get_page` | +| `schema-drift` | payload parsed but a field vanished, changed type, or changed units | `schema.check_drift` | + +Retry policy: retryable statuses (408/425/429/5xx) get 4 attempts with 2s/8s/30s +backoff; a refusal such as 403 or 451 is **not** retried, because repeating the +request will not change it. + +Drift is checked against `schema/offering-manifest.json` on every run, because "the +pipeline did not crash" is not evidence that the source is unchanged. A field that +disappears or changes type is breaking. So is a numeric field whose values move +more than 10x outside their recorded span — that is what a *unit* change looks like +from the outside (fractions rescaled to percentages, per-1M prices restated +per-1k), and it parses perfectly while meaning something else. A **new** field is +informational only: additive change is how AA ships new benchmarks and never fails +a run. On breaking drift the run alerts and publishes nothing, because a stale +snapshot is recoverable and a silently wrong one is not. + +Shape guards reject a thin capture before it can be published: `ShapeError` when +fewer than 850 offerings, 45 providers or 250 openness rows are parsed, when under +95% of offerings carry a model slug, or when `offering_id` is not unique. + +### Sending alerts somewhere else + +Delivery is one function, `deliver_via_github_issue`. Any `(Alert) -> str` +callable is a channel; pass it as `send_alert(alert, delivery=…)` or change +`DEFAULT_DELIVERY`. Nothing else in the service knows how alerts are delivered, so +adding Teams or email is a single new function. diff --git a/services/aa-sync/schema/offering-manifest.json b/services/aa-sync/schema/offering-manifest.json new file mode 100644 index 0000000..3b7a07b --- /dev/null +++ b/services/aa-sync/schema/offering-manifest.json @@ -0,0 +1,502 @@ +{ + "record_count": 1082, + "fields": { + "features.contextWindowTokens": { + "types": [ + "number" + ], + "populated_ratio": 1.0, + "minimum": 4096.0, + "maximum": 2000000.0 + }, + "features.functionCalling": { + "types": [ + "bool" + ], + "populated_ratio": 1.0 + }, + "features.jsonMode": { + "types": [ + "bool" + ], + "populated_ratio": 1.0 + }, + "features.openaiCompatible": { + "types": [ + "bool" + ], + "populated_ratio": 1.0 + }, + "footnotes": { + "types": [ + "null", + "string" + ], + "populated_ratio": 0.025 + }, + "host.color": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "host.functionCallingUrl": { + "types": [ + "null", + "string" + ], + "populated_ratio": 0.8549 + }, + "host.jsonModeUrl": { + "types": [ + "null", + "string" + ], + "populated_ratio": 0.8614 + }, + "host.logo": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "host.name": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "host.slug": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "hostApiId": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "id": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "label": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "model.aime25": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.4584, + "minimum": 0.0, + "maximum": 0.99 + }, + "model.apexAgents": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.1433, + "minimum": 0.00737463126843658, + "maximum": 0.470501474926254 + }, + "model.automationBench": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.1405, + "minimum": 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"types": [ + "null", + "number" + ], + "populated_ratio": 0.4954, + "minimum": 0.00211640211640212, + "maximum": 0.917460317460317 + }, + "model.mmmuPro": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.4621, + "minimum": 0.145086705202312, + "maximum": 0.854913294797688 + }, + "model.omniscience": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.9436, + "minimum": -84.4166666666667, + "maximum": 43.3 + }, + "model.omniscienceAccuracy": { + "types": [ + "number" + ], + "populated_ratio": 0.9436, + "minimum": 0.0375, + "maximum": 0.6535 + }, + "model.omniscienceNonHallucination": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.9436, + "minimum": 0.0155008719240457, + "maximum": 0.8833808844507846 + }, + "model.reasoningModel": { + "types": [ + "bool" + ], + "populated_ratio": 1.0 + }, + "model.scicode": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.9843, + "minimum": 0.0, + "maximum": 0.601851851851852 + }, + "model.sizeClass": { + "types": [ + "null", + "string" + ], + "populated_ratio": 0.9982 + }, + "model.slug": { + "types": [ + "string" + ], + "populated_ratio": 1.0 + }, + "model.tau2": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.8549, + "minimum": 0.0, + "maximum": 0.991228070175439 + }, + "model.tauBanking": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.488, + "minimum": 0.00206185567010309, + "maximum": 0.51340206185567 + }, + "model.terminalbenchHard": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.8484, + "minimum": 0.0, + "maximum": 0.659090909090909 + }, + "model.terminalbenchV21": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.5397, + "minimum": 0.0, + "maximum": 0.895131086142322 + }, + "performance.medianEndToEndResponseTimeSeconds": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 0.9212771973034348, + "maximum": 379.1163103460784 + }, + "performance.medianOutputTokensPerSecond": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 6.77081935192625, + "maximum": 1741.60717168212 + }, + "performance.medianReasoningTimeSeconds": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.3484, + "minimum": 1.1483645867559864, + "maximum": 326.1974048004891 + }, + "performance.medianTimeToFirstAnswerTokenSeconds": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 0.28731636949999, + "maximum": 327.9239994859891 + }, + "performance.medianTimeToFirstTokenSeconds": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 0.225839045500038, + "maximum": 214.8594473925 + }, + "performance.percentile05OutputTokensPerSecond": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 3.34139166170614, + "maximum": 1242.0925775297 + }, + "performance.percentile05TimeToFirstTokenSeconds": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 0.19727731295003, + "maximum": 109.5900858161 + }, + "performance.percentile95OutputTokensPerSecond": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 11.6353995454393, + "maximum": 3007.91430849967 + }, + "performance.percentile95TimeToFirstTokenSeconds": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 0.380386290249964, + "maximum": 443.404310084 + }, + "performance.quartile25OutputTokensPerSecond": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 4.81885556971971, + "maximum": 1586.92870223862 + }, + "performance.quartile25TimeToFirstTokenSeconds": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 0.215531554749987, + "maximum": 146.352216131 + }, + "performance.quartile75OutputTokensPerSecond": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 10.644349004231, + "maximum": 1939.13279548338 + }, + "performance.quartile75TimeToFirstTokenSeconds": { + "types": [ + "number" + ], + "populated_ratio": 0.8189, + "minimum": 0.250766401500073, + "maximum": 258.4857551165 + }, + "pricing.cacheHitPrice": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.4704, + "minimum": 0.002, + "maximum": 8.25 + }, + "pricing.cacheWritePrice": { + "types": [ + "null", + "number" + ], + "populated_ratio": 0.1192, + "minimum": 0.0, + "maximum": 18.75 + }, + "pricing.costPerTask": { + "types": [ + "number" + ], + "populated_ratio": 0.439, + "minimum": 0.0, + "maximum": 10.155232380864094 + }, + "pricing.price1mInputTokens": { + "types": [ + "number" + ], + "populated_ratio": 1.0, + "minimum": 0.0, + "maximum": 150.0 + }, + "pricing.price1mOutputTokens": { + "types": [ + "number" + ], + "populated_ratio": 1.0, + "minimum": 0.0, + "maximum": 600.0 + }, + "pricing.priceClass": { + "types": [ + "null", + "string" + ], + "populated_ratio": 0.9741 + } + } +} diff --git a/services/aa-sync/src/tokenpricing_aa/alerting.py b/services/aa-sync/src/tokenpricing_aa/alerting.py new file mode 100644 index 0000000..de203e8 --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/alerting.py @@ -0,0 +1,234 @@ +"""Failure and drift alerting for the Artificial Analysis sync. + +Three failure modes are worth waking someone for, and they are kept distinct +because each has a different remedy. An alert that says "sync failed" tells you +nothing; these name the failure: + +``payload-not-found`` + AA no longer ships page data as a flight payload in the initial HTML. The + acquisition strategy itself needs revisiting. +``request-blocked`` + The page could not be retrieved after retries -- a 403, a persistent 5xx, or a + transport failure. Nothing to fix in this repo; check whether we are blocked. +``schema-drift`` + The payload parsed, but a field vanished, changed type, or changed units. The + projection in ``parse.py`` needs updating before the data can be trusted. + +Delivery is deliberately one function. ``deliver_via_github_issue`` is the default +because it reaches no external service and needs no account decisions: it opens (or +comments on) an issue in this repository. To route alerts somewhere else -- Teams, +email, PagerDuty -- write a function with the same ``(Alert) -> str`` shape and pass +it as ``delivery`` to ``send_alert``, or change the default below. No other module +knows how alerts are delivered. +""" + +from __future__ import annotations + +import logging +import os +from collections.abc import Callable +from dataclasses import dataclass +from enum import Enum +from typing import Any + +import httpx + +logger = logging.getLogger(__name__) + +ISSUE_LABEL = "aa-sync-failure" +GITHUB_API = "https://api.github.com" + + +class AlertKind(str, Enum): + PAYLOAD_NOT_FOUND = "payload-not-found" + REQUEST_BLOCKED = "request-blocked" + SCHEMA_DRIFT = "schema-drift" + + +@dataclass +class Alert: + kind: AlertKind + subject: str + """What the failure was about (a URL, a dataset name) -- part of the identity.""" + detail: str + body: str + + @property + def title(self) -> str: + """Stable, human-readable, and unique per (kind, subject). + + Deduplication matches on this exact string, so it must not embed anything + that changes run to run -- no timestamps, no counts. + """ + return f"[aa-sync] {self.kind.value}: {self.subject}" + + +def _run_context() -> str: + server = os.environ.get("GITHUB_SERVER_URL", "https://github.com") + repo = os.environ.get("GITHUB_REPOSITORY") + run_id = os.environ.get("GITHUB_RUN_ID") + if repo and run_id: + return f"\n\nRun: {server}/{repo}/actions/runs/{run_id}" + return "" + + +def payload_not_found_alert(url: str, error: Exception) -> Alert: + detail = str(error) + body = ( + f"The flight payload could not be extracted from `{url}`.\n\n" + f"```\n{detail}\n```\n\n" + "This service reads AA's data from the `self.__next_f.push([1,...])` chunks " + "in the initial HTML document. This error means those chunks are gone, " + "renamed, or no longer decodable -- most likely AA changed how the page " + "ships its data (client-side fetch, different bundler, different " + "serialisation).\n\n" + "**What to check:** fetch the page manually and look for the chunk calls. If " + "they are gone, the acquisition strategy in `flight.py` needs rethinking; " + "the raw capture is attached to the failing workflow run." + + _run_context() + ) + return Alert(AlertKind.PAYLOAD_NOT_FOUND, url, detail, body) + + +def request_blocked_alert(url: str, error: Exception) -> Alert: + detail = str(error) + status = getattr(error, "status", None) + attempts = getattr(error, "attempts", None) + history = getattr(error, "history", None) or [] + body = ( + f"`{url}` could not be retrieved.\n\n" + f"- Final status: **{status if status is not None else 'transport failure'}**\n" + f"- Attempts: {attempts}\n" + + ("- History:\n" + "".join(f" - {h}\n" for h in history) if history else "") + + "\nRetries with backoff already ran, so this is not a transient blip. A 403 " + "or 451 means we are being refused and no code change here will help; a " + "persistent 5xx means AA is down.\n\n" + f"```\n{detail}\n```" + _run_context() + ) + return Alert(AlertKind.REQUEST_BLOCKED, url, detail, body) + + +def schema_drift_alert(subject: str, report: Any) -> Alert: + lines = [] + for label, items in ( + ("Missing / entirely null", report.missing), + ("Type changed", report.type_changed), + ("Range shifted (possible unit change)", report.range_shifted), + ): + if items: + lines.append(f"**{label}**\n" + "".join(f"- `{i}`\n" for i in sorted(items))) + if report.new: + lines.append( + "**New fields (informational, not a failure)**\n" + + "".join(f"- `{i}`\n" for i in sorted(report.new)) + ) + body = ( + f"The `{subject}` payload parsed successfully but no longer matches " + f"`schema/offering-manifest.json` over {report.record_count} records.\n\n" + + "\n".join(lines) + + "\nA field disappearing or changing type breaks the columns built from it. " + "A shifted numeric range usually means the *units* changed -- fractions " + "rescaled to percentages, per-1M prices restated per-1k -- which parses " + "cleanly and silently corrupts everything downstream.\n\n" + "**What to check:** compare against the manifest, update the projection in " + "`parse.py` if the change is real, then regenerate the manifest with " + "`uv run tokenpricing-aa-sync build-manifest`." + _run_context() + ) + return Alert(AlertKind.SCHEMA_DRIFT, subject, report.summary(), body) + + +# --------------------------------------------------------------------------- # +# Delivery. Swap this one function to change the channel. +# --------------------------------------------------------------------------- # + + +def deliver_via_github_issue( + alert: Alert, + *, + repo: str | None = None, + token: str | None = None, + client: httpx.Client | None = None, +) -> str: + """Open an issue for ``alert``, or comment on the open one that matches it. + + Deduplication is by exact issue title among open issues carrying + ``aa-sync-failure``, so a failure that persists for weeks accumulates comments + on one issue instead of opening a new issue every run. + """ + repo = repo or os.environ.get("GITHUB_REPOSITORY") or "" + token = token or os.environ.get("GITHUB_TOKEN") or "" + if not repo or not token: + logger.error( + "no GITHUB_REPOSITORY/GITHUB_TOKEN; alert not delivered:\n%s\n\n%s", + alert.title, + alert.body, + ) + return "undelivered: missing GITHUB_REPOSITORY or GITHUB_TOKEN" + + owns_client = client is None + http = client or httpx.Client(timeout=30.0) + headers = { + "Authorization": f"Bearer {token}", + "Accept": "application/vnd.github+json", + "X-GitHub-Api-Version": "2022-11-28", + } + try: + existing = http.get( + f"{GITHUB_API}/repos/{repo}/issues", + params={"state": "open", "labels": ISSUE_LABEL, "per_page": 100}, + headers=headers, + ) + existing.raise_for_status() + match = next( + ( + issue + for issue in existing.json() + if issue.get("title") == alert.title + ), + None, + ) + + if match is not None: + number = match["number"] + response = http.post( + f"{GITHUB_API}/repos/{repo}/issues/{number}/comments", + json={"body": f"Still failing.\n\n{alert.body}"}, + headers=headers, + ) + response.raise_for_status() + return f"commented on #{number}" + + response = http.post( + f"{GITHUB_API}/repos/{repo}/issues", + json={ + "title": alert.title, + "body": alert.body, + "labels": [ISSUE_LABEL], + }, + headers=headers, + ) + response.raise_for_status() + return f"opened #{response.json().get('number')}" + finally: + if owns_client: + http.close() + + +Delivery = Callable[[Alert], str] + +DEFAULT_DELIVERY: Delivery = deliver_via_github_issue + + +def send_alert(alert: Alert, delivery: Delivery | None = None) -> str: + """Deliver ``alert``, logging it regardless of whether delivery succeeds. + + Never raises: an alerting failure must not mask the failure being alerted on. + """ + logger.error("ALERT %s -- %s", alert.title, alert.detail) + try: + outcome = (delivery or DEFAULT_DELIVERY)(alert) + except Exception as exc: + logger.exception("alert delivery failed") + return f"delivery failed: {exc}" + logger.info("alert delivered: %s", outcome) + return outcome diff --git a/services/aa-sync/src/tokenpricing_aa/cli.py b/services/aa-sync/src/tokenpricing_aa/cli.py index b581bc7..81f4858 100644 --- a/services/aa-sync/src/tokenpricing_aa/cli.py +++ b/services/aa-sync/src/tokenpricing_aa/cli.py @@ -7,16 +7,42 @@ from pathlib import Path from typing import Any -from tokenpricing_aa.fetch import fetch_openness_page, fetch_provider_pages +from tokenpricing_aa.alerting import ( + Delivery, + payload_not_found_alert, + request_blocked_alert, + schema_drift_alert, + send_alert, +) +from tokenpricing_aa.fetch import ( + LEADERBOARD_URL, + FetchBlockedError, + fetch_leaderboard, + fetch_openness_page, +) +from tokenpricing_aa.flight import PayloadNotFoundError from tokenpricing_aa.normalize import normalize_sources +from tokenpricing_aa.parse import offering_objects from tokenpricing_aa.paths import ( CURRENT_DATABASE_DIR, HISTORY_DIR, + OFFERING_MANIFEST_PATH, RAW_CAPTURE_DIR, ) +from tokenpricing_aa.schema import build_manifest, check_drift DATASET_NAME = "artificial-analysis" +logger = logging.getLogger(__name__) + + +class DriftError(RuntimeError): + """Breaking drift was detected, so nothing was published. + + A stale snapshot is recoverable; a snapshot silently missing a column or + carrying rescaled units is not. The run stops before writing. + """ + def ensure_directories() -> None: CURRENT_DATABASE_DIR.mkdir(parents=True, exist_ok=True) @@ -26,8 +52,8 @@ def ensure_directories() -> None: def write_json(path: Path, payload: Any) -> None: path.parent.mkdir(parents=True, exist_ok=True) - # Explicit UTF-8: benchmark names carry non-ASCII (τ²-Bench), which the - # platform default encoding rejects on Windows. + # Explicit UTF-8: benchmark names carry non-ASCII, which the platform default + # encoding rejects on Windows. path.write_text( json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8" ) @@ -38,29 +64,61 @@ def read_json(path: Path) -> Any: def _raw_paths() -> tuple[Path, Path]: - return RAW_CAPTURE_DIR / "providers.json", RAW_CAPTURE_DIR / "openness.json" + return RAW_CAPTURE_DIR / "leaderboard.json", RAW_CAPTURE_DIR / "openness.json" -def sync() -> dict[str, Any]: - """Fetch AA pages, keep the raw capture, then normalize it.""" +def sync(delivery: Delivery | None = None) -> dict[str, Any]: + """Fetch both AA pages, keep the raw capture, check shape, then publish. + + Each of the three failure modes alerts under its own name before propagating. + """ ensure_directories() - providers = fetch_provider_pages() - openness = fetch_openness_page() - providers_path, openness_path = _raw_paths() - write_json(providers_path, providers) + try: + leaderboard = fetch_leaderboard() + openness = fetch_openness_page() + except FetchBlockedError as exc: + send_alert(request_blocked_alert(exc.url, exc), delivery) + raise + + leaderboard_path, openness_path = _raw_paths() + write_json(leaderboard_path, leaderboard) write_json(openness_path, openness) - return _build(providers, openness) + return _build(leaderboard, openness, delivery) -def normalize_only() -> dict[str, Any]: +def normalize_only(delivery: Delivery | None = None) -> dict[str, Any]: """Rebuild the dataset from the raw capture already on disk.""" ensure_directories() - providers_path, openness_path = _raw_paths() - return _build(read_json(providers_path), read_json(openness_path)) - + leaderboard_path, openness_path = _raw_paths() + return _build(read_json(leaderboard_path), read_json(openness_path), delivery) + + +def _build( + leaderboard: dict[str, Any], + openness: dict[str, Any], + delivery: Delivery | None = None, +) -> dict[str, Any]: + try: + objects = offering_objects(leaderboard["page"]) + except PayloadNotFoundError as exc: + send_alert(payload_not_found_alert(str(leaderboard.get("source_url")), exc), delivery) + raise + + drift = check_drift(objects) + if drift.new: + logger.info("new payload fields (informational): %s", ", ".join(drift.new)) + if drift.breaking: + send_alert(schema_drift_alert(DATASET_NAME, drift), delivery) + raise DriftError( + f"breaking payload drift, nothing published -- {drift.summary()}" + ) + + try: + dataset = normalize_sources(leaderboard, openness, drift=drift) + except PayloadNotFoundError as exc: + send_alert(payload_not_found_alert(str(openness.get("source_url")), exc), delivery) + raise -def _build(providers: dict[str, Any], openness: dict[str, Any]) -> dict[str, Any]: - dataset = normalize_sources(providers, openness) payload = dataset.model_dump(mode="json") timestamp = datetime.now(UTC).strftime("%Y%m%dT%H%M%SZ") current_path = CURRENT_DATABASE_DIR / f"{DATASET_NAME}.json" @@ -72,12 +130,47 @@ def _build(providers: dict[str, Any], openness: dict[str, Any]) -> dict[str, Any "history_snapshot": str(history_path), "providers": dataset.metadata.provider_count, "offerings": dataset.metadata.offering_count, + "deprecated_offerings": dataset.metadata.deprecated_offering_count, "openness_rows": dataset.metadata.openness_row_count, "openness_matched": dataset.metadata.openness_matched, "openness_unmatched": dataset.metadata.openness_unmatched, "openness_ambiguous": dataset.metadata.openness_ambiguous, - "match_tiers": dataset.metadata.match_tiers, - "unreachable_provider_slugs": dataset.metadata.unreachable_provider_slugs, + "new_payload_fields": drift.new, + } + + +def build_manifest_command(from_capture: bool = False) -> dict[str, Any]: + """Regenerate ``schema/offering-manifest.json`` from the live payload. + + Run this deliberately, after reviewing what changed -- it is the act of + accepting a new payload shape as expected. + """ + if from_capture: + leaderboard = read_json(_raw_paths()[0]) + else: + leaderboard = fetch_leaderboard() + manifest = build_manifest(offering_objects(leaderboard["page"])) + write_json(OFFERING_MANIFEST_PATH, manifest) + return { + "manifest": str(OFFERING_MANIFEST_PATH), + "records": manifest["record_count"], + "fields": len(manifest["fields"]), + "source": str(leaderboard.get("source_url", LEADERBOARD_URL)), + } + + +def check_command() -> dict[str, Any]: + """Compare the live payload against the manifest without publishing.""" + leaderboard = fetch_leaderboard() + drift = check_drift(offering_objects(leaderboard["page"])) + return { + "records": drift.record_count, + "breaking": drift.breaking, + "missing": drift.missing, + "type_changed": drift.type_changed, + "range_shifted": drift.range_shifted, + "new": drift.new, + "summary": drift.summary(), } @@ -92,6 +185,17 @@ def build_parser() -> argparse.ArgumentParser: subparsers.add_parser( "normalize", help="rebuild the AA dataset from the local raw capture" ) + subparsers.add_parser( + "check", help="compare the live payload against the expected-shape manifest" + ) + manifest = subparsers.add_parser( + "build-manifest", help="regenerate the expected-shape manifest" + ) + manifest.add_argument( + "--from-capture", + action="store_true", + help="use the local raw capture instead of fetching", + ) return parser @@ -105,4 +209,10 @@ def main() -> None: if args.command == "normalize": print(json.dumps(normalize_only(), indent=2)) return + if args.command == "check": + print(json.dumps(check_command(), indent=2)) + return + if args.command == "build-manifest": + print(json.dumps(build_manifest_command(args.from_capture), indent=2)) + return parser.error("unknown command") diff --git a/services/aa-sync/src/tokenpricing_aa/fetch.py b/services/aa-sync/src/tokenpricing_aa/fetch.py index c90e22a..172991f 100644 --- a/services/aa-sync/src/tokenpricing_aa/fetch.py +++ b/services/aa-sync/src/tokenpricing_aa/fetch.py @@ -2,35 +2,68 @@ Data source: Artificial Analysis (https://artificialanalysis.ai). -Everything this module needs is server-rendered into the initial HTML document: -there is no JSON API behind these pages and no browser is required. The spike -(`experiments/aa-scrape-spike`) recommended Playwright, but that was only needed -to reach the *expanded* column view of the aggregate leaderboard. The per-provider -pages and the Openness Index table are fully rendered on a plain GET, so this -workload is plain HTTP. +Two GETs per run. Everything this service needs is server-rendered into the initial +HTML document of the provider leaderboard, whose flight payload carries every +offering of every provider with every field -- including the superseded offerings +its rendered table hides. There is no JSON API behind these pages, no browser is +required, and the per-provider ``/providers/`` pages add nothing: see +``flight.py`` for the measurements behind that. """ from __future__ import annotations -import re +import logging from datetime import UTC, datetime from typing import Any import httpx BASE_URL = "https://artificialanalysis.ai" -PROVIDER_INDEX_URL = f"{BASE_URL}/leaderboards/providers" +LEADERBOARD_URL = f"{BASE_URL}/leaderboards/providers" OPENNESS_URL = f"{BASE_URL}/evaluations/artificial-analysis-openness-index" -PROVIDER_PAGE_URL = f"{BASE_URL}/providers/{{slug}}" -REQUEST_TIMEOUT = 60.0 +REQUEST_TIMEOUT = 90.0 USER_AGENT = "tokenpricing-aa-sync/0.1.0 (https://github.com/Atena-IT/tokenpricing)" -# Politeness: the whole capture is ~60 requests once a week, so there is nothing to -# rate limit against. A small delay keeps us well inside any reasonable budget. -REQUEST_DELAY_SECONDS = 1.0 +# Two requests a week. Retries exist to ride out a transient blip, not to grind +# against a block: a 403 is returned as-is on the first attempt. +MAX_ATTEMPTS = 4 +BACKOFF_SECONDS = (2.0, 8.0, 30.0) -_PROVIDER_HREF = re.compile(r"/providers/([a-z0-9._-]+)") +# Statuses worth a retry. Anything else is a decision by the far end, and +# repeating the request will not change it. +RETRY_STATUSES = frozenset({408, 425, 429, 500, 502, 503, 504}) + +logger = logging.getLogger(__name__) + + +class FetchBlockedError(RuntimeError): + """A page could not be retrieved after exhausting retries. + + Carries the evidence needed to tell a block from an outage without digging + through logs: the final status (``None`` for a transport failure), how many + attempts were made, and every status seen along the way. + """ + + def __init__( + self, + url: str, + status: int | None, + attempts: int, + history: list[str], + detail: str = "", + ) -> None: + self.url = url + self.status = status + self.attempts = attempts + self.history = history + self.detail = detail + described = f"HTTP {status}" if status is not None else "transport failure" + super().__init__( + f"{url} unreachable after {attempts} attempt(s): {described}" + + (f" ({detail})" if detail else "") + + f"; attempts: {', '.join(history)}" + ) def _client(client: httpx.Client | None = None) -> httpx.Client: @@ -43,30 +76,16 @@ def _client(client: httpx.Client | None = None) -> httpx.Client: ) -def discover_provider_slugs(html: str) -> list[str]: - """Extract provider slugs from the aggregate leaderboard markup. - - The leaderboard is only used for *discovery* — its rows are filtered - (``Status: Current``) and cover roughly half of what the per-provider pages - expose, so the row data itself is taken from the provider pages instead. - - Some discovered slugs do not resolve to a live page; callers must tolerate - 404s rather than assuming every slug is fetchable. - """ - slugs = { - slug - for slug in _PROVIDER_HREF.findall(html) - # Next.js chunk filenames land under the same prefix in the asset graph. - if not slug.endswith(".js") - } - return sorted(slugs) - - -def fetch_provider_pages( +def get_page( + url: str, client: httpx.Client | None = None, sleep: Any = None, -) -> dict[str, Any]: - """Fetch the provider index plus every discoverable provider page.""" +) -> str: + """GET ``url``, retrying only transient failures. + + Raises ``FetchBlockedError`` when the failure is persistent or is a refusal + (403, 404, 451, ...) that retrying cannot fix. + """ if sleep is None: import time @@ -74,51 +93,75 @@ def fetch_provider_pages( owns_client = client is None http = _client(client) + history: list[str] = [] try: - index = http.get(PROVIDER_INDEX_URL) - index.raise_for_status() - slugs = discover_provider_slugs(index.text) - if not slugs: - raise ValueError("No provider slugs discovered on the provider leaderboard") - - pages: dict[str, str] = {} - missing: list[dict[str, Any]] = [] - for slug in slugs: - sleep(REQUEST_DELAY_SECONDS) - response = http.get(PROVIDER_PAGE_URL.format(slug=slug)) - if response.status_code != 200: - missing.append({"slug": slug, "status": response.status_code}) + for attempt in range(1, MAX_ATTEMPTS + 1): + try: + response = http.get(url) + except httpx.HTTPError as exc: + history.append(f"attempt {attempt}: {type(exc).__name__}") + if attempt == MAX_ATTEMPTS: + raise FetchBlockedError( + url, None, attempt, history, str(exc) + ) from exc + sleep(BACKOFF_SECONDS[min(attempt - 1, len(BACKOFF_SECONDS) - 1)]) continue - pages[slug] = response.text + + history.append(f"attempt {attempt}: HTTP {response.status_code}") + if response.status_code == 200: + return response.text + + if response.status_code not in RETRY_STATUSES: + raise FetchBlockedError( + url, + response.status_code, + attempt, + history, + "not a retryable status", + ) + + if attempt == MAX_ATTEMPTS: + raise FetchBlockedError( + url, + response.status_code, + attempt, + history, + "retryable status persisted", + ) + logger.warning( + "%s returned %s, retrying (attempt %s/%s)", + url, + response.status_code, + attempt, + MAX_ATTEMPTS, + ) + sleep(BACKOFF_SECONDS[min(attempt - 1, len(BACKOFF_SECONDS) - 1)]) finally: if owns_client: http.close() + raise AssertionError("unreachable") # pragma: no cover + + +def fetch_leaderboard( + client: httpx.Client | None = None, sleep: Any = None +) -> dict[str, Any]: + """Fetch the provider leaderboard page.""" return { - "source": "artificial_analysis_providers", - "source_url": PROVIDER_INDEX_URL, + "source": "artificial_analysis_leaderboard", + "source_url": LEADERBOARD_URL, "fetched_at": datetime.now(UTC).isoformat(), - "discovered_slugs": slugs, - "unreachable_slugs": missing, - "pages": pages, + "page": get_page(LEADERBOARD_URL, client=client, sleep=sleep), } -def fetch_openness_page(client: httpx.Client | None = None) -> dict[str, Any]: - """Fetch the Openness Index leaderboard.""" - owns_client = client is None - http = _client(client) - try: - response = http.get(OPENNESS_URL) - response.raise_for_status() - html = response.text - finally: - if owns_client: - http.close() - +def fetch_openness_page( + client: httpx.Client | None = None, sleep: Any = None +) -> dict[str, Any]: + """Fetch the Openness Index page.""" return { "source": "artificial_analysis_openness", "source_url": OPENNESS_URL, "fetched_at": datetime.now(UTC).isoformat(), - "page": html, + "page": get_page(OPENNESS_URL, client=client, sleep=sleep), } diff --git a/services/aa-sync/src/tokenpricing_aa/flight.py b/services/aa-sync/src/tokenpricing_aa/flight.py new file mode 100644 index 0000000..0e7a6a0 --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/flight.py @@ -0,0 +1,151 @@ +"""Extraction of the React flight payload embedded in Artificial Analysis pages. + +AA is a Next.js app. Every page streams its server-rendered data into the initial +HTML document as a sequence of ``self.__next_f.push([1,""])`` calls; +concatenating those string literals reconstructs one flight payload containing the +full dataset behind the page. + +This is the *only* acquisition mechanism this service needs, because the payload is +a superset of what the page's HTML table renders: + +* The rendered table shows 12 columns collapsed and 50 expanded, but "Expand + Columns" is a client-side toggle over data that already shipped -- expanding + issues no network request. +* The provider leaderboard renders only non-deprecated offerings (its ``Status: + Current`` filter), while its payload carries every offering. Measured + 2026-08-14: Nebius rendered 25 of 35, Azure rendered 20 of 84, and + ``rendered + deprecated == payload`` exactly for both. + +So one GET of the leaderboard yields every offering of every provider with every +field, and the per-provider pages are redundant. +""" + +from __future__ import annotations + +import json +import re +from typing import Any + +# Only [1, ""] chunks carry payload text; [0]/[2] chunks are framing. +_CHUNK = re.compile(r'self\.__next_f\.push\(\[1,("(?:[^"\\]|\\.)*")\]\)') + +# React encodes `undefined` as this sentinel string rather than omitting the key. +UNDEFINED = "$undefined" + + +class PayloadNotFoundError(RuntimeError): + """The flight payload could not be located or reconstructed from a page. + + This is the failure mode to expect if AA stops shipping data in the initial + document -- moving to a client-side fetch, a different bundler, or a + different serialisation. It is deliberately distinct from a parse error over + a payload that *was* found: the remedy is different. + """ + + +def reconstruct_payload(html: str) -> str: + """Concatenate every flight chunk in ``html`` into one payload string.""" + literals = _CHUNK.findall(html) + if not literals: + raise PayloadNotFoundError( + "no self.__next_f.push([1,...]) chunks found in the document; " + "AA may no longer ship page data in the initial HTML" + ) + try: + payload = "".join(json.loads(literal) for literal in literals) + except json.JSONDecodeError as exc: + raise PayloadNotFoundError( + f"flight chunks found ({len(literals)}) but a chunk is not a decodable " + f"JSON string literal: {exc}" + ) from exc + if not payload.strip(): + raise PayloadNotFoundError( + f"flight chunks found ({len(literals)}) but they reconstructed to an " + "empty payload" + ) + return payload + + +def _enclosing_object(text: str, pos: int) -> str | None: + """Return the smallest complete JSON object containing ``pos``. + + Walks backwards to the object's opening brace, then forwards with a + string-aware depth counter to its close. + """ + depth = 0 + start = None + for i in range(pos, -1, -1): + char = text[i] + if char == "}": + depth += 1 + elif char == "{": + if depth == 0: + start = i + break + depth -= 1 + if start is None: + return None + + depth = 0 + in_string = False + escaped = False + for j in range(start, len(text)): + char = text[j] + if escaped: + escaped = False + continue + if char == "\\": + escaped = True + continue + if char == '"': + in_string = not in_string + continue + if in_string: + continue + if char == "{": + depth += 1 + elif char == "}": + depth -= 1 + if depth == 0: + return text[start : j + 1] + return None + + +def objects_with_key(payload: str, key: str) -> list[dict[str, Any]]: + """Every JSON object in ``payload`` that carries ``key``, de-duplicated by identity. + + The payload is a flat stream rather than one document, so records are located + by a marker key and brace-matched outwards instead of being indexed by path. + """ + found: list[dict[str, Any]] = [] + seen: set[str] = set() + for match in re.finditer(re.escape(f'"{key}"'), payload): + raw = _enclosing_object(payload, match.start()) + if raw is None or raw in seen: + continue + seen.add(raw) + try: + obj = json.loads(raw) + except json.JSONDecodeError: + # A marker can appear inside a label dictionary or a chart config, + # where the enclosing braces are not a self-contained object. + continue + if isinstance(obj, dict) and key in obj: + found.append(obj) + return found + + +def clean(value: Any) -> Any: + """Normalise React's ``$undefined`` sentinel to ``None``, recursively. + + ``$undefined`` and an absent key mean the same thing; the leaderboard omits + the key where a provider page emits the sentinel. Collapsing both to ``None`` + is what makes the two sources compare equal. + """ + if isinstance(value, dict): + return {k: clean(v) for k, v in value.items() if v != UNDEFINED} + if isinstance(value, list): + return [clean(v) for v in value] + if value == UNDEFINED: + return None + return value diff --git a/services/aa-sync/src/tokenpricing_aa/matching.py b/services/aa-sync/src/tokenpricing_aa/matching.py index 2dcc6bd..21dd782 100644 --- a/services/aa-sync/src/tokenpricing_aa/matching.py +++ b/services/aa-sync/src/tokenpricing_aa/matching.py @@ -1,36 +1,28 @@ -"""Join between the provider offering rows and the Openness Index rows. +"""Reasoning-variant identification and the Openness Index join. -The two AA datasets carry their model identity differently, and neither is -sufficient alone: +Both jobs used to be hard because the only identifiers available from the rendered +tables were display names, so joining meant normalising human-readable strings and +hoping. The payload removes that: -* Provider pages have a stable ``/models/`` anchor **and** an effort-suffixed - display name (``GPT-5.6 Sol (xhigh)``). -* The Openness Index has no anchors at all — its only identity is the rendered - display name plus a ``Creator`` column, and it disambiguates variants - differently (``(Reasoning)`` / ``(Non-reasoning)``, sometimes - ``(Reasoning, Max Effort)``). +* ``model.reasoningModel`` is a boolean, so reasoning mode is read, not inferred. +* Every offering carries ``model.slug``, and the Openness Index page carries a slug + for each scored model, so the cross-dataset join is an exact slug match. The + three-tier confidence matching, creator-prefix stripping, quantisation and + snapshot-token vocabularies, and date-token recognition that the display-name + join needed are all gone. -So the join runs on a normalised name key, not on slugs and not on raw strings. -The parenthetical suffix space is wider than reasoning effort alone: it also -carries quantisation (``FP8``, ``NVFP4``), serving tier (``FAST``, ``Turbo``), -hosting platform (``Vertex``, ``AI Studio``) and snapshot dates. Openness is a -property of the *model*, not of how a given provider serves it, so those -serving-side tokens are dropped from the key while reasoning identity is kept. - -Ambiguity is never resolved by guessing: candidates that disagree are reported -as ambiguous and left unmatched. +Reasoning *effort* is the one thing still read from names, because AA encodes it in +the slug suffix and the label parenthetical rather than as a field. """ from __future__ import annotations import re -from collections.abc import Iterable from dataclasses import dataclass, field from typing import Any -# Reasoning-effort levels. These are part of model identity for openness purposes -# only insofar as AA publishes separate rows for them; where AA does not, the -# effort level is dropped by the tier-2 fallback below. +# AA's effort vocabulary. ``max`` is the unsuffixed slug: claude-opus-5 is max, +# claude-opus-5-high is high. EFFORT_TOKENS = { "minimal": "minimal", "low": "low", @@ -45,214 +37,49 @@ "xhigh effort": "xhigh", } -REASONING_TOKENS = {"reasoning": "reasoning", "non-reasoning": "non-reasoning"} - -# Serving-side variation: quantisation, serving tier, hosting platform, snapshot -# labels. None of these change a model's openness, so they are not part of the key. -SERVING_TOKENS = { - "fp4", - "fp8", - "fp16", - "bf16", - "int4", - "int8", - "nvfp4", - "mxfp4", - "mxfp8", - "awq", - "gptq", - "fast", - "turbo", - "base", - "preview", - "exp", - "experimental", - "with fallback", - "ai studio", - "vertex", - "bedrock", - "chatgpt", - "vision", - "standard", - "batch", -} - -_MONTHS = ( - "jan|feb|mar|apr|may|jun|june|jul|july|aug|sep|sept|oct|nov|dec" -) -_DATE_TOKEN = re.compile(rf"^({_MONTHS})\b[\s'’]*\d{{0,4}}$", re.IGNORECASE) -_BARE_YEAR = re.compile(r"^'?\d{2,4}$") +_SLUG_EFFORT_SUFFIXES = ("minimal", "low", "medium", "high", "xhigh") _PARENTHETICAL = re.compile(r"\(([^)]*)\)") -_NON_ALNUM = re.compile(r"[^a-z0-9]+") - - -def _slugify(text: str) -> str: - """Normalise a name to lowercase alphanumeric tokens. - - ``+`` is spelled out rather than stripped: ``Command A`` and ``Command A+`` - are different models, and folding the plus away silently merges them. - """ - lowered = str(text or "").lower().replace("+", " plus ") - return _NON_ALNUM.sub(" ", lowered).strip() - - -def normalize_creator(creator: str | None) -> str | None: - """Creators are compared without spacing or punctuation (``Z AI`` -> ``zai``).""" - if not creator: - return None - return _NON_ALNUM.sub("", str(creator).lower()) or None - - -def _is_serving_token(token: str) -> bool: - if token in SERVING_TOKENS: - return True - if _DATE_TOKEN.match(token) or _BARE_YEAR.match(token): - return True - # "Sep '25", "Feb 2026" - return bool(re.match(rf"^({_MONTHS})\s", token, re.IGNORECASE)) @dataclass(frozen=True) -class NameKey: - """Normalised model identity used on both sides of the join.""" - - base: str - reasoning: str | None - effort: str | None - #: Serving-side suffixes recognised as safe to drop from the key. - dropped: tuple[str, ...] = () - #: Suffixes that matched no known vocabulary. Also dropped from the key, but - #: surfaced in dataset metadata so a new AA suffix type gets noticed rather - #: than silently changing what the join considers the same model. - unknown: tuple[str, ...] = () - - -def parse_display_name(name: str) -> NameKey: - """Split an AA display name into a base name and its classified suffixes.""" - raw_tokens: list[str] = [] - for group in _PARENTHETICAL.findall(name or ""): - # "(Turbo, FP8)" and "(Reasoning, Max Effort)" pack several tokens in one group. - raw_tokens.extend(part.strip() for part in group.split(",") if part.strip()) - - base = _slugify(_PARENTHETICAL.sub(" ", name or "")) +class Variant: + reasoning_mode: str | None + """``reasoning`` or ``non-reasoning``, read from the payload's boolean.""" + reasoning_effort: str | None + """``minimal``..``max``, or ``None`` where AA publishes no effort variant.""" + + +def parse_variant( + display_name: str, model_slug: str | None, reasoning_model: Any +) -> Variant: + """Identify the reasoning variant of one offering.""" + mode: str | None = None + if reasoning_model is True: + mode = "reasoning" + elif reasoning_model is False: + mode = "non-reasoning" + + slug = (model_slug or "").lower() + if slug.endswith("-non-reasoning"): + mode = "non-reasoning" effort: str | None = None - reasoning: str | None = None - dropped: list[str] = [] - unknown: list[str] = [] - for token in raw_tokens: - lowered = token.strip().lower() - if lowered in REASONING_TOKENS: - reasoning = REASONING_TOKENS[lowered] - elif lowered in EFFORT_TOKENS: - effort = EFFORT_TOKENS[lowered] - # An explicit effort level implies the model is reasoning, unless the - # name also says otherwise (AA emits "(Non-reasoning, high)"). - if reasoning is None: - reasoning = "reasoning" - elif _is_serving_token(lowered): - dropped.append(token) - else: - unknown.append(token) - - # "(Non-reasoning, high)" — the explicit mode wins over the effort implication. - for token in raw_tokens: - if token.strip().lower() == "non-reasoning": - reasoning = "non-reasoning" - - return NameKey( - base=base, - reasoning=reasoning, - effort=effort, - dropped=tuple(dropped), - unknown=tuple(unknown), - ) - - -# Order matters: "-non-reasoning" must be tested before "-reasoning". -_SLUG_REASONING_SUFFIXES = ( - ("-non-reasoning", "non-reasoning"), - ("-nonreasoning", "non-reasoning"), - ("-reasoning", "reasoning"), -) - - -def enrich_key_from_slug(key: NameKey, model_slug: str | None) -> NameKey: - """Fill in variant identity the display name omits but the slug states. - - Provider pages sometimes render a bare name (``Grok 4 Fast``) while the - ``/models/`` href spells the variant out (``grok-4-fast-reasoning``). The - Openness Index splits those models into ``(Reasoning)`` / ``(Non-reasoning)`` - rows, so without this the row is only resolvable as ambiguous. - - This reads variant identity **only** from an explicit trailing suffix. It - never infers effort from a bare slug: which effort level owns the - unsuffixed slug varies per model (``gpt-oss-120b`` is high, ``claude-opus-5`` - is max), so that is not derivable by rule. - """ - if not model_slug: - return key - slug = model_slug.lower() - reasoning, effort = key.reasoning, key.effort - - if reasoning is None: - for suffix, mode in _SLUG_REASONING_SUFFIXES: - if slug.endswith(suffix): - reasoning = mode - break + for suffix in _SLUG_EFFORT_SUFFIXES: + if slug.endswith(f"-{suffix}"): + effort = suffix + break if effort is None: - for level in EFFORT_TOKENS: - if " " in level: - continue - if slug.endswith(f"-{level}"): - effort = level - if reasoning is None: - reasoning = "reasoning" + for group in _PARENTHETICAL.findall(display_name or ""): + token = group.strip().lower() + if token in EFFORT_TOKENS: + effort = EFFORT_TOKENS[token] break - if reasoning == key.reasoning and effort == key.effort: - return key - return NameKey( - base=key.base, - reasoning=reasoning, - effort=effort, - dropped=key.dropped, - unknown=key.unknown, - ) + return Variant(reasoning_mode=mode, reasoning_effort=effort) -def _strip_creator_prefix(base: str, creator: str | None) -> str | None: - """Drop a leading creator name from a base name, if it is spelled there. - - ``NVIDIA Nemotron 3 Nano 30B A3B`` and ``Nemotron 3 Ultra 550B A55B`` both - appear in the Openness Index, so the creator prefix cannot be assumed either - way and both spellings have to be tried. - """ - wanted = normalize_creator(creator) - if not wanted: - return None - words = base.split() - accumulated = "" - for index, word in enumerate(words): - accumulated += _NON_ALNUM.sub("", word) - if not wanted.startswith(accumulated): - return None - if accumulated == wanted and index + 1 < len(words): - return " ".join(words[index + 1 :]) - return None - - -def _base_variants(key: NameKey, creator: str | None) -> list[str]: - """Openness sometimes prefixes the creator onto the model name, sometimes not.""" - variants = [key.base] - trimmed = _strip_creator_prefix(key.base, creator) - if trimmed: - variants.append(trimmed) - return variants - - -OPENNESS_COMPONENTS = ( +BREAKDOWN_FIELDS = ( "openness_index", "model_availability", "model_transparency", @@ -260,166 +87,77 @@ def _base_variants(key: NameKey, creator: str | None) -> list[str]: "pre_training_data_license", "post_training_data_access", "post_training_data_license", + "transparency_methodology", + "transparency_pre_training_data", + "transparency_post_training_data", ) -def _component_signature(row: dict[str, Any]) -> tuple[Any, ...]: - return tuple(row.get(field_name) for field_name in OPENNESS_COMPONENTS) - - -@dataclass -class OpennessIndexTable: - """Lookup structure over the Openness Index rows.""" - - rows: list[dict[str, Any]] - _exact: dict[tuple[str, str | None, str | None], list[dict[str, Any]]] = field( - default_factory=dict - ) - _by_base_mode: dict[tuple[str, str | None], list[dict[str, Any]]] = field( - default_factory=dict - ) - _by_base: dict[str, list[dict[str, Any]]] = field(default_factory=dict) - - def __post_init__(self) -> None: - for row in self.rows: - key = parse_display_name(row["display_name"]) - row.setdefault("_key", key) - for base in _base_variants(key, row.get("creator")): - self._exact.setdefault((base, key.reasoning, key.effort), []).append(row) - self._by_base_mode.setdefault((base, key.reasoning), []).append(row) - self._by_base.setdefault(base, []).append(row) - - def candidates(self, tier: str, bases: list[str], key: NameKey) -> list[dict[str, Any]]: - """Look up candidate openness rows for one confidence tier.""" - found: list[dict[str, Any]] = [] - for base in bases: - if tier == "exact": - found.extend(self._exact.get((base, key.reasoning, key.effort), [])) - elif tier == "base+mode": - found.extend(self._by_base_mode.get((base, key.reasoning), [])) - else: - found.extend(self._by_base.get(base, [])) - # The same row can be reached through several base variants. - unique: list[dict[str, Any]] = [] - for row in found: - if not any(row is seen for seen in unique): - unique.append(row) - return unique - - @staticmethod - def collapse( - candidates: Iterable[dict[str, Any]], creator: str | None - ) -> tuple[dict[str, Any] | None, list[dict[str, Any]]]: - """Return a single row when candidates agree on every openness component. - - A creator, when known on both sides, is a hard filter: two different - creators shipping a similarly named model must never be merged. - """ - pool = list(candidates) - wanted = normalize_creator(creator) - if wanted: - filtered = [ - row for row in pool if normalize_creator(row.get("creator")) == wanted - ] - if filtered: - pool = filtered - elif any(normalize_creator(row.get("creator")) for row in pool): - # Every candidate names a different creator: same model name, - # different lab. Reject outright rather than falling back. - return None, [] - if not pool: - return None, [] - signatures = {_component_signature(row) for row in pool} - if len(signatures) == 1: - return pool[0], pool - return None, pool - - @dataclass class JoinResult: matched: int = 0 + """Offerings that received an openness breakdown.""" unmatched: list[dict[str, Any]] = field(default_factory=list) + """Offerings whose model has no Openness Index row.""" ambiguous: list[dict[str, Any]] = field(default_factory=list) - tiers: dict[str, int] = field(default_factory=dict) + """Offerings whose model slug maps to more than one openness row.""" + openness_without_offering: list[str] = field(default_factory=list) + """Scored models no tracked provider serves.""" def join_openness( offerings: list[dict[str, Any]], openness_rows: list[dict[str, Any]] ) -> JoinResult: - """Attach openness components to offering rows in place. - - Matching runs in descending order of confidence: + """Attach openness breakdowns to offerings by exact model slug. - 1. base name + reasoning mode + effort level - 2. base name + reasoning mode, when every candidate agrees on the components - (AA frequently publishes one openness row for all effort levels) - 3. base name alone, when every candidate agrees - - Anything that survives all three without a unique, self-consistent answer is - recorded as ambiguous or unmatched — never guessed. + Mutates each offering in place, setting ``openness`` where a row exists. A + model with no openness row is normal -- AA scores far fewer models than + providers serve -- so it is recorded rather than warned about. """ - table = OpennessIndexTable(openness_rows) + by_slug: dict[str, list[dict[str, Any]]] = {} + for row in openness_rows: + slug = row.get("model_slug") + if slug: + by_slug.setdefault(str(slug), []).append(row) + result = JoinResult() + used: set[str] = set() for offering in offerings: - key = enrich_key_from_slug( - parse_display_name(offering["display_name"]), offering.get("model_slug") - ) - creator = offering.get("creator") - offering["openness"] = None - offering["openness_match"] = None - - chosen: dict[str, Any] | None = None - tier_used: str | None = None - pool: list[dict[str, Any]] = [] - - bases = _base_variants(key, creator) - for tier in ("exact", "base+mode", "base"): - candidates = table.candidates(tier, bases, key) - if not candidates: - continue - chosen, pool = table.collapse(candidates, creator) - if chosen is not None: - tier_used = tier - break - if pool: - # Candidates exist but disagree: stop here rather than falling - # through to a looser tier that would only be more ambiguous. - break + slug = offering.get("model_slug") + candidates = by_slug.get(str(slug), []) if slug else [] - if chosen is not None and tier_used is not None: - offering["openness"] = { - name: chosen.get(name) for name in OPENNESS_COMPONENTS - } - offering["openness_match"] = { - "tier": tier_used, - "openness_display_name": chosen["display_name"], - "openness_creator": chosen.get("creator"), - } - result.matched += 1 - result.tiers[tier_used] = result.tiers.get(tier_used, 0) + 1 - elif pool: - result.ambiguous.append( + if not candidates: + offering["openness"] = None + result.unmatched.append( { "provider_slug": offering.get("provider_slug"), - "model_slug": offering.get("model_slug"), - "display_name": offering["display_name"], - "creator": creator, - "normalized_base": key.base, - "candidates": sorted({row["display_name"] for row in pool}), - "reason": "candidates disagree on openness components", + "model_slug": slug, + "display_name": offering.get("display_name"), + "creator": offering.get("creator"), + "reason": "no openness row for this model slug", } ) - else: - result.unmatched.append( + continue + + if len(candidates) > 1: + offering["openness"] = None + result.ambiguous.append( { "provider_slug": offering.get("provider_slug"), - "model_slug": offering.get("model_slug"), - "display_name": offering["display_name"], - "creator": creator, - "normalized_base": key.base, - "reason": "no openness row for this model", + "model_slug": slug, + "display_name": offering.get("display_name"), + "creator": offering.get("creator"), + "candidates": [str(c.get("display_name")) for c in candidates], + "reason": "model slug maps to multiple openness rows", } ) + continue + + row = candidates[0] + offering["openness"] = {name: row.get(name) for name in BREAKDOWN_FIELDS} + used.add(str(slug)) + result.matched += 1 + result.openness_without_offering = sorted(set(by_slug) - used) return result diff --git a/services/aa-sync/src/tokenpricing_aa/modeling.py b/services/aa-sync/src/tokenpricing_aa/modeling.py index b9f6a37..c4a0265 100644 --- a/services/aa-sync/src/tokenpricing_aa/modeling.py +++ b/services/aa-sync/src/tokenpricing_aa/modeling.py @@ -1,18 +1,14 @@ """Pydantic models for the Artificial Analysis dataset. These live in the service rather than in ``libraries/python`` (the public SDK) -for two reasons: +because the grain differs: ``tokenpricing.modeling.ModelInfo`` is keyed by model +with a single provider and a single price, while AA's data is one row per +(provider x model x reasoning variant x serving endpoint) -- 1082 rows over 58 +providers as of 2026-08-14. -* The grain differs. ``tokenpricing.modeling.ModelInfo`` is keyed by model with a - single provider and a single price; AA's data is one row per - (model x reasoning variant x serving platform) — 1045 rows over 51 providers. -* AA's public pages carry no per-token pricing at all (only ``Cost per Task USD``), - so these rows cannot populate ``PricingInfo``/``SourceInfo``, whose - ``input_per_million``/``output_per_million`` are required. - -The dataset therefore sits alongside the canonical database and links to it by -``model_slug``/``display_name`` rather than trying to impersonate ``ModelInfo``. -The SDK's public API is unchanged. +The dataset sits alongside the canonical database and links to it by +``model_slug``/``display_name`` rather than impersonating ``ModelInfo``. The SDK's +public API is unchanged. Data source: Artificial Analysis (https://artificialanalysis.ai). """ @@ -34,11 +30,10 @@ class OpennessBreakdown(BaseModel): - """Openness Index components as published, on their raw published scales. + """Openness Index components on their raw published scales. - The live table publishes ``Model Availability`` as an aggregate and does not - publish the two methodology subcomponents at all, so this is what AA exposes - rather than the full six-part 0-18 breakdown described in the spec. + The payload carries the two methodology subcomponents that the rendered + Openness Index table omits, so this is a fuller breakdown than the site shows. """ openness_index: float | None = Field( @@ -50,53 +45,111 @@ class OpennessBreakdown(BaseModel): pre_training_data_license: float | None = None post_training_data_access: float | None = None post_training_data_license: float | None = None - - -class OpennessMatch(BaseModel): - """How an offering was matched to its Openness Index row.""" - - tier: str = Field( - description="Confidence tier that produced the match: exact, base+mode, or base" - ) - openness_display_name: str - openness_creator: str | None = None + transparency_methodology: float | None = None + transparency_pre_training_data: float | None = None + transparency_post_training_data: float | None = None class Offering(BaseModel): - """One model as served by one provider, as measured by Artificial Analysis.""" + """One model as served by one provider at one reasoning effort. + + ``offering_id`` is AA's own uuid for the row and is the primary key. No + composite of the human-readable fields is unique: verified over the full + 1082-row payload, ``(provider_slug, model_slug, host_api_id, display_name)`` + still collides on 4 groups, because AA publishes distinct endpoints that differ + only in price and measured performance (``openai``/``o3`` appears at both + $10/$40 and $2/$8 per 1M tokens under one ``host_api_id``). + """ + offering_id: str provider_slug: str - model_slug: str + provider_name: str | None = None + model_slug: str | None = None display_name: str + host_api_id: str | None = None + footnotes: str | None = None creator: str | None = None + + # Identity / lifecycle + is_open_weights: bool | None = Field( + default=None, + description="AA's License column is the rendering of this boolean", + ) + deprecated: bool | None = Field( + default=None, + description=( + "Superseded offering. The leaderboard's Status: Current view hides " + "these; the payload retains them, so price history stays continuous " + "when a model is replaced. Filter at query time, not acquisition time." + ), + ) + reasoning_model: bool | None = None + size_class: str | None = None + reasoning_mode: str | None = Field( + default=None, description="reasoning or non-reasoning" + ) + reasoning_effort: str | None = Field( + default=None, description="minimal, low, medium, high, xhigh, or max" + ) + + # Features context_window: int | None = None - supports_function_calling: bool = False - supports_json_mode: bool = False - license: str | None = None + supports_function_calling: bool | None = None + supports_json_mode: bool | None = None + openai_compatible: bool | None = None + + # Intelligence intelligence_index: float | None = None - intelligence_index_estimated: bool = False + intelligence_index_estimated: bool | None = None + omniscience_index: float | None = None + omniscience_accuracy: float | None = None + omniscience_non_hallucination: float | None = None + gdpval_normalized: float | None = None + briefcase_elo: float | None = None + terminalbench_hard: float | None = None + terminalbench_v21: float | None = None + tau2_bench_telecom: float | None = None + tau3_banking: float | None = None + aa_lcr: float | None = None + humanitys_last_exam: float | None = None + gpqa_diamond: float | None = None + scicode: float | None = None + ifbench: float | None = None + critpt: float | None = None + apex_agents: float | None = None + itbench_sre: float | None = None + mmmu_pro: float | None = None + livecodebench: float | None = None + aime_2025: float | None = None + automation_bench: float | None = None + harvey_lab: float | None = None + + # Price cost_per_task_usd: float | None = None - cost_per_task_estimated: bool = False + price_class: str | None = None + input_price_usd_per_1m: float | None = None + output_price_usd_per_1m: float | None = None + cache_hit_price_usd_per_1m: float | None = None + cache_write_price_usd_per_1m: float | None = None + + # Speed median_output_tokens_per_second: float | None = None + p5_output_tokens_per_second: float | None = None + p25_output_tokens_per_second: float | None = None + p75_output_tokens_per_second: float | None = None + p95_output_tokens_per_second: float | None = None + + # Latency median_first_chunk_seconds: float | None = None + p5_first_chunk_seconds: float | None = None + p25_first_chunk_seconds: float | None = None + p75_first_chunk_seconds: float | None = None + p95_first_chunk_seconds: float | None = None + first_answer_token_seconds: float | None = None total_response_seconds: float | None = None reasoning_time_seconds: float | None = None - reasoning_mode: str | None = Field( - default=None, description="reasoning, non-reasoning, or None when unspecified" - ) - reasoning_effort: str | None = Field( - default=None, description="minimal, low, medium, high, xhigh, or max" - ) - variant_tokens: list[str] = Field( - default_factory=list, - description="Serving-side name suffixes (quantisation, tier, platform, snapshot)", - ) - unclassified_variant_tokens: list[str] = Field( - default_factory=list, - description="Name suffixes matching no known vocabulary; dropped from the join key", - ) + openness: OpennessBreakdown | None = None - openness_match: OpennessMatch | None = None class UnmatchedOffering(BaseModel): @@ -104,11 +157,20 @@ class UnmatchedOffering(BaseModel): model_slug: str | None = None display_name: str creator: str | None = None - normalized_base: str | None = None candidates: list[str] = Field(default_factory=list) reason: str +class DriftSummary(BaseModel): + """Outcome of the manifest check for this run.""" + + breaking: bool = False + missing: list[str] = Field(default_factory=list) + type_changed: list[str] = Field(default_factory=list) + range_shifted: list[str] = Field(default_factory=list) + new: list[str] = Field(default_factory=list) + + class AAMetadata(BaseModel): fetched_at: datetime sources: list[str] @@ -116,19 +178,13 @@ class AAMetadata(BaseModel): openness_spec_version: str = OPENNESS_SPEC_VERSION provider_count: int offering_count: int + deprecated_offering_count: int = 0 openness_row_count: int openness_matched: int openness_unmatched: int openness_ambiguous: int - match_tiers: dict[str, int] = Field(default_factory=dict) - unreachable_provider_slugs: list[str] = Field(default_factory=list) - unclassified_variant_tokens: list[str] = Field( - default_factory=list, - description=( - "Distinct name suffixes matching no known vocabulary. A new entry here " - "means AA introduced a suffix type the join key does not account for." - ), - ) + openness_without_offering: int = 0 + drift: DriftSummary = Field(default_factory=DriftSummary) class AADataset(BaseModel): diff --git a/services/aa-sync/src/tokenpricing_aa/normalize.py b/services/aa-sync/src/tokenpricing_aa/normalize.py index 6e3a1a7..dae6da9 100644 --- a/services/aa-sync/src/tokenpricing_aa/normalize.py +++ b/services/aa-sync/src/tokenpricing_aa/normalize.py @@ -6,28 +6,30 @@ from datetime import UTC, datetime from typing import Any -from tokenpricing_aa.matching import ( - enrich_key_from_slug, - join_openness, - parse_display_name, -) +from tokenpricing_aa.matching import join_openness, parse_variant from tokenpricing_aa.modeling import ( AADataset, AAMetadata, + DriftSummary, Offering, OpennessBreakdown, - OpennessMatch, UnmatchedOffering, ) -from tokenpricing_aa.parse import parse_openness_page, parse_provider_page +from tokenpricing_aa.parse import ( + offering_objects, + parse_leaderboard, + parse_openness, +) +from tokenpricing_aa.schema import DriftReport, check_drift logger = logging.getLogger(__name__) # Shape guards. A silent partial capture writing a truncated dataset into # database/ is the realistic failure mode here, not an HTTP error, so the job -# fails loudly instead of publishing a thin snapshot. -MIN_PROVIDERS = 40 -MIN_OFFERINGS = 800 +# fails loudly instead of publishing a thin snapshot. Baselines from the +# 2026-08-14 capture: 1082 offerings, 58 providers, 298 openness rows. +MIN_PROVIDERS = 45 +MIN_OFFERINGS = 850 MIN_OPENNESS_ROWS = 250 MIN_SLUG_COVERAGE = 0.95 @@ -42,88 +44,80 @@ def _check(condition: bool, message: str) -> None: def normalize_sources( - providers_payload: dict[str, Any], openness_payload: dict[str, Any] + leaderboard_payload: dict[str, Any], + openness_payload: dict[str, Any], + drift: DriftReport | None = None, ) -> AADataset: - pages: dict[str, str] = providers_payload.get("pages") or {} - _check( - len(pages) >= MIN_PROVIDERS, - f"only {len(pages)} provider pages captured, expected >= {MIN_PROVIDERS}", - ) + """Project both captured pages into the published dataset. - raw_offerings: list[dict[str, Any]] = [] - for slug in sorted(pages): - rows = parse_provider_page(pages[slug], slug) - if not rows: - logger.warning("provider page %s parsed to zero rows", slug) - raw_offerings.extend(rows) + ``drift`` is the manifest comparison for this run, recorded in the dataset's + metadata so a snapshot carries the evidence that its shape was checked. + """ + page = leaderboard_payload["page"] + rows = parse_leaderboard(page) _check( - len(raw_offerings) >= MIN_OFFERINGS, - f"only {len(raw_offerings)} offerings parsed, expected >= {MIN_OFFERINGS}", + len(rows) >= MIN_OFFERINGS, + f"only {len(rows)} offerings parsed, expected >= {MIN_OFFERINGS}", ) - with_slug = sum(1 for row in raw_offerings if row.get("model_slug")) - coverage = with_slug / len(raw_offerings) + providers = {row["provider_slug"] for row in rows if row.get("provider_slug")} + _check( + len(providers) >= MIN_PROVIDERS, + f"only {len(providers)} providers in the payload, expected >= {MIN_PROVIDERS}", + ) + with_slug = sum(1 for row in rows if row.get("model_slug")) + coverage = with_slug / len(rows) _check( coverage >= MIN_SLUG_COVERAGE, - f"only {coverage:.1%} of offerings carry a /models/ anchor, " + f"only {coverage:.1%} of offerings carry a model slug, " f"expected >= {MIN_SLUG_COVERAGE:.0%}", ) - openness_rows = parse_openness_page(openness_payload["page"]) + ids = [row["offering_id"] for row in rows] + _check( + len(set(ids)) == len(ids), + f"offering_id is not unique: {len(ids) - len(set(ids))} duplicate(s)", + ) + + openness_rows = parse_openness(openness_payload["page"]) _check( len(openness_rows) >= MIN_OPENNESS_ROWS, f"only {len(openness_rows)} openness rows parsed, " f"expected >= {MIN_OPENNESS_ROWS}", ) - join = join_openness(raw_offerings, openness_rows) + if drift is None: + drift = check_drift(offering_objects(page)) + + join = join_openness(rows, openness_rows) offerings: list[Offering] = [] - for row in raw_offerings: - key = enrich_key_from_slug( - parse_display_name(row["display_name"]), row.get("model_slug") + for row in rows: + variant = parse_variant( + row.get("display_name") or "", + row.get("model_slug"), + row.get("reasoning_model"), ) openness = row.get("openness") - match = row.get("openness_match") offerings.append( Offering( - **{ - name: row[name] - for name in ( - "provider_slug", - "model_slug", - "display_name", - "creator", - "context_window", - "supports_function_calling", - "supports_json_mode", - "license", - "intelligence_index", - "intelligence_index_estimated", - "cost_per_task_usd", - "cost_per_task_estimated", - "median_output_tokens_per_second", - "median_first_chunk_seconds", - "total_response_seconds", - "reasoning_time_seconds", - ) - }, - reasoning_mode=key.reasoning, - reasoning_effort=key.effort, - variant_tokens=list(key.dropped), - unclassified_variant_tokens=list(key.unknown), + **{k: v for k, v in row.items() if k != "openness"}, + reasoning_mode=variant.reasoning_mode, + reasoning_effort=variant.reasoning_effort, openness=OpennessBreakdown(**openness) if openness else None, - openness_match=OpennessMatch(**match) if match else None, ) ) - for entry in join.unmatched: - logger.info( - "openness unmatched: %s @ %s (%s)", - entry["display_name"], - entry["provider_slug"], - entry["reason"], - ) + logger.info( + "%s offerings over %s providers; openness matched %s, unmatched %s, " + "ambiguous %s; %s scored models served by nobody", + len(offerings), + len(providers), + join.matched, + len(join.unmatched), + len(join.ambiguous), + len(join.openness_without_offering), + ) for entry in join.ambiguous: logger.warning( "openness ambiguous: %s @ %s -> %s", @@ -133,44 +127,36 @@ def normalize_sources( ) fetched_at = max( - datetime.fromisoformat(str(providers_payload["fetched_at"])), + datetime.fromisoformat(str(leaderboard_payload["fetched_at"])), datetime.fromisoformat(str(openness_payload["fetched_at"])), ) - unreachable = [ - str(item.get("slug")) - for item in providers_payload.get("unreachable_slugs") or [] - ] - unclassified = sorted( - {token for offering in offerings for token in offering.unclassified_variant_tokens} - ) - if unclassified: - logger.warning( - "unclassified name suffixes (dropped from the join key): %s", unclassified - ) return AADataset( generated_at=datetime.now(UTC), metadata=AAMetadata( fetched_at=fetched_at, sources=[ - str(providers_payload.get("source_url")), + str(leaderboard_payload.get("source_url")), str(openness_payload.get("source_url")), ], - provider_count=len(pages), + provider_count=len(providers), offering_count=len(offerings), + deprecated_offering_count=sum(1 for o in offerings if o.deprecated), openness_row_count=len(openness_rows), openness_matched=join.matched, openness_unmatched=len(join.unmatched), openness_ambiguous=len(join.ambiguous), - match_tiers=dict(sorted(join.tiers.items())), - unreachable_provider_slugs=sorted(unreachable), - unclassified_variant_tokens=unclassified, + openness_without_offering=len(join.openness_without_offering), + drift=DriftSummary( + breaking=drift.breaking, + missing=sorted(drift.missing), + type_changed=sorted(drift.type_changed), + range_shifted=sorted(drift.range_shifted), + new=sorted(drift.new), + ), ), offerings=offerings, - openness_index=[ - {name: value for name, value in row.items() if not name.startswith("_")} - for row in openness_rows - ], + openness_index=openness_rows, unmatched=[UnmatchedOffering(**entry) for entry in join.unmatched], ambiguous=[UnmatchedOffering(**entry) for entry in join.ambiguous], ) diff --git a/services/aa-sync/src/tokenpricing_aa/parse.py b/services/aa-sync/src/tokenpricing_aa/parse.py index cda02bc..dac4bbc 100644 --- a/services/aa-sync/src/tokenpricing_aa/parse.py +++ b/services/aa-sync/src/tokenpricing_aa/parse.py @@ -1,243 +1,218 @@ -"""HTML parsing for Artificial Analysis pages. +"""Projection of the Artificial Analysis flight payload into flat records. -Every value parser here exists because of a rendering quirk observed in the -captured pages; see ``tests/test_parse.py`` for the fixtures behind each one. +There is no HTML table parsing here and no value cleaning. The payload carries +typed JSON -- floats at full precision, real booleans, explicit ``null`` -- so the +em-dash / U+2212 / currency-symbol / thousands-separator / trailing-asterisk +handling that a table-scraping parser needs has no counterpart in this module. + +Two things AA's own table cannot express survive here: + +* ``intelligence_index_estimated`` is a real boolean field rather than a trailing + asterisk on a rendered score. +* ``deprecated`` distinguishes superseded offerings, which the leaderboard's + ``Status: Current`` view hides but the payload retains. """ from __future__ import annotations from typing import Any -from bs4 import BeautifulSoup, Tag - -# The per-provider offering table, after the two-deep header is flattened. -PROVIDER_COLUMNS = ( - "Model", - "Context Window", - "Function Calling", - "JSON Mode", - "License", - "Artificial Analysis Intelligence Index", - "Cost per Task USD", - "Median Tokens/s", - "Median First Chunk (s)", - "Total Response (s)", - "Reasoning Time (s)", - "Further Analysis", +from tokenpricing_aa.flight import ( + PayloadNotFoundError, + clean, + objects_with_key, + reconstruct_payload, ) -OPENNESS_COLUMNS = ( - "Creator", - "Model", - "Openness Index", - "Intelligence Index", - "Model Availability", - "Model Transparency", - "Pre-training Data Access", - "Pre-training Data License", - "Post-training Data Access", - "Post-training Data License", +# Marker keys used to locate each record type in the payload stream. +OFFERING_MARKER = "hostApiId" +OPENNESS_MARKER = "opennessIndex" + +# (output field, path into the offering object). The single source of truth for +# the projection; ``schema.py`` builds its drift manifest from these paths, so a +# field cannot be read here without being covered there. +OFFERING_FIELDS: tuple[tuple[str, tuple[str, ...]], ...] = ( + ("offering_id", ("id",)), + ("display_name", ("label",)), + ("host_api_id", ("hostApiId",)), + ("footnotes", ("footnotes",)), + ("provider_slug", ("host", "slug")), + ("provider_name", ("host", "name")), + ("model_slug", ("model", "slug")), + ("creator", ("model", "creator", "name")), + ("is_open_weights", ("model", "isOpenWeights")), + ("deprecated", ("model", "deprecated")), + ("reasoning_model", ("model", "reasoningModel")), + ("size_class", ("model", "sizeClass")), + ("context_window", ("features", "contextWindowTokens")), + ("supports_function_calling", ("features", "functionCalling")), + ("supports_json_mode", ("features", "jsonMode")), + ("openai_compatible", ("features", "openaiCompatible")), + # Intelligence + ("intelligence_index", ("model", "intelligenceIndex")), + ("intelligence_index_estimated", ("model", "intelligenceIndexIsEstimated")), + ("omniscience_index", ("model", "omniscience")), + ("omniscience_accuracy", ("model", "omniscienceAccuracy")), + ("omniscience_non_hallucination", ("model", "omniscienceNonHallucination")), + ("gdpval_normalized", ("model", "gdpvalNormalized")), + ("briefcase_elo", ("model", "briefcase", "elo")), + ("terminalbench_hard", ("model", "terminalbenchHard")), + ("terminalbench_v21", ("model", "terminalbenchV21")), + ("tau2_bench_telecom", ("model", "tau2")), + ("tau3_banking", ("model", "tauBanking")), + ("aa_lcr", ("model", "lcr")), + ("humanitys_last_exam", ("model", "hle")), + ("gpqa_diamond", ("model", "gpqa")), + ("scicode", ("model", "scicode")), + ("ifbench", ("model", "ifbench")), + ("critpt", ("model", "critpt")), + ("apex_agents", ("model", "apexAgents")), + ("itbench_sre", ("model", "itbenchSre")), + ("mmmu_pro", ("model", "mmmuPro")), + ("livecodebench", ("model", "livecodebench")), + ("aime_2025", ("model", "aime25")), + ("automation_bench", ("model", "automationBench")), + ("harvey_lab", ("model", "harveyLab")), + # Price + ("cost_per_task_usd", ("pricing", "costPerTask")), + ("price_class", ("pricing", "priceClass")), + ("input_price_usd_per_1m", ("pricing", "price1mInputTokens")), + ("output_price_usd_per_1m", ("pricing", "price1mOutputTokens")), + ("cache_hit_price_usd_per_1m", ("pricing", "cacheHitPrice")), + ("cache_write_price_usd_per_1m", ("pricing", "cacheWritePrice")), + # Speed + ("median_output_tokens_per_second", ("performance", "medianOutputTokensPerSecond")), + ("p5_output_tokens_per_second", ("performance", "percentile05OutputTokensPerSecond")), + ("p25_output_tokens_per_second", ("performance", "quartile25OutputTokensPerSecond")), + ("p75_output_tokens_per_second", ("performance", "quartile75OutputTokensPerSecond")), + ("p95_output_tokens_per_second", ("performance", "percentile95OutputTokensPerSecond")), + # Latency + ("median_first_chunk_seconds", ("performance", "medianTimeToFirstTokenSeconds")), + ("p5_first_chunk_seconds", ("performance", "percentile05TimeToFirstTokenSeconds")), + ("p25_first_chunk_seconds", ("performance", "quartile25TimeToFirstTokenSeconds")), + ("p75_first_chunk_seconds", ("performance", "quartile75TimeToFirstTokenSeconds")), + ("p95_first_chunk_seconds", ("performance", "percentile95TimeToFirstTokenSeconds")), + ( + "first_answer_token_seconds", + ("performance", "medianTimeToFirstAnswerTokenSeconds"), + ), + ("total_response_seconds", ("performance", "medianEndToEndResponseTimeSeconds")), + ("reasoning_time_seconds", ("performance", "medianReasoningTimeSeconds")), ) -# AA renders "no data" as an em dash or a double hyphen. It is not zero. -_NO_DATA = {"", "--", "—", "–", "N/A", "n/a"} - -# U+2212 MINUS SIGN, not ASCII hyphen. ``float("−31")`` raises ValueError. -_MINUS_SIGN = "−" +OPENNESS_FIELDS: tuple[tuple[str, tuple[str, ...]], ...] = ( + ("openness_record_id", ("id",)), + ("model_id", ("modelId",)), + ("openness_index", ("opennessIndex",)), + ("model_availability", ("modelAvailability",)), + ("model_transparency", ("modelTransparency",)), + ("pre_training_data_access", ("dataPretrainAccess",)), + ("pre_training_data_license", ("dataPretrainLicense",)), + ("post_training_data_access", ("dataPosttrainAccess",)), + ("post_training_data_license", ("dataPosttrainLicense",)), + # Published in the payload but absent from the rendered table. + ("transparency_methodology", ("transparencyMethodology",)), + ("transparency_pre_training_data", ("transparencyPreTrainingData",)), + ("transparency_post_training_data", ("transparencyPostTrainingData",)), +) class ParseError(ValueError): - """Raised when a page no longer matches the shape this module expects.""" - + """The payload was found and decoded but does not hold the expected records.""" -def cell_text(node: Tag | None) -> str: - if node is None: - return "" - return " ".join(node.get_text(" ", strip=True).split()) +def dig(obj: Any, path: tuple[str, ...]) -> Any: + """Follow ``path`` through nested dicts, returning ``None`` if it dead-ends.""" + cursor: Any = obj + for part in path: + if not isinstance(cursor, dict): + return None + cursor = cursor.get(part) + return cursor -def is_estimated(value: str | None) -> bool: - """A trailing asterisk marks an estimated / partial score.""" - return str(value or "").strip().endswith("*") +def project(obj: dict[str, Any], fields: tuple[tuple[str, tuple[str, ...]], ...]) -> dict[str, Any]: + return {name: dig(obj, path) for name, path in fields} -def parse_number(value: str | None) -> float | None: - """Parse an AA-rendered numeric cell. - Handles the U+2212 minus sign, currency and percent decoration, thousands - separators, the estimated-score asterisk, and the several spellings of - "no data". Returns ``None`` for absent values so that absent stays - distinguishable from zero. - """ - if value is None: - return None - text = str(value).strip() - if text in _NO_DATA: - return None - text = ( - text.replace(_MINUS_SIGN, "-") - .replace("$", "") - .replace("%", "") - .replace(",", "") - .rstrip("*") - .strip() - ) - if text in _NO_DATA: - return None - try: - return float(text) - except ValueError: - return None - - -def parse_context_window(value: str | None) -> int | None: - """Context windows are rendered human-readable: ``1M``, ``1.05M``, ``262k``.""" - text = str(value or "").strip() - if text in _NO_DATA: - return None - multiplier = 1 - if text[-1:] in {"M", "m"}: - multiplier, text = 1_000_000, text[:-1] - elif text[-1:] in {"k", "K"}: - multiplier, text = 1_000, text[:-1] - try: - return round(float(text.replace(",", "")) * multiplier) - except ValueError: - return None - - -def parse_flag(cell: Tag | None) -> bool: - """Feature columns render a check icon only when the feature is supported. - - AA emits ```` for supported features and renders - nothing at all otherwise — there is no "No" icon anywhere in the captured - pages, so absence is the negative signal. - """ - if cell is None: - return False - return any( - svg.get("aria-label") == "Yes" for svg in cell.select("svg[aria-label]") - ) - - -def _creator_from_cell(cell: Tag | None) -> str | None: - """The model cell carries the creator's logo; its alt text names the creator.""" - if cell is None: - return None - for img in cell.select("img[alt]"): - alt = str(img.get("alt") or "").strip() - if alt.lower().endswith(" logo"): - return alt[: -len(" logo")].strip() or None - return None - - -def _first_table(html: str) -> Tag: - soup = BeautifulSoup(html, "html.parser") - table = soup.find("table") - if table is None: - raise ParseError("no found in document") - return table - - -def _columns_from(headers: list[str], expected: tuple[str, ...], what: str) -> None: - if tuple(headers) != expected: +def offering_objects(html: str) -> list[dict[str, Any]]: + """Raw offering objects from a page's payload, ``$undefined`` normalised away.""" + payload = reconstruct_payload(html) + objects = [clean(obj) for obj in objects_with_key(payload, OFFERING_MARKER)] + if not objects: raise ParseError( - f"{what} columns changed: expected {list(expected)}, got {headers}" + "payload reconstructed but contains no offering objects " + f"(no {OFFERING_MARKER!r} records)" ) + return objects -def parse_provider_page(html: str, provider_slug: str) -> list[dict[str, Any]]: - """Parse one ``/providers/`` page into offering rows. +def parse_leaderboard(html: str) -> list[dict[str, Any]]: + """Project every offering on the provider leaderboard into a flat record. - The header is two-deep (a column-group row then the real column row); the - real columns are located by finding the ``Model`` header rather than by - assuming a fixed offset. + One offering is one (provider x model x reasoning variant x serving endpoint). + ``offering_id`` is AA's own uuid and the only field unique across the set -- + see ``docs/database.md`` for why no composite of the human-readable fields is. """ - table = _first_table(html) - headers = [cell_text(th) for th in table.select("thead th")] - try: - start = headers.index("Model") - except ValueError as exc: # pragma: no cover - guarded by shape test - raise ParseError( - f"{provider_slug}: no 'Model' column in header {headers}" - ) from exc - _columns_from(headers[start:], PROVIDER_COLUMNS, f"provider page {provider_slug}") - - rows: list[dict[str, Any]] = [] - for tr in table.select("tbody tr"): - cells = tr.find_all("td", recursive=False) or tr.select("td") - if len(cells) < len(PROVIDER_COLUMNS): - continue - name_cell = cells[0] - anchor = name_cell.select_one('a[href^="/models/"]') or tr.select_one( - 'a[href^="/models/"]' - ) - model_slug = ( - str(anchor.get("href", "")).removeprefix("/models/") if anchor else "" - ) - display_name = cell_text(name_cell) - if not display_name: - continue - cost = cell_text(cells[6]) - intelligence = cell_text(cells[5]) - rows.append( - { - "provider_slug": provider_slug, - "model_slug": model_slug, - "display_name": display_name, - "creator": _creator_from_cell(name_cell), - "context_window": parse_context_window(cell_text(cells[1])), - "supports_function_calling": parse_flag(cells[2]), - "supports_json_mode": parse_flag(cells[3]), - "license": cell_text(cells[4]) or None, - "intelligence_index": parse_number(intelligence), - "intelligence_index_estimated": is_estimated(intelligence), - "cost_per_task_usd": parse_number(cost), - "cost_per_task_estimated": is_estimated(cost), - "median_output_tokens_per_second": parse_number(cell_text(cells[7])), - "median_first_chunk_seconds": parse_number(cell_text(cells[8])), - "total_response_seconds": parse_number(cell_text(cells[9])), - "reasoning_time_seconds": parse_number(cell_text(cells[10])), - } - ) - return rows + records = [project(obj, OFFERING_FIELDS) for obj in offering_objects(html)] + missing = [r for r in records if not r.get("offering_id")] + if missing: + raise ParseError(f"{len(missing)} offering records carry no id") + return records -def parse_openness_page(html: str) -> list[dict[str, Any]]: - """Parse the Openness Index leaderboard. +def parse_openness(html: str) -> list[dict[str, Any]]: + """Project the Openness Index, resolving each score to its model slug. - The table body carries no anchors at all — there are no model slugs on this - page, which is why the join runs on normalised display names plus creator. + Openness records live in their own id space and reference a model by + ``modelId``; that uuid does not appear anywhere in the leaderboard payload, so + the cross-dataset join runs on the model *slug* the openness page itself + supplies for each scored model. """ - table = _first_table(html) - headers = [cell_text(th) for th in table.select("thead th")] - try: - start = headers.index("Creator") - except ValueError as exc: # pragma: no cover - guarded by shape test - raise ParseError(f"no 'Creator' column in openness header {headers}") from exc - _columns_from(headers[start:], OPENNESS_COLUMNS, "openness index") + payload = reconstruct_payload(html) + records = [clean(obj) for obj in objects_with_key(payload, OPENNESS_MARKER)] + if not records: + raise ParseError( + f"openness payload contains no {OPENNESS_MARKER!r} records" + ) + + entities = { + obj["id"]: obj + for obj in objects_with_key(payload, "slug") + if obj.get("id") and obj.get("slug") + } rows: list[dict[str, Any]] = [] - for tr in table.select("tbody tr"): - cells = [cell_text(td) for td in tr.select("td")] - if len(cells) < len(OPENNESS_COLUMNS) + 1: - continue - rank, creator, name = cells[0], cells[1], cells[2] - if not name: - continue - rows.append( - { - "rank": int(parse_number(rank) or 0) or None, - "creator": creator or None, - "display_name": name, - "openness_index": parse_number(cells[3]), - "intelligence_index": parse_number(cells[4]), - "model_availability": parse_number(cells[5]), - "model_transparency": parse_number(cells[6]), - "pre_training_data_access": parse_number(cells[7]), - "pre_training_data_license": parse_number(cells[8]), - "post_training_data_access": parse_number(cells[9]), - "post_training_data_license": parse_number(cells[10]), - } + unresolved = 0 + for record in records: + row = project(record, OPENNESS_FIELDS) + entity = entities.get(str(row.get("model_id"))) + if entity is None: + unresolved += 1 + row["model_slug"] = None + row["display_name"] = None + else: + row["model_slug"] = entity.get("slug") + row["display_name"] = entity.get("name") + row["model_family_slug"] = entity.get("model_family_slug") + rows.append(row) + + if unresolved == len(rows): + raise ParseError( + f"none of the {len(rows)} openness records resolved to a model entity; " + "the modelId -> model relationship has changed" ) return rows + + +__all__ = [ + "OFFERING_FIELDS", + "OPENNESS_FIELDS", + "ParseError", + "PayloadNotFoundError", + "dig", + "offering_objects", + "parse_leaderboard", + "parse_openness", + "project", +] diff --git a/services/aa-sync/src/tokenpricing_aa/paths.py b/services/aa-sync/src/tokenpricing_aa/paths.py index d0442ec..65ef682 100644 --- a/services/aa-sync/src/tokenpricing_aa/paths.py +++ b/services/aa-sync/src/tokenpricing_aa/paths.py @@ -7,7 +7,13 @@ CURRENT_DATABASE_DIR = DATABASE_ROOT / "current" HISTORY_DIR = DATABASE_ROOT / "history" CHANGELOG_DIR = DATABASE_ROOT / "changelog" -# The raw HTML capture is ~60MB per run — far too large to commit weekly. It is -# kept out of the tracked database (gitignored, uploaded as a CI artifact) so that -# a failing parse still has its exact input available for diagnosis. +# The raw HTML capture is ~12MB per run (two pages) — kept out of the tracked +# database (gitignored, uploaded as a CI artifact) so that a failing parse still +# has its exact input available for diagnosis. RAW_CAPTURE_DIR = REPO_ROOT / ".capture" / "artificial-analysis" + +# The expected-shape manifest every run is checked against. Tracked in git: it is +# the record of what AA's payload looked like when the projection was last verified. +SERVICE_ROOT = Path(__file__).resolve().parents[2] +SCHEMA_DIR = SERVICE_ROOT / "schema" +OFFERING_MANIFEST_PATH = SCHEMA_DIR / "offering-manifest.json" diff --git a/services/aa-sync/src/tokenpricing_aa/schema.py b/services/aa-sync/src/tokenpricing_aa/schema.py new file mode 100644 index 0000000..7eb8957 --- /dev/null +++ b/services/aa-sync/src/tokenpricing_aa/schema.py @@ -0,0 +1,239 @@ +"""Structural and semantic drift detection against a checked-in manifest. + +"The pipeline did not crash" is not evidence that the source is unchanged. A +payload can stay perfectly parseable while a field quietly disappears, flips type, +or changes units -- and a scraper that only fails on exceptions will publish the +resulting nonsense. + +So the expected shape is recorded explicitly in ``schema/offering-manifest.json`` +and compared against the live payload on every run. Four signals, deliberately +separated because they need different responses: + +``missing`` + A field the manifest expects is gone, or has collapsed to entirely null after + having been populated. Breaks downstream columns. +``type_changed`` + A field now carries a different JSON type. Breaks downstream columns. +``range_shifted`` + A numeric field's values have moved far outside their recorded range, which is + how a *unit* change looks from the outside -- fractions rescaled to + percentages, per-1M prices restated per-1k. Parses fine, means something else. +``new`` + A field appeared. Informational only: additive change is how AA ships new + benchmarks, and it is never a reason to fail a run. +""" + +from __future__ import annotations + +import json +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +from tokenpricing_aa.paths import OFFERING_MANIFEST_PATH as MANIFEST_PATH + +__all__ = [ + "MANIFEST_PATH", + "RANGE_TOLERANCE", + "DriftReport", + "FieldSpec", + "build_manifest", + "check_drift", + "flatten", + "load_manifest", + "type_name", +] + +# A numeric field's live values may sit this many times outside the recorded +# span before it reads as a unit change rather than ordinary week-to-week drift. +RANGE_TOLERANCE = 10.0 + +# Below this recorded fill rate a field is too sparse for "entirely null now" to +# mean anything; several benchmarks are legitimately scored for a handful of models. +SPARSE_FIELD_THRESHOLD = 0.2 + + +def type_name(value: Any) -> str: + """JSON type name for ``value``. ``bool`` is checked before ``int`` deliberately.""" + if value is None: + return "null" + if isinstance(value, bool): + return "bool" + if isinstance(value, (int, float)): + return "number" + if isinstance(value, str): + return "string" + if isinstance(value, list): + return "array" + if isinstance(value, dict): + return "object" + return type(value).__name__ + + +def flatten(obj: dict[str, Any], prefix: str = "") -> dict[str, Any]: + """Flatten nested dicts to dotted paths. Leaf values only.""" + out: dict[str, Any] = {} + for key, value in obj.items(): + path = f"{prefix}{key}" + if isinstance(value, dict): + out.update(flatten(value, path + ".")) + else: + out[path] = value + return out + + +@dataclass +class FieldSpec: + types: set[str] + populated_ratio: float + minimum: float | None = None + maximum: float | None = None + + def to_json(self) -> dict[str, Any]: + out: dict[str, Any] = { + "types": sorted(self.types), + "populated_ratio": round(self.populated_ratio, 4), + } + if self.minimum is not None: + out["minimum"] = self.minimum + if self.maximum is not None: + out["maximum"] = self.maximum + return out + + @classmethod + def from_json(cls, data: dict[str, Any]) -> FieldSpec: + return cls( + types=set(data["types"]), + populated_ratio=float(data.get("populated_ratio", 0.0)), + minimum=data.get("minimum"), + maximum=data.get("maximum"), + ) + + +@dataclass +class DriftReport: + missing: list[str] = field(default_factory=list) + type_changed: list[str] = field(default_factory=list) + range_shifted: list[str] = field(default_factory=list) + new: list[str] = field(default_factory=list) + record_count: int = 0 + + @property + def breaking(self) -> bool: + """New fields are additive and do not make a report breaking.""" + return bool(self.missing or self.type_changed or self.range_shifted) + + def summary(self) -> str: + if not self.breaking and not self.new: + return f"no drift over {self.record_count} records" + parts = [] + for label, items in ( + ("missing", self.missing), + ("type changed", self.type_changed), + ("range shifted", self.range_shifted), + ("new", self.new), + ): + if items: + parts.append(f"{label}: {', '.join(sorted(items))}") + return " | ".join(parts) + + +def build_manifest(objects: list[dict[str, Any]]) -> dict[str, Any]: + """Derive a manifest from live payload objects.""" + flats = [flatten(obj) for obj in objects] + paths: set[str] = set() + for flat in flats: + paths |= set(flat) + + specs: dict[str, FieldSpec] = {} + for path in sorted(paths): + types: set[str] = set() + populated = 0 + numbers: list[float] = [] + for flat in flats: + if path not in flat: + continue + value = flat[path] + types.add(type_name(value)) + if value is not None: + populated += 1 + if isinstance(value, (int, float)) and not isinstance(value, bool): + numbers.append(float(value)) + spec = FieldSpec( + types=types, + populated_ratio=populated / len(flats) if flats else 0.0, + ) + if numbers: + spec.minimum = min(numbers) + spec.maximum = max(numbers) + specs[path] = spec + + return { + "record_count": len(objects), + "fields": {path: spec.to_json() for path, spec in specs.items()}, + } + + +def load_manifest(path: Path | None = None) -> dict[str, FieldSpec]: + data = json.loads((path or MANIFEST_PATH).read_text(encoding="utf-8")) + return {p: FieldSpec.from_json(s) for p, s in data["fields"].items()} + + +def check_drift( + objects: list[dict[str, Any]], manifest: dict[str, FieldSpec] | None = None +) -> DriftReport: + """Compare live payload objects against the expected manifest.""" + expected = manifest if manifest is not None else load_manifest() + flats = [flatten(obj) for obj in objects] + report = DriftReport(record_count=len(flats)) + if not flats: + report.missing = sorted(expected) + return report + + observed: dict[str, list[Any]] = {} + for flat in flats: + for path, value in flat.items(): + observed.setdefault(path, []).append(value) + + for path, spec in expected.items(): + values = observed.get(path) + if values is None: + report.missing.append(path) + continue + + non_null = [v for v in values if v is not None] + if ( + not non_null + and spec.populated_ratio >= SPARSE_FIELD_THRESHOLD + ): + report.missing.append(f"{path} (present but entirely null)") + continue + + live_types = {type_name(v) for v in non_null} + unexpected = live_types - spec.types + if unexpected: + report.type_changed.append( + f"{path} ({'/'.join(sorted(unexpected))} not in " + f"{'/'.join(sorted(spec.types))})" + ) + continue + + if spec.minimum is not None and spec.maximum is not None: + numbers = [ + float(v) for v in non_null if isinstance(v, (int, float)) and not isinstance(v, bool) + ] + if numbers: + span = max(abs(spec.maximum - spec.minimum), 1e-9) + allowance = span * RANGE_TOLERANCE + low = spec.minimum - allowance + high = spec.maximum + allowance + if min(numbers) < low or max(numbers) > high: + report.range_shifted.append( + f"{path} (observed {min(numbers):.4g}..{max(numbers):.4g}, " + f"expected within {low:.4g}..{high:.4g})" + ) + + for path in sorted(set(observed) - set(expected)): + report.new.append(path) + + return report diff --git a/services/aa-sync/tests/factories.py b/services/aa-sync/tests/factories.py new file mode 100644 index 0000000..fba9728 --- /dev/null +++ b/services/aa-sync/tests/factories.py @@ -0,0 +1,154 @@ +"""Fixture builders that reproduce the real shape of an AA page. + +Pages are built the way AA ships them -- payload text split across several +``self.__next_f.push([1,"..."])`` chunks -- so that tests exercise chunk +reconstruction rather than a convenient single-blob shortcut. Every test that needs +a *broken* page starts from one of these and damages it deliberately. +""" + +from __future__ import annotations + +import json +from typing import Any + + +def offering(**overrides: Any) -> dict[str, Any]: + """One offering object shaped exactly like the live payload's.""" + obj: dict[str, Any] = { + "id": "10b47ef3-f0b9-4d9e-808a-9ed9ad14e73f", + "label": "Kimi K2.6", + "hostApiId": "moonshotai/Kimi-K2.6", + "footnotes": None, + "host": { + "name": "Nebius", + "slug": "nebius", + "logo": "nebius_small.svg", + "color": "#112a40", + "functionCallingUrl": "https://docs.nebius.com/tool-calling", + "jsonModeUrl": "https://docs.nebius.com/json/", + }, + "model": { + "slug": "kimi-k2-6", + "isOpenWeights": True, + "deprecated": True, + "reasoningModel": True, + "intelligenceIndex": 45.1382483763163, + "intelligenceIndexIsEstimated": False, + "omniscience": 5.3, + "omniscienceAccuracy": 0.326, + "omniscienceNonHallucination": 0.5949554896142433, + "briefcase": {"elo": 818.82}, + "harveyLab": 0.841221595020987, + "automationBench": 0.19552711052930297, + "gdpvalNormalized": 0.34519, + "terminalbenchHard": 0.439393939393939, + "terminalbenchV21": 0.659176029962547, + "tau2": 0.95906432748538, + "tauBanking": 0.232989690721649, + "lcr": 0.766666666666667, + "hle": 0.374884151992586, + "gpqa": 0.911111111111111, + "scicode": 0.534722222222222, + "livecodebench": None, + "aime25": None, + "ifbench": 0.759863945578231, + "critpt": 0.08, + "apexAgents": 0.284660766961652, + "itbenchSre": 0.311864406779661, + "mmmuPro": 0.79364161849711, + "sizeClass": "large", + "creator": {"name": "Kimi", "logo": "kimi.jpg"}, + }, + "features": { + "contextWindowTokens": 262144, + "functionCalling": True, + "jsonMode": True, + "openaiCompatible": True, + }, + "pricing": { + "costPerTask": 0.7586225879503231, + "priceClass": "high", + "price1mInputTokens": 0.95, + "price1mOutputTokens": 4, + "cacheHitPrice": None, + "cacheWritePrice": None, + }, + "performance": { + "medianOutputTokensPerSecond": 161.843217857469, + "percentile05OutputTokensPerSecond": 86.2935722234904, + "quartile25OutputTokensPerSecond": 110.732402161215, + "quartile75OutputTokensPerSecond": 238.691468618383, + "percentile95OutputTokensPerSecond": 330.205485545576, + "medianTimeToFirstTokenSeconds": 1.935748112, + "percentile05TimeToFirstTokenSeconds": 1.84192147900001, + "quartile25TimeToFirstTokenSeconds": 1.91706955349997, + "quartile75TimeToFirstTokenSeconds": 2.01518286099997, + "percentile95TimeToFirstTokenSeconds": 5.47746819690005, + "medianTimeToFirstAnswerTokenSeconds": 29.43767286932827, + "medianEndToEndResponseTimeSeconds": 32.52708252528517, + "medianReasoningTimeSeconds": 27.501924757328272, + }, + } + for key, value in overrides.items(): + if isinstance(value, dict) and isinstance(obj.get(key), dict): + obj[key] = {**obj[key], **value} + else: + obj[key] = value + return obj + + +def make_page(payload_obj: Any, chunks: int = 3) -> str: + """Render ``payload_obj`` into an HTML document, split across flight chunks.""" + text = json.dumps(payload_obj, ensure_ascii=False) + size = max(1, len(text) // chunks + 1) + pieces = [text[i : i + size] for i in range(0, len(text), size)] + scripts = "".join( + f"" + for piece in pieces + ) + return ( + "Artificial Analysis" + f"
Model
" + f'{scripts}' + ) + + +def leaderboard_page(offerings: list[dict[str, Any]] | None = None, **kw: Any) -> str: + objects = offerings if offerings is not None else [offering()] + return make_page({"rsc": "payload", "data": objects}, **kw) + + +def openness_page( + records: list[dict[str, Any]] | None = None, + entities: list[dict[str, Any]] | None = None, + **kw: Any, +) -> str: + """An Openness Index page: score records plus the model entities they point at.""" + if records is None: + records = [ + { + "id": "9b8adea3-ffcb-46fe-8fcf-acd121000a80", + "modelId": "f0083258-8646-45b8-8082-7aaf6c2ea82a", + "dataPretrainAccess": 0, + "dataPretrainLicense": 0, + "dataPosttrainAccess": 0, + "dataPosttrainLicense": 0, + "opennessIndex": 38.888888888888886, + "modelAvailability": 6, + "modelTransparency": 1, + "transparencyMethodology": 1, + "transparencyPostTrainingData": 0, + "transparencyPreTrainingData": 0, + } + ] + if entities is None: + entities = [ + { + "id": "f0083258-8646-45b8-8082-7aaf6c2ea82a", + "slug": "kimi-k2-6", + "name": "Kimi K2.6", + "model_family_slug": "kimi-k2", + "deprecated": False, + } + ] + return make_page({"scores": records, "models": entities}, **kw) diff --git a/services/aa-sync/tests/test_alerting.py b/services/aa-sync/tests/test_alerting.py new file mode 100644 index 0000000..4e1f1af --- /dev/null +++ b/services/aa-sync/tests/test_alerting.py @@ -0,0 +1,340 @@ +"""Each of the three failure modes must produce its own named alert. + +These drive the real code paths against deliberately broken inputs -- a page with +no payload, an HTTP transport that refuses, a payload whose shape moved -- and +assert on what actually gets delivered. Delivery itself is injected, so no test +touches the network or opens an issue. +""" + +from __future__ import annotations + +import httpx +import pytest + +from tests.factories import leaderboard_page, offering, openness_page +from tokenpricing_aa import cli +from tokenpricing_aa.alerting import ( + Alert, + AlertKind, + deliver_via_github_issue, + payload_not_found_alert, + request_blocked_alert, + schema_drift_alert, + send_alert, +) +from tokenpricing_aa.fetch import FetchBlockedError, get_page +from tokenpricing_aa.flight import PayloadNotFoundError +from tokenpricing_aa.normalize import ShapeError +from tokenpricing_aa.schema import DriftReport, FieldSpec, build_manifest, check_drift + + +class Recorder: + """A delivery channel that records instead of sending.""" + + def __init__(self) -> None: + self.alerts: list[Alert] = [] + + def __call__(self, alert: Alert) -> str: + self.alerts.append(alert) + return "recorded" + + @property + def kinds(self) -> list[str]: + return [a.kind.value for a in self.alerts] + + +def _capture(page: str, url: str = "https://artificialanalysis.ai/leaderboards/providers"): + return {"source_url": url, "fetched_at": "2026-08-14T00:00:00+00:00", "page": page} + + +# --------------------------------------------------------------------------- # +# Failure mode 1: the payload cannot be found +# --------------------------------------------------------------------------- # + + +def test_payload_not_found_alert_names_the_failure(tmp_path, monkeypatch): + monkeypatch.setattr(cli, "CURRENT_DATABASE_DIR", tmp_path / "current") + monkeypatch.setattr(cli, "HISTORY_DIR", tmp_path / "history") + recorder = Recorder() + + broken = _capture("no payload here at all") + with pytest.raises(PayloadNotFoundError): + cli._build(broken, _capture(openness_page()), recorder) + + assert recorder.kinds == ["payload-not-found"] + alert = recorder.alerts[0] + assert alert.title.startswith("[aa-sync] payload-not-found:") + assert "self.__next_f.push" in alert.body + assert "flight payload could not be extracted" in alert.body + + +def test_payload_not_found_alert_does_not_publish_anything(tmp_path, monkeypatch): + current = tmp_path / "current" + monkeypatch.setattr(cli, "CURRENT_DATABASE_DIR", current) + monkeypatch.setattr(cli, "HISTORY_DIR", tmp_path / "history") + with pytest.raises(PayloadNotFoundError): + cli._build(_capture(""), _capture(openness_page()), Recorder()) + assert not list(tmp_path.glob("**/*.json")) + + +def test_payload_not_found_alert_body_explains_the_remedy(): + alert = payload_not_found_alert("https://x/y", RuntimeError("no chunks")) + assert alert.kind is AlertKind.PAYLOAD_NOT_FOUND + assert "acquisition strategy" in alert.body + + +# --------------------------------------------------------------------------- # +# Failure mode 2: the request is blocked +# --------------------------------------------------------------------------- # + + +def test_forbidden_is_not_retried_and_raises_blocked(): + attempts = [] + + def handler(request): + attempts.append(request.url) + return httpx.Response(403, text="forbidden") + + client = httpx.Client(transport=httpx.MockTransport(handler)) + with pytest.raises(FetchBlockedError) as exc: + get_page("https://artificialanalysis.ai/x", client=client, sleep=lambda _: None) + + assert exc.value.status == 403 + assert len(attempts) == 1, "a refusal must not be retried" + + +def test_transient_5xx_is_retried_then_succeeds(): + statuses = [503, 503, 200] + + def handler(request): + code = statuses.pop(0) + return httpx.Response(code, text="ok") + + client = httpx.Client(transport=httpx.MockTransport(handler)) + assert ( + get_page("https://artificialanalysis.ai/x", client=client, sleep=lambda _: None) + == "ok" + ) + assert statuses == [] + + +def test_persistent_5xx_exhausts_retries_then_raises_blocked(): + calls = [] + + def handler(request): + calls.append(1) + return httpx.Response(503) + + client = httpx.Client(transport=httpx.MockTransport(handler)) + with pytest.raises(FetchBlockedError) as exc: + get_page("https://artificialanalysis.ai/x", client=client, sleep=lambda _: None) + + assert exc.value.status == 503 + assert len(calls) > 1, "a transient status must be retried before alerting" + assert "retryable status persisted" in str(exc.value) + + +def test_transport_failure_is_retried_then_raises_blocked(): + def handler(request): + raise httpx.ConnectError("no route to host") + + client = httpx.Client(transport=httpx.MockTransport(handler)) + with pytest.raises(FetchBlockedError) as exc: + get_page("https://artificialanalysis.ai/x", client=client, sleep=lambda _: None) + assert exc.value.status is None + + +def test_blocked_alert_carries_the_status_and_attempt_history(): + error = FetchBlockedError( + "https://artificialanalysis.ai/leaderboards/providers", + 403, + 1, + ["attempt 1: HTTP 403"], + "not a retryable status", + ) + alert = request_blocked_alert(error.url, error) + assert alert.kind is AlertKind.REQUEST_BLOCKED + assert "**403**" in alert.body + assert "attempt 1: HTTP 403" in alert.body + assert "not a transient blip" in alert.body + + +def test_sync_alerts_on_blocked_fetch(monkeypatch, tmp_path): + monkeypatch.setattr(cli, "CURRENT_DATABASE_DIR", tmp_path / "current") + monkeypatch.setattr(cli, "HISTORY_DIR", tmp_path / "history") + monkeypatch.setattr(cli, "RAW_CAPTURE_DIR", tmp_path / "capture") + + def blocked(): + raise FetchBlockedError("https://artificialanalysis.ai/x", 403, 1, ["attempt 1: HTTP 403"]) + + monkeypatch.setattr(cli, "fetch_leaderboard", blocked) + recorder = Recorder() + with pytest.raises(FetchBlockedError): + cli.sync(recorder) + assert recorder.kinds == ["request-blocked"] + + +# --------------------------------------------------------------------------- # +# Failure mode 3: schema drift +# --------------------------------------------------------------------------- # + + +def _specs(objects): + return {p: FieldSpec.from_json(s) for p, s in build_manifest(objects)["fields"].items()} + + +def test_schema_drift_alert_lists_the_offending_fields(): + baseline = [offering(id=f"id-{i}") for i in range(5)] + broken = [ + offering(id=f"id-{i}", features={"contextWindowTokens": "262k"}) for i in range(5) + ] + report = check_drift(broken, _specs(baseline)) + alert = schema_drift_alert("artificial-analysis", report) + + assert alert.kind is AlertKind.SCHEMA_DRIFT + assert alert.title == "[aa-sync] schema-drift: artificial-analysis" + assert "features.contextWindowTokens" in alert.body + assert "Type changed" in alert.body + + +def test_schema_drift_alert_explains_a_unit_change(): + baseline = [offering(id=f"id-{i}", model={"gpqa": 0.5}) for i in range(5)] + live = [offering(id=f"id-{i}", model={"gpqa": 50.0}) for i in range(5)] + report = check_drift(live, _specs(baseline)) + alert = schema_drift_alert("artificial-analysis", report) + assert "Range shifted" in alert.body + assert "units" in alert.body + + +def test_build_alerts_on_drift_and_publishes_nothing(tmp_path, monkeypatch): + """A payload that parses fine but moved must not reach database/.""" + current = tmp_path / "current" + monkeypatch.setattr(cli, "CURRENT_DATABASE_DIR", current) + monkeypatch.setattr(cli, "HISTORY_DIR", tmp_path / "history") + + # Drift the live payload against the real checked-in manifest: prices per 1k. + drifted = [ + offering(id=f"id-{i}", pricing={"price1mInputTokens": 950000.0}) + for i in range(5) + ] + recorder = Recorder() + with pytest.raises(cli.DriftError): + cli._build(_capture(leaderboard_page(drifted)), _capture(openness_page()), recorder) + + assert recorder.kinds == ["schema-drift"] + assert not list(tmp_path.glob("**/*.json")) + + +def test_new_field_alone_does_not_alert_or_block(tmp_path, monkeypatch): + """AA ships new benchmarks constantly; that must never page anyone.""" + monkeypatch.setattr(cli, "CURRENT_DATABASE_DIR", tmp_path / "current") + monkeypatch.setattr(cli, "HISTORY_DIR", tmp_path / "history") + # Isolate the additive case from the checked-in manifest, which a handful of + # stub offerings cannot satisfy on sparsely-scored benchmarks. + monkeypatch.setattr( + cli, + "check_drift", + lambda _objects: DriftReport(record_count=3, new=["model.brandNewBench2027"]), + ) + recorder = Recorder() + + additive = [offering(id=f"id-{i}", model={"brandNewBench2027": 0.4}) for i in range(3)] + # The shape guard still rejects a 3-row capture -- but for its own reason, and + # without an alert for the additive field. + with pytest.raises(ShapeError, match="expected >="): + cli._build(_capture(leaderboard_page(additive)), _capture(openness_page()), recorder) + + assert recorder.alerts == [] + + +# --------------------------------------------------------------------------- # +# Delivery: dedupe behaviour, and the swappable seam +# --------------------------------------------------------------------------- # + + +def test_github_delivery_opens_an_issue_when_none_is_open(): + posted = {} + + def handler(request): + if request.method == "GET": + return httpx.Response(200, json=[]) + posted["url"] = str(request.url) + return httpx.Response(201, json={"number": 91}) + + client = httpx.Client(transport=httpx.MockTransport(handler)) + alert = payload_not_found_alert("https://x/y", RuntimeError("boom")) + outcome = deliver_via_github_issue( + alert, repo="Atena-IT/tokenpricing", token="t", client=client + ) + assert outcome == "opened #91" + assert posted["url"].endswith("/repos/Atena-IT/tokenpricing/issues") + + +def test_github_delivery_comments_instead_of_duplicating(): + alert = payload_not_found_alert("https://x/y", RuntimeError("boom")) + seen = {} + + def handler(request): + if request.method == "GET": + return httpx.Response(200, json=[{"number": 42, "title": alert.title}]) + seen["url"] = str(request.url) + return httpx.Response(201, json={"id": 1}) + + client = httpx.Client(transport=httpx.MockTransport(handler)) + outcome = deliver_via_github_issue( + alert, repo="Atena-IT/tokenpricing", token="t", client=client + ) + assert outcome == "commented on #42" + assert seen["url"].endswith("/issues/42/comments") + + +def test_a_different_failure_mode_opens_its_own_issue(): + """Dedupe is per (kind, subject), so two distinct failures do not collapse.""" + drift = payload_not_found_alert("https://x/y", RuntimeError("boom")) + other = request_blocked_alert("https://x/y", FetchBlockedError("https://x/y", 403, 1, [])) + assert drift.title != other.title + + def handler(request): + if request.method == "GET": + return httpx.Response(200, json=[{"number": 42, "title": drift.title}]) + return httpx.Response(201, json={"number": 43}) + + client = httpx.Client(transport=httpx.MockTransport(handler)) + assert ( + deliver_via_github_issue(other, repo="a/b", token="t", client=client) + == "opened #43" + ) + + +def test_titles_are_stable_across_runs(): + """Dedupe matches on the title, so it must not carry run-specific detail.""" + first = request_blocked_alert("https://x/y", FetchBlockedError("https://x/y", 403, 1, ["a"])) + second = request_blocked_alert("https://x/y", FetchBlockedError("https://x/y", 403, 4, ["b"])) + assert first.title == second.title + + +def test_delivery_without_credentials_reports_undelivered_rather_than_raising(monkeypatch): + monkeypatch.delenv("GITHUB_REPOSITORY", raising=False) + monkeypatch.delenv("GITHUB_TOKEN", raising=False) + alert = payload_not_found_alert("https://x/y", RuntimeError("boom")) + assert "undelivered" in deliver_via_github_issue(alert) + + +def test_send_alert_never_raises_when_delivery_fails(): + """An alerting failure must not mask the failure being alerted on.""" + + def broken(_alert): + raise RuntimeError("channel down") + + alert = payload_not_found_alert("https://x/y", RuntimeError("boom")) + assert "delivery failed" in send_alert(alert, broken) + + +def test_delivery_is_a_single_swappable_function(): + """Documents the seam: any (Alert) -> str callable is a channel.""" + sent: list[str] = [] + assert send_alert( + payload_not_found_alert("https://x/y", RuntimeError("boom")), + lambda alert: sent.append(alert.title) or "sent to teams", + ) == "sent to teams" + assert sent == ["[aa-sync] payload-not-found: https://x/y"] diff --git a/services/aa-sync/tests/test_flight.py b/services/aa-sync/tests/test_flight.py new file mode 100644 index 0000000..0aa48d1 --- /dev/null +++ b/services/aa-sync/tests/test_flight.py @@ -0,0 +1,92 @@ +"""Flight payload extraction, including the "AA changed how it ships data" mode.""" + +from __future__ import annotations + +import json + +import pytest + +from tests.factories import leaderboard_page, make_page, offering +from tokenpricing_aa.flight import ( + UNDEFINED, + PayloadNotFoundError, + clean, + objects_with_key, + reconstruct_payload, +) + + +def test_reconstructs_payload_across_chunk_boundaries(): + html = leaderboard_page(chunks=7) + payload = reconstruct_payload(html) + assert '"hostApiId"' in payload + # Reconstruction must be lossless: the joined text parses as the original. + assert json.loads(payload)["data"][0]["label"] == "Kimi K2.6" + + +def test_finds_offering_objects_in_a_flat_payload_stream(): + html = leaderboard_page([offering(id="a"), offering(id="b", label="Other")]) + objects = objects_with_key(reconstruct_payload(html), "hostApiId") + assert {o["id"] for o in objects} == {"a", "b"} + + +def test_ignores_marker_occurrences_that_are_not_self_contained_objects(): + # A label dictionary mentioning the marker key must not become a record. + html = make_page( + { + "labels": {"hostApiId": "API ID", "price": "Price"}, + "data": [offering(id="real")], + } + ) + objects = objects_with_key(reconstruct_payload(html), "hostApiId") + ids = [o.get("id") for o in objects] + assert "real" in ids + + +# --- failure mode 1: the payload is no longer there ------------------------- # + + +def test_missing_flight_chunks_raise_payload_not_found(): + html = "
rendered but no payload
" + with pytest.raises(PayloadNotFoundError, match="no self.__next_f.push"): + reconstruct_payload(html) + + +def test_chunks_present_but_undecodable_raise_payload_not_found(): + # Chunk syntax survives, contents do not: a bundler/serialisation change. + html = '' + with pytest.raises(PayloadNotFoundError): + reconstruct_payload(html) + + +def test_chunks_that_reconstruct_to_nothing_raise_payload_not_found(): + html = '' + with pytest.raises(PayloadNotFoundError, match="empty payload"): + reconstruct_payload(html) + + +def test_client_side_fetch_migration_looks_like_a_missing_payload(): + # The realistic future break: data arrives by XHR, HTML ships only the shell. + html = ( + "
" + "" + "" + ) + with pytest.raises(PayloadNotFoundError): + reconstruct_payload(html) + + +# --- the $undefined sentinel ----------------------------------------------- # + + +def test_clean_collapses_the_undefined_sentinel_to_none(): + assert clean({"a": UNDEFINED, "b": 1}) == {"b": 1} + assert clean({"n": {"deep": UNDEFINED}}) == {"n": {}} + assert clean([UNDEFINED, 2]) == [None, 2] + + +def test_sentinel_and_absent_key_normalise_the_same_way(): + """Why the two sources compared equal: one omits the key, one sends $undefined.""" + with_sentinel = clean({"pricing": {"costPerTask": UNDEFINED, "priceClass": "high"}}) + without_key = clean({"pricing": {"priceClass": "high"}}) + assert with_sentinel == without_key diff --git a/services/aa-sync/tests/test_matching.py b/services/aa-sync/tests/test_matching.py index f0e0669..5c123d4 100644 --- a/services/aa-sync/tests/test_matching.py +++ b/services/aa-sync/tests/test_matching.py @@ -1,349 +1,148 @@ -from typing import Any +"""Reasoning-variant identification and the exact-slug Openness join.""" -from tokenpricing_aa.matching import ( - enrich_key_from_slug, - join_openness, - normalize_creator, - parse_display_name, +from __future__ import annotations + +import pytest + +from tokenpricing_aa.matching import join_openness, parse_variant + + +def test_reasoning_mode_is_read_from_the_boolean_not_the_name(): + assert parse_variant("Kimi K2.6", "kimi-k2-6", True).reasoning_mode == "reasoning" + assert ( + parse_variant("Kimi K2.6", "kimi-k2-6", False).reasoning_mode + == "non-reasoning" + ) + assert parse_variant("Kimi K2.6", "kimi-k2-6", None).reasoning_mode is None + + +@pytest.mark.parametrize( + ("slug", "expected"), + [ + ("claude-opus-5-low", "low"), + ("claude-opus-5-medium", "medium"), + ("claude-opus-5-high", "high"), + ("claude-opus-5-xhigh", "xhigh"), + ("gpt-5-nano-minimal", "minimal"), + ], ) +def test_effort_comes_from_the_slug_suffix(slug, expected): + assert parse_variant("whatever", slug, True).reasoning_effort == expected -def openness_row( - display_name: str, - creator: str = "Alibaba", - openness_index: float = 66.67, - availability: float = 6.0, - transparency: float = 6.0, -) -> dict[str, Any]: - return { - "rank": 1, - "creator": creator, - "display_name": display_name, - "openness_index": openness_index, - "intelligence_index": 50.0, - "model_availability": availability, - "model_transparency": transparency, - "pre_training_data_access": 1.0, - "pre_training_data_license": 1.0, - "post_training_data_access": 1.0, - "post_training_data_license": 1.0, - } +def test_max_effort_is_the_unsuffixed_slug_and_read_from_the_label(): + """claude-opus-5 is max; only the label says so.""" + variant = parse_variant("Claude Opus 5 (max)", "claude-opus-5", True) + assert variant.reasoning_effort == "max" + + +def test_effort_falls_back_to_the_label_parenthetical(): + variant = parse_variant("GPT-5.2 Codex (xhigh)", "gpt-5-2-codex", True) + assert variant.reasoning_effort == "xhigh" + + +def test_non_reasoning_slug_suffix_overrides_the_boolean(): + variant = parse_variant("GPT-5.6 Sol (Non-reasoning)", "gpt-5-6-sol-non-reasoning", None) + assert variant.reasoning_mode == "non-reasoning" + + +def test_serving_side_parentheticals_are_not_efforts(): + """Quantisation and platform tags used to need a whole vocabulary to exclude.""" + for name in ( + "GLM-5.2 (FP4)", + "Gemini 2.5 Flash (AI Studio)", + "Hermes 4 405B (FP8)", + "Llama 3.3 70B Base", + ): + assert parse_variant(name, "some-model", True).reasoning_effort is None + + +def test_no_effort_where_aa_publishes_no_variant(): + assert parse_variant("Nemotron 3 Ultra", "nemotron-3-ultra", True).reasoning_effort is None -def offering( - display_name: str, creator: str = "Alibaba", provider: str = "fireworks" -) -> dict[str, Any]: - return { - "provider_slug": provider, - "model_slug": "some-slug", - "display_name": display_name, - "creator": creator, +# --- the openness join ----------------------------------------------------- # + + +def _offering(**kw): + base = { + "provider_slug": "nebius", + "model_slug": "kimi-k2-6", + "display_name": "Kimi K2.6", + "creator": "Kimi", } + base.update(kw) + return base + + +def _openness(slug="kimi-k2-6", **kw): + row = { + "model_slug": slug, + "display_name": "Kimi K2.6", + "openness_index": 38.9, + "model_availability": 6, + "model_transparency": 1, + "pre_training_data_access": 0, + "pre_training_data_license": 0, + "post_training_data_access": 0, + "post_training_data_license": 0, + "transparency_methodology": 1, + "transparency_pre_training_data": 0, + "transparency_post_training_data": 0, + } + row.update(kw) + return row + + +def test_exact_slug_match_attaches_the_breakdown(): + offerings = [_offering()] + result = join_openness(offerings, [_openness()]) + assert result.matched == 1 + assert offerings[0]["openness"]["openness_index"] == 38.9 + assert offerings[0]["openness"]["transparency_methodology"] == 1 + + +def test_model_with_no_openness_row_is_recorded_not_guessed(): + offerings = [_offering(model_slug="unscored-model")] + result = join_openness(offerings, [_openness()]) + assert result.matched == 0 + assert offerings[0]["openness"] is None + assert result.unmatched[0]["model_slug"] == "unscored-model" + + +def test_display_name_differences_do_not_affect_the_join(): + """The old join normalised names; this one never looks at them.""" + offerings = [_offering(display_name="Kimi K2.6 (FP8, Turbo) Sept '26")] + result = join_openness(offerings, [_openness()]) + assert result.matched == 1 + + +def test_every_offering_of_a_model_gets_the_same_breakdown(): + offerings = [ + _offering(provider_slug="nebius"), + _offering(provider_slug="azure"), + _offering(provider_slug="novita"), + ] + result = join_openness(offerings, [_openness()]) + assert result.matched == 3 + assert all(o["openness"]["openness_index"] == 38.9 for o in offerings) + + +def test_duplicate_openness_slugs_are_ambiguous_not_arbitrary(): + offerings = [_offering()] + result = join_openness(offerings, [_openness(), _openness(openness_index=12.0)]) + assert result.matched == 0 + assert len(result.ambiguous) == 1 + assert offerings[0]["openness"] is None + + +def test_scored_models_nobody_serves_are_reported(): + result = join_openness([_offering()], [_openness(), _openness(slug="orphan")]) + assert result.openness_without_offering == ["orphan"] -class TestParseDisplayName: - def test_reasoning_effort_is_captured(self) -> None: - key = parse_display_name("GPT-5.6 Sol (xhigh)") - assert key.base == "gpt 5 6 sol" - assert key.effort == "xhigh" - assert key.reasoning == "reasoning" - - def test_non_reasoning_is_captured(self) -> None: - key = parse_display_name("Qwen3.5 397B A17B (Non-reasoning)") - assert key.reasoning == "non-reasoning" - assert key.effort is None - - def test_bare_reasoning_token(self) -> None: - assert parse_display_name("Nemotron 3 Ultra (Reasoning)").reasoning == "reasoning" - - def test_compound_reasoning_and_effort(self) -> None: - key = parse_display_name("K2-V2 (Reasoning, Max Effort)") - assert key.reasoning == "reasoning" - assert key.effort == "max" - - def test_explicit_non_reasoning_beats_effort_implication(self) -> None: - """AA emits "(Non-reasoning, high)"; the stated mode must win.""" - key = parse_display_name("Some Model (Non-reasoning, high)") - assert key.reasoning == "non-reasoning" - assert key.effort == "high" - - def test_quantisation_and_serving_tokens_are_dropped_not_part_of_identity( - self, - ) -> None: - """Openness is a model property; quantisation and serving tier are not.""" - for name in ( - "Llama 4 Maverick (FP8)", - "Llama 4 Maverick (NVFP4)", - "Llama 4 Maverick (Turbo, FP8)", - "Llama 4 Maverick (FAST)", - ): - key = parse_display_name(name) - assert key.base == "llama 4 maverick", name - assert key.effort is None, name - - def test_platform_and_snapshot_tokens_are_dropped(self) -> None: - for name in ( - "Gemini 3 Pro (AI Studio)", - "Gemini 3 Pro (Vertex)", - "Gemini 3 Pro (Sep '25)", - "Gemini 3 Pro (Feb 2026)", - ): - assert parse_display_name(name).base == "gemini 3 pro", name - - def test_known_serving_tokens_are_classified_separately_from_unknown_ones( - self, - ) -> None: - """An unrecognised suffix is still dropped from the key, but flagged so a - new AA suffix type does not change join behaviour unnoticed.""" - known = parse_display_name("Some Model (FP8)") - assert known.dropped == ("FP8",) - assert known.unknown == () - - novel = parse_display_name("Some Model (Warp Drive)") - assert novel.dropped == () - assert novel.unknown == ("Warp Drive",) - assert novel.base == "some model" - - def test_effort_survives_alongside_dropped_tokens(self) -> None: - key = parse_display_name("Kimi K3 (max) (FAST)") - assert key.base == "kimi k3" - assert key.effort == "max" - assert "FAST" in key.dropped - - def test_plus_suffix_is_significant_not_stripped(self) -> None: - """``Command A`` and ``Command A+`` are different Cohere models.""" - assert parse_display_name("Command A").base == "command a" - assert parse_display_name("Command A+").base == "command a plus" - assert ( - parse_display_name("Command A").base != parse_display_name("Command A+").base - ) - - def test_bare_name_has_no_variant_identity(self) -> None: - key = parse_display_name("Kimi K2.6") - assert key.base == "kimi k2 6" - assert key.reasoning is None - assert key.effort is None - - -class TestEnrichKeyFromSlug: - def test_slug_supplies_reasoning_mode_the_name_omits(self) -> None: - key = enrich_key_from_slug( - parse_display_name("Grok 4 Fast"), "grok-4-fast-reasoning" - ) - assert key.reasoning == "reasoning" - - def test_slug_supplies_non_reasoning_mode(self) -> None: - key = enrich_key_from_slug( - parse_display_name("GPT-5.1"), "gpt-5-1-non-reasoning" - ) - assert key.reasoning == "non-reasoning" - - def test_slug_supplies_effort_level(self) -> None: - key = enrich_key_from_slug(parse_display_name("GPT-5.1"), "gpt-5-1-high") - assert key.effort == "high" - assert key.reasoning == "reasoning" - - def test_display_name_wins_over_slug(self) -> None: - key = enrich_key_from_slug( - parse_display_name("Some Model (Non-reasoning)"), "some-model-reasoning" - ) - assert key.reasoning == "non-reasoning" - - def test_bare_slug_effort_is_never_guessed(self) -> None: - """Which effort owns the unsuffixed slug varies per model, so it is not - derivable by rule — gpt-oss-120b is high, claude-opus-5 is max.""" - for slug in ("gpt-oss-120b", "claude-opus-5"): - key = enrich_key_from_slug(parse_display_name("Whatever"), slug) - assert key.effort is None, slug - - def test_missing_slug_leaves_key_untouched(self) -> None: - original = parse_display_name("Kimi K3 (max)") - assert enrich_key_from_slug(original, None) is original - assert enrich_key_from_slug(original, "") is original - - -class TestNormalizeCreator: - def test_spacing_and_case_are_ignored(self) -> None: - assert normalize_creator("Z AI") == normalize_creator("ZAI") == "zai" - - def test_missing_creator_is_none(self) -> None: - assert normalize_creator(None) is None - assert normalize_creator("") is None - - -class TestJoin: - def test_exact_variant_match(self) -> None: - offerings = [offering("Qwen3.5 397B A17B (Non-reasoning)")] - rows = [ - openness_row("Qwen3.5 397B A17B (Reasoning)", openness_index=66.67), - openness_row("Qwen3.5 397B A17B (Non-reasoning)", openness_index=55.55), - ] - - result = join_openness(offerings, rows) - - assert result.matched == 1 - assert result.tiers == {"exact": 1} - assert offerings[0]["openness"]["openness_index"] == 55.55 - assert offerings[0]["openness_match"]["tier"] == "exact" - - def test_reasoning_variants_are_not_collapsed(self) -> None: - """The spike's headline risk: a naive name join merges these two.""" - offerings = [ - offering("Qwen3.5 397B A17B (max)"), - offering("Qwen3.5 397B A17B (Non-reasoning)"), - ] - rows = [ - openness_row("Qwen3.5 397B A17B (Reasoning)", openness_index=66.67), - openness_row("Qwen3.5 397B A17B (Non-reasoning)", openness_index=55.55), - ] - - join_openness(offerings, rows) - - assert offerings[0]["openness"]["openness_index"] == 66.67 - assert offerings[1]["openness"]["openness_index"] == 55.55 - - def test_effort_is_dropped_when_openness_publishes_one_row_per_model(self) -> None: - """AA usually publishes a single openness row for all effort levels.""" - offerings = [offering("GPT-5.6 Sol (xhigh)", creator="OpenAI")] - rows = [openness_row("GPT-5.6 Sol (Reasoning)", creator="OpenAI")] - - result = join_openness(offerings, rows) - - assert result.matched == 1 - assert result.tiers == {"base+mode": 1} - - def test_quantisation_variants_all_match_the_same_openness_row(self) -> None: - offerings = [ - offering("Llama 4 Maverick (FP8)", creator="Meta"), - offering("Llama 4 Maverick (NVFP4)", creator="Meta"), - offering("Llama 4 Maverick", creator="Meta"), - ] - rows = [openness_row("Llama 4 Maverick", creator="Meta", openness_index=77.78)] - - result = join_openness(offerings, rows) - - assert result.matched == 3 - assert all(o["openness"]["openness_index"] == 77.78 for o in offerings) - - def test_creator_prefix_on_openness_side_still_matches(self) -> None: - offerings = [offering("Nemotron 3 Nano 30B A3B (Non-reasoning)", creator="NVIDIA")] - rows = [ - openness_row( - "NVIDIA Nemotron 3 Nano 30B A3B (Non-reasoning)", creator="NVIDIA" - ) - ] - - result = join_openness(offerings, rows) - - assert result.matched == 1 - - def test_different_creators_are_never_merged(self) -> None: - offerings = [offering("Nova Pro", creator="Amazon")] - rows = [openness_row("Nova Pro", creator="Some Other Lab")] - - result = join_openness(offerings, rows) - - assert result.matched == 0 - assert len(result.unmatched) == 1 - - def test_plus_variant_does_not_borrow_the_base_models_openness(self) -> None: - offerings = [offering("Command A+", creator="Cohere")] - rows = [ - openness_row("Command A", creator="Cohere", openness_index=33.33), - openness_row("Command A+", creator="Cohere", openness_index=22.22), - ] - - result = join_openness(offerings, rows) - - assert result.matched == 1 - assert offerings[0]["openness"]["openness_index"] == 22.22 - - def test_bare_name_against_split_reasoning_rows_is_ambiguous(self) -> None: - """Observed live: provider pages list a bare ``GPT-5.1`` while the - Openness Index splits it into ``(Non-reasoning)`` and ``(high)``.""" - offerings = [offering("GPT-5.1", creator="OpenAI")] - rows = [ - openness_row("GPT-5.1 (Non-reasoning)", creator="OpenAI", openness_index=11.11), - openness_row("GPT-5.1 (high)", creator="OpenAI", openness_index=22.22), - ] - - result = join_openness(offerings, rows) - - assert result.matched == 0 - assert len(result.ambiguous) == 1 - assert offerings[0]["openness"] is None - - def test_slug_resolves_what_the_display_name_leaves_ambiguous(self) -> None: - """Observed live: azure lists a bare ``Grok 4 Fast`` under the slug - ``grok-4-fast-reasoning``, which resolves the split openness rows.""" - offer = offering("Grok 4 Fast", creator="SpaceXAI") - offer["model_slug"] = "grok-4-fast-reasoning" - rows = [ - openness_row("Grok 4 Fast (Reasoning)", creator="SpaceXAI", openness_index=22.22), - openness_row( - "Grok 4 Fast (Non-reasoning)", creator="SpaceXAI", openness_index=11.11 - ), - ] - - result = join_openness([offer], rows) - - assert result.matched == 1 - assert result.ambiguous == [] - assert offer["openness"]["openness_index"] == 22.22 - - def test_disagreeing_candidates_are_reported_ambiguous_not_guessed(self) -> None: - offerings = [offering("Mystery Model")] - rows = [ - openness_row("Mystery Model (Reasoning)", openness_index=88.89), - openness_row("Mystery Model (Non-reasoning)", openness_index=11.11), - ] - - result = join_openness(offerings, rows) - - assert result.matched == 0 - assert len(result.ambiguous) == 1 - entry = result.ambiguous[0] - assert entry["candidates"] == [ - "Mystery Model (Non-reasoning)", - "Mystery Model (Reasoning)", - ] - assert offerings[0]["openness"] is None - - def test_agreeing_candidates_collapse_to_a_match(self) -> None: - offerings = [offering("Agreeable Model")] - rows = [ - openness_row("Agreeable Model (Reasoning)", openness_index=44.44), - openness_row("Agreeable Model (Non-reasoning)", openness_index=44.44), - ] - - result = join_openness(offerings, rows) - - assert result.matched == 1 - assert offerings[0]["openness"]["openness_index"] == 44.44 - - def test_absent_openness_is_recorded_not_zeroed(self) -> None: - """Coverage genuinely differs between the two datasets.""" - offerings = [offering("Claude Opus 5 (max)", creator="Anthropic")] - rows = [openness_row("Claude Opus 4.5", creator="Anthropic")] - - result = join_openness(offerings, rows) - - assert result.matched == 0 - assert offerings[0]["openness"] is None - entry = result.unmatched[0] - assert entry["display_name"] == "Claude Opus 5 (max)" - assert entry["normalized_base"] == "claude opus 5" - assert entry["reason"] == "no openness row for this model" - - def test_unmatched_rows_are_never_dropped_from_the_dataset(self) -> None: - offerings = [ - offering("Known Model", creator="Alibaba"), - offering("Unknown Model", creator="Alibaba"), - ] - rows = [openness_row("Known Model", creator="Alibaba")] - - result = join_openness(offerings, rows) - - assert result.matched == 1 - assert len(result.unmatched) == 1 - assert len(offerings) == 2 +def test_offering_without_a_slug_is_unmatched_rather_than_crashing(): + offerings = [_offering(model_slug=None)] + result = join_openness(offerings, [_openness()]) + assert result.matched == 0 + assert len(result.unmatched) == 1 diff --git a/services/aa-sync/tests/test_normalize.py b/services/aa-sync/tests/test_normalize.py index cb2fe9f..04b367d 100644 --- a/services/aa-sync/tests/test_normalize.py +++ b/services/aa-sync/tests/test_normalize.py @@ -1,183 +1,154 @@ -from datetime import UTC, datetime -from typing import Any +"""Dataset assembly and the shape guards that stop a thin capture publishing.""" + +from __future__ import annotations import pytest -from tokenpricing_aa.fetch import discover_provider_slugs +from tests.factories import leaderboard_page, offering, openness_page from tokenpricing_aa.normalize import ShapeError, normalize_sources -from tokenpricing_aa.parse import OPENNESS_COLUMNS, PROVIDER_COLUMNS +from tokenpricing_aa.schema import DriftReport + + +def _capture(page: str, url: str = "https://artificialanalysis.ai/leaderboards/providers"): + return {"source_url": url, "fetched_at": "2026-08-14T09:00:00+00:00", "page": page} + + +def _wide_payload(providers: int = 50, per_provider: int = 20): + """A capture big enough to clear the shape guards.""" + objects = [] + for p in range(providers): + for m in range(per_provider): + objects.append( + offering( + id=f"off-{p}-{m}", + label=f"Model {m}", + host={"slug": f"provider-{p}", "name": f"Provider {p}"}, + model={"slug": f"model-{m}"}, + ) + ) + return objects + + +def _build(objects, openness_html=None, drift=None): + return normalize_sources( + _capture(leaderboard_page(objects)), + _capture(openness_html or _wide_openness(), url="https://artificialanalysis.ai/o"), + drift=drift or DriftReport(record_count=len(objects)), + ) -from .test_parse import CHECK_ICON, GROUP_HEADER -NOW = datetime(2026, 8, 14, tzinfo=UTC).isoformat() +def _wide_openness(count: int = 300): + records, entities = [], [] + for i in range(count): + records.append( + { + "id": f"score-{i}", + "modelId": f"model-uuid-{i}", + "opennessIndex": 38.0 + (i % 10), + "modelAvailability": 6, + "modelTransparency": 1, + "dataPretrainAccess": 0, + "dataPretrainLicense": 0, + "dataPosttrainAccess": 0, + "dataPosttrainLicense": 0, + "transparencyMethodology": 1, + "transparencyPreTrainingData": 0, + "transparencyPostTrainingData": 0, + } + ) + entities.append( + {"id": f"model-uuid-{i}", "slug": f"model-{i}", "name": f"Model {i}"} + ) + return openness_page(records, entities) -def provider_page(rows: list[tuple[str, str, str]]) -> str: - group = "".join(f"{cell}" for cell in GROUP_HEADER) - header = "".join(f"{cell}" for cell in PROVIDER_COLUMNS) - body = "" - for name, slug, creator in rows: - model_cell = ( - f'
{creator} logo{name}' - f'
' - ) - cells = [ - model_cell, - "1M", - CHECK_ICON, - CHECK_ICON, - "Open", - "60", - "$0.89", - "48", - "1.26", - "53.76", - "42.00", - "x", - ] - body += "" + "".join(f"{c}" for c in cells) + "" - return ( - f"{group}{header}" - f"{body}
" - ) +def test_builds_a_dataset_from_a_healthy_capture(): + dataset = _build(_wide_payload()) + assert dataset.metadata.offering_count == 1000 + assert dataset.metadata.provider_count == 50 + assert dataset.metadata.openness_row_count == 300 -def openness_page(rows: list[tuple[str, str, str]]) -> str: - header = "".join(f"{c}" for c in ("", *OPENNESS_COLUMNS)) - body = "" - for rank, (creator, name, index) in enumerate(rows, 1): - cells = [ - str(rank), - creator, - name, - index, - "50.0", - "6.00", - "6.00", - "1.00", - "1.00", - "1.00", - "1.00", - ] - body += "" + "".join(f"{c}" for c in cells) + "" - return f"{header}{body}
" - - -def payloads( - provider_count: int = 45, - rows_per_provider: int = 20, - openness_count: int = 260, -) -> tuple[dict[str, Any], dict[str, Any]]: - pages = { - f"provider-{index:02d}": provider_page( - [ - (f"Model {n} (max)", f"model-{n}", "Alibaba") - for n in range(rows_per_provider) - ] - ) - for index in range(provider_count) - } - openness_rows = [ - ("Alibaba", f"Model {n} (Reasoning)", "66.67") for n in range(openness_count) - ] - providers_payload = { - "source": "artificial_analysis_providers", - "source_url": "https://artificialanalysis.ai/leaderboards/providers", - "fetched_at": NOW, - "discovered_slugs": sorted(pages), - "unreachable_slugs": [{"slug": "hyperbolic", "status": 404}], - "pages": pages, - } - openness_payload = { - "source": "artificial_analysis_openness", - "source_url": "https://artificialanalysis.ai/evaluations/artificial-analysis-openness-index", - "fetched_at": NOW, - "page": openness_page(openness_rows), - } - return providers_payload, openness_payload - - -class TestDiscoverProviderSlugs: - def test_extracts_slugs_and_ignores_chunk_filenames(self) -> None: - html = ( - 'a' - 'b' - '' - 'dupe' - ) - assert discover_provider_slugs(html) == ["fireworks", "togetherai"] - - -class TestNormalizeSources: - def test_builds_dataset_with_metadata_and_attribution(self) -> None: - dataset = normalize_sources(*payloads()) - - assert dataset.metadata.provider_count == 45 - assert dataset.metadata.offering_count == 900 - assert dataset.metadata.openness_row_count == 260 - assert dataset.metadata.openness_matched == 900 - assert dataset.metadata.unreachable_provider_slugs == ["hyperbolic"] - assert "Artificial Analysis" in dataset.metadata.attribution - assert dataset.metadata.openness_spec_version.startswith("Openness Spec V1.0") - - def test_offerings_carry_variant_identity(self) -> None: - dataset = normalize_sources(*payloads()) - offering = dataset.offerings[0] - - assert offering.reasoning_effort == "max" - assert offering.reasoning_mode == "reasoning" - assert offering.openness is not None - assert offering.openness.openness_index == 66.67 - assert offering.openness_match is not None - - def test_unmatched_offerings_are_reported_and_kept(self) -> None: - providers, openness = payloads(rows_per_provider=21) - dataset = normalize_sources(providers, openness) - - # Model 20 has no openness row (openness covers Model 0..259 by name, but - # each provider page only emits Model 0..20, so all are covered) — assert - # instead on the join accounting being complete and lossless. - assert dataset.metadata.offering_count == len(dataset.offerings) - assert ( - dataset.metadata.openness_matched - + dataset.metadata.openness_unmatched - + dataset.metadata.openness_ambiguous - == dataset.metadata.offering_count - ) +def test_deprecated_offerings_are_kept_and_counted(): + """Superseded rows are the whole reason the payload beats the rendered table.""" + objects = _wide_payload() + objects[0]["model"]["deprecated"] = False + dataset = _build(objects) + assert dataset.metadata.deprecated_offering_count == 999 + assert any(o.deprecated for o in dataset.offerings) + assert any(not o.deprecated for o in dataset.offerings) - def test_openness_absent_for_a_model_is_recorded_not_zeroed(self) -> None: - providers, openness = payloads() - providers["pages"]["provider-00"] = provider_page( - [("Claude Opus 5 (max)", "claude-opus-5", "Anthropic")] - ) - dataset = normalize_sources(providers, openness) - - missing = [o for o in dataset.offerings if o.display_name == "Claude Opus 5 (max)"] - assert len(missing) == 1 - assert missing[0].openness is None - assert any(u.display_name == "Claude Opus 5 (max)" for u in dataset.unmatched) - - def test_too_few_providers_fails_loudly(self) -> None: - providers, openness = payloads(provider_count=10) - with pytest.raises(ShapeError, match="provider pages captured"): - normalize_sources(providers, openness) - - def test_too_few_offerings_fails_loudly(self) -> None: - providers, openness = payloads(provider_count=41, rows_per_provider=1) - with pytest.raises(ShapeError, match="offerings parsed"): - normalize_sources(providers, openness) - - def test_too_few_openness_rows_fails_loudly(self) -> None: - providers, openness = payloads(openness_count=10) - with pytest.raises(ShapeError, match="openness rows parsed"): - normalize_sources(providers, openness) - - def test_missing_model_anchors_fail_loudly(self) -> None: - """A restyle that drops the /models/ hrefs must not publish silently.""" - providers, openness = payloads() - providers["pages"] = { - slug: html.replace('href="/models/', 'href="/broken/') - for slug, html in providers["pages"].items() - } - with pytest.raises(ShapeError, match="/models/ anchor"): - normalize_sources(providers, openness) + +def test_offering_id_is_carried_onto_every_row(): + dataset = _build(_wide_payload()) + ids = {o.offering_id for o in dataset.offerings} + assert len(ids) == len(dataset.offerings) + + +def test_openness_breakdown_is_attached_by_slug(): + dataset = _build(_wide_payload(providers=50, per_provider=20)) + scored = [o for o in dataset.offerings if o.openness is not None] + assert scored, "model-N slugs should match openness entities of the same slug" + assert scored[0].openness.openness_index is not None + + +def test_reasoning_variant_is_recorded(): + objects = _wide_payload() + objects[0]["label"] = "Claude Opus 5 (max)" + objects[0]["model"]["slug"] = "claude-opus-5" + objects[1]["model"]["slug"] = "claude-opus-5-low" + dataset = _build(objects) + by_slug = {o.model_slug: o for o in dataset.offerings} + assert by_slug["claude-opus-5"].reasoning_effort == "max" + assert by_slug["claude-opus-5-low"].reasoning_effort == "low" + assert by_slug["claude-opus-5"].reasoning_mode == "reasoning" + + +def test_drift_summary_is_recorded_in_metadata(): + report = DriftReport(record_count=1000, new=["model.newBench"]) + dataset = _build(_wide_payload(), drift=report) + assert dataset.metadata.drift.new == ["model.newBench"] + assert dataset.metadata.drift.breaking is False + + +# --- shape guards ---------------------------------------------------------- # + + +def test_too_few_offerings_is_rejected(): + with pytest.raises(ShapeError, match="offerings parsed"): + _build(_wide_payload(providers=5, per_provider=5)) + + +def test_too_few_providers_is_rejected(): + with pytest.raises(ShapeError, match="providers in the payload"): + _build(_wide_payload(providers=10, per_provider=100)) + + +def test_duplicate_offering_ids_are_rejected(): + """The primary key must be verified, not assumed.""" + objects = _wide_payload() + objects[1]["id"] = objects[0]["id"] + with pytest.raises(ShapeError, match="offering_id is not unique"): + _build(objects) + + +def test_missing_model_slugs_are_rejected(): + objects = _wide_payload() + for obj in objects[:200]: + obj["model"]["slug"] = None + with pytest.raises(ShapeError, match="carry a model slug"): + _build(objects) + + +def test_too_few_openness_rows_is_rejected(): + with pytest.raises(ShapeError, match="openness rows parsed"): + _build(_wide_payload(), openness_html=_wide_openness(count=10)) + + +def test_sources_are_recorded_for_attribution(): + dataset = _build(_wide_payload()) + assert dataset.metadata.sources == [ + "https://artificialanalysis.ai/leaderboards/providers", + "https://artificialanalysis.ai/o", + ] + assert "Artificial Analysis" in dataset.metadata.attribution diff --git a/services/aa-sync/tests/test_parse.py b/services/aa-sync/tests/test_parse.py index 164eb02..183ee20 100644 --- a/services/aa-sync/tests/test_parse.py +++ b/services/aa-sync/tests/test_parse.py @@ -1,228 +1,135 @@ +"""Projection of payload objects into flat records.""" + +from __future__ import annotations + import pytest +from tests.factories import leaderboard_page, make_page, offering, openness_page from tokenpricing_aa.parse import ( - OPENNESS_COLUMNS, - PROVIDER_COLUMNS, + OFFERING_FIELDS, ParseError, - is_estimated, - parse_context_window, - parse_number, - parse_openness_page, - parse_provider_page, + dig, + parse_leaderboard, + parse_openness, ) -GROUP_HEADER = [ - "", - "Features", - "Model Intelligence", - "Price", - "Speed", - "Latency", - "End-to-End Response Time", - "", -] - -CHECK_ICON = '' - - -def provider_html(rows: list[list[str]], columns: tuple[str, ...] = PROVIDER_COLUMNS) -> str: - group = "".join(f"{cell}" for cell in GROUP_HEADER) - header = "".join(f"{cell}" for cell in columns) - body = "".join( - "" + "".join(f"{cell}" for cell in row) + "" for row in rows - ) - return ( - f"{group}{header}" - f"{body}
" - ) - - -def offering_row( - name: str = "Kimi K3 (max)", - slug: str = "kimi-k3", - creator: str = "Kimi", - context: str = "1.05M", - function_calling: str = CHECK_ICON, - json_mode: str = CHECK_ICON, - license_: str = "Open", - intelligence: str = "60", - cost: str = "$0.89", - tokens_per_second: str = "48", - first_chunk: str = "1.26", - total: str = "53.76", - reasoning: str = "42.00", -) -> list[str]: - model_cell = ( - f'
{creator} logo{name}' - f'
' - ) - return [ - model_cell, - context, - function_calling, - json_mode, - license_, - intelligence, - cost, - tokens_per_second, - first_chunk, - total, - reasoning, - 'Model Provider', - ] - - -class TestParseNumber: - def test_unicode_minus_sign_parses_as_negative(self) -> None: - """AA renders negatives with U+2212, which float() rejects outright.""" - with pytest.raises(ValueError): - float("−31") - assert parse_number("−31") == -31.0 - assert parse_number("−38.5") == -38.5 - - def test_ascii_hyphen_still_parses(self) -> None: - assert parse_number("-31") == -31.0 - - def test_no_data_is_none_not_zero(self) -> None: - for value in ("--", "—", "–", "", "N/A", None): - assert parse_number(value) is None - - def test_zero_is_preserved_as_zero(self) -> None: - assert parse_number("0") == 0.0 - - def test_strips_currency_percent_and_thousands_separators(self) -> None: - assert parse_number("$0.89") == 0.89 - assert parse_number("67%") == 67.0 - assert parse_number("1,715") == 1715.0 - - def test_estimated_asterisk_is_stripped_but_flagged(self) -> None: - assert parse_number("33*") == 33.0 - assert is_estimated("33*") is True - assert is_estimated("33") is False - - def test_unparseable_text_is_none(self) -> None: - assert parse_number("coming soon") is None - - -class TestParseContextWindow: - @pytest.mark.parametrize( - ("rendered", "expected"), - [ - ("1M", 1_000_000), - ("1.05M", 1_050_000), - ("262k", 262_000), - ("205k", 205_000), - ("128K", 128_000), - ], - ) - def test_human_readable_suffixes(self, rendered: str, expected: int) -> None: - assert parse_context_window(rendered) == expected - - def test_missing_is_none(self) -> None: - assert parse_context_window("--") is None - assert parse_context_window(None) is None - - -class TestParseProviderPage: - def test_extracts_offering_fields(self) -> None: - (row,) = parse_provider_page(provider_html([offering_row()]), "fireworks") - - assert row["provider_slug"] == "fireworks" - assert row["model_slug"] == "kimi-k3" - assert row["display_name"] == "Kimi K3 (max)" - assert row["creator"] == "Kimi" - assert row["context_window"] == 1_050_000 - assert row["license"] == "Open" - assert row["intelligence_index"] == 60.0 - assert row["cost_per_task_usd"] == 0.89 - assert row["median_output_tokens_per_second"] == 48.0 - assert row["reasoning_time_seconds"] == 42.0 - - def test_check_icon_presence_drives_feature_flags(self) -> None: - """AA emits a check icon for yes and renders nothing at all for no.""" - (yes,) = parse_provider_page(provider_html([offering_row()]), "p") - assert yes["supports_function_calling"] is True - assert yes["supports_json_mode"] is True - - (no,) = parse_provider_page( - provider_html([offering_row(function_calling="", json_mode="")]), "p" - ) - assert no["supports_function_calling"] is False - assert no["supports_json_mode"] is False - - def test_missing_reasoning_time_is_none(self) -> None: - (row,) = parse_provider_page( - provider_html([offering_row(reasoning="--")]), "openai" - ) - assert row["reasoning_time_seconds"] is None - - def test_same_slug_twice_is_kept_as_two_offerings(self) -> None: - """A provider can serve one model under several serving tiers.""" - rows = parse_provider_page( - provider_html( - [ - offering_row(name="Kimi K3 (max)"), - offering_row(name="Kimi K3 (max) (FAST)", cost="$1.34"), - ] - ), - "fireworks", - ) - assert len(rows) == 2 - assert {row["display_name"] for row in rows} == { - "Kimi K3 (max)", - "Kimi K3 (max) (FAST)", - } - - def test_changed_columns_fail_loudly(self) -> None: - mutated = ("Model", "Context Window", "Surprise") + PROVIDER_COLUMNS[3:] - with pytest.raises(ParseError, match="columns changed"): - parse_provider_page(provider_html([], columns=mutated), "p") - - def test_missing_table_fails_loudly(self) -> None: - with pytest.raises(ParseError, match="no "): - parse_provider_page("nope", "p") - - -def openness_html(rows: list[list[str]]) -> str: - header = "".join(f"" for cell in ("", *OPENNESS_COLUMNS)) - body = "".join( - "" + "".join(f"" for cell in row) + "" for row in rows - ) - return f"
{cell}
{cell}
{header}{body}
" - - -class TestParseOpennessPage: - def test_extracts_components(self) -> None: - (row,) = parse_openness_page( - openness_html( - [ - [ - "1", - "Allen Institute for AI", - "Olmo 3.1 32B Think", - "88.89", - "7.90", - "6.00", - "10.00", - "3.00", - "1.00", - "3.00", - "1.00", - ] - ] - ) - ) - - assert row["rank"] == 1 - assert row["creator"] == "Allen Institute for AI" - assert row["display_name"] == "Olmo 3.1 32B Think" - assert row["openness_index"] == 88.89 - assert row["pre_training_data_access"] == 3.0 - assert row["post_training_data_license"] == 1.0 - - def test_changed_columns_fail_loudly(self) -> None: - header = "".join( - f"{cell}" for cell in ("", "Creator", "Model", "Something Else") - ) - with pytest.raises(ParseError, match="columns changed"): - parse_openness_page(f"{header}
") + +def test_projects_every_declared_field(): + (row,) = parse_leaderboard(leaderboard_page()) + assert set(row) == {name for name, _ in OFFERING_FIELDS} + + +def test_reads_values_at_full_precision(): + """No rounding, no currency stripping: the payload is already typed.""" + (row,) = parse_leaderboard(leaderboard_page()) + assert row["intelligence_index"] == 45.1382483763163 + assert row["input_price_usd_per_1m"] == 0.95 + assert row["output_price_usd_per_1m"] == 4 + assert row["cost_per_task_usd"] == 0.7586225879503231 + assert row["context_window"] == 262144 + + +def test_reads_booleans_as_booleans(): + (row,) = parse_leaderboard(leaderboard_page()) + assert row["intelligence_index_estimated"] is False + assert row["supports_function_calling"] is True + assert row["deprecated"] is True + assert row["is_open_weights"] is True + + +def test_carries_identity_fields_the_rendered_table_lacks(): + (row,) = parse_leaderboard(leaderboard_page()) + assert row["offering_id"] == "10b47ef3-f0b9-4d9e-808a-9ed9ad14e73f" + assert row["host_api_id"] == "moonshotai/Kimi-K2.6" + assert row["omniscience_index"] == 5.3 + assert row["p95_output_tokens_per_second"] == 330.205485545576 + + +def test_absent_nested_values_become_none_not_zero(): + row_obj = offering(model={"livecodebench": None}, pricing={"cacheHitPrice": None}) + (row,) = parse_leaderboard(leaderboard_page([row_obj])) + assert row["livecodebench"] is None + assert row["cache_hit_price_usd_per_1m"] is None + + +def test_nested_path_reads_through_sub_objects(): + (row,) = parse_leaderboard(leaderboard_page()) + assert row["briefcase_elo"] == 818.82 + assert row["creator"] == "Kimi" + assert row["provider_slug"] == "nebius" + + +def test_dig_dead_ends_to_none_rather_than_raising(): + assert dig({"a": {"b": 1}}, ("a", "b")) == 1 + assert dig({"a": {"b": 1}}, ("a", "missing")) is None + assert dig({"a": 1}, ("a", "b")) is None + assert dig(None, ("a",)) is None + + +def test_payload_without_offerings_is_a_parse_error(): + html = make_page({"data": [{"unrelated": True}]}) + with pytest.raises(ParseError, match="no offering objects"): + parse_leaderboard(html) + + +def test_offerings_without_ids_are_a_parse_error(): + html = leaderboard_page([offering(id=None)]) + with pytest.raises(ParseError, match="no id"): + parse_leaderboard(html) + + +# --- openness ------------------------------------------------------------- # + +# The default fixture entity has this id; "dangling" deliberately points nowhere. +_RESOLVABLE_MODEL_ID = "f0083258-8646-45b8-8082-7aaf6c2ea82a" + + +def _score(record_id: str = "resolvable", index: float = 38.888888888888886): + return { + "id": record_id, + "modelId": _RESOLVABLE_MODEL_ID if record_id == "resolvable" else "nowhere", + "opennessIndex": index, + "modelAvailability": 6, + "modelTransparency": 1, + "dataPretrainAccess": 0, + "dataPretrainLicense": 0, + "dataPosttrainAccess": 0, + "dataPosttrainLicense": 0, + "transparencyMethodology": 1, + "transparencyPreTrainingData": 0, + "transparencyPostTrainingData": 0, + } + + +def test_openness_resolves_each_score_to_a_model_slug(): + (row,) = parse_openness(openness_page()) + assert row["model_slug"] == "kimi-k2-6" + assert row["display_name"] == "Kimi K2.6" + assert row["openness_index"] == 38.888888888888886 + + +def test_openness_includes_components_absent_from_the_rendered_table(): + (row,) = parse_openness(openness_page()) + assert row["transparency_methodology"] == 1 + assert row["transparency_pre_training_data"] == 0 + assert row["transparency_post_training_data"] == 0 + + +def test_openness_record_with_unresolvable_model_id_keeps_its_score(): + """One dangling modelId must not discard its score, nor fail the run.""" + rows = parse_openness(openness_page(records=[_score(), _score("dangling", 12.5)])) + by_id = {row["openness_record_id"]: row for row in rows} + assert by_id["dangling"]["model_slug"] is None + assert by_id["dangling"]["openness_index"] == 12.5 + assert by_id["resolvable"]["model_slug"] == "kimi-k2-6" + + +def test_openness_with_no_resolvable_models_at_all_is_a_parse_error(): + page = openness_page(entities=[{"id": "other", "slug": None, "name": "X"}]) + with pytest.raises(ParseError, match="modelId -> model relationship"): + parse_openness(page) + + +def test_openness_without_score_records_is_a_parse_error(): + with pytest.raises(ParseError, match="no 'opennessIndex' records"): + parse_openness(make_page({"models": [{"id": "a", "slug": "b"}]})) diff --git a/services/aa-sync/tests/test_schema.py b/services/aa-sync/tests/test_schema.py new file mode 100644 index 0000000..75045bc --- /dev/null +++ b/services/aa-sync/tests/test_schema.py @@ -0,0 +1,188 @@ +"""Drift detection: the check that a parseable payload still means what it did.""" + +from __future__ import annotations + +import json + +from tests.factories import offering +from tokenpricing_aa.parse import OFFERING_FIELDS +from tokenpricing_aa.paths import OFFERING_MANIFEST_PATH +from tokenpricing_aa.schema import ( + build_manifest, + check_drift, + flatten, + load_manifest, + type_name, +) + + +def _manifest(objects): + """A manifest as specs, the way check_drift consumes it.""" + from tokenpricing_aa.schema import FieldSpec + + data = build_manifest(objects) + return {p: FieldSpec.from_json(s) for p, s in data["fields"].items()} + + +def _baseline(count: int = 5): + """Several objects so populated_ratio is meaningful.""" + return [offering(id=f"id-{i}") for i in range(count)] + + +def test_type_name_distinguishes_bool_from_number(): + """bool is an int subclass in Python; a silent bool->number change must not pass.""" + assert type_name(True) == "bool" + assert type_name(1) == "number" + assert type_name(1.5) == "number" + assert type_name(None) == "null" + assert type_name("x") == "string" + + +def test_flatten_produces_dotted_leaf_paths(): + flat = flatten(offering()) + assert flat["model.briefcase.elo"] == 818.82 + assert flat["host.slug"] == "nebius" + assert "model" not in flat + + +def test_identical_payload_reports_no_drift(): + objects = _baseline() + report = check_drift(objects, _manifest(objects)) + assert not report.breaking + assert report.new == [] + assert "no drift" in report.summary() + + +# --- failure mode 3a: a field disappears ----------------------------------- # + + +def test_removed_field_is_flagged_missing(): + manifest = _manifest(_baseline()) + broken = [] + for i in range(5): + obj = offering(id=f"id-{i}") + del obj["pricing"]["price1mInputTokens"] + broken.append(obj) + report = check_drift(broken, manifest) + assert report.breaking + assert "pricing.price1mInputTokens" in report.missing + + +def test_field_that_went_entirely_null_is_flagged_missing(): + """Present but never populated is as broken as absent, for a populated field.""" + manifest = _manifest(_baseline()) + broken = [ + offering(id=f"id-{i}", pricing={"price1mInputTokens": None}) for i in range(5) + ] + report = check_drift(broken, manifest) + assert report.breaking + assert any("price1mInputTokens" in m for m in report.missing) + + +def test_sparse_field_going_null_is_not_flagged(): + """Benchmarks scored for a handful of models must not cause weekly false alarms.""" + baseline = [ + offering(id=f"id-{i}", model={"livecodebench": 0.5 if i == 0 else None}) + for i in range(10) + ] + manifest = _manifest(baseline) + live = [offering(id=f"id-{i}", model={"livecodebench": None}) for i in range(10)] + report = check_drift(live, manifest) + assert not any("livecodebench" in m for m in report.missing) + + +# --- failure mode 3b: a field changes type --------------------------------- # + + +def test_type_change_is_flagged(): + manifest = _manifest(_baseline()) + broken = [ + offering(id=f"id-{i}", features={"contextWindowTokens": "262k"}) + for i in range(5) + ] + report = check_drift(broken, manifest) + assert report.breaking + assert any("features.contextWindowTokens" in t for t in report.type_changed) + assert any("string" in t for t in report.type_changed) + + +def test_boolean_becoming_numeric_is_flagged(): + manifest = _manifest(_baseline()) + broken = [offering(id=f"id-{i}", features={"functionCalling": 1}) for i in range(5)] + report = check_drift(broken, manifest) + assert report.breaking + assert any("features.functionCalling" in t for t in report.type_changed) + + +# --- failure mode 3c: meaning changes while the type stays ------------------ # + + +def test_unit_rescale_is_flagged_as_a_range_shift(): + """0-1 fractions restated as 0-100 percentages parse perfectly and mean something else.""" + baseline = [offering(id=f"id-{i}", model={"gpqa": 0.5 + i / 100}) for i in range(10)] + manifest = _manifest(baseline) + live = [offering(id=f"id-{i}", model={"gpqa": 50.0 + i}) for i in range(10)] + report = check_drift(live, manifest) + assert report.breaking + assert any("model.gpqa" in r for r in report.range_shifted) + + +def test_price_restated_per_1k_is_flagged_as_a_range_shift(): + baseline = [ + offering(id=f"id-{i}", pricing={"price1mOutputTokens": 4.0 + i}) for i in range(10) + ] + manifest = _manifest(baseline) + live = [ + offering(id=f"id-{i}", pricing={"price1mOutputTokens": 4000.0 + i}) + for i in range(10) + ] + report = check_drift(live, manifest) + assert report.breaking + assert any("price1mOutputTokens" in r for r in report.range_shifted) + + +def test_ordinary_week_to_week_movement_is_not_a_range_shift(): + baseline = [offering(id=f"id-{i}", model={"gpqa": 0.5 + i / 100}) for i in range(10)] + manifest = _manifest(baseline) + live = [offering(id=f"id-{i}", model={"gpqa": 0.42 + i / 50}) for i in range(10)] + report = check_drift(live, manifest) + assert not report.range_shifted + + +# --- additive change is not a failure -------------------------------------- # + + +def test_new_field_is_reported_but_not_breaking(): + manifest = _manifest(_baseline()) + live = [offering(id=f"id-{i}", model={"brandNewBench2027": 0.4}) for i in range(5)] + report = check_drift(live, manifest) + assert not report.breaking + assert "model.brandNewBench2027" in report.new + + +def test_empty_payload_reports_everything_missing(): + manifest = _manifest(_baseline()) + report = check_drift([], manifest) + assert report.breaking + assert len(report.missing) == len(manifest) + + +# --- the checked-in manifest ----------------------------------------------- # + + +def test_checked_in_manifest_is_loadable(): + specs = load_manifest() + assert len(specs) > 50 + assert all(spec.types for spec in specs.values()) + + +def test_checked_in_manifest_covers_every_projected_field(): + """The projection and the manifest cannot drift apart silently.""" + specs = load_manifest() + for _name, path in OFFERING_FIELDS: + assert ".".join(path) in specs, f"{'.'.join(path)} not covered by the manifest" + + +def test_checked_in_manifest_records_the_capture_it_came_from(): + data = json.loads(OFFERING_MANIFEST_PATH.read_text(encoding="utf-8")) + assert data["record_count"] > 800