diff --git a/README.md b/README.md index a97c575..ff96795 100644 --- a/README.md +++ b/README.md @@ -87,11 +87,11 @@ Total ████████████░░░░░ "repo": "freeCodeCamp/freeCodeCamp", "description": "freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…", "createdAt": "2014-12-24T17:49:19Z", - "updatedAt": "2025-10-27T07:40:58Z", - "pushedAt": "2025-10-26T11:31:08Z", - "stars": 430828, - "watchers": 8582, - "forks": 42136, + "updatedAt": "2025-10-28T11:58:08Z", + "pushedAt": "2025-10-28T10:17:16Z", + "stars": 430886, + "watchers": 8583, + "forks": 42146, "defaultBranch": "main" }, { @@ -100,11 +100,11 @@ Total ████████████░░░░░ "repo": "codecrafters-io/build-your-own-x", "description": "Master programming by recreating your favorite technologies from scratch.", "createdAt": "2018-05-09T12:03:18Z", - "updatedAt": "2025-10-27T07:43:25Z", + "updatedAt": "2025-10-28T12:37:11Z", "pushedAt": "2025-10-10T18:45:01Z", - "stars": 430102, - "watchers": 6322, - "forks": 40388, + "stars": 430877, + "watchers": 6332, + "forks": 40453, "defaultBranch": "master" }, { @@ -113,11 +113,11 @@ Total ████████████░░░░░ "repo": "sindresorhus/awesome", "description": "😎 Awesome lists about all kinds of interesting topics", "createdAt": "2014-07-11T13:42:37Z", - "updatedAt": "2025-10-27T07:44:27Z", - "pushedAt": "2025-10-23T17:26:53Z", - "stars": 409760, - "watchers": 8016, - "forks": 32015, + "updatedAt": "2025-10-28T12:40:21Z", + "pushedAt": "2025-10-27T17:57:31Z", + "stars": 410052, + "watchers": 8017, + "forks": 32029, "defaultBranch": "main" } ] @@ -128,9 +128,9 @@ Total ████████████░░░░░ ``` repositories[3]{id,name,repo,description,createdAt,updatedAt,pushedAt,stars,watchers,forks,defaultBranch}: - 28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2025-10-27T07:40:58Z","2025-10-26T11:31:08Z",430828,8582,42136,main - 132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2025-10-27T07:43:25Z","2025-10-10T18:45:01Z",430102,6322,40388,master - 21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2025-10-27T07:44:27Z","2025-10-23T17:26:53Z",409760,8016,32015,main + 28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2025-10-28T11:58:08Z","2025-10-28T10:17:16Z",430886,8583,42146,main + 132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2025-10-28T12:37:11Z","2025-10-10T18:45:01Z",430877,6332,40453,master + 21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2025-10-28T12:40:21Z","2025-10-27T17:57:31Z",410052,8017,32029,main ``` --- @@ -208,36 +208,36 @@ metrics[5]{date,views,clicks,conversions,revenue,bounceRate}: > [!NOTE] > Measured with [`gpt-tokenizer`](https://github.com/niieani/gpt-tokenizer) using `o200k_base` encoding (used by GPT-5 and other modern models). Savings will vary across models and tokenizers. - + ### Retrieval Accuracy -Accuracy across **3 LLMs** on **159 data retrieval questions**: +Accuracy across **3 LLMs** on **154 data retrieval questions**: ``` -gpt-5-nano - toon ████████████████████ 99.4% (158/159) - yaml ███████████████████░ 95.0% (151/159) - csv ██████████████████░░ 92.5% (147/159) - json ██████████████████░░ 92.5% (147/159) - xml ██████████████████░░ 91.2% (145/159) - -claude-haiku-4-5 - toon ███████████████░░░░░ 75.5% (120/159) - xml ███████████████░░░░░ 75.5% (120/159) - csv ███████████████░░░░░ 75.5% (120/159) - json ███████████████░░░░░ 75.5% (120/159) - yaml ███████████████░░░░░ 74.2% (118/159) - gemini-2.5-flash - xml ██████████████████░░ 91.8% (146/159) - csv █████████████████░░░ 86.2% (137/159) - toon █████████████████░░░ 84.9% (135/159) - json ████████████████░░░░ 81.8% (130/159) - yaml ████████████████░░░░ 78.6% (125/159) + xml ██████████████████░░ 90.3% (139/154) + csv ██████████████████░░ 89.0% (137/154) + toon █████████████████░░░ 87.0% (134/154) + json ████████████████░░░░ 79.2% (122/154) + yaml ███████████████░░░░░ 76.0% (117/154) + +gpt-5-nano + toon ███████████████████░ 96.1% (148/154) + csv ██████████████████░░ 90.3% (139/154) + yaml ██████████████████░░ 89.0% (137/154) + json ██████████████████░░ 87.7% (135/154) + xml █████████████████░░░ 83.8% (129/154) + +claude-haiku-4-5-20251001 + json ██████████░░░░░░░░░░ 48.7% (75/154) + toon ██████████░░░░░░░░░░ 48.1% (74/154) + xml █████████░░░░░░░░░░░ 47.4% (73/154) + yaml █████████░░░░░░░░░░░ 47.4% (73/154) + csv █████████░░░░░░░░░░░ 45.5% (70/154) ``` -**Advantage:** TOON achieves **86.6% accuracy** (vs JSON's 83.2%) while using **46.3% fewer tokens**. +**Advantage:** TOON achieves **77.1% accuracy** (vs JSON's 71.9%) while using **46.3% fewer tokens**.
Performance by dataset and model @@ -248,73 +248,73 @@ gemini-2.5-flash | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `toon` | 87.4% | 2.483 | 152/174 | -| `csv` | 82.8% | 2.337 | 144/174 | -| `yaml` | 83.9% | 4.969 | 146/174 | -| `json` | 83.9% | 6.347 | 146/174 | -| `xml` | 88.5% | 7.314 | 154/174 | +| `csv` | 74.7% | 2,337 | 112/150 | +| `toon` | 76.7% | 2,483 | 115/150 | +| `yaml` | 70.7% | 4,969 | 106/150 | +| `xml` | 77.3% | 7,314 | 116/150 | +| `json` | 69.3% | 6,347 | 104/150 | ##### E-commerce orders with nested structures | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `toon` | 90.9% | 5.967 | 120/132 | -| `csv` | 93.9% | 6.735 | 124/132 | -| `yaml` | 87.1% | 7.328 | 115/132 | -| `json` | 87.9% | 9.694 | 116/132 | -| `xml` | 93.2% | 10.992 | 123/132 | +| `toon` | 80.0% | 5,967 | 96/120 | +| `csv` | 75.8% | 6,735 | 91/120 | +| `yaml` | 74.2% | 7,328 | 89/120 | +| `json` | 79.2% | 9,694 | 95/120 | +| `xml` | 78.3% | 10,992 | 94/120 | ##### Time-series analytics data | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `csv` | 89.7% | 1.393 | 78/87 | -| `toon` | 88.5% | 1.515 | 77/87 | -| `yaml` | 83.9% | 2.938 | 73/87 | -| `json` | 88.5% | 3.665 | 77/87 | -| `xml` | 85.1% | 4.376 | 74/87 | +| `csv` | 75.5% | 1,393 | 77/102 | +| `toon` | 76.5% | 1,515 | 78/102 | +| `yaml` | 74.5% | 2,938 | 76/102 | +| `json` | 76.5% | 3,665 | 78/102 | +| `xml` | 74.5% | 4,376 | 76/102 | ##### Top 100 GitHub repositories | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `toon` | 76.2% | 8.745 | 64/84 | -| `csv` | 69.0% | 8.513 | 58/84 | -| `yaml` | 71.4% | 13.129 | 60/84 | -| `json` | 69.0% | 15.145 | 58/84 | -| `xml` | 71.4% | 17.095 | 60/84 | +| `toon` | 74.4% | 8,745 | 67/90 | +| `csv` | 73.3% | 8,513 | 66/90 | +| `yaml` | 62.2% | 13,129 | 56/90 | +| `json` | 61.1% | 15,145 | 55/90 | +| `xml` | 61.1% | 17,095 | 55/90 | #### Performance by Model -##### gpt-5-nano - -| Format | Accuracy | Correct/Total | -| ------ | -------- | ------------- | -| `toon` | 99.4% | 158/159 | -| `yaml` | 95.0% | 151/159 | -| `csv` | 92.5% | 147/159 | -| `json` | 92.5% | 147/159 | -| `xml` | 91.2% | 145/159 | - -##### claude-haiku-4-5 - -| Format | Accuracy | Correct/Total | -| ------ | -------- | ------------- | -| `toon` | 75.5% | 120/159 | -| `xml` | 75.5% | 120/159 | -| `csv` | 75.5% | 120/159 | -| `json` | 75.5% | 120/159 | -| `yaml` | 74.2% | 118/159 | - ##### gemini-2.5-flash | Format | Accuracy | Correct/Total | | ------ | -------- | ------------- | -| `xml` | 91.8% | 146/159 | -| `csv` | 86.2% | 137/159 | -| `toon` | 84.9% | 135/159 | -| `json` | 81.8% | 130/159 | -| `yaml` | 78.6% | 125/159 | +| `xml` | 90.3% | 139/154 | +| `csv` | 89.0% | 137/154 | +| `toon` | 87.0% | 134/154 | +| `json` | 79.2% | 122/154 | +| `yaml` | 76.0% | 117/154 | + +##### gpt-5-nano + +| Format | Accuracy | Correct/Total | +| ------ | -------- | ------------- | +| `toon` | 96.1% | 148/154 | +| `csv` | 90.3% | 139/154 | +| `yaml` | 89.0% | 137/154 | +| `json` | 87.7% | 135/154 | +| `xml` | 83.8% | 129/154 | + +##### claude-haiku-4-5-20251001 + +| Format | Accuracy | Correct/Total | +| ------ | -------- | ------------- | +| `json` | 48.7% | 75/154 | +| `toon` | 48.1% | 74/154 | +| `xml` | 47.4% | 73/154 | +| `yaml` | 47.4% | 73/154 | +| `csv` | 45.5% | 70/154 |
@@ -336,32 +336,34 @@ Four datasets designed to test different structural patterns: #### Question Types -159 questions are generated dynamically across three categories: +154 questions are generated dynamically across three categories: -- **Field retrieval (50%)**: Direct value lookups +- **Field retrieval (40%)**: Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths) - Example: "What is Alice's salary?" → `75000` + - Example: "How many items are in order ORD-0042?" → `3` - Example: "What is the customer name for order ORD-0042?" → `John Doe` -- **Aggregation (25%)**: Counting and summation tasks +- **Aggregation (32%)**: Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons) - Example: "How many employees work in Engineering?" → `17` - Example: "What is the total revenue across all orders?" → `45123.50` + - Example: "How many employees have salary > 80000?" → `23` -- **Filtering (25%)**: Conditional queries +- **Filtering (28%)**: Multi-condition queries requiring compound logic (AND constraints across fields) - Example: "How many employees in Sales have salary > 80000?" → `5` - - Example: "How many orders have total > 400?" → `12` + - Example: "How many active employees have more than 10 years of experience?" → `8` #### Evaluation Process -1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, JSON, YAML, CSV, XML). +1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, CSV, XML, JSON, YAML). 2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer. -4. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`). +3. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`). #### Models & Configuration -- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5`, `gemini-2.5-flash` +- **Models tested**: `gemini-2.5-flash`, `gpt-5-nano`, `claude-haiku-4-5-20251001` - **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding (GPT-5 tokenizer) - **Temperature**: 0 (for non-reasoning models) -- **Total evaluations**: 159 questions × 5 formats × 3 models = 2,385 LLM calls +- **Total evaluations**: 154 questions × 5 formats × 3 models = 2,310 LLM calls diff --git a/benchmarks/data/github-repos.json b/benchmarks/data/github-repos.json index b7ed072..343e7c0 100644 --- a/benchmarks/data/github-repos.json +++ b/benchmarks/data/github-repos.json @@ -5,11 +5,11 @@ "repo": "freeCodeCamp/freeCodeCamp", "description": "freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.", "createdAt": "2014-12-24T17:49:19Z", - "updatedAt": "2025-10-27T07:40:58Z", - "pushedAt": "2025-10-26T11:31:08Z", - "stars": 430828, - "watchers": 8582, - "forks": 42136, + "updatedAt": "2025-10-28T11:58:08Z", + "pushedAt": "2025-10-28T10:17:16Z", + "stars": 430886, + "watchers": 8583, + "forks": 42146, "defaultBranch": "main" }, { @@ -18,11 +18,11 @@ "repo": "codecrafters-io/build-your-own-x", "description": "Master programming by recreating your favorite technologies from scratch.", "createdAt": "2018-05-09T12:03:18Z", - "updatedAt": "2025-10-27T07:43:25Z", + "updatedAt": "2025-10-28T12:37:11Z", "pushedAt": "2025-10-10T18:45:01Z", - "stars": 430102, - "watchers": 6322, - "forks": 40388, + "stars": 430877, + "watchers": 6332, + "forks": 40453, "defaultBranch": "master" }, { @@ -31,11 +31,11 @@ "repo": "sindresorhus/awesome", "description": "😎 Awesome lists about all kinds of interesting topics", "createdAt": "2014-07-11T13:42:37Z", - "updatedAt": "2025-10-27T07:44:27Z", - "pushedAt": "2025-10-23T17:26:53Z", - "stars": 409760, - "watchers": 8016, - "forks": 32015, + "updatedAt": "2025-10-28T12:40:21Z", + "pushedAt": "2025-10-27T17:57:31Z", + "stars": 410052, + "watchers": 8017, + "forks": 32029, "defaultBranch": "main" }, { @@ -44,11 +44,11 @@ "repo": "EbookFoundation/free-programming-books", "description": ":books: Freely available programming books", "createdAt": "2013-10-11T06:50:37Z", - "updatedAt": "2025-10-27T07:36:14Z", - "pushedAt": "2025-10-26T23:24:34Z", - "stars": 375134, - "watchers": 9788, - "forks": 65149, + "updatedAt": "2025-10-28T12:16:59Z", + "pushedAt": "2025-10-28T01:52:13Z", + "stars": 375307, + "watchers": 9786, + "forks": 65199, "defaultBranch": "main" }, { @@ -57,11 +57,11 @@ "repo": "public-apis/public-apis", "description": "A collective list of free APIs", "createdAt": "2016-03-20T23:49:42Z", - "updatedAt": "2025-10-27T07:45:41Z", + "updatedAt": "2025-10-28T12:33:14Z", "pushedAt": "2025-05-20T15:56:34Z", - "stars": 373288, - "watchers": 4392, - "forks": 39386, + "stars": 374003, + "watchers": 4400, + "forks": 39473, "defaultBranch": "master" }, { @@ -70,11 +70,11 @@ "repo": "kamranahmedse/developer-roadmap", "description": "Interactive roadmaps, guides and other educational content to help developers grow in their careers.", "createdAt": "2017-03-15T13:45:52Z", - "updatedAt": "2025-10-27T07:26:36Z", - "pushedAt": "2025-10-24T10:20:46Z", - "stars": 342038, - "watchers": 6887, - "forks": 43222, + "updatedAt": "2025-10-28T12:31:02Z", + "pushedAt": "2025-10-28T11:09:58Z", + "stars": 342136, + "watchers": 6886, + "forks": 43234, "defaultBranch": "master" }, { @@ -83,11 +83,11 @@ "repo": "jwasham/coding-interview-university", "description": "A complete computer science study plan to become a software engineer.", "createdAt": "2016-06-06T02:34:12Z", - "updatedAt": "2025-10-27T07:46:31Z", + "updatedAt": "2025-10-28T12:21:02Z", "pushedAt": "2025-08-28T14:42:47Z", - "stars": 331885, - "watchers": 8512, - "forks": 81046, + "stars": 331947, + "watchers": 8511, + "forks": 81057, "defaultBranch": "main" }, { @@ -96,11 +96,11 @@ "repo": "donnemartin/system-design-primer", "description": "Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.", "createdAt": "2017-02-26T16:15:28Z", - "updatedAt": "2025-10-27T07:38:55Z", + "updatedAt": "2025-10-28T12:32:56Z", "pushedAt": "2025-05-21T11:13:33Z", - "stars": 324162, - "watchers": 6818, - "forks": 52866, + "stars": 324409, + "watchers": 6819, + "forks": 52904, "defaultBranch": "master" }, { @@ -109,11 +109,11 @@ "repo": "996icu/996.ICU", "description": "Repo for counting stars and contributing. Press F to pay respect to glorious developers.", "createdAt": "2019-03-26T07:31:14Z", - "updatedAt": "2025-10-27T07:35:11Z", + "updatedAt": "2025-10-28T11:07:13Z", "pushedAt": "2025-08-22T06:01:29Z", - "stars": 274700, - "watchers": 4217, - "forks": 21033, + "stars": 274706, + "watchers": 4216, + "forks": 21029, "defaultBranch": "master" }, { @@ -122,11 +122,11 @@ "repo": "vinta/awesome-python", "description": "An opinionated list of awesome Python frameworks, libraries, software and resources.", "createdAt": "2014-06-27T21:00:06Z", - "updatedAt": "2025-10-27T07:40:04Z", + "updatedAt": "2025-10-28T12:28:13Z", "pushedAt": "2025-10-16T13:40:58Z", - "stars": 266460, - "watchers": 6127, - "forks": 26579, + "stars": 266661, + "watchers": 6128, + "forks": 26604, "defaultBranch": "master" }, { @@ -135,11 +135,11 @@ "repo": "awesome-selfhosted/awesome-selfhosted", "description": "A list of Free Software network services and web applications which can be hosted on your own servers", "createdAt": "2015-06-01T02:33:17Z", - "updatedAt": "2025-10-27T07:43:02Z", - "pushedAt": "2025-10-23T10:47:33Z", - "stars": 254916, - "watchers": 2995, - "forks": 11798, + "updatedAt": "2025-10-28T12:24:53Z", + "pushedAt": "2025-10-27T21:40:26Z", + "stars": 255143, + "watchers": 2990, + "forks": 11802, "defaultBranch": "master" }, { @@ -148,11 +148,11 @@ "repo": "practical-tutorials/project-based-learning", "description": "Curated list of project-based tutorials", "createdAt": "2017-04-12T05:07:46Z", - "updatedAt": "2025-10-27T07:45:41Z", + "updatedAt": "2025-10-28T12:22:51Z", "pushedAt": "2024-08-15T05:33:54Z", - "stars": 247930, - "watchers": 3445, - "forks": 32413, + "stars": 248050, + "watchers": 3446, + "forks": 32431, "defaultBranch": "master" }, { @@ -161,11 +161,11 @@ "repo": "facebook/react", "description": "The library for web and native user interfaces.", "createdAt": "2013-05-24T16:15:54Z", - "updatedAt": "2025-10-27T06:47:16Z", - "pushedAt": "2025-10-24T22:08:43Z", - "stars": 240059, - "watchers": 6687, - "forks": 49664, + "updatedAt": "2025-10-28T12:24:55Z", + "pushedAt": "2025-10-28T01:25:20Z", + "stars": 240100, + "watchers": 6686, + "forks": 49682, "defaultBranch": "main" }, { @@ -174,11 +174,11 @@ "repo": "TheAlgorithms/Python", "description": "All Algorithms implemented in Python", "createdAt": "2016-07-16T09:44:01Z", - "updatedAt": "2025-10-27T07:26:23Z", + "updatedAt": "2025-10-28T12:25:22Z", "pushedAt": "2025-10-20T00:59:36Z", - "stars": 212044, + "stars": 212119, "watchers": 5975, - "forks": 48986, + "forks": 49025, "defaultBranch": "master" }, { @@ -187,11 +187,11 @@ "repo": "vuejs/vue", "description": "This is the repo for Vue 2. For Vue 3, go to https://github.com/vuejs/core", "createdAt": "2013-07-29T03:24:51Z", - "updatedAt": "2025-10-27T05:37:40Z", + "updatedAt": "2025-10-28T10:39:45Z", "pushedAt": "2024-10-10T07:24:15Z", - "stars": 209624, - "watchers": 5787, - "forks": 33796, + "stars": 209636, + "watchers": 5786, + "forks": 33795, "defaultBranch": "main" }, { @@ -200,11 +200,11 @@ "repo": "torvalds/linux", "description": "Linux kernel source tree", "createdAt": "2011-09-04T22:48:12Z", - "updatedAt": "2025-10-27T07:25:34Z", - "pushedAt": "2025-10-26T23:00:24Z", - "stars": 205761, - "watchers": 7739, - "forks": 58023, + "updatedAt": "2025-10-28T12:39:23Z", + "pushedAt": "2025-10-27T18:11:32Z", + "stars": 205858, + "watchers": 7743, + "forks": 58047, "defaultBranch": "master" }, { @@ -213,11 +213,11 @@ "repo": "ossu/computer-science", "description": "🎓 Path to a free self-taught education in Computer Science!", "createdAt": "2014-05-04T00:18:39Z", - "updatedAt": "2025-10-27T07:25:53Z", + "updatedAt": "2025-10-28T12:41:20Z", "pushedAt": "2025-08-23T18:48:52Z", - "stars": 196024, - "watchers": 5935, - "forks": 24465, + "stars": 196086, + "watchers": 5936, + "forks": 24474, "defaultBranch": "master" }, { @@ -226,11 +226,11 @@ "repo": "trekhleb/javascript-algorithms", "description": "📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings", "createdAt": "2018-03-24T07:47:04Z", - "updatedAt": "2025-10-27T07:26:50Z", + "updatedAt": "2025-10-28T12:37:32Z", "pushedAt": "2025-10-22T15:03:29Z", - "stars": 193648, - "watchers": 4267, - "forks": 30919, + "stars": 193744, + "watchers": 4268, + "forks": 30929, "defaultBranch": "master" }, { @@ -239,11 +239,11 @@ "repo": "tensorflow/tensorflow", "description": "An Open Source Machine Learning Framework for Everyone", "createdAt": "2015-11-07T01:19:20Z", - "updatedAt": "2025-10-27T07:33:01Z", - "pushedAt": "2025-10-27T06:15:29Z", - "stars": 192220, + "updatedAt": "2025-10-28T11:56:54Z", + "pushedAt": "2025-10-28T12:37:04Z", + "stars": 192240, "watchers": 7431, - "forks": 74928, + "forks": 74932, "defaultBranch": "master" }, { @@ -252,11 +252,11 @@ "repo": "trimstray/the-book-of-secret-knowledge", "description": "A collection of inspiring lists, manuals, cheatsheets, blogs, hacks, one-liners, cli/web tools and more.", "createdAt": "2018-06-23T10:43:14Z", - "updatedAt": "2025-10-27T07:43:08Z", + "updatedAt": "2025-10-28T12:40:20Z", "pushedAt": "2024-11-19T14:00:38Z", - "stars": 191315, - "watchers": 2679, - "forks": 11763, + "stars": 191487, + "watchers": 2678, + "forks": 11764, "defaultBranch": "master" }, { @@ -265,11 +265,11 @@ "repo": "getify/You-Dont-Know-JS", "description": "A book series (2 published editions) on the JS language.", "createdAt": "2013-11-16T02:37:24Z", - "updatedAt": "2025-10-27T07:25:47Z", + "updatedAt": "2025-10-28T11:34:43Z", "pushedAt": "2025-05-20T14:22:36Z", - "stars": 183631, - "watchers": 5802, - "forks": 33668, + "stars": 183653, + "watchers": 5803, + "forks": 33671, "defaultBranch": "2nd-ed" }, { @@ -278,11 +278,11 @@ "repo": "CyC2018/CS-Notes", "description": ":books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计", "createdAt": "2018-02-13T14:56:24Z", - "updatedAt": "2025-10-27T07:19:57Z", + "updatedAt": "2025-10-28T11:56:57Z", "pushedAt": "2024-08-21T09:40:10Z", - "stars": 182646, - "watchers": 5252, - "forks": 51251, + "stars": 182661, + "watchers": 5249, + "forks": 51249, "defaultBranch": "master" }, { @@ -291,11 +291,11 @@ "repo": "ohmyzsh/ohmyzsh", "description": "🙃 A delightful community-driven (with 2,400+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python, etc), 140+ themes to spice up your morning, and an auto-update tool that makes it easy to keep up with the latest updates from the community.", "createdAt": "2009-08-28T18:15:37Z", - "updatedAt": "2025-10-27T07:25:29Z", - "pushedAt": "2025-10-26T13:17:47Z", - "stars": 182297, - "watchers": 2618, - "forks": 26259, + "updatedAt": "2025-10-28T12:39:19Z", + "pushedAt": "2025-10-27T18:37:07Z", + "stars": 182331, + "watchers": 2620, + "forks": 26261, "defaultBranch": "master" }, { @@ -304,11 +304,11 @@ "repo": "Significant-Gravitas/AutoGPT", "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.", "createdAt": "2023-03-16T09:21:07Z", - "updatedAt": "2025-10-27T07:34:44Z", - "pushedAt": "2025-10-27T00:10:36Z", - "stars": 179292, + "updatedAt": "2025-10-28T12:01:03Z", + "pushedAt": "2025-10-28T11:50:06Z", + "stars": 179337, "watchers": 1547, - "forks": 46077, + "forks": 46094, "defaultBranch": "master" }, { @@ -317,11 +317,11 @@ "repo": "microsoft/vscode", "description": "Visual Studio Code", "createdAt": "2015-09-03T20:23:38Z", - "updatedAt": "2025-10-27T07:26:11Z", - "pushedAt": "2025-10-27T07:29:25Z", - "stars": 177925, - "watchers": 3364, - "forks": 35788, + "updatedAt": "2025-10-28T12:22:53Z", + "pushedAt": "2025-10-28T12:33:55Z", + "stars": 177962, + "watchers": 3366, + "forks": 35810, "defaultBranch": "main" }, { @@ -330,11 +330,11 @@ "repo": "jackfrued/Python-100-Days", "description": "Python - 100天从新手到大师", "createdAt": "2018-03-01T16:05:52Z", - "updatedAt": "2025-10-27T07:26:50Z", + "updatedAt": "2025-10-28T12:40:38Z", "pushedAt": "2025-03-28T10:29:23Z", - "stars": 173752, + "stars": 173818, "watchers": 6098, - "forks": 54771, + "forks": 54782, "defaultBranch": "master" }, { @@ -343,11 +343,11 @@ "repo": "twbs/bootstrap", "description": "The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.", "createdAt": "2011-07-29T21:19:00Z", - "updatedAt": "2025-10-27T07:25:34Z", - "pushedAt": "2025-10-26T18:41:31Z", - "stars": 173599, - "watchers": 6681, - "forks": 79156, + "updatedAt": "2025-10-28T12:25:19Z", + "pushedAt": "2025-10-28T10:02:33Z", + "stars": 173612, + "watchers": 6680, + "forks": 79159, "defaultBranch": "main" }, { @@ -356,11 +356,11 @@ "repo": "flutter/flutter", "description": "Flutter makes it easy and fast to build beautiful apps for mobile and beyond", "createdAt": "2015-03-06T22:54:58Z", - "updatedAt": "2025-10-27T07:31:00Z", - "pushedAt": "2025-10-27T05:33:32Z", - "stars": 173546, + "updatedAt": "2025-10-28T12:35:50Z", + "pushedAt": "2025-10-28T12:35:51Z", + "stars": 173572, "watchers": 3481, - 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"updatedAt": "2025-10-27T07:49:02Z", + "updatedAt": "2025-10-28T12:41:21Z", "pushedAt": "2025-10-07T20:06:10Z", - "stars": 157565, - "watchers": 1154, - "forks": 29246, + "stars": 157629, + "watchers": 1156, + "forks": 29254, "defaultBranch": "master" }, { @@ -408,11 +408,11 @@ "repo": "avelino/awesome-go", "description": "A curated list of awesome Go frameworks, libraries and software", "createdAt": "2014-07-06T13:42:15Z", - "updatedAt": "2025-10-27T07:49:36Z", + "updatedAt": "2025-10-28T12:41:20Z", "pushedAt": "2025-10-22T12:15:14Z", - "stars": 155801, - "watchers": 2818, - "forks": 12706, + "stars": 155912, + "watchers": 2820, + "forks": 12712, "defaultBranch": "main" }, { @@ -421,11 +421,11 @@ "repo": "ollama/ollama", "description": "Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.", "createdAt": "2023-06-26T19:39:32Z", - "updatedAt": "2025-10-27T07:43:05Z", - "pushedAt": "2025-10-27T01:25:05Z", - "stars": 154808, - "watchers": 877, - "forks": 13467, + "updatedAt": "2025-10-28T12:05:06Z", + "pushedAt": "2025-10-28T08:16:13Z", + "stars": 154883, + "watchers": 876, + "forks": 13480, "defaultBranch": "main" }, { @@ -434,11 +434,11 @@ "repo": "massgravel/Microsoft-Activation-Scripts", "description": "Open-source Windows and Office activator featuring HWID, Ohook, TSforge, KMS38, and Online KMS activation methods, along with advanced troubleshooting.", "createdAt": "2020-01-12T23:03:34Z", - "updatedAt": "2025-10-27T07:44:35Z", + "updatedAt": "2025-10-28T12:40:24Z", "pushedAt": "2025-09-30T22:22:59Z", - "stars": 153864, + "stars": 154022, "watchers": 1319, - "forks": 14861, + "forks": 14869, "defaultBranch": "master" }, { @@ -447,11 +447,11 @@ "repo": "Snailclimb/JavaGuide", "description": "「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！", "createdAt": "2018-05-07T13:27:00Z", - "updatedAt": "2025-10-27T07:25:13Z", - "pushedAt": "2025-10-20T08:53:33Z", - "stars": 152308, - "watchers": 4470, - "forks": 46020, + "updatedAt": "2025-10-28T12:01:53Z", + "pushedAt": "2025-10-27T11:09:05Z", + "stars": 152325, + "watchers": 4469, + "forks": 46021, "defaultBranch": "main" }, { @@ -460,11 +460,11 @@ "repo": "n8n-io/n8n", "description": "Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.", "createdAt": "2019-06-22T09:24:21Z", - "updatedAt": "2025-10-27T07:48:50Z", - "pushedAt": "2025-10-27T07:12:52Z", - "stars": 151975, - "watchers": 880, - "forks": 48459, + "updatedAt": "2025-10-28T12:41:23Z", + "pushedAt": "2025-10-28T12:34:50Z", + "stars": 152300, + "watchers": 889, + "forks": 48578, "defaultBranch": "master" }, { @@ -473,11 +473,11 @@ "repo": "huggingface/transformers", "description": "🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. ", "createdAt": "2018-10-29T13:56:00Z", - "updatedAt": "2025-10-27T07:45:24Z", - "pushedAt": "2025-10-25T16:31:22Z", - "stars": 151659, - "watchers": 1166, - "forks": 30955, + "updatedAt": "2025-10-28T12:41:10Z", + "pushedAt": "2025-10-28T12:38:18Z", + "stars": 151745, + "watchers": 1167, + "forks": 30971, "defaultBranch": "main" }, { @@ -486,11 +486,11 @@ "repo": "airbnb/javascript", "description": "JavaScript Style Guide", "createdAt": "2012-11-01T23:13:50Z", - "updatedAt": "2025-10-27T06:50:33Z", + "updatedAt": "2025-10-28T11:07:36Z", "pushedAt": "2025-09-17T18:12:44Z", - "stars": 147687, - "watchers": 3705, - "forks": 26797, + "stars": 147700, + "watchers": 3702, + "forks": 26795, "defaultBranch": "master" }, { @@ -499,24 +499,37 @@ "repo": "ytdl-org/youtube-dl", "description": "Command-line program to download videos from YouTube.com and other video sites", "createdAt": "2010-10-31T14:35:07Z", - "updatedAt": "2025-10-27T07:30:15Z", + "updatedAt": "2025-10-28T12:01:08Z", "pushedAt": "2025-10-18T10:02:28Z", - 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"updatedAt": "2025-10-27T07:38:47Z", - "pushedAt": "2025-10-27T07:02:37Z", - "stars": 135306, - "watchers": 1497, - "forks": 29680, + "updatedAt": "2025-10-28T12:19:30Z", + "pushedAt": "2025-10-28T12:22:48Z", + "stars": 135333, + "watchers": 1495, + "forks": 29693, "defaultBranch": "canary" }, - { - "id": 599320067, - "name": "langflow", - "repo": "langflow-ai/langflow", - "description": "Langflow is a powerful tool for building and deploying AI-powered agents and workflows.", - "createdAt": "2023-02-08T22:28:03Z", - "updatedAt": "2025-10-27T07:22:05Z", - "pushedAt": "2025-10-27T00:28:51Z", - "stars": 134904, - "watchers": 453, - "forks": 7853, - "defaultBranch": "main" - }, { "id": 307260205, "name": "yt-dlp", "repo": "yt-dlp/yt-dlp", "description": "A feature-rich command-line audio/video downloader", "createdAt": "2020-10-26T04:22:55Z", - "updatedAt": "2025-10-27T07:35:17Z", - "pushedAt": "2025-10-25T22:47:00Z", - "stars": 132793, - "watchers": 675, - "forks": 10659, + "updatedAt": "2025-10-28T12:38:42Z", + "pushedAt": "2025-10-27T23:21:38Z", + "stars": 132949, + "watchers": 678, + "forks": 10668, "defaultBranch": "master" }, { @@ -564,10 +564,10 @@ "repo": "521xueweihan/HelloGitHub", "description": ":octocat: 分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.", "createdAt": "2016-05-04T06:24:11Z", - "updatedAt": "2025-10-27T07:49:37Z", - "pushedAt": "2025-09-28T02:00:22Z", - "stars": 132228, - "watchers": 4182, + "updatedAt": "2025-10-28T12:13:38Z", + "pushedAt": "2025-10-28T00:14:25Z", + "stars": 132365, + "watchers": 4187, "forks": 10822, "defaultBranch": "master" }, @@ -577,11 +577,11 @@ "repo": "yangshun/tech-interview-handbook", "description": "💯 Curated coding interview preparation materials for busy software engineers", "createdAt": "2016-07-05T05:00:48Z", - "updatedAt": "2025-10-27T07:26:22Z", + "updatedAt": "2025-10-28T09:33:23Z", "pushedAt": "2025-08-27T00:17:33Z", - "stars": 131399, + "stars": 131430, "watchers": 2182, - "forks": 15942, + "forks": 15945, "defaultBranch": "main" }, { @@ -590,11 +590,11 @@ "repo": "golang/go", "description": "The Go programming language", "createdAt": "2014-08-19T04:33:40Z", - "updatedAt": "2025-10-27T07:25:58Z", - "pushedAt": "2025-10-27T04:49:52Z", - "stars": 130538, - "watchers": 3346, - "forks": 18415, + "updatedAt": "2025-10-28T11:52:10Z", + "pushedAt": "2025-10-28T06:29:46Z", + "stars": 130554, + "watchers": 3347, + "forks": 18419, "defaultBranch": "master" }, { @@ -603,11 +603,11 @@ "repo": "Genymobile/scrcpy", "description": "Display and control your Android device", "createdAt": "2017-11-21T18:00:27Z", - "updatedAt": "2025-10-27T07:30:24Z", - "pushedAt": "2025-10-26T10:52:03Z", - "stars": 130238, - "watchers": 1321, - "forks": 12191, + "updatedAt": "2025-10-28T12:05:50Z", + "pushedAt": "2025-10-27T08:59:41Z", + "stars": 130304, + "watchers": 1322, + "forks": 12194, "defaultBranch": "master" }, { @@ -616,11 +616,11 @@ "repo": "labuladong/fucking-algorithm", "description": "刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why. ", "createdAt": "2020-02-19T09:01:23Z", - "updatedAt": "2025-10-27T07:27:20Z", + "updatedAt": "2025-10-28T08:35:53Z", "pushedAt": "2025-10-08T04:06:00Z", - "stars": 129651, + "stars": 129669, "watchers": 2283, - "forks": 23450, + "forks": 23452, "defaultBranch": "master" }, { @@ -629,11 +629,11 @@ "repo": "Chalarangelo/30-seconds-of-code", "description": "Coding articles to level up your development skills", "createdAt": "2017-11-29T17:35:03Z", - "updatedAt": "2025-10-27T07:26:47Z", + "updatedAt": "2025-10-28T09:14:02Z", "pushedAt": "2025-10-22T12:51:11Z", - "stars": 125630, + "stars": 125639, "watchers": 2594, - "forks": 12358, + "forks": 12362, "defaultBranch": "master" }, { @@ -642,11 +642,11 @@ "repo": "microsoft/PowerToys", "description": "Microsoft PowerToys is a collection of utilities that help you customize Windows and streamline everyday tasks", "createdAt": "2019-05-01T17:44:02Z", - "updatedAt": "2025-10-27T07:50:46Z", - "pushedAt": "2025-10-27T02:44:52Z", - "stars": 125223, - 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"forks": 16578, + "forks": 16584, "defaultBranch": "main" }, - { - "id": 20580498, - "name": "kubernetes", - "repo": "kubernetes/kubernetes", - "description": "Production-Grade Container Scheduling and Management", - "createdAt": "2014-06-06T22:56:04Z", - "updatedAt": "2025-10-27T07:31:13Z", - "pushedAt": "2025-10-26T22:21:34Z", - "stars": 118226, - "watchers": 3189, - "forks": 41578, - "defaultBranch": "master" - }, { "id": 552661142, "name": "langchain", "repo": "langchain-ai/langchain", "description": "🦜🔗 Build context-aware reasoning applications", "createdAt": "2022-10-17T02:58:36Z", - "updatedAt": "2025-10-27T07:37:09Z", - "pushedAt": "2025-10-27T07:39:14Z", - "stars": 118140, - "watchers": 775, - "forks": 19453, + "updatedAt": "2025-10-28T12:37:33Z", + "pushedAt": "2025-10-27T23:47:43Z", + "stars": 118261, + "watchers": 776, + "forks": 19476, + "defaultBranch": "master" + }, + { + "id": 20580498, + "name": "kubernetes", + "repo": "kubernetes/kubernetes", + "description": "Production-Grade Container Scheduling and Management", + "createdAt": "2014-06-06T22:56:04Z", + "updatedAt": "2025-10-28T12:19:38Z", + "pushedAt": "2025-10-28T10:29:37Z", + "stars": 118246, + "watchers": 3189, + "forks": 41587, "defaultBranch": "master" }, { @@ -707,10 +707,10 @@ "repo": "krahets/hello-algo", "description": "《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version in translation", "createdAt": "2022-11-04T11:08:34Z", - "updatedAt": "2025-10-27T07:28:05Z", + "updatedAt": "2025-10-28T12:30:36Z", "pushedAt": "2025-10-16T21:33:36Z", - "stars": 118081, - "watchers": 582, + "stars": 118105, + "watchers": 583, "forks": 14500, "defaultBranch": "main" }, @@ -720,11 +720,11 @@ "repo": "langgenius/dify", "description": "Production-ready platform for agentic workflow development.", "createdAt": "2023-04-12T07:40:24Z", - "updatedAt": "2025-10-27T07:45:31Z", - "pushedAt": "2025-10-27T07:48:43Z", - "stars": 117359, - "watchers": 697, - "forks": 18125, + "updatedAt": "2025-10-28T12:18:46Z", + "pushedAt": "2025-10-28T10:48:12Z", + "stars": 117486, + "watchers": 698, + "forks": 18151, "defaultBranch": "main" }, { @@ -733,10 +733,10 @@ "repo": "justjavac/free-programming-books-zh_CN", "description": ":books: 免费的计算机编程类中文书籍，欢迎投稿", "createdAt": "2013-11-04T01:59:19Z", - "updatedAt": "2025-10-27T07:25:46Z", + "updatedAt": "2025-10-28T09:19:09Z", "pushedAt": "2024-07-15T08:55:20Z", - "stars": 115537, - "watchers": 5860, + "stars": 115543, + "watchers": 5859, "forks": 28362, "defaultBranch": "main" }, @@ -746,11 +746,11 @@ "repo": "ripienaar/free-for-dev", "description": "A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev", "createdAt": "2015-03-18T21:06:26Z", - "updatedAt": "2025-10-27T07:26:05Z", + "updatedAt": "2025-10-28T11:38:56Z", "pushedAt": "2025-10-23T04:49:00Z", - "stars": 114093, - "watchers": 1734, - "forks": 11683, + "stars": 114128, + "watchers": 1735, + "forks": 11684, "defaultBranch": "master" }, { @@ -759,11 +759,11 @@ "repo": "nodejs/node", "description": "Node.js JavaScript runtime ✨🐢🚀✨", "createdAt": "2014-11-26T19:57:11Z", - "updatedAt": "2025-10-27T07:38:07Z", - "pushedAt": "2025-10-27T01:02:07Z", - "stars": 113974, - "watchers": 2964, - "forks": 33571, + "updatedAt": "2025-10-28T12:34:32Z", + "pushedAt": "2025-10-28T11:29:04Z", + "stars": 114019, + "watchers": 2963, + "forks": 33580, "defaultBranch": "main" }, { @@ -772,24 +772,11 @@ "repo": "open-webui/open-webui", "description": "User-friendly AI Interface (Supports Ollama, OpenAI API, ...)", "createdAt": "2023-10-06T22:08:27Z", - "updatedAt": "2025-10-27T07:32:58Z", - "pushedAt": "2025-10-27T05:20:59Z", - "stars": 113474, - "watchers": 516, - "forks": 15764, - "defaultBranch": "main" - }, - { - "id": 943149, - "name": "d3", - "repo": "d3/d3", - "description": "Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:", - "createdAt": "2010-09-27T17:22:42Z", - "updatedAt": "2025-10-27T07:25:31Z", - "pushedAt": "2025-07-27T11:30:40Z", - "stars": 111683, - "watchers": 3558, - "forks": 22851, + "updatedAt": "2025-10-28T12:22:47Z", + "pushedAt": "2025-10-28T08:46:37Z", + "stars": 113575, + "watchers": 515, + "forks": 15783, "defaultBranch": "main" }, { @@ -798,11 +785,24 @@ "repo": "DigitalPlatDev/FreeDomain", "description": "DigitalPlat FreeDomain: Free Domain For Everyone", "createdAt": "2024-05-30T13:23:00Z", - "updatedAt": "2025-10-27T07:49:47Z", + "updatedAt": "2025-10-28T12:40:49Z", "pushedAt": "2025-09-25T12:12:01Z", - "stars": 111350, + "stars": 111985, "watchers": 120, - "forks": 2066, + "forks": 2068, + "defaultBranch": "main" + }, + { + "id": 943149, + "name": "d3", + "repo": "d3/d3", + "description": "Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:", + "createdAt": "2010-09-27T17:22:42Z", + "updatedAt": "2025-10-28T09:47:08Z", + "pushedAt": "2025-07-27T11:30:40Z", + "stars": 111693, + "watchers": 3558, + "forks": 22850, "defaultBranch": "main" }, { @@ -811,11 +811,11 @@ "repo": "excalidraw/excalidraw", "description": "Virtual whiteboard for sketching hand-drawn like diagrams", "createdAt": "2020-01-02T01:04:43Z", - 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"forks": 11366, + "forks": 11371, "defaultBranch": "v1.x" }, { @@ -850,11 +850,11 @@ "repo": "rust-lang/rust", "description": "Empowering everyone to build reliable and efficient software.", "createdAt": "2010-06-16T20:39:03Z", - "updatedAt": "2025-10-27T06:39:34Z", - "pushedAt": "2025-10-27T07:25:41Z", - "stars": 107453, - "watchers": 1467, - "forks": 13897, + "updatedAt": "2025-10-28T12:40:15Z", + "pushedAt": "2025-10-28T11:12:51Z", + "stars": 107478, + "watchers": 1468, + "forks": 13900, "defaultBranch": "master" }, { @@ -863,11 +863,11 @@ "repo": "microsoft/TypeScript", "description": "TypeScript is a superset of JavaScript that compiles to clean JavaScript output.", "createdAt": "2014-06-17T15:28:39Z", - "updatedAt": "2025-10-27T07:20:39Z", - "pushedAt": "2025-10-27T00:06:54Z", - "stars": 106530, + "updatedAt": "2025-10-28T12:19:23Z", + "pushedAt": "2025-10-27T23:52:12Z", + "stars": 106557, "watchers": 2148, - "forks": 13084, + "forks": 13086, "defaultBranch": "main" }, { @@ -876,11 +876,11 @@ "repo": "denoland/deno", "description": "A modern runtime for JavaScript and TypeScript.", "createdAt": "2018-05-15T01:34:26Z", - "updatedAt": "2025-10-27T07:14:57Z", - "pushedAt": "2025-10-24T23:41:20Z", - "stars": 104915, + "updatedAt": "2025-10-28T12:27:16Z", + "pushedAt": "2025-10-28T09:10:45Z", + "stars": 104939, "watchers": 1398, - "forks": 5753, + "forks": 5754, "defaultBranch": "main" }, { @@ -889,11 +889,11 @@ "repo": "goldbergyoni/nodebestpractices", "description": ":white_check_mark: The Node.js best practices list (July 2024)", "createdAt": "2017-09-15T08:33:19Z", - "updatedAt": "2025-10-27T07:26:43Z", + "updatedAt": "2025-10-28T11:50:28Z", "pushedAt": "2025-04-15T21:52:42Z", - "stars": 104439, + "stars": 104455, "watchers": 1944, - "forks": 10627, + "forks": 10625, "defaultBranch": "master" }, { @@ -902,11 +902,11 @@ "repo": "facebook/create-react-app", "description": "Set up a modern web app by running one command.", "createdAt": "2016-07-17T14:55:11Z", - "updatedAt": "2025-10-27T07:26:24Z", + "updatedAt": "2025-10-28T12:35:24Z", "pushedAt": "2025-02-15T01:32:11Z", - "stars": 103811, - "watchers": 1892, - "forks": 27146, + "stars": 103813, + "watchers": 1891, + "forks": 27148, "defaultBranch": "main" }, { @@ -915,11 +915,11 @@ "repo": "GrowingGit/GitHub-Chinese-Top-Charts", "description": ":cn: GitHub中文排行榜，各语言分设「软件 | 资料」榜单，精准定位中文好项目。各取所需，高效学习。", "createdAt": "2019-09-05T03:01:56Z", - "updatedAt": "2025-10-27T06:04:01Z", + "updatedAt": "2025-10-28T10:36:09Z", "pushedAt": "2024-10-12T06:51:36Z", - "stars": 103336, + "stars": 103358, "watchers": 2607, - "forks": 13364, + "forks": 13363, "defaultBranch": "master" }, { @@ -928,11 +928,11 @@ "repo": "godotengine/godot", "description": "Godot Engine – Multi-platform 2D and 3D game engine", "createdAt": "2014-01-04T16:05:36Z", - "updatedAt": "2025-10-27T07:16:51Z", - "pushedAt": "2025-10-25T20:48:20Z", - "stars": 102604, + "updatedAt": "2025-10-28T11:39:26Z", + "pushedAt": "2025-10-28T08:43:09Z", + "stars": 102655, "watchers": 1493, - "forks": 23450, + "forks": 23457, "defaultBranch": "master" }, { @@ -941,11 +941,11 @@ "repo": "rustdesk/rustdesk", "description": "An open-source remote desktop application designed for self-hosting, as an alternative to TeamViewer.", "createdAt": "2020-09-28T15:36:08Z", - "updatedAt": "2025-10-27T07:42:29Z", - "pushedAt": "2025-10-26T13:28:57Z", - "stars": 101456, + "updatedAt": "2025-10-28T12:27:03Z", + "pushedAt": "2025-10-28T12:25:33Z", + "stars": 101531, "watchers": 548, - "forks": 14837, + "forks": 14850, "defaultBranch": "master" }, { @@ -954,11 +954,11 @@ "repo": "microsoft/generative-ai-for-beginners", "description": "21 Lessons, Get Started Building with Generative AI ", "createdAt": "2023-06-19T16:28:59Z", - "updatedAt": "2025-10-27T07:38:12Z", + "updatedAt": "2025-10-28T12:25:17Z", "pushedAt": "2025-10-27T03:19:39Z", - "stars": 100935, - "watchers": 889, - "forks": 53478, + "stars": 101010, + "watchers": 887, + "forks": 53526, "defaultBranch": "main" }, { @@ -967,9 +967,9 @@ "repo": "microsoft/terminal", "description": "The new Windows Terminal and the original Windows console host, all in the same place!", "createdAt": "2017-08-11T18:38:22Z", - "updatedAt": "2025-10-27T05:40:24Z", - "pushedAt": "2025-10-22T01:31:33Z", - "stars": 100726, + "updatedAt": "2025-10-28T12:08:57Z", + "pushedAt": "2025-10-28T03:04:50Z", + "stars": 100746, "watchers": 1334, "forks": 8879, "defaultBranch": "main" @@ -980,11 +980,11 @@ "repo": "fatedier/frp", "description": "A fast reverse proxy to help you expose a local server behind a NAT or firewall to the internet.", "createdAt": "2015-12-21T15:24:59Z", - "updatedAt": "2025-10-27T07:00:25Z", - "pushedAt": "2025-10-17T02:53:43Z", - "stars": 100015, - "watchers": 1563, - "forks": 14562, + "updatedAt": "2025-10-28T11:57:26Z", + "pushedAt": "2025-10-28T09:52:35Z", + "stars": 100048, + "watchers": 1564, + "forks": 14567, "defaultBranch": "dev" }, { @@ -993,11 +993,11 @@ "repo": "deepseek-ai/DeepSeek-V3", "description": null, "createdAt": "2024-12-26T09:52:40Z", - "updatedAt": "2025-10-27T07:28:30Z", + "updatedAt": "2025-10-28T12:11:53Z", "pushedAt": "2025-08-28T03:24:37Z", - "stars": 99981, - "watchers": 750, - "forks": 16309, + "stars": 100020, + "watchers": 752, + "forks": 16313, "defaultBranch": "main" }, { @@ -1006,11 +1006,11 @@ "repo": "Hack-with-Github/Awesome-Hacking", "description": "A collection of various awesome lists for hackers, pentesters and security researchers", "createdAt": "2016-03-30T15:47:10Z", - "updatedAt": "2025-10-27T07:49:40Z", + "updatedAt": "2025-10-28T12:11:25Z", "pushedAt": "2025-01-18T01:48:02Z", - "stars": 99684, - "watchers": 3931, - "forks": 9634, + "stars": 99746, + "watchers": 3932, + "forks": 9633, "defaultBranch": "master" }, { @@ -1019,9 +1019,9 @@ "repo": "papers-we-love/papers-we-love", "description": "Papers from the computer science community to read and discuss.", "createdAt": "2013-12-15T14:31:41Z", - "updatedAt": "2025-10-27T07:49:42Z", + "updatedAt": "2025-10-28T12:35:57Z", "pushedAt": "2025-10-10T15:35:14Z", - "stars": 99626, + "stars": 99660, "watchers": 3159, "forks": 6144, "defaultBranch": "main" @@ -1032,11 +1032,11 @@ "repo": "angular/angular", "description": "Deliver web apps with confidence 🚀", "createdAt": "2014-09-18T16:12:01Z", - "updatedAt": "2025-10-27T07:05:22Z", - "pushedAt": "2025-10-24T19:28:33Z", - "stars": 99167, + "updatedAt": "2025-10-28T11:07:05Z", + "pushedAt": "2025-10-28T10:04:30Z", + "stars": 99174, "watchers": 2980, - "forks": 26724, + "forks": 26730, "defaultBranch": "main" }, { @@ -1045,11 +1045,11 @@ "repo": "shadcn-ui/ui", "description": "A set of beautifully-designed, accessible components and a code distribution platform. Works with your favorite frameworks. Open Source. Open Code.", "createdAt": "2023-01-04T12:43:27Z", - "updatedAt": "2025-10-27T07:34:00Z", - "pushedAt": "2025-10-27T07:18:39Z", - "stars": 98464, - "watchers": 306, - "forks": 7031, + "updatedAt": "2025-10-28T12:32:30Z", + "pushedAt": "2025-10-28T12:41:17Z", + "stars": 98552, + "watchers": 307, + "forks": 7046, "defaultBranch": "main" }, { @@ -1058,11 +1058,11 @@ "repo": "tauri-apps/tauri", "description": "Build smaller, faster, and more secure desktop and mobile applications with a web frontend.", "createdAt": "2019-07-13T09:09:37Z", - "updatedAt": "2025-10-27T07:27:10Z", - "pushedAt": "2025-10-26T13:55:16Z", - "stars": 98199, + "updatedAt": "2025-10-28T12:32:07Z", + "pushedAt": "2025-10-28T10:29:35Z", + "stars": 98262, "watchers": 530, - "forks": 3133, + "forks": 3139, "defaultBranch": "dev" }, { @@ -1071,11 +1071,11 @@ "repo": "iptv-org/iptv", "description": "Collection of publicly available IPTV channels from all over the world", "createdAt": "2018-11-14T22:00:57Z", - "updatedAt": "2025-10-27T07:13:48Z", - "pushedAt": "2025-10-27T00:13:17Z", - "stars": 98051, - "watchers": 1950, - "forks": 4195, + "updatedAt": "2025-10-28T12:32:18Z", + "pushedAt": "2025-10-28T00:11:46Z", + "stars": 98083, + "watchers": 1952, + "forks": 4199, "defaultBranch": "master" }, { @@ -1084,9 +1084,9 @@ "repo": "mui/material-ui", "description": "Material UI: Comprehensive React component library that implements Google's Material Design. Free forever.", "createdAt": "2014-08-18T19:11:54Z", - "updatedAt": "2025-10-27T07:25:58Z", - "pushedAt": "2025-10-27T07:11:45Z", - "stars": 96875, + "updatedAt": "2025-10-28T08:02:20Z", + "pushedAt": "2025-10-28T06:08:34Z", + "stars": 96887, "watchers": 1312, "forks": 32696, "defaultBranch": "master" @@ -1097,11 +1097,11 @@ "repo": "ant-design/ant-design", "description": "An enterprise-class UI design language and React UI library", "createdAt": "2015-04-24T15:37:24Z", - "updatedAt": "2025-10-27T07:19:39Z", - "pushedAt": "2025-10-27T07:44:37Z", - "stars": 96467, + "updatedAt": "2025-10-28T11:00:38Z", + "pushedAt": "2025-10-28T10:52:44Z", + "stars": 96472, "watchers": 236, - "forks": 53873, + "forks": 53890, "defaultBranch": "master" }, { @@ -1110,11 +1110,11 @@ "repo": "Anduin2017/HowToCook", "description": "程序员在家做饭方法指南。Programmer's guide about how to cook at home (Simplified Chinese only).", "createdAt": "2020-02-29T10:43:49Z", - "updatedAt": "2025-10-27T07:31:17Z", - "pushedAt": "2025-10-23T12:40:47Z", - "stars": 95393, + "updatedAt": "2025-10-28T12:35:03Z", + "pushedAt": "2025-10-28T11:30:11Z", + "stars": 95425, "watchers": 488, - "forks": 10650, + "forks": 10651, "defaultBranch": "master" }, { @@ -1123,9 +1123,9 @@ "repo": "nvbn/thefuck", "description": "Magnificent app which corrects your previous console command.", "createdAt": "2015-04-08T15:08:04Z", - "updatedAt": "2025-10-27T07:26:06Z", + "updatedAt": "2025-10-28T12:34:25Z", "pushedAt": "2024-07-19T14:56:13Z", - "stars": 94482, + "stars": 94497, "watchers": 825, "forks": 3792, "defaultBranch": "master" @@ -1136,11 +1136,11 @@ "repo": "pytorch/pytorch", "description": "Tensors and Dynamic neural networks in Python with strong GPU acceleration", "createdAt": "2016-08-13T05:26:41Z", - "updatedAt": "2025-10-27T07:51:08Z", - "pushedAt": "2025-10-27T07:51:03Z", - "stars": 94273, - "watchers": 1771, - "forks": 25671, + "updatedAt": "2025-10-28T12:25:28Z", + "pushedAt": "2025-10-28T12:40:19Z", + "stars": 94326, + "watchers": 1770, + "forks": 25678, "defaultBranch": "main" }, { @@ -1149,11 +1149,11 @@ "repo": "ryanmcdermott/clean-code-javascript", "description": "Clean Code concepts adapted for JavaScript", "createdAt": "2016-11-25T22:25:41Z", - "updatedAt": "2025-10-27T03:36:56Z", + "updatedAt": "2025-10-28T08:55:17Z", "pushedAt": "2024-07-29T07:24:37Z", - "stars": 93960, + "stars": 93959, "watchers": 1744, - "forks": 12496, + "forks": 12495, "defaultBranch": "master" }, { @@ -1162,11 +1162,11 @@ "repo": "mtdvio/every-programmer-should-know", "description": "A collection of (mostly) technical things every software developer should know about", "createdAt": "2017-08-24T13:18:26Z", - "updatedAt": "2025-10-27T07:26:42Z", + "updatedAt": "2025-10-28T11:51:44Z", "pushedAt": "2025-10-22T15:21:18Z", - "stars": 93814, + "stars": 93832, "watchers": 2011, - "forks": 8436, + "forks": 8437, "defaultBranch": "master" }, { @@ -1175,11 +1175,11 @@ "repo": "neovim/neovim", "description": "Vim-fork focused on extensibility and usability", "createdAt": "2014-01-31T13:39:22Z", - "updatedAt": "2025-10-27T07:30:43Z", - "pushedAt": "2025-10-27T05:15:23Z", - "stars": 93731, - "watchers": 971, - "forks": 6378, + "updatedAt": "2025-10-28T12:38:30Z", + "pushedAt": "2025-10-28T08:45:46Z", + "stars": 93768, + "watchers": 972, + "forks": 6376, "defaultBranch": "master" }, { @@ -1188,11 +1188,11 @@ "repo": "x1xhlol/system-prompts-and-models-of-ai-tools", "description": "FULL Augment Code, Claude Code, Cluely, CodeBuddy, Comet, Cursor, Devin AI, Junie, Kiro, Leap.new, Lovable, Manus Agent Tools, NotionAI, Orchids.app, Perplexity, Poke, Qoder, Replit, Same.dev, Trae, Traycer AI, VSCode Agent, Warp.dev, Windsurf, Xcode, Z.ai Code, dia & v0. (And other Open Sourced) System Prompts, Internal Tools & AI Models", "createdAt": "2025-03-05T16:38:29Z", - "updatedAt": "2025-10-27T07:37:40Z", + "updatedAt": "2025-10-28T12:37:42Z", "pushedAt": "2025-10-19T18:44:24Z", - "stars": 93282, + "stars": 93450, "watchers": 1183, - "forks": 25228, + "forks": 25250, "defaultBranch": "main" }, { @@ -1201,11 +1201,11 @@ "repo": "iluwatar/java-design-patterns", "description": "Design patterns implemented in Java", "createdAt": "2014-08-09T16:45:18Z", - "updatedAt": "2025-10-27T07:35:54Z", + "updatedAt": "2025-10-28T11:55:32Z", "pushedAt": "2025-10-21T21:30:34Z", - "stars": 93215, + "stars": 93230, "watchers": 3717, - "forks": 27309, + "forks": 27312, "defaultBranch": "master" }, { @@ -1214,9 +1214,9 @@ "repo": "puppeteer/puppeteer", "description": "JavaScript API for Chrome and Firefox", "createdAt": "2017-05-09T22:16:13Z", - "updatedAt": "2025-10-27T07:31:12Z", - "pushedAt": "2025-10-26T04:03:55Z", - "stars": 92724, + "updatedAt": "2025-10-28T11:55:21Z", + "pushedAt": "2025-10-28T11:35:29Z", + "stars": 92732, "watchers": 1184, "forks": 9314, "defaultBranch": "main" @@ -1227,11 +1227,11 @@ "repo": "microsoft/Web-Dev-For-Beginners", "description": "24 Lessons, 12 Weeks, Get Started as a Web Developer", "createdAt": "2020-11-10T02:44:00Z", - "updatedAt": "2025-10-27T07:27:35Z", - "pushedAt": "2025-10-25T00:47:36Z", - "stars": 92476, + "updatedAt": "2025-10-28T12:11:24Z", + "pushedAt": "2025-10-27T13:01:13Z", + "stars": 92494, "watchers": 2690, - "forks": 14330, + "forks": 14334, "defaultBranch": "main" }, { @@ -1240,11 +1240,11 @@ "repo": "comfyanonymous/ComfyUI", "description": "The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.", "createdAt": "2023-01-17T03:15:56Z", - "updatedAt": "2025-10-27T07:46:53Z", - "pushedAt": "2025-10-27T00:23:05Z", - "stars": 92036, - "watchers": 614, - "forks": 10341, + "updatedAt": "2025-10-28T12:38:44Z", + "pushedAt": "2025-10-28T08:45:49Z", + "stars": 92150, + "watchers": 615, + "forks": 10367, "defaultBranch": "master" }, { @@ -1253,11 +1253,11 @@ "repo": "jaywcjlove/awesome-mac", "description": " Now we have become very big, Different from the original idea. Collect premium software in various categories.", "createdAt": "2016-07-17T15:33:47Z", - "updatedAt": "2025-10-27T07:50:40Z", - "pushedAt": "2025-10-25T04:02:03Z", - "stars": 91815, + "updatedAt": "2025-10-28T12:29:52Z", + "pushedAt": "2025-10-27T17:27:24Z", + "stars": 91942, "watchers": 1517, - "forks": 6947, + "forks": 6956, "defaultBranch": "master" }, { @@ -1266,11 +1266,11 @@ "repo": "deepseek-ai/DeepSeek-R1", "description": null, "createdAt": "2025-01-20T11:57:28Z", - "updatedAt": "2025-10-27T06:56:07Z", + "updatedAt": "2025-10-28T12:33:45Z", "pushedAt": "2025-06-27T08:35:54Z", - "stars": 91380, + "stars": 91406, "watchers": 607, - "forks": 11766, + "forks": 11768, "defaultBranch": "main" }, { @@ -1279,11 +1279,11 @@ "repo": "fastapi/fastapi", "description": "FastAPI framework, high performance, easy to learn, fast to code, ready for production", "createdAt": "2018-12-08T08:21:47Z", - "updatedAt": "2025-10-27T07:49:54Z", - "pushedAt": "2025-10-23T20:55:59Z", - "stars": 91203, - "watchers": 723, - "forks": 8123, + "updatedAt": "2025-10-28T11:31:45Z", + "pushedAt": "2025-10-28T07:50:29Z", + "stars": 91252, + "watchers": 721, + "forks": 8135, "defaultBranch": "master" }, { @@ -1292,11 +1292,11 @@ "repo": "tailwindlabs/tailwindcss", "description": "A utility-first CSS framework for rapid UI development.", "createdAt": "2017-10-06T14:59:14Z", - "updatedAt": "2025-10-27T07:48:03Z", - "pushedAt": "2025-10-24T11:53:16Z", - "stars": 90800, + "updatedAt": "2025-10-28T12:25:13Z", + "pushedAt": "2025-10-28T12:25:08Z", + "stars": 90816, "watchers": 615, - "forks": 4771, + "forks": 4766, "defaultBranch": "main" } ] diff --git a/benchmarks/package.json b/benchmarks/package.json index 1b071c8..d4316a6 100644 --- a/benchmarks/package.json +++ b/benchmarks/package.json @@ -5,7 +5,7 @@ "scripts": { "benchmark:token-efficiency": "tsx scripts/token-efficiency-benchmark.ts", "benchmark:accuracy": "tsx --env-file=.env scripts/accuracy-benchmark.ts", - "fetch-github-data": "tsx scripts/fetch-github-data.ts", + "fetch:github-repos": "tsx scripts/fetch-github-repos.ts", "test": "vitest" }, "devDependencies": { @@ -14,14 +14,16 @@ "@ai-sdk/openai": "^2.0.53", "@ai-sdk/provider": "^2.0.0", "@antfu/eslint-config": "^6.1.0", + "@clack/prompts": "^0.11.0", "@faker-js/faker": "^10.1.0", "ai": "^5.0.80", - "consola": "^3.4.2", "csv-stringify": "^6.6.0", "fast-xml-parser": "^5.3.0", "gpt-tokenizer": "^3.2.0", "ofetch": "^1.4.1", "p-map": "^7.0.3", + "p-queue": "^9.0.0", + "unstorage": "^1.17.1", "yaml": "^2.8.1" } } diff --git a/benchmarks/results/accuracy/models/claude-haiku-4-5-20251001 b/benchmarks/results/accuracy/models/claude-haiku-4-5-20251001 new file mode 100644 index 0000000..16cc232 --- /dev/null +++ b/benchmarks/results/accuracy/models/claude-haiku-4-5-20251001 @@ -0,0 +1 @@ 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"totalCount": 477 - }, - { - "format": "xml", - "accuracy": 0.8616352201257862, - "totalTokens": 9944, - "averageLatency": 6035, - "correctCount": 411, - "totalCount": 477 - }, - { - "format": "csv", - "accuracy": 0.8469601677148847, - "totalTokens": 4745, - "averageLatency": 6551, - "correctCount": 404, - "totalCount": 477 - }, - { - "format": "json", - "accuracy": 0.8322851153039832, - "totalTokens": 8713, - "averageLatency": 7981, - "correctCount": 397, - "totalCount": 477 - }, - { - "format": "yaml", - "accuracy": 0.8259958071278826, - "totalTokens": 7091, - "averageLatency": 5561, - "correctCount": 394, - "totalCount": 477 - } - ], - "questions": 159, - "models": [ - "gpt-5-nano", - "claude-haiku-4-5", - "gemini-2.5-flash" - ], - "datasets": [ - { - "name": "tabular", - "description": "Uniform employee records (TOON optimal format)" - }, - { - "name": "nested", - "description": "E-commerce orders with nested structures" - }, - { - "name": "analytics", - "description": "Time-series analytics data" - }, - { - "name": "github", - "description": "Top 100 GitHub repositories" - } - ], - "tokenCounts": { - "json-tabular": 6347, - "json-nested": 9694, - "json-analytics": 3665, - "json-github": 15145, - "toon-tabular": 2483, - "toon-nested": 5967, - "toon-analytics": 1515, - "toon-github": 8745, - "csv-tabular": 2337, - "csv-nested": 6735, - "csv-analytics": 1393, - "csv-github": 8513, - "xml-tabular": 7314, - "xml-nested": 10992, - "xml-analytics": 4376, - "xml-github": 17095, - "yaml-tabular": 4969, - "yaml-nested": 7328, - "yaml-analytics": 2938, - "yaml-github": 13129 - }, - "timestamp": "2025-10-28T07:39:09.360Z" -} diff --git a/benchmarks/results/accuracy/report.md b/benchmarks/results/retrieval-accuracy.md similarity index 52% rename from benchmarks/results/accuracy/report.md rename to benchmarks/results/retrieval-accuracy.md index 3ddd0f2..3650edd 100644 --- a/benchmarks/results/accuracy/report.md +++ b/benchmarks/results/retrieval-accuracy.md @@ -1,31 +1,31 @@ ### Retrieval Accuracy -Accuracy across **3 LLMs** on **159 data retrieval questions**: +Accuracy across **3 LLMs** on **154 data retrieval questions**: ``` gpt-5-nano - toon ████████████████████ 99.4% (158/159) - yaml ███████████████████░ 95.0% (151/159) - csv ██████████████████░░ 92.5% (147/159) - json ██████████████████░░ 92.5% (147/159) - xml ██████████████████░░ 91.2% (145/159) - -claude-haiku-4-5 - toon ███████████████░░░░░ 75.5% (120/159) - xml ███████████████░░░░░ 75.5% (120/159) - csv ███████████████░░░░░ 75.5% (120/159) - json ███████████████░░░░░ 75.5% (120/159) - yaml ███████████████░░░░░ 74.2% (118/159) + toon ███████████████████░ 96.1% (148/154) + csv ██████████████████░░ 90.3% (139/154) + yaml ██████████████████░░ 89.0% (137/154) + json ██████████████████░░ 87.7% (135/154) + xml █████████████████░░░ 83.8% (129/154) gemini-2.5-flash - xml ██████████████████░░ 91.8% (146/159) - csv █████████████████░░░ 86.2% (137/159) - toon █████████████████░░░ 84.9% (135/159) - json ████████████████░░░░ 81.8% (130/159) - yaml ████████████████░░░░ 78.6% (125/159) + xml ██████████████████░░ 90.3% (139/154) + csv ██████████████████░░ 89.0% (137/154) + toon █████████████████░░░ 87.0% (134/154) + json ████████████████░░░░ 79.2% (122/154) + yaml ███████████████░░░░░ 76.0% (117/154) + +claude-haiku-4-5-20251001 + json ██████████░░░░░░░░░░ 48.7% (75/154) + toon ██████████░░░░░░░░░░ 48.1% (74/154) + xml █████████░░░░░░░░░░░ 47.4% (73/154) + yaml █████████░░░░░░░░░░░ 47.4% (73/154) + csv █████████░░░░░░░░░░░ 45.5% (70/154) ``` -**Advantage:** TOON achieves **86.6% accuracy** (vs JSON's 83.2%) while using **46.3% fewer tokens**. +**Advantage:** TOON achieves **77.1% accuracy** (vs JSON's 71.9%) while using **46.3% fewer tokens**.
Performance by dataset and model @@ -36,41 +36,41 @@ gemini-2.5-flash | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `toon` | 87.4% | 2.483 | 152/174 | -| `csv` | 82.8% | 2.337 | 144/174 | -| `yaml` | 83.9% | 4.969 | 146/174 | -| `json` | 83.9% | 6.347 | 146/174 | -| `xml` | 88.5% | 7.314 | 154/174 | +| `csv` | 74.7% | 2,337 | 112/150 | +| `toon` | 76.7% | 2,483 | 115/150 | +| `yaml` | 70.7% | 4,969 | 106/150 | +| `xml` | 77.3% | 7,314 | 116/150 | +| `json` | 69.3% | 6,347 | 104/150 | ##### E-commerce orders with nested structures | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `toon` | 90.9% | 5.967 | 120/132 | -| `csv` | 93.9% | 6.735 | 124/132 | -| `yaml` | 87.1% | 7.328 | 115/132 | -| `json` | 87.9% | 9.694 | 116/132 | -| `xml` | 93.2% | 10.992 | 123/132 | +| `toon` | 80.0% | 5,967 | 96/120 | +| `csv` | 75.8% | 6,735 | 91/120 | +| `yaml` | 74.2% | 7,328 | 89/120 | +| `json` | 79.2% | 9,694 | 95/120 | +| `xml` | 78.3% | 10,992 | 94/120 | ##### Time-series analytics data | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `csv` | 89.7% | 1.393 | 78/87 | -| `toon` | 88.5% | 1.515 | 77/87 | -| `yaml` | 83.9% | 2.938 | 73/87 | -| `json` | 88.5% | 3.665 | 77/87 | -| `xml` | 85.1% | 4.376 | 74/87 | +| `csv` | 75.5% | 1,393 | 77/102 | +| `toon` | 76.5% | 1,515 | 78/102 | +| `yaml` | 74.5% | 2,938 | 76/102 | +| `json` | 76.5% | 3,665 | 78/102 | +| `xml` | 74.5% | 4,376 | 76/102 | ##### Top 100 GitHub repositories | Format | Accuracy | Tokens | Correct/Total | | ------ | -------- | ------ | ------------- | -| `toon` | 76.2% | 8.745 | 64/84 | -| `csv` | 69.0% | 8.513 | 58/84 | -| `yaml` | 71.4% | 13.129 | 60/84 | -| `json` | 69.0% | 15.145 | 58/84 | -| `xml` | 71.4% | 17.095 | 60/84 | +| `toon` | 74.4% | 8,745 | 67/90 | +| `csv` | 73.3% | 8,513 | 66/90 | +| `yaml` | 62.2% | 13,129 | 56/90 | +| `json` | 61.1% | 15,145 | 55/90 | +| `xml` | 61.1% | 17,095 | 55/90 | #### Performance by Model @@ -78,31 +78,31 @@ gemini-2.5-flash | Format | Accuracy | Correct/Total | | ------ | -------- | ------------- | -| `toon` | 99.4% | 158/159 | -| `yaml` | 95.0% | 151/159 | -| `csv` | 92.5% | 147/159 | -| `json` | 92.5% | 147/159 | -| `xml` | 91.2% | 145/159 | - -##### claude-haiku-4-5 - -| Format | Accuracy | Correct/Total | -| ------ | -------- | ------------- | -| `toon` | 75.5% | 120/159 | -| `xml` | 75.5% | 120/159 | -| `csv` | 75.5% | 120/159 | -| `json` | 75.5% | 120/159 | -| `yaml` | 74.2% | 118/159 | +| `toon` | 96.1% | 148/154 | +| `csv` | 90.3% | 139/154 | +| `yaml` | 89.0% | 137/154 | +| `json` | 87.7% | 135/154 | +| `xml` | 83.8% | 129/154 | ##### gemini-2.5-flash | Format | Accuracy | Correct/Total | | ------ | -------- | ------------- | -| `xml` | 91.8% | 146/159 | -| `csv` | 86.2% | 137/159 | -| `toon` | 84.9% | 135/159 | -| `json` | 81.8% | 130/159 | -| `yaml` | 78.6% | 125/159 | +| `xml` | 90.3% | 139/154 | +| `csv` | 89.0% | 137/154 | +| `toon` | 87.0% | 134/154 | +| `json` | 79.2% | 122/154 | +| `yaml` | 76.0% | 117/154 | + +##### claude-haiku-4-5-20251001 + +| Format | Accuracy | Correct/Total | +| ------ | -------- | ------------- | +| `json` | 48.7% | 75/154 | +| `toon` | 48.1% | 74/154 | +| `xml` | 47.4% | 73/154 | +| `yaml` | 47.4% | 73/154 | +| `csv` | 45.5% | 70/154 |
@@ -124,31 +124,33 @@ Four datasets designed to test different structural patterns: #### Question Types -159 questions are generated dynamically across three categories: +154 questions are generated dynamically across three categories: -- **Field retrieval (50%)**: Direct value lookups +- **Field retrieval (40%)**: Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths) - Example: "What is Alice's salary?" → `75000` + - Example: "How many items are in order ORD-0042?" → `3` - Example: "What is the customer name for order ORD-0042?" → `John Doe` -- **Aggregation (25%)**: Counting and summation tasks +- **Aggregation (32%)**: Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons) - Example: "How many employees work in Engineering?" → `17` - Example: "What is the total revenue across all orders?" → `45123.50` + - Example: "How many employees have salary > 80000?" → `23` -- **Filtering (25%)**: Conditional queries +- **Filtering (28%)**: Multi-condition queries requiring compound logic (AND constraints across fields) - Example: "How many employees in Sales have salary > 80000?" → `5` - - Example: "How many orders have total > 400?" → `12` + - Example: "How many active employees have more than 10 years of experience?" → `8` #### Evaluation Process -1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, JSON, YAML, CSV, XML). +1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, CSV, XML, JSON, YAML). 2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer. -4. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`). +3. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`). #### Models & Configuration -- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5`, `gemini-2.5-flash` +- **Models tested**: `claude-haiku-4-5-20251001`, `gemini-2.5-flash`, `gpt-5-nano` - **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding (GPT-5 tokenizer) - **Temperature**: 0 (for non-reasoning models) -- **Total evaluations**: 159 questions × 5 formats × 3 models = 2,385 LLM calls +- **Total evaluations**: 154 questions × 5 formats × 3 models = 2,310 LLM calls diff --git a/benchmarks/results/token-efficiency.md b/benchmarks/results/token-efficiency.md index 8c382e8..f15963e 100644 --- a/benchmarks/results/token-efficiency.md +++ b/benchmarks/results/token-efficiency.md @@ -39,11 +39,11 @@ Total ████████████░░░░░ "repo": "freeCodeCamp/freeCodeCamp", "description": "freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…", "createdAt": "2014-12-24T17:49:19Z", - "updatedAt": "2025-10-27T07:40:58Z", - "pushedAt": "2025-10-26T11:31:08Z", - "stars": 430828, - "watchers": 8582, - "forks": 42136, + "updatedAt": "2025-10-28T11:58:08Z", + "pushedAt": "2025-10-28T10:17:16Z", + "stars": 430886, + "watchers": 8583, + "forks": 42146, "defaultBranch": "main" }, { @@ -52,11 +52,11 @@ Total ████████████░░░░░ "repo": "codecrafters-io/build-your-own-x", "description": "Master programming by recreating your favorite technologies from scratch.", "createdAt": "2018-05-09T12:03:18Z", - "updatedAt": "2025-10-27T07:43:25Z", + "updatedAt": "2025-10-28T12:37:11Z", "pushedAt": "2025-10-10T18:45:01Z", - "stars": 430102, - "watchers": 6322, - "forks": 40388, + "stars": 430877, + "watchers": 6332, + "forks": 40453, "defaultBranch": "master" }, { @@ -65,11 +65,11 @@ Total ████████████░░░░░ "repo": "sindresorhus/awesome", "description": "😎 Awesome lists about all kinds of interesting topics", "createdAt": "2014-07-11T13:42:37Z", - "updatedAt": "2025-10-27T07:44:27Z", - "pushedAt": "2025-10-23T17:26:53Z", - "stars": 409760, - "watchers": 8016, - "forks": 32015, + "updatedAt": "2025-10-28T12:40:21Z", + "pushedAt": "2025-10-27T17:57:31Z", + "stars": 410052, + "watchers": 8017, + "forks": 32029, "defaultBranch": "main" } ] @@ -80,9 +80,9 @@ Total ████████████░░░░░ ``` repositories[3]{id,name,repo,description,createdAt,updatedAt,pushedAt,stars,watchers,forks,defaultBranch}: - 28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2025-10-27T07:40:58Z","2025-10-26T11:31:08Z",430828,8582,42136,main - 132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2025-10-27T07:43:25Z","2025-10-10T18:45:01Z",430102,6322,40388,master - 21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2025-10-27T07:44:27Z","2025-10-23T17:26:53Z",409760,8016,32015,main + 28457823,freeCodeCamp,freeCodeCamp/freeCodeCamp,"freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming,…","2014-12-24T17:49:19Z","2025-10-28T11:58:08Z","2025-10-28T10:17:16Z",430886,8583,42146,main + 132750724,build-your-own-x,codecrafters-io/build-your-own-x,Master programming by recreating your favorite technologies from scratch.,"2018-05-09T12:03:18Z","2025-10-28T12:37:11Z","2025-10-10T18:45:01Z",430877,6332,40453,master + 21737465,awesome,sindresorhus/awesome,😎 Awesome lists about all kinds of interesting topics,"2014-07-11T13:42:37Z","2025-10-28T12:40:21Z","2025-10-27T17:57:31Z",410052,8017,32029,main ``` --- diff --git a/benchmarks/scripts/accuracy-benchmark.ts b/benchmarks/scripts/accuracy-benchmark.ts index 70172a1..e80ecc1 100644 --- a/benchmarks/scripts/accuracy-benchmark.ts +++ b/benchmarks/scripts/accuracy-benchmark.ts @@ -1,51 +1,53 @@ -/** - * LLM Retrieval Accuracy Benchmark - * - * Main entry point that orchestrates the full benchmark: - * 1. Generate questions from datasets - * 2. Format data in all formats (JSON, TOON, YAML, Markdown-kv) - * 3. Evaluate each question with each format using LLMs - * 4. Generate reports - */ - -import type { EvaluationResult, Question } from '../src/types' -import * as fsp from 'node:fs/promises' +import type { Question } from '../src/types' import * as path from 'node:path' -import { consola } from 'consola' -import pMap from 'p-map' -import { BENCHMARKS_DIR, DEFAULT_CONCURRENCY, DRY_RUN, DRY_RUN_LIMITS, ROOT_DIR } from '../src/constants' +import process from 'node:process' +import * as prompts from '@clack/prompts' +import PQueue from 'p-queue' +import { DEFAULT_CONCURRENCY, DRY_RUN, DRY_RUN_LIMITS, MODEL_RPM_LIMITS, ROOT_DIR } from '../src/constants' import { datasets } from '../src/datasets' import { evaluateQuestion, models } from '../src/evaluate' import { formatters } from '../src/formatters' import { generateQuestions } from '../src/questions' import { calculateFormatResults, calculateTokenCounts, saveResults } from '../src/report' +import { getAllModelResults, hasModelResults, saveModelResults } from '../src/storage' -consola.start('Retrieval Accuracy Benchmark for TOON') +prompts.intro('Retrieval Accuracy Benchmark') -// Check if results already exist -const resultsDir = path.join(BENCHMARKS_DIR, 'results', 'accuracy') -const rawResultsPath = path.join(resultsDir, 'raw-results.json') -const summaryPath = path.join(resultsDir, 'summary.json') +// Prompt user to select which models to benchmark +const modelChoices = models.map(({ modelId }) => ({ + value: modelId, + label: modelId, +})) -let existingResults: EvaluationResult[] | undefined -let existingTokenCounts: Record | undefined +const selectedModels = await prompts.multiselect({ + message: 'Select models to benchmark (Space to select, Enter to confirm)', + options: modelChoices, + required: true, +}) -try { - const [rawData, summaryData] = await Promise.all([ - fsp.readFile(rawResultsPath, 'utf-8'), - fsp.readFile(summaryPath, 'utf-8'), - ]) - existingResults = JSON.parse(rawData) - const summary = JSON.parse(summaryData) - existingTokenCounts = summary.tokenCounts - consola.info('Found existing results – regenerating report only') +if (prompts.isCancel(selectedModels)) { + prompts.cancel('Benchmark cancelled') + process.exit(0) } -catch { - // Results don't exist, will run full evaluation + +const activeModels = models.filter(m => selectedModels.includes(m.modelId)) + +prompts.log.info(`Selected ${activeModels.length} model(s): ${activeModels.map(m => m.modelId).join(', ')}`) + +// Check which models already have results +const existingModelResults: Record = {} +for (const model of activeModels) { + const existingResult = await hasModelResults(model.modelId) + if (existingResult) + existingModelResults[model.modelId] = existingResult +} + +if (Object.keys(existingModelResults).length > 0) { + prompts.log.info(`Found existing results for ${Object.values(existingModelResults).length} model(s)`) } if (DRY_RUN) { - consola.info('Limiting questions and models for dry run') + prompts.log.info('Limiting questions and models for dry run') } let questions = generateQuestions() @@ -55,79 +57,98 @@ if (DRY_RUN && DRY_RUN_LIMITS.maxQuestions) { questions = questions.slice(0, DRY_RUN_LIMITS.maxQuestions) } -// Filter models for dry run -const activeModels = DRY_RUN && DRY_RUN_LIMITS.allowedModels.length > 0 - ? Object.fromEntries( - Object.entries(models).filter(([name]) => DRY_RUN_LIMITS.allowedModels.includes(name)), - ) - : models +prompts.log.info(`Evaluating ${questions.length} questions`) +prompts.log.info(`Testing ${Object.keys(formatters).length} formats`) -let results: EvaluationResult[] -let tokenCounts: Record +// Evaluate each model separately and save results incrementally +for (const model of activeModels) { + const modelId = model.modelId -if (existingResults && existingTokenCounts) { - // Reuse existing results - results = existingResults - tokenCounts = existingTokenCounts -} -else { - // Run full evaluation - consola.info(`Evaluating ${questions.length} questions`) - consola.info(`Testing ${Object.keys(formatters).length} formats`) - consola.info(`Using ${Object.keys(activeModels).length} models: ${Object.keys(activeModels).join(', ')}`) + // Skip if results already exist + if (existingModelResults[modelId]) { + prompts.log.info(`Skipping ${modelId} (results already exist)`) + continue + } - // Calculate token counts for all format+dataset combinations - tokenCounts = calculateTokenCounts(formatters) - - // Generate evaluation tasks - const tasks: { question: Question, formatName: string, modelName: string }[] = [] + prompts.log.step(`Running benchmark for ${modelId}`) + // Generate evaluation tasks for this model + const tasks: { question: Question, formatName: string }[] = [] for (const question of questions) { for (const [formatName] of Object.entries(formatters)) { - for (const [modelName] of Object.entries(activeModels)) { - tasks.push({ question, formatName, modelName }) - } + tasks.push({ question, formatName }) } } const total = tasks.length - consola.start(`Running ${total} evaluations with concurrency: ${DEFAULT_CONCURRENCY}`) + const rpmLimit = MODEL_RPM_LIMITS[modelId] + const queue = new PQueue({ + concurrency: DEFAULT_CONCURRENCY, + intervalCap: rpmLimit, + interval: rpmLimit ? 60_000 : undefined, + }) - results = await pMap( - tasks, - async (task, index) => { + const evalSpinner = prompts.spinner() + evalSpinner.start(`Running ${total} evaluations (concurrency: ${DEFAULT_CONCURRENCY}, RPM limit: ${rpmLimit ?? 'unlimited'})`) + + let completed = 0 + + // Queue all tasks + const modelResultPromises = tasks.map(task => + queue.add(async () => { // Format data on-demand const dataset = datasets.find(d => d.name === task.question.dataset)! const formatter = formatters[task.formatName]! const formattedData = formatter(dataset.data) - const model = activeModels[task.modelName as keyof typeof activeModels]! const result = await evaluateQuestion({ question: task.question, formatName: task.formatName, formattedData, model, - modelName: task.modelName, }) // Progress update after task completes - if ((index + 1) % 10 === 0 || (index + 1) === total) { - const percent = (((index + 1) / total) * 100).toFixed(1) - consola.start(`Progress: ${index + 1}/${total} (${percent}%)`) + completed++ + if (completed % 10 === 0 || completed === total) { + const percent = ((completed / total) * 100).toFixed(1) + evalSpinner.message(`Progress: ${completed}/${total} (${percent}%)`) } return result - }, - { concurrency: DEFAULT_CONCURRENCY }, + }), ) - consola.success('Evaluation complete!') + // Wait for all tasks to complete + const modelResults = await Promise.all(modelResultPromises) + + evalSpinner.stop(`Evaluation complete for ${modelId}`) + + // Save results immediately for this model + await saveModelResults(modelId, modelResults) + prompts.log.success(`Saved results for ${modelId}`) } -// Generate/regenerate markdown report -consola.start('Generating report and saving results…') -const formatResults = calculateFormatResults(results, tokenCounts) -await saveResults(results, formatResults, questions, tokenCounts) +// Generate/regenerate markdown report from all available model results +const reportSpinner = prompts.spinner() +reportSpinner.start('Generating report from all model results') -consola.info(`Results saved to: \`${path.relative(ROOT_DIR, resultsDir)}\``) -consola.success(existingResults ? 'Markdown report regenerated!' : 'Evaluation complete!') +// Load all available model results (including any that were skipped) +const allModelResults = await getAllModelResults() +const allResults = Object.values(allModelResults).flat() + +if (allResults.length === 0) { + prompts.log.warn('No results available to generate report') + process.exit(0) +} + +// Calculate token counts freshly (deterministic, no need to persist) +const tokenCounts = calculateTokenCounts(formatters) + +// Calculate format statistics and save report +const formatResults = calculateFormatResults(allResults, tokenCounts) +const resultsDir = await saveResults(allResults, formatResults, questions, tokenCounts) + +const reportPath = path.join(resultsDir, 'retrieval-accuracy.md') +prompts.log.info(`Report saved to: \`${path.relative(ROOT_DIR, reportPath)}\``) +reportSpinner.stop('Report generation complete!') diff --git a/benchmarks/scripts/fetch-github-data.ts b/benchmarks/scripts/fetch-github-repos.ts similarity index 72% rename from benchmarks/scripts/fetch-github-data.ts rename to benchmarks/scripts/fetch-github-repos.ts index 1ded0ee..3b75fd9 100644 --- a/benchmarks/scripts/fetch-github-data.ts +++ b/benchmarks/scripts/fetch-github-repos.ts @@ -1,18 +1,20 @@ import * as path from 'node:path' import process from 'node:process' -import { consola } from 'consola' +import * as prompts from '@clack/prompts' import { ofetch } from 'ofetch' import pMap from 'p-map' import { BENCHMARKS_DIR } from '../src/constants' import { ensureDir, saveJsonFile } from '../src/utils' +prompts.intro('GitHub Repositories Fetcher') + try { // Fetch top 100 repos from GitHub const repoList = await searchTop100Repos() const repos = await fetchRepoDetails(repoList) if (repos.length === 0) { - consola.error('❌ No repositories fetched. Exiting.') + prompts.log.error('No repositories fetched. Exiting.') process.exit(1) } @@ -21,15 +23,16 @@ try { await saveRepos(repos) - consola.success('Done!') + prompts.log.success('Done!') } catch (error) { - consola.error(error) + prompts.log.error(String(error)) process.exit(1) } async function searchTop100Repos(): Promise { - consola.start('Fetching top 100 starred repositories from GitHub API…') + const s = prompts.spinner() + s.start('Fetching top 100 starred repositories') const response = await ofetch<{ items: { full_name: string }[] }>( 'https://api.github.com/search/repositories', @@ -47,23 +50,26 @@ async function searchTop100Repos(): Promise { }, ) + s.stop('Fetched top 100 repositories') + return response.items.map(item => item.full_name) } async function fetchRepoDetails(repoList: string[]): Promise[]> { - consola.start(`Fetching ${repoList.length} GitHub repositories…`) + const s = prompts.spinner() + s.start(`Fetching ${repoList.length} GitHub repositories`) const repos = await pMap( repoList, async (repoPath, index) => { - consola.info(`[${index + 1}/${repoList.length}] Fetching ${repoPath}…`) + s.message(`[${index + 1}/${repoList.length}] Fetching ${repoPath}`) const { repo } = await ofetch(`https://ungh.cc/repos/${repoPath}`) return repo }, { concurrency: 5 }, ) - consola.success(`Successfully fetched ${repos.length}/${repoList.length} repositories`) + s.stop(`Successfully fetched ${repos.length}/${repoList.length} repositories`) return repos } @@ -76,5 +82,5 @@ async function saveRepos(repos: Record[]): Promise { await saveJsonFile(outputFile, repos) const relativePath = path.relative(BENCHMARKS_DIR, outputFile) - consola.info(`Saved to \`${relativePath}\``) + prompts.log.info(`Result saved to \`${relativePath}\``) } diff --git a/benchmarks/scripts/token-efficiency-benchmark.ts b/benchmarks/scripts/token-efficiency-benchmark.ts index 34c8c32..afefafe 100644 --- a/benchmarks/scripts/token-efficiency-benchmark.ts +++ b/benchmarks/scripts/token-efficiency-benchmark.ts @@ -1,6 +1,6 @@ import * as fsp from 'node:fs/promises' import * as path from 'node:path' -import { consola } from 'consola' +import * as prompts from '@clack/prompts' import { encode } from '../../src/index' import githubRepos from '../data/github-repos.json' with { type: 'json' } import { BENCHMARKS_DIR, ROOT_DIR } from '../src/constants' @@ -24,8 +24,6 @@ interface BenchmarkResult { showDetailed: boolean } -const outputFilePath = path.join(BENCHMARKS_DIR, 'results', 'token-efficiency.md') - const BENCHMARK_EXAMPLES = [ { name: 'GitHub Repositories', @@ -50,6 +48,8 @@ const BENCHMARK_EXAMPLES = [ }, ] as const +prompts.intro('Token Efficiency Benchmark') + // Calculate total savings let totalJsonTokens = 0 let totalToonTokens = 0 @@ -204,9 +204,12 @@ ${detailedExamples} `.trimStart() -console.log(`${barChartSection}\n`) +prompts.log.message(`${barChartSection}\n`) -await ensureDir(path.join(BENCHMARKS_DIR, 'results')) +const resultsDir = path.join(BENCHMARKS_DIR, 'results') +await ensureDir(resultsDir) + +const outputFilePath = path.join(resultsDir, 'token-efficiency.md') await fsp.writeFile(outputFilePath, markdown, 'utf-8') -consola.success(`Benchmark written to \`${path.relative(ROOT_DIR, outputFilePath)}\``) +prompts.log.success(`Result saved to \`${path.relative(ROOT_DIR, outputFilePath)}\``) diff --git a/benchmarks/src/constants.ts b/benchmarks/src/constants.ts index e9301d9..35eb2c1 100644 --- a/benchmarks/src/constants.ts +++ b/benchmarks/src/constants.ts @@ -5,9 +5,22 @@ export const ROOT_DIR: string = url.fileURLToPath(new URL('../../', import.meta. export const BENCHMARKS_DIR: string = url.fileURLToPath(new URL('../', import.meta.url)) /** - * Default concurrency for parallel evaluations + * Model-specific RPM (requests per minute) limits to handle API quotas + * + * @remarks + * Set `undefined` for models without specific limits */ -export const DEFAULT_CONCURRENCY = 20 +/// keep-sorted +export const MODEL_RPM_LIMITS: Record = { + 'claude-haiku-4-5-20251001': 50, + 'gemini-2.5-flash': 25, + 'gpt-5-nano': undefined, +} + +/** + * Default concurrency for parallel evaluations to prevent bursting + */ +export const DEFAULT_CONCURRENCY = 10 /** * Progress bar configuration @@ -28,13 +41,83 @@ export const PROGRESS_BAR = { export const DRY_RUN: boolean = process.env.DRY_RUN === 'true' /** - * Limits applied when DRY_RUN is enabled + * Limits applied during dry run mode */ export const DRY_RUN_LIMITS = { /** Maximum number of questions to evaluate */ maxQuestions: 10, - /** Maximum number of formats to test */ - maxFormats: undefined as number | undefined, - /** Models to use in dry run */ - allowedModels: [] as string[], } + +/** + * Threshold values for filtering and aggregation questions + */ +export const QUESTION_THRESHOLDS = { + tabular: { + salaryRanges: [60000, 80000, 100000, 120000], + experienceYears: [5, 10, 15, 20], + departmentSalaryThreshold: 80000, + departmentExperienceThreshold: 10, + }, + nested: { + highValueOrders: [200, 400, 600], + statusValueThreshold: 300, + itemCountThreshold: 3, + totalThresholdsForItems: [300, 500], + }, + analytics: { + views: [5000, 7000], + conversions: [10, 30], + viewsForFiltering: [6000, 7000], + conversionsForFiltering: 15, + revenueThresholds: [500, 1000, 1500, 2000, 2500], + viewsThresholdForRevenue: 6000, + clicksForFiltering: [250, 400], + conversionsForClickFiltering: 15, + revenueForBounceRate: [1000, 1500], + bounceRateThreshold: 0.5, + }, + github: { + stars: [100000, 150000, 200000], + forks: [20000, 35000, 50000], + watchers: [5000, 8000], + starForkCombinations: [ + { stars: 75000, forks: 15000 }, + { stars: 100000, forks: 20000 }, + { stars: 150000, forks: 30000 }, + { stars: 200000, forks: 45000 }, + ], + starWatcherCombinations: [ + { stars: 100000, watchers: 7000 }, + { stars: 150000, watchers: 9000 }, + ], + }, +} as const + +/** + * Question generation configuration + */ +export const QUESTION_LIMITS = { + tabular: { + fieldRetrieval: 20, + aggregationDepartments: 6, + filteringMultiConditionDepartments: 6, + filteringExperience: 4, + filteringDepartmentExp: 3, + filteringDepartmentActive: 3, + }, + nested: { + fieldRetrievalOrders: 8, + fieldRetrievalCustomers: 10, + aggregationStatuses: 5, + filteringStatusAndValue: 5, + filteringStatusAndItems: 3, + }, + analytics: { + fieldRetrievalDates: 13, + }, + github: { + fieldRetrievalRepos: 11, + aggregationBranches: 2, + filteringStarsAndForks: 8, + }, +} as const diff --git a/benchmarks/src/datasets.ts b/benchmarks/src/datasets.ts index 49de5fb..fc2d274 100644 --- a/benchmarks/src/datasets.ts +++ b/benchmarks/src/datasets.ts @@ -1,12 +1,3 @@ -/** - * Datasets for TOON benchmarks - * - * These datasets are designed to test TOON's strengths and weaknesses: - * - Tabular: Uniform records (TOON optimal) - * - Nested: Complex structures with nested objects - * - Analytics: Time-series data - */ - import type { Dataset } from './types' import { faker } from '@faker-js/faker' import githubRepos from '../data/github-repos.json' with { type: 'json' } @@ -128,7 +119,7 @@ const tabularDataset: Dataset = { description: 'Uniform employee records (TOON optimal format)', data: { employees: Array.from({ length: 100 }, (_, i): Employee => { - const yearsExp = faker.number.int({ min: 1, max: 20 }) + const yearsExp = faker.number.int({ min: 1, max: 25 }) return { id: i + 1, name: faker.person.fullName(), diff --git a/benchmarks/src/evaluate.ts b/benchmarks/src/evaluate.ts index 6bb39c4..468390c 100644 --- a/benchmarks/src/evaluate.ts +++ b/benchmarks/src/evaluate.ts @@ -1,28 +1,19 @@ -/** - * LLM evaluation logic for TOON benchmarks - * - * Handles: - * - Model configuration - * - Question evaluation with LLMs - * - Answer validation using LLM-as-judge - */ - import type { LanguageModelV2 } from '@ai-sdk/provider' import type { EvaluationResult, Question } from './types' import { anthropic } from '@ai-sdk/anthropic' import { google } from '@ai-sdk/google' import { openai } from '@ai-sdk/openai' +import * as prompts from '@clack/prompts' import { generateText } from 'ai' -import { consola } from 'consola' /** * Models used for evaluation */ -export const models: Record = { - 'gpt-5-nano': openai('gpt-5-nano'), - 'claude-haiku-4-5': anthropic('claude-haiku-4-5-20251001'), - 'gemini-2.5-flash': google('gemini-2.5-flash'), -} +export const models: LanguageModelV2[] = [ + openai('gpt-5-nano'), + google('gemini-2.5-flash'), + anthropic('claude-haiku-4-5-20251001'), +] /** * Evaluate a single question with a specific format and model @@ -33,14 +24,12 @@ export async function evaluateQuestion( formatName, formattedData, model, - modelName, }: { question: Question formatName: string formattedData: string model: LanguageModelV2 - modelName: string }, ): Promise { const prompt = ` @@ -59,10 +48,11 @@ Provide only the direct answer, without any additional explanation or formatting const { text, usage } = await generateText({ model, prompt, - temperature: !model.modelId.startsWith('gpt-') ? 0 : undefined, + temperature: !model.modelId.startsWith('gpt-5') ? 0 : undefined, }) const latencyMs = performance.now() - startTime + const isCorrect = await validateAnswer({ actual: text.trim(), expected: question.groundTruth, @@ -72,7 +62,7 @@ Provide only the direct answer, without any additional explanation or formatting return { questionId: question.id, format: formatName, - model: modelName, + model: model.modelId, expected: question.groundTruth, actual: text.trim(), isCorrect, @@ -115,14 +105,14 @@ Respond with only "YES" or "NO". try { const { text } = await generateText({ - model: models['gpt-5-nano']!, + model: models.find(m => m.modelId === 'gpt-5-nano')!, prompt, }) return text.trim().toUpperCase() === 'YES' } catch (error) { - consola.error('Validation error:', error) + prompts.log.error(`Validation error: ${error}`) // Fallback to simple string comparison return actual.toLowerCase().trim() === expected.toLowerCase().trim() } diff --git a/benchmarks/src/formatters.ts b/benchmarks/src/formatters.ts index 23326e5..b20b1b7 100644 --- a/benchmarks/src/formatters.ts +++ b/benchmarks/src/formatters.ts @@ -1,20 +1,3 @@ -/** - * Format converters for TOON benchmarks - * - * Converts data to different formats for comparison: - * - JSON - * - TOON - * - CSV - * - XML - * - YAML - * - * ## Semantic Equivalence - * - * All formatters attempt to preserve semantic equivalence with the source data, - * meaning the converted data should represent the same information. However, - * CSV has inherent limitations with nested structures (see `toCSV` docs). - */ - import { stringify as stringifyCSV } from 'csv-stringify/sync' import { XMLBuilder } from 'fast-xml-parser' import { stringify as stringifyYAML } from 'yaml' @@ -23,7 +6,10 @@ import { encode as encodeToon } from '../../src/index' /** * Format converters registry * - * Each formatter takes unknown data and returns a string representation + * @remarks + * All formatters attempt to preserve semantic equivalence with the source data, + * meaning the converted data should represent the same information. However, + * CSV has inherent limitations with nested structures (see `toCSV` docs). */ export const formatters: Record string> = { json: data => JSON.stringify(data, undefined, 2), @@ -37,11 +23,13 @@ export const formatters: Record string> = { * Convert data to CSV format * * @remarks - * **Limitations**: CSV is designed for flat tabular data only. This formatter: - * - Only handles top-level objects with arrays of flat objects - * - Cannot properly represent deeply nested structures (nested arrays/objects within rows) - * - Loses nested structure information during conversion - * - May produce misleading results for datasets with complex nesting (e.g., e-commerce orders with nested items) + * Limitations: CSV is designed for flat tabular data only. + * + * This formatter: + * - Only handles top-level objects with arrays of flat objects + * - Cannot properly represent deeply nested structures (nested arrays/objects within rows) + * - Loses nested structure information during conversion + * - May produce misleading results for datasets with complex nesting (e.g., e-commerce orders with nested items) * * For datasets with nested structures, CSV comparisons may not be fair or representative * of how CSV would typically be used in practice. diff --git a/benchmarks/src/questions.ts b/benchmarks/src/questions.ts index cbd5ce8..a644ec2 100644 --- a/benchmarks/src/questions.ts +++ b/benchmarks/src/questions.ts @@ -1,24 +1,18 @@ /** * Question generation for TOON benchmarks * - * Generates ~160 questions across different types: - * - Field retrieval (50%): "What is X's Y?" - * - Aggregation (25%): "How many X have Y?" - * - Filtering (25%): "List/count X where Y" - * - * Questions are generated dynamically based on actual data values - * - * TODO: Balance question distribution across datasets to ensure fair representation. - * Current distribution: - * - Tabular: 70 questions (43%) - * - Nested: 50 questions (31%) - * - Analytics: 40 questions (25%) - * - GitHub: 40 questions (25%) + * Generates ~150-160 questions across different question types and datasets: + * - Field Retrieval: Direct field access with no computation + * Examples: "What is X's salary?", "What is the status of order Y?" + * - Aggregation: Counts, sums, averages, min/max operations (including single-condition filters) + * Examples: "How many X?", "What is the total/average?", "How many X > threshold?" + * - Filtering: Multi-condition queries requiring complex logical operations + * Examples: "How many X WHERE condition1 AND condition2?" */ import type { AnalyticsMetric, Employee, Order, Repository } from './datasets' import type { Question } from './types' -import { consola } from 'consola' +import { QUESTION_LIMITS, QUESTION_THRESHOLDS } from './constants' import { datasets } from './datasets' /** @@ -34,19 +28,15 @@ export function generateQuestions(): Question[] { const analytics = (datasets.find(d => d.name === 'analytics')?.data.metrics as AnalyticsMetric[]) ?? [] const github = (datasets.find(d => d.name === 'github')?.data.repositories as Repository[]) ?? [] - // ======================================== - // TABULAR DATASET QUESTIONS (70 questions) - // ======================================== - if (tabular.length > 0) { - // Field retrieval: specific employees (40 questions) - for (let i = 0; i < Math.min(40, tabular.length); i++) { + // Field retrieval: specific employees + for (let i = 0; i < Math.min(QUESTION_LIMITS.tabular.fieldRetrieval, tabular.length); i++) { const emp = tabular[i * 2] || tabular[i] if (!emp) continue - // Alternate between different field types - if (i % 3 === 0) { + // Rotate through all field types + if (i % 5 === 0) { questions.push({ id: `q${idCounter++}`, prompt: `What is the salary of ${emp.name}?`, @@ -55,7 +45,7 @@ export function generateQuestions(): Question[] { dataset: 'tabular', }) } - else if (i % 3 === 1) { + else if (i % 5 === 1) { questions.push({ id: `q${idCounter++}`, prompt: `What department does ${emp.name} work in?`, @@ -64,7 +54,7 @@ export function generateQuestions(): Question[] { dataset: 'tabular', }) } - else { + else if (i % 5 === 2) { questions.push({ id: `q${idCounter++}`, prompt: `What is the email address of ${emp.name}?`, @@ -73,11 +63,29 @@ export function generateQuestions(): Question[] { dataset: 'tabular', }) } + else if (i % 5 === 3) { + questions.push({ + id: `q${idCounter++}`, + prompt: `How many years of experience does ${emp.name} have?`, + groundTruth: String(emp.yearsExperience), + type: 'field-retrieval', + dataset: 'tabular', + }) + } + else { + questions.push({ + id: `q${idCounter++}`, + prompt: `Is ${emp.name} an active employee?`, + groundTruth: emp.active ? 'yes' : 'no', + type: 'field-retrieval', + dataset: 'tabular', + }) + } } // Aggregation: count by department const departments = [...new Set(tabular.map(e => e.department))] - for (const dept of departments.slice(0, 6)) { + for (const dept of departments.slice(0, QUESTION_LIMITS.tabular.aggregationDepartments)) { const count = tabular.filter(e => e.department === dept).length questions.push({ id: `q${idCounter++}`, @@ -88,9 +96,8 @@ export function generateQuestions(): Question[] { }) } - // Aggregation: salary ranges (4 questions) - const salaryThresholds = [60000, 80000, 100000, 120000] - for (const threshold of salaryThresholds) { + // Aggregation: salary ranges (single-condition filters) + for (const threshold of QUESTION_THRESHOLDS.tabular.salaryRanges) { const count = tabular.filter(e => e.salary > threshold).length questions.push({ id: `q${idCounter++}`, @@ -101,39 +108,57 @@ export function generateQuestions(): Question[] { }) } - // Filtering: active status + // Aggregation: totals and averages + const totalEmployees = tabular.length + const avgSalary = Math.round(tabular.reduce((sum, e) => sum + e.salary, 0) / totalEmployees) const activeCount = tabular.filter(e => e.active).length const inactiveCount = tabular.filter(e => !e.active).length + questions.push( + { + id: `q${idCounter++}`, + prompt: 'How many employees are in the dataset?', + groundTruth: String(totalEmployees), + type: 'aggregation', + dataset: 'tabular', + }, + { + id: `q${idCounter++}`, + prompt: 'What is the average salary across all employees?', + groundTruth: String(avgSalary), + type: 'aggregation', + dataset: 'tabular', + }, { id: `q${idCounter++}`, prompt: 'How many employees are active?', groundTruth: String(activeCount), - type: 'filtering', + type: 'aggregation', dataset: 'tabular', }, { id: `q${idCounter++}`, prompt: 'How many employees are inactive?', groundTruth: String(inactiveCount), - type: 'filtering', + type: 'aggregation', dataset: 'tabular', }, ) - // Complex filtering: multi-condition (8 questions) - for (const dept of departments.slice(0, 4)) { - const count = tabular.filter(e => e.department === dept && e.salary > 80000).length + // Filtering: count by department with salary filter (multi-condition) + for (const dept of departments.slice(0, QUESTION_LIMITS.tabular.filteringMultiConditionDepartments)) { + const count = tabular.filter(e => e.department === dept && e.salary > QUESTION_THRESHOLDS.tabular.departmentSalaryThreshold).length questions.push({ id: `q${idCounter++}`, - prompt: `How many employees in ${dept} have a salary greater than 80000?`, + prompt: `How many employees in ${dept} have a salary greater than ${QUESTION_THRESHOLDS.tabular.departmentSalaryThreshold}?`, groundTruth: String(count), type: 'filtering', dataset: 'tabular', }) } - for (const exp of [5, 10]) { + // Filtering: active employees by experience (multi-condition) + for (const exp of QUESTION_THRESHOLDS.tabular.experienceYears.slice(0, QUESTION_LIMITS.tabular.filteringExperience)) { const count = tabular.filter(e => e.yearsExperience > exp && e.active).length questions.push({ id: `q${idCounter++}`, @@ -143,15 +168,35 @@ export function generateQuestions(): Question[] { dataset: 'tabular', }) } + + // Filtering: department by experience (multi-condition) + for (const dept of departments.slice(0, QUESTION_LIMITS.tabular.filteringDepartmentExp)) { + const count = tabular.filter(e => e.department === dept && e.yearsExperience > QUESTION_THRESHOLDS.tabular.departmentExperienceThreshold).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many employees in ${dept} have more than ${QUESTION_THRESHOLDS.tabular.departmentExperienceThreshold} years of experience?`, + groundTruth: String(count), + type: 'filtering', + dataset: 'tabular', + }) + } + + // Filtering: department by active status (multi-condition) + for (const dept of departments.slice(0, QUESTION_LIMITS.tabular.filteringDepartmentActive)) { + const count = tabular.filter(e => e.department === dept && e.active).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many active employees work in ${dept}?`, + groundTruth: String(count), + type: 'filtering', + dataset: 'tabular', + }) + } } - // ======================================== - // NESTED DATASET QUESTIONS (50 questions) - // ======================================== - if (nested.length > 0) { - // Field retrieval: order totals (20 questions) - for (let i = 0; i < Math.min(20, nested.length); i++) { + // Field retrieval: order totals and statuses + for (let i = 0; i < Math.min(QUESTION_LIMITS.nested.fieldRetrievalOrders, nested.length); i++) { const order = nested[i * 2] || nested[i] if (!order) continue @@ -159,7 +204,7 @@ export function generateQuestions(): Question[] { if (i % 2 === 0) { questions.push({ id: `q${idCounter++}`, - prompt: `What is the total amount for order ${order.orderId}?`, + prompt: `What is the total for order ${order.orderId}?`, groundTruth: String(order.total), type: 'field-retrieval', dataset: 'nested', @@ -176,51 +221,143 @@ export function generateQuestions(): Question[] { } } - // Field retrieval: customer info (15 questions) - for (let i = 0; i < Math.min(15, nested.length); i++) { - const order = nested[i * 3] || nested[i] + // Field retrieval: customer info and order dates (expanded) + for (let i = 0; i < Math.min(QUESTION_LIMITS.nested.fieldRetrievalCustomers, nested.length); i++) { + const order = nested[i * 2 + 1] || nested[i] if (!order) continue - questions.push({ - id: `q${idCounter++}`, - prompt: `What is the customer name for order ${order.orderId}?`, - groundTruth: order.customer.name, - type: 'field-retrieval', - dataset: 'nested', - }) + if (i % 4 === 0) { + questions.push({ + id: `q${idCounter++}`, + prompt: `What is the customer name for order ${order.orderId}?`, + groundTruth: order.customer.name, + type: 'field-retrieval', + dataset: 'nested', + }) + } + else if (i % 4 === 1) { + questions.push({ + id: `q${idCounter++}`, + prompt: `What is the customer email for order ${order.orderId}?`, + groundTruth: order.customer.email, + type: 'field-retrieval', + dataset: 'nested', + }) + } + else if (i % 4 === 2) { + questions.push({ + id: `q${idCounter++}`, + prompt: `What is the order date for order ${order.orderId}?`, + groundTruth: order.orderDate || '', + type: 'field-retrieval', + dataset: 'nested', + }) + } + else { + questions.push({ + id: `q${idCounter++}`, + prompt: `How many items are in order ${order.orderId}?`, + groundTruth: String(order.items.length), + type: 'field-retrieval', + dataset: 'nested', + }) + } } - // Aggregation: count by status + // Aggregation: totals and averages + const totalRevenue = nested.reduce((sum, o) => sum + o.total, 0) + const avgOrderValue = totalRevenue / nested.length + const totalOrders = nested.length + const maxOrderValue = Math.max(...nested.map(o => o.total)) + + // Count by status const statuses = [...new Set(nested.map(o => o.status))] - for (const status of statuses) { + for (const status of statuses.slice(0, QUESTION_LIMITS.nested.aggregationStatuses)) { const count = nested.filter(o => o.status === status).length questions.push({ id: `q${idCounter++}`, prompt: `How many orders have status "${status}"?`, groundTruth: String(count), + type: 'aggregation', + dataset: 'nested', + }) + } + + questions.push( + { + id: `q${idCounter++}`, + prompt: 'What is the total revenue across all orders?', + groundTruth: String(totalRevenue.toFixed(2)), + type: 'aggregation', + dataset: 'nested', + }, + { + id: `q${idCounter++}`, + prompt: 'What is the average order value?', + groundTruth: String(avgOrderValue.toFixed(2)), + type: 'aggregation', + dataset: 'nested', + }, + { + id: `q${idCounter++}`, + prompt: 'How many orders are in the dataset?', + groundTruth: String(totalOrders), + type: 'aggregation', + dataset: 'nested', + }, + { + id: `q${idCounter++}`, + prompt: 'What is the highest order total?', + groundTruth: String(maxOrderValue.toFixed(2)), + type: 'aggregation', + dataset: 'nested', + }, + ) + + // Aggregation: high-value orders (single-condition filter) + for (const threshold of QUESTION_THRESHOLDS.nested.highValueOrders) { + const count = nested.filter(o => o.total > threshold).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many orders have a total greater than ${threshold}?`, + groundTruth: String(count), + type: 'aggregation', + dataset: 'nested', + }) + } + + // Filtering: multi-condition queries (status AND value) + const orderStatuses = [...new Set(nested.map(o => o.status))] + for (const status of orderStatuses.slice(0, QUESTION_LIMITS.nested.filteringStatusAndValue)) { + const count = nested.filter(o => o.status === status && o.total > QUESTION_THRESHOLDS.nested.statusValueThreshold).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many orders have status "${status}" and total greater than ${QUESTION_THRESHOLDS.nested.statusValueThreshold}?`, + groundTruth: String(count), type: 'filtering', dataset: 'nested', }) } - // Aggregation: total revenue - const totalRevenue = nested.reduce((sum, o) => sum + o.total, 0) - questions.push({ - id: `q${idCounter++}`, - prompt: 'What is the total revenue across all orders?', - groundTruth: String(totalRevenue.toFixed(2)), - type: 'aggregation', - dataset: 'nested', - }) - - // Filtering: high-value orders (3 questions) - const highValueThresholds = [200, 400, 600] - for (const threshold of highValueThresholds) { - const count = nested.filter(o => o.total > threshold).length + // Filtering: status AND items count (multi-condition) + for (const status of orderStatuses.slice(0, QUESTION_LIMITS.nested.filteringStatusAndItems)) { + const count = nested.filter(o => o.status === status && o.items.length >= QUESTION_THRESHOLDS.nested.itemCountThreshold).length questions.push({ id: `q${idCounter++}`, - prompt: `How many orders have a total greater than ${threshold}?`, + prompt: `How many orders have status "${status}" and at least ${QUESTION_THRESHOLDS.nested.itemCountThreshold} items?`, + groundTruth: String(count), + type: 'filtering', + dataset: 'nested', + }) + } + + // Filtering: total AND items count (multi-condition) + for (const threshold of QUESTION_THRESHOLDS.nested.totalThresholdsForItems) { + const count = nested.filter(o => o.total > threshold && o.items.length >= QUESTION_THRESHOLDS.nested.itemCountThreshold).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many orders have a total greater than ${threshold} and at least ${QUESTION_THRESHOLDS.nested.itemCountThreshold} items?`, groundTruth: String(count), type: 'filtering', dataset: 'nested', @@ -228,18 +365,14 @@ export function generateQuestions(): Question[] { } } - // ======================================== - // ANALYTICS DATASET QUESTIONS (40 questions) - // ======================================== - if (analytics.length > 0) { - // Field retrieval: specific dates (20 questions) - for (let i = 0; i < Math.min(20, analytics.length); i++) { + // Field retrieval: specific dates (expanded with all metrics) + for (let i = 0; i < Math.min(QUESTION_LIMITS.analytics.fieldRetrievalDates, analytics.length); i++) { const metric = analytics[i * 3] || analytics[i] if (!metric) continue - if (i % 2 === 0) { + if (i % 5 === 0) { questions.push({ id: `q${idCounter++}`, prompt: `How many views were recorded on ${metric.date}?`, @@ -248,7 +381,7 @@ export function generateQuestions(): Question[] { dataset: 'analytics', }) } - else { + else if (i % 5 === 1) { questions.push({ id: `q${idCounter++}`, prompt: `What was the revenue on ${metric.date}?`, @@ -257,12 +390,42 @@ export function generateQuestions(): Question[] { dataset: 'analytics', }) } + else if (i % 5 === 2) { + questions.push({ + id: `q${idCounter++}`, + prompt: `What was the conversion count on ${metric.date}?`, + groundTruth: String(metric.conversions), + type: 'field-retrieval', + dataset: 'analytics', + }) + } + else if (i % 5 === 3) { + questions.push({ + id: `q${idCounter++}`, + prompt: `How many clicks were recorded on ${metric.date}?`, + groundTruth: String(metric.clicks), + type: 'field-retrieval', + dataset: 'analytics', + }) + } + else { + questions.push({ + id: `q${idCounter++}`, + prompt: `What was the bounce rate on ${metric.date}?`, + groundTruth: String(metric.bounceRate), + type: 'field-retrieval', + dataset: 'analytics', + }) + } } - // Aggregation: totals (4 questions) + // Aggregation: totals and averages const totalViews = analytics.reduce((sum, m) => sum + m.views, 0) const totalRevenue = analytics.reduce((sum, m) => sum + m.revenue, 0) const totalConversions = analytics.reduce((sum, m) => sum + m.conversions, 0) + const avgViews = Math.round(totalViews / analytics.length) + const avgRevenue = totalRevenue / analytics.length + const avgConversions = Math.round(totalConversions / analytics.length) questions.push( { @@ -286,27 +449,97 @@ export function generateQuestions(): Question[] { type: 'aggregation', dataset: 'analytics', }, + { + id: `q${idCounter++}`, + prompt: 'What is the average number of views per day?', + groundTruth: String(avgViews), + type: 'aggregation', + dataset: 'analytics', + }, + { + id: `q${idCounter++}`, + prompt: 'What is the average revenue per day?', + groundTruth: String(avgRevenue.toFixed(2)), + type: 'aggregation', + dataset: 'analytics', + }, + { + id: `q${idCounter++}`, + prompt: 'What is the average number of conversions per day?', + groundTruth: String(avgConversions), + type: 'aggregation', + dataset: 'analytics', + }, + { + id: `q${idCounter++}`, + prompt: 'How many days are included in the analytics data?', + groundTruth: String(analytics.length), + type: 'aggregation', + dataset: 'analytics', + }, + { + id: `q${idCounter++}`, + prompt: 'What is the highest number of views recorded in a single day?', + groundTruth: String(Math.max(...analytics.map(m => m.views))), + type: 'aggregation', + dataset: 'analytics', + }, ) - // Filtering: high-performing days (10 questions) - const viewThresholds = [5000, 6000, 7000] - for (const threshold of viewThresholds) { + // Aggregation: high-performing days (single-condition filters) + for (const threshold of QUESTION_THRESHOLDS.analytics.views) { const count = analytics.filter(m => m.views > threshold).length questions.push({ id: `q${idCounter++}`, prompt: `How many days had more than ${threshold} views?`, groundTruth: String(count), + type: 'aggregation', + dataset: 'analytics', + }) + } + + // Filtering: multi-condition queries (views AND conversions) + for (const viewThreshold of QUESTION_THRESHOLDS.analytics.viewsForFiltering) { + const count = analytics.filter(m => m.views > viewThreshold && m.conversions > QUESTION_THRESHOLDS.analytics.conversionsForFiltering).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many days had more than ${viewThreshold} views and more than ${QUESTION_THRESHOLDS.analytics.conversionsForFiltering} conversions?`, + groundTruth: String(count), type: 'filtering', dataset: 'analytics', }) } - const conversionThresholds = [10, 20, 30] - for (const threshold of conversionThresholds) { - const count = analytics.filter(m => m.conversions > threshold).length + // Filtering: views AND revenue (expanded) + for (const revenueThreshold of QUESTION_THRESHOLDS.analytics.revenueThresholds.slice(0, 5)) { + const count = analytics.filter(m => m.views > QUESTION_THRESHOLDS.analytics.viewsThresholdForRevenue && m.revenue > revenueThreshold).length questions.push({ id: `q${idCounter++}`, - prompt: `How many days had more than ${threshold} conversions?`, + prompt: `How many days had more than ${QUESTION_THRESHOLDS.analytics.viewsThresholdForRevenue} views and revenue greater than ${revenueThreshold}?`, + groundTruth: String(count), + type: 'filtering', + dataset: 'analytics', + }) + } + + // Filtering: clicks AND conversions (multi-condition) + for (const clickThreshold of QUESTION_THRESHOLDS.analytics.clicksForFiltering) { + const count = analytics.filter(m => m.clicks > clickThreshold && m.conversions > QUESTION_THRESHOLDS.analytics.conversionsForClickFiltering).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many days had more than ${clickThreshold} clicks and more than ${QUESTION_THRESHOLDS.analytics.conversionsForClickFiltering} conversions?`, + groundTruth: String(count), + type: 'filtering', + dataset: 'analytics', + }) + } + + // Filtering: revenue AND bounce rate (multi-condition) + for (const revenueThreshold of QUESTION_THRESHOLDS.analytics.revenueForBounceRate) { + const count = analytics.filter(m => m.revenue > revenueThreshold && m.bounceRate < QUESTION_THRESHOLDS.analytics.bounceRateThreshold).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many days had revenue greater than ${revenueThreshold} and bounce rate less than ${QUESTION_THRESHOLDS.analytics.bounceRateThreshold}?`, groundTruth: String(count), type: 'filtering', dataset: 'analytics', @@ -314,79 +547,159 @@ export function generateQuestions(): Question[] { } } - // ======================================== - // GITHUB DATASET QUESTIONS (40 questions) - // ======================================== - if (github.length > 0) { - // Field retrieval: specific repos (20 questions) - for (let i = 0; i < Math.min(20, github.length); i++) { - const repo = github[i * 10] || github[i] + // Helper to extract owner from repo field + const getOwner = (repoFullName: string) => repoFullName.split('/')[0]! + + // Field retrieval: specific repos (diverse fields) + for (let i = 0; i < Math.min(QUESTION_LIMITS.github.fieldRetrievalRepos, github.length); i++) { + const repo = github[i * 7] if (!repo) continue - if (i % 2 === 0) { + if (i % 5 === 0) { questions.push({ id: `q${idCounter++}`, - prompt: `How many stars does ${repo.owner}/${repo.name} have?`, + prompt: `How many stars does ${repo.repo} have?`, groundTruth: String(repo.stars), type: 'field-retrieval', dataset: 'github', }) } + else if (i % 5 === 1) { + questions.push({ + id: `q${idCounter++}`, + prompt: `How many forks does ${repo.repo} have?`, + groundTruth: String(repo.forks), + type: 'field-retrieval', + dataset: 'github', + }) + } + else if (i % 5 === 2) { + questions.push({ + id: `q${idCounter++}`, + prompt: `Who is the owner of ${repo.repo}?`, + groundTruth: getOwner(repo.repo), + type: 'field-retrieval', + dataset: 'github', + }) + } + else if (i % 5 === 3) { + questions.push({ + id: `q${idCounter++}`, + prompt: `What is the default branch of ${repo.repo}?`, + groundTruth: repo.defaultBranch, + type: 'field-retrieval', + dataset: 'github', + }) + } else { questions.push({ id: `q${idCounter++}`, - prompt: `How many forks does ${repo.owner}/${repo.name} have?`, - groundTruth: String(repo.forks), + prompt: `How many watchers does ${repo.repo} have?`, + groundTruth: String(repo.watchers), type: 'field-retrieval', dataset: 'github', }) } } - // Aggregation: count by owner (5 questions) - const owners = [...new Set(github.map(r => r.owner))] - for (const owner of owners.slice(0, 5)) { - const count = github.filter(r => r.owner === owner).length + // Aggregation: popular repositories + const totalStars = github.reduce((sum, r) => sum + r.stars, 0) + const totalRepos = github.length + const avgStars = Math.round(totalStars / totalRepos) + + questions.push( + { + id: `q${idCounter++}`, + prompt: 'What is the total number of stars across all repositories?', + groundTruth: String(totalStars), + type: 'aggregation', + dataset: 'github', + }, + { + id: `q${idCounter++}`, + prompt: 'How many repositories are in the dataset?', + groundTruth: String(totalRepos), + type: 'aggregation', + dataset: 'github', + }, + { + id: `q${idCounter++}`, + prompt: 'What is the average number of stars per repository?', + groundTruth: String(avgStars), + type: 'aggregation', + dataset: 'github', + }, + ) + + // Aggregation: star thresholds (single-condition filters) + for (const threshold of QUESTION_THRESHOLDS.github.stars) { + const count = github.filter(r => r.stars > threshold).length questions.push({ id: `q${idCounter++}`, - prompt: `How many repositories does ${owner} have in the dataset?`, + prompt: `How many repositories have more than ${threshold} stars?`, groundTruth: String(count), type: 'aggregation', dataset: 'github', }) } - // Aggregation: total stars - const totalStars = github.reduce((sum, r) => sum + r.stars, 0) - questions.push({ - id: `q${idCounter++}`, - prompt: 'What is the total number of stars across all repositories?', - groundTruth: String(totalStars), - type: 'aggregation', - dataset: 'github', - }) - - // Filtering: popular repos (8 questions) - const starThresholds = [10000, 50000, 100000] - for (const threshold of starThresholds) { - const count = github.filter(r => r.stars > threshold).length + // Aggregation: fork thresholds (single-condition filters) + for (const threshold of QUESTION_THRESHOLDS.github.forks) { + const count = github.filter(r => r.forks > threshold).length questions.push({ id: `q${idCounter++}`, - prompt: `How many repositories have more than ${threshold} stars?`, + prompt: `How many repositories have more than ${threshold} forks?`, + groundTruth: String(count), + type: 'aggregation', + dataset: 'github', + }) + } + + // Aggregation: watcher thresholds (single-condition filters) + for (const threshold of QUESTION_THRESHOLDS.github.watchers) { + const count = github.filter(r => r.watchers > threshold).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many repositories have more than ${threshold} watchers?`, + groundTruth: String(count), + type: 'aggregation', + dataset: 'github', + }) + } + + // Aggregation: default branch counts + const branches = [...new Set(github.map(r => r.defaultBranch))] + for (const branch of branches.slice(0, QUESTION_LIMITS.github.aggregationBranches)) { + const count = github.filter(r => r.defaultBranch === branch).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many repositories use "${branch}" as their default branch?`, + groundTruth: String(count), + type: 'aggregation', + dataset: 'github', + }) + } + + // Filtering: multi-condition queries (stars AND forks) + for (const combo of QUESTION_THRESHOLDS.github.starForkCombinations.slice(0, QUESTION_LIMITS.github.filteringStarsAndForks)) { + const count = github.filter(r => r.stars > combo.stars && r.forks > combo.forks).length + questions.push({ + id: `q${idCounter++}`, + prompt: `How many repositories have more than ${combo.stars} stars and more than ${combo.forks} forks?`, groundTruth: String(count), type: 'filtering', dataset: 'github', }) } - const forkThresholds = [1000, 5000, 10000] - for (const threshold of forkThresholds) { - const count = github.filter(r => r.forks > threshold).length + // Filtering: stars AND watchers (multi-condition) + for (const combo of QUESTION_THRESHOLDS.github.starWatcherCombinations) { + const count = github.filter(r => r.stars > combo.stars && r.watchers > combo.watchers).length questions.push({ id: `q${idCounter++}`, - prompt: `How many repositories have more than ${threshold} forks?`, + prompt: `How many repositories have more than ${combo.stars} stars and more than ${combo.watchers} watchers?`, groundTruth: String(count), type: 'filtering', dataset: 'github', @@ -394,14 +707,5 @@ export function generateQuestions(): Question[] { } } - consola.info(`Question breakdown:`) - consola.box(` -Tabular: ${questions.filter(q => q.dataset === 'tabular').length} -Nested: ${questions.filter(q => q.dataset === 'nested').length} -Analytics: ${questions.filter(q => q.dataset === 'analytics').length} -GitHub: ${questions.filter(q => q.dataset === 'github').length} -Total: ${questions.length} -`.trim()) - return questions } diff --git a/benchmarks/src/report.ts b/benchmarks/src/report.ts index dbc5987..845d048 100644 --- a/benchmarks/src/report.ts +++ b/benchmarks/src/report.ts @@ -1,21 +1,9 @@ -/** - * Report generation for TOON benchmarks - * - * Handles: - * - Statistical analysis - * - Markdown report generation with visual elements - * - Per-dataset breakdowns - * - Cost analysis - * - Result file saving - */ - import type { EvaluationResult, FormatResult, Question } from './types' import * as fsp from 'node:fs/promises' import * as path from 'node:path' import { BENCHMARKS_DIR } from './constants' import { datasets } from './datasets' -import { models } from './evaluate' -import { createProgressBar, ensureDir, saveJsonFile, tokenize } from './utils' +import { createProgressBar, ensureDir, tokenize } from './utils' /** * Calculate per-format statistics from evaluation results @@ -63,8 +51,8 @@ export function generateMarkdownReport( const json = formatResults.find(r => r.format === 'json') // Build model-by-model breakdown with ASCII bars - const modelCount = Object.keys(models).length - const modelNames = Object.keys(models) + const modelNames = [...new Set(results.map(r => r.model))].reverse() + const modelCount = modelNames.length const modelBreakdown = modelNames.map((modelName, i) => { const modelResults = formatResults.map((fr) => { @@ -136,7 +124,7 @@ export function generateMarkdownReport( }) const tableRows = datasetResults.slice(0, 6).map(result => - `| \`${result.format}\` | ${(result.accuracy * 100).toFixed(1)}% | ${result.tokens.toLocaleString()} | ${result.correctCount}/${result.totalCount} |`, + `| \`${result.format}\` | ${(result.accuracy * 100).toFixed(1)}% | ${result.tokens.toLocaleString('en-US')} | ${result.correctCount}/${result.totalCount} |`, ).join('\n') return ` @@ -180,6 +168,27 @@ ${tableRows} // Calculate total unique questions const totalQuestions = [...new Set(results.map(r => r.questionId))].length + // Calculate question type distribution + const fieldRetrievalCount = questions.filter(q => q.type === 'field-retrieval').length + const aggregationCount = questions.filter(q => q.type === 'aggregation').length + const filteringCount = questions.filter(q => q.type === 'filtering').length + + const fieldRetrievalPercent = ((fieldRetrievalCount / totalQuestions) * 100).toFixed(0) + const aggregationPercent = ((aggregationCount / totalQuestions) * 100).toFixed(0) + const filteringPercent = ((filteringCount / totalQuestions) * 100).toFixed(0) + + // Calculate dataset sizes + const tabularSize = datasets.find(d => d.name === 'tabular')?.data.employees?.length || 0 + const nestedSize = datasets.find(d => d.name === 'nested')?.data.orders?.length || 0 + const analyticsSize = datasets.find(d => d.name === 'analytics')?.data.metrics?.length || 0 + const githubSize = datasets.find(d => d.name === 'github')?.data.repositories?.length || 0 + + // Calculate number of formats and models + const formatCount = formatResults.length + const modelsUsed = [...new Set(results.map(r => r.model))] + const modelsListStr = modelsUsed.map(m => `\`${m}\``).join(', ') + const totalEvaluations = totalQuestions * formatCount * modelsUsed.length + return ` ### Retrieval Accuracy @@ -213,39 +222,41 @@ This benchmark tests **LLM comprehension and data retrieval accuracy** across di Four datasets designed to test different structural patterns: -1. **Tabular** (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format. -2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays. -3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values. -4. **GitHub** (100 repositories): Real-world data from top GitHub repos by stars. +1. **Tabular** (${tabularSize} employee records): Uniform objects with identical fields – optimal for TOON's tabular format. +2. **Nested** (${nestedSize} e-commerce orders): Complex structures with nested customer objects and item arrays. +3. **Analytics** (${analyticsSize} days of metrics): Time-series data with dates and numeric values. +4. **GitHub** (${githubSize} repositories): Real-world data from top GitHub repos by stars. #### Question Types ${totalQuestions} questions are generated dynamically across three categories: -- **Field retrieval (50%)**: Direct value lookups +\- **Field retrieval (${fieldRetrievalPercent}%)**: Direct value lookups or values that can be read straight off a record (including booleans and simple counts such as array lengths) - Example: "What is Alice's salary?" → \`75000\` + - Example: "How many items are in order ORD-0042?" → \`3\` - Example: "What is the customer name for order ORD-0042?" → \`John Doe\` -- **Aggregation (25%)**: Counting and summation tasks +- **Aggregation (${aggregationPercent}%)**: Dataset-level totals and averages plus single-condition filters (counts, sums, min/max comparisons) - Example: "How many employees work in Engineering?" → \`17\` - Example: "What is the total revenue across all orders?" → \`45123.50\` + - Example: "How many employees have salary > 80000?" → \`23\` -- **Filtering (25%)**: Conditional queries +- **Filtering (${filteringPercent}%)**: Multi-condition queries requiring compound logic (AND constraints across fields) - Example: "How many employees in Sales have salary > 80000?" → \`5\` - - Example: "How many orders have total > 400?" → \`12\` + - Example: "How many active employees have more than 10 years of experience?" → \`8\` #### Evaluation Process -1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, JSON, YAML, CSV, XML). +1. **Format conversion:** Each dataset is converted to all ${formatCount} formats (${formatResults.map(f => f.format.toUpperCase()).join(', ')}). 2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer. -4. **Validate with LLM-as-judge**: \`gpt-5-nano\` validates if the answer is semantically correct (e.g., \`50000\` = \`$50,000\`, \`Engineering\` = \`engineering\`, \`2025-01-01\` = \`January 1, 2025\`). +3. **Validate with LLM-as-judge**: \`gpt-5-nano\` validates if the answer is semantically correct (e.g., \`50000\` = \`$50,000\`, \`Engineering\` = \`engineering\`, \`2025-01-01\` = \`January 1, 2025\`). #### Models & Configuration -- **Models tested**: \`gpt-5-nano\`, \`claude-haiku-4-5\`, \`gemini-2.5-flash\` +- **Models tested**: ${modelsListStr} - **Token counting**: Using \`gpt-tokenizer\` with \`o200k_base\` encoding (GPT-5 tokenizer) - **Temperature**: 0 (for non-reasoning models) -- **Total evaluations**: 159 questions × 5 formats × 3 models = 2,385 LLM calls +- **Total evaluations**: ${totalQuestions} questions × ${formatCount} formats × ${modelsUsed.length} models = ${totalEvaluations.toLocaleString('en-US')} LLM calls `.trimStart() @@ -272,6 +283,10 @@ export function calculateTokenCounts( /** * Save results to disk + * + * @remarks + * Per-model results are managed separately via storage.ts + * This function only generates the aggregated markdown report */ export async function saveResults( results: EvaluationResult[], @@ -279,31 +294,12 @@ export async function saveResults( questions: Question[], tokenCounts: Record, ): Promise { - const resultsDir = path.join(BENCHMARKS_DIR, 'results', 'accuracy') + const resultsDir = path.join(BENCHMARKS_DIR, 'results') await ensureDir(resultsDir) - // Save raw results - await saveJsonFile(path.join(resultsDir, 'raw-results.json'), results) - - // Save summary - await saveJsonFile( - path.join(resultsDir, 'summary.json'), - { - formatResults, - questions: questions.length, - models: Object.keys(models), - datasets: datasets.map(d => ({ name: d.name, description: d.description })), - tokenCounts, - timestamp: new Date().toISOString(), - }, - ) - - // Generate markdown report + // Generate markdown report from all available model results const report = generateMarkdownReport(formatResults, results, questions, tokenCounts) - await fsp.writeFile( - path.join(resultsDir, 'report.md'), - report, - ) + await fsp.writeFile(path.join(resultsDir, 'retrieval-accuracy.md'), report) return resultsDir } diff --git a/benchmarks/src/storage.ts b/benchmarks/src/storage.ts new file mode 100644 index 0000000..aab9287 --- /dev/null +++ b/benchmarks/src/storage.ts @@ -0,0 +1,46 @@ +import type { Storage, StorageValue } from 'unstorage' +import type { EvaluationResult } from './types' +import * as path from 'node:path' +import { createStorage } from 'unstorage' +import fsDriver from 'unstorage/drivers/fs' +import { BENCHMARKS_DIR } from './constants' + +/** + * Storage instance for model results + * + * @remarks + * Stores results in: `benchmarks/results/accuracy/models/` + */ +export const resultsStorage: Storage = createStorage({ + driver: fsDriver({ + base: path.join(BENCHMARKS_DIR, 'results', 'accuracy', 'models'), + }), +}) + +export async function loadModelResults(modelId: string): Promise { + const data = await resultsStorage.getItem(modelId) + return data ?? undefined +} + +export async function saveModelResults(modelId: string, results: EvaluationResult[]): Promise { + await resultsStorage.setItem(modelId, results) +} + +export async function getAllModelResults(): Promise> { + const keys = await resultsStorage.getKeys() + const results: Record = {} + + await Promise.all( + keys.map(async (modelId) => { + const data = await resultsStorage.getItem(modelId) + if (data) + results[modelId] = data + }), + ) + + return results +} + +export async function hasModelResults(modelId: string): Promise { + return await resultsStorage.hasItem(modelId) +} diff --git a/benchmarks/src/utils.ts b/benchmarks/src/utils.ts index 3b0a735..bbd44c0 100644 --- a/benchmarks/src/utils.ts +++ b/benchmarks/src/utils.ts @@ -1,13 +1,3 @@ -/** - * Shared utility functions for TOON benchmarks - * - * Provides common functionality used across multiple benchmark scripts: - * - Progress bar visualization - * - Token counting - * - File I/O operations - * - Retry logic for API calls - */ - import * as fsp from 'node:fs/promises' import { encode } from 'gpt-tokenizer' diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index a645f96..2363e29 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -56,15 +56,15 @@ importers: '@antfu/eslint-config': specifier: ^6.1.0 version: 6.1.0(@vue/compiler-sfc@3.5.22)(eslint@9.38.0(jiti@2.6.1))(typescript@5.9.3)(vitest@4.0.3(@types/debug@4.1.12)(@types/node@24.9.1)(jiti@2.6.1)(tsx@4.20.6)(yaml@2.8.1)) + '@clack/prompts': + specifier: ^0.11.0 + version: 0.11.0 '@faker-js/faker': specifier: ^10.1.0 version: 10.1.0 ai: specifier: ^5.0.80 version: 5.0.80(zod@4.1.12) - consola: - specifier: ^3.4.2 - version: 3.4.2 csv-stringify: specifier: ^6.6.0 version: 6.6.0 @@ -80,6 +80,12 @@ importers: p-map: specifier: ^7.0.3 version: 7.0.3 + p-queue: + specifier: ^9.0.0 + version: 9.0.0 + unstorage: + specifier: ^1.17.1 + version: 1.17.1 yaml: specifier: ^2.8.1 version: 2.8.1 @@ -985,6 +991,10 @@ packages: resolution: {integrity: 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'@capacitor/preferences': + optional: true + '@deno/kv': + optional: true + '@netlify/blobs': + optional: true + '@planetscale/database': + optional: true + '@upstash/redis': + optional: true + '@vercel/blob': + optional: true + '@vercel/functions': + optional: true + '@vercel/kv': + optional: true + aws4fetch: + optional: true + db0: + optional: true + idb-keyval: + optional: true + ioredis: + optional: true + uploadthing: + optional: true + untyped@2.0.0: resolution: {integrity: sha512-nwNCjxJTjNuLCgFr42fEak5OcLuB3ecca+9ksPFNvtfYSLpjf+iJqSIaSnIile6ZPbKYxI5k2AfXqeopGudK/g==} hasBin: true @@ -3143,6 +3254,11 @@ snapshots: ansis@4.2.0: {} + anymatch@3.1.3: + dependencies: + normalize-path: 3.0.0 + picomatch: 2.3.1 + are-docs-informative@0.0.2: {} argparse@2.0.1: {} @@ -3289,6 +3405,8 @@ snapshots: consola@3.4.2: {} + cookie-es@1.2.2: {} + core-js-compat@3.46.0: dependencies: browserslist: 4.27.0 @@ -3299,6 +3417,10 @@ snapshots: shebang-command: 2.0.0 which: 2.0.2 + crossws@0.3.5: + 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