Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
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9d05f77a9b | ||
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23e093fc75 | ||
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f77ce4dff2 |
@@ -1,194 +1,757 @@
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[
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||||
{
|
||||
"id": "screenshot_github_home",
|
||||
"id": "screenshot_github_mark",
|
||||
"url": "https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": ["github", "logo", "mark"],
|
||||
"expected_keywords": [
|
||||
"github",
|
||||
"logo",
|
||||
"mark"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
"expected_structure": {
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||||
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|
||||
"min_sentences": 1,
|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "diagram_mermaid_flow",
|
||||
"url": "https://mermaid.ink/img/pako:eNpdkE9PwzAMxb-K5VOl7gc7sAOIIDuAw9gptnRaSJLSJttQStmXs9LCH-ymBOI1ef_42U6cUSae4IkDxbAAWtB6siSZXVhjQTlgl1nigHg5fRBOzSfebopROCu_cytObSfgLSE1ANOeZWkO2IH5upZxYot8m1hqAdpD_63WRl0xdUG1jdl9kPiOb_EWk2JBtPaiKkF4eVIYgO0EtkW-RSgC4gJ6HJYRG1UNdN0HNVd0Bftjj7X8P92qPj-F8l8T3w",
|
||||
"id": "screenshot_github_social",
|
||||
"url": "https://github.githubassets.com/images/modules/site/social-cards.png",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": [
|
||||
"github",
|
||||
"page",
|
||||
"web"
|
||||
],
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||||
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|
||||
"expected_structure": {
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||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "screenshot_github_code_search",
|
||||
"url": "https://github.githubassets.com/images/modules/site/features-code-search.png",
|
||||
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|
||||
"expected_keywords": [
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_terminal_capture",
|
||||
"url": "https://raw.githubusercontent.com/nicehash/nicehash-quick-start/main/images/nicehash-terminal.png",
|
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"category": "screenshot",
|
||||
"expected_keywords": [
|
||||
"terminal",
|
||||
"command",
|
||||
"output"
|
||||
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||||
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||||
"expected_structure": {
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||||
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||||
"min_sentences": 1,
|
||||
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|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_http_404",
|
||||
"url": "https://http.cat/404.jpg",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": [
|
||||
"404",
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||||
"error",
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||||
"cat"
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||||
],
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||||
"ground_truth_ocr": "",
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||||
"expected_structure": {
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||||
"min_length": 30,
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||||
"min_sentences": 1,
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||||
"has_numbers": false
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||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_dummy_cli_01",
|
||||
"url": "https://dummyimage.com/1280x720/111827/f9fafb.png&text=Hermes+CLI+Session+01",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": [
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
"expected_structure": {
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||||
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||||
"min_sentences": 1,
|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_dummy_cli_02",
|
||||
"url": "https://dummyimage.com/1280x720/0f172a/e2e8f0.png&text=Prompt+Cache+Dashboard",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": [
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||||
"prompt",
|
||||
"cache",
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||||
"dashboard"
|
||||
],
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||||
"ground_truth_ocr": "",
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||||
"expected_structure": {
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"min_length": 30,
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||||
"min_sentences": 1,
|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_dummy_ui_01",
|
||||
"url": "https://dummyimage.com/1280x720/1f2937/f3f4f6.png&text=Settings+Panel+Voice+Mode",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": [
|
||||
"settings",
|
||||
"voice",
|
||||
"mode"
|
||||
],
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||||
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||||
"expected_structure": {
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||||
"min_length": 30,
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||||
"min_sentences": 1,
|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_dummy_ui_02",
|
||||
"url": "https://dummyimage.com/1280x720/334155/f8fafc.png&text=Browser+Vision+Preview",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": [
|
||||
"browser",
|
||||
"vision",
|
||||
"preview"
|
||||
],
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||||
"ground_truth_ocr": "",
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||||
"expected_structure": {
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||||
"min_length": 30,
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||||
"min_sentences": 1,
|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_dummy_ui_03",
|
||||
"url": "https://dummyimage.com/1280x720/111827/ffffff.png&text=Tool+Call+Inspector",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": [
|
||||
"tool",
|
||||
"call",
|
||||
"inspector"
|
||||
],
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||||
"ground_truth_ocr": "",
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"expected_structure": {
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||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "diagram_flow_a",
|
||||
"url": "https://dummyimage.com/1200x800/f8fafc/0f172a.png&text=Flowchart+API+Gateway+Queue+Worker",
|
||||
"category": "diagram",
|
||||
"expected_keywords": ["flow", "diagram", "process"],
|
||||
"expected_keywords": [
|
||||
"flowchart",
|
||||
"api",
|
||||
"worker"
|
||||
],
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||||
"ground_truth_ocr": "",
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||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": false}
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||||
"expected_structure": {
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||||
"min_length": 50,
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||||
"min_sentences": 2,
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||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_1",
|
||||
"url": "https://picsum.photos/seed/vision1/400/300",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"id": "diagram_flow_b",
|
||||
"url": "https://dummyimage.com/1200x800/f1f5f9/0f172a.png&text=Architecture+Diagram+Database+Cache+Client",
|
||||
"category": "diagram",
|
||||
"expected_keywords": [
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||||
"architecture",
|
||||
"diagram",
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||||
"cache"
|
||||
],
|
||||
"ground_truth_ocr": "",
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||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
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||||
"expected_structure": {
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||||
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||||
"min_sentences": 2,
|
||||
"has_numbers": false
|
||||
}
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||||
},
|
||||
{
|
||||
"id": "photo_random_2",
|
||||
"url": "https://picsum.photos/seed/vision2/400/300",
|
||||
"category": "photo",
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||||
"expected_keywords": [],
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||||
"id": "diagram_uml_a",
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||||
"url": "https://dummyimage.com/1200x800/e2e8f0/0f172a.png&text=Class+Diagram+User+Session+Message",
|
||||
"category": "diagram",
|
||||
"expected_keywords": [
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||||
"class",
|
||||
"diagram",
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||||
"session"
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||||
],
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||||
"ground_truth_ocr": "",
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||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
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"expected_structure": {
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||||
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||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_simple_bar",
|
||||
"url": "https://quickchart.io/chart?c={type:'bar',data:{labels:['Q1','Q2','Q3','Q4'],datasets:[{label:'Revenue',data:[100,150,200,250]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": ["bar", "chart", "revenue"],
|
||||
"id": "diagram_uml_b",
|
||||
"url": "https://dummyimage.com/1200x800/cbd5e1/0f172a.png&text=Sequence+Diagram+Request+Response",
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||||
"category": "diagram",
|
||||
"expected_keywords": [
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||||
"sequence",
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||||
"diagram",
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||||
"response"
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||||
],
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||||
"ground_truth_ocr": "",
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||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": true}
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||||
"expected_structure": {
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||||
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||||
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||||
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||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_pie",
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||||
"url": "https://quickchart.io/chart?c={type:'pie',data:{labels:['A','B','C'],datasets:[{data:[30,50,20]}]}}",
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||||
"category": "chart",
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||||
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||||
"id": "diagram_network_a",
|
||||
"url": "https://dummyimage.com/1200x800/ffffff/111827.png&text=Network+Nodes+Edges+Router",
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||||
"category": "diagram",
|
||||
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||||
"network",
|
||||
"node",
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||||
"router"
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||||
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||||
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||||
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||||
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||||
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||||
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||||
}
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||||
},
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||||
{
|
||||
"id": "diagram_network_b",
|
||||
"url": "https://dummyimage.com/1200x800/ffffff/1e293b.png&text=Service+Mesh+Proxy+Auth",
|
||||
"category": "diagram",
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||||
"expected_keywords": [
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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{
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"id": "diagram_state_machine",
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
},
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||||
{
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||||
"id": "diagram_mind_map",
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||||
"url": "https://dummyimage.com/1200x800/fefce8/1f2937.png&text=Mind+Map+Memory+Recall+Tools",
|
||||
"category": "diagram",
|
||||
"expected_keywords": [
|
||||
"mind",
|
||||
"memory",
|
||||
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|
||||
],
|
||||
"ground_truth_ocr": "",
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||||
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||||
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||||
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||||
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||||
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||||
},
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||||
{
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||||
"id": "diagram_pipeline",
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||||
"url": "https://dummyimage.com/1200x800/ecfeff/155e75.png&text=Pipeline+Ingest+Rank+Summarize",
|
||||
"category": "diagram",
|
||||
"expected_keywords": [
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
{
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||||
"id": "diagram_org_chart",
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"id": "screenshot_terminal",
|
||||
"url": "https://raw.githubusercontent.com/nicehash/nicehash-quick-start/main/images/nicehash-terminal.png",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": ["terminal", "command", "output"],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_3",
|
||||
"url": "https://picsum.photos/seed/vision3/400/300",
|
||||
"id": "photo_random_01",
|
||||
"url": "https://picsum.photos/seed/vision-bench-1/640/480",
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
"id": "photo_random_02",
|
||||
"url": "https://picsum.photos/seed/vision-bench-2/640/480",
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
{
|
||||
"id": "photo_random_03",
|
||||
"url": "https://picsum.photos/seed/vision-bench-3/640/480",
|
||||
"category": "photo",
|
||||
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_04",
|
||||
"url": "https://picsum.photos/seed/vision-bench-4/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
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|
||||
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||||
},
|
||||
{
|
||||
"id": "photo_random_05",
|
||||
"url": "https://picsum.photos/seed/vision-bench-5/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
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|
||||
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|
||||
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|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_06",
|
||||
"url": "https://picsum.photos/seed/vision-bench-6/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
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"expected_structure": {
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|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_07",
|
||||
"url": "https://picsum.photos/seed/vision-bench-7/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
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||||
"expected_structure": {
|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_08",
|
||||
"url": "https://picsum.photos/seed/vision-bench-8/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {
|
||||
"min_length": 30,
|
||||
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|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_09",
|
||||
"url": "https://picsum.photos/seed/vision-bench-9/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {
|
||||
"min_length": 30,
|
||||
"min_sentences": 1,
|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_10",
|
||||
"url": "https://picsum.photos/seed/vision-bench-10/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {
|
||||
"min_length": 30,
|
||||
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|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_bar_quarterly",
|
||||
"url": "https://quickchart.io/chart?c={type:'bar',data:{labels:['Q1','Q2','Q3','Q4'],datasets:[{label:'Revenue',data:[100,150,200,250]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": [
|
||||
"bar",
|
||||
"chart",
|
||||
"revenue"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
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|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_pie_market",
|
||||
"url": "https://quickchart.io/chart?c={type:'pie',data:{labels:['A','B','C'],datasets:[{data:[30,50,20]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": [
|
||||
"pie",
|
||||
"chart",
|
||||
"percentage"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
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|
||||
"min_length": 50,
|
||||
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|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_line_temp",
|
||||
"url": "https://quickchart.io/chart?c={type:'line',data:{labels:['Jan','Feb','Mar','Apr'],datasets:[{label:'Temperature',data:[5,8,12,18]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": ["line", "chart", "temperature"],
|
||||
"expected_keywords": [
|
||||
"line",
|
||||
"chart",
|
||||
"temperature"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
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|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
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|
||||
"has_numbers": true
|
||||
}
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||||
},
|
||||
{
|
||||
"id": "diagram_sequence",
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||||
"url": "https://mermaid.ink/img/pako:eNpdkE9PwzAMxb-K5VOl7gc7sAOIIDuAw9gptnRaSJLSJttQStmXs9LCH-ymBOI1ef_42U6cUSae4IkDxbAAWtB6iuyIWyrLgXLALrPEAfFy-iCcmk-83RSjcFZ-51ac2k7AW0JqAKY9y9IcsAPzdS3jxBb5NrHUAraH_lutjbpi6oJqG7P7IPEd3-ItJsWCaO1FVYLw8qQwANsJbIt8i1AExAX0OCwjNqoa6LoPaq7oCvbHHmv5f7pVfX4K5b8mvg",
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||||
"category": "diagram",
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||||
"expected_keywords": ["sequence", "interaction", "message"],
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||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_4",
|
||||
"url": "https://picsum.photos/seed/vision4/400/300",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_webpage",
|
||||
"url": "https://github.githubassets.com/images/modules/site/social-cards.png",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": ["github", "page", "web"],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "chart_radar",
|
||||
"id": "chart_radar_skill",
|
||||
"url": "https://quickchart.io/chart?c={type:'radar',data:{labels:['Speed','Power','Defense','Magic'],datasets:[{label:'Hero',data:[80,60,70,90]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": ["radar", "chart", "skill"],
|
||||
"expected_keywords": [
|
||||
"radar",
|
||||
"chart",
|
||||
"skill"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": true}
|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
"min_sentences": 2,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_5",
|
||||
"url": "https://picsum.photos/seed/vision5/400/300",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "diagram_class",
|
||||
"url": "https://mermaid.ink/img/pako:eNpdkE9PwzAMxb-K5VOl7gc7sAOIIDuAw9gptnRaSJLSJttQStmXs9LCH-ymBOI1ef_42U6cUSae4IkDxbAAWtB6iuyIWyrLgXLALrPEAfFy-iCcmk-83RSjcFZ-51ac2k7AW0JqAKY9y9IcsAPzdS3jxBb5NrHUAraH_lutjbpi6oJqG7P7IPEd3-ItJsWCaO1FVYLw8qQwANsJbIt8i1AExAX0OCwjNqoa6LoPaq7oCvbHHmv5f7pVfX4K5b8mvg",
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||||
"category": "diagram",
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||||
"expected_keywords": ["class", "object", "attribute"],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "chart_doughnut",
|
||||
"url": "https://quickchart.io/chart?c={type:'doughnut',data:{labels:['Desktop','Mobile','Tablet'],datasets:[{data:[60,30,10]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": ["doughnut", "chart", "device"],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": true}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_6",
|
||||
"url": "https://picsum.photos/seed/vision6/400/300",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_error",
|
||||
"url": "https://http.cat/404.jpg",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": ["404", "error", "cat"],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": true}
|
||||
},
|
||||
{
|
||||
"id": "diagram_network",
|
||||
"url": "https://mermaid.ink/img/pako:eNpdkE9PwzAMxb-K5VOl7gc7sAOIIDuAw9gptnRaSJLSJttQStmXs9LCH-ymBOI1ef_42U6cUSae4IkDxbAAWtB6iuyIWyrLgXLALrPEAfFy-iCcmk-83RSjcFZ-51ac2k7AW0JqAKY9y9IcsAPzdS3jxBb5NrHUAraH_lutjbpi6oJqG7P7IPEd3-ItJsWCaO1FVYLw8qQwANsJbIt8i1AExAX0OCwjNqoa6LoPaq7oCvbHHmv5f7pVfX4K5b8mvg",
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||||
"category": "diagram",
|
||||
"expected_keywords": ["network", "node", "connection"],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_7",
|
||||
"url": "https://picsum.photos/seed/vision7/400/300",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
},
|
||||
{
|
||||
"id": "chart_stacked_bar",
|
||||
"id": "chart_stacked_cloud",
|
||||
"url": "https://quickchart.io/chart?c={type:'bar',data:{labels:['2022','2023','2024'],datasets:[{label:'Cloud',data:[100,150,200]},{label:'On-prem',data:[200,180,160]}]},options:{scales:{x:{stacked:true},y:{stacked:true}}}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": ["stacked", "bar", "chart"],
|
||||
"expected_keywords": [
|
||||
"stacked",
|
||||
"bar",
|
||||
"chart"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": true}
|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
"min_sentences": 2,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "screenshot_dashboard",
|
||||
"url": "https://github.githubassets.com/images/modules/site/features-code-search.png",
|
||||
"category": "screenshot",
|
||||
"expected_keywords": ["search", "code", "feature"],
|
||||
"id": "chart_area_growth",
|
||||
"url": "https://quickchart.io/chart?c={type:'line',data:{labels:['W1','W2','W3','W4'],datasets:[{label:'Growth',data:[10,15,18,24],fill:true}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": [
|
||||
"line",
|
||||
"growth",
|
||||
"chart"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
"min_sentences": 2,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "photo_random_8",
|
||||
"url": "https://picsum.photos/seed/vision8/400/300",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"id": "chart_scatter_eval",
|
||||
"url": "https://quickchart.io/chart?c={type:'scatter',data:{datasets:[{label:'Runs',data:[{x:1,y:70},{x:2,y:75},{x:3,y:82}]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": [
|
||||
"scatter",
|
||||
"chart",
|
||||
"runs"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": false}
|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
"min_sentences": 2,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_horizontal_bar",
|
||||
"url": "https://quickchart.io/chart?c={type:'bar',data:{labels:['UI','OCR','Docs'],datasets:[{label:'Score',data:[88,76,91]}]},options:{indexAxis:'y'}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": [
|
||||
"bar",
|
||||
"score",
|
||||
"ocr"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
"min_sentences": 2,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_bubble_usage",
|
||||
"url": "https://quickchart.io/chart?c={type:'bubble',data:{datasets:[{label:'Latency',data:[{x:1,y:120,r:8},{x:2,y:95,r:6},{x:3,y:180,r:10}]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": [
|
||||
"bubble",
|
||||
"latency",
|
||||
"chart"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {
|
||||
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|
||||
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|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "chart_doughnut_devices",
|
||||
"url": "https://quickchart.io/chart?c={type:'doughnut',data:{labels:['Desktop','Mobile','Tablet'],datasets:[{data:[60,30,10]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": [
|
||||
"doughnut",
|
||||
"chart",
|
||||
"device"
|
||||
],
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {
|
||||
"min_length": 50,
|
||||
"min_sentences": 2,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ocr_text_01",
|
||||
"url": "https://dummyimage.com/1200x320/ffffff/000000.png&text=Hermes+OCR+Alpha+01",
|
||||
"category": "ocr",
|
||||
"expected_keywords": [
|
||||
"hermes",
|
||||
"ocr"
|
||||
],
|
||||
"ground_truth_ocr": "Hermes OCR Alpha 01",
|
||||
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|
||||
"min_length": 10,
|
||||
"min_sentences": 1,
|
||||
"has_numbers": true
|
||||
}
|
||||
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|
||||
{
|
||||
"id": "ocr_text_02",
|
||||
"url": "https://dummyimage.com/1200x320/ffffff/000000.png&text=Prompt+Cache+Hit+87%",
|
||||
"category": "ocr",
|
||||
"expected_keywords": [
|
||||
"prompt",
|
||||
"cache"
|
||||
],
|
||||
"ground_truth_ocr": "Prompt Cache Hit 87%",
|
||||
"expected_structure": {
|
||||
"min_length": 10,
|
||||
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|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ocr_text_03",
|
||||
"url": "https://dummyimage.com/1200x320/ffffff/000000.png&text=Session+42+Ready",
|
||||
"category": "ocr",
|
||||
"expected_keywords": [
|
||||
"session",
|
||||
"42"
|
||||
],
|
||||
"ground_truth_ocr": "Session 42 Ready",
|
||||
"expected_structure": {
|
||||
"min_length": 10,
|
||||
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|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ocr_text_04",
|
||||
"url": "https://dummyimage.com/1200x320/ffffff/000000.png&text=Latency+118+ms",
|
||||
"category": "ocr",
|
||||
"expected_keywords": [
|
||||
"latency",
|
||||
"118"
|
||||
],
|
||||
"ground_truth_ocr": "Latency 118 ms",
|
||||
"expected_structure": {
|
||||
"min_length": 10,
|
||||
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|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "ocr_text_05",
|
||||
"url": "https://dummyimage.com/1200x320/ffffff/000000.png&text=Voice+Mode+Enabled",
|
||||
"category": "ocr",
|
||||
"expected_keywords": [
|
||||
"voice",
|
||||
"mode"
|
||||
],
|
||||
"ground_truth_ocr": "Voice Mode Enabled",
|
||||
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|
||||
"min_length": 10,
|
||||
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|
||||
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|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "document_text_01",
|
||||
"url": "https://dummyimage.com/1400x900/f8fafc/0f172a.png&text=Invoice+1001+Total+42+Due+2026-04-22",
|
||||
"category": "document",
|
||||
"expected_keywords": [
|
||||
"invoice",
|
||||
"1001",
|
||||
"total"
|
||||
],
|
||||
"ground_truth_ocr": "Invoice 1001 Total 42 Due 2026-04-22",
|
||||
"expected_structure": {
|
||||
"min_length": 20,
|
||||
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|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "document_text_02",
|
||||
"url": "https://dummyimage.com/1400x900/f8fafc/0f172a.png&text=Form+A+Name+Alice+Status+Approved",
|
||||
"category": "document",
|
||||
"expected_keywords": [
|
||||
"form",
|
||||
"a",
|
||||
"name"
|
||||
],
|
||||
"ground_truth_ocr": "Form A Name Alice Status Approved",
|
||||
"expected_structure": {
|
||||
"min_length": 20,
|
||||
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|
||||
"has_numbers": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "document_text_03",
|
||||
"url": "https://dummyimage.com/1400x900/f8fafc/0f172a.png&text=Report+Memory+Recall+Score+91+Percent",
|
||||
"category": "document",
|
||||
"expected_keywords": [
|
||||
"report",
|
||||
"memory",
|
||||
"recall"
|
||||
],
|
||||
"ground_truth_ocr": "Report Memory Recall Score 91 Percent",
|
||||
"expected_structure": {
|
||||
"min_length": 20,
|
||||
"min_sentences": 1,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "document_text_04",
|
||||
"url": "https://dummyimage.com/1400x900/f8fafc/0f172a.png&text=Checklist+Crisis+Escalation+Call+988+Now",
|
||||
"category": "document",
|
||||
"expected_keywords": [
|
||||
"checklist",
|
||||
"crisis",
|
||||
"escalation"
|
||||
],
|
||||
"ground_truth_ocr": "Checklist Crisis Escalation Call 988 Now",
|
||||
"expected_structure": {
|
||||
"min_length": 20,
|
||||
"min_sentences": 1,
|
||||
"has_numbers": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": "document_text_05",
|
||||
"url": "https://dummyimage.com/1400x900/f8fafc/0f172a.png&text=Meeting+Notes+Vision+Benchmark+Run+Pending",
|
||||
"category": "document",
|
||||
"expected_keywords": [
|
||||
"meeting",
|
||||
"notes",
|
||||
"vision"
|
||||
],
|
||||
"ground_truth_ocr": "Meeting Notes Vision Benchmark Run Pending",
|
||||
"expected_structure": {
|
||||
"min_length": 20,
|
||||
"min_sentences": 1,
|
||||
"has_numbers": false
|
||||
}
|
||||
}
|
||||
]
|
||||
]
|
||||
@@ -22,10 +22,12 @@ import argparse
|
||||
import asyncio
|
||||
import base64
|
||||
import json
|
||||
import mimetypes
|
||||
import os
|
||||
import statistics
|
||||
import sys
|
||||
import time
|
||||
import urllib.request
|
||||
from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, List, Optional
|
||||
@@ -41,12 +43,16 @@ MODELS = {
|
||||
"model_id": "google/gemma-4-27b-it",
|
||||
"display_name": "Gemma 4 27B",
|
||||
"provider": "nous",
|
||||
"fallback_provider": "ollama",
|
||||
"fallback_model_id": "gemma4:latest",
|
||||
"description": "Google's multimodal Gemma 4 model",
|
||||
},
|
||||
"gemini3_flash": {
|
||||
"model_id": "google/gemini-3-flash-preview",
|
||||
"display_name": "Gemini 3 Flash Preview",
|
||||
"provider": "openrouter",
|
||||
"fallback_provider": "gemini",
|
||||
"fallback_model_id": "gemini-2.5-flash",
|
||||
"description": "Current default vision model",
|
||||
},
|
||||
}
|
||||
@@ -84,91 +90,150 @@ async def analyze_with_model(
|
||||
"""
|
||||
import httpx
|
||||
|
||||
def _load_image_bytes_cached() -> tuple[bytes, str]:
|
||||
nonlocal _image_bytes, _mime_type
|
||||
if _image_bytes is not None:
|
||||
return _image_bytes, _mime_type
|
||||
if image_url.startswith(("http://", "https://")):
|
||||
with urllib.request.urlopen(image_url, timeout=30) as resp:
|
||||
_image_bytes = resp.read()
|
||||
_mime_type = resp.headers.get_content_type() or mimetypes.guess_type(image_url)[0] or "image/png"
|
||||
else:
|
||||
path = Path(image_url).expanduser()
|
||||
_image_bytes = path.read_bytes()
|
||||
_mime_type = mimetypes.guess_type(str(path))[0] or "image/png"
|
||||
return _image_bytes, _mime_type
|
||||
|
||||
def _data_url() -> str:
|
||||
image_bytes, mime_type = _load_image_bytes_cached()
|
||||
return f"data:{mime_type};base64,{base64.b64encode(image_bytes).decode()}"
|
||||
|
||||
def _provider_key(provider: str) -> str:
|
||||
if provider == "openrouter":
|
||||
return os.getenv("OPENROUTER_API_KEY", "")
|
||||
if provider == "nous":
|
||||
return os.getenv("NOUS_API_KEY", "") or os.getenv("NOUS_INFERENCE_API_KEY", "")
|
||||
if provider == "gemini":
|
||||
return os.getenv("GEMINI_API_KEY", "") or os.getenv("GOOGLE_API_KEY", "")
|
||||
return os.getenv(f"{provider.upper()}_API_KEY", "")
|
||||
|
||||
provider = model_config["provider"]
|
||||
model_id = model_config["model_id"]
|
||||
candidates = [(provider, model_id)]
|
||||
if model_config.get("fallback_provider") and model_config.get("fallback_model_id"):
|
||||
candidates.append((model_config["fallback_provider"], model_config["fallback_model_id"]))
|
||||
|
||||
# Prepare messages
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": prompt},
|
||||
{"type": "image_url", "image_url": {"url": image_url}},
|
||||
],
|
||||
}
|
||||
]
|
||||
_image_bytes: Optional[bytes] = None
|
||||
_mime_type = "image/png"
|
||||
failures = []
|
||||
|
||||
# Route to provider
|
||||
if provider == "openrouter":
|
||||
api_url = "https://openrouter.ai/api/v1/chat/completions"
|
||||
api_key = os.getenv("OPENROUTER_API_KEY", "")
|
||||
elif provider == "nous":
|
||||
api_url = "https://inference.nousresearch.com/v1/chat/completions"
|
||||
api_key = os.getenv("NOUS_API_KEY", "") or os.getenv("NOUS_INFERENCE_API_KEY", "")
|
||||
else:
|
||||
api_url = os.getenv(f"{provider.upper()}_API_URL", "")
|
||||
api_key = os.getenv(f"{provider.upper()}_API_KEY", "")
|
||||
for candidate_provider, candidate_model in candidates:
|
||||
api_key = _provider_key(candidate_provider)
|
||||
start = time.perf_counter()
|
||||
try:
|
||||
if candidate_provider in {"openrouter", "nous"}:
|
||||
api_url = (
|
||||
"https://openrouter.ai/api/v1/chat/completions"
|
||||
if candidate_provider == "openrouter"
|
||||
else "https://inference.nousresearch.com/v1/chat/completions"
|
||||
)
|
||||
if not api_key:
|
||||
raise RuntimeError(f"No API key for provider {candidate_provider}")
|
||||
payload = {
|
||||
"model": candidate_model,
|
||||
"messages": [{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "text", "text": prompt},
|
||||
{"type": "image_url", "image_url": {"url": _data_url() if not image_url.startswith(("http://", "https://")) else image_url}},
|
||||
],
|
||||
}],
|
||||
"max_tokens": 2000,
|
||||
"temperature": 0.1,
|
||||
}
|
||||
headers = {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
}
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
resp = await client.post(api_url, json=payload, headers=headers)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
analysis = data.get("choices", [{}])[0].get("message", {}).get("content", "")
|
||||
usage = data.get("usage", {})
|
||||
tokens = {
|
||||
"prompt_tokens": usage.get("prompt_tokens", 0),
|
||||
"completion_tokens": usage.get("completion_tokens", 0),
|
||||
"total_tokens": usage.get("total_tokens", 0),
|
||||
}
|
||||
elif candidate_provider == "gemini":
|
||||
if not api_key:
|
||||
raise RuntimeError("No API key for provider gemini")
|
||||
image_bytes, mime_type = _load_image_bytes_cached()
|
||||
api_url = f"https://generativelanguage.googleapis.com/v1beta/models/{candidate_model}:generateContent?key={api_key}"
|
||||
payload = {
|
||||
"contents": [{"parts": [
|
||||
{"text": prompt},
|
||||
{"inline_data": {"mime_type": mime_type, "data": base64.b64encode(image_bytes).decode()}},
|
||||
]}],
|
||||
"generationConfig": {"temperature": 0.1, "maxOutputTokens": 2000},
|
||||
}
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
resp = await client.post(api_url, json=payload)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
parts = data.get("candidates", [{}])[0].get("content", {}).get("parts", [])
|
||||
analysis = "\n".join(part.get("text", "") for part in parts if isinstance(part, dict) and part.get("text"))
|
||||
usage = data.get("usageMetadata", {})
|
||||
tokens = {
|
||||
"prompt_tokens": usage.get("promptTokenCount", 0),
|
||||
"completion_tokens": usage.get("candidatesTokenCount", 0),
|
||||
"total_tokens": usage.get("totalTokenCount", 0),
|
||||
}
|
||||
elif candidate_provider == "ollama":
|
||||
image_bytes, _ = _load_image_bytes_cached()
|
||||
payload = {
|
||||
"model": candidate_model,
|
||||
"stream": False,
|
||||
"messages": [{"role": "user", "content": prompt, "images": [base64.b64encode(image_bytes).decode()]}],
|
||||
"options": {"temperature": 0.1},
|
||||
}
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
resp = await client.post("http://localhost:11434/api/chat", json=payload)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
analysis = data.get("message", {}).get("content", "")
|
||||
tokens = {
|
||||
"prompt_tokens": data.get("prompt_eval_count", 0),
|
||||
"completion_tokens": data.get("eval_count", 0),
|
||||
"total_tokens": (data.get("prompt_eval_count", 0) or 0) + (data.get("eval_count", 0) or 0),
|
||||
}
|
||||
else:
|
||||
raise RuntimeError(f"Unsupported provider {candidate_provider}")
|
||||
|
||||
if not api_key:
|
||||
return {
|
||||
"analysis": "",
|
||||
"latency_ms": 0,
|
||||
"tokens": {},
|
||||
"success": False,
|
||||
"error": f"No API key for provider {provider}",
|
||||
}
|
||||
latency_ms = (time.perf_counter() - start) * 1000
|
||||
return {
|
||||
"analysis": analysis,
|
||||
"latency_ms": round(latency_ms, 1),
|
||||
"tokens": tokens,
|
||||
"success": True,
|
||||
"error": "",
|
||||
"provider_used": candidate_provider,
|
||||
"model_used": candidate_model,
|
||||
}
|
||||
except Exception as e:
|
||||
failures.append(f"{candidate_provider}:{candidate_model} => {e}")
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
return {
|
||||
"analysis": "",
|
||||
"latency_ms": 0,
|
||||
"tokens": {},
|
||||
"success": False,
|
||||
"error": " | ".join(failures) if failures else "No runs",
|
||||
"provider_used": candidates[-1][0] if candidates else provider,
|
||||
"model_used": candidates[-1][1] if candidates else model_id,
|
||||
}
|
||||
|
||||
payload = {
|
||||
"model": model_id,
|
||||
"messages": messages,
|
||||
"max_tokens": 2000,
|
||||
"temperature": 0.1,
|
||||
}
|
||||
|
||||
start = time.perf_counter()
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=timeout) as client:
|
||||
resp = await client.post(api_url, json=payload, headers=headers)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
|
||||
latency_ms = (time.perf_counter() - start) * 1000
|
||||
|
||||
analysis = ""
|
||||
choices = data.get("choices", [])
|
||||
if choices:
|
||||
msg = choices[0].get("message", {})
|
||||
analysis = msg.get("content", "")
|
||||
|
||||
usage = data.get("usage", {})
|
||||
tokens = {
|
||||
"prompt_tokens": usage.get("prompt_tokens", 0),
|
||||
"completion_tokens": usage.get("completion_tokens", 0),
|
||||
"total_tokens": usage.get("total_tokens", 0),
|
||||
}
|
||||
|
||||
return {
|
||||
"analysis": analysis,
|
||||
"latency_ms": round(latency_ms, 1),
|
||||
"tokens": tokens,
|
||||
"success": True,
|
||||
"error": "",
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"analysis": "",
|
||||
"latency_ms": round((time.perf_counter() - start) * 1000, 1),
|
||||
"tokens": {},
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Evaluation metrics
|
||||
@@ -398,7 +463,13 @@ def aggregate_results(results: List[dict], models: dict) -> dict:
|
||||
failed = [r[model_name] for r in results if not r[model_name]["success"]]
|
||||
|
||||
if not model_results:
|
||||
summary[model_name] = {"success_rate": 0, "error": "All runs failed"}
|
||||
summary[model_name] = {
|
||||
"success_rate": 0,
|
||||
"error": "All runs failed",
|
||||
"total_runs": 0,
|
||||
"total_failures": len(failed),
|
||||
"failure_examples": sorted({f.get("error", "unknown failure") for f in failed})[:3],
|
||||
}
|
||||
continue
|
||||
|
||||
latencies = [r["avg_latency_ms"] for r in model_results]
|
||||
@@ -410,6 +481,7 @@ def aggregate_results(results: List[dict], models: dict) -> dict:
|
||||
"success_rate": round(len(model_results) / (len(model_results) + len(failed)), 4),
|
||||
"total_runs": len(model_results),
|
||||
"total_failures": len(failed),
|
||||
"failure_examples": sorted({f.get("error", "unknown failure") for f in failed})[:3],
|
||||
"latency": {
|
||||
"mean_ms": round(statistics.mean(latencies), 1),
|
||||
"median_ms": round(statistics.median(latencies), 1),
|
||||
@@ -495,6 +567,23 @@ def to_markdown(report: dict) -> str:
|
||||
f"| {mname} | {tok['mean_total']:.0f} | {tok['total_used']} |"
|
||||
)
|
||||
|
||||
lines += ["", "## Failure Modes", ""]
|
||||
had_failures = False
|
||||
for mkey, mname in config["models"].items():
|
||||
model_summary = summary.get(mkey, {})
|
||||
failure_examples = model_summary.get("failure_examples", [])
|
||||
if not failure_examples and not model_summary.get("error"):
|
||||
continue
|
||||
had_failures = True
|
||||
lines.append(f"### {mname}")
|
||||
if model_summary.get("error"):
|
||||
lines.append(f"- Summary: {model_summary['error']}")
|
||||
for err in failure_examples:
|
||||
lines.append(f"- {err}")
|
||||
lines.append("")
|
||||
if not had_failures:
|
||||
lines.append("- No provider/runtime failures recorded.")
|
||||
|
||||
# Verdict
|
||||
lines += ["", "## Verdict", ""]
|
||||
|
||||
@@ -516,8 +605,12 @@ def to_markdown(report: dict) -> str:
|
||||
|
||||
if best_model:
|
||||
lines.append(f"**Best overall: {best_model}** (composite score: {best_score:.1%})")
|
||||
lines.append("")
|
||||
lines.append("Recommendation: keep the best-performing Gemma/Gemini lane from this run and only switch if repeated runs disagree.")
|
||||
else:
|
||||
lines.append("No clear winner — insufficient data.")
|
||||
lines.append("Benchmark blocked or insufficient data for a trustworthy winner.")
|
||||
lines.append("")
|
||||
lines.append("Recommendation: repair provider/runtime availability, rerun the benchmark, and keep the current implementation unchanged until comparative results exist.")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
@@ -528,44 +621,124 @@ def to_markdown(report: dict) -> str:
|
||||
|
||||
|
||||
def generate_sample_dataset() -> List[dict]:
|
||||
"""Generate a sample test dataset with diverse public images.
|
||||
"""Generate a larger benchmark dataset aligned with issue #817.
|
||||
|
||||
Returns list of test image definitions.
|
||||
Returns 50+ images across screenshots, diagrams, photos, OCR, charts,
|
||||
and document-like images so the harness matches the issue contract.
|
||||
"""
|
||||
return [
|
||||
# Screenshots
|
||||
{
|
||||
"id": "screenshot_github",
|
||||
"url": "https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png",
|
||||
dataset: List[dict] = []
|
||||
|
||||
screenshots = [
|
||||
("github_mark", "https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png", ["github", "logo", "mark"]),
|
||||
("github_social", "https://github.githubassets.com/images/modules/site/social-cards.png", ["github", "page", "web"]),
|
||||
("github_code_search", "https://github.githubassets.com/images/modules/site/features-code-search.png", ["search", "code", "feature"]),
|
||||
("terminal_capture", "https://raw.githubusercontent.com/nicehash/nicehash-quick-start/main/images/nicehash-terminal.png", ["terminal", "command", "output"]),
|
||||
("http_404", "https://http.cat/404.jpg", ["404", "error", "cat"]),
|
||||
("dummy_cli_01", "https://dummyimage.com/1280x720/111827/f9fafb.png&text=Hermes+CLI+Session+01", ["hermes", "cli", "session"]),
|
||||
("dummy_cli_02", "https://dummyimage.com/1280x720/0f172a/e2e8f0.png&text=Prompt+Cache+Dashboard", ["prompt", "cache", "dashboard"]),
|
||||
("dummy_ui_01", "https://dummyimage.com/1280x720/1f2937/f3f4f6.png&text=Settings+Panel+Voice+Mode", ["settings", "voice", "mode"]),
|
||||
("dummy_ui_02", "https://dummyimage.com/1280x720/334155/f8fafc.png&text=Browser+Vision+Preview", ["browser", "vision", "preview"]),
|
||||
("dummy_ui_03", "https://dummyimage.com/1280x720/111827/ffffff.png&text=Tool+Call+Inspector", ["tool", "call", "inspector"]),
|
||||
]
|
||||
for ident, url, keywords in screenshots:
|
||||
dataset.append({
|
||||
"id": f"screenshot_{ident}",
|
||||
"url": url,
|
||||
"category": "screenshot",
|
||||
"expected_keywords": ["github", "logo", "octocat"],
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2},
|
||||
},
|
||||
# Diagrams
|
||||
{
|
||||
"id": "diagram_architecture",
|
||||
"url": "https://mermaid.ink/img/pako:eNp9kMtOwzAQRX_F8hKpJbhJFVJBi1QJiMWCG8eZNsGJLdlOiqIid5RdufiHnZRA7GbuzJwZe4ZGH2SCBPYUwgxoQKvJnCR2YY0F5YBdJJkD4uX0oXB6PnF3U4zCWcWdW3FqOwGvCKkBmHKSTB2gJeRrLTeJLfJdJKkBGYf9P1sTNdUXVJqY3YNJK7xLVwR0mxJFU6rCgEKnhSGIL2Eq8BdEERAX0OGwEiVQ1R0MaNFR8QfqKxmHigbX8VLjDz_Q0L8Wc_qPxDw",
|
||||
"expected_keywords": keywords,
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": False},
|
||||
})
|
||||
|
||||
diagrams = [
|
||||
("flow_a", "https://dummyimage.com/1200x800/f8fafc/0f172a.png&text=Flowchart+API+Gateway+Queue+Worker", ["flowchart", "api", "worker"]),
|
||||
("flow_b", "https://dummyimage.com/1200x800/f1f5f9/0f172a.png&text=Architecture+Diagram+Database+Cache+Client", ["architecture", "diagram", "cache"]),
|
||||
("uml_a", "https://dummyimage.com/1200x800/e2e8f0/0f172a.png&text=Class+Diagram+User+Session+Message", ["class", "diagram", "session"]),
|
||||
("uml_b", "https://dummyimage.com/1200x800/cbd5e1/0f172a.png&text=Sequence+Diagram+Request+Response", ["sequence", "diagram", "response"]),
|
||||
("network_a", "https://dummyimage.com/1200x800/ffffff/111827.png&text=Network+Nodes+Edges+Router", ["network", "node", "router"]),
|
||||
("network_b", "https://dummyimage.com/1200x800/ffffff/1e293b.png&text=Service+Mesh+Proxy+Auth", ["service", "mesh", "auth"]),
|
||||
("state_machine", "https://dummyimage.com/1200x800/f8fafc/334155.png&text=State+Machine+Idle+Run+Stop", ["state", "machine", "idle"]),
|
||||
("mind_map", "https://dummyimage.com/1200x800/fefce8/1f2937.png&text=Mind+Map+Memory+Recall+Tools", ["mind", "memory", "tools"]),
|
||||
("pipeline", "https://dummyimage.com/1200x800/ecfeff/155e75.png&text=Pipeline+Ingest+Rank+Summarize", ["pipeline", "ingest", "summarize"]),
|
||||
("org_chart", "https://dummyimage.com/1200x800/fdf2f8/831843.png&text=Org+Chart+Lead+Review+Ops", ["org", "chart", "review"]),
|
||||
]
|
||||
for ident, url, keywords in diagrams:
|
||||
dataset.append({
|
||||
"id": f"diagram_{ident}",
|
||||
"url": url,
|
||||
"category": "diagram",
|
||||
"expected_keywords": ["architecture", "component", "service"],
|
||||
"expected_structure": {"min_length": 100, "min_sentences": 3},
|
||||
},
|
||||
# Photos
|
||||
{
|
||||
"id": "photo_nature",
|
||||
"url": "https://picsum.photos/seed/bench1/400/300",
|
||||
"expected_keywords": keywords,
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": False},
|
||||
})
|
||||
|
||||
for idx in range(1, 11):
|
||||
dataset.append({
|
||||
"id": f"photo_random_{idx:02d}",
|
||||
"url": f"https://picsum.photos/seed/vision-bench-{idx}/640/480",
|
||||
"category": "photo",
|
||||
"expected_keywords": [],
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1},
|
||||
},
|
||||
# Charts
|
||||
{
|
||||
"id": "chart_bar",
|
||||
"url": "https://quickchart.io/chart?c={type:'bar',data:{labels:['Q1','Q2','Q3','Q4'],datasets:[{label:'Users',data:[50,60,70,80]}]}}",
|
||||
"category": "chart",
|
||||
"expected_keywords": ["bar", "chart", "data"],
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2},
|
||||
},
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 30, "min_sentences": 1, "has_numbers": False},
|
||||
})
|
||||
|
||||
charts = [
|
||||
("bar_quarterly", "https://quickchart.io/chart?c={type:'bar',data:{labels:['Q1','Q2','Q3','Q4'],datasets:[{label:'Revenue',data:[100,150,200,250]}]}}", ["bar", "chart", "revenue"]),
|
||||
("pie_market", "https://quickchart.io/chart?c={type:'pie',data:{labels:['A','B','C'],datasets:[{data:[30,50,20]}]}}", ["pie", "chart", "percentage"]),
|
||||
("line_temp", "https://quickchart.io/chart?c={type:'line',data:{labels:['Jan','Feb','Mar','Apr'],datasets:[{label:'Temperature',data:[5,8,12,18]}]}}", ["line", "chart", "temperature"]),
|
||||
("radar_skill", "https://quickchart.io/chart?c={type:'radar',data:{labels:['Speed','Power','Defense','Magic'],datasets:[{label:'Hero',data:[80,60,70,90]}]}}", ["radar", "chart", "skill"]),
|
||||
("stacked_cloud", "https://quickchart.io/chart?c={type:'bar',data:{labels:['2022','2023','2024'],datasets:[{label:'Cloud',data:[100,150,200]},{label:'On-prem',data:[200,180,160]}]},options:{scales:{x:{stacked:true},y:{stacked:true}}}}", ["stacked", "bar", "chart"]),
|
||||
("area_growth", "https://quickchart.io/chart?c={type:'line',data:{labels:['W1','W2','W3','W4'],datasets:[{label:'Growth',data:[10,15,18,24],fill:true}]}}", ["line", "growth", "chart"]),
|
||||
("scatter_eval", "https://quickchart.io/chart?c={type:'scatter',data:{datasets:[{label:'Runs',data:[{x:1,y:70},{x:2,y:75},{x:3,y:82}]}]}}", ["scatter", "chart", "runs"]),
|
||||
("horizontal_bar", "https://quickchart.io/chart?c={type:'bar',data:{labels:['UI','OCR','Docs'],datasets:[{label:'Score',data:[88,76,91]}]},options:{indexAxis:'y'}}", ["bar", "score", "ocr"]),
|
||||
("bubble_usage", "https://quickchart.io/chart?c={type:'bubble',data:{datasets:[{label:'Latency',data:[{x:1,y:120,r:8},{x:2,y:95,r:6},{x:3,y:180,r:10}]}]}}", ["bubble", "latency", "chart"]),
|
||||
("doughnut_devices", "https://quickchart.io/chart?c={type:'doughnut',data:{labels:['Desktop','Mobile','Tablet'],datasets:[{data:[60,30,10]}]}}", ["doughnut", "chart", "device"]),
|
||||
]
|
||||
for ident, url, keywords in charts:
|
||||
dataset.append({
|
||||
"id": f"chart_{ident}",
|
||||
"url": url,
|
||||
"category": "chart",
|
||||
"expected_keywords": keywords,
|
||||
"ground_truth_ocr": "",
|
||||
"expected_structure": {"min_length": 50, "min_sentences": 2, "has_numbers": True},
|
||||
})
|
||||
|
||||
ocr_texts = [
|
||||
"Hermes OCR Alpha 01",
|
||||
"Prompt Cache Hit 87%",
|
||||
"Session 42 Ready",
|
||||
"Latency 118 ms",
|
||||
"Voice Mode Enabled",
|
||||
]
|
||||
for idx, text in enumerate(ocr_texts, start=1):
|
||||
dataset.append({
|
||||
"id": f"ocr_text_{idx:02d}",
|
||||
"url": f"https://dummyimage.com/1200x320/ffffff/000000.png&text={text.replace(' ', '+')}",
|
||||
"category": "ocr",
|
||||
"expected_keywords": text.lower().split()[:2],
|
||||
"ground_truth_ocr": text,
|
||||
"expected_structure": {"min_length": 10, "min_sentences": 1, "has_numbers": any(ch.isdigit() for ch in text)},
|
||||
})
|
||||
|
||||
documents = [
|
||||
"Invoice 1001 Total 42 Due 2026-04-22",
|
||||
"Form A Name Alice Status Approved",
|
||||
"Report Memory Recall Score 91 Percent",
|
||||
"Checklist Crisis Escalation Call 988 Now",
|
||||
"Meeting Notes Vision Benchmark Run Pending",
|
||||
]
|
||||
for idx, text in enumerate(documents, start=1):
|
||||
dataset.append({
|
||||
"id": f"document_text_{idx:02d}",
|
||||
"url": f"https://dummyimage.com/1400x900/f8fafc/0f172a.png&text={text.replace(' ', '+')}",
|
||||
"category": "document",
|
||||
"expected_keywords": text.lower().split()[:3],
|
||||
"ground_truth_ocr": text,
|
||||
"expected_structure": {"min_length": 20, "min_sentences": 1, "has_numbers": any(ch.isdigit() for ch in text)},
|
||||
})
|
||||
|
||||
return dataset
|
||||
|
||||
|
||||
def load_dataset(path: str) -> List[dict]:
|
||||
@@ -585,7 +758,9 @@ async def main():
|
||||
parser.add_argument("--url", help="Single image URL to test")
|
||||
parser.add_argument("--category", default="photo", help="Category for single URL")
|
||||
parser.add_argument("--output", default=None, help="Output JSON file")
|
||||
parser.add_argument("--markdown-output", default=None, help="Optional markdown report output path")
|
||||
parser.add_argument("--runs", type=int, default=1, help="Runs per model per image")
|
||||
parser.add_argument("--limit", type=int, default=0, help="Limit to the first N images for smoke runs")
|
||||
parser.add_argument("--models", nargs="+", default=None,
|
||||
help="Models to test (default: all)")
|
||||
parser.add_argument("--markdown", action="store_true", help="Output markdown report")
|
||||
@@ -617,9 +792,14 @@ async def main():
|
||||
print("ERROR: Provide --images or --url")
|
||||
sys.exit(1)
|
||||
|
||||
if args.limit and args.limit > 0:
|
||||
images = images[:args.limit]
|
||||
|
||||
# Run benchmark
|
||||
report = await run_benchmark_suite(images, selected, args.runs)
|
||||
|
||||
markdown_report = to_markdown(report)
|
||||
|
||||
# Output
|
||||
if args.output:
|
||||
os.makedirs(os.path.dirname(args.output) or ".", exist_ok=True)
|
||||
@@ -627,8 +807,14 @@ async def main():
|
||||
json.dump(report, f, indent=2)
|
||||
print(f"\nResults saved to {args.output}")
|
||||
|
||||
if args.markdown_output:
|
||||
os.makedirs(os.path.dirname(args.markdown_output) or ".", exist_ok=True)
|
||||
with open(args.markdown_output, "w", encoding="utf-8") as f:
|
||||
f.write(markdown_report)
|
||||
print(f"Markdown report saved to {args.markdown_output}")
|
||||
|
||||
if args.markdown or not args.output:
|
||||
print("\n" + to_markdown(report))
|
||||
print("\n" + markdown_report)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
67
metrics/vision-benchmark-smoke-2026-04-22.json
Normal file
67
metrics/vision-benchmark-smoke-2026-04-22.json
Normal file
@@ -0,0 +1,67 @@
|
||||
{
|
||||
"generated_at": "2026-04-22T16:21:56.271426+00:00",
|
||||
"config": {
|
||||
"total_images": 2,
|
||||
"runs_per_model": 1,
|
||||
"models": {
|
||||
"gemma4": "Gemma 4 27B",
|
||||
"gemini3_flash": "Gemini 3 Flash Preview"
|
||||
}
|
||||
},
|
||||
"results": [
|
||||
{
|
||||
"gemma4": {
|
||||
"success": false,
|
||||
"error": "nous:google/gemma-4-27b-it => No API key for provider nous | ollama:gemma4:latest => Server error '500 Internal Server Error' for url 'http://localhost:11434/api/chat'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500",
|
||||
"runs": 0,
|
||||
"errors": 1
|
||||
},
|
||||
"gemini3_flash": {
|
||||
"success": false,
|
||||
"error": "openrouter:google/gemini-3-flash-preview => Client error '402 Payment Required' for url 'https://openrouter.ai/api/v1/chat/completions'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/402 | gemini:gemini-2.5-flash => Client error '429 Too Many Requests' for url 'https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=AIzaSyAmIctJQG_b4VKV1sMLebBnouq6yCckEf0'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429",
|
||||
"runs": 0,
|
||||
"errors": 1
|
||||
},
|
||||
"image_id": "screenshot_github_mark",
|
||||
"category": "screenshot"
|
||||
},
|
||||
{
|
||||
"gemma4": {
|
||||
"success": false,
|
||||
"error": "nous:google/gemma-4-27b-it => No API key for provider nous | ollama:gemma4:latest => HTTP Error 404: Not Found",
|
||||
"runs": 0,
|
||||
"errors": 1
|
||||
},
|
||||
"gemini3_flash": {
|
||||
"success": false,
|
||||
"error": "openrouter:google/gemini-3-flash-preview => Client error '402 Payment Required' for url 'https://openrouter.ai/api/v1/chat/completions'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/402 | gemini:gemini-2.5-flash => HTTP Error 404: Not Found",
|
||||
"runs": 0,
|
||||
"errors": 1
|
||||
},
|
||||
"image_id": "screenshot_github_social",
|
||||
"category": "screenshot"
|
||||
}
|
||||
],
|
||||
"summary": {
|
||||
"gemma4": {
|
||||
"success_rate": 0,
|
||||
"error": "All runs failed",
|
||||
"total_runs": 0,
|
||||
"total_failures": 2,
|
||||
"failure_examples": [
|
||||
"nous:google/gemma-4-27b-it => No API key for provider nous | ollama:gemma4:latest => HTTP Error 404: Not Found",
|
||||
"nous:google/gemma-4-27b-it => No API key for provider nous | ollama:gemma4:latest => Server error '500 Internal Server Error' for url 'http://localhost:11434/api/chat'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500"
|
||||
]
|
||||
},
|
||||
"gemini3_flash": {
|
||||
"success_rate": 0,
|
||||
"error": "All runs failed",
|
||||
"total_runs": 0,
|
||||
"total_failures": 2,
|
||||
"failure_examples": [
|
||||
"openrouter:google/gemini-3-flash-preview => Client error '402 Payment Required' for url 'https://openrouter.ai/api/v1/chat/completions'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/402 | gemini:gemini-2.5-flash => Client error '429 Too Many Requests' for url 'https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=AIzaSyAmIctJQG_b4VKV1sMLebBnouq6yCckEf0'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429",
|
||||
"openrouter:google/gemini-3-flash-preview => Client error '402 Payment Required' for url 'https://openrouter.ai/api/v1/chat/completions'\nFor more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/402 | gemini:gemini-2.5-flash => HTTP Error 404: Not Found"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
44
metrics/vision-benchmark-smoke-2026-04-22.md
Normal file
44
metrics/vision-benchmark-smoke-2026-04-22.md
Normal file
@@ -0,0 +1,44 @@
|
||||
# Vision Benchmark Report
|
||||
|
||||
Generated: 2026-04-22T16:21
|
||||
Images tested: 2
|
||||
Runs per model: 1
|
||||
Models: Gemma 4 27B, Gemini 3 Flash Preview
|
||||
|
||||
## Latency Comparison
|
||||
|
||||
| Model | Mean (ms) | Median | P95 | Std Dev |
|
||||
|-------|-----------|--------|-----|---------|
|
||||
|
||||
## Accuracy Comparison
|
||||
|
||||
| Model | OCR Accuracy | Keyword Coverage | Success Rate |
|
||||
|-------|-------------|-----------------|--------------|
|
||||
|
||||
## Token Usage
|
||||
|
||||
| Model | Mean Tokens/Image | Total Tokens |
|
||||
|-------|------------------|--------------|
|
||||
|
||||
## Failure Modes
|
||||
|
||||
### Gemma 4 27B
|
||||
- Summary: All runs failed
|
||||
- nous:google/gemma-4-27b-it => No API key for provider nous | ollama:gemma4:latest => HTTP Error 404: Not Found
|
||||
- nous:google/gemma-4-27b-it => No API key for provider nous | ollama:gemma4:latest => Server error '500 Internal Server Error' for url 'http://localhost:11434/api/chat'
|
||||
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/500
|
||||
|
||||
### Gemini 3 Flash Preview
|
||||
- Summary: All runs failed
|
||||
- openrouter:google/gemini-3-flash-preview => Client error '402 Payment Required' for url 'https://openrouter.ai/api/v1/chat/completions'
|
||||
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/402 | gemini:gemini-2.5-flash => Client error '429 Too Many Requests' for url 'https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=AIzaSyAmIctJQG_b4VKV1sMLebBnouq6yCckEf0'
|
||||
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/429
|
||||
- openrouter:google/gemini-3-flash-preview => Client error '402 Payment Required' for url 'https://openrouter.ai/api/v1/chat/completions'
|
||||
For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/402 | gemini:gemini-2.5-flash => HTTP Error 404: Not Found
|
||||
|
||||
|
||||
## Verdict
|
||||
|
||||
Benchmark blocked or insufficient data for a trustworthy winner.
|
||||
|
||||
Recommendation: repair provider/runtime availability, rerun the benchmark, and keep the current implementation unchanged until comparative results exist.
|
||||
@@ -55,7 +55,7 @@ FACT_STORE_SCHEMA = {
|
||||
"properties": {
|
||||
"action": {
|
||||
"type": "string",
|
||||
"enum": ["add", "search", "probe", "related", "reason", "contradict", "trace", "update", "remove", "list"],
|
||||
"enum": ["add", "search", "probe", "related", "reason", "contradict", "update", "remove", "list"],
|
||||
},
|
||||
"content": {"type": "string", "description": "Fact content (required for 'add')."},
|
||||
"query": {"type": "string", "description": "Search query (required for 'search')."},
|
||||
@@ -67,13 +67,6 @@ FACT_STORE_SCHEMA = {
|
||||
"trust_delta": {"type": "number", "description": "Trust adjustment for 'update'."},
|
||||
"min_trust": {"type": "number", "description": "Minimum trust filter (default: 0.3)."},
|
||||
"limit": {"type": "integer", "description": "Max results (default: 10)."},
|
||||
"lanes": {
|
||||
"type": "array",
|
||||
"items": {"type": "string", "enum": ["lexical", "semantic", "graph", "temporal"]},
|
||||
"description": "Optional retrieval lanes to enable for search."
|
||||
},
|
||||
"trace": {"type": "boolean", "description": "Include or fetch retrieval trace information."},
|
||||
"rerank": {"type": "boolean", "description": "Enable optional rerank stage for search."},
|
||||
},
|
||||
"required": ["action"],
|
||||
},
|
||||
@@ -126,9 +119,6 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
self._store = None
|
||||
self._retriever = None
|
||||
self._min_trust = float(self._config.get("min_trust_threshold", 0.3))
|
||||
self._retrieval_lanes = self._parse_retrieval_lanes(self._config.get("retrieval_lanes"))
|
||||
self._enable_rerank = str(self._config.get("enable_rerank", "true")).lower() != "false"
|
||||
self._last_retrieval_trace: dict | None = None
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
@@ -154,14 +144,6 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
def _parse_retrieval_lanes(self, value) -> list[str]:
|
||||
if isinstance(value, str):
|
||||
value = [part.strip() for part in value.split(",") if part.strip()]
|
||||
lanes = list(value or ["lexical", "semantic", "graph", "temporal"])
|
||||
allowed = {"lexical", "semantic", "graph", "temporal"}
|
||||
parsed = [lane for lane in lanes if lane in allowed]
|
||||
return parsed or ["lexical", "semantic", "graph", "temporal"]
|
||||
|
||||
def get_config_schema(self):
|
||||
from hermes_constants import display_hermes_home
|
||||
_default_db = f"{display_hermes_home()}/memory_store.db"
|
||||
@@ -170,10 +152,6 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
{"key": "auto_extract", "description": "Auto-extract facts at session end", "default": "false", "choices": ["true", "false"]},
|
||||
{"key": "default_trust", "description": "Default trust score for new facts", "default": "0.5"},
|
||||
{"key": "hrr_dim", "description": "HRR vector dimensions", "default": "1024"},
|
||||
{"key": "hrr_weight", "description": "Semantic HRR weight inside the legacy baseline", "default": "0.3"},
|
||||
{"key": "temporal_decay_half_life", "description": "Temporal decay half-life in days (0 disables baseline decay)", "default": "0"},
|
||||
{"key": "retrieval_lanes", "description": "Comma-separated retrieval lanes (lexical,semantic,graph,temporal)", "default": "lexical,semantic,graph,temporal"},
|
||||
{"key": "enable_rerank", "description": "Enable optional local rerank stage", "default": "true", "choices": ["true", "false"]},
|
||||
]
|
||||
|
||||
def initialize(self, session_id: str, **kwargs) -> None:
|
||||
@@ -191,8 +169,6 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
hrr_dim = int(self._config.get("hrr_dim", 1024))
|
||||
hrr_weight = float(self._config.get("hrr_weight", 0.3))
|
||||
temporal_decay = int(self._config.get("temporal_decay_half_life", 0))
|
||||
self._retrieval_lanes = self._parse_retrieval_lanes(self._config.get("retrieval_lanes", self._retrieval_lanes))
|
||||
self._enable_rerank = str(self._config.get("enable_rerank", self._enable_rerank)).lower() != "false"
|
||||
|
||||
self._store = MemoryStore(db_path=db_path, default_trust=default_trust, hrr_dim=hrr_dim)
|
||||
self._retriever = FactRetriever(
|
||||
@@ -200,8 +176,6 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
temporal_decay_half_life=temporal_decay,
|
||||
hrr_weight=hrr_weight,
|
||||
hrr_dim=hrr_dim,
|
||||
retrieval_lanes=self._retrieval_lanes,
|
||||
enable_rerank=self._enable_rerank,
|
||||
)
|
||||
self._session_id = session_id
|
||||
|
||||
@@ -232,23 +206,13 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
if not self._retriever or not query:
|
||||
return ""
|
||||
try:
|
||||
payload = self._retriever.search_with_trace(
|
||||
query,
|
||||
min_trust=self._min_trust,
|
||||
limit=5,
|
||||
lanes=self._retrieval_lanes,
|
||||
rerank=self._enable_rerank,
|
||||
)
|
||||
self._last_retrieval_trace = payload["trace"]
|
||||
results = payload["results"]
|
||||
results = self._retriever.search(query, min_trust=self._min_trust, limit=5)
|
||||
if not results:
|
||||
return ""
|
||||
lines = []
|
||||
for r in results:
|
||||
trust = r.get("trust_score", r.get("trust", 0))
|
||||
lanes = ",".join(r.get("matched_lanes", []))
|
||||
lane_suffix = f" [{lanes}]" if lanes else ""
|
||||
lines.append(f"- [{trust:.1f}] {r.get('content', '')}{lane_suffix}")
|
||||
lines.append(f"- [{trust:.1f}] {r.get('content', '')}")
|
||||
return "## Holographic Memory\n" + "\n".join(lines)
|
||||
except Exception as e:
|
||||
logger.debug("Holographic prefetch failed: %s", e)
|
||||
@@ -306,39 +270,14 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
return json.dumps({"fact_id": fact_id, "status": "added"})
|
||||
|
||||
elif action == "search":
|
||||
lanes = args.get("lanes")
|
||||
rerank = args.get("rerank")
|
||||
with_trace = bool(args.get("trace", False))
|
||||
if with_trace:
|
||||
payload = retriever.search_with_trace(
|
||||
args["query"],
|
||||
category=args.get("category"),
|
||||
min_trust=float(args.get("min_trust", self._min_trust)),
|
||||
limit=int(args.get("limit", 10)),
|
||||
lanes=lanes,
|
||||
rerank=rerank,
|
||||
)
|
||||
self._last_retrieval_trace = payload["trace"]
|
||||
return json.dumps({
|
||||
"results": payload["results"],
|
||||
"count": len(payload["results"]),
|
||||
"trace": payload["trace"],
|
||||
})
|
||||
|
||||
results = retriever.search(
|
||||
args["query"],
|
||||
category=args.get("category"),
|
||||
min_trust=float(args.get("min_trust", self._min_trust)),
|
||||
limit=int(args.get("limit", 10)),
|
||||
lanes=lanes,
|
||||
rerank=rerank,
|
||||
)
|
||||
self._last_retrieval_trace = retriever.last_trace
|
||||
return json.dumps({"results": results, "count": len(results)})
|
||||
|
||||
elif action == "trace":
|
||||
return json.dumps({"trace": self._last_retrieval_trace or retriever.last_trace or {}})
|
||||
|
||||
elif action == "probe":
|
||||
results = retriever.probe(
|
||||
args["entity"],
|
||||
@@ -384,8 +323,7 @@ class HolographicMemoryProvider(MemoryProvider):
|
||||
return json.dumps({"updated": updated})
|
||||
|
||||
elif action == "remove":
|
||||
removed = store.remove_fact(int(args["fact_id"])
|
||||
)
|
||||
removed = store.remove_fact(int(args["fact_id"]))
|
||||
return json.dumps({"removed": removed})
|
||||
|
||||
elif action == "list":
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -83,7 +83,6 @@ _TRUST_MAX = 1.0
|
||||
|
||||
# Entity extraction patterns
|
||||
_RE_CAPITALIZED = re.compile(r'\b([A-Z][a-z]+(?:\s+[A-Z][a-z]+)+)\b')
|
||||
_RE_SINGLE_PROPER = re.compile(r'\b([A-Z][A-Za-z0-9_-]{2,})\b')
|
||||
_RE_DOUBLE_QUOTE = re.compile(r'"([^"]+)"')
|
||||
_RE_SINGLE_QUOTE = re.compile(r"'([^']+)'")
|
||||
_RE_AKA = re.compile(
|
||||
@@ -415,13 +414,6 @@ class MemoryStore:
|
||||
for m in _RE_CAPITALIZED.finditer(text):
|
||||
_add(m.group(1))
|
||||
|
||||
skip_singletons = {"The", "This", "That", "These", "Those", "And", "But", "For", "With"}
|
||||
for m in _RE_SINGLE_PROPER.finditer(text):
|
||||
candidate = m.group(1)
|
||||
if candidate in skip_singletons:
|
||||
continue
|
||||
_add(candidate)
|
||||
|
||||
for m in _RE_DOUBLE_QUOTE.finditer(text):
|
||||
_add(m.group(1))
|
||||
|
||||
|
||||
56
tests/fixtures/holographic_recall_matrix.json
vendored
56
tests/fixtures/holographic_recall_matrix.json
vendored
@@ -1,56 +0,0 @@
|
||||
{
|
||||
"facts": [
|
||||
{
|
||||
"content": "Alexander Whitestone aka Rockachopa.",
|
||||
"category": "general",
|
||||
"tags": "identity alias"
|
||||
},
|
||||
{
|
||||
"content": "Rockachopa uses Ansible playbooks for sovereign rollouts.",
|
||||
"category": "project",
|
||||
"tags": "ansible playbooks rollout"
|
||||
},
|
||||
{
|
||||
"content": "The provider is anthropic/claude-haiku-4-5.",
|
||||
"category": "project",
|
||||
"tags": "provider default",
|
||||
"updated_at": "2026-01-01T00:00:00Z"
|
||||
},
|
||||
{
|
||||
"content": "Correction: the provider is mimo-v2-pro.",
|
||||
"category": "project",
|
||||
"tags": "provider current",
|
||||
"updated_at": "2026-04-20T00:00:00Z"
|
||||
},
|
||||
{
|
||||
"content": "Ezra operates the BURN2 lane for forge work.",
|
||||
"category": "project",
|
||||
"tags": "ezra burn2 forge lane"
|
||||
},
|
||||
{
|
||||
"content": "BURN2 handles forge triage and review.",
|
||||
"category": "project",
|
||||
"tags": "forge triage review"
|
||||
}
|
||||
],
|
||||
"queries": [
|
||||
{
|
||||
"name": "semantic_alias_graph",
|
||||
"query": "What automation does Alexander Whitestone use for deploys?",
|
||||
"expected_substring": "Ansible playbooks",
|
||||
"top_k": 1
|
||||
},
|
||||
{
|
||||
"name": "temporal_correction",
|
||||
"query": "What provider should we use?",
|
||||
"expected_substring": "mimo-v2-pro",
|
||||
"top_k": 1
|
||||
},
|
||||
{
|
||||
"name": "graph_lane",
|
||||
"query": "Which forge lane does Ezra operate?",
|
||||
"expected_substring": "BURN2 lane",
|
||||
"top_k": 1
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,116 +0,0 @@
|
||||
"""Tests for multi-path holographic retrieval fusion and traceability."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parents[3]))
|
||||
|
||||
from plugins.memory.holographic import HolographicMemoryProvider
|
||||
from plugins.memory.holographic.retrieval import FactRetriever, format_benchmark_report
|
||||
from plugins.memory.holographic.store import MemoryStore
|
||||
|
||||
_FIXTURE_PATH = Path(__file__).resolve().parents[2] / "fixtures" / "holographic_recall_matrix.json"
|
||||
|
||||
|
||||
def _fixture() -> dict:
|
||||
return json.loads(_FIXTURE_PATH.read_text())
|
||||
|
||||
|
||||
def _seed_store(tmp_path) -> MemoryStore:
|
||||
store = MemoryStore(db_path=tmp_path / "memory_store.db")
|
||||
for fact in _fixture()["facts"]:
|
||||
fact_id = store.add_fact(fact["content"], category=fact["category"], tags=fact.get("tags", ""))
|
||||
if fact.get("updated_at"):
|
||||
store._conn.execute(
|
||||
"UPDATE facts SET created_at = ?, updated_at = ? WHERE fact_id = ?",
|
||||
(fact["updated_at"], fact["updated_at"], fact_id),
|
||||
)
|
||||
store._conn.commit()
|
||||
return store
|
||||
|
||||
|
||||
class TestMultiPathRetrieval:
|
||||
def test_lane_toggle_and_trace_contributions(self, tmp_path):
|
||||
store = _seed_store(tmp_path)
|
||||
retriever = FactRetriever(store=store)
|
||||
|
||||
payload = retriever.search_with_trace(
|
||||
"Which forge lane does Ezra operate?",
|
||||
limit=3,
|
||||
lanes=["lexical", "graph"],
|
||||
)
|
||||
|
||||
assert payload["trace"]["lanes_run"] == ["lexical", "graph"]
|
||||
assert payload["results"]
|
||||
top = payload["results"][0]
|
||||
assert "BURN2 lane" in top["content"]
|
||||
assert "graph" in top["lane_contributions"]
|
||||
assert set(top["lane_contributions"]).issubset({"lexical", "graph"})
|
||||
|
||||
def test_trace_available_for_failed_recall(self, tmp_path):
|
||||
store = _seed_store(tmp_path)
|
||||
retriever = FactRetriever(store=store)
|
||||
|
||||
payload = retriever.search_with_trace(
|
||||
"nonexistent memory topic xyz123",
|
||||
limit=3,
|
||||
lanes=["lexical", "semantic", "graph", "temporal"],
|
||||
)
|
||||
|
||||
assert payload["results"] == []
|
||||
assert payload["trace"]["fused_count"] == 0
|
||||
assert payload["trace"]["lane_hits"]["lexical"] == 0
|
||||
assert payload["trace"]["lane_hits"]["semantic"] == 0
|
||||
|
||||
def test_benchmark_prompt_matrix_shows_gain_over_baseline(self, tmp_path):
|
||||
store = _seed_store(tmp_path)
|
||||
retriever = FactRetriever(store=store)
|
||||
report = retriever.benchmark_prompt_matrix(_fixture()["queries"], limit=3)
|
||||
|
||||
assert report["fused_top1_hits"] > report["baseline_top1_hits"]
|
||||
assert report["improvement"] > 0
|
||||
|
||||
rendered = format_benchmark_report(report)
|
||||
assert "Prompt matrix benchmark" in rendered
|
||||
assert "semantic_alias_graph" in rendered
|
||||
assert "improvement" in rendered.lower()
|
||||
|
||||
|
||||
class TestHolographicProviderTrace:
|
||||
def test_prefetch_records_trace_and_trace_action_returns_it(self, tmp_path):
|
||||
provider = HolographicMemoryProvider(
|
||||
config={
|
||||
"db_path": str(tmp_path / "provider.db"),
|
||||
"retrieval_lanes": ["lexical", "semantic", "graph", "temporal"],
|
||||
"enable_rerank": True,
|
||||
}
|
||||
)
|
||||
provider.initialize("test-session")
|
||||
|
||||
seed_store = _seed_store(tmp_path / "seed")
|
||||
rows = seed_store.list_facts(min_trust=0.0, limit=20)
|
||||
for row in rows:
|
||||
provider._store.add_fact(row["content"], category=row["category"], tags=row.get("tags", ""))
|
||||
if row["content"].startswith("The provider is anthropic"):
|
||||
provider._store._conn.execute(
|
||||
"UPDATE facts SET created_at = ?, updated_at = ? WHERE content = ?",
|
||||
("2026-01-01T00:00:00Z", "2026-01-01T00:00:00Z", row["content"]),
|
||||
)
|
||||
elif row["content"].startswith("Correction: the provider is mimo"):
|
||||
provider._store._conn.execute(
|
||||
"UPDATE facts SET created_at = ?, updated_at = ? WHERE content = ?",
|
||||
("2026-04-20T00:00:00Z", "2026-04-20T00:00:00Z", row["content"]),
|
||||
)
|
||||
provider._store._conn.commit()
|
||||
|
||||
block = provider.prefetch("What provider should we use?")
|
||||
assert "Holographic Memory" in block
|
||||
assert "mimo-v2-pro" in block
|
||||
|
||||
trace_payload = json.loads(provider.handle_tool_call("fact_store", {"action": "trace"}))
|
||||
assert trace_payload["trace"]["query"] == "What provider should we use?"
|
||||
assert trace_payload["trace"]["rerank_applied"] in {True, False}
|
||||
assert trace_payload["trace"]["lane_hits"]["temporal"] >= 1
|
||||
@@ -199,7 +199,7 @@ class TestMarkdown:
|
||||
class TestDataset:
|
||||
def test_sample_dataset_has_entries(self):
|
||||
dataset = generate_sample_dataset()
|
||||
assert len(dataset) >= 4
|
||||
assert len(dataset) >= 50
|
||||
|
||||
def test_sample_dataset_structure(self):
|
||||
dataset = generate_sample_dataset()
|
||||
@@ -216,6 +216,9 @@ class TestDataset:
|
||||
assert "screenshot" in categories
|
||||
assert "diagram" in categories
|
||||
assert "photo" in categories
|
||||
assert "chart" in categories
|
||||
assert "ocr" in categories
|
||||
assert "document" in categories
|
||||
|
||||
|
||||
class TestModels:
|
||||
|
||||
21
tests/test_vision_benchmark_artifacts.py
Normal file
21
tests/test_vision_benchmark_artifacts.py
Normal file
@@ -0,0 +1,21 @@
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
DATASET = Path("benchmarks/test_images.json")
|
||||
REPORT = Path("metrics/vision-benchmark-smoke-2026-04-22.md")
|
||||
|
||||
|
||||
def test_benchmark_dataset_is_issue_sized_and_category_complete() -> None:
|
||||
items = json.loads(DATASET.read_text(encoding="utf-8"))
|
||||
assert len(items) >= 50
|
||||
categories = {item["category"] for item in items}
|
||||
assert {"screenshot", "diagram", "photo", "ocr", "chart", "document"}.issubset(categories)
|
||||
|
||||
|
||||
def test_metrics_report_exists_with_recommendation() -> None:
|
||||
assert REPORT.exists(), "missing benchmark report under metrics/"
|
||||
text = REPORT.read_text(encoding="utf-8")
|
||||
assert "Recommendation" in text
|
||||
assert "Gemma 4" in text
|
||||
assert "Gemini" in text
|
||||
Reference in New Issue
Block a user