chore: recovery checkpoint - v3.0 API successfully recovered
CRITICAL CHECKPOINT - DO NOT PUSH TO GITHUB Recovery Status: - Successfully recovered brainy.ts from compiled JavaScript - All core v3.0 API methods functional (add, get, update, delete, relate, find, etc.) - Neural subsystem intact (562KB embedded patterns, NLP working) - Augmentation pipeline operational (20+ augmentations) - HNSW clustering system complete - Triple Intelligence compiled (needs constructor fix) - Test suite validates functionality Changes preserved: - 898 files with changes from last 3 days - 144,475 insertions - All augmentation improvements - All test coverage enhancements - Complete v3.0 feature set This is a LOCAL checkpoint only - contains recovered work after corruption incident. Created backup in .backups/brainy-full-20250910-151314.tar.gz Branch: recovery-checkpoint-20250910-151433 Date: Wed Sep 10 03:18:04 PM PDT 2025
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|
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||||
"template": {
|
||||
"like": "${1}",
|
||||
"where": { "${4}": { "greaterThan": "${3}" } }
|
||||
},
|
||||
"confidence": 0.9
|
||||
},
|
||||
{
|
||||
"id": "filter_less_than",
|
||||
"category": "filtering",
|
||||
"examples": ["models with less than 1M parameters", "papers with under 10 citations"],
|
||||
"pattern": "(.+) with (less than|under|fewer than) (\\d+) (.+)",
|
||||
"template": {
|
||||
"like": "${1}",
|
||||
"where": { "${4}": { "lessThan": "${3}" } }
|
||||
},
|
||||
"confidence": 0.9
|
||||
},
|
||||
{
|
||||
"id": "filter_exactly",
|
||||
"category": "filtering",
|
||||
"examples": ["papers with exactly 5 authors", "models with 12 layers"],
|
||||
"pattern": "(.+) with (exactly |)(\\d+) (.+)",
|
||||
"template": {
|
||||
"like": "${1}",
|
||||
"where": { "${4}": "${3}" }
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "aggregation_count",
|
||||
"category": "aggregation",
|
||||
"examples": ["count papers", "number of models", "how many datasets"],
|
||||
"pattern": "(count|number of|how many) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"aggregate": "count"
|
||||
},
|
||||
"confidence": 0.9
|
||||
},
|
||||
{
|
||||
"id": "aggregation_average",
|
||||
"category": "aggregation",
|
||||
"examples": ["average citations", "mean accuracy", "average performance"],
|
||||
"pattern": "(average|mean) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"aggregate": "avg"
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "aggregation_sum",
|
||||
"category": "aggregation",
|
||||
"examples": ["total citations", "sum of parameters", "total cost"],
|
||||
"pattern": "(total|sum of|sum) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"aggregate": "sum"
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "aggregation_max",
|
||||
"category": "aggregation",
|
||||
"examples": ["highest accuracy", "maximum performance", "largest model"],
|
||||
"pattern": "(highest|maximum|largest|biggest) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"aggregate": "max"
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "aggregation_min",
|
||||
"category": "aggregation",
|
||||
"examples": ["lowest error", "minimum cost", "smallest model"],
|
||||
"pattern": "(lowest|minimum|smallest|least) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"aggregate": "min"
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "question_can",
|
||||
"category": "informational",
|
||||
"examples": ["can transformers handle images", "can I use this for NLP"],
|
||||
"pattern": "can (.+)",
|
||||
"template": {
|
||||
"like": "${1}",
|
||||
"where": { "type": "capability" }
|
||||
},
|
||||
"confidence": 0.8
|
||||
},
|
||||
{
|
||||
"id": "question_should",
|
||||
"category": "informational",
|
||||
"examples": ["should I use tensorflow", "should we implement caching"],
|
||||
"pattern": "should (I|we|you) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"where": { "type": "recommendation" }
|
||||
},
|
||||
"confidence": 0.8
|
||||
},
|
||||
{
|
||||
"id": "question_which",
|
||||
"category": "commercial",
|
||||
"examples": ["which model is best", "which framework to use"],
|
||||
"pattern": "which (.+)",
|
||||
"template": {
|
||||
"like": "${1}",
|
||||
"where": { "type": "selection" }
|
||||
},
|
||||
"confidence": 0.8
|
||||
},
|
||||
{
|
||||
"id": "comparative_better",
|
||||
"category": "commercial",
|
||||
"examples": ["is BERT better than GPT", "pytorch better than tensorflow"],
|
||||
"pattern": "(is )? (.+) better than (.+)",
|
||||
"template": {
|
||||
"like": ["${2}", "${3}"],
|
||||
"where": { "type": "comparison" }
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "comparative_faster",
|
||||
"category": "commercial",
|
||||
"examples": ["fastest model", "quickest training", "faster than BERT"],
|
||||
"pattern": "(fastest|quickest|faster) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"orderBy": { "speed": "desc" }
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "comparative_more_accurate",
|
||||
"category": "commercial",
|
||||
"examples": ["most accurate model", "higher accuracy than"],
|
||||
"pattern": "(most accurate|highest accuracy|more accurate) (.+)",
|
||||
"template": {
|
||||
"like": "${2}",
|
||||
"orderBy": { "accuracy": "desc" }
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "action_show",
|
||||
"category": "navigational",
|
||||
"examples": ["show me papers", "display results", "list models"],
|
||||
"pattern": "(show|display|list) (me )? (.+)",
|
||||
"template": {
|
||||
"like": "${3}"
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "action_find",
|
||||
"category": "navigational",
|
||||
"examples": ["find papers about AI", "search for models", "look for datasets"],
|
||||
"pattern": "(find|search for|look for) (.+)",
|
||||
"template": {
|
||||
"like": "${2}"
|
||||
},
|
||||
"confidence": 0.9
|
||||
},
|
||||
{
|
||||
"id": "action_get",
|
||||
"category": "transactional",
|
||||
"examples": ["get all papers", "fetch datasets", "retrieve models"],
|
||||
"pattern": "(get|fetch|retrieve) (all )? (.+)",
|
||||
"template": {
|
||||
"like": "${3}"
|
||||
},
|
||||
"confidence": 0.85
|
||||
},
|
||||
{
|
||||
"id": "combined_complex_1",
|
||||
"category": "combined",
|
||||
"examples": ["recent papers by Hinton with more than 50 citations"],
|
||||
"pattern": "recent (.+) by (.+) with more than (\\d+) (.+)",
|
||||
"template": {
|
||||
"like": "${1}",
|
||||
"connected": { "from": "${2}" },
|
||||
"where": { "${4}": { "greaterThan": "${3}" } },
|
||||
"boost": "recent"
|
||||
},
|
||||
"confidence": 0.75
|
||||
},
|
||||
{
|
||||
"id": "combined_complex_2",
|
||||
"category": "combined",
|
||||
"examples": ["best machine learning papers from 2023 at Stanford"],
|
||||
"pattern": "best (.+) from (\\d{4}) at (.+)",
|
||||
"template": {
|
||||
"like": "${1}",
|
||||
"where": { "year": "${2}", "organization": "${3}" },
|
||||
"boost": "popular"
|
||||
},
|
||||
"confidence": 0.75
|
||||
},
|
||||
{
|
||||
"id": "combined_complex_3",
|
||||
"category": "combined",
|
||||
"examples": ["compare tensorflow and pytorch for computer vision"],
|
||||
"pattern": "compare (.+) and (.+) for (.+)",
|
||||
"template": {
|
||||
"like": ["${1}", "${2}", "${3}"],
|
||||
"where": { "type": "comparison", "domain": "${3}" }
|
||||
},
|
||||
"confidence": 0.75
|
||||
},
|
||||
{
|
||||
"id": "contextual_more_like",
|
||||
"category": "contextual",
|
||||
"examples": ["more like this", "similar papers", "find similar"],
|
||||
"pattern": "(more like|similar to|like) (this|that|these)",
|
||||
"template": {
|
||||
"similar": "__context__"
|
||||
},
|
||||
"confidence": 0.8
|
||||
},
|
||||
{
|
||||
"id": "contextual_same_but",
|
||||
"category": "contextual",
|
||||
"examples": ["same but newer", "same query but from 2023"],
|
||||
"pattern": "same (query |search |)but (.+)",
|
||||
"template": {
|
||||
"__modifier__": "${2}"
|
||||
},
|
||||
"confidence": 0.75
|
||||
}
|
||||
]
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue