ci: infer component labels for issues filed without the form (#6471)
This commit is contained in:
Kartik
@@ -12,7 +12,7 @@ body:
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- Python SDK
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- TypeScript SDK
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- Vector Store
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- OpenClaw
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- Plugin
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- REST API
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- Other
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validations:
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@@ -12,7 +12,7 @@ body:
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- Python SDK
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- TypeScript SDK
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- Vector Store
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- OpenClaw
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- Plugin
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- REST API
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- Other
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validations:
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@@ -9,7 +9,7 @@ policy:
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keys: ['TypeScript SDK']
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- name: 'vector-store'
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keys: ['Vector Store']
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- name: 'openclaw'
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keys: ['OpenClaw']
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- name: 'plugin'
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keys: ['Plugin']
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- name: 'rest-api'
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keys: ['REST API']
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@@ -0,0 +1,44 @@
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{
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"language": {
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"sdk-python": [
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"python", "pip install", "pypi", "pyproject", "requirements.txt",
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"from mem0", "import mem0", "traceback", "pydantic", "asyncmemory",
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"poetry", "virtualenv", "venv", "conda", "pytest", "async def"
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],
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"sdk-typescript": [
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"typescript", "javascript", "pnpm", "yarn", "node.js", "nodejs",
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"mem0-ts", "mem0ai/oss", "tsconfig", "await import",
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"=> {", "undefined is not"
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]
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},
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"area": {
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"plugin": [
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"openclaw", "openclaw-mem0", "openclaw.json", "openclaw plugin",
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"claude code", "opencode", "pi agent", "mem0-plugin",
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"cursor plugin", "codex plugin", "editor plugin"
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],
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"openmemory": [
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"openmemory", "open memory", "localhost:8765", "localhost:3000",
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"openmemory ui", "openmemory/api", "openmemory/ui"
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],
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"cli": ["mem0-cli", "@mem0/cli", "npx mem0", "command line"],
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"vector-store": [
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"pgvector", "pinecone", "chroma", "chromadb", "weaviate",
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"milvus", "faiss", "vector store", "vectorstore",
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"elasticsearch", "supabase", "azure ai search",
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"s3 vectors", "mongodb"
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],
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"integrations": [
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"vercel ai", "vercel-ai-sdk", "@mem0/vercel-ai-provider",
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"llamaindex", "crewai", "autogen", "langgraph"
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],
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"rest-api": [
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"rest api", "fastapi", "docker-compose", "/v1/memories",
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"localhost:8000", "localhost:8888", "curl -x", "http endpoint"
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],
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"documentation": [
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"docs.mem0.ai", "documentation", "typo", "readme", "docstring", "broken link",
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"issue on docs", "docs:", "link to the docs page", "issue with current documentation"
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]
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}
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}
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+36
-12
@@ -1,14 +1,23 @@
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vector-store:
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- changed-files:
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- any-glob-to-any-file: 'mem0/vector_stores/**'
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sdk-python:
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- changed-files:
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- any-glob-to-any-file: ['mem0/**', 'tests/**']
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- any-glob-to-any-file:
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- 'mem0/**'
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- 'tests/**'
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- 'cli/python/**'
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- 'pyproject.toml'
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- 'poetry.lock'
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sdk-typescript:
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- changed-files:
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- any-glob-to-any-file: 'mem0-ts/**'
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- any-glob-to-any-file:
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- 'mem0-ts/**'
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- 'cli/node/**'
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vector-store:
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- changed-files:
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- any-glob-to-any-file:
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- 'mem0/vector_stores/**'
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- 'mem0-ts/src/oss/src/vector_stores/**'
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rest-api:
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- changed-files:
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@@ -18,22 +27,37 @@ openmemory:
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- changed-files:
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- any-glob-to-any-file: 'openmemory/**'
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openclaw:
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- changed-files:
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- any-glob-to-any-file: 'integrations/openclaw/**'
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integrations:
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- changed-files:
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- any-glob-to-any-file: 'integrations/**'
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plugin:
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- changed-files:
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- all-globs-to-any-file:
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- 'integrations/**'
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- '!integrations/vercel-ai-sdk/**'
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- any-glob-to-any-file:
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- 'skills/**'
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- '.agents/**'
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- '.claude-plugin/**'
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- '.codex-plugin/**'
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- '.cursor-plugin/**'
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- 'marketplace.json'
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cli:
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- changed-files:
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- any-glob-to-any-file: 'cli/**'
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documentation:
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- changed-files:
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- any-glob-to-any-file: 'docs/**'
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- any-glob-to-any-file:
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- 'docs/**'
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- 'examples/**'
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- '*.md'
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ci:
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- changed-files:
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- any-glob-to-any-file: '.github/**'
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- any-glob-to-any-file:
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- '.github/**'
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- 'scripts/**'
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- '.pre-commit-config.yaml'
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@@ -0,0 +1,44 @@
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const fs = require('fs');
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function componentLabels(keywords) {
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return Object.values(keywords).flatMap(Object.keys);
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}
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function toMatcher(term) {
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const escaped = term.replace(/[.*+?^${}()|[\]\\]/g, '\\$&');
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const prefix = /^[a-z0-9]/i.test(term) ? '\\b' : '';
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return new RegExp(prefix + escaped, 'i');
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}
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function scoreGroup(text, group) {
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let winner = null;
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let best = 0;
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for (const [label, terms] of Object.entries(group)) {
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const score = terms.reduce((n, term) => n + (toMatcher(term).test(text) ? 1 : 0), 0);
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if (score > best) {
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winner = label;
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best = score;
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}
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}
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return winner;
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}
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const UMBRELLA = { plugin: 'integrations' };
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function inferComponentLabels(text, keywords) {
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if (!text) return [];
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const labels = [scoreGroup(text, keywords.language), scoreGroup(text, keywords.area)].filter(
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Boolean,
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);
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for (const label of labels.slice()) {
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const parent = UMBRELLA[label];
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if (parent && !labels.includes(parent)) labels.push(parent);
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}
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return labels;
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}
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function loadKeywords(file) {
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return JSON.parse(fs.readFileSync(file, 'utf8'));
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}
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module.exports = { componentLabels, inferComponentLabels, loadKeywords };
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@@ -0,0 +1,108 @@
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const assert = require('assert');
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const path = require('path');
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const { inferComponentLabels, loadKeywords } = require('./infer-component-labels.js');
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const keywords = loadKeywords(path.join(__dirname, '..', 'component-keywords.json'));
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const cases = [
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{
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number: 6210,
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title: "but(anthropic): sampling parameters returns 400 error for new model",
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body: "### Component\n\nCore / Python SDK\n\n### Description\n\n### Summary\n\nWhen using Anthropic latest models such as `claude-opus-4-7`, `claude-opus-4-8`, or `claude-sonnet-5`, Mem0 still sends sampling parameters like `temperature` / `top_p`. These models do not support those parameters, causing Anthropic API requests to fail.\n\nSee https://platform.claude.com/docs/en/about-claude/models/migration-guide\n\n### Steps to Reproduce\n\n```python\n from mem0 import Memory\n\n m = Memory.from_config({\n \"llm\": {\n \"provider\": \"anthropic\",\n \"config\": {\n \"model\": \"claude-opus-4-8\",\n \"api_key\": \"your-anthropic-api-key\"\n },\n },\n ...\n })\n```\n\n### Expected Behavior\n\nMem0 should detect Anthropic models that do not support sampling parameters and omit temperature and top_p from the request.\n\nFor models that still support sampling parameters, such as claude-opus-4-6, claude-sonnet-4-6, and claude-haiku-4-5, Mem0 should continue sending supported sampling parameters till they're deprecated.\n\n### Actual Behavior\n\nMem0 includes temperature by default for Anthropic requests. With newer Anthropic models that do not support sampling parameters, the API request fails because unsupported parameters are sent.\n\n### Environment\n\n - mem0 version: 2.0.11\n - Python/Node version: Python 3.11\n - OS: macOS\n",
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expected: ["sdk-python"],
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},
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{
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number: 5770,
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title: "feat(ts-sdk): add FastEmbed embedding provider",
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body: "## Summary\n\nThe Python SDK supports **FastEmbed** as an embedding provider, but the TypeScript OSS SDK (`mem0ai/oss`) does not. Add it to bring the TS SDK to parity.\n\n| | |\n|---|---|\n| Python reference | `mem0/embeddings/fastembed.py` |\n| Registered in (Python) | `mem0/utils/factory.py` (EmbedderFactory) |\n| Target file (TypeScript) | `mem0-ts/src/oss/src/embeddings/fastembed.ts` |\n| Suggested implementation | Use the `fastembed` npm package (ONNX local embeddings). |\n\n## Requirements\n\n- [ ] Implement `FastEmbedEmbedder` in `mem0-ts/src/oss/src/embeddings/fastembed.ts`, extending `Embedder` (`mem0-ts/src/oss/src/embeddings/base.ts`) and mirroring the Python provider's behavior (embed / embedBatch).\n- [ ] Register the `\"fastembed\"` provider in `mem0-ts/src/oss/src/utils/factory.ts` (EmbedderFactory).\n- [ ] Add config typing in `mem0-ts/src/oss/src/types/`.\n- [ ] Add a unit test under `mem0-ts/src/oss/src/tests/`.\n- [ ] Add `fastembed` to `mem0-ts/package.json` (optional/peer dependency, lazy-imported like other providers).\n- [ ] Update docs under `docs/` if this provider is user-facing.\n\n## Reference pattern\n\nMirror an existing TS provider: `embeddings/openai.ts`.\n\n## Notes\n\n`fastembed` (v2.x) is the JS port of Qdrant's FastEmbed — local/offline embeddings. Mirror the default model in `mem0/embeddings/fastembed.py`.\n\n---\n_Part of the TypeScript ↔ Python SDK provider-parity effort. One provider per issue (atomic)._\n",
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expected: ["sdk-typescript"],
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},
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{
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number: 3940,
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title: "Milvus database will return distance not similarity score",
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body: "### 🐛 Describe the bug\n\nMilvus database will return distance not similarity score\n\n## in milvus.py\n\ndef _parse_output(self, data: list):\n \"\"\"\n Parse the output data.\n\n Args:\n data (Dict): Output data.\n\n Returns:\n List[OutputData]: Parsed output data.\n \"\"\"\n memory = []\n\n for value in data:\n uid, score, metadata = (\n value.get(\"id\"),\n value.get(\"distance\"), # here\n value.get(\"entity\", {}).get(\"metadata\"),\n )\n\n memory_obj = OutputData(id=uid, score=score, payload=metadata)\n memory.append(memory_obj)\n\n return memory\n",
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expected: ["vector-store"],
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},
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{
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number: 5290,
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title: "Recall search failed: Bad Request Using OpenAI Embedding Model",
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body: "### Component\n\nOpenClaw\n\n### Description\n\n### Summary\nuse openclaw.json config:\n\n```json\n...\n\"embedder\": {\n \"provider\": \"openai\",\n \"config\": {\n \"model\": \"bge-base-zh-v1.5\",\n \"embedding_dims\": 1024,\n \"embeddingDims\": 1024,\n \"url\": \"https://xxxxxxxxx/v1\",\n \"apiKey\": \"xxxxxxxxxxxx\"\n }\n },\n\"vectorStore\": {\n \"provider\": \"qdrant\",\n \"config\": {\n \"url\": \"http://qdrant:6333\",\n \"apiKey\": \"${QDRANT_API_KEY}\",\n \"collectionName\": \"mem0\",\n \"embeddingModelDims\": 1024\n }\n }\n```\n```\n\nopenclaw log info is：\n\n```\n23:14:20 Api key is used with unsecure connection.\n23:14:21 [mem0] Recall search failed: Bad Request\n23:14:21 [plugins] openclaw-mem0: skills-mode recall (strategy=smart) injecting 0 memories (~20 tokens)\n23:14:22 [ws] ⇄ res ✓ sessions.list 256ms conn=d1eb9bc4…17da id=201b8113…c9dc\n23:14:22 [ws] ⇄ res ✓ sessions.list 264ms conn=d1eb9bc4…17da id=4939f962…2f16\n23:14:34 [ws] ⇄ res ✓ sessions.list 250ms conn=d1eb9bc4…17da id=f7ad503f…baa6\n23:15:12 [mem0] **Recall search failed: Bad Request**\n23:15:12 [plugins] openclaw-mem0: skills-mode recall (strategy=smart) injecting 0 memories (~20 tokens)\n23:15:12 [ws] ⇄ res ✓ sessions.list 288ms conn=d1eb9bc4…17da id=9e20bb86…371e\n23:15:13 [ws] ⇄ res ✓ sessions.list 268ms conn=d1eb9bc4…17da id=3b49a2ad…7ada\n23:15:20 [ws] ⇄ res ✓ sessions.list 235ms conn=d1eb9bc4…17da id=a192da30…069f\n```\n\n### Actual Behavior\n\nembedding model response ok,response message has 1024 vectors,but the vectors are submitted to vector-db:qdrant with all zero vectors,and vectors has only 256 size.\n\n```http\nPOST /collections/mem0/points/search HTTP/1.1\nhost: qdrant:6333\nconnection: keep-alive\nuser-agent: qdrant-js/1.13.0\napi-key: xxxxxxxxxxxxxxxxxxxxxxxxxxxx\nContent-Type: application/json\nAccept: application/json\naccept-language: *\nsec-fetch-mode: cors\naccept-encoding: gzip, deflate\ncontent-length: 651\n\n{\"vector\":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],\"limit\":120,\"offset\":0,\"filter\":{\"must\":[{\"key\":\"user_id\",\"match\":{\"value\":\"agent\"}}]},\"with_payload\":true,\"with_vector\":false}\n\n**HTTP/1.1 400 Bad Request**\ntransfer-encoding: chunked\ncontent-type: application/json\nvary: accept-encoding, Origin, Access-Control-Request-Method, Access-Control-Request-Headers\ncontent-encoding: gzip\n\n```\n\n### Expected Behavior\n\nembedding model response ok by tcpdump, response message has 1024 vectors,and this vectors are submitted to vector-db:qdrant with the same vectors,and vectors has also 1024 size.\n\n\n### Environment\n\n- openclaw-mem0 version: 1.0.11\n- qdrant: 1.13.6\n",
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expected: ["plugin", "integrations"],
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},
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{
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number: 3696,
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title: "Cannot set expiration_date for memory in REST API server (Docker Compose)",
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body: "### 🐛 Describe the bug\n\nI'm using docker compose to deploy a REST API server. When adding memory, I'm unable to set the expiration_date. Is this feature not supported?",
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expected: ["rest-api"],
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},
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{
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number: 3444,
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title: "Fix: Openmemory run.sh non-existent vector-store route",
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body: "### 🐛 Describe the bug\n\n# Vector_store not implemented\nThere is many references to ` ${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store` in lines 280, 293, 306, 319, 332, 345, 358, and 371. \n```bash\ncurl -fsS -X PUT \"${NEXT_PUBLIC_API_URL}/api/v1/config/mem0/vector_store\" # Line 280 and for each vector store\n```\nBut the api route is not implemented in `api/app/routers/config.py`.\n# Suggested solution\nI would implement `vector_store` route or remove and use `update_configuration` for all config updates. Also Create class with all config keys for vector_store",
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expected: ["openmemory"],
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},
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{
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number: 6252,
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title: "cursor: on_file_read_cursor.sh ignores auto_search / MEM0_AUTO_SEARCH",
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body: "### Component\n\nCursor / mem0-plugin\n\n### Description\n\n`on_file_read_cursor.sh` never checks `MEM0_AUTO_SEARCH`. In Claude Code, #6065/#6071 added a guard on `on_file_read.sh`, but the Cursor PreToolUse variant still always calls `file_context.py` (and thus Platform search) once `MEM0_API_KEY` is set.\n\n### Expected\n\nWhen `auto_search: false` / `MEM0_AUTO_SEARCH=false`, `on_file_read_cursor.sh` should exit 0 without searching.\n\n### Actual\n\nTimeline search still runs.\n\n### Related\n\n#6065, #6071, #6250\n",
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expected: ["plugin", "integrations"],
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},
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{
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number: 6032,
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title: "docs: fix typos and punctuation errors across docs",
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body: "### Description\n\n### Page\nMultiple pages — see list below.\n\n### What's Wrong or Missing\n1. https://docs.mem0.ai/components/llms/overview — \"a llm\" should be \"an LLM\"\n2. https://docs.mem0.ai/components/vectordbs/dbs/azure — 2 comma splices + \"setup\" used as a verb (should be \"set up\")\n3. https://docs.mem0.ai/components/embedders/models/azure_openai — \"from the Azure.\" is an incomplete sentence\n4. https://docs.mem0.ai/components/llms/models/azure_openai — same incomplete \"from the Azure\" phrasing\n5. https://docs.mem0.ai/cookbooks/companions/voice-companion-openai — \"an important information\" (uncountable noun)\n6. https://docs.mem0.ai/cookbooks/essentials/exporting-memories — comma splice\n7. https://docs.mem0.ai/cookbooks/integrations/tavily-search — \"usecase\" should be \"use case\"\n8. https://docs.mem0.ai/cookbooks/overview — broken parallelism in bullet list\n9. README.md — \"Github App\" should be \"GitHub App\"\n10. https://docs.mem0.ai/platform/overview — table cell not capitalized like other rows\n\n### Suggested Fix\nApply the corrections listed above for each page. I will submit a PR soon addressing all of the issues mentioned.",
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expected: ["documentation"],
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||||
},
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];
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const cliRegressionCase = {
|
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number: 3144,
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title: "Bug Report: Memory Score Does Not Match Expected Relevance in Local Search",
|
||||
body: "### 🐛 Describe the bug\n\n#### Description\n\nWhen using the locally deployed `mem0` server, the returned memory `score` from the `search` interface does not align with the expected semantic relevance. In particular, irrelevant or less relevant memories sometimes receive higher scores than directly related ones.\n\n#### Reproduction Steps\n\n```python\nmem0 = mem0_client(mode=\"local\")\nprint(\"Mem0 client initialized successfully.\")\n\nprint(\"Adding memories...\")\nresult = mem0.add(messages=[\n {\"role\": \"user\", \"content\": \"I like drinking coffee in the morning\"},\n {\"role\": \"user\", \"content\": \"I enjoy reading books at night\"}\n], user_id=\"alice\")\nprint(\"Memory added:\", result)\n\nprint(\"Searching memories...\")\nsearch_result = mem0.search(query=\"coffee\", user_id=\"alice\", top_k=2)\nprint(\"Search results:\", search_result)\n```\n\n#### Actual Output\n\n```json\n{\n \"results\": [\n {\n \"id\": \"5099b5be-c673-4f09-99de-a196f43b6476\",\n \"memory\": \"Likes drinking coffee in the morning\",\n \"score\": 0.5115111920687857\n },\n {\n \"id\": \"08df5c51-c52b-4c45-a5b6-b3f864ea149a\",\n \"memory\": \"Enjoys reading books at night\",\n \"score\": 0.7755568273863331\n }\n ],\n \"relations\": [\n {\"source\": \"coffee\", \"relationship\": \"consumed_in\", \"destination\": \"morning\"},\n {\"source\": \"user_id:_alice\", \"relationship\": \"likes\", \"destination\": \"coffee\"},\n {\"source\": \"user_id:_alice\", \"relationship\": \"likes_drinking\", \"destination\": \"coffee\"},\n {\"source\": \"user_id:_alice\", \"relationship\": \"in_time\", \"destination\": \"morning\"},\n {\"source\": \"user_id:_alice\", \"relationship\": \"drinks_in\", \"destination\": \"morning\"}\n ]\n}\n```\n\n#### Expected Behavior\n\nThe memory `\"Likes drinking coffee in the morning\"` should have a **higher score** than `\"Enjoys reading books at night\"` when querying for `\"coffee\"`, since it is directly semantically related.",
|
||||
};
|
||||
|
||||
let failures = 0;
|
||||
|
||||
function run(name, fn) {
|
||||
try {
|
||||
fn();
|
||||
console.log(`PASS ${name}`);
|
||||
} catch (err) {
|
||||
failures++;
|
||||
console.error(`FAIL ${name}: ${err.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
for (const { number, title, body, expected } of cases) {
|
||||
const text = `${title}
|
||||
|
||||
${body}`;
|
||||
run(`#${number}`, () => {
|
||||
assert.deepStrictEqual(inferComponentLabels(text, keywords), expected);
|
||||
});
|
||||
}
|
||||
|
||||
run('#3144 cliKeywordPrefixSubstringRegression', () => {
|
||||
const text = `${cliRegressionCase.title}
|
||||
|
||||
${cliRegressionCase.body}`;
|
||||
const inferred = inferComponentLabels(text, keywords);
|
||||
assert.ok(!inferred.includes('cli'), `expected 'cli' absent (body contains 'Mem0 client', a substring of the removed 'mem0 cli' term), got ${JSON.stringify(inferred)}`);
|
||||
});
|
||||
|
||||
run('noKeywordMatchReturnsEmptyArray', () => {
|
||||
const text = 'The weather today is sunny and I went for a walk in the park with my dog.';
|
||||
assert.deepStrictEqual(inferComponentLabels(text, keywords), []);
|
||||
});
|
||||
|
||||
run('emptyStringReturnsEmptyArray', () => {
|
||||
assert.deepStrictEqual(inferComponentLabels('', keywords), []);
|
||||
});
|
||||
|
||||
if (failures > 0) {
|
||||
console.error(`
|
||||
${failures} test(s) failed.`);
|
||||
process.exit(1);
|
||||
}
|
||||
console.log(`
|
||||
All ${cases.length + 3} tests passed.`);
|
||||
@@ -16,11 +16,52 @@ jobs:
|
||||
|
||||
- uses: stefanbuck/github-issue-parser@v3
|
||||
id: issue-parser
|
||||
continue-on-error: true
|
||||
with:
|
||||
template-path: .github/ISSUE_TEMPLATE/bug_report.yml
|
||||
|
||||
- uses: redhat-plumbers-in-action/advanced-issue-labeler@v3
|
||||
continue-on-error: true
|
||||
with:
|
||||
issue-form: ${{ steps.issue-parser.outputs.jsonString }}
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
config-path: .github/advanced-issue-labeler.yml
|
||||
|
||||
- name: Infer component from text when the form was not used
|
||||
uses: actions/github-script@v7
|
||||
with:
|
||||
script: |
|
||||
const {
|
||||
componentLabels,
|
||||
inferComponentLabels,
|
||||
loadKeywords,
|
||||
} = require(`${process.env.GITHUB_WORKSPACE}/.github/scripts/infer-component-labels.js`);
|
||||
|
||||
const { data: issue } = await github.rest.issues.get({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
issue_number: context.issue.number,
|
||||
});
|
||||
|
||||
const keywords = loadKeywords(`${process.env.GITHUB_WORKSPACE}/.github/component-keywords.json`);
|
||||
const known = componentLabels(keywords);
|
||||
const existing = issue.labels.map((label) => label.name || label);
|
||||
if (existing.some((name) => known.includes(name))) {
|
||||
core.info(`Component label already present: ${existing.join(', ')}`);
|
||||
return;
|
||||
}
|
||||
|
||||
const labels = inferComponentLabels(`${issue.title}\n\n${issue.body || ''}`, keywords);
|
||||
|
||||
if (labels.length === 0) {
|
||||
core.info('No component could be inferred from the issue text');
|
||||
return;
|
||||
}
|
||||
|
||||
core.info(`Inferred: ${labels.join(', ')}`);
|
||||
await github.rest.issues.addLabels({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
issue_number: context.issue.number,
|
||||
labels,
|
||||
});
|
||||
|
||||
@@ -4,6 +4,10 @@ on:
|
||||
pull_request_target:
|
||||
types: [opened, synchronize, reopened, edited]
|
||||
|
||||
concurrency:
|
||||
group: pr-labeler-${{ github.event.pull_request.number }}
|
||||
cancel-in-progress: true
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
pull-requests: write
|
||||
@@ -22,31 +26,34 @@ jobs:
|
||||
with:
|
||||
script: |
|
||||
const allowed = new Set([
|
||||
'sdk-python', 'sdk-typescript', 'vector-store', 'openclaw',
|
||||
'sdk-python', 'sdk-typescript', 'vector-store', 'plugin',
|
||||
'rest-api', 'openmemory', 'documentation', 'ci', 'cli', 'integrations',
|
||||
]);
|
||||
const body = context.payload.pull_request.body || '';
|
||||
const matches = [...body.matchAll(/\b(?:close[sd]?|fix(?:e[sd])?|resolve[sd]?)\s*:?\s*(?:#|https:\/\/github\.com\/mem0ai\/mem0\/issues\/)(\d+)/gi)];
|
||||
const numbers = [...new Set(matches.map((m) => Number(m[1])))];
|
||||
const umbrella = { plugin: 'integrations' };
|
||||
const { repository } = await github.graphql(
|
||||
`query ($owner: String!, $repo: String!, $number: Int!) {
|
||||
repository(owner: $owner, name: $repo) {
|
||||
pullRequest(number: $number) {
|
||||
closingIssuesReferences(first: 20) {
|
||||
nodes { labels(first: 50) { nodes { name } } }
|
||||
}
|
||||
}
|
||||
}
|
||||
}`,
|
||||
{ owner: context.repo.owner, repo: context.repo.repo, number: context.issue.number },
|
||||
);
|
||||
|
||||
const labels = new Set();
|
||||
for (const issue_number of numbers) {
|
||||
let issue;
|
||||
try {
|
||||
({ data: issue } = await github.rest.issues.get({
|
||||
owner: context.repo.owner,
|
||||
repo: context.repo.repo,
|
||||
issue_number,
|
||||
}));
|
||||
} catch {
|
||||
continue;
|
||||
}
|
||||
if (issue.pull_request) continue;
|
||||
for (const label of issue.labels) {
|
||||
for (const issue of repository.pullRequest.closingIssuesReferences.nodes) {
|
||||
for (const label of issue.labels.nodes) {
|
||||
if (allowed.has(label.name)) labels.add(label.name);
|
||||
}
|
||||
}
|
||||
|
||||
for (const label of [...labels]) {
|
||||
if (umbrella[label]) labels.add(umbrella[label]);
|
||||
}
|
||||
|
||||
if (labels.size > 0) {
|
||||
await github.rest.issues.addLabels({
|
||||
owner: context.repo.owner,
|
||||
|
||||
Reference in New Issue
Block a user