evrmem

# evrmem Skill ## Name `evrmem` ## Description Local Chinese Vector Memory System. Provides semantic memory search and storage for AI agents using local Chinese embedding models (text2vec) and ChromaDB. Supports RAG-based context augmentation. ## When to Use Use this skill when the user asks to: - "Search memories" or "Find related memories" - "Save this to memory" - "Remember this information" - "Search my knowledge base" - "Find past notes about X" - "Add this to my memory" - "What do I know about X" - "RAG retrieval" or "context augmentation" - Query or recall previous learnings ## Prerequisites Install evrmem and initialize: ```bash pip install evrmem evrmem init ``` For China users (mirror): ```bash set HF_ENDPOINT=https://hf-mirror.com # Windows # or export HF_ENDPOINT=https://hf-mirror.com # Linux/Mac evrmem init ``` ## Core Workflow ### 1. Semantic Search (Most Common) ```python from qmd.core.vector_db import vector_db results = vector_db.search("React form warning", top_k=5) for r in results: print(f"[{r['distance']:.3f}] {r['content'][:80]}") ``` Or via CLI: ```bash evrmem search "React form warning" evrmem search "deployment issue" --project myproject ``` ### 2. Add Memory ```python memory_id = vector_db.add_memory( "React StrictMode causes Form.useForm warning", metadata={"project": "mes-demo", "tags": "react,antd"} ) ``` Or via CLI: ```bash evrmem add "Important finding about X" --project myproject --tags react,bug ``` ### 3. Structured Query ```bash # Query by project evrmem query --project mes-demo # Query by tag evrmem query --tag react # List all projects evrmem query --list-projects # List all tags evrmem query --list-tags ``` ### 4. RAG Retrieval ```python result = vector_db.rag("how to fix the form warning", top_k=3) print(result["context"]) ``` Or via CLI: ```bash evrmem rag "how to fix the form warning" evrmem rag "how to fix the form warning" --prompt ``` ### 5. Statistics ```bash evrmem stats ``` ## Configuration Create `~/.evrmem/config.yaml`: ```yaml vector_db: persist_directory: "~/.evrmem/data/qmd_memory" embedding: model_name: "shibing624/text2vec-base-chinese" device: "cpu" # or "cuda" cache_folder: "~/.evrmem/models" rag: top_k: 5 min_similarity: 0.5 logging: level: "WARNING" ``` ## Environment Variables | Variable | Description | Default | |----------|-------------|---------| | `EVREM_DATA_DIR` | Data directory | `~/.evrmem/data/qmd_memory` | | `EVREM_MODEL_NAME` | HuggingFace model name | `shibing624/text2vec-base-chinese` | | `EVREM_LOCAL_MODEL` | Local model path (highest priority) | - | | `EVREM_DEVICE` | Device for inference | `cpu` | | `EVREM_TOP_K` | Default retrieval count | `5` | | `EVREM_MIN_SIM` | Minimum similarity threshold | `0.5` | | `EVREM_LOG_LEVEL` | Logging level | `WARNING` | | `EVREM_LOCAL_FILES_ONLY` | Disable network access | `false` | | `HF_ENDPOINT` | HuggingFace mirror endpoint | - | ## Response Format When reporting search results, use this format: ``` ## evrmem Search Results **Query:** "user query" **Results:** N memories found | Score | Project | Content | |-------|---------|---------| | 0.723 | mes-demo | React StrictMode causes Form.useForm warning... | | 0.681 | docs | Deployment script timeout issue... | ### Top Match **Project:** mes-demo | **Tags:** react,antd > React StrictMode causes Form.useForm warning... ``` When adding memory: ``` ## Memory Saved **ID:** abc123 **Project:** mes-demo **Tags:** react **Content:** React StrictMode causes Form.useForm warning... Use `evrmem search "React StrictMode"` to retrieve later. ``` ## Installation for Agent If `evrmem` is not installed: ```python import subprocess subprocess.run(["pip", "install", "evrmem"], check=True) # Initialize on first use (downloads ~400MB model) subprocess.run(["evrmem", "init"], check=True) ``` For China users, set mirror before init: ```python import os os.environ["HF_ENDPOINT"] = "https://hf-mirror.com" subprocess.run(["evrmem", "init"], check=True) ``` ## Edge Cases - **Model download fails**: Set `HF_ENDPOINT=https://hf-mirror.com` before `evrmem init` - **NumPy errors**: Run `pip install "numpy<2" --force-reinstall` - **Offline/air-gapped**: Download model on connected machine, copy `~/.evrmem/models` to offline machine, set `EVREM_LOCAL_FILES_ONLY=true` - **Empty search results**: Try broader terms or check if memories exist with `evrmem query --list-projects` - **Similarity too low**: Adjust `--top-k` or lower `EVREM_MIN_SIM` threshold - **Slow search**: Use CPU by default; set `EVREM_DEVICE=cuda` if GPU available