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:

Prerequisites

Install evrmem and initialize:

pip install evrmem
evrmem init

For China users (mirror):

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)

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:

evrmem search "React form warning"
evrmem search "deployment issue" --project myproject

2. Add Memory

memory_id = vector_db.add_memory(
    "React StrictMode causes Form.useForm warning",
    metadata={"project": "mes-demo", "tags": "react,antd"}
)

Or via CLI:

evrmem add "Important finding about X" --project myproject --tags react,bug

3. Structured Query

# 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

result = vector_db.rag("how to fix the form warning", top_k=3)
print(result["context"])

Or via CLI:

evrmem rag "how to fix the form warning"
evrmem rag "how to fix the form warning" --prompt

5. Statistics

evrmem stats

Configuration

Create ~/.evrmem/config.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:

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:

import os
os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
subprocess.run(["evrmem", "init"], check=True)

Edge Cases

Create ~/.evrmem/config.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"