EverMemOS Configuration Guide

EverMemOS Configuration Guide

This guide provides a detailed explanation of the configuration options in env.template. Before deploying EverMemOS, please copy env.template to .env and fill in your actual configuration values according to this guide.

⚠️ Security Notice: The .env file contains sensitive information (such as API keys and database passwords). Be sure to add it to .gitignore and NEVER commit it to version control systems.

1. LLM Configuration

Configuration for the LLM service used for memory extraction, Agentic retrieval, and Q&A generation.

2. Vectorize Service Configuration

Configuration for converting text to vectors (Embeddings), supporting DeepInfra and vLLM.

Advanced Settings:

• VECTORIZE_TIMEOUT: Request timeout (seconds)
• VECTORIZE_MAX_RETRIES: Maximum retry attempts
• VECTORIZE_BATCH_SIZE: Batch size
• VECTORIZE_MAX_CONCURRENT: Maximum concurrent requests
• VECTORIZE_ENCODING_FORMAT: Encoding format, usually float

3. Rerank Service Configuration

Configuration for re-ranking retrieval results to improve relevance.

Advanced Settings:

• RERANK_TIMEOUT: Timeout (seconds)
• RERANK_BATCH_SIZE: Batch size
• RERANK_MAX_CONCURRENT: Maximum concurrent requests

4. Database Configuration

EverMemOS relies on multiple database services, typically started via Docker Compose.

Redis

Used for caching and distributed locks.

• REDIS_HOST: Host address (default localhost)
• REDIS_PORT: Port (default 6379)
• REDIS_DB: Database index (default 8)

MongoDB

Primary database, stores memory cells, profiles, and conversation records.

• MONGODB_HOST: Host address (default localhost)
• MONGODB_PORT: Port (default 27017)
• MONGODB_USERNAME: Username (default admin)
• MONGODB_PASSWORD: Password (default memsys123)
• MONGODB_DATABASE: Database name (default memsys)

Elasticsearch

Used for keyword retrieval (BM25).

• ES_HOSTS: Service address (default http://localhost:19200)
• SELF_ES_INDEX_NS: Index namespace (default memsys)

Milvus

Vector database, used for semantic retrieval.

• MILVUS_HOST: Host address (default localhost)
• MILVUS_PORT: Port (default 19530)
• SELF_MILVUS_COLLECTION_NS: Collection namespace (default memsys)

5. Other Configuration

API Server

• API_BASE_URL: Base URL for V1 API, used for client connections (default http://localhost:1995)

Environment & Logging

• LOG_LEVEL: Log level (INFO, DEBUG, WARNING, ERROR)
• ENV: Environment identifier (dev, prod)
• MEMORY_LANGUAGE: Primary system language (zh, en)

Configuration Examples

1. Using DeepInfra (Recommended)

VECTORIZE_PROVIDER=deepinfra
VECTORIZE_API_KEY=your_key_here
VECTORIZE_BASE_URL=https://api.deepinfra.com/v1/openai
VECTORIZE_MODEL=Qwen/Qwen3-Embedding-4B

RERANK_PROVIDER=deepinfra
RERANK_API_KEY=your_key_here
RERANK_BASE_URL=https://api.deepinfra.com/v1/inference
RERANK_MODEL=Qwen/Qwen3-Reranker-4B

2. Using Local vLLM

VECTORIZE_PROVIDER=vllm
VECTORIZE_API_KEY=none
VECTORIZE_BASE_URL=http://localhost:8000/v1
VECTORIZE_MODEL=Qwen3-Embedding-4B
VECTORIZE_DIMENSIONS=0  # vLLM sometimes requires disabling this parameter

RERANK_PROVIDER=vllm
RERANK_API_KEY=none
RERANK_BASE_URL=http://localhost:12000/score
RERANK_MODEL=Qwen3-Reranker-4B

ℹ️ vLLM Deployment Tips:

• Embedding Models (Supported since v0.4.0+):

  vllm serve Qwen/Qwen3-Embedding-4B --task embed --trust-remote-code
  

• Reward/Reranker Models (See vLLM PR #19260 for details):

  vllm serve Qwen/Qwen3-Reranker-4B --task reward --trust-remote-code
  

  Note: Use --task reward for Reranker models.