vLLM

Configure vLLM as an LLM provider in Mem0 for high-performance local inference with GPU-optimized serving.

vLLM is a high-performance inference engine for large language models that provides significant performance improvements for local inference. It’s designed to maximize throughput and memory efficiency for serving LLMs.

Prerequisites

1. Install vLLM:

   pip install vllm
   

2. Start vLLM server:

   # For testing with a small model
   vllm serve microsoft/DialoGPT-medium --port 8000
   
   # For production with a larger model (requires GPU)
   vllm serve Qwen/Qwen2.5-32B-Instruct --port 8000
   

Usage

Python

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your-api-key"  # used for embedding model

config = {
    "llm": {
        "provider": "vllm",
        "config": {
            "model": "Qwen/Qwen2.5-32B-Instruct",
            "vllm_base_url": "http://localhost:8000/v1",
            "temperature": 0.1,
            "max_tokens": 2000,
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thrillers, but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})

import { Memory } from "mem0ai/oss";

const config = {
  llm: {
    provider: "vllm",
    config: {
      model: "Qwen/Qwen2.5-32B-Instruct",
      baseURL: "http://localhost:8000/v1",
      apiKey: process.env.VLLM_API_KEY || "vllm-api-key",
      temperature: 0.1,
      maxTokens: 2000,
    },
  },
};

const memory = new Memory(config);
const messages = [
  {
    role: "user",
    content: "I'm planning to watch a movie tonight. Any recommendations?",
  },
  {
    role: "assistant",
    content: "How about thriller movies? They can be quite engaging.",
  },
  {
    role: "user",
    content: "I'm not a big fan of thrillers, but I love sci-fi movies.",
  },
  {
    role: "assistant",
    content: "Got it! I'll avoid thrillers and suggest sci-fi movies instead.",
  },
];
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });

Configuration Parameters

Parameter	Description	Default	Environment Variable
model	Model name running on vLLM server	"Qwen/Qwen2.5-32B-Instruct"	-
vllm_base_url	vLLM server URL	"http://localhost:8000/v1"	VLLM_BASE_URL
api_key	API key (dummy for local)	"vllm-api-key"	VLLM_API_KEY
temperature	Sampling temperature	0.1	-
max_tokens	Maximum tokens to generate	2000	-

Environment Variables

You can set these environment variables instead of specifying them in config:

export VLLM_BASE_URL="http://localhost:8000/v1"
export VLLM_API_KEY="your-vllm-api-key"
export OPENAI_API_KEY="your-openai-api-key"  # for embeddings

Benefits

• High Performance: 2-24x faster inference than standard implementations
• Memory Efficient: Optimized memory usage with PagedAttention
• Local Deployment: Keep your data private and reduce API costs
• Easy Integration: Drop-in replacement for other LLM providers
• Flexible: Works with any model supported by vLLM

Troubleshooting

1. Server not responding: Make sure vLLM server is running

   curl http://localhost:8000/health
   

2. 404 errors: Ensure correct base URL format

   "vllm_base_url": "http://localhost:8000/v1"  # Note the /v1
   

3. Model not found: Check model name matches server

4. Out of memory: Try smaller models or reduce max_model_len

   vllm serve Qwen/Qwen2.5-32B-Instruct --max-model-len 4096
   

Config

All available parameters for the vllm config are present in Master List of All Params in Config.