Azure OpenAI - Mem0

Documentation Index

Fetch the complete documentation index at: /llms.txt

Use this file to discover all available pages before exploring further.

To use Azure OpenAI embedding models, set the EMBEDDING_AZURE_OPENAI_API_KEY, EMBEDDING_AZURE_DEPLOYMENT, EMBEDDING_AZURE_ENDPOINT and EMBEDDING_AZURE_API_VERSION environment variables. You can obtain the Azure OpenAI API key from the Azure Portal.

Usage

Python

import os
from mem0 import Memory

os.environ["EMBEDDING_AZURE_OPENAI_API_KEY"] = "your-api-key"
os.environ["EMBEDDING_AZURE_DEPLOYMENT"] = "your-deployment-name"
os.environ["EMBEDDING_AZURE_ENDPOINT"] = "your-api-base-url"
os.environ["EMBEDDING_AZURE_API_VERSION"] = "version-to-use"

os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM

config = {
    "embedder": {
        "provider": "azure_openai",
        "config": {
            "model": "text-embedding-3-large",
            "azure_kwargs": {
                  "api_version": "",
                  "azure_deployment": "",
                  "azure_endpoint": "",
                  "api_key": "",
                  "default_headers": {
                    "CustomHeader": "your-custom-header",
                  }
              }
        }
    }
}

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 thriller movies but I love sci-fi movies."},\
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}\
]
m.add(messages, user_id="john")

TypeScript

import { Memory } from 'mem0ai/oss';

const config = {
    embedder: {
        provider: "azure_openai",
        config: {
            model: "text-embedding-3-large",
            modelProperties: {
                endpoint: "your-api-base-url",
                deployment: "your-deployment-name",
                apiVersion: "version-to-use",
            }
        }
    }
}

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 thriller movies but I love sci-fi movies."},\
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}\
]

await memory.add(messages, { userId: "john" });

As an alternative to using an API key, the Azure Identity credential chain can be used to authenticate with Azure OpenAI role-based security.

If an API key is provided, it will be used for authentication over an Azure Identity.

Below is a sample configuration for using Mem0 with Azure OpenAI and Azure Identity:

import os
from mem0 import Memory
# You can set the values directly in the config dictionary or use environment variables

os.environ["LLM_AZURE_DEPLOYMENT"] = "your-deployment-name"
os.environ["LLM_AZURE_ENDPOINT"] = "your-api-base-url"
os.environ["LLM_AZURE_API_VERSION"] = "version-to-use"

config = {
    "llm": {
        "provider": "azure_openai_structured",
        "config": {
            "model": "your-deployment-name",
            "temperature": 0.1,
            "max_tokens": 2000,
            "azure_kwargs": {
                  "azure_deployment": "<your-deployment-name>",
                  "api_version": "<version-to-use>",
                  "azure_endpoint": "<your-api-base-url>",
                  "default_headers": {
                    "CustomHeader": "your-custom-header",
                  }
              }
        }
    }
}

Config

Here are the parameters available for configuring Azure OpenAI embedder: