docs/components/embedders/models/azure_openai.mdx
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.
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 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.
<Note> If an API key is provided, it will be used for authentication over an Azure Identity </Note>
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",
}
}
}
}
}
Refer to Azure Identity troubleshooting tips for setting up an Azure Identity credential.
Here are the parameters available for configuring Azure OpenAI embedder: <Tabs> <Tab title="Python">
| Parameter | Description | Default Value |
|---|---|---|
model | The name of the embedding model to use | text-embedding-3-small |
embedding_dims | Dimensions of the embedding model | 1536 |
azure_kwargs | The Azure OpenAI configs | config_keys |