# Why weaviate client (typescript) is not using configured text2VecAzureOpenAI vectorizer?

**URL:** <https://forum.weaviate.io/t/why-weaviate-client-typescript-is-not-using-configured-text2vecazureopenai-vectorizer/7549>\
**Category:** Support\
**Tags:** typescript, azure\
**Created:** [November 12, 2024, 9:03am UTC](https://forum.weaviate.io/t/why-weaviate-client-typescript-is-not-using-configured-text2vecazureopenai-vectorizer/7549 "2024-11-12T09:03:29Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![Oleksandr\_Yakovliev](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/oleksandr_yakovliev/32/2848_2.png) [@Oleksandr\_Yakovliev](https://forum.weaviate.io/u/Oleksandr_Yakovliev)\
**Post date:** [November 12, 2024, 9:03am UTC](https://forum.weaviate.io/t/why-weaviate-client-typescript-is-not-using-configured-text2vecazureopenai-vectorizer/7549/1 "2024-11-12T09:03:29Z")

</div>

### Description

I’m trying to insert documents with inverted indexes into weaviate (local instance) but embeddings aren’t being created. As embedding model I’m using Azure OpenAI model “text-embedding-3-small”. When collection is being inserted I’m getting the next error for each document:

```auto
{
      message: 'API Key: no api key found neither in request header: X-Openai-Api-Key nor in environment variable under OPENAI_APIKEY',
      object: [Object],
      originalUuid: undefined
    }

```

**Question:** why the client is trying to use **X-Openai-Api-Key** key instead of **X-Azure-Api-Key** for **text2VecAzureOpenAI** vectorizer? I also tried to replace **text2vec-openai** module in docker with **text2vec-azure-openai** one but got the error that such module doesn’t exists. When I replaced **X-Azure-Api-Key** with **X-Openai-Api-Key** the client tried to connect to OpenAI API and not Azure.

Is it possible to use remote (azure) embedding model for local weaviate instance running in docker?

Here is my config:

Connection to local instance (working):

```auto
const client = await weaviate.connectToLocal({
      host: "172.16.41.55",
      port: 8080,
      grpcPort: 50051, 
      headers: {
        'X-Azure-Api-Key': this.embeddings.azureOpenAIApiKey || '',
      }
    });
await client.isReady()

```

Create collection function call:

```auto
client.collections.create({
      name: `${collection}_${this.context.id}`,
      properties: [
        {
          name: 'document',
          dataType: dataType.TEXT,
          description: 'Splitted document' as const,
          vectorizePropertyName: true,
        },
      ],
      invertedIndex: configure.invertedIndex({
        indexNullState: true,
        indexPropertyLength: true,
        indexTimestamps: true,
      }),
      vectorizers: [
        weaviate.configure.vectorizer.text2VecAzureOpenAI(
          {
            name: 'title_vector',
            sourceProperties: ['title'],
            resourceName: this.embeddings.azureOpenAIApiInstanceName || '',
            deploymentId: this.embeddings.azureOpenAIApiDeploymentName || '',
          },
        ),
      ],
});

```

### Server Setup Information

- Weaviate Server Version: **1.27.1**
- Deployment Method: **docker**
- Multi Node? Number of Running Nodes: 1
- Client Language and Version: **TS (3.2.2)**
- Multitenancy?:

### Any additional Information

Weaviate service in docker-compose file:

```auto
weaviate:
    command: --host 0.0.0.0 --port '8080' --scheme http
    container_name: dowow-weaviate
    image: cr.weaviate.io/semitechnologies/weaviate:1.27.1
    restart: always
    volumes:
      - weaviate_data:/var/lib/weaviate
    networks:
      dowow:
        ipv4_address: 172.16.41.55
    ports:
    - 8086:8080
    - 50051:50051
    - 2112:2112
    environment:
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      DEFAULT_VECTORIZER_MODULE: 'text2vec-openai'
      ENABLE_MODULES: 'text2vec-openai'
      CLUSTER_HOSTNAME: 'node1'

volumes:
  weaviate_data:
    driver: local

```

Response from /v1/meta endpoint:

```auto
{
    "grpcMaxMessageSize": 10485760,
    "hostname": "http://[::]:8080",
    "modules": {
        "text2vec-openai": {
            "documentationHref": "https://platform.openai.com/docs/guides/embeddings/what-are-embeddings",
            "name": "OpenAI Module"
        }
    },
    "version": "1.27.1"
}

```

---

<div class="post-metadata">

**Author:** ![DudaNogueira](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/dudanogueira/32/7846_2.png) [@DudaNogueira](https://forum.weaviate.io/u/DudaNogueira)\
**Post date:** [November 20, 2024, 6:54pm UTC](https://forum.weaviate.io/t/why-weaviate-client-typescript-is-not-using-configured-text2vecazureopenai-vectorizer/7549/2 "2024-11-20T18:54:02Z")

</div>

Hi!

Sorry for the delay here ☹

I was not able to reproduce this.

Here is the code I used:

```typescript
import weaviate, { Collection, WeaviateClient } from 'weaviate-client';

async function runFullExample() {
    const client = await weaviate.connectToLocal({
        host: process.env.WEAVIATE_HOST || 'localhost',
        port: parseInt(process.env.WEAVIATE_PORT || '8080'),
        grpcPort: parseInt(process.env.WEAVIATE_GRPC_PORT || '50051'),
        headers: {
            'X-Azure-Api-Key': "my api key here",
        }
    });
    console.log(`Server Version: ${(await client.getMeta()).version}`)
    // delete test collection
    await client.collections.delete("JeopardyQuestions");
    // create test collection
    const collection = await client.collections.create({
        name: 'JeopardyQuestions',
        properties: [
            {
                name: 'category',
                dataType: 'text',
            },
            {
                name: 'question',
                dataType: 'text',
            },
            {
                name: 'answer',
                dataType: 'text',
            },
        ],

        vectorizers: [
            weaviate.configure.vectorizer.text2VecAzureOpenAI({
                name: 'my_vector',
                sourceProperties: ['category', 'answer', 'question'],
                resourceName: 'duda-instance',
                deploymentId: 'duda-deployment-id'
              },
            ),
        ],
    });
    // add some data
    await collection.data.insert({
        "category": "example",
        "question": "is this an example?",
        "answer": "yes! this is an example."
    })
    // show data
    const query = await collection.query.fetchObjects({
        includeVector: true
    })
    await query.objects.map(object=>{
        console.log(object.vectors)
    })
}
runFullExample();

```

Let me know if this helps!