Create a Fireworks AI inference endpoint

PUT /_inference/{task_type}/{fireworksai_inference_id}

Create an inference endpoint to perform an inference task with the fireworksai service.

Required authorization

Authentication

Authorization: <value>

Elasticsearch APIs use key-based authentication. You must create an API key and use the encoded value in the request header. For example:

		curl -X GET "${ES_URL}/_cat/indices?v=true" \
  -H "Authorization: ApiKey ${API_KEY}"

For more information about where to find API keys for the Elasticsearch endpoint (${ES_URL}) for a project, go to Get started with Elasticsearch Serverless.

Path parameters

Query parameters

Request body

chunking_settings object: The chunking configuration object. Applies only to the text_embedding task type. Not applicable to the completion or chat_completion task types.; Reference
service enum required: The type of service supported for the specified task type. In this case, fireworksai.; Values: fireworksai
service_settings object required: Settings used to install the inference model. These settings are specific to the fireworksai service.
task_settings object: Settings to configure the inference task. Applies only to the completion or chat_completion task types. Not applicable to the text_embedding task type. These settings are specific to the task type you specified.

Response

application/json

chunking_settings object: The chunking configuration object. Applies only to the embedding, sparse_embedding and text_embedding task types. Not applicable to the rerank, completion, or chat_completion task types.
service string required: The service type
service_settings object required: Settings specific to the service
task_settings object: Task settings specific to the service and task type
inference_id string required: The inference Id
task_type enum required: The task type; Values: chat_completion completion text_embedding

Examples

Request Console 1 / 8 languages

Run PUT _inference/text_embedding/my-fireworks-embeddings to create an inference endpoint that performs a text_embedding task.

Console Kibana Dev Tools

PUT _inference/text_embedding/my-fireworks-embeddings
{
    "service": "fireworksai",
    "service_settings": {
        "api_key": "your-api-key",
        "model_id": "fireworks/qwen3-embedding-8b"
    }
}

JavaScript Elasticsearch JavaScript Client

const response = await client.inference.put({
  task_type: "text_embedding",
  inference_id: "my-fireworks-embeddings",
  inference_config: {
    service: "fireworksai",
    service_settings: {
      api_key: "your-api-key",
      model_id: "fireworks/qwen3-embedding-8b",
    },
  },
});

Python Elasticsearch Python Client

resp = client.inference.put(
    task_type="text_embedding",
    inference_id="my-fireworks-embeddings",
    inference_config={
        "service": "fireworksai",
        "service_settings": {
            "api_key": "your-api-key",
            "model_id": "fireworks/qwen3-embedding-8b"
        }
    },
)

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/text_embedding/my-fireworks-embeddings" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "service": "fireworksai",
    "service_settings": {
      "api_key": "your-api-key",
      "model_id": "fireworks/qwen3-embedding-8b"
    }
  }'

Java Elasticsearch Java Client

client.inference().put(p -> p
    .inferenceId("my-fireworks-embeddings")
    .taskType(TaskType.TextEmbedding)
    .inferenceConfig(i -> i
        .service("fireworksai")
        .serviceSettings(JsonData.fromJson("""
{"api_key":"your-api-key","model_id":"fireworks/qwen3-embedding-8b"}
"""))
    )
);

C# Elasticsearch .NET Client

var response = await client.Inference
    .PutAsync(new PutInferenceRequest()
    {
        InferenceId = "my-fireworks-embeddings",
        TaskType = TaskType.TextEmbedding,
        InferenceConfig = new()
        {
            Service = "fireworksai",
            ServiceSettings = JsonSerializer.Deserialize<JsonElement>("""
            {
              "api_key": "your-api-key",
              "model_id": "fireworks/qwen3-embedding-8b"
            }
            """)
        }
    });

PHP Elasticsearch PHP Client

$resp = $client->inference()->put([
    "task_type" => "text_embedding",
    "inference_id" => "my-fireworks-embeddings",
    "body" => [
        "service" => "fireworksai",
        "service_settings" => [
            "api_key" => "your-api-key",
            "model_id" => "fireworks/qwen3-embedding-8b",
        ],
    ],
]);

Ruby Elasticsearch Ruby Client

response = client.inference.put(
  task_type: "text_embedding",
  inference_id: "my-fireworks-embeddings",
  body: {
    "service": "fireworksai",
    "service_settings": {
      "api_key": "your-api-key",
      "model_id": "fireworks/qwen3-embedding-8b"
    }
  }
)

Response

A successful response when creating a Fireworks AI text_embedding inference endpoint.

{
    "inference_id": "my-fireworks-embeddings",
    "task_type": "text_embedding",
    "service": "fireworksai",
    "service_settings": {
        "model_id": "fireworks/qwen3-embedding-8b",
        "url": "https://api.fireworks.ai/inference/v1/embeddings",
        "similarity": "cosine",
        "dimensions": 4096,
        "rate_limit": {
            "requests_per_minute": 6000
        }
    },
    "chunking_settings": {
        "strategy": "sentence",
        "max_chunk_size": 250,
        "sentence_overlap": 1
    }
}

Request Console 1 / 2 languages

Create a Fireworks AI text embedding endpoint with custom dimensions, similarity, and rate limit.

Console Kibana Dev Tools

PUT /_inference/TASK_TYPE/FIREWORKSAI_INFERENCE_ID
{
    "service": "fireworksai",
    "service_settings": {
        "api_key": "your-api-key",
        "model_id": "fireworks/qwen3-embedding-8b",
        "dimensions": 1024,
        "similarity": "cosine",
        "rate_limit": {
            "requests_per_minute": 6000
        }
    }
}

Generated from the API spec

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/${TASK_TYPE}/${FIREWORKSAI_INFERENCE_ID}" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
      "service": "fireworksai",
      "service_settings": {
          "api_key": "your-api-key",
          "model_id": "fireworks/qwen3-embedding-8b",
          "dimensions": 1024,
          "similarity": "cosine",
          "rate_limit": {
              "requests_per_minute": 6000
          }
      }
  }'

Generated from the API spec

Response

A successful response when creating a Fireworks AI text_embedding inference endpoint.

{
    "inference_id": "my-fireworks-embeddings",
    "task_type": "text_embedding",
    "service": "fireworksai",
    "service_settings": {
        "model_id": "fireworks/qwen3-embedding-8b",
        "url": "https://api.fireworks.ai/inference/v1/embeddings",
        "similarity": "cosine",
        "dimensions": 4096,
        "rate_limit": {
            "requests_per_minute": 6000
        }
    },
    "chunking_settings": {
        "strategy": "sentence",
        "max_chunk_size": 250,
        "sentence_overlap": 1
    }
}

Request Console 1 / 2 languages

Run PUT _inference/chat_completion/my-fireworks-chat to create an inference endpoint that performs a chat_completion task.

Console Kibana Dev Tools

PUT _inference/chat_completion/my-fireworks-chat
{
    "service": "fireworksai",
    "service_settings": {
        "api_key": "your-api-key",
        "model_id": "accounts/fireworks/models/deepseek-v3p1"
    }
}

Generated from the API spec

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/chat_completion/my-fireworks-chat" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
      "service": "fireworksai",
      "service_settings": {
          "api_key": "your-api-key",
          "model_id": "accounts/fireworks/models/deepseek-v3p1"
      }
  }'

Generated from the API spec

Response

A successful response when creating a Fireworks AI text_embedding inference endpoint.

{
    "inference_id": "my-fireworks-embeddings",
    "task_type": "text_embedding",
    "service": "fireworksai",
    "service_settings": {
        "model_id": "fireworks/qwen3-embedding-8b",
        "url": "https://api.fireworks.ai/inference/v1/embeddings",
        "similarity": "cosine",
        "dimensions": 4096,
        "rate_limit": {
            "requests_per_minute": 6000
        }
    },
    "chunking_settings": {
        "strategy": "sentence",
        "max_chunk_size": 250,
        "sentence_overlap": 1
    }
}

Request Console 1 / 2 languages

Run PUT _inference/completion/my-fireworks-completion to create an inference endpoint that performs a completion task.

Console Kibana Dev Tools

PUT _inference/completion/my-fireworks-completion
{
    "service": "fireworksai",
    "service_settings": {
        "api_key": "your-api-key",
        "model_id": "accounts/fireworks/models/deepseek-v3p1"
    }
}

Generated from the API spec

curl Shell

curl -X PUT "$ELASTICSEARCH_URL/_inference/completion/my-fireworks-completion" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
      "service": "fireworksai",
      "service_settings": {
          "api_key": "your-api-key",
          "model_id": "accounts/fireworks/models/deepseek-v3p1"
      }
  }'

Generated from the API spec

Response

A successful response when creating a Fireworks AI text_embedding inference endpoint.

{
    "inference_id": "my-fireworks-embeddings",
    "task_type": "text_embedding",
    "service": "fireworksai",
    "service_settings": {
        "model_id": "fireworks/qwen3-embedding-8b",
        "url": "https://api.fireworks.ai/inference/v1/embeddings",
        "similarity": "cosine",
        "dimensions": 4096,
        "rate_limit": {
            "requests_per_minute": 6000
        }
    },
    "chunking_settings": {
        "strategy": "sentence",
        "max_chunk_size": 250,
        "sentence_overlap": 1
    }
}

Required authorization

Authentication

Path parameters

Query parameters

Request body { }

Response

Request body