> ## Documentation Index
> Fetch the complete documentation index at: https://docs.keywordsai.co/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

You could use Keywords AI's unified LLM API to call Embeddings model to turn text into numbers, unlocking use cases like search.

Keywords AI now supports `text-embedding-3-large`, `text-embedding-3-small`, and `text-embedding-ada-002` models from OpenAI.

| Model                    | Description                                                                       | Output Dimension |
| ------------------------ | --------------------------------------------------------------------------------- | ---------------- |
| `text-embedding-3-large` | Most capable embedding model for both english and non-english tasks               | 3,072            |
| `text-embedding-3-small` | Increased performance over 2nd generation ada embedding model                     | 1,536            |
| `text-embedding-ada-002` | Most capable 2nd generation embedding model, replacing 16 first generation models | 1,536            |

## Integration steps:

<Steps>
  <Step title="Get your OpenAI API key">
    Go to [OpenAI API plaform](https://platform.openai.com/api-keys) to get your OpenAI API key.
  </Step>

  <Step title="Add OpenAI's API key in Providers">
    You should add your OpenAI's API key on [Keywords AI credentials page](https://platform.keywordsai.co/platform/api/providers).

    <img height="200" width="500" src="https://keywordsai-static.s3.us-east-1.amazonaws.com/docs/settings/providers.jpg" />
  </Step>

  <Step title="Call your Embeddings model">
    <CodeGroup>
      ```Python OpenAI Python theme={"system"}
      from openai import OpenAI

      client = OpenAI(
          base_url="https://api.keywordsai.co/api/",
          api_key="YOUR_KEYWORDSAI_API_KEY",
      )

      response = client.embeddings.create(
          model="text-embedding-3-small", # or "text-embedding-3-large" or "text-embedding-ada-002"
          input="Your text string goes here",
          extra_body={"customer_identifier": "customer_11"}  # All Keywords AI parameters are supported
      )
      ```

      ```TypeScript OpenAI TypeScript theme={"system"}
      import OpenAI from "openai";

      const openai = new OpenAI({
        baseURL: "https://api.keywordsai.co/api/",
        apiKey: "YOUR_KEYWORDSAI_API_KEY",
      });

      const embedding = await openai.embeddings.create({
        model: "text-embedding-3-small",
        input: "Your text string goes here",
        encoding_format: "float",
        extra_body: { customer_identifier: "customer_11" } // All Keywords AI parameters are supported
      });

      ```
    </CodeGroup>
  </Step>
</Steps>

## OpenAI parameters

<ParamField path="model" type="string" required>
  ID of the model to use. Supported models are `text-embedding-3-large`, `text-embedding-3-small`, and `text-embedding-ada-002`.
</ParamField>

<ParamField path="input" type="string or array" required>
  nput text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less.
</ParamField>

<ParamField path="encoding_format" type="string" Defaults={"float"}>
  The format to return the embeddings in. Can be either `float` or `base64`.
</ParamField>

<ParamField path="dimensions" type="integer">
  The number of dimensions the resulting output embeddings should have. Only supported in `text-embedding-3` and later models.
</ParamField>

## Keywords AI parameters

See how to make a standard Keywords AI API call in the [Quick Start](/get-started/quickstart/gateway) guide.

### Generation parameters

<ParamField path="customer_credentials" type="object">
  You can pass in your customer's credentials for [supported providers](/integration/overview) and use their credits when our proxy is calling models from those providers. <br />See details [here](/documentation/admin/llm_provider_keys)

  <Accordion title="Example">
    ```json theme={"system"}
    "customer_credentials": {

      "openai": {
        "api_key": "YOUR_OPENAI_API_KEY",
      }
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="disable_log" type="boolean" deafault={false}>
  When set to true, only the request and [performance
  metrics](/documentation/products/dashboard/quickstart) will be recorded, input
  and output messages will be omitted from the log.
</ParamField>

### Observability parameters

<ParamField path="metadata" type="dict">
  You can add any key-value pair to this metadata field for your reference. Check the [details of metadata here.](/documentation/products/logs/configuration/custom_properties)

  Contact [team@keywordsai.co](mailto:team@keywordsai.co) if you need extra parameter support for your use case.

  <Accordion title="Example">
    ```json theme={"system"}
    {
      "my_value_key": "my_value"
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="customer_identifier" type="string">
  Use this as a tag to identify the user associated with the API call. See the [details of customer identifier here.](/documentation/products/users/customer-identifier)

  <Accordion title="Example">
    ```json theme={"system"}
    {
        //...other_params,
        "customer_identifier": "user_123"
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="customer_email" type="string">
  This is the email address of the user associated with the API call. You can add your corresponding user's email address to the request.

  You could also edit customer's emails on the platform. Check the [details of user editing here.](/api-endpoints/observe/users/user-creation-endpoint)

  <Accordion title="Example">
    ```json theme={"system"}
    {
        //...other_params,
        "customer_email": "hendrix@keywordsai.co"
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="thread_identifier" type="string">
  See logs as a conversation log thread. Pass all logs with the same `thread_identifier` to see them in the same thread.

  <Accordion title="Example">
    ```json theme={"system"}
    {
        //...other_params,
        "thread_identifier": "test_thread"
    }
    ```
  </Accordion>
</ParamField>

<ParamField path="request_breakdown" type="boolean" default={false}>
  Adding this returns the summarization of the response in the response body. If streaming is on, the metrics will be streamed as the last chunk.

  <Accordion title="Properties">
    <CodeGroup>
      ```json Regular Response theme={"system"}
      {
        "id": "chatcmpl-7476cf3f-fcc9-4902-a548-a12489856d8a",
        //... main part of the response body ...
        "request_breakdown": {
          "prompt_tokens": 6,
          "completion_tokens": 9,
          "cost": 4.8e-5,
          "prompt_messages": [
            {
              "role": "user",
              "content": "How are you doing today?"
            }
          ],
          "completion_message": {
            "content": " I'm doing well, thanks for asking!",
            "role": "assistant"
          },
          "model": "claude-2",
          "cached": false,
          "timestamp": "2024-02-20T01:23:39.329729Z",
          "status_code": 200,
          "stream": false,
          "latency": 1.8415491580963135,
          "scores": {},
          "category": "Questions",
          "metadata": {},
          "routing_time": 0.18612787732854486,
          "full_request": {
            "messages": [
              {
                "role": "user",
                "content": "How are you doing today?"
              }
            ],
            "model": "claude-2",
            "logprobs": true
          },
          "sentiment_score": 0
        }
      }
      ```

      ```json Streaming Response theme={"system"}
      //... other chunks ...
      // The following is the last chunk
      {
        "id": "request_breakdown",
        "choices": [
          {
            "delta": { "content": null, "role": "assistant" },
            "finish_reason": "stop",
            "request_breakdown": {
              "prompt_tokens": 6,
              "completion_tokens": 9,
              "cost": 4.8e-5, //  In usd
              "prompt_messages": [
                {
                  "role": "user",
                  "content": "How are you doing today?"
                }
              ],
              "completion_message": {
                "content": " I'm doing well, thanks for asking!",
                "role": "assistant"
              },
              "model": "claude-2",
              "cached": false,
              "timestamp": "2024-02-20T01:23:39.329729Z",
              "status_code": 200,
              "stream": false,
              "latency": 1.8415491580963135, // in seconds
              "scores": {},
              "category": "Questions",
              "metadata": {},
              "routing_time": 0.18612787732854486, // in seconds
              "full_request": {
                "messages": [
                  {
                    "role": "user",
                    "content": "How are you doing today?"
                  }
                ],
                "model": "claude-2",
                "logprobs": true
              },
              "sentiment_score": 0
            },
            "index": 0,
            "message": { "content": null, "role": "assistant" }
          }
        ],
        "created": 1706100589,
        "model": "extra_parameter",
        "object": "chat.completion.chunk",
        "system_fingerprint": null,
        "usage": {}
      }
      ```
    </CodeGroup>
  </Accordion>
</ParamField>

<RequestExample />