openai

Will be used to build the Authorization header, like so: Authorization: Bearer $api_key

Default: env.OPENAI_API_KEY

The base URL for the API.

Default: https://api.openai.com/v1

Additional headers to send with the request.

Example:

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client<llm> MyClient {
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  provider openai
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  options {
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    api_key env.MY_OPENAI_KEY
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    model "gpt-3.5-turbo"
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    headers {
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      "X-My-Header" "my-value"
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    }
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  }
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}

The role to use if the role is not in the allowed_roles. Default: "user" usually, but some models like OpenAI’s gpt-4o will use "system"

Picked the first role in allowed_roles if not “user”, otherwise “user”.

Which roles should we forward to the API? Default: ["system", "user", "assistant"] usually, but some models like OpenAI’s o1-mini will use ["user", "assistant"]

When building prompts, any role not in this list will be set to the default_role.

Which role metadata should we forward to the API? Default: []

For example you can set this to ["foo", "bar"] to forward the cache policy to the API.

If you do not set allowed_role_metadata, we will not forward any role metadata to the API even if it is set in the prompt.

Then in your prompt you can use something like:

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client<llm> Foo {
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  provider openai
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  options {
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    allowed_role_metadata: ["foo", "bar"]
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  }
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}
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client<llm> FooWithout {
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  provider openai
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  options {
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  }
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}
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template_string Foo() #"
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  {{ _.role('user', foo={"type": "ephemeral"}, bar="1", cat=True) }}
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  This will be have foo and bar, but not cat metadata. But only for Foo, not FooWithout.
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  {{ _.role('user') }}
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  This will have none of the role metadata for Foo or FooWithout.
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"#

You can use the playground to see the raw curl request to see what is being sent to the API.

Whether the internal LLM client should use the streaming API. Default: <auto>

Then in your prompt you can use something like:

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client<llm> MyClientWithoutStreaming {
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  provider openai
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  options {
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    model gpt-4o
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    api_key env.OPENAI_API_KEY
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    supports_streaming false 
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  }
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}
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function MyFunction() -> string {
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  client MyClientWithoutStreaming
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  prompt #"Write a short story"#
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}

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# This will be streamed from your python code perspective, 
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# but under the hood it will call the non-streaming HTTP API
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# and then return a streamable response with a single event
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b.stream.MyFunction()
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# This will work exactly the same as before
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b.MyFunction()

Which finish reasons are allowed? Default: null

Will raise a BamlClientFinishReasonError if the finish reason is not in the allow list. See Exceptions for more details.

Note, only one of finish_reason_allow_list or finish_reason_deny_list can be set.

For example you can set this to ["stop"] to only allow the stop finish reason, all other finish reasons (e.g. length) will treated as failures that PREVENT fallbacks and retries (similar to parsing errors).

Then in your code you can use something like:

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client<llm> MyClient {
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  provider "openai"
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  options {
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    model "gpt-4o-mini"
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    api_key env.OPENAI_API_KEY
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    // Finish reason allow list will only allow the stop finish reason
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    finish_reason_allow_list ["stop"]
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  }
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}

Which finish reasons are denied? Default: null

Will raise a BamlClientFinishReasonError if the finish reason is in the deny list. See Exceptions for more details.

Note, only one of finish_reason_allow_list or finish_reason_deny_list can be set.

For example you can set this to ["length"] to stop the function from continuing if the finish reason is length. (e.g. LLM was cut off because it was too long).

Then in your code you can use something like:

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client<llm> MyClient {
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  provider "openai"
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  options {
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    model "gpt-4o-mini"
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    api_key env.OPENAI_API_KEY
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    // Finish reason deny list will allow all finish reasons except length
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    finish_reason_deny_list ["length"]
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  }
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}

BAML will auto construct this field for you from the prompt

BAML will auto construct this field for you based on how you call the client in your code

The model to use.

See openai docs for the list of openai models. You can pass any model name you wish, we will not check if it exists.