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Foundation model REST API reference

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This article provides general API information for Databricks Foundation Model APIs and the models they support. The Foundation Model APIs are designed to be similar to OpenAI’s REST API to make migrating existing projects easier. Both the pay-per-token and provisioned throughput endpoints accept the same REST API request format.

Endpoints

Each pay-per-token model has a single endpoint, and users can interact with these endpoints using HTTP POST requests. Provisioned throughput endpoints can be created using the API or the Serving UI. These endpoints also support multiple models per endpoint for A/B testing, as long as both served models expose the same API format. For example, both models are chat models.

Requests and responses use JSON, the exact JSON structure depends on an endpoint’s task type. Chat and completion endpoints support streaming responses.

Pay-per-token workloads support certain models, see Supported models for pay-per-token for those models and accepted API formats.

Usage

Responses include a usage sub-message which reports the number of tokens in the request and response. The format of this sub-message is the same across all task types.

Field Type Description
completion_tokens Integer Number of generated tokens. Not included in embedding responses.
prompt_tokens Integer Number of tokens from the input prompt(s).
total_tokens Integer Number of total tokens.

For models like llama-2-70b-chat a user prompt is transformed using a prompt template before being passed into the model. For pay-per-token endpoints, a system prompt might also be added. prompt_tokens includes all text added by our server.

Chat task

Chat tasks are optimized for multi-turn conversations with a model. Each request describes the conversation so far, where the messages field must alternate between user and assistant roles, ending with a user message. The model response provides the next assistant message in the conversation.

Chat request

Field Default Type Description
messages ChatMessage list Required. A list of messages representing the current conversation.
max_tokens nil Integer greater than zero or nil, which represents infinity The maximum number of tokens to generate.
stream true Boolean Stream responses back to a client in order to allow partial results for requests. If this parameter is included in the request, responses are sent using the Server-sent events standard.
temperature 1.0 Float in [0,2] The sampling temperature. 0 is deterministic and higher values introduce more randomness.
top_p 1.0 Float in (0,1] The probability threshold used for nucleus sampling.
top_k nil Integer greater than zero or nil, which represents infinity Defines the number of k most likely tokens to use for top-k-filtering. Set this value to 1 to make outputs deterministic.
stop [] String or List[String] Model stops generating further tokens when any one of the sequences in stop is encountered.
n 1 Integer greater than zero The API returns n independent chat completions when n is specified. Recommended for workloads that generate multiple completions on the same input for additional inference efficiency and cost savings. Only available for provisioned throughput endpoints.
tool_choice nil String or ToolChoiceObject Used only in conjunction with the tools field. tool_choice supports a variety of keyword strings such as auto, required, and none. auto means that you are letting the model decide which (if any) tool is relevant to use. With auto if the model doesn’t believe any of the tools in tools are relevant, the model generates a standard assistant message instead of a tool call. required means that the model picks the most relevant tool in tools and must generate a tool call. none means that the model does not generate any tool calls and instead must generate a standard assistant message. To force a tool call with a specific tool defined in tools, use a ToolChoiceObject. By default, if the tools field is populated tool_choice = "auto". Else, the tools field defaults to tool_choice = "none"
tools nil ToolObject A list of tools that the model can call. Currently, function is the only supported tool type and a max of 32 functions are supported.

ChatMessage

Field Type Description
role String Required. The role of the author of the message. Can be "system", "user", "assistant" or "tool".
content String The content of the message. Required for chat tasks that do not involve tool calls.
tool_calls ToolCall list The list of tool_calls that the model generated. Must have role as "assistant" and no specification for the content field.
tool_call_id String When role is "tool", the ID associated with the ToolCall that the message is responding to. Must be empty for other role options.

The system role can only be used once, as the first message in a conversation. It overrides the model’s default system prompt.

ToolCall

A tool call action suggestion by the model. See Function calling on Azure Databricks.

Field Type Description
id String Required. A unique identifier for this tool call suggestion.
type String Required. Only "function" is supported.
function FunctionCallCompletion Required. A function call suggested by the model.

FunctionCallCompletion

Field Type Description
name String Required. The name of the function the model recommended.
arguments Object Required. Arguments to the function as a serialized JSON dictionary.

ToolChoiceObject

See Function calling on Azure Databricks.

Field Type Description
type String Required. The type of the tool. Currently, only "function" is supported.
function Object Required. An object defining which tool to call of the form {"type": "function", "function": {"name": "my_function"}} where "my_function is the name of a FunctionObject in the tools field.

ToolObject

See Function calling on Azure Databricks.

Field Type Description
type String Required. The type of the tool. Currently, only function is supported.
function FunctionObject Required. The function definition associated with the tool.

FunctionObject

Field Type Description
name String Required. The name of the function to be called.
description Object Required. The detailed description of the function. The model uses this description to understand the relevance of the function to the prompt and generate the tool calls with higher accuracy.
parameters Object The parameters the function accepts, described as a valid JSON Schema object. If the tool is called, then the tool call is fit to the JSON Schema provided. Omitting parameters defines a function without any parameters. The number of properties is limited to 15 keys.

Chat response

For non-streaming requests, the response is a single chat completion object. For streaming requests, the response is a text/event-stream where each event is a completion chunk object. The top-level structure of completion and chunk objects is almost identical: only choices has a different type.

Field Type Description
id String Unique identifier for the chat completion.
choices List[ChatCompletionChoice] or List[ChatCompletionChunk] (streaming) List of chat completion texts. n choices are returned if the n parameter is specified.
object String The object type. Equal to either "chat.completions" for non-streaming or "chat.completion.chunk" for streaming.
created Integer The time the chat completion was generated in seconds.
model String The model version used to generate the response.
usage Usage Token usage metadata. May not be present on streaming responses.

ChatCompletionChoice

Field Type Description
index Integer The index of the choice in the list of generated choices.
message ChatMessage A chat completion message returned by the model. The role will be assistant.
finish_reason String The reason the model stopped generating tokens.

ChatCompletionChunk

Field Type Description
index Integer The index of the choice in the list of generated choices.
delta ChatMessage A chat completion message part of generated streamed responses from the model. Only the first chunk is guaranteed to have role populated.
finish_reason String The reason the model stopped generating tokens. Only the last chunk will have this populated.

Completion task

Text completion tasks are for generating responses to a single prompt. Unlike Chat, this task supports batched inputs: multiple independent prompts can be sent in one request.

Completion request

Field Default Type Description
prompt String or List[String] Required. The prompt(s) for the model.
max_tokens nil Integer greater than zero or nil, which represents infinity The maximum number of tokens to generate.
stream true Boolean Stream responses back to a client in order to allow partial results for requests. If this parameter is included in the request, responses are sent using the Server-sent events standard.
temperature 1.0 Float in [0,2] The sampling temperature. 0 is deterministic and higher values introduce more randomness.
top_p 1.0 Float in (0,1] The probability threshold used for nucleus sampling.
top_k nil Integer greater than zero or nil, which represents infinity Defines the number of k most likely tokens to use for top-k-filtering. Set this value to 1 to make outputs deterministic.
error_behavior "error" "truncate" or "error" For timeouts and context-length-exceeded errors. One of: "truncate" (return as many tokens as possible) and "error" (return an error). This parameter is only accepted by pay per token endpoints.
n 1 Integer greater than zero The API returns n independent chat completions when n is specified. Recommended for workloads that generate multiple completions on the same input for additional inference efficiency and cost savings. Only available for provisioned throughput endpoints.
stop [] String or List[String] Model stops generating further tokens when any one of the sequences in stop is encountered.
suffix "" String A string that is appended to the end of every completion.
echo false Boolean Returns the prompt along with the completion.
use_raw_prompt false Boolean If true, pass the prompt directly into the model without any transformation.

Completion response

Field Type Description
id String Unique identifier for the text completion.
choices CompletionChoice A list of text completions. For every prompt passed in, n choices are generated if n is specified. Default n is 1.
object String The object type. Equal to "text_completion"
created Integer The time the completion was generated in seconds.
usage Usage Token usage metadata.

CompletionChoice

Field Type Description
index Integer The index of the prompt in request.
text String The generated completion.
finish_reason String The reason the model stopped generating tokens.

Embedding task

Embedding tasks map input strings into embedding vectors. Many inputs can be batched together in each request.

Embedding request

Field Type Description
input String or List[String] Required. The input text to embed. Can be a string or a list of strings.
instruction String An optional instruction to pass to the embedding model.

Instructions are optional and highly model specific. For instance the The BGE authors recommend no instruction when indexing chunks and recommend using the instruction "Represent this sentence for searching relevant passages:" for retrieval queries. Other models like Instructor-XL support a wide range of instruction strings.

Embeddings response

Field Type Description
id String Unique identifier for the embedding.
object String The object type. Equal to "list".
model String The name of the embedding model used to create the embedding.
data EmbeddingObject The embedding object.
usage Usage Token usage metadata.

EmbeddingObject

Field Type Description
object String The object type. Equal to "embedding".
index Integer The index of the embedding in the list of embeddings generated by the model.
embedding List[Float] The embedding vector. Each model will return a fixed size vector (1024 for BGE-Large)

Additional resources