Movatterモバイル変換

[0]ホーム
URL:

Skip to main content
Optimization
Evaluations
  • Overview
API Reference

API Reference: Inference

API reference for the/inference endpoint.

POST /inference

The inference endpoint is the core of the TensorZero Gateway API.Under the hood, the gateway validates the request, samples a variant from the function, handles templating when applicable, and routes the inference to the appropriate model provider.If a problem occurs, it attempts to gracefully fallback to a different model provider or variant.After a successful inference, it returns the data to the client and asynchronously stores structured information in the database.
See theAPI Reference forPOST /openai/v1/chat/completions for an inference endpoint compatible with the OpenAI API.

Request

additional_tools

  • Type: a list of tools (see below)
  • Required: no (default:[])
A list of tools defined at inference time that the model is allowed to call.This field allows for dynamic tool use, i.e. defining tools at runtime.You should prefer to define tools in the configuration file if possible.Only use this field if dynamic tool use is necessary for your use case.
Function Tools
Function tools are the typical tools used with LLMs.Function tools use JSON Schema to define their parameters.Each function tool is an object with the following fields:
  • name (string, required): The name of the tool
  • description (string, required): A description of what the tool does
  • parameters (object, required): A JSON Schema defining the tool’s parameters
  • strict (boolean, optional): Whether to enforce strict schema validation (defaults tofalse)
SeeConfiguration Reference for more details.
OpenAI Custom Tools
OpenAI custom tools are only supported by OpenAI models (both Chat Completions and Responses APIs).Using custom tools with other providers will result in an error.
OpenAI custom tools support alternative output formats beyond JSON Schema, such as freeform text or grammar-constrained output.Each custom tool is an object with the following fields:
  • type (string, required): Must be"openai_custom"
  • name (string, required): The name of the tool
  • description (string, optional): A description of what the tool does
  • format (object, optional): The output format for the tool (see below)
Theformat field can be one of:
  • {"type": "text"}: Freeform text output
  • {"type": "grammar", "grammar": {"syntax": "lark", "definition": "..."}}: Output constrained by aLark grammar
  • {"type": "grammar", "grammar": {"syntax": "regex", "definition": "..."}}: Output constrained by a regular expression
{  "model_name":"openai::gpt-5-mini",  "input": {    "messages": [      {        "role":"user",        "content":"Generate Python code to print 'Hello, World!'"      }    ]  },  "additional_tools": [    {      "type":"openai_custom",      "name":"code_generator",      "description":"Generates Python code snippets",      "format": {"type":"text" }    }  ]}
{  "model_name":"openai::gpt-5-mini",  "input": {    "messages": [      {"role":"user","content":"Format the phone number 4155550123" }    ]  },  "additional_tools": [    {      "type":"openai_custom",      "name":"phone_formatter",      "description":"Formats phone numbers in XXX-XXX-XXXX format",      "format": {        "type":"grammar",        "grammar": {          "syntax":"regex",          "definition":"^\\d{3}-\\d{3}-\\d{4}$"        }      }    }  ]}

allowed_tools

  • Type: list of strings
  • Required: no
A list of tool names that the model is allowed to call.The tools must be defined in the configuration file or provided dynamically viaadditional_tools.Some providers (notably OpenAI) natively support restricting allowed tools.For these providers, we send all tools (both configured and dynamic) to the provider, and separately specify which ones are allowed to be called.For providers that do not natively support this feature, we filter the tool list ourselves and only send the allowed tools to the provider.

cache_options

  • Type: object
  • Required: no (default:{"enabled": "write_only"})
Options for controlling inference caching behavior.The object has the fields below.SeeInference Caching for more details.
cache_options.enabled
  • Type: string
  • Required: no (default:"write_only")
The cache mode to use.Must be one of:
  • "write_only" (default): Only write to cache but don’t serve cached responses
  • "read_only": Only read from cache but don’t write new entries
  • "on": Both read from and write to cache
  • "off": Disable caching completely
Note: When usingdryrun=true, the gateway never writes to the cache.
cache_options.max_age_s
  • Type: integer
  • Required: no (default:null)
Maximum age in seconds for cache entries.If set, cached responses older than this value will not be used.For example, if you setmax_age_s=3600, the gateway will only use cache entries that were created in the last hour.

credentials

  • Type: object (a map from dynamic credential names to API keys)
  • Required: no (default: no credentials)
Each model provider in your TensorZero configuration can be configured to accept credentials at inference time by using thedynamic location (e.g.dynamic::my_dynamic_api_key_name).See theconfiguration reference for more details.The gateway expects the credentials to be provided in thecredentials field of the request body as specified below.The gateway will return a 400 error if the credentials are not provided and the model provider has been configured with dynamic credentials.
[models.my_model_name.providers.my_provider_name]# ...# Note: the name of the credential field (e.g. `api_key_location`) depends on the provider typeapi_key_location ="dynamic::my_dynamic_api_key_name"# ...
{  // ...  "credentials": {    // ...    "my_dynamic_api_key_name":"sk-..."    // ...  }  // ...}

dryrun

  • Type: boolean
  • Required: no
Iftrue, the inference request will be executed but won’t be stored to the database.The gateway will still call the downstream model providers.This field is primarily for debugging and testing, and you should generally not use it in production.

episode_id

  • Type: UUID
  • Required: no
The ID of an existing episode to associate the inference with.If null, the gateway will generate a new episode ID and return it in the response.SeeEpisodes for more information.

extra_body

  • Type: array of objects (see below)
  • Required: no
Theextra_body field allows you to modify the request body that TensorZero sends to a model provider.This advanced feature is an “escape hatch” that lets you use provider-specific functionality that TensorZero hasn’t implemented yet.Each object in the array must have two or three fields:
  • pointer: AJSON Pointer string specifying where to modify the request body
  • One of the following:
    • value: The value to insert at that location; it can be of any type including nested types
    • delete = true: Deletes the field at the specified location, if present.
  • Optional: If one of the following is specified, the modification will only be applied to the specified variant, model, or model provider. If neither is specified, the modification applies to all model inferences.
    • variant_name
    • model_name
    • model_name andprovider_name
You can also setextra_body in the configuration file.The values provided at inference-time take priority over the values in the configuration file.
If TensorZero would normally send this request body to the provider…
{  "project":"tensorzero",  "safety_checks": {    "no_internet":false,    "no_agi":true  }}
…then the followingextra_body in the inference request…
{  // ...  "extra_body": [    {      "variant_name":"my_variant",// or "model_name": "my_model", "provider_name": "my_provider"      "pointer":"/agi",      "value":true    },    {      // No `variant_name` or `model_name`/`provider_name` specified, so it applies to all variants and providers      "pointer":"/safety_checks/no_agi",      "value": {        "bypass":"on"      }    }  ]}
…overrides the request body to:
{  "agi":true,  "project":"tensorzero",  "safety_checks": {    "no_internet":false,    "no_agi": {      "bypass":"on"    }  }}

extra_headers

  • Type: array of objects (see below)
  • Required: no
Theextra_headers field allows you to modify the request headers that TensorZero sends to a model provider.This advanced feature is an “escape hatch” that lets you use provider-specific functionality that TensorZero hasn’t implemented yet.Each object in the array must have two or three fields:
  • name: The name of the header to modify
  • value: The value to set the header to
  • Optional: If one of the following is specified, the modification will only be applied to the specified variant, model, or model provider. If neither is specified, the modification applies to all model inferences.
    • variant_name
    • model_name
    • model_name andprovider_name
You can also setextra_headers in the configuration file.The values provided at inference-time take priority over the values in the configuration file.
If TensorZero would normally send the following request headers to the provider…
Safety-Checks: on
…then the followingextra_headers…
{  "extra_headers": [    {      "variant_name":"my_variant",// or "model_name": "my_model", "provider_name": "my_provider"      "name":"Safety-Checks",      "value":"off"    },    {      // No `variant_name` or `model_name`/`provider_name` specified, so it applies to all variants and providers      "name":"Intelligence-Level",      "value":"AGI"    }  ]}
…overrides the request headers so thatSafety-Checks is set tooff only formy_variant, whileIntelligence-Level: AGI is applied globally to all variants and providers:
Safety-Checks: offIntelligence-Level: AGI

function_name

  • Type: string
  • Required: eitherfunction_name ormodel_name must be provided
The name of the function to call.The function must be defined in the configuration file.Alternatively, you can use themodel_name field to call a model directly, without the need to define a function.See below for more details.

include_original_response

  • Type: boolean
  • Required: no
Iftrue, the original response from the model will be included in the response in theoriginal_response field as a string.Seeoriginal_response in theresponse section for more details.

input

  • Type: varies
  • Required: yes
The input to the function.The type of the input depends on the function type.
input.messages
  • Type: list of messages (see below)
  • Required: no (default:[])
A list of messages to provide to the model.Each message is an object with the following fields:
  • role: The role of the message (assistant oruser).
  • content: The content of the message (see below).
Thecontent field can be have one of the following types:
  • string: the text for a text message (only allowed if there is no schema for that role)
  • list of content blocks: the content blocks for the message (see below)
A content block is an object with the fieldtype and additional fields depending on the type.If the content block has typetext, it must have either of the following additional fields:
  • text: The text for the content block.
  • arguments: A JSON object containing the function arguments for TensorZero functions with templates and schemas (seeCreate a prompt template for details).
If the content block has typetool_call, it must have the following additional fields:
  • arguments: The arguments for the tool call.
  • id: The ID for the content block.
  • name: The name of the tool for the content block.
If the content block has typetool_result, it must have the following additional fields:
  • id: The ID for the content block.
  • name: The name of the tool for the content block.
  • result: The result of the tool call.
If the content block has typefile, it must have exactly one of the following additional fields:
  • File URLs
    • file_type: must beurl
    • url
    • mime_type (optional): override the MIME type of the file
    • detail (optional): controls the fidelity of image processing. Only applies to image files; ignored for other file types. Can below,high, orauto. Affects token consumption and image quality. Only supported by some model providers; ignored otherwise.
    • filename (optional): a filename to associate with the file
  • Base64-encoded Files
    • file_type: must bebase64
    • data:base64-encoded data for an embedded file
    • mime_type: the MIME type (e.g.image/png,image/jpeg,application/pdf)
    • detail (optional): controls the fidelity of image processing. Only applies to image files; ignored for other file types. Can below,high, orauto. Affects token consumption and image quality. Only supported by some model providers; ignored otherwise.
    • filename (optional): a filename to associate with the file
See theMultimodal Inference guide for more details on how to use images in inference.If the content block has typeraw_text, it must have the following additional fields:
  • value: The text for the content block.This content block will ignore any relevant templates and schemas for this function.
If the content block has typethought, it must have the following additional fields:
  • text: The text for the content block.
If the content block has typeunknown, it must have the following additional fields:
  • data: The original content block from the provider, without any validation or transformation by TensorZero.
  • model_provider_name (optional): A string specifying when this content block should be included in the model provider input.If set, the content block will only be provided to this specific model provider.If not set, the content block is passed to all model providers.
For example, the following hypothetical unknown content block will send thedaydreaming content block to inference requests targeting theyour_model_provider_name model provider.
{  "type":"unknown",  "data": {    "type":"daydreaming",    "dream":"..."  },  "model_provider_name":"tensorzero::model_name::your_model_name::provider_name::your_model_provider_name"}
This is the most complex field in the entire API. See this example for more details.
{  // ...  "input": {    "messages": [      // If you don't have a user (or assistant) schema...      {        "role":"user",// (or "assistant")        "content":"What is the weather in Tokyo?"      },      // If you have a user (or assistant) schema...      {        "role":"user",// (or "assistant")        "content": [          {            "type":"text",            "arguments": {              "location":"Tokyo"            }          }        ]      },      // If the model previously called a tool...      {        "role":"assistant",        "content": [          {            "type":"tool_call",            "id":"0",            "name":"get_temperature",            "arguments":"{\"location\":\"Tokyo\"}"          }        ]      },      // ...and you're providing the result of that tool call...      {        "role":"user",        "content": [          {            "type":"tool_result",            "id":"0",            "name":"get_temperature",            "result":"70"          }        ]      },      // You can also specify a text message using a content block...      {        "role":"user",        "content": [          {            "type":"text",            "text":"What about NYC?" // (or object if there is a schema)          }        ]      },      // You can also provide multiple content blocks in a single message...      {        "role":"assistant",        "content": [          {            "type":"text",            "text":"Sure, I can help you with that." // (or object if there is a schema)          },          {            "type":"tool_call",            "id":"0",            "name":"get_temperature",            "arguments":"{\"location\":\"New York\"}"          }        ]      }      // ...    ]    // ...  }  // ...}
input.system
  • Type: string or object
  • Required: no
The input for the system message.If the function does not have a system schema, this field should be a string.If the function has a system schema, this field should be an object that matches the schema.

model_name

  • Type: string
  • Required: eithermodel_name orfunction_name must be provided
The name of the model to call.Under the hood, the gateway will use a built-in passthrough chat function calledtensorzero::default.
To call…Use this format…
A function defined as[functions.my_function] in yourtensorzero.toml configuration filefunction_name="my_function" (notmodel_name)
A model defined as[models.my_model] in yourtensorzero.tomlconfiguration filemodel_name="my_model"
A model offered by a model provider, without defining it in yourtensorzero.toml configuration file (if supported, see below)model_name="{provider_type}::{model_name}"
The following model providers support short-hand model names:anthropic,deepseek,fireworks,gcp_vertex_anthropic,gcp_vertex_gemini,google_ai_studio_gemini,groq,hyperbolic,mistral,openai,openrouter,together, andxai.
For example, if you have the following configuration:
tensorzero.toml
[models.gpt-4o]routing = ["openai","azure"][models.gpt-4o.providers.openai]# ...[models.gpt-4o.providers.azure]# ...[functions.extract-data]# ...
Then:
  • function_name="extract-data" calls theextract-data function defined above.
  • model_name="gpt-4o" calls thegpt-4o model in your configuration, which supports fallback fromopenai toazure. SeeRetries & Fallbacks for details.
  • model_name="openai::gpt-4o" calls the OpenAI API directly for thegpt-4o model, ignoring thegpt-4o model defined above.
Be careful about the different prefixes:model_name="gpt-4o" will use the[models.gpt-4o] model defined in thetensorzero.toml file, whereasmodel_name="openai::gpt-4o" will call the OpenAI API directly for thegpt-4o model.

output_schema

  • Type: object (valid JSON Schema)
  • Required: no
If set, this schema will override theoutput_schema defined in the function configuration for a JSON function.This dynamic output schema is used for validating the output of the function, and sent to providers which support structured outputs.

otlp_traces_extra_headers

  • Type: object (a map from string to string)
  • Required: no (default:{})
Dynamic headers to include in OTLP trace exports for this specific inference request.This is useful for adding per-request metadata to OTLP trace exports (e.g. user IDs, request sources).The headers are automatically prefixed withtensorzero-otlp-traces-extra-header- before being sent to the OTLP endpoint.These headers are merged with any static headers configured inexport.otlp.traces.extra_headers.When the same header key is present in both static and dynamic headers, the dynamic header value takes precedence.SeeExport OpenTelemetry traces for more details and examples.

parallel_tool_calls

  • Type: boolean
  • Required: no
Iftrue, the function will be allowed to request multiple tool calls in a single conversation turn.If not set, we default to the configuration value for the function being called.Most model providers do not support parallel tool calls. In those cases, the gateway ignores this field.At the moment, only Fireworks AI and OpenAI support parallel tool calls.

params

  • Type: object (see below)
  • Required: no (default:{})
Override inference-time parameters for a particular variant type.This fields allows for dynamic inference parameters, i.e. defining parameters at runtime.This field’s format is{ variant_type: { param: value, ... }, ... }.You should prefer to set these parameters in the configuration file if possible.Only use this field if you need to set these parameters dynamically at runtime.Note that the parameters will apply to every variant of the specified type.Currently, we support the following:
  • chat_completion
    • frequency_penalty
    • json_mode
    • max_tokens
    • presence_penalty
    • reasoning_effort
    • seed
    • service_tier
    • stop_sequences
    • temperature
    • thinking_budget_tokens
    • top_p
    • verbosity
SeeConfiguration Reference for more details on the parameters, and Examples below for usage.
For example, if you wanted to dynamically override thetemperature parameter for achat_completion variants, you’d include the following in the request body:
{  // ...  "params": {    "chat_completion": {      "temperature":0.7    }  }  // ...}
See“Chat Function with Dynamic Inference Parameters” for a complete example.

provider_tools

  • Type: array of objects
  • Required: no (default:[])
A list of provider-specific built-in tools defined at inference time that can be used by the model.These are tools that run server-side on the provider’s infrastructure, such as OpenAI’s web search tool.Each object in the array has the following fields:
  • scope (object, optional): Limits which model/provider combination can use this tool. If omitted, the tool is available to all compatible providers.
    • model_name (string): The model name as defined in your configuration
    • model_provider_name (string): The provider name for that model
  • tool (object, required): The provider-specific tool configuration as defined by the provider’s API
This field allows for dynamic provider tool use at runtime.You should prefer to define provider tools in the configuration file if possible (seeConfiguration Reference).Only use this field if dynamic provider tool configuration is necessary for your use case.
{  "function_name":"my_function",  "input": {    "messages": [      {        "role":"user",        "content":"What were the latest developments in AI this week?"      }    ]  },  "provider_tools": [    {      "tool": {        "type":"web_search"      }    }  ]}
This makes the web search tool available to all compatible providers configured for the function.
{  "function_name":"my_function",  "input": {    "messages": [      {        "role":"user",        "content":"What were the latest developments in AI this week?"      }    ]  },  "provider_tools": [    {      "scope": {        "model_name":"gpt-5-mini",        "model_provider_name":"openai"      },      "tool": {        "type":"web_search"      }    }  ]}
This makes the web search tool available only to the OpenAI provider for thegpt-5-mini model.

stream

  • Type: boolean
  • Required: no
Iftrue, the gateway will stream the response from the model provider.

tags

  • Type: flat JSON object with string keys and values
  • Required: no
User-provided tags to associate with the inference.For example,{"user_id": "123"} or{"author": "Alice"}.

tool_choice

  • Type: string
  • Required: no
If set, overrides the tool choice strategy for the request.The supported tool choice strategies are:
  • none: The function should not use any tools.
  • auto: The model decides whether or not to use a tool. If it decides to use a tool, it also decides which tools to use.
  • required: The model should use a tool. If multiple tools are available, the model decides which tool to use.
  • { specific = "tool_name" }: The model should use a specific tool. The tool must be defined in thetools section of the configuration file or provided inadditional_tools.

variant_name

  • Type: string
  • Required: no
If set, pins the inference request to a particular variant (not recommended).You should generally not set this field, and instead let the TensorZero gateway assign a variant.This field is primarily used for testing or debugging purposes.

Response

The response format depends on the function type (as defined in the configuration file) and whether the response is streamed or not.

Chat Function

When the function type ischat, the response is structured as follows.
  • Regular
  • Streaming
In regular (non-streaming) mode, the response is a JSON object with the following fields:
content
  • Type: a list of content blocks (see below)
The content blocks generated by the model.A content block can havetype equal totext andtool_call.Reasoning models (e.g. DeepSeek R1) might also includethought content blocks.Iftype istext, the content block has the following fields:
  • text: The text for the content block.
Iftype istool_call, the content block has the following fields:
  • arguments (object): The validated arguments for the tool call (null if invalid).
  • id (string): The ID of the content block.
  • name (string): The validated name of the tool (null if invalid).
  • raw_arguments (string): The arguments for the tool call generated by the model (which might be invalid).
  • raw_name (string): The name of the tool generated by the model (which might be invalid).
Iftype isthought, the content block has the following fields:
  • text (string): The text of the thought.
If the model provider responds with a content block of an unknown type, it will be included in the response as a content block of typeunknown with the following additional fields:
  • data: The original content block from the provider, without any validation or transformation by TensorZero.
  • model_provider_name: The fully-qualified name of the model provider that returned the content block.
For example, if the model provideryour_model_provider_name returns a content block of typedaydreaming, it will be included in the response like this:
{  "type":"unknown",  "data": {    "type":"daydreaming",    "dream":"..."  },  "model_provider_name":"tensorzero::model_name::your_model_name::provider_name::your_model_provider_name"}
episode_id
  • Type: UUID
The ID of the episode associated with the inference.
inference_id
  • Type: UUID
The ID assigned to the inference.
original_response
  • Type: string (optional)
The original response from the model provider (only available wheninclude_original_response istrue).The returned data depends on the variant type:
  • chat_completion: raw response from the inference to themodel
  • experimental_best_of_n_sampling: raw response from the inference to theevaluator
  • experimental_mixture_of_n_sampling: raw response from the inference to thefuser
  • experimental_dynamic_in_context_learning: raw response from the inference to themodel
  • experimental_chain_of_thought: raw response from the inference to themodel
variant_name
  • Type: string
The name of the variant used for the inference.
usage
  • Type: object (optional)
The usage metrics for the inference.The object has the following fields:
  • input_tokens: The number of input tokens used for the inference.
  • output_tokens: The number of output tokens used for the inference.
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
content
  • Type: a list of content block chunks (see below)
The content deltas for the inference.A content block chunk can havetype equal totext ortool_call.Reasoning models (e.g. DeepSeek R1) might also includethought content block chunks.Iftype istext, the chunk has the following fields:
  • id: The ID of the content block.
  • text: The text delta for the content block.
Iftype istool_call, the chunk has the following fields (all strings):
  • id: The ID of the content block.
  • raw_name: The string delta of the name of the tool.
  • raw_arguments: The string delta of the arguments for the tool call.
Iftype isthought, the chunk has the following fields:
  • id: The ID of the content block.
  • text: The text delta for the thought.
episode_id
  • Type: UUID
The ID of the episode associated with the inference.
inference_id
  • Type: UUID
The ID assigned to the inference.
variant_name
  • Type: string
The name of the variant used for the inference.
usage
  • Type: object (optional)
The usage metrics for the inference.The object has the following fields:
  • input_tokens: The number of input tokens used for the inference.
  • output_tokens: The number of output tokens used for the inference.

JSON Function

When the function type isjson, the response is structured as follows.
  • Regular
  • Streaming
In regular (non-streaming) mode, the response is a JSON object with the following fields:
inference_id
  • Type: UUID
The ID assigned to the inference.
episode_id
  • Type: UUID
The ID of the episode associated with the inference.
original_response
  • Type: string (optional)
The original response from the model provider (only available wheninclude_original_response istrue).The returned data depends on the variant type:
  • chat_completion: raw response from the inference to themodel
  • experimental_best_of_n_sampling: raw response from the inference to theevaluator
  • experimental_mixture_of_n_sampling: raw response from the inference to thefuser
  • experimental_dynamic_in_context_learning: raw response from the inference to themodel
  • experimental_chain_of_thought: raw response from the inference to themodel
output
  • Type: object (see below)
The output object contains the following fields:
  • raw: The raw response from the model provider (which might be invalid JSON).
  • parsed: The parsed response from the model provider (null if invalid JSON).
variant_name
  • Type: string
The name of the variant used for the inference.
usage
  • Type: object (optional)
The usage metrics for the inference.The object has the following fields:
  • input_tokens: The number of input tokens used for the inference.
  • output_tokens: The number of output tokens used for the inference.
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
episode_id
  • Type: UUID
The ID of the episode associated with the inference.
inference_id
  • Type: UUID
The ID assigned to the inference.
raw
  • Type: string
The raw response delta from the model provider.The TensorZero Gateway does not provide aparsed field for streaming JSON inferences.If your application depends on a well-formed JSON response, we recommend using regular (non-streaming) inference.
variant_name
  • Type: string
The name of the variant used for the inference.
usage
  • Type: object (optional)
The usage metrics for the inference.The object has the following fields:
  • input_tokens: The number of input tokens used for the inference.
  • output_tokens: The number of output tokens used for the inference.

Examples

Chat Function

Configuration
// tensorzero.toml# ...[functions.draft_email]type ="chat"# ...
Request
  • Python
  • HTTP
POST /inference
from tensorzeroimport AsyncTensorZeroGatewayasync with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000")as client:    result= await client.inference(        function_name="draft_email",        input={            "system":"You are an AI assistant...",            "messages": [                {                  "role":"user",                  "content":"I need to write an email to Gabriel explaining..."                }            ]        }        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/inference \  -H "Content-Type: application/json" \  -d '{    "function_name": "draft_email",    "input": {      "system": "You are an AI assistant...",      "messages": [        {          "role": "user",          "content": "I need to write an email to Gabriel explaining..."        }      ]    }    // optional: "stream": true  }'
Response
  • Regular
  • Streaming
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"text",      "text":"Hi Gabriel,\n\nI noticed...",    }  ]  "usage": {    "input_tokens":100,    "output_tokens":100  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"text",      "id":"0",      "text":"Hi Gabriel," // a text content delta    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}

Chat Function with Schemas

Configuration
// tensorzero.toml# ...[functions.draft_email]type ="chat"system_schema ="system_schema.json"user_schema ="user_schema.json"# ...
// system_schema.json{  "$schema":"http://json-schema.org/draft-07/schema#",  "type":"object",  "properties": {    "tone": {      "type":"string"    }  },  "required": ["tone"],  "additionalProperties":false}
// user_schema.json{  "$schema":"http://json-schema.org/draft-07/schema#",  "type":"object",  "properties": {    "recipient": {      "type":"string"    },    "email_purpose": {      "type":"string"    }  },  "required": ["recipient","email_purpose"],  "additionalProperties":false}
Request
  • Python
  • HTTP
POST /inference
from tensorzeroimport AsyncTensorZeroGatewayasync with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000")as client:    result= await client.inference(        function_name="draft_email",        input={            "system": {"tone":"casual"},            "messages": [                {                    "role":"user",                    "content": [                        {                            "type":"text",                            "arguments": {                                "recipient":"Gabriel",                                "email_purpose":"Request a meeting to..."                            }                        }                    ]                }            ]        }        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/inference \  -H "Content-Type: application/json" \  -d '{    "function_name": "draft_email",    "input": {      "system": {"tone": "casual"},      "messages": [        {          "role": "user",          "content": [            {              "type": "text",              "arguments": {                "recipient": "Gabriel",                "email_purpose": "Request a meeting to..."              }            }          ]        }      ]    }    // optional: "stream": true  }'
Response
  • Regular
  • Streaming
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"text",      "text":"Hi Gabriel,\n\nI noticed...",    }  ]  "usage": {    "input_tokens":100,    "output_tokens":100  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"text",      "id":"0",      "text":"Hi Gabriel," // a text content delta    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}

Chat Function with Tool Use

Configuration
// tensorzero.toml# ...[functions.weather_bot]type ="chat"tools = ["get_temperature"]# ...[tools.get_temperature]description ="Get the current temperature in a given location"parameters ="get_temperature.json"# ...
// get_temperature.json{  "$schema":"http://json-schema.org/draft-07/schema#",  "type":"object",  "properties": {    "location": {      "type":"string",      "description":"The location to get the temperature for (e.g.\"New York\")"    },    "units": {      "type":"string",      "description":"The units to get the temperature in (must be\"fahrenheit\" or\"celsius\")",      "enum": ["fahrenheit","celsius"]    }  },  "required": ["location"],  "additionalProperties":false}
Request
  • Python
  • HTTP
POST /inference
from tensorzeroimport AsyncTensorZeroGatewayasync with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000")as client:    result= await client.inference(        function_name="weather_bot",        input={            "messages": [                {                    "role":"user",                    "content":"What is the weather like in Tokyo?"                }            ]        }        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/inference \  -H "Content-Type: application/json" \  -d '{    "function_name": "weather_bot",    "input": {      "messages": [        {          "role": "user",          "content": "What is the weather like in Tokyo?"        }      ]    }    // optional: "stream": true  }'
Response
  • Regular
  • Streaming
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"tool_call",      "arguments": {        "location":"Tokyo",        "units":"celsius"      },      "id":"123456789",      "name":"get_temperature",      "raw_arguments":"{\"location\":\"Tokyo\",\"units\":\"celsius\"}",      "raw_name":"get_temperature"    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"tool_call",      "id":"123456789",      "name":"get_temperature",      "arguments":"{\"location\":" // a tool arguments delta    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}

Chat Function with Multi-Turn Tool Use

Configuration
// tensorzero.toml# ...[functions.weather_bot]type ="chat"tools = ["get_temperature"]# ...[tools.get_temperature]description ="Get the current temperature in a given location"parameters ="get_temperature.json"# ...
// get_temperature.json{  "$schema":"http://json-schema.org/draft-07/schema#",  "type":"object",  "properties": {    "location": {      "type":"string",      "description":"The location to get the temperature for (e.g.\"New York\")"    },    "units": {      "type":"string",      "description":"The units to get the temperature in (must be\"fahrenheit\" or\"celsius\")",      "enum": ["fahrenheit","celsius"]    }  },  "required": ["location"],  "additionalProperties":false}
Request
  • Python
  • HTTP
POST /inference
from tensorzeroimport AsyncTensorZeroGatewayasync with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000")as client:    result= await client.inference(        function_name="weather_bot",        input={            "messages": [                {                    "role":"user",                    "content":"What is the weather like in Tokyo?"                },                {                    "role":"assistant",                    "content": [                        {                            "type":"tool_call",                            "arguments": {                                "location":"Tokyo",                                "units":"celsius"                            },                            "id":"123456789",                            "name":"get_temperature",                        }                    ]                },                {                    "role":"user",                    "content": [                        {                            "type":"tool_result",                            "id":"123456789",                            "name":"get_temperature",                            "result":"25"  # the tool result must be a string                        }                    ]                }            ]        }        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/inference \  -H "Content-Type: application/json" \  -d '{    "function_name": "weather_bot",    "input": {      "messages": [        {          "role": "user",          "content": "What is the weather like in Tokyo?"        },        {          "role": "assistant",          "content": [            {              "type": "tool_call",              "arguments": {                "location": "Tokyo",                "units": "celsius"              },              "id": "123456789",              "name": "get_temperature",            }          ]        },        {          "role": "user",          "content": [            {              "type": "tool_result",              "id": "123456789",              "name": "get_temperature",              "result": "25"  // the tool result must be a string            }          ]        }      ]    }    // optional: "stream": true  }'
Response
  • Regular
  • Streaming
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"text",      "content": [        {          "type":"text",          "text":"The weather in Tokyo is 25 degrees Celsius."        }      ]    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"text",      "id":"0",      "text":"The weather in" // a text content delta    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}

Chat Function with Dynamic Tool Use

Configuration
// tensorzero.toml# ...[functions.weather_bot]type ="chat"# Note: no `tools = ["get_temperature"]` field in configuration# ...
Request
  • Python
  • HTTP
POST /inference
from tensorzeroimport AsyncTensorZeroGatewayasync with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000")as client:    result= await client.inference(        function_name="weather_bot",        input={            "messages": [                {                    "role":"user",                    "content":"What is the weather like in Tokyo?"                }            ]        },        additional_tools=[            {                "name":"get_temperature",                "description":"Get the current temperature in a given location",                "parameters": {                    "$schema":"http://json-schema.org/draft-07/schema#",                    "type":"object",                    "properties": {                        "location": {                            "type":"string",                            "description":"The location to get the temperature for (e.g.\"New York\")"                        },                        "units": {                            "type":"string",                            "description":"The units to get the temperature in (must be\"fahrenheit\" or\"celsius\")",                            "enum": ["fahrenheit","celsius"]                        }                    },                    "required": ["location"],                    "additionalProperties": false                }            }        ],        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/inference \  -H "Content-Type: application/json" \  -d '{    "function_name": "weather_bot",    input: {      "messages": [        {          "role": "user",          "content": "What is the weather like in Tokyo?"        }      ]    },    additional_tools: [      {        "name": "get_temperature",        "description": "Get the current temperature in a given location",        "parameters": {          "$schema": "http://json-schema.org/draft-07/schema#",          "type": "object",          "properties": {            "location": {              "type": "string",              "description": "The location to get the temperature for (e.g. \"New York\")"            },            "units": {              "type": "string",              "description": "The units to get the temperature in (must be \"fahrenheit\" or \"celsius\")",              "enum": ["fahrenheit", "celsius"]            }          },          "required": ["location"],          "additionalProperties": false        }      }    ]    // optional: "stream": true  }'
Response
  • Regular
  • Streaming
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"tool_call",      "arguments": {        "location":"Tokyo",        "units":"celsius"      },      "id":"123456789",      "name":"get_temperature",      "raw_arguments":"{\"location\":\"Tokyo\",\"units\":\"celsius\"}",      "raw_name":"get_temperature"    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"tool_call",      "id":"123456789",      "name":"get_temperature",      "arguments":"{\"location\":" // a tool arguments delta    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}

Chat Function with Dynamic Inference Parameters

Configuration
// tensorzero.toml# ...[functions.draft_email]type ="chat"# ...[functions.draft_email.variants.prompt_v1]type ="chat_completion"temperature =0.5  # the API request will override this value# ...
Request
  • Python
  • HTTP
POST /inference
from tensorzeroimport AsyncTensorZeroGatewayasync with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000")as client:    result= await client.inference(        function_name="draft_email",        input={            "system":"You are an AI assistant...",            "messages": [                {                    "role":"user",                    "content":"I need to write an email to Gabriel explaining..."                }            ]        },        # Override parameters for every variant with type "chat_completion"        params={            "chat_completion": {                "temperature":0.7,            }        },        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/inference \  -H "Content-Type: application/json" \  -d '{    "function_name": "draft_email",    "input": {      "system": "You are an AI assistant...",      "messages": [        {          "role": "user",          "content": "I need to write an email to Gabriel explaining..."        }      ]    },    params={      // Override parameters for every variant with type "chat_completion"      "chat_completion": {        "temperature": 0.7,      }    }    // optional: "stream": true  }'
Response
  • Regular
  • Streaming
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "type":"text",      "text":"Hi Gabriel,\n\nI noticed...",    }  ]  "usage": {    "input_tokens":100,    "output_tokens":100  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "content": [    {      "id":"0",      "text":"Hi Gabriel," // a text content delta    }  ],  "usage": {    "input_tokens":100,    "output_tokens":100  }}

JSON Function

Configuration
// tensorzero.toml# ...[functions.extract_email]type ="json"output_schema ="output_schema.json"# ...
// output_schema.json{  "$schema":"http://json-schema.org/draft-07/schema#",  "type":"object",  "properties": {    "email": {      "type":"string"    }  },  "required": ["email"]}
Request
  • Python
  • HTTP
POST /inference
from tensorzeroimport AsyncTensorZeroGatewayasync with await AsyncTensorZeroGateway.build_http(gateway_url="http://localhost:3000")as client:    result= await client.inference(        function_name="extract_email",        input={            "system":"You are an AI assistant...",            "messages": [                {                    "role":"user",                    "content":"...blah blah blah[email protected] blah blah blah..."                }            ]        }        # optional: stream=True,    )
POST /inference
curl -X POST http://localhost:3000/inference \  -H "Content-Type: application/json" \  -d '{    "function_name": "extract_email",    "input": {      "system": "You are an AI assistant...",      "messages": [        {          "role": "user",          "content": "...blah blah blah[email protected] blah blah blah..."        }      ]    }    // optional: "stream": true  }'
Response
  • Regular
  • Streaming
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "output": {    "raw":"{\"email\":\"[email protected]\"}",    "parsed": {      "email":"[email protected]"    }  }  "usage": {    "input_tokens":100,    "output_tokens":100  }}
In streaming mode, the response is anSSE stream of JSON messages, followed by a final[DONE] message.Each JSON message has the following fields:
POST /inference
{  "inference_id":"00000000-0000-0000-0000-000000000000",  "episode_id":"11111111-1111-1111-1111-111111111111",  "variant_name":"prompt_v1",  "raw":"{\"email\":",// a JSON content delta  "usage": {    "input_tokens":100,    "output_tokens":100  }}
⌘I
[8]ページ先頭
©2009-2025 Movatter.jp