Truefoundry Docs

TrueFoundry AI Gateway provides a universal API for all supported models via the standard OpenAI /chat/completions endpoint. This unified interface allows you to seamlessly work with models from different providers through a consistent API.

Section	Description
Getting Started	Basic setup and configuration
Input Controls	System prompts and request parameters
Working with Media	Images, audio, and video support
Function Calling	Enabling models to invoke functions
Response Format	Structured JSON outputs
Prompt Caching	Optimize API usage with caching
Reasoning Models	Access model reasoning processes

Getting Started

You can use the standard OpenAI client to send requests to the gateway:

from openai import OpenAI

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm/api/inference/openai" # e.g. https://my-company.truefoundry.cloud/api/llm/api/inference/openai
)

response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini", # this is the truefoundry model id
    messages=[{"role": "user", "content": "Hello, how are you?"}]
)

print(response.choices[0].message.content)

Configuration

You will need to configure the following:

base_url: The base URL of the TrueFoundry dashboard
api_key: API key generated from Personal Access Tokens
model: TrueFoundry model ID in the format provider_account/model_name (available in the LLM playground UI)

Input Controls

System Prompts

System prompts set the behavior and context for the model by defining the assistant’s role, tone, and constraints:

response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant that specializes in Python programming."},
        {"role": "user", "content": "How do I write a function to calculate factorial?"}
    ]
)

Request Parameters

Fine-tune model behavior with these common parameters:

response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello, how are you?"}],
    temperature=0.7,       # Controls randomness (0.0 to 1.0)
    max_tokens=100,        # Maximum tokens to generate
    top_p=0.9,             # Nucleus sampling parameter
    frequency_penalty=0.0, # Reduces repetition
    presence_penalty=0.0,  # Encourages new topics
    stop=["\n", "Human:"]  # Stop sequences
)

Some models don’t support all parameters. For example, temperature is not supported by o series models like o3-mini.

Working with Media

The API supports various media types including images, audio, video and pdf.

Images

Supported Models: GPT-4o, GPT-4 Vision, Claude 3, Gemini Pro VisionSend images as part of your chat completion requests using either URLs or base64 encoding:

Using Image URLs

from openai import OpenAI

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm"
)

response = client.chat.completions.create(
    model="openai-main/gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg"
                    }
                }
            ]
        }
    ]
)

Using Base64 Encoded Images

import base64

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

response = client.chat.completions.create(
    model="openai-main/gpt-4o",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{encode_image('image.jpeg')}"
                    }
                }
            ]
        }
    ]
)

Audio

Supported Models: Google Gemini models (Gemini 2.0 Flash, etc.)Send audio files in supported formats (MP3, WAV, etc.). Currently supported for Google Gemini models:

Using Audio URLs

response = client.chat.completions.create(
    model="internal-google/gemini-2-0-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Transcribe this audio"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/audio.wav",
                        "mime_type": "audio/wav" # required for gemini models
                    }
                }
            ]
        }
    ]
)

Using Base64 Encoded Audio

import base64

def encode_audio(audio_path):
    with open(audio_path, "rb") as audio_file:
        return base64.b64encode(audio_file.read()).decode('utf-8')

response = client.chat.completions.create(
    model="internal-google/gemini-2-0-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Transcribe this audio"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:audio/wav;base64,{encode_audio('audio.wav')}"
                    }
                }
            ]
        }
    ]
)

Video

Supported Models: Google Gemini models (Gemini 2.0 Flash, etc.)Video processing is natively supported for Google Gemini models:

Using Video URLs

response = client.chat.completions.create(
    model="internal-google/gemini-2-0-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe what's happening in this video"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://www.youtube.com/watch?v=example",
                        "mime_type": "video/mp4" # required for gemini models
                    }
                }
            ]
        }
    ]
)

Using Base64 Encoded Video

import base64

def encode_video(video_path):
    with open(video_path, "rb") as video_file:
        return base64.b64encode(video_file.read()).decode('utf-8')

response = client.chat.completions.create(
    model="internal-google/gemini-2-0-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe what's happening in this video"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:video/mp4;base64,{encode_video('video.mp4')}",
                        "mime_type": "video/mp4" # required for gemini models
                    }
                }
            ]
        }
    ]
)

PDF Documents

Supported Models: Google Gemini models (Gemini 2.5 Flash, etc.)PDF document processing allows models to analyze and extract information from PDF files:

Using Base64 Encoded PDF

from openai import OpenAI

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm"
)

import base64

def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

response = client.chat.completions.create(
    model="internal-google/gemini-2-5-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this pdf?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:application/pdf;base64,{encode_image('sample.pdf')}"
                    }
                }
            ]
        }
    ]
)
print(response.choices[0].message.content)

Using PDF URLs

response = client.chat.completions.create(
    model="internal-google/gemini-2-5-flash",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Summarize this PDF document"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/document.pdf",
                        "mime_type": "application/pdf"
                    }
                }
            ]
        }
    ]
)
print(response.choices[0].message.content)

Function Calling

Function calling allows models to invoke defined functions during conversations, enabling them to perform specific actions or retrieve external information.

Basic Usage

Define functions that the model can call:

from openai import OpenAI
import json

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm/api/inference/openai"
)

# Define a function
tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"]
                }
            },
            "required": ["location"]
        }
    }
}]

# Make the request
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather in New York?"}],
    tools=tools
)

# Check if the model wants to call a function
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    function_name = tool_call.function.name
    function_args = json.loads(tool_call.function.arguments)

    print(f"Function called: {function_name}")
    print(f"Arguments: {function_args}")

Function Definition Reference

Creating Well-Structured Function Definitions

When defining functions, you need to provide:

name: The function name
description: What the function does
parameters: JSON Schema object describing the parameters

function_schema = {
    "name": "get_weather",
    "description": "Get current weather information",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "City name"
            }
        },
        "required": ["location"]
    }
}

Supported Parameter Types for Function Arguments

Functions support various parameter types:

function_schema = {
    "name": "process_data",
    "description": "Process data with various parameters",
    "parameters": {
        "type": "object",
        "properties": {
            "text": {
                "type": "string",
                "description": "Text to process"
            },
            "count": {
                "type": "integer",
                "description": "Number of items"
            },
            "confidence": {
                "type": "number",
                "description": "Threshold (0.0 to 1.0)"
            },
            "enabled": {
                "type": "boolean",
                "description": "Whether processing is enabled"
            },
            "categories": {
                "type": "array",
                "items": {"type": "string"},
                "description": "List of categories"
            }
        },
        "required": ["text"]
    }
}

Implementation Workflows

Working with Multiple Function Definitions

Define multiple functions for the model to choose from:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather information",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["location"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "search_web",
            "description": "Search the web for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "max_results": {"type": "integer", "default": 5}
                },
                "required": ["query"]
            }
        }
    }
]

Processing and Responding to Function Calls

Process function calls and continue the conversation:

# Initial request
messages = [{"role": "user", "content": "What's the weather in Tokyo?"}]
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=messages,
    tools=tools
)

# Handle function call
if response.choices[0].message.tool_calls:
    messages.append(response.choices[0].message)

    for tool_call in response.choices[0].message.tool_calls:
        function_name = tool_call.function.name
        function_args = json.loads(tool_call.function.arguments)

        # Execute your function (simulated here)
        if function_name == "get_weather":
            result = f"The weather in {function_args['location']} is 22°C and sunny"

        # Add the function result to the conversation
        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": result
        })

    # Continue the conversation
    final_response = client.chat.completions.create(
        model="openai-main/gpt-4o-mini",
        messages=messages
    )

    print(final_response.choices[0].message.content)

Controlling When and How Functions Are Called

Control when and how functions are called:

# Force a specific function call
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice={"type": "function", "function": {"name": "get_weather"}}
)

# Allow automatic function calling (default)
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="auto"
)

# Prevent function calling
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="none"
)

# Force any function call
response = client.chat.completions.create(
    model="openai-main/gpt-4o-mini",
    messages=[{"role": "user", "content": "What's the weather?"}],
    tools=tools,
    tool_choice="required"
)

Response Format

The chat completions API supports structured response formats, enabling you to receive consistent, predictable outputs in JSON format. This is useful for parsing responses programmatically.

JSON Response Options

Basic JSON Mode: Getting Valid JSON Without Structure Constraints

JSON mode ensures the model’s output is valid JSON without enforcing a specific structure:

from openai import OpenAI

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm/api/inference/openai"
)

response = client.chat.completions.create(
    model="openai-main/gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant designed to output JSON."},
        {"role": "user", "content": "Extract information about the 2020 World Series winner"}
    ],
    response_format={"type": "json_object"}
)

print(response.choices[0].message.content)

Output:

{
  "team": "Los Angeles Dodgers",
  "year": 2020,
  "opponent": "Tampa Bay Rays",
  "games_played": 6,
  "series_result": "4-2"
}

JSON Schema Mode: Enforcing Specific Data Structures

JSON Schema mode provides strict structure validation using predefined schemas:

from openai import OpenAI
import json

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm/api/inference/openai"
)

# Define JSON schema
user_info_schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer", "minimum": 0},
        "occupation": {"type": "string"},
        "location": {"type": "string"},
        "skills": {
            "type": "array",
            "items": {"type": "string"}
        }
    },
    "required": ["name", "age", "occupation", "location", "skills"],
    "additionalProperties": False
}

response = client.chat.completions.create(
    model="openai-main/gpt-4o",
    messages=[
        {
            "role": "system",
            "content": "Extract user information and respond according to the provided JSON schema."
        },
        {
            "role": "user",
            "content": "My name is Sarah Johnson, I'm 28 years old, and I work as a data scientist in New York. I'm skilled in Python, SQL, and machine learning."
        }
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "user_info",
            "schema": user_info_schema,
            "strict": True
        }
    }
)

# Parse response
result = json.loads(response.choices[0].message.content)

When using JSON schema with strict mode set to true, all properties defined in the schema must be included in the required array. If any property is defined but not marked as required, the API will return a 400 Bad Request Error.

Advanced Schema Integration

Python Type Validation with Pydantic Models

Pydantic provides automatic validation, serialization, and type hints for structured data:

from openai import OpenAI
from pydantic import BaseModel, Field
from typing import List

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm/api/inference/openai"
)

# Define Pydantic model
class UserInfo(BaseModel):
    name: str = Field(description="Full name of the user")
    age: int = Field(ge=0, description="Age in years")
    occupation: str = Field(description="Job title or profession")
    location: str = Field(description="City or location")
    skills: List[str] = Field(description="List of professional skills")

    class Config:
        extra = "forbid"  # Prevent additional fields

response = client.chat.completions.create(
    model="openai-main/gpt-4o",
    messages=[
        {
            "role": "system",
            "content": "Extract user information and respond according to the provided schema."
        },
        {
            "role": "user",
            "content": "Hi, I'm Mike Chen, a 32-year-old software architect from Seattle. I specialize in cloud computing, microservices, and Kubernetes."
        }
    ],
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "user_info",
            "schema": UserInfo.model_json_schema(),
            "strict": True
        }
    }
)

# Parse and validate with Pydantic
user_data = UserInfo.model_validate_json(response.choices[0].message.content)

When using OpenAI models with Pydantic Models, there should not be any optional fields in the pydantic model when strict mode is true. This is because the corresponding JSON schema will have missing fields in the “required” section.

Streamlined Pydantic Integration with OpenAI's Beta Parse API

The beta parse client provides the most streamlined approach for Pydantic integration:

from openai import OpenAI
from pydantic import BaseModel, Field
from typing import List, Optional

class UserInfo(BaseModel):
    name: str = Field(description="Full name of the user")
    age: int = Field(ge=0, description="Age in years")
    occupation: str = Field(description="Job title or profession")
    location: Optional[str] = Field(None, description="City or location")
    skills: List[str] = Field(default=[], description="List of professional skills")

client = OpenAI(
    api_key="your_truefoundry_api_key",
    base_url="<truefoundry-base-url>/api/llm/api/inference/openai"
)

completion = client.beta.chat.completions.parse(
    model="openai-main/gpt-4o",
    messages=[
        {
            "role": "system",
            "content": "Extract user information from the provided text."
        },
        {
            "role": "user",
            "content": "Hello, I'm Alex Rodriguez, a 29-year-old product manager from Austin. I have experience in agile methodologies, data analysis, and team leadership."
        }
    ],
    response_format=UserInfo,
)

user_result = completion.choices[0].message.parsed

This approach allows for optional fields in your Pydantic model and provides a cleaner API for structured responses.

Prompt Caching

Prompt caching optimizes API usage by allowing resumption from specific prefixes in your prompts. This significantly reduces processing time and costs for repetitive tasks or prompts with consistent elements.

Currently, only Anthropic models support this caching feature.

Basic Usage

Add the cache_control parameter to any message content you want to cache:

import requests
import json

URL = "https://{controlPlaneUrl}/api/llm/chat/completions"
API_KEY = "TFY_API_KEY"

headers = {
    "Authorization": f"Bearer {API_KEY}",
    "X-TFY-METADATA": '{"tfy_log_request":"true"}'
}

payload = {
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "<TEXT_TO_CACHE>",
                    "cache_control": {
                        "type": "ephemeral"
                    }
                }
            ]
        }
    ],
    "model": "anthropic-main/claude-3-opus",
    "stream": True
}

response = requests.post(URL, headers=headers, json=payload)

Minimum Cacheable Length

Model	Minimum Token Length
Claude Opus 4, Claude Sonnet 4, Claude Sonnet 3.7, Claude Sonnet 3.5, Claude Opus 3	1024 tokens
Claude Haiku 3.5, Claude Haiku 3	2048 tokens

This feature is only available through direct REST API calls. The OpenAI SDK doesn’t recognize the cache_control field.

Reasoning Models

TrueFoundry AI Gateway provides access to model reasoning processes through thinking/reasoning tokens, currently available for Claude 3.7 Sonnet (via Anthropic, AWS Bedrock, and Google Vertex AI). These models expose their internal reasoning process, allowing you to see how they arrive at conclusions. The thinking/reasoning tokens provide step-by-step insights into the model’s cognitive process.

Enabling Reasoning Tokens

To enable thinking/reasoning tokens, your request must include:

The header: X-TFY-STRICT-OPENAI: false
A thinking field in the request body

import requests
import json

url = "https://{controlPlaneUrl}/api/llm/chat/completions"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "X-TFY-STRICT-OPENAI": "false"
}

payload = {
    "messages": [
        {"role": "user", "content": "How to compute 3^3^3?"}
    ],
    "model": "anthropic/claude-3-7",
    "thinking": {
        "type": "enabled",
        "budget_tokens": 16000
    },
    "max_tokens": 18000
}

response = requests.post(url, headers=headers, json=payload)

When the X-TFY-STRICT-OPENAI header is set to false, the response is no longer OpenAI-compliant, as it introduces an additional reasoning layer that OpenAI’s compliance framework does not support.

Response Format

When reasoning tokens are enabled, the response includes both thinking and content sections:

{
  "id": "1742890579083",
  "object": "chat.completion",
  "created": 1742890579,
  "model": "",
  "provider": "aws",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": [
          {
            "type": "thinking",
            "thinking": "The user has asked a complex question about quantum mechanics. To provide a useful answer, I should first break down the core concepts and then explain them in simple terms before diving into advanced details."
          },
          {
            "type": "text",
            "text": "Quantum mechanics is a branch of physics that explains how particles behave at very small scales. Unlike classical physics, where objects have definite positions and velocities, quantum particles exist in a superposition of states until measured. Would you like a more detailed explanation or examples?"
          }
        ]
      },
      "finish_reason": "end_turn"
    }
  ],
  "usage": {
    "prompt_tokens": 45,
    "completion_tokens": 180,
    "total_tokens": 225
  }
}

Streaming with Reasoning Tokens

For streaming responses, the thinking section is always sent before the content section.

API Reference

For detailed API specifications, parameters, and response schemas, see the Chat Completions API Reference.

Get Started

Developer Guide

MCP Registry and Gateway

Observability

Integrations

Deployment

API Reference

Chat

Agent

MCP

Embeddings

Rerank

Responses

Image

Audio

Batch

Files

Moderations

​Contents

​Getting Started

​Configuration

​Input Controls

​System Prompts

​Request Parameters

​Working with Media

​Function Calling

​Basic Usage

​Function Definition Reference

​Implementation Workflows

​Response Format

​JSON Response Options

​Advanced Schema Integration

​Prompt Caching

​Basic Usage

​Minimum Cacheable Length

​Reasoning Models

​Enabling Reasoning Tokens

​Response Format

​Streaming with Reasoning Tokens

​API Reference

Contents

Getting Started

Configuration

Input Controls

System Prompts

Request Parameters

Working with Media

Function Calling

Basic Usage

Function Definition Reference

Implementation Workflows

Response Format

JSON Response Options

Advanced Schema Integration

Prompt Caching

Basic Usage

Minimum Cacheable Length

Reasoning Models

Enabling Reasoning Tokens

Response Format

Streaming with Reasoning Tokens

API Reference