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The OpenAICompletionsClient class provides a wrapper around the OpenAI Completions API (legacy text completion endpoint) using the AsyncOpenAI client.

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Overview

This client implements the Client interface for OpenAI’s legacy Completions API, which is used for text completion models. It handles:
  • Message conversion to plain text format (Completions API only accepts text prompts)
  • Text-only content validation (rejects images and other multimodal content)
  • Token usage and logprobs parsing
  • Context length error handling
The Completions API does not support tools, function calling, or multimodal content. Use OpenAIChatCompletionsClient for these features.

​
Type Aliases

OpenAITextMessages = str
OpenAITextResponse = Completion

​
Class Definition

class OpenAICompletionsClient(
    Client[
        AsyncOpenAI,
        OpenAITextMessages,
        OpenAITextResponse,
        None,
    ]
)
Generic type parameters:
  • ClientT: AsyncOpenAI - The OpenAI async client
  • MessagesT: OpenAITextMessages - Plain text string (concatenated messages)
  • ResponseT: OpenAITextResponse - OpenAI Completion object
  • ToolT: None - Tools are not supported

​
Constructor

OpenAICompletionsClient(client_or_config: AsyncOpenAI | ClientConfig)
​
client_or_config
AsyncOpenAI | ClientConfig
required
Either a pre-configured AsyncOpenAI client or a ClientConfig to create one.

​
Example

from verifiers.clients.openai_completions_client import OpenAICompletionsClient
from verifiers.types import ClientConfig

# Using ClientConfig
client = OpenAICompletionsClient(
    ClientConfig(
        api_key="sk-...",
        base_url="https://api.openai.com/v1"
    )
)

# Using pre-configured AsyncOpenAI client
from openai import AsyncOpenAI
client = OpenAICompletionsClient(
    AsyncOpenAI(api_key="sk-...", base_url="https://api.openai.com/v1")
)

​
Methods

​
setup_client

def setup_client(self, config: ClientConfig) -> AsyncOpenAI
Creates an AsyncOpenAI client from a ClientConfig.
​
config
ClientConfig
required
Configuration with API key, base URL, and other settings.
Returns: Configured AsyncOpenAI instance.

​
close

async def close(self) -> None
Closes the underlying AsyncOpenAI client connection.

​
to_native_prompt

async def to_native_prompt(
    self, messages: Messages
) -> tuple[OpenAITextMessages, dict]
Converts Verifiers messages to plain text format for the Completions API.
​
messages
Messages
required
List of Verifiers message objects. All messages will be concatenated with double newlines.
Returns: Tuple of (text_prompt, extra_kwargs). The text prompt is a string with all message contents joined by "\n\n". The extra_kwargs dict is currently empty. Raises:
  • ValueError if any message contains non-text content (e.g., images)

​
to_native_tool

async def to_native_tool(self, tool: Tool) -> None
Not supported for Completions API.
​
tool
Tool
required
A Verifiers tool definition.
Raises: ValueError with message “Tools are not supported for Completions API”

​
get_native_response

@handle_openai_overlong_prompt
async def get_native_response(
    self,
    prompt: OpenAITextMessages,
    model: str,
    sampling_args: SamplingArgs,
    tools: list[None] | None = None,
    **kwargs,
) -> OpenAITextResponse
Calls the OpenAI Completions API and returns the native response.
​
prompt
OpenAITextMessages
required
Plain text prompt string.
​
model
str
required
OpenAI model identifier (e.g., "gpt-3.5-turbo-instruct").
​
sampling_args
SamplingArgs
required
Sampling parameters. None values are filtered out before sending to API.
​
tools
list[None] | None
default:"None"
Must be None or empty. Tools are not supported.
Returns: OpenAI Completion object. Raises:
  • ValueError if tools are provided
  • OverlongPromptError if the prompt exceeds the model’s context length

​
raise_from_native_response

async def raise_from_native_response(self, response: OpenAITextResponse) -> None
Validates the OpenAI response and raises errors if invalid.
​
response
OpenAITextResponse
required
The OpenAI Completion response.
Raises:
  • EmptyModelResponseError if response is None, has no choices, or the text is empty
  • InvalidModelResponseError if the response has more than 1 choice

​
from_native_response

async def from_native_response(self, response: OpenAITextResponse) -> Response
Converts an OpenAI Completion to a Verifiers Response.
​
response
OpenAITextResponse
required
The OpenAI Completion response.
Returns: Verifiers Response object with:
  • id: Response ID from OpenAI
  • created: Timestamp from OpenAI
  • model: Model name from response
  • usage: Token counts (prompt_tokens, completion_tokens, total_tokens, reasoning_tokens=0)
  • message: Response message with text content and metadata
Parsed fields:
  • Content: Text from response.choices[0].text
  • Finish reason: Mapped from OpenAI values ("stop" → "stop", "length" → "length", others → None)
  • Is truncated: True if finish_reason is "length"
  • Tokens: If available (vLLM with return_tokens=true), includes prompt_token_ids, token_ids, logprobs
  • Reasoning content: Always None (not supported by Completions API)
  • Tool calls: Always None (not supported by Completions API)

​
Usage Example

import asyncio
from verifiers.clients.openai_completions_client import OpenAICompletionsClient
from verifiers.types import (
    ClientConfig,
    UserMessage,
    SamplingArgs,
)

async def main():
    # Initialize client
    client = OpenAICompletionsClient(
        ClientConfig(api_key="sk-...")
    )
    
    # Simple completion
    messages = [UserMessage(content="The capital of France is")]
    sampling_args = SamplingArgs(
        temperature=0.7,
        max_tokens=50
    )
    
    response = await client.get_response(
        prompt=messages,
        model="gpt-3.5-turbo-instruct",
        sampling_args=sampling_args
    )
    
    print(response.message.content)
    # " Paris."
    
    # Multi-message conversation (concatenated)
    messages = [
        UserMessage(content="User: What is 2+2?"),
        UserMessage(content="Assistant: 2+2 equals 4."),
        UserMessage(content="User: What about 3+3?"),
    ]
    
    response = await client.get_response(
        prompt=messages,
        model="gpt-3.5-turbo-instruct",
        sampling_args=sampling_args
    )
    
    print(response.message.content)
    # "Assistant: 3+3 equals 6."
    
    await client.close()

asyncio.run(main())

​
Limitations

​
No Tool Support

# This will raise ValueError
try:
    await client.get_response(
        prompt=messages,
        model="gpt-3.5-turbo-instruct",
        sampling_args=sampling_args,
        tools=[some_tool]  # NOT SUPPORTED
    )
except ValueError as e:
    print(e)  # "Completions API does not support tools..."

​
No Multimodal Content

# This will raise ValueError
from verifiers.types import ImageContent

try:
    messages = [
        UserMessage(content=[
            TextContent(text="What's in this image?"),
            ImageContent(url="https://example.com/image.jpg")
        ])
    ]
    await client.to_native_prompt(messages)
except ValueError as e:
    print(e)  # "Completions API does not support non-text content..."

​
Token Details

The client attempts to parse token-level information from the response: 
response = await client.get_response(...)

if response.message.tokens:
    # Available when using vLLM with return_tokens=true
    print("Prompt token IDs:", response.message.tokens.prompt_ids)
    print("Completion token IDs:", response.message.tokens.completion_ids)
    print("Log probabilities:", response.message.tokens.completion_logprobs)

​
Error Handling

The @handle_openai_overlong_prompt decorator catches BadRequestError and converts context length errors to OverlongPromptError. It detects phrases like:
  • “this model’s maximum context length is”
  • “is longer than the model’s context length”
  • “prompt_too_long”
  • “context length”
Authentication and permission errors are re-raised without wrapping.

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See Also

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