622 lines
19 KiB
Python
622 lines
19 KiB
Python
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"""OpenAI-specific utilities.
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This module contains utilities specific to the OpenAI provider,
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including reask functions, response handlers, and message formatting.
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"""
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from __future__ import annotations
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import json
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from textwrap import dedent
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from typing import Any, cast
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from openai import pydantic_function_tool
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from ...dsl.parallel import ParallelModel, handle_parallel_model
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from ...core.exceptions import ConfigurationError
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from ...mode import Mode
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from ...utils.core import dump_message, merge_consecutive_messages
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from ...processing.schema import generate_openai_schema
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def _is_stream_response(response: Any) -> bool:
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"""Check if response is a Stream object rather than a ChatCompletion.
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Stream objects don't have 'choices' attribute and can't be used
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for detailed reask messages that reference the response content.
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"""
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return response is None or not hasattr(response, "choices")
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def _filter_responses_tool_calls(output_items: list[Any]) -> list[Any]:
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"""Return response output items that represent tool calls."""
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tool_calls: list[Any] = []
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for item in output_items:
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item_type = getattr(item, "type", None)
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if item_type in {"function_call", "tool_call"}:
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tool_calls.append(item)
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continue
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if item_type is None and hasattr(item, "arguments"):
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tool_calls.append(item)
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return tool_calls
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def _format_responses_tool_call_details(tool_call: Any) -> str:
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"""Format tool call name/id details for reask messages."""
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tool_name = getattr(tool_call, "name", None)
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tool_id = (
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getattr(tool_call, "id", None)
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or getattr(tool_call, "call_id", None)
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or getattr(tool_call, "tool_call_id", None)
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)
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details: list[str] = []
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if tool_name:
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details.append(f"name={tool_name}")
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if tool_id:
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details.append(f"id={tool_id}")
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if not details:
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return ""
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return f" (tool call {', '.join(details)})"
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def reask_tools(
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kwargs: dict[str, Any],
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response: Any,
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exception: Exception,
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failed_attempts: list[Any] | None = None, # noqa: ARG001
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):
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"""
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Handle reask for OpenAI tools mode when validation fails.
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Kwargs modifications:
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- Adds: "messages" (tool response messages indicating validation errors)
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"""
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kwargs = kwargs.copy()
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# Handle Stream objects which don't have choices attribute
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# This happens when streaming mode is used with retries
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if _is_stream_response(response):
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kwargs["messages"].append(
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{
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"role": "user",
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"content": (
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f"Validation Error found:\n{exception}\n"
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"Recall the function correctly, fix the errors"
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),
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}
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)
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return kwargs
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reask_msgs = [dump_message(response.choices[0].message)]
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for tool_call in response.choices[0].message.tool_calls:
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reask_msgs.append(
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{
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"role": "tool", # type: ignore
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"tool_call_id": tool_call.id,
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"name": tool_call.function.name,
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"content": (
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f"Validation Error found:\n{exception}\nRecall the function correctly, fix the errors"
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),
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}
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)
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kwargs["messages"].extend(reask_msgs)
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return kwargs
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def reask_responses_tools(
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kwargs: dict[str, Any],
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response: Any,
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exception: Exception,
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failed_attempts: list[Any] | None = None, # noqa: ARG001
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):
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"""
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Handle reask for OpenAI responses tools mode when validation fails.
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Kwargs modifications:
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- Adds: "messages" (user messages with validation errors)
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"""
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kwargs = kwargs.copy()
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# Handle Stream objects which don't have output attribute
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if response is None or not hasattr(response, "output"):
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kwargs["messages"].append(
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{
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"role": "user",
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"content": (
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f"Validation Error found:\n{exception}\n"
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"Recall the function correctly, fix the errors"
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),
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}
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)
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return kwargs
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reask_messages = []
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for tool_call in _filter_responses_tool_calls(response.output):
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details = _format_responses_tool_call_details(tool_call)
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reask_messages.append(
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{
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"role": "user", # type: ignore
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"content": (
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f"Validation Error found:\n{exception}\n"
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"Recall the function correctly, fix the errors with "
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f"{tool_call.arguments}{details}"
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),
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}
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)
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kwargs["messages"].extend(reask_messages)
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return kwargs
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def reask_md_json(
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kwargs: dict[str, Any],
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response: Any,
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exception: Exception,
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failed_attempts: list[Any] | None = None, # noqa: ARG001
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):
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"""
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Handle reask for OpenAI JSON modes when validation fails.
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Kwargs modifications:
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- Adds: "messages" (user message requesting JSON correction)
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"""
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kwargs = kwargs.copy()
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# Handle Stream objects which don't have choices attribute
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if _is_stream_response(response):
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kwargs["messages"].append(
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{
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"role": "user",
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"content": f"Correct your JSON ONLY RESPONSE, based on the following errors:\n{exception}",
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}
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)
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return kwargs
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reask_msgs = [dump_message(response.choices[0].message)]
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reask_msgs.append(
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{
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"role": "user",
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"content": f"Correct your JSON ONLY RESPONSE, based on the following errors:\n{exception}",
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}
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)
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kwargs["messages"].extend(reask_msgs)
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return kwargs
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def reask_default(
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kwargs: dict[str, Any],
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response: Any,
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exception: Exception,
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failed_attempts: list[Any] | None = None, # noqa: ARG001
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):
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"""
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Handle reask for OpenAI default mode when validation fails.
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Kwargs modifications:
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- Adds: "messages" (user message requesting function correction)
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"""
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kwargs = kwargs.copy()
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# Handle Stream objects which don't have choices attribute
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if _is_stream_response(response):
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kwargs["messages"].append(
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{
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"role": "user",
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"content": (
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f"Recall the function correctly, fix the errors, exceptions found\n{exception}"
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),
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}
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)
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return kwargs
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reask_msgs = [dump_message(response.choices[0].message)]
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reask_msgs.append(
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{
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"role": "user",
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"content": (
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f"Recall the function correctly, fix the errors, exceptions found\n{exception}"
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),
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}
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)
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kwargs["messages"].extend(reask_msgs)
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return kwargs
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# Response handlers
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def handle_parallel_tools(
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response_model: type[Any], new_kwargs: dict[str, Any]
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) -> tuple[type[Any], dict[str, Any]]:
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"""
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Handle OpenAI parallel tools mode for concurrent function calls.
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This mode enables making multiple independent function calls in a single request,
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useful for batch processing or when you need to extract multiple structured outputs
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simultaneously. The response_model should be a list/iterable type or use the
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ParallelModel wrapper.
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Example usage:
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# Define models for parallel extraction
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class PersonInfo(BaseModel):
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name: str
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age: int
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class EventInfo(BaseModel):
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date: str
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location: str
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# Use with PARALLEL_TOOLS mode
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result = client.chat.completions.create(
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model="gpt-4",
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response_model=[PersonInfo, EventInfo],
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mode=instructor.Mode.PARALLEL_TOOLS,
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messages=[{"role": "user", "content": "Extract person and event info..."}]
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)
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Kwargs modifications:
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- Adds: "tools" (multiple function schemas from parallel model)
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- Adds: "tool_choice" ("auto" to allow model to choose which tools to call)
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- Validates: stream=False (streaming not supported in parallel mode)
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"""
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if new_kwargs.get("stream", False):
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raise ConfigurationError(
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"stream=True is not supported when using PARALLEL_TOOLS mode"
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)
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new_kwargs["tools"] = handle_parallel_model(response_model)
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new_kwargs["tool_choice"] = "auto"
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return cast(type[Any], ParallelModel(typehint=response_model)), new_kwargs
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def handle_functions(
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response_model: type[Any] | None, new_kwargs: dict[str, Any]
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) -> tuple[type[Any] | None, dict[str, Any]]:
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"""
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Handle OpenAI functions mode (deprecated).
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Kwargs modifications:
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- When response_model is None: No modifications
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- When response_model is provided:
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- Adds: "functions" (list with function schema)
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- Adds: "function_call" (forced function call)
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"""
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Mode.warn_mode_functions_deprecation()
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if response_model is None:
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return None, new_kwargs
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new_kwargs["functions"] = [generate_openai_schema(response_model)]
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new_kwargs["function_call"] = {
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"name": generate_openai_schema(response_model)["name"]
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}
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return response_model, new_kwargs
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def handle_tools_strict(
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response_model: type[Any] | None, new_kwargs: dict[str, Any]
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) -> tuple[type[Any] | None, dict[str, Any]]:
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"""
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Handle OpenAI strict tools mode.
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Kwargs modifications:
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- When response_model is None: No modifications
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- When response_model is provided:
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- Adds: "tools" (list with strict function schema)
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- Adds: "tool_choice" (forced function call)
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"""
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if response_model is None:
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return None, new_kwargs
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response_model_schema = pydantic_function_tool(response_model)
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response_model_schema["function"]["strict"] = True
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new_kwargs["tools"] = [response_model_schema]
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new_kwargs["tool_choice"] = {
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"type": "function",
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"function": {"name": response_model_schema["function"]["name"]},
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}
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return response_model, new_kwargs
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def handle_tools(
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response_model: type[Any] | None, new_kwargs: dict[str, Any]
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) -> tuple[type[Any] | None, dict[str, Any]]:
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"""
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Handle OpenAI tools mode.
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Kwargs modifications:
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- When response_model is None: No modifications
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- When response_model is provided:
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- Adds: "tools" (list with function schema)
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- Adds: "tool_choice" (forced function call)
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"""
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if response_model is None:
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return None, new_kwargs
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new_kwargs["tools"] = [
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{
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"type": "function",
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"function": generate_openai_schema(response_model),
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}
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]
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new_kwargs["tool_choice"] = {
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"type": "function",
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"function": {"name": generate_openai_schema(response_model)["name"]},
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}
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return response_model, new_kwargs
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def handle_responses_tools(
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response_model: type[Any] | None, new_kwargs: dict[str, Any]
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) -> tuple[type[Any] | None, dict[str, Any]]:
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"""
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Handle OpenAI responses tools mode.
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Kwargs modifications:
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- When response_model is None: No modifications
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- When response_model is provided:
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- Adds: "tools" (list with function schema)
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- Adds: "tool_choice" (forced function call)
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- Adds: "max_output_tokens" (converted from max_tokens)
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"""
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# Handle max_tokens to max_output_tokens conversion for RESPONSES_TOOLS modes
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if new_kwargs.get("max_tokens") is not None:
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new_kwargs["max_output_tokens"] = new_kwargs.pop("max_tokens")
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# If response_model is None, just return without setting up tools
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if response_model is None:
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return None, new_kwargs
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schema = pydantic_function_tool(response_model)
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del schema["function"]["strict"]
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tool_definition = {
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"type": "function",
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"name": schema["function"]["name"],
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"parameters": schema["function"]["parameters"],
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}
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if "description" in schema["function"]:
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tool_definition["description"] = schema["function"]["description"]
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else:
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tool_definition["description"] = (
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f"Correctly extracted `{response_model.__name__}` with all "
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f"the required parameters with correct types"
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)
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new_kwargs["tools"] = [tool_definition]
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new_kwargs["tool_choice"] = {
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"type": "function",
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"name": tool_definition["name"],
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}
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return response_model, new_kwargs
|
||
|
|
|
||
|
|
|
||
|
|
def handle_responses_tools_with_inbuilt_tools(
|
||
|
|
response_model: type[Any] | None, new_kwargs: dict[str, Any]
|
||
|
|
) -> tuple[type[Any] | None, dict[str, Any]]:
|
||
|
|
"""
|
||
|
|
Handle OpenAI responses tools with inbuilt tools mode.
|
||
|
|
|
||
|
|
Kwargs modifications:
|
||
|
|
- When response_model is None: No modifications
|
||
|
|
- When response_model is provided:
|
||
|
|
- Adds: "tools" (list with function schema)
|
||
|
|
- Adds: "tool_choice" (forced function call)
|
||
|
|
- Adds: "max_output_tokens" (converted from max_tokens)
|
||
|
|
"""
|
||
|
|
# Handle max_tokens to max_output_tokens conversion for RESPONSES_TOOLS modes
|
||
|
|
if new_kwargs.get("max_tokens") is not None:
|
||
|
|
new_kwargs["max_output_tokens"] = new_kwargs.pop("max_tokens")
|
||
|
|
|
||
|
|
# If response_model is None, just return without setting up tools
|
||
|
|
if response_model is None:
|
||
|
|
return None, new_kwargs
|
||
|
|
|
||
|
|
schema = pydantic_function_tool(response_model)
|
||
|
|
del schema["function"]["strict"]
|
||
|
|
|
||
|
|
tool_definition = {
|
||
|
|
"type": "function",
|
||
|
|
"name": schema["function"]["name"],
|
||
|
|
"parameters": schema["function"]["parameters"],
|
||
|
|
}
|
||
|
|
|
||
|
|
if "description" in schema["function"]:
|
||
|
|
tool_definition["description"] = schema["function"]["description"]
|
||
|
|
else:
|
||
|
|
tool_definition["description"] = (
|
||
|
|
f"Correctly extracted `{response_model.__name__}` with all "
|
||
|
|
f"the required parameters with correct types"
|
||
|
|
)
|
||
|
|
|
||
|
|
if not new_kwargs.get("tools"):
|
||
|
|
new_kwargs["tools"] = [tool_definition]
|
||
|
|
new_kwargs["tool_choice"] = {
|
||
|
|
"type": "function",
|
||
|
|
"name": generate_openai_schema(response_model)["name"],
|
||
|
|
}
|
||
|
|
else:
|
||
|
|
new_kwargs["tools"].append(tool_definition)
|
||
|
|
|
||
|
|
return response_model, new_kwargs
|
||
|
|
|
||
|
|
|
||
|
|
def handle_json_o1(
|
||
|
|
response_model: type[Any] | None, new_kwargs: dict[str, Any]
|
||
|
|
) -> tuple[type[Any] | None, dict[str, Any]]:
|
||
|
|
"""
|
||
|
|
Handle OpenAI o1 JSON mode.
|
||
|
|
|
||
|
|
Kwargs modifications:
|
||
|
|
- When response_model is None: No modifications
|
||
|
|
- When response_model is provided:
|
||
|
|
- Modifies: "messages" (appends user message with JSON schema)
|
||
|
|
- Validates: No system messages allowed for O1 models
|
||
|
|
"""
|
||
|
|
roles = [message["role"] for message in new_kwargs.get("messages", [])]
|
||
|
|
if "system" in roles:
|
||
|
|
raise ValueError("System messages are not supported For the O1 models")
|
||
|
|
|
||
|
|
if response_model is None:
|
||
|
|
return None, new_kwargs
|
||
|
|
|
||
|
|
message = dedent(
|
||
|
|
f"""
|
||
|
|
Understand the content and provide
|
||
|
|
the parsed objects in json that match the following json_schema:\n
|
||
|
|
|
||
|
|
{json.dumps(response_model.model_json_schema(), indent=2, ensure_ascii=False)}
|
||
|
|
|
||
|
|
Make sure to return an instance of the JSON, not the schema itself
|
||
|
|
"""
|
||
|
|
)
|
||
|
|
|
||
|
|
new_kwargs["messages"].append(
|
||
|
|
{
|
||
|
|
"role": "user",
|
||
|
|
"content": message,
|
||
|
|
},
|
||
|
|
)
|
||
|
|
return response_model, new_kwargs
|
||
|
|
|
||
|
|
|
||
|
|
def handle_json_modes(
|
||
|
|
response_model: type[Any] | None, new_kwargs: dict[str, Any], mode: Mode
|
||
|
|
) -> tuple[type[Any] | None, dict[str, Any]]:
|
||
|
|
"""
|
||
|
|
Handle OpenAI JSON modes (JSON, MD_JSON, JSON_SCHEMA).
|
||
|
|
|
||
|
|
Kwargs modifications:
|
||
|
|
- When response_model is None: No modifications
|
||
|
|
- When response_model is provided:
|
||
|
|
- Mode.JSON_SCHEMA: Adds "response_format" with json_schema
|
||
|
|
- Mode.JSON: Adds "response_format" with type="json_object", modifies system message
|
||
|
|
- Mode.MD_JSON: Appends user message for markdown JSON response
|
||
|
|
"""
|
||
|
|
if response_model is None:
|
||
|
|
return None, new_kwargs
|
||
|
|
|
||
|
|
# Use a neutral prompt that doesn't impose a persona
|
||
|
|
# This allows the JSON mode to work with character-based applications
|
||
|
|
# See: https://github.com/instructor-ai/instructor/issues/1514
|
||
|
|
message = dedent(
|
||
|
|
f"""
|
||
|
|
Parse the content and return a JSON object matching this schema:
|
||
|
|
|
||
|
|
{json.dumps(response_model.model_json_schema(), indent=2, ensure_ascii=False)}
|
||
|
|
|
||
|
|
Return a valid JSON instance, not the schema definition."""
|
||
|
|
)
|
||
|
|
|
||
|
|
if mode == Mode.JSON:
|
||
|
|
new_kwargs["response_format"] = {"type": "json_object"}
|
||
|
|
elif mode == Mode.JSON_SCHEMA:
|
||
|
|
new_kwargs["response_format"] = {
|
||
|
|
"type": "json_schema",
|
||
|
|
"json_schema": {
|
||
|
|
"name": response_model.__name__,
|
||
|
|
"schema": response_model.model_json_schema(),
|
||
|
|
},
|
||
|
|
}
|
||
|
|
elif mode == Mode.MD_JSON:
|
||
|
|
new_kwargs["messages"].append(
|
||
|
|
{
|
||
|
|
"role": "user",
|
||
|
|
"content": "Return the correct JSON response within a ```json codeblock. not the JSON_SCHEMA",
|
||
|
|
},
|
||
|
|
)
|
||
|
|
new_kwargs["messages"] = merge_consecutive_messages(new_kwargs["messages"])
|
||
|
|
|
||
|
|
if mode != Mode.JSON_SCHEMA:
|
||
|
|
if new_kwargs["messages"][0]["role"] != "system":
|
||
|
|
new_kwargs["messages"].insert(
|
||
|
|
0,
|
||
|
|
{
|
||
|
|
"role": "system",
|
||
|
|
"content": message,
|
||
|
|
},
|
||
|
|
)
|
||
|
|
elif isinstance(new_kwargs["messages"][0]["content"], str):
|
||
|
|
new_kwargs["messages"][0]["content"] += f"\n\n{message}"
|
||
|
|
elif isinstance(new_kwargs["messages"][0]["content"], list):
|
||
|
|
new_kwargs["messages"][0]["content"][0]["text"] += f"\n\n{message}"
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Invalid message format, must be a string or a list of messages"
|
||
|
|
)
|
||
|
|
|
||
|
|
return response_model, new_kwargs
|
||
|
|
|
||
|
|
|
||
|
|
def handle_openrouter_structured_outputs(
|
||
|
|
response_model: type[Any], new_kwargs: dict[str, Any]
|
||
|
|
) -> tuple[type[Any], dict[str, Any]]:
|
||
|
|
"""
|
||
|
|
Handle OpenRouter structured outputs mode.
|
||
|
|
|
||
|
|
Kwargs modifications:
|
||
|
|
- Adds: "response_format" (json_schema with strict mode enabled)
|
||
|
|
"""
|
||
|
|
schema = response_model.model_json_schema()
|
||
|
|
schema["additionalProperties"] = False
|
||
|
|
new_kwargs["response_format"] = {
|
||
|
|
"type": "json_schema",
|
||
|
|
"json_schema": {
|
||
|
|
"name": response_model.__name__,
|
||
|
|
"schema": schema,
|
||
|
|
"strict": True,
|
||
|
|
},
|
||
|
|
}
|
||
|
|
return response_model, new_kwargs
|
||
|
|
|
||
|
|
|
||
|
|
# Handler registry for OpenAI
|
||
|
|
OPENAI_HANDLERS = {
|
||
|
|
Mode.TOOLS: {
|
||
|
|
"reask": reask_tools,
|
||
|
|
"response": handle_tools,
|
||
|
|
},
|
||
|
|
Mode.TOOLS_STRICT: {
|
||
|
|
"reask": reask_tools,
|
||
|
|
"response": handle_tools_strict,
|
||
|
|
},
|
||
|
|
Mode.FUNCTIONS: {
|
||
|
|
"reask": reask_default,
|
||
|
|
"response": handle_functions,
|
||
|
|
},
|
||
|
|
Mode.JSON: {
|
||
|
|
"reask": reask_md_json,
|
||
|
|
"response": lambda rm, nk: handle_json_modes(rm, nk, Mode.JSON),
|
||
|
|
},
|
||
|
|
Mode.MD_JSON: {
|
||
|
|
"reask": reask_md_json,
|
||
|
|
"response": lambda rm, nk: handle_json_modes(rm, nk, Mode.MD_JSON),
|
||
|
|
},
|
||
|
|
Mode.JSON_SCHEMA: {
|
||
|
|
"reask": reask_md_json,
|
||
|
|
"response": lambda rm, nk: handle_json_modes(rm, nk, Mode.JSON_SCHEMA),
|
||
|
|
},
|
||
|
|
Mode.JSON_O1: {
|
||
|
|
"reask": reask_md_json,
|
||
|
|
"response": handle_json_o1,
|
||
|
|
},
|
||
|
|
Mode.PARALLEL_TOOLS: {
|
||
|
|
"reask": reask_tools,
|
||
|
|
"response": handle_parallel_tools,
|
||
|
|
},
|
||
|
|
Mode.RESPONSES_TOOLS: {
|
||
|
|
"reask": reask_responses_tools,
|
||
|
|
"response": handle_responses_tools,
|
||
|
|
},
|
||
|
|
Mode.RESPONSES_TOOLS_WITH_INBUILT_TOOLS: {
|
||
|
|
"reask": reask_responses_tools,
|
||
|
|
"response": handle_responses_tools_with_inbuilt_tools,
|
||
|
|
},
|
||
|
|
Mode.OPENROUTER_STRUCTURED_OUTPUTS: {
|
||
|
|
"reask": reask_md_json,
|
||
|
|
"response": handle_openrouter_structured_outputs,
|
||
|
|
},
|
||
|
|
}
|