Remove OpenPipe integration
OpenPipe was acquired by CoreWeave in September 2025. The Python package hasn't been updated since June 2025 and the repo since 2024. The openpipe package caps openai<=1.97.1, creating dependency conflicts with other extras. Remove the dead integration to clean up the codebase.
This commit is contained in:
Mark Backman
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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import sys
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from pipecat.services import DeprecatedModuleProxy
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from .llm import *
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sys.modules[__name__] = DeprecatedModuleProxy(globals(), "openpipe", "openpipe.llm")
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@@ -1,143 +0,0 @@
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#
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# Copyright (c) 2024-2026, Daily
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#
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# SPDX-License-Identifier: BSD 2-Clause License
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#
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"""OpenPipe LLM service implementation for Pipecat.
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This module provides an OpenPipe-specific implementation of the OpenAI LLM service,
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enabling integration with OpenPipe's fine-tuning and monitoring capabilities.
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"""
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from dataclasses import dataclass
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from typing import Dict, Optional
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from loguru import logger
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from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
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from pipecat.services.openai.base_llm import BaseOpenAILLMService
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from pipecat.services.openai.llm import OpenAILLMService
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try:
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from openpipe import AsyncOpenAI as OpenPipeAI
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except ModuleNotFoundError as e:
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logger.error(f"Exception: {e}")
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logger.error("In order to use OpenPipe, you need to `pip install pipecat-ai[openpipe]`.")
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raise Exception(f"Missing module: {e}")
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@dataclass
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class OpenPipeLLMSettings(BaseOpenAILLMService.Settings):
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"""Settings for OpenPipeLLMService."""
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pass
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class OpenPipeLLMService(OpenAILLMService):
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"""OpenPipe-powered Large Language Model service.
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Extends OpenAI's LLM service to integrate with OpenPipe's fine-tuning and
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monitoring platform. Provides enhanced request logging and tagging capabilities
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for model training and evaluation.
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"""
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Settings = OpenPipeLLMSettings
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_settings: Settings
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def __init__(
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self,
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*,
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model: Optional[str] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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openpipe_api_key: Optional[str] = None,
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openpipe_base_url: str = "https://app.openpipe.ai/api/v1",
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tags: Optional[Dict[str, str]] = None,
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settings: Optional[Settings] = None,
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**kwargs,
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):
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"""Initialize OpenPipe LLM service.
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Args:
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model: The model name to use. Defaults to "gpt-4.1".
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.. deprecated:: 0.0.105
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Use ``settings=OpenPipeLLMService.Settings(model=...)`` instead.
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api_key: OpenAI API key for authentication. If None, reads from environment.
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base_url: Custom OpenAI API endpoint URL. Uses default if None.
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openpipe_api_key: OpenPipe API key for enhanced features. If None, reads from environment.
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openpipe_base_url: OpenPipe API endpoint URL. Defaults to "https://app.openpipe.ai/api/v1".
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tags: Optional dictionary of tags to apply to all requests for tracking.
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settings: Runtime-updatable settings. When provided alongside deprecated
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parameters, ``settings`` values take precedence.
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**kwargs: Additional arguments passed to parent OpenAILLMService.
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"""
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# 1. Initialize default_settings with hardcoded defaults
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default_settings = self.Settings(model="gpt-4.1")
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# 2. Apply direct init arg overrides (deprecated)
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if model is not None:
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self._warn_init_param_moved_to_settings("model", "model")
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default_settings.model = model
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# 3. (No step 3, as there's no params object to apply)
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# 4. Apply settings delta (canonical API, always wins)
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if settings is not None:
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default_settings.apply_update(settings)
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super().__init__(
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api_key=api_key,
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base_url=base_url,
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openpipe_api_key=openpipe_api_key,
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openpipe_base_url=openpipe_base_url,
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settings=default_settings,
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**kwargs,
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)
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self._tags = tags
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def create_client(self, api_key=None, base_url=None, **kwargs):
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"""Create an OpenPipe client instance.
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Args:
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api_key: OpenAI API key for authentication.
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base_url: OpenAI API base URL.
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**kwargs: Additional arguments including openpipe_api_key and openpipe_base_url.
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Returns:
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Configured OpenPipe AsyncOpenAI client instance.
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"""
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openpipe_api_key = kwargs.get("openpipe_api_key") or ""
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openpipe_base_url = kwargs.get("openpipe_base_url") or ""
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client = OpenPipeAI(
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api_key=api_key,
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base_url=base_url,
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openpipe={"api_key": openpipe_api_key, "base_url": openpipe_base_url},
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)
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return client
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def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
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"""Build parameters for OpenPipe chat completion request.
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Adds OpenPipe-specific logging and tagging parameters.
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Args:
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params_from_context: Parameters, derived from the LLM context, to
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use for the chat completion. Contains messages, tools, and tool
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choice.
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Returns:
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Dictionary of parameters for the chat completion request.
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"""
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# Start with base parameters
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params = super().build_chat_completion_params(params_from_context)
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# Add OpenPipe-specific parameters
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params["openpipe"] = {
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"tags": self._tags,
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"log_request": True,
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}
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return params
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