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Merge pull request #11 from Serverless-Devs/fix-add-include-usage
feat: 添加 Google ADK、CrewAI、LangChain 和 PydanticAI 模型适配器的流选项支持，更新数据 AP…
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‎.github/workflows/release-test.yml‎

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name: Release Test Package
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on:
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# 支持手动触发，用户可以在 GitHub Actions 页面点击 "Run workflow"
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workflow_dispatch:
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inputs:
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version_bump:
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description: '版本递增类型'
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required: true
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default: 'patch'
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type: choice
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options:
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- patch # 0.0.1 -> 0.0.2
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- minor # 0.0.1 -> 0.1.0
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- major # 0.0.1 -> 1.0.0
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jobs:
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release-test:
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runs-on: ubuntu-latest
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permissions:
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contents: write
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id-token: write
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0 # 获取所有历史和 tags
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.10'
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- name: Get latest test version and calculate next version
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id: version
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run: |
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# 获取所有 agentrun-inner-test-v* 的 tags，找到最新版本
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LATEST_TAG=$(git tag -l "agentrun-inner-test-v*" | sort -V | tail -n 1)
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if [ -z "$LATEST_TAG" ]; then
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# 如果没有找到任何 tag，从 0.0.0 开始
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CURRENT_VERSION="0.0.0"
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echo "No existing test tags found, starting from 0.0.0"
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else
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# 从 tag 中提取版本号
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CURRENT_VERSION="${LATEST_TAG#agentrun-inner-test-v}"
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echo "Latest test tag: $LATEST_TAG (version: $CURRENT_VERSION)"
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fi
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# 解析版本号
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IFS='.' read -r MAJOR MINOR PATCH <<< "$CURRENT_VERSION"
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# 根据用户选择递增版本
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BUMP_TYPE="${{ inputs.version_bump }}"
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case "$BUMP_TYPE" in
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major)
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MAJOR=$((MAJOR + 1))
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MINOR=0
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PATCH=0
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;;
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minor)
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MINOR=$((MINOR + 1))
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PATCH=0
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;;
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patch)
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PATCH=$((PATCH + 1))
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;;
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esac
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NEW_VERSION="${MAJOR}.${MINOR}.${PATCH}"
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NEW_TAG="agentrun-inner-test-v${NEW_VERSION}"
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echo "VERSION=${NEW_VERSION}" >> $GITHUB_OUTPUT
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echo "TAG=${NEW_TAG}" >> $GITHUB_OUTPUT
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echo "New version: ${NEW_VERSION}"
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echo "New tag: ${NEW_TAG}"
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- name: Update package name and version in pyproject.toml
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run: |
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VERSION="${{ steps.version.outputs.VERSION }}"
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# 修改包名为 agentrun-inner-test
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sed -i 's/name = "agentrun-sdk"/name = "agentrun-inner-test"/' pyproject.toml
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# 修改版本号
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sed -i 's/version = "[^"]*"/version = "'${VERSION}'"/' pyproject.toml
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echo "Updated pyproject.toml:"
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head -10 pyproject.toml
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- name: Update __version__ in __init__.py
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run: |
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VERSION="${{ steps.version.outputs.VERSION }}"
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if grep -q "__version__" agentrun/__init__.py; then
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sed -i 's/__version__ = "[^"]*"/__version__ = "'${VERSION}'"/' agentrun/__init__.py
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else
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sed -i '1a __version__ = "'${VERSION}'"' agentrun/__init__.py
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fi
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echo "Updated __init__.py version to ${VERSION}"
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grep "__version__" agentrun/__init__.py
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- name: Build package
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run: |
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python -m pip install --upgrade pip
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pip install build twine
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python -m build
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echo "Package built successfully"
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ls -la dist/
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- name: Verify package
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run: |
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python -m twine check dist/*
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echo "Package verification completed"
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- name: Publish to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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password: ${{ secrets.PYPI_API_TOKEN }}
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verify-metadata: false
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- name: Create and push tag
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run: |
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TAG="${{ steps.version.outputs.TAG }}"
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git config --local user.email "[email protected]"
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git config --local user.name "GitHub Action"
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git tag -a "$TAG" -m "Release test package version ${{ steps.version.outputs.VERSION }}"
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git push origin "$TAG"
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echo "Created and pushed tag: $TAG"
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- name: Summary
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run: |
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echo "## 🎉 Test Package Released!" >> $GITHUB_STEP_SUMMARY
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echo "" >> $GITHUB_STEP_SUMMARY
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echo "- **Package Name:** agentrun-inner-test" >> $GITHUB_STEP_SUMMARY
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echo "- **Version:** ${{ steps.version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
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echo "- **Tag:** ${{ steps.version.outputs.TAG }}" >> $GITHUB_STEP_SUMMARY
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echo "- **Commit:** ${{ github.sha }}" >> $GITHUB_STEP_SUMMARY
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echo "" >> $GITHUB_STEP_SUMMARY
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echo "Install with: \`pip install agentrun-inner-test==${{ steps.version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY

‎agentrun/integration/agentscope/model_adapter.py‎

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@@ -56,4 +56,5 @@ def wrap_model(self, common_model: CommonModel) -> Any:
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"base_url": info.base_url,
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"http_client": AsyncClient(headers=info.headers),
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},
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generate_kwargs={"stream_options": {"include_usage": True}},
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)

‎agentrun/integration/crewai/model_adapter.py‎

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model=f"{info.provider or 'openai'}/{info.model}",
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base_url=info.base_url,
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default_headers=info.headers,
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stream_options={"include_usage": True},
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# async_client=AsyncClient(headers=info.headers),
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)

‎agentrun/integration/google_adk/model_adapter.py‎

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api_base=info.base_url,
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api_key=info.api_key,
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extra_headers=info.headers,
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stream_options={"include_usage": True},
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)

‎agentrun/integration/langchain/model_adapter.py‎

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model=info.model,
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base_url=info.base_url,
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async_client=AsyncClient(headers=info.headers),
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stream_usage=True,
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)

‎agentrun/integration/pydantic_ai/model_adapter.py‎

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"""PydanticAI 模型适配器 / PydanticAI Model Adapter"""
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from contextlib import asynccontextmanager
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import json
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from typing import Any, AsyncIterator
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from typing import Any
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from agentrun.integration.utils.adapter import ModelAdapter
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from agentrun.integration.utils.model import CommonModel
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try:
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.openai import OpenAIProvider
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from pydantic_ai.settings import ModelSettings
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except Exception as e:
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raise ImportError(
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"PydanticAI is not installed. "
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api_key=info.api_key,
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http_client=AsyncClient(headers=info.headers),
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),
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settings=ModelSettings(
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extra_body={"stream_options": {"include_usage": True}}
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),
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)
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‎agentrun/model/api/data.py‎

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**self.headers,
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**kwargs.get("headers", {}),
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}
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if kwargs.get("stream_options") is None:
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kwargs["stream_options"] = {}
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kwargs["stream_options"]["include_usage"] = True
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from litellm import completion
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return completion(
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**self.headers,
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**kwargs.get("headers", {}),
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}
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if kwargs.get("stream_options") is None:
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kwargs["stream_options"] = {}
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kwargs["stream_options"]["include_usage"] = True
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from litellm import responses
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return responses(

‎tests/unittests/integration/test_integration.py‎

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messages = kwargs.get("messages") or []
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tools_payload = kwargs.get("tools")
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assert kwargs.get("stream") in (None, False)
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assert pydash.get(kwargs, "stream_options.include_usage") is True
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return self._build_model_response(messages, tools_payload)
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async def fake_acompletion(*args, **kwargs):
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messages = kwargs.get("messages") or []
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tools_payload = kwargs.get("tools")
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assert kwargs.get("stream") in (None, False)
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assert pydash.get(kwargs, "stream_options.include_usage") is True
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return self._build_model_response(messages, tools_payload)
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monkeypatch.setattr("litellm.completion", fake_completion)

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