Merge pull request #152 from TencentCloudBase/feature/http_functions_python

Feature/http functions python

Author: jiwenchi

httpfunctions/crewai-python/.env.example

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+# OpenAI-Compatible API Configuration
+# Copy this file to .env and fill in your values
+
+# Model name (e.g., gpt-4o-mini, qwen-plus, glm-4)
+OPENAI_MODEL=gpt-4o-mini
+
+# API Base URL (optional, defaults to OpenAI)
+OPENAI_BASE_URL=your_base_url_here
+
+# API Key (required)
+OPENAI_API_KEY=your_api_key_here
+
+# Temperature (default: 0.7)
+OPENAI_TEMPERATURE=0.7

httpfunctions/crewai-python/agent.py

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+"""Agentic Chat Agent Implementation.
+
+This module implements a conversational chat agent using CrewAI Flow framework
+integrated with Cloudbase Agent. The agent uses LiteLLM for model interactions and
+supports streaming responses.
+"""
+
+import os
+import sys
+import pysqlite3
+from pathlib import Path
+
+# Configure HOME and CrewAI storage directories for SCF environment
+os.environ["HOME"] = "/tmp"
+os.environ["CREWAI_STORAGE_DIR"] = "/tmp"
+
+# Create required directories
+credentials_dir = Path("/tmp/.local/share/crewai/credentials")
+credentials_dir.mkdir(parents=True, exist_ok=True)
+
+# Replace sqlite3 with pysqlite3 for SCF compatibility
+sys.modules['sqlite3'] = pysqlite3 
+  
+from crewai import Crew, Agent, Task
+
+try:
+    from crewai.flow import Flow, start, persist
+except ModuleNotFoundError as exc:
+    raise ImportError(
+        "crewai.flow is required. Please install a CrewAI version that includes Flow (e.g., crewai>=1.7.2)."
+    ) from exc
+from litellm import acompletion
+from crewai.events.event_bus import crewai_event_bus
+from ag_ui.core import EventType
+from cloudbase_agent.crewai import CrewAIAgent as _BaseCrewAIAgent
+from cloudbase_agent.crewai.converters import CopilotKitState
+from cloudbase_agent.crewai.context import flow_context
+from cloudbase_agent.crewai.events import BridgedTextMessageChunkEvent
+
+from dotenv import load_dotenv
+load_dotenv()
+
+class CrewAIAgent(_BaseCrewAIAgent):
+    """Override run to avoid BaseAgent.as_current cross-context reset."""
+
+    async def run(self, run_input):
+        if getattr(self, "_should_fix_event_ids", True):
+            async for event in self._run_internal(run_input):
+                yield super()._fix_event_ids(event, run_input.thread_id, run_input.run_id)
+        else:
+            async for event in self._run_internal(run_input):
+                yield event
+
+
+@persist()
+class AgenticChatFlow(Flow[CopilotKitState]):
+    """Conversational chat flow using CrewAI framework.
+
+    This flow implements a basic chat agent that processes user messages
+    and generates streaming responses using LiteLLM completion API.
+
+    :ivar state: Flow state containing conversation messages and CopilotKit context
+    :type state: CopilotKitState
+    """
+
+    @start()
+    async def chat(self) -> None:
+        """Process chat messages and generate streaming responses.
+
+        This method is the entry point of the flow. It sends messages to the LLM,
+        streams the response back, and updates the conversation state.
+
+        The method:
+        1. Constructs messages with system prompt and conversation history
+        2. Calls LiteLLM completion API with streaming enabled
+        3. Wraps the response in copilotkit_stream for proper formatting
+        4. Appends the assistant's response to conversation state
+
+        :raises Exception: If LLM completion fails or streaming encounters errors
+        """
+        system_prompt = "You are a helpful assistant."
+
+        try:
+            model_name = os.getenv("OPENAI_MODEL")
+            base_url = os.getenv("OPENAI_BASE_URL")
+            api_key = os.getenv("OPENAI_API_KEY")
+            tools = getattr(self.state.copilotkit, "actions", [])
+            tools_arg = tools if tools else None
+
+            # Run the model and stream the response
+            stream = await acompletion(
+                model=model_name,
+                messages=[{"role": "system", "content": system_prompt}, *self.state.messages],
+                tools=tools_arg,
+                parallel_tool_calls=False,
+                stream=False, 
+                base_url=base_url,
+                api_key=api_key,
+                custom_llm_provider="openai",
+            )
+
+            message = stream.choices[0].message
+            content = message.content if hasattr(message, "content") else None
+
+            if content:
+                flow = flow_context.get(None)
+                if flow is not None:
+                    crewai_event_bus.emit(
+                        flow,
+                        BridgedTextMessageChunkEvent(
+                            type=EventType.TEXT_MESSAGE_CHUNK,
+                            message_id=getattr(message, "id", None) or stream.id,
+                            role="assistant",
+                            delta=content,
+                        ),
+                    )
+
+            self.state.messages.append(message)
+        except Exception as e:
+            print(f"[CrewAI Flow Chat] {e}")
+
+
+def build_chat_workflow() -> AgenticChatFlow:
+    """Build and return a new chat workflow instance.
+
+    This factory function creates a fresh instance of AgenticChatFlow
+    for each conversation to ensure proper isolation.
+
+    :return: New instance of the chat workflow
+    :rtype: AgenticChatFlow
+    """
+    return AgenticChatFlow()

httpfunctions/crewai-python/app.py

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+# -*- coding: utf-8 -*-
+from cloudbase_agent.server import AgentServiceApp
+
+import compatibility
+
+from agent import build_chat_workflow, CrewAIAgent
+
+if __name__ == "__main__":
+    agent = CrewAIAgent(
+        flow=build_chat_workflow(),
+    )
+    AgentServiceApp().run(lambda:{"agent": agent})

httpfunctions/crewai-python/requirements.txt

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+fastapi==0.125.0
+cloudbase-agent-server==0.1.11
+cloudbase-agent-core==0.1.11
+cloudbase-agent-crewai==0.1.11
+crewai==1.7.2
+pysqlite3-binary==0.5.4
+litellm==1.50.0
+openai==1.83.0
+python-dotenv==1.1.1

httpfunctions/crewai-python/scf_bootstrap

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+#!/bin/bash
+export PYTHONPATH="./env:$PYTHONPATH"
+/var/lang/python310/bin/python3 -u app.py

httpfunctions/langgraph-python/.env.example

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+# OpenAI-Compatible API Configuration
+# Copy this file to .env and fill in your values
+
+# Model name (e.g., gpt-4o-mini, qwen-plus, glm-4)
+OPENAI_MODEL=gpt-4o-mini
+
+# API Base URL (optional, defaults to OpenAI)
+OPENAI_BASE_URL=your_base_url_here
+
+# API Key (required)
+OPENAI_API_KEY=your_api_key_here
+
+# Temperature (default: 0.7)
+OPENAI_TEMPERATURE=0.7

httpfunctions/langgraph-python/README.md

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+# LangGraph Python Agent 快速开始
+
+本文档介绍如何将一个基于 LangGraph 的 AI Agent 应用部署到 CloudBase HTTP 云函数。该项目使用 `cloudbase-agent-server` 作为应用程序运行框架。
+
+我们这里使用 `python3.10` 进行开发。
+
+## 第1步: 编写基础应用
+
+创建名为 `langgraph-python` 的新项目，并进入此目录中:
+
+```bash
+mkdir langgraph-python
+cd langgraph-python
+```
+
+创建虚拟环境
+
+```bash
+python3.10 -m venv venv
+source venv/bin/activate  # 激活虚拟环境
+```
+
+安装依赖组件
+
+```bash
+python -m pip install -r ./requirements.txt \
+    --platform manylinux_2_17_x86_64 \
+    --target ./env \
+    --python-version 3.10 \
+    --only-binary=:all: \
+    --upgrade
+```
+
+## 第2步：配置环境变量
+
+创建 `.env` 文件（参考 `.env.example`）:
+
+```bash
+OPENAI_API_KEY=your_api_key_here
+OPENAI_BASE_URL=https://api.openai.com/v1
+OPENAI_MODEL=gpt-4o-mini
+```
+
+**注意**: 部署到 SCF 时，需要在云函数控制台配置这些环境变量。
+
+## 第3步：编写代码
+
+### `agent.py` - Agent 实现
+
+```python
+from langgraph.graph import StateGraph, MessagesState
+from langchain_openai import ChatOpenAI
+import os
+
+def chat_node(state, config=None):
+    api_key = os.getenv("OPENAI_API_KEY")
+    base_url = os.getenv("OPENAI_BASE_URL")
+    
+    if not api_key or not base_url:
+        raise ValueError("Environment variables not set")
+    
+    chat_model = ChatOpenAI(
+        model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
+        api_key=api_key,
+        base_url=base_url,
+    )
+    # ... 实现 chat 逻辑
+```
+
+### `app.py` - 应用入口
+
+```python
+from cloudbase_agent.server import AgentServiceApp
+from cloudbase_agent.langgraph import LangGraphAgent
+from agent import build_agentic_chat_workflow
+
+if __name__ == "__main__":
+    agent = LangGraphAgent(graph=build_agentic_chat_workflow())
+    AgentServiceApp().run(lambda: {"agent": agent})
+```
+
+**服务端口**: 默认使用 9000 端口（由 `cloudbase-agent-server` 管理）。
+
+### `scf_bootstrap` - SCF 启动脚本
+
+```bash
+#!/bin/bash
+export PYTHONPATH="./env:$PYTHONPATH"
+/var/lang/python310/bin/python3 -u app.py
+```
+
+**说明**:
+- 设置 `PYTHONPATH` 指向 `./env` 目录，让 Python 能找到依赖包
+- 所有通过 pip 安装的依赖包都存放在 `env/` 目录中
+
+## 第4步：管理项目依赖
+
+### 打包部署
+
+将项目文件打成 zip 包（包含本地依赖）:
+
+```bash
+zip -r langgraph-python.zip .
+```
+
+### 上传部署
+
+1. 选择 **HTTP 云函数**
+2. Python 运行时选择 **3.10**
+3. 上传 zip 包
+4. 在控制台配置环境变量：
+   - `OPENAI_API_KEY`
+   - `OPENAI_BASE_URL`
+   - `OPENAI_MODEL`（可选）
+5. 点击部署