{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# OpenAI 助手代理\n\n[Open AI 助手](https://platform.openai.com/docs/assistants/overview) \n和 [Azure OpenAI 助手](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/assistant)\n是用于构建代理的服务器端API。\n它们可用于在AutoGen中构建代理。本示例演示如何\n使用OpenAI助手创建一个可以运行代码和进行文档问答的代理。\n\n## 消息协议\n\n首先，我们需要为基于OpenAI助手的代理指定消息协议。消息协议定义了\n代理处理和发布的消息结构。\n为便于说明，我们定义了一个简单的\n包含4种消息类型的协议：`Message`、`Reset`、`UploadForCodeInterpreter`和`UploadForFileSearch`。\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from dataclasses import dataclass\n", "\n", "\n", "@dataclass\n", "class TextMessage:\n", " content: str\n", " source: str\n", "\n", "\n", "@dataclass\n", "class Reset:\n", " pass\n", "\n", "\n", "@dataclass\n", "class UploadForCodeInterpreter:\n", " file_path: str\n", "\n", "\n", "@dataclass\n", "class UploadForFileSearch:\n", " file_path: str\n", " vector_store_id: str" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`TextMessage`消息类型用于与代理通信。它有一个\n包含消息内容的`content`字段，以及一个表示发送者的`source`字段。\n`Reset`消息类型是重置代理内存的控制消息。它没有字段。\n当我们需要与代理开始新对话时，这非常有用。\n\n`UploadForCodeInterpreter`消息类型用于上传代码解释器的数据文件，\n而`UploadForFileSearch`消息类型用于上传文件搜索的文档。\n两种消息类型都有一个`file_path`字段，包含要上传文件的本地路径。\n\n## 定义代理\n\n接下来，我们定义代理类。\n代理类构造函数有以下参数：`description`、\n`client`、`assistant_id`、`thread_id`和`assistant_event_handler_factory`。\n`client`参数是OpenAI异步客户端对象，\n`assistant_event_handler_factory`用于创建助手事件处理器\n来处理OpenAI助手事件。\n这可用于创建来自助手的流式输出。\n\n代理类有以下消息处理器：\n- `handle_message`：处理`TextMessage`消息类型，并返回来自助手的响应。\n- `handle_reset`：处理`Reset`消息类型，并重置助手代理的内存。\n- `handle_upload_for_code_interpreter`：处理`UploadForCodeInterpreter`\n 消息类型，并将文件上传至代码解释器。\n- `handle_upload_for_file_search`：处理`UploadForFileSearch`\n 消息类型，并将文档上传至文件搜索。\n\n助手的内存存储在一个线程中，该线程保存在服务器端。\n线程由`thread_id`参数引用。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import asyncio\n", "import os\n", "from typing import Any, Callable, List\n", "\n", "import aiofiles\n", "from autogen_core import AgentId, MessageContext, RoutedAgent, message_handler\n", "from openai import AsyncAssistantEventHandler, AsyncClient\n", "from openai.types.beta.thread import ToolResources, ToolResourcesFileSearch\n", "\n", "\n", "class OpenAIAssistantAgent(RoutedAgent):\n", " \"\"\"An agent implementation that uses the OpenAI Assistant API to generate\n", " responses.\n", "\n", " Args:\n", " description (str): The description of the agent.\n", " client (openai.AsyncClient): The client to use for the OpenAI API.\n", " assistant_id (str): The assistant ID to use for the OpenAI API.\n", " thread_id (str): The thread ID to use for the OpenAI API.\n", " assistant_event_handler_factory (Callable[[], AsyncAssistantEventHandler], optional):\n", " A factory function to create an async assistant event handler. Defaults to None.\n", " If provided, the agent will use the streaming mode with the event handler.\n", " If not provided, the agent will use the blocking mode to generate responses.\n", " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " description: str,\n", " client: AsyncClient,\n", " assistant_id: str,\n", " thread_id: str,\n", " assistant_event_handler_factory: Callable[[], AsyncAssistantEventHandler],\n", " ) -> None:\n", " super().__init__(description)\n", " self._client = client\n", " self._assistant_id = assistant_id\n", " self._thread_id = thread_id\n", " self._assistant_event_handler_factory = assistant_event_handler_factory\n", "\n", " @message_handler\n", " async def handle_message(self, message: TextMessage, ctx: MessageContext) -> TextMessage:\n", " \"\"\"Handle a message. This method adds the message to the thread and publishes a response.\"\"\"\n", " # 将消息保存到线程中。\n", " await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(\n", " self._client.beta.threads.messages.create(\n", " thread_id=self._thread_id,\n", " content=message.content,\n", " role=\"user\",\n", " metadata={\"sender\": message.source},\n", " )\n", " )\n", " )\n", " # 生成响应。\n", " async with self._client.beta.threads.runs.stream(\n", " thread_id=self._thread_id,\n", " assistant_id=self._assistant_id,\n", " event_handler=self._assistant_event_handler_factory(),\n", " ) as stream:\n", " await ctx.cancellation_token.link_future(asyncio.ensure_future(stream.until_done()))\n", "\n", " # 获取最后一条消息。\n", " messages = await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(self._client.beta.threads.messages.list(self._thread_id, order=\"desc\", limit=1))\n", " )\n", " last_message_content = messages.data[0].content\n", "\n", " # 获取最后一条消息的文本内容\n", " text_content = [content for content in last_message_content if content.type == \"text\"]\n", " if not text_content:\n", " raise ValueError(f\"Expected text content in the last message: {last_message_content}\")\n", "\n", " return TextMessage(content=text_content[0].text.value, source=self.metadata[\"type\"])\n", "\n", " @message_handler()\n", " async def on_reset(self, message: Reset, ctx: MessageContext) -> None:\n", " \"\"\"Handle a reset message. This method deletes all messages in the thread.\"\"\"\n", " # 获取此线程中的所有消息\n", " all_msgs: List[str] = []\n", " while True:\n", " if not all_msgs:\n", " msgs = await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(self._client.beta.threads.messages.list(self._thread_id))\n", " )\n", " else:\n", " msgs = await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(self._client.beta.threads.messages.list(self._thread_id, after=all_msgs[-1]))\n", " )\n", " for msg in msgs.data:\n", " all_msgs.append(msg.id)\n", " if not msgs.has_next_page():\n", " break\n", " # 删除所有消息\n", " for msg_id in all_msgs:\n", " status = await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(\n", " self._client.beta.threads.messages.delete(message_id=msg_id, thread_id=self._thread_id)\n", " )\n", " )\n", " assert status.deleted is True\n", "\n", " @message_handler()\n", " async def on_upload_for_code_interpreter(self, message: UploadForCodeInterpreter, ctx: MessageContext) -> None:\n", " \"\"\"Handle an upload for code interpreter. This method uploads a file and updates the thread with the file.\"\"\"\n", " # 获取文件内容\n", " async with aiofiles.open(message.file_path, mode=\"rb\") as f:\n", " file_content = await ctx.cancellation_token.link_future(asyncio.ensure_future(f.read()))\n", " file_name = os.path.basename(message.file_path)\n", " # 上传文件\n", " file = await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(self._client.files.create(file=(file_name, file_content), purpose=\"assistants\"))\n", " )\n", " # 从工具资源中获取现有文件ID。\n", " thread = await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(self._client.beta.threads.retrieve(thread_id=self._thread_id))\n", " )\n", " tool_resources: ToolResources = thread.tool_resources if thread.tool_resources else ToolResources()\n", " assert tool_resources.code_interpreter is not None\n", " if tool_resources.code_interpreter.file_ids:\n", " file_ids = tool_resources.code_interpreter.file_ids\n", " else:\n", " file_ids = [file.id]\n", " # 使用新文件更新线程。\n", " await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(\n", " self._client.beta.threads.update(\n", " thread_id=self._thread_id,\n", " tool_resources={\n", " \"code_interpreter\": {\"file_ids\": file_ids},\n", " },\n", " )\n", " )\n", " )\n", "\n", " @message_handler()\n", " async def on_upload_for_file_search(self, message: UploadForFileSearch, ctx: MessageContext) -> None:\n", " \"\"\"Handle an upload for file search. This method uploads a file and updates the vector store.\"\"\"\n", " # 获取文件内容。\n", " async with aiofiles.open(message.file_path, mode=\"rb\") as file:\n", " file_content = await ctx.cancellation_token.link_future(asyncio.ensure_future(file.read()))\n", " file_name = os.path.basename(message.file_path)\n", " # 上传文件。\n", " await ctx.cancellation_token.link_future(\n", " asyncio.ensure_future(\n", " self._client.vector_stores.file_batches.upload_and_poll(\n", " vector_store_id=message.vector_store_id,\n", " files=[(file_name, file_content)],\n", " )\n", " )\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Agent类是对OpenAI Assistant API的轻量级封装，用于实现\n消息协议。更多功能，如多模态消息处理，\n可以通过扩展消息协议来添加。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 助手事件处理器\n\n助手事件处理器提供了处理 Assistant API 特定事件的回调函数。这对于处理助手的流式输出和进一步的用户界面集成非常有用。\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from openai import AsyncAssistantEventHandler, AsyncClient\n", "from openai.types.beta.threads import Message, Text, TextDelta\n", "from openai.types.beta.threads.runs import RunStep, RunStepDelta\n", "from typing_extensions import override\n", "\n", "\n", "class EventHandler(AsyncAssistantEventHandler):\n", " @override\n", " async def on_text_delta(self, delta: TextDelta, snapshot: Text) -> None:\n", " print(delta.value, end=\"\", flush=True)\n", "\n", " @override\n", " async def on_run_step_created(self, run_step: RunStep) -> None:\n", " details = run_step.step_details\n", " if details.type == \"tool_calls\":\n", " for tool in details.tool_calls:\n", " if tool.type == \"code_interpreter\":\n", " print(\"\\nGenerating code to interpret:\\n\\n```python\")\n", "\n", " @override\n", " async def on_run_step_done(self, run_step: RunStep) -> None:\n", " details = run_step.step_details\n", " if details.type == \"tool_calls\":\n", " for tool in details.tool_calls:\n", " if tool.type == \"code_interpreter\":\n", " print(\"\\n```\\nExecuting code...\")\n", "\n", " @override\n", " async def on_run_step_delta(self, delta: RunStepDelta, snapshot: RunStep) -> None:\n", " details = delta.step_details\n", " if details is not None and details.type == \"tool_calls\":\n", " for tool in details.tool_calls or []:\n", " if tool.type == \"code_interpreter\" and tool.code_interpreter and tool.code_interpreter.input:\n", " print(tool.code_interpreter.input, end=\"\", flush=True)\n", "\n", " @override\n", " async def on_message_created(self, message: Message) -> None:\n", " print(f\"{'-'*80}\\nAssistant:\\n\")\n", "\n", " @override\n", " async def on_message_done(self, message: Message) -> None:\n", " # 打印所搜索文件的引用信息\n", " if not message.content:\n", " return\n", " content = message.content[0]\n", " if not content.type == \"text\":\n", " return\n", " text_content = content.text\n", " annotations = text_content.annotations\n", " citations: List[str] = []\n", " for index, annotation in enumerate(annotations):\n", " text_content.value = text_content.value.replace(annotation.text, f\"[{index}]\")\n", " if file_citation := getattr(annotation, \"file_citation\", None):\n", " client = AsyncClient()\n", " cited_file = await client.files.retrieve(file_citation.file_id)\n", " citations.append(f\"[{index}] {cited_file.filename}\")\n", " if citations:\n", " print(\"\\n\".join(citations))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 使用代理\n\n首先我们需要使用 `openai` 客户端创建实际的助手、线程和向量存储。我们的 AutoGen 代理将使用这些资源。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import openai\n", "\n", "# 创建一个带有代码解释器和文件搜索工具的助手。\n", "oai_assistant = openai.beta.assistants.create(\n", " model=\"gpt-4o-mini\",\n", " description=\"An AI assistant that helps with everyday tasks.\",\n", " instructions=\"Help the user with their task.\",\n", " tools=[{\"type\": \"code_interpreter\"}, {\"type\": \"file_search\"}],\n", ")\n", "\n", "# 创建一个用于文件搜索的向量存储。\n", "vector_store = openai.vector_stores.create()\n", "\n", "# 创建一个线程作为助手的记忆存储\n", "thread = openai.beta.threads.create(\n", " tool_resources={\"file_search\": {\"vector_store_ids\": [vector_store.id]}},\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "然后，我们创建一个运行时环境，并为该智能体注册一个代理工厂函数\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from autogen_core import SingleThreadedAgentRuntime\n", "\n", "runtime = SingleThreadedAgentRuntime()\n", "await OpenAIAssistantAgent.register(\n", " runtime,\n", " \"assistant\",\n", " lambda: OpenAIAssistantAgent(\n", " description=\"OpenAI Assistant Agent\",\n", " client=openai.AsyncClient(),\n", " assistant_id=oai_assistant.id,\n", " thread_id=thread.id,\n", " assistant_event_handler_factory=lambda: EventHandler(),\n", " ),\n", ")\n", "agent = AgentId(\"assistant\", \"default\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "开启日志记录功能，观察底层运行情况\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import logging\n", "\n", "logging.basicConfig(level=logging.WARNING)\n", "logging.getLogger(\"autogen_core\").setLevel(logging.DEBUG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "向智能体发送问候消息，查看流式返回的响应\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Sending message of type TextMessage to assistant: {'content': 'Hello, how are you today!', 'source': 'user'}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type TextMessage sent by Unknown\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "Assistant:\n", "\n", "Hello! I'm here and ready to assist you. How can I help you today?" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Resolving response with message type TextMessage for recipient None from assistant: {'content': \"Hello! I'm here and ready to assist you. How can I help you today?\", 'source': 'assistant'}\n" ] } ], "source": [ "runtime.start()\n", "await runtime.send_message(TextMessage(content=\"Hello, how are you today!\", source=\"user\"), agent)\n", "await runtime.stop_when_idle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 带代码解释器的助手\n\n向智能体提出数学问题，观察它如何使用代码解释器来回答问题\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Sending message of type TextMessage to assistant: {'content': 'What is 1332322 x 123212?', 'source': 'user'}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type TextMessage sent by Unknown\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "# Calculating the product of 1332322 and 123212\n", "result = 1332322 * 123212\n", "result\n", "```\n", "Executing code...\n", "--------------------------------------------------------------------------------\n", "Assistant:\n", "\n", "The product of 1,332,322 and 123,212 is 164,158,058,264." ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Resolving response with message type TextMessage for recipient None from assistant: {'content': 'The product of 1,332,322 and 123,212 is 164,158,058,264.', 'source': 'assistant'}\n" ] } ], "source": [ "runtime.start()\n", "await runtime.send_message(TextMessage(content=\"What is 1332322 x 123212?\", source=\"user\"), agent)\n", "await runtime.stop_when_idle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "让我们从西雅图开放数据门户获取一些数据。我们将使用\n[西雅图城市工资数据](https://data.seattle.gov/City-Business/City-of-Seattle-Wage-Data/2khk-5ukd/)。\n首先下载它。\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "import requests\n", "\n", "response = requests.get(\"https://data.seattle.gov/resource/2khk-5ukd.csv\")\n", "with open(\"seattle_city_wages.csv\", \"wb\") as file:\n", " file.write(response.content)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "让我们使用 `UploadForCodeInterpreter` 消息将文件发送给代理。\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Sending message of type UploadForCodeInterpreter to assistant: {'file_path': 'seattle_city_wages.csv'}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type UploadForCodeInterpreter sent by Unknown\n", "INFO:autogen_core:Resolving response with message type NoneType for recipient None from assistant: None\n" ] } ], "source": [ "runtime.start()\n", "await runtime.send_message(UploadForCodeInterpreter(file_path=\"seattle_city_wages.csv\"), agent)\n", "await runtime.stop_when_idle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "现在我们可以向代理询问一些关于数据的问题。\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Sending message of type TextMessage to assistant: {'content': 'Take a look at the uploaded CSV file.', 'source': 'user'}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type TextMessage sent by Unknown\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "import pandas as pd\n", "\n", "# Load the uploaded CSV file to examine its contents\n", "file_path = '/mnt/data/file-oEvRiyGyHc2jZViKyDqL8aoh'\n", "csv_data = pd.read_csv(file_path)\n", "\n", "# Display the first few rows of the dataframe to understand its structure\n", "csv_data.head()\n", "```\n", "Executing code...\n", "--------------------------------------------------------------------------------\n", "Assistant:\n", "\n", "The uploaded CSV file contains the following columns:\n", "\n", "1. **department**: The department in which the individual works.\n", "2. **last_name**: The last name of the employee.\n", "3. **first_name**: The first name of the employee.\n", "4. **job_title**: The job title of the employee.\n", "5. **hourly_rate**: The hourly rate for the employee's position.\n", "\n", "Here are the first few entries from the file:\n", "\n", "| department | last_name | first_name | job_title | hourly_rate |\n", "|--------------------------------|-----------|------------|------------------------------------|-------------|\n", "| Police Department | Aagard | Lori | Pol Capt-Precinct | 112.70 |\n", "| Police Department | Aakervik | Dag | Pol Ofcr-Detective | 75.61 |\n", "| Seattle City Light | Aaltonen | Evan | Pwrline Clear Tree Trimmer | 53.06 |\n", "| Seattle Public Utilities | Aar | Abdimallik | Civil Engrng Spec,Sr | 64.43 |\n", "| Seattle Dept of Transportation | Abad | Abigail | Admin Spec II-BU | 37.40 |\n", "\n", "If you need any specific analysis or information from this data, please let me know!" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Resolving response with message type TextMessage for recipient None from assistant: {'content': \"The uploaded CSV file contains the following columns:\\n\\n1. **department**: The department in which the individual works.\\n2. **last_name**: The last name of the employee.\\n3. **first_name**: The first name of the employee.\\n4. **job_title**: The job title of the employee.\\n5. **hourly_rate**: The hourly rate for the employee's position.\\n\\nHere are the first few entries from the file:\\n\\n| department | last_name | first_name | job_title | hourly_rate |\\n|--------------------------------|-----------|------------|------------------------------------|-------------|\\n| Police Department | Aagard | Lori | Pol Capt-Precinct | 112.70 |\\n| Police Department | Aakervik | Dag | Pol Ofcr-Detective | 75.61 |\\n| Seattle City Light | Aaltonen | Evan | Pwrline Clear Tree Trimmer | 53.06 |\\n| Seattle Public Utilities | Aar | Abdimallik | Civil Engrng Spec,Sr | 64.43 |\\n| Seattle Dept of Transportation | Abad | Abigail | Admin Spec II-BU | 37.40 |\\n\\nIf you need any specific analysis or information from this data, please let me know!\", 'source': 'assistant'}\n" ] } ], "source": [ "runtime.start()\n", "await runtime.send_message(TextMessage(content=\"Take a look at the uploaded CSV file.\", source=\"user\"), agent)\n", "await runtime.stop_when_idle()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Sending message of type TextMessage to assistant: {'content': 'What are the top-10 salaries?', 'source': 'user'}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type TextMessage sent by Unknown\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "# Sorting the data by hourly_rate in descending order and selecting the top 10 salaries\n", "top_10_salaries = csv_data[['first_name', 'last_name', 'job_title', 'hourly_rate']].sort_values(by='hourly_rate', ascending=False).head(10)\n", "top_10_salaries.reset_index(drop=True, inplace=True)\n", "top_10_salaries\n", "```\n", "Executing code...\n", "--------------------------------------------------------------------------------\n", "Assistant:\n", "\n", "Here are the top 10 salaries based on the hourly rates from the CSV file:\n", "\n", "| First Name | Last Name | Job Title | Hourly Rate |\n", "|------------|-----------|------------------------------------|-------------|\n", "| Eric | Barden | Executive4 | 139.61 |\n", "| Idris | Beauregard| Executive3 | 115.90 |\n", "| Lori | Aagard | Pol Capt-Precinct | 112.70 |\n", "| Krista | Bair | Pol Capt-Precinct | 108.74 |\n", "| Amy | Bannister | Fire Chief, Dep Adm-80 Hrs | 104.07 |\n", "| Ginger | Armbruster| Executive2 | 102.42 |\n", "| William | Andersen | Executive2 | 102.42 |\n", "| Valarie | Anderson | Executive2 | 102.42 |\n", "| Paige | Alderete | Executive2 | 102.42 |\n", "| Kathryn | Aisenberg | Executive2 | 100.65 |\n", "\n", "If you need any further details or analysis, let me know!" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Resolving response with message type TextMessage for recipient None from assistant: {'content': 'Here are the top 10 salaries based on the hourly rates from the CSV file:\\n\\n| First Name | Last Name | Job Title | Hourly Rate |\\n|------------|-----------|------------------------------------|-------------|\\n| Eric | Barden | Executive4 | 139.61 |\\n| Idris | Beauregard| Executive3 | 115.90 |\\n| Lori | Aagard | Pol Capt-Precinct | 112.70 |\\n| Krista | Bair | Pol Capt-Precinct | 108.74 |\\n| Amy | Bannister | Fire Chief, Dep Adm-80 Hrs | 104.07 |\\n| Ginger | Armbruster| Executive2 | 102.42 |\\n| William | Andersen | Executive2 | 102.42 |\\n| Valarie | Anderson | Executive2 | 102.42 |\\n| Paige | Alderete | Executive2 | 102.42 |\\n| Kathryn | Aisenberg | Executive2 | 100.65 |\\n\\nIf you need any further details or analysis, let me know!', 'source': 'assistant'}\n" ] } ], "source": [ "runtime.start()\n", "await runtime.send_message(TextMessage(content=\"What are the top-10 salaries?\", source=\"user\"), agent)\n", "await runtime.stop_when_idle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 带文件搜索功能的助手\n\n让我们尝试文档问答功能。首先下载\n关于第三次英阿战争的维基百科页面。\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "response = requests.get(\"https://en.wikipedia.org/wiki/Third_Anglo-Afghan_War\")\n", "with open(\"third_anglo_afghan_war.html\", \"wb\") as file:\n", " file.write(response.content)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用 `UploadForFileSearch` 消息将文件发送给代理。\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Sending message of type UploadForFileSearch to assistant: {'file_path': 'third_anglo_afghan_war.html', 'vector_store_id': 'vs_h3xxPbJFnd1iZ9WdjsQwNdrp'}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type UploadForFileSearch sent by Unknown\n", "INFO:autogen_core:Resolving response with message type NoneType for recipient None from assistant: None\n" ] } ], "source": [ "runtime.start()\n", "await runtime.send_message(\n", " UploadForFileSearch(file_path=\"third_anglo_afghan_war.html\", vector_store_id=vector_store.id), agent\n", ")\n", "await runtime.stop_when_idle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "让我们向代理询问一些关于文档的问题。在提问之前，\n我们重置了代理的记忆以开始新的对话。\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Sending message of type Reset to assistant: {}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type Reset sent by Unknown\n", "INFO:autogen_core:Resolving response with message type NoneType for recipient None from assistant: None\n", "INFO:autogen_core:Sending message of type TextMessage to assistant: {'content': 'When and where was the treaty of Rawalpindi signed? Answer using the document provided.', 'source': 'user'}\n", "INFO:autogen_core:Calling message handler for assistant:default with message type TextMessage sent by Unknown\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "--------------------------------------------------------------------------------\n", "Assistant:\n", "\n", "The Treaty of Rawalpindi was signed on **8 August 1919**. The location of the signing was in **Rawalpindi**, which is in present-day Pakistan【6:0†source】." ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:autogen_core:Resolving response with message type TextMessage for recipient None from assistant: {'content': 'The Treaty of Rawalpindi was signed on **8 August 1919**. The location of the signing was in **Rawalpindi**, which is in present-day Pakistan【6:0†source】.', 'source': 'assistant'}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0] third_anglo_afghan_war.html\n" ] } ], "source": [ "runtime.start()\n", "await runtime.send_message(Reset(), agent)\n", "await runtime.send_message(\n", " TextMessage(\n", " content=\"When and where was the treaty of Rawalpindi signed? Answer using the document provided.\", source=\"user\"\n", " ),\n", " agent,\n", ")\n", "await runtime.stop_when_idle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "就是这样！我们已成功构建了一个由OpenAI Assistant支持的代理。\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }