206 lines
7.5 KiB
Plaintext
206 lines
7.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-12T15:52:56.909385Z",
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"start_time": "2025-09-12T15:52:56.417088Z"
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}
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},
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"source": [
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"import os\n",
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"\n",
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"os.environ['DASHSCOPE_API_KEY'] = 'sk-e2a05bbcfac84e53b73f98acef15a009'\n",
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"\n",
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"# Step 0: Define tools and model\n",
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"\n",
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"from langchain_core.tools import tool\n",
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"from langchain_community.chat_models.tongyi import ChatTongyi\n",
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"\n",
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"llm = ChatTongyi(\n",
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" model=\"qwen-max\", # 此处以qwen-max为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models\n",
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" streaming=True,\n",
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" # other params...\n",
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")"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-12T15:54:08.425580Z",
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"start_time": "2025-09-12T15:54:08.374623Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"\n",
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"# Define tools\n",
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"@tool\n",
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"def multiply(a: int, b: int) -> int:\n",
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" \"\"\"Multiply a and b.\n",
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"\n",
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" Args:\n",
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" a: first int\n",
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" b: second int\n",
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" \"\"\"\n",
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" return a * b\n",
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"\n",
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"\n",
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"@tool\n",
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"def add(a: int, b: int) -> int:\n",
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" \"\"\"Adds a and b.\n",
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"\n",
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" Args:\n",
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" a: first int\n",
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" b: second int\n",
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" \"\"\"\n",
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" return a + b\n",
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"\n",
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"\n",
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"@tool\n",
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"def divide(a: int, b: int) -> float:\n",
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" \"\"\"Divide a and b.\n",
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"\n",
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" Args:\n",
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" a: first int\n",
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" b: second int\n",
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" \"\"\"\n",
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" return a / b\n",
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"\n",
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"\n",
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"# Augment the LLM with tools\n",
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"tools = [add, multiply, divide]\n",
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"tools_by_name = {tool.name: tool for tool in tools}\n",
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"llm_with_tools = llm.bind_tools(tools)\n",
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"\n",
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"from langgraph.graph import add_messages\n",
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"from langchain_core.messages import (\n",
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" SystemMessage,\n",
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" HumanMessage,\n",
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" BaseMessage,\n",
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" ToolCall,\n",
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")\n",
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"from langgraph.func import entrypoint, task\n",
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"\n",
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"# Step 1: define model node\n",
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"@task\n",
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"def call_llm(messages: list[BaseMessage]):\n",
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" \"\"\"LLM decides whether to call a tool or not\"\"\"\n",
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" return llm_with_tools.invoke(\n",
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" [\n",
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" SystemMessage(\n",
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" content=\"You are a helpful assistant tasked with performing arithmetic on a set of inputs.\"\n",
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" )\n",
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" ]\n",
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" + messages\n",
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" )\n",
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"\n",
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"\n",
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"# Step 2: define tool node\n",
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"@task\n",
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"def call_tool(tool_call: ToolCall):\n",
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" \"\"\"Performs the tool call\"\"\"\n",
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" tool = tools_by_name[tool_call[\"name\"]]\n",
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" return tool.invoke(tool_call)\n",
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"\n",
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"\n",
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"# Step 3: define agent\n",
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"@entrypoint()\n",
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"def agent(messages: list[BaseMessage]):\n",
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" llm_response = call_llm(messages).result()\n",
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"\n",
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" while True:\n",
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" if not llm_response.tool_calls:\n",
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" break\n",
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"\n",
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" # Execute tools\n",
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" tool_result_futures = [\n",
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" call_tool(tool_call) for tool_call in llm_response.tool_calls\n",
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" ]\n",
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" tool_results = [fut.result() for fut in tool_result_futures]\n",
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" messages = add_messages(messages, [llm_response, *tool_results])\n",
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" llm_response = call_llm(messages).result()\n",
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"\n",
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" messages = add_messages(messages, llm_response)\n",
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" return messages"
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],
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"id": "8a77a9b24ee9616d",
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"outputs": [],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-09-12T15:54:11.693756Z",
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"start_time": "2025-09-12T15:54:10.101700Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"# Invoke\n",
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"messages = [HumanMessage(content=\"Add 3 and 4.\")]\n",
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"for chunk in agent.stream(messages, stream_mode=\"updates\"):\n",
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" print(chunk)\n",
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" print(\"\\n\")"
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],
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"id": "7c4b06da8200b106",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'call_llm': AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'function', 'function': {'name': 'add', 'arguments': '{\"a\": 3, \"b\": 4}'}}]}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'tool_calls', 'request_id': '6d8c6555-1a67-4cc9-a93f-57e94bc20842', 'token_usage': {'input_tokens': 354, 'output_tokens': 22, 'total_tokens': 376, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--afcea3de-940e-45c6-ba96-bbd7e41fa115-0', tool_calls=[{'name': 'add', 'args': {'a': 3, 'b': 4}, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'tool_call'}], chunk_position=None)}\n",
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"\n",
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"\n",
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"{'call_tool': ToolMessage(content='7', name='add', id='aeaf3d29-254b-48ab-a933-814e9ea72394', tool_call_id='call_ef8c897dd4f84afbbf0927')}\n",
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"\n",
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"\n",
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"{'call_llm': AIMessage(content='The sum of 3 and 4 is 7.', additional_kwargs={}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'stop', 'request_id': '310102ab-48dc-4e80-bc57-ca8814239a65', 'token_usage': {'input_tokens': 386, 'output_tokens': 13, 'total_tokens': 399, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--b3dffae8-42c2-492e-a1f4-e659eba6a879-0', chunk_position=None)}\n",
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"\n",
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"\n",
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"{'agent': [HumanMessage(content='Add 3 and 4.', additional_kwargs={}, response_metadata={}, id='40fc3758-a8ab-4302-aff9-8dfbdec16fa0'), AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'function', 'function': {'name': 'add', 'arguments': '{\"a\": 3, \"b\": 4}'}}]}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'tool_calls', 'request_id': '6d8c6555-1a67-4cc9-a93f-57e94bc20842', 'token_usage': {'input_tokens': 354, 'output_tokens': 22, 'total_tokens': 376, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--afcea3de-940e-45c6-ba96-bbd7e41fa115-0', tool_calls=[{'name': 'add', 'args': {'a': 3, 'b': 4}, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'tool_call'}], chunk_position=None), ToolMessage(content='7', name='add', id='aeaf3d29-254b-48ab-a933-814e9ea72394', tool_call_id='call_ef8c897dd4f84afbbf0927'), AIMessage(content='The sum of 3 and 4 is 7.', additional_kwargs={}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'stop', 'request_id': '310102ab-48dc-4e80-bc57-ca8814239a65', 'token_usage': {'input_tokens': 386, 'output_tokens': 13, 'total_tokens': 399, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--b3dffae8-42c2-492e-a1f4-e659eba6a879-0', chunk_position=None)]}\n",
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"\n",
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"\n"
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]
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}
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],
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"execution_count": 3
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "",
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"id": "7e55492ae0289f06"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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