20250912

2025-09-12 23:56:37 +08:00
parent 46694bfb5b
commit 0fb54e8a95
7 changed files with 785 additions and 30 deletions
--- a/test/langgraph/quickstart_graph_api.ipynb
+++ b/test/langgraph/quickstart_graph_api.ipynb
--- a/test/langgraph/quickstart_graph_api.py
+++ b/test/langgraph/quickstart_graph_api.py
@@ -0,0 +1,144 @@
+# Step 0: Define tools and model
+
+from langchain_core.tools import tool
+from langchain_community.chat_models.tongyi import ChatTongyi
+
+llm = ChatTongyi(
+    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
+    streaming=True,
+    # other params...
+)
+
+# Define tools
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a * b
+
+
+@tool
+def add(a: int, b: int) -> int:
+    """Adds a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a + b
+
+
+@tool
+def divide(a: int, b: int) -> float:
+    """Divide a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a / b
+
+
+# Augment the LLM with tools
+tools = [add, multiply, divide]
+tools_by_name = {tool.name: tool for tool in tools}
+llm_with_tools = llm.bind_tools(tools)
+
+# Step 1: Define state
+
+from langchain_core.messages import AnyMessage
+from typing_extensions import TypedDict, Annotated
+import operator
+
+class MessagesState(TypedDict):
+    messages: Annotated[list[AnyMessage], operator.add]
+    llm_calls: int
+
+# Step 2: Define model node
+from langchain_core.messages import SystemMessage
+def llm_call(state: dict):
+    """LLM decides whether to call a tool or not"""
+
+    return {
+        "messages": [
+            llm_with_tools.invoke(
+                [
+                    SystemMessage(
+                        content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
+                    )
+                ]
+                + state["messages"]
+            )
+        ],
+        "llm_calls": state.get('llm_calls', 0) + 1
+    }
+
+
+# Step 3: Define tool node
+
+
+from langchain_core.messages import ToolMessage
+
+def tool_node(state: dict):
+    """Performs the tool call"""
+
+    result = []
+    for tool_call in state["messages"][-1].tool_calls:
+        tool = tools_by_name[tool_call["name"]]
+        observation = tool.invoke(tool_call["args"])
+        result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
+    return {"messages": result}
+
+# Step 4: Define logic to determine whether to end
+
+from typing import Literal
+from langgraph.graph import StateGraph, START, END
+
+# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
+def should_continue(state: MessagesState) -> Literal["tool_node", END]:
+    """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""
+
+    messages = state["messages"]
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then perform an action
+    if last_message.tool_calls:
+        return "tool_node"
+    # Otherwise, we stop (reply to the user)
+    return END
+
+# Step 5: Build agent
+
+# Build workflow
+agent_builder = StateGraph(MessagesState)
+
+# Add nodes
+agent_builder.add_node("llm_call", llm_call)
+agent_builder.add_node("tool_node", tool_node)
+
+# Add edges to connect nodes
+agent_builder.add_edge(START, "llm_call")
+agent_builder.add_conditional_edges(
+    "llm_call",
+    should_continue,
+    ["tool_node", END]
+)
+agent_builder.add_edge("tool_node", "llm_call")
+
+# Compile the agent
+agent = agent_builder.compile()
+
+
+from IPython.display import Image, display
+# Show the agent
+display(Image(agent.get_graph(xray=True).draw_mermaid_png()))
+
+# Invoke
+# from langchain_core.messages import HumanMessage
+# messages = [HumanMessage(content="Add 3 and 4.")]
+# messages = agent.invoke({"messages": messages})
+# for m in messages["messages"]:
+#     m.pretty_print()
--- a/test/langgraph/quistart_functional_api.ipynb
+++ b/test/langgraph/quistart_functional_api.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-12T15:52:56.909385Z",
+     "start_time": "2025-09-12T15:52:56.417088Z"
+    }
+   },
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ['DASHSCOPE_API_KEY'] = 'sk-e2a05bbcfac84e53b73f98acef15a009'\n",
+    "\n",
+    "# Step 0: Define tools and model\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_community.chat_models.tongyi import ChatTongyi\n",
+    "\n",
+    "llm = ChatTongyi(\n",
+    "    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",
+    "    streaming=True,\n",
+    "    # other params...\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-12T15:54:08.425580Z",
+     "start_time": "2025-09-12T15:54:08.374623Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "\n",
+    "# Define tools\n",
+    "@tool\n",
+    "def multiply(a: int, b: int) -> int:\n",
+    "    \"\"\"Multiply a and b.\n",
+    "\n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a * b\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def add(a: int, b: int) -> int:\n",
+    "    \"\"\"Adds a and b.\n",
+    "\n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a + b\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def divide(a: int, b: int) -> float:\n",
+    "    \"\"\"Divide a and b.\n",
+    "\n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a / b\n",
+    "\n",
+    "\n",
+    "# Augment the LLM with tools\n",
+    "tools = [add, multiply, divide]\n",
+    "tools_by_name = {tool.name: tool for tool in tools}\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
+    "\n",
+    "from langgraph.graph import add_messages\n",
+    "from langchain_core.messages import (\n",
+    "    SystemMessage,\n",
+    "    HumanMessage,\n",
+    "    BaseMessage,\n",
+    "    ToolCall,\n",
+    ")\n",
+    "from langgraph.func import entrypoint, task\n",
+    "\n",
+    "# Step 1: define model node\n",
+    "@task\n",
+    "def call_llm(messages: list[BaseMessage]):\n",
+    "    \"\"\"LLM decides whether to call a tool or not\"\"\"\n",
+    "    return llm_with_tools.invoke(\n",
+    "        [\n",
+    "            SystemMessage(\n",
+    "                content=\"You are a helpful assistant tasked with performing arithmetic on a set of inputs.\"\n",
+    "            )\n",
+    "        ]\n",
+    "        + messages\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Step 2: define tool node\n",
+    "@task\n",
+    "def call_tool(tool_call: ToolCall):\n",
+    "    \"\"\"Performs the tool call\"\"\"\n",
+    "    tool = tools_by_name[tool_call[\"name\"]]\n",
+    "    return tool.invoke(tool_call)\n",
+    "\n",
+    "\n",
+    "# Step 3: define agent\n",
+    "@entrypoint()\n",
+    "def agent(messages: list[BaseMessage]):\n",
+    "    llm_response = call_llm(messages).result()\n",
+    "\n",
+    "    while True:\n",
+    "        if not llm_response.tool_calls:\n",
+    "            break\n",
+    "\n",
+    "        # Execute tools\n",
+    "        tool_result_futures = [\n",
+    "            call_tool(tool_call) for tool_call in llm_response.tool_calls\n",
+    "        ]\n",
+    "        tool_results = [fut.result() for fut in tool_result_futures]\n",
+    "        messages = add_messages(messages, [llm_response, *tool_results])\n",
+    "        llm_response = call_llm(messages).result()\n",
+    "\n",
+    "    messages = add_messages(messages, llm_response)\n",
+    "    return messages"
+   ],
+   "id": "8a77a9b24ee9616d",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-12T15:54:11.693756Z",
+     "start_time": "2025-09-12T15:54:10.101700Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "# Invoke\n",
+    "messages = [HumanMessage(content=\"Add 3 and 4.\")]\n",
+    "for chunk in agent.stream(messages, stream_mode=\"updates\"):\n",
+    "    print(chunk)\n",
+    "    print(\"\\n\")"
+   ],
+   "id": "7c4b06da8200b106",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'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",
+      "\n",
+      "\n",
+      "{'call_tool': ToolMessage(content='7', name='add', id='aeaf3d29-254b-48ab-a933-814e9ea72394', tool_call_id='call_ef8c897dd4f84afbbf0927')}\n",
+      "\n",
+      "\n",
+      "{'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",
+      "\n",
+      "\n",
+      "{'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",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "7e55492ae0289f06"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
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+ "nbformat": 4,
+ "nbformat_minor": 5
+}