LangGraph

官方文档

按顺序进行

const model = new ChatOpenAI({
  streaming: true,
  openAIApiKey: modelSecret,
  modelName: modelOptions.openaiModelType,
  temperature: modelOptions.temperature,
});

const graph = new MessageGraph();

graph.addNode("oracle", async (state: BaseMessage[]) => {
  return model.invoke(state);
});

graph.addEdge("oracle", END);

graph.setEntryPoint("oracle");

const runnable = graph.compile();

const results = await runnable.invoke(new HumanMessage("What is 1 + 1?"));

return res.status(200).json({
  results,
});

{
  "results": [
    {
      "lc": 1,
      "type": "constructor",
      "id": ["langchain_core", "messages", "HumanMessage"],
      "kwargs": {
        "content": "What is 1 + 1?",
        "additional_kwargs": {},
        "response_metadata": {}
      }
    },
    {
      "lc": 1,
      "type": "constructor",
      "id": ["langchain_core", "messages", "AIMessageChunk"],
      "kwargs": {
        "content": "1 + 1 equals 2.",
        "additional_kwargs": {},
        "response_metadata": {
          "estimatedTokenUsage": {
            "promptTokens": 15,
            "completionTokens": 8,
            "totalTokens": 23
          },
          "prompt": 0,
          "completion": 0
        },
        "tool_call_chunks": [],
        "tool_calls": [],
        "invalid_tool_calls": []
      }
    }
  ]
}

使用条件支线

const model = new ChatOpenAI({
  streaming: true,
  openAIApiKey: modelSecret,
  modelName: modelOptions.openaiModelType,
  temperature: modelOptions.temperature,
}).bind({
  tools: [convertToOpenAITool(new Calculator())],
  tool_choice: "auto",
});

const graph = new MessageGraph();

graph.addNode("oracle", async (state: BaseMessage[]) => {
  return model.invoke(state);
});

graph.addNode("calculator", async (state: BaseMessage[]) => {
  const tool = new Calculator();
  const toolCalls = state[state.length - 1].additional_kwargs.tool_calls ?? [];
  const calculatorCall = toolCalls.find(
    (toolCall) => toolCall.function.name === "calculator"
  );
  if (calculatorCall === undefined) {
    throw new Error("Calculator call not found");
  }
  const result = await tool.invoke(
    JSON.parse(calculatorCall.function.arguments)
  );
  return new ToolMessage({
    tool_call_id: calculatorCall.id,
    content: result,
  });
});

graph.addEdge("calculator", END);

graph.setEntryPoint("oracle");

const router = (state: BaseMessage[]) => {
  const toolCalls = state[state.length - 1].additional_kwargs.tool_calls ?? [];
  if (toolCalls.length) {
    return "calculator";
  }
  return "end";
};

graph.addConditionalEdges("oracle", router, {
  calculator: "calculator",
  end: END,
});

const runnable = graph.compile();

const results = await runnable.invoke(new HumanMessage("What is 1 + 1?"));

return res.status(200).json({
  results,
});

{
  // Input: "What is 1 + 1?"
  "results": [
    {
      "lc": 1,
      "type": "constructor",
      "id": ["langchain_core", "messages", "HumanMessage"],
      "kwargs": {
        "content": "What is 1 + 1?",
        "additional_kwargs": {},
        "response_metadata": {}
      }
    },
    {
      "lc": 1,
      "type": "constructor",
      "id": ["langchain_core", "messages", "AIMessageChunk"],
      "kwargs": {
        "content": "",
        "additional_kwargs": {
          "tool_calls": [
            {
              "index": 0,
              "id": "call_31gAUfN0KeI7O22p6Lt0avVq",
              "type": "function",
              "function": {
                "name": "calculator",
                "arguments": "{\"input\":\"1+1\"}"
              }
            }
          ]
        },
        "response_metadata": {
          "estimatedTokenUsage": {
            "promptTokens": 15,
            "completionTokens": 0,
            "totalTokens": 15
          },
          "prompt": 0,
          "completion": 0
        },
        "tool_call_chunks": [],
        "tool_calls": [],
        "invalid_tool_calls": []
      }
    },
    {
      "lc": 1,
      "type": "constructor",
      "id": ["langchain_core", "messages", "ToolMessage"],
      "kwargs": {
        "tool_call_id": "call_31gAUfN0KeI7O22p6Lt0avVq",
        "content": "2",
        "additional_kwargs": {},
        "response_metadata": {}
      }
    }
  ]
}

// Input: "What is your name?"
{
  "results": [
    {
      "lc": 1,
      "type": "constructor",
      "id": [
        "langchain_core",
        "messages",
        "HumanMessage"
      ],
      "kwargs": {
        "content": "What is your name?",
        "additional_kwargs": {},
        "response_metadata": {}
      }
    },
    {
      "lc": 1,
      "type": "constructor",
      "id": [
        "langchain_core",
        "messages",
        "AIMessageChunk"
      ],
      "kwargs": {
        "content": "My name is Assistant. How can I assist you today?",
        "additional_kwargs": {},
        "response_metadata": {
          "estimatedTokenUsage": {
            "promptTokens": 12,
            "completionTokens": 12,
            "totalTokens": 24
          },
          "prompt": 0,
          "completion": 0
        },
        "tool_call_chunks": [],
        "tool_calls": [],
        "invalid_tool_calls": []
      }
    }
  ]
}

搭建一个 cycle graph

const tools = [
  new TavilySearchResults({ maxResults: 1 }), // Need to TAVILY_API_KEY in env
  // new Calculator()
];
const toolExecutor = new ToolExecutor({ tools });

const model = new ChatOpenAI({
  streaming: true,
  openAIApiKey: modelSecret,
  modelName: modelOptions.openaiModelType,
  temperature: modelOptions.temperature,
});
const toolsAsOpenAIFunctions = tools.map((tool) =>
  convertToOpenAIFunction(tool)
);
const model_with_tools = model.bind({
  functions: toolsAsOpenAIFunctions,
});

const agentState = {
  messages: {
    value: (x: BaseMessage[], y: BaseMessage[]) => x.concat(y),
    default: () => [],
  },
};

const shouldContinue = (state: { messages: Array<BaseMessage> }) => {
  const { messages } = state;
  const lastMessage = messages[messages.length - 1];

  if (
    !("function_call" in lastMessage.additional_kwargs) ||
    !lastMessage.additional_kwargs.function_call
  ) {
    return "end";
  }

  return "continue";
};

const _getAction = (state: { messages: Array<BaseMessage> }): AgentAction => {
  const { messages } = state;
  const lastMessage = messages[messages.length - 1];
  if (!lastMessage) {
    throw new Error("No messages found");
  }
  if (!lastMessage.additional_kwargs.function_call) {
    throw new Error("No function call found");
  }

  return {
    tool: lastMessage.additional_kwargs.function_call.name,
    toolInput: lastMessage.additional_kwargs.function_call.arguments,
    log: "",
  };
};

const callModel = async (state: { messages: Array<BaseMessage> }) => {
  const { messages } = state;

  const prompt = ChatPromptTemplate.fromMessages([
    ["system", "You are a helpful assistant."],
    new MessagesPlaceholder("messages"),
  ]);
  const response = await prompt.pipe(model_with_tools).invoke({ messages });
  return {
    messages: [response],
  };
};

const callTool = async (state: { messages: Array<BaseMessage> }) => {
  const action = _getAction(state);

  const response = await toolExecutor.invoke(action);

  const functionMessage = new FunctionMessage({
    content: response,
    name: action.tool,
  });

  return {
    messages: [functionMessage],
  };
};

const workflow = new StateGraph({
  channels: agentState,
});

workflow.addNode("agent", callModel);
workflow.addNode("action", callTool);

workflow.setEntryPoint("agent");

workflow.addConditionalEdges("agent", shouldContinue, {
  continue: "action",
  end: END,
});

workflow.addEdge("action", "agent");

const app = workflow.compile();

const inputs = {
  messages: [new HumanMessage("what is the weather in paris")],
};
const results = await app.invoke(inputs);

return res.status(200).json({
  results,
});

{
  "results": {
    "messages": [
      {
        "lc": 1,
        "type": "constructor",
        "id": ["langchain_core", "messages", "HumanMessage"],
        "kwargs": {
          "content": "what is the weather in paris",
          "additional_kwargs": {},
          "response_metadata": {}
        }
      },
      {
        "lc": 1,
        "type": "constructor",
        "id": ["langchain_core", "messages", "AIMessageChunk"],
        "kwargs": {
          "content": "",
          "additional_kwargs": {
            "function_call": {
              "name": "tavily_search_results_json",
              "arguments": "{\"input\":\"current weather in Paris\"}"
            }
          },
          "response_metadata": {
            "estimatedTokenUsage": {
              "promptTokens": 86,
              "completionTokens": 21,
              "totalTokens": 107
            },
            "prompt": 0,
            "completion": 0
          },
          "tool_call_chunks": [],
          "tool_calls": [],
          "invalid_tool_calls": []
        }
      },
      {
        "lc": 1,
        "type": "constructor",
        "id": ["langchain_core", "messages", "FunctionMessage"],
        "kwargs": {
          "content": "[{\"title\":\"Weather in Paris\",\"url\":\"https://www.weatherapi.com/\",\"content\":\"{'location': {'name': 'Paris', 'region': 'Ile-de-France', 'country': 'France', 'lat': 48.87, 'lon': 2.33, 'tz_id': 'Europe/Paris', 'localtime_epoch': 1717085019, 'localtime': '2024-05-30 18:03'}, 'current': {'last_updated_epoch': 1717084800, 'last_updated': '2024-05-30 18:00', 'temp_c': 15.0, 'temp_f': 59.0, 'is_day': 1, 'condition': {'text': 'Light rain', 'icon': '//cdn.weatherapi.com/weather/64x64/day/296.png', 'code': 1183}, 'wind_mph': 10.5, 'wind_kph': 16.9, 'wind_degree': 300, 'wind_dir': 'WNW', 'pressure_mb': 1010.0, 'pressure_in': 29.83, 'precip_mm': 0.27, 'precip_in': 0.01, 'humidity': 72, 'cloud': 75, 'feelslike_c': 14.0, 'feelslike_f': 57.1, 'windchill_c': 12.7, 'windchill_f': 54.8, 'heatindex_c': 14.0, 'heatindex_f': 57.1, 'dewpoint_c': 11.6, 'dewpoint_f': 52.9, 'vis_km': 10.0, 'vis_miles': 6.0, 'uv': 3.0, 'gust_mph': 17.4, 'gust_kph': 28.1}}\",\"score\":0.98324,\"raw_content\":null}]",
          "name": "tavily_search_results_json",
          "additional_kwargs": {},
          "response_metadata": {}
        }
      },
      {
        "lc": 1,
        "type": "constructor",
        "id": ["langchain_core", "messages", "AIMessageChunk"],
        "kwargs": {
          "content": "The current weather in Paris is as follows:\n- Temperature: 15.0°C (59.0°F)\n- Condition: Light rain\n- Wind: 16.9 km/h from WNW\n- Pressure: 1010.0 mb\n- Humidity: 72%\n- Cloudiness: 75%\n- Visibility: 10.0 km\n- UV Index: 3.0\n\nFor more details, you can visit [Weather in Paris](https://www.weatherapi.com/).",
          "additional_kwargs": {},
          "response_metadata": {
            "estimatedTokenUsage": {
              "promptTokens": 537,
              "completionTokens": 104,
              "totalTokens": 641
            },
            "prompt": 0,
            "completion": 0
          },
          "tool_call_chunks": [],
          "tool_calls": [],
          "invalid_tool_calls": []
        }
      }
    ]
  }
}

自定义 tools

const model = new ChatOpenAI({
  streaming: true,
  openAIApiKey: modelSecret,
  modelName: modelOptions.openaiModelType,
  temperature: modelOptions.temperature,
});

const tools = [
  new DynamicTool({
    name: "FOO",
    description:
      "call this to get the value of foo. input should be an empty string.",
    func: async () => "baz",
  }),
  new DynamicStructuredTool({
    name: "random-number-generator",
    description: "generates a random number between two input numbers",
    schema: z.object({
      low: z.number().describe("The lower bound of the generated number"),
      high: z.number().describe("The upper bound of the generated number"),
    }),
    func: async ({ low, high }) => {
      return (Math.random() * (high - low) + low).toString();
    },
  }),
];

const prompt = await pull<ChatPromptTemplate>(
  "hwchase17/openai-functions-agent"
);

const agent = await createOpenAIFunctionsAgent({
  llm: model,
  tools,
  prompt,
});

const agentExecutor = new AgentExecutor({
  agent,
  tools,
  verbose: true,
});

const results = await agentExecutor.invoke({
  input: `What is the value of foo?`,
});

return res.status(200).json({
  results,
});

{
  "results": {
    "input": "What is the value of foo?",
    "output": "The value of foo is \"baz\"."
  }
}

{
  "results": {
    "input": "Generate a random number between 1 and 10.",
    "output": "I have generated a random number between 1 and 10 for you. The number is 4.31."
  }
}

两个核心问题 ❤️

• 每个 agent 分别是做什么的？
• 每个 agent 之间是怎么联系的？

Resource

• 🦜🕸️LangGraph.js: https://js.langchain.com/v0.1/docs/langgraph/
• How to build your custom tool ?: https://js.langchain.com/v0.1/docs/modules/agents/tools/dynamic/
• Langchain Blog LangGraph: https://blog.langchain.dev/langgraph/
• LangGraph: Multi-Agent Workflows: https://blog.langchain.dev/langgraph-multi-agent-workflows/
• LangGraph: Multi-Agent Workflows - youtube: https://www.youtube.com/watch?v=hvAPnpSfSGo
• Hierarchical Agent Teams: https://github.com/langchain-ai/langgraphjs/blob/main/examples/multi_agent/hierarchical_agent_teams.ipynb