How to attach runtime arguments to a Runnable

Prerequisites

This guide assumes familiarity with the following concepts:

• LangChain Expression Language (LCEL)
• Chaining runnables
• Tool calling

Sometimes we want to invoke a Runnable within a RunnableSequence with constant arguments that are not part of the output of the preceding Runnable in the sequence, and which are not part of the user input. We can use the Runnable.bind() method to set these arguments ahead of time.

Binding stop sequences

Suppose we have a simple prompt + model chain:

tip

See this section for general instructions on installing integration packages.

npm

npm i @langchain/openai

yarn

yarn add @langchain/openai

pnpm

pnpm add @langchain/openai

import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { ChatOpenAI } from "@langchain/openai";

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "Write out the following equation using algebraic symbols then solve it. Use the format\n\nEQUATION:...\nSOLUTION:...\n\n",
  ],
  ["human", "{equation_statement}"],
]);

const model = new ChatOpenAI({ temperature: 0 });

const runnable = prompt.pipe(model).pipe(new StringOutputParser());

const res = await runnable.invoke({
  equation_statement: "x raised to the third plus seven equals 12",
});

console.log(res);

EQUATION: x^3 + 7 = 12

SOLUTION:
Subtract 7 from both sides:
x^3 = 5

Take the cube root of both sides:
x = ∛5

and want to call the model with certain stop words so that we shorten the output, which is useful in certain types of prompting techniques. While we can pass some arguments into the constructor, other runtime args use the .bind() method as follows:

const runnable = prompt
  .pipe(model.bind({ stop: "SOLUTION" }))
  .pipe(new StringOutputParser());

const res = await runnable.invoke({
  equation_statement: "x raised to the third plus seven equals 12",
});

console.log(res);

EQUATION: x^3 + 7 = 12

What you can bind to a Runnable will depend on the extra parameters you can pass when invoking it.

Attaching OpenAI tools

Another common use-case is tool calling. While you should generally use the .bind_tools() method for tool-calling models, you can also bind provider-specific args directly if you want lower level control:

const tools = [
  {
    type: "function",
    function: {
      name: "get_current_weather",
      description: "Get the current weather in a given location",
      parameters: {
        type: "object",
        properties: {
          location: {
            type: "string",
            description: "The city and state, e.g. San Francisco, CA",
          },
          unit: { type: "string", enum: ["celsius", "fahrenheit"] },
        },
        required: ["location"],
      },
    },
  },
];

const model = new ChatOpenAI({ model: "gpt-4o" }).bind({ tools });

await model.invoke("What's the weather in SF, NYC and LA?");

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: "",
    tool_calls: [
      {
        name: "get_current_weather",
        args: { location: "San Francisco, CA" },
        id: "call_iDKz4zU8PKBaaIT052fJkMMF"
      },
      {
        name: "get_current_weather",
        args: { location: "New York, NY" },
        id: "call_niQwZDOqO6OJTBiDBFG8FODc"
      },
      {
        name: "get_current_weather",
        args: { location: "Los Angeles, CA" },
        id: "call_zLXH2cDVQy0nAVC0ViWuEP4m"
      }
    ],
    invalid_tool_calls: [],
    additional_kwargs: {
      function_call: undefined,
      tool_calls: [
        {
          id: "call_iDKz4zU8PKBaaIT052fJkMMF",
          type: "function",
          function: [Object]
        },
        {
          id: "call_niQwZDOqO6OJTBiDBFG8FODc",
          type: "function",
          function: [Object]
        },
        {
          id: "call_zLXH2cDVQy0nAVC0ViWuEP4m",
          type: "function",
          function: [Object]
        }
      ]
    },
    response_metadata: {}
  },
  lc_namespace: [ "langchain_core", "messages" ],
  content: "",
  name: undefined,
  additional_kwargs: {
    function_call: undefined,
    tool_calls: [
      {
        id: "call_iDKz4zU8PKBaaIT052fJkMMF",
        type: "function",
        function: {
          name: "get_current_weather",
          arguments: '{"location": "San Francisco, CA"}'
        }
      },
      {
        id: "call_niQwZDOqO6OJTBiDBFG8FODc",
        type: "function",
        function: {
          name: "get_current_weather",
          arguments: '{"location": "New York, NY"}'
        }
      },
      {
        id: "call_zLXH2cDVQy0nAVC0ViWuEP4m",
        type: "function",
        function: {
          name: "get_current_weather",
          arguments: '{"location": "Los Angeles, CA"}'
        }
      }
    ]
  },
  response_metadata: {
    tokenUsage: { completionTokens: 70, promptTokens: 82, totalTokens: 152 },
    finish_reason: "tool_calls"
  },
  tool_calls: [
    {
      name: "get_current_weather",
      args: { location: "San Francisco, CA" },
      id: "call_iDKz4zU8PKBaaIT052fJkMMF"
    },
    {
      name: "get_current_weather",
      args: { location: "New York, NY" },
      id: "call_niQwZDOqO6OJTBiDBFG8FODc"
    },
    {
      name: "get_current_weather",
      args: { location: "Los Angeles, CA" },
      id: "call_zLXH2cDVQy0nAVC0ViWuEP4m"
    }
  ],
  invalid_tool_calls: []
}

Next steps

You now know how to bind runtime arguments to a Runnable.

Next, you might be interested in our how-to guides on passing data through a chain.