You can use LangChain Node.js SDK to interact with FriendliAI. This makes migration of existing applications already using LangChain particularly easy.

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How to use

Before you start, ensure you’ve already obtained the FRIENDLI_TOKEN from the Friendli Suite. Our products are entirely compatible with OpenAI, so we use the @langchain/openai package by referring to the FriendliAI baseURL.

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Instantiation

Now we can instantiate our model object and generate chat completions. We provide usage examples for each type of endpoint. Choose the one that best suits your needs:

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Runnable interface

We support both synchronous and asynchronous runnable methods to generate a response.

import { HumanMessage, SystemMessage } from "@langchain/core/messages";

const messages = [
  new SystemMessage("Translate the following from English into Italian"),
  new HumanMessage("hi!"),
];

const result = await model.invoke(messages);
console.log(result);

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Chaining

We can chain our model with a prompt template. Prompt templates convert raw user input to better input to the LLM.

import { ChatPromptTemplate } from "@langchain/core/prompts";

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a world class technical documentation writer."],
  ["user", "{input}"],
]);

const chain = prompt.pipe(model);

console.log(
  await chain.invoke({ input: "how can langsmith help with testing?" })
);

To get the string value instead of the message, we can add an output parser to the chain.

import { StringOutputParser } from "@langchain/core/output_parsers";

const outputParser = new StringOutputParser();

const chain = prompt.pipe(model).pipe(outputParser);

console.log(
  await chain.invoke({ input: "how can langsmith help with testing?" })
);

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Tool calling

Describe tools and their parameters, and let the model return a tool to invoke with the input arguments. Tool calling is extremely useful for enhancing the model’s capability to provide more comprehensive and actionable responses.

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Define tools to use

We can define tools with Zod schemas and use them to generate tool calls.

import { tool } from "@langchain/core/tools";
import { z } from "zod";

/**
 * Note that the descriptions here are crucial, as they will be passed along
 * to the model along with the class name.
 */
const calculatorSchema = z.object({
  operation: z
    .enum(["add", "subtract", "multiply", "divide"])
    .describe("The type of operation to execute."),
  number1: z.number().describe("The first number to operate on."),
  number2: z.number().describe("The second number to operate on."),
});

const calculatorTool = tool(
  async ({ operation, number1, number2 }) => {
    // Functions must return strings
    if (operation === "add") {
      return `${number1 + number2}`;
    } else if (operation === "subtract") {
      return `${number1 - number2}`;
    } else if (operation === "multiply") {
      return `${number1 * number2}`;
    } else if (operation === "divide") {
      return `${number1 / number2}`;
    } else {
      throw new Error("Invalid operation.");
    }
  },
  {
    name: "calculator",
    description: "Can perform mathematical operations.",
    schema: calculatorSchema,
  }
);

console.log(
  await calculatorTool.invoke({ operation: "add", number1: 3, number2: 4 })
);

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Bind tools to the model

Now models can generate a tool calling response.

const modelWithTools = model.bindTools([calculatorTool]);

const messages = [new HumanMessage("What is 3 * 12? Also, what is 11 + 49?")];

const aiMessage = await modelWithTools.invoke(messages);

console.log(aiMessage);

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Generate a tool assisted message

Use the tool call results to generate a message.

messages.push(aiMessage);

const toolsByName = {
  calculator: calculatorTool,
};

for (const toolCall of aiMessage.tool_calls) {
  const selectedTool = toolsByName[toolCall.name];
  const toolMessage = await selectedTool.invoke(toolCall);
  messages.push(toolMessage);
}

console.log(await modelWithTools.invoke(messages));

For more information on how to use tools, check out the LangChain documentation.

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