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Anthropic Functions

LangChain offers an experimental wrapper around Anthropic that gives it the same API as OpenAI Functions.

Setup​

First, you'll need to install the popular fast-xml-parser package as a peer dependency:

npm install fast-xml-parser

Initialize model​

You can initialize this wrapper the same way you'd initialize a standard ChatAnthropic instance:

import { AnthropicFunctions } from "langchain/experimental/chat_models/anthropic_functions";

const model = new AnthropicFunctions({
  temperature: 0.1,
  anthropicApiKey: "YOUR-API-KEY", // In Node.js defaults to process.env.ANTHROPIC_API_KEY
});

Passing in functions​

You can now pass in functions the same way as OpenAI:

import { AnthropicFunctions } from "langchain/experimental/chat_models/anthropic_functions";
import { HumanMessage } from "langchain/schema";

const model = new AnthropicFunctions({
  temperature: 0.1,
}).bind({
  functions: [
    {
      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"],
      },
    },
  ],
  // You can set the `function_call` arg to force the model to use a function
  function_call: {
    name: "get_current_weather",
  },
});

const response = await model.invoke([
  new HumanMessage({
    content: "What's the weather in Boston?",
  }),
]);

console.log(response);

/*
  AIMessage {
    content: '',
    additional_kwargs: {
      function_call: {
        name: 'get_current_weather',
        arguments: '{"location":"Boston, MA","unit":"fahrenheit"}'
      }
    }
  }
*/

API Reference:

Using for extraction​

import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

import { AnthropicFunctions } from "langchain/experimental/chat_models/anthropic_functions";
import { PromptTemplate } from "langchain/prompts";
import { JsonOutputFunctionsParser } from "langchain/output_parsers";

const EXTRACTION_TEMPLATE = `Extract and save the relevant entities mentioned in the following passage together with their properties.

Passage:
{input}
`;

const prompt = PromptTemplate.fromTemplate(EXTRACTION_TEMPLATE);

// Use Zod for easier schema declaration
const schema = z.object({
  people: z.array(
    z.object({
      name: z.string().describe("The name of a person"),
      height: z.number().describe("The person's height"),
      hairColor: z.optional(z.string()).describe("The person's hair color"),
    })
  ),
});

const model = new AnthropicFunctions({
  temperature: 0.1,
}).bind({
  functions: [
    {
      name: "information_extraction",
      description: "Extracts the relevant information from the passage.",
      parameters: {
        type: "object",
        properties: zodToJsonSchema(schema),
      },
    },
  ],
  function_call: {
    name: "information_extraction",
  },
});

// Use a JsonOutputFunctionsParser to get the parsed JSON response directly.
const chain = await prompt.pipe(model).pipe(new JsonOutputFunctionsParser());

const response = await chain.invoke({
  input:
    "Alex is 5 feet tall. Claudia is 1 foot taller than Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.",
});

console.log(response);

/*
  {
    people: [
      { name: 'Alex', height: 5, hairColor: 'blonde' },
      { name: 'Claudia', height: 6, hairColor: 'brunette' }
    ]
  }
*/

API Reference: