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Use RunnableMaps

tip

See this section for general instructions on installing integration packages.

npm install @langchain/anthropic @langchain/community

RunnableMaps allow you to execute multiple Runnables in parallel, and to return the output of these Runnables as a map.

import { PromptTemplate } from "@langchain/core/prompts";
import { RunnableMap } from "@langchain/core/runnables";
import { ChatAnthropic } from "@langchain/anthropic";

const model = new ChatAnthropic({});
const jokeChain = PromptTemplate.fromTemplate(
  "Tell me a joke about {topic}"
).pipe(model);
const poemChain = PromptTemplate.fromTemplate(
  "write a 2-line poem about {topic}"
).pipe(model);

const mapChain = RunnableMap.from({
  joke: jokeChain,
  poem: poemChain,
});

const result = await mapChain.invoke({ topic: "bear" });
console.log(result);
/*
  {
    joke: AIMessage {
      content: " Here's a silly joke about a bear:\n" +
        '\n' +
        'What do you call a bear with no teeth?\n' +
        'A gummy bear!',
      additional_kwargs: {}
    },
    poem: AIMessage {
      content: ' Here is a 2-line poem about a bear:\n' +
        '\n' +
        'Furry and wild, the bear roams free  \n' +
        'Foraging the forest, strong as can be',
      additional_kwargs: {}
    }
  }
*/

API Reference:

Manipulating outputs/inputs​

Maps can be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence.

Note below that the object within the RunnableSequence.from() call is automatically coerced into a runnable map. All keys of the object must have values that are runnables or can be themselves coerced to runnables (functions to RunnableLambdas or objects to RunnableMaps). This coercion will also occur when composing chains via the .pipe() method.

import { CohereEmbeddings } from "@langchain/cohere";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { PromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import {
  RunnablePassthrough,
  RunnableSequence,
} from "@langchain/core/runnables";
import { Document } from "@langchain/core/documents";
import { ChatAnthropic } from "@langchain/anthropic";

const model = new ChatAnthropic();
const vectorstore = await HNSWLib.fromDocuments(
  [{ pageContent: "mitochondria is the powerhouse of the cell", metadata: {} }],
  new CohereEmbeddings()
);
const retriever = vectorstore.asRetriever();
const template = `Answer the question based only on the following context:
{context}

Question: {question}`;

const prompt = PromptTemplate.fromTemplate(template);

const formatDocs = (docs: Document[]) => docs.map((doc) => doc.pageContent);

const retrievalChain = RunnableSequence.from([
  { context: retriever.pipe(formatDocs), question: new RunnablePassthrough() },
  prompt,
  model,
  new StringOutputParser(),
]);

const result = await retrievalChain.invoke(
  "what is the powerhouse of the cell?"
);
console.log(result);

/*
 Based on the given context, the powerhouse of the cell is mitochondria.
*/

API Reference:

Here the input to prompt is expected to be a map with keys "context" and "question". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the "question" key.

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