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How to handle multiple retrievers

Sometimes, a query analysis technique may allow for selection of which retriever to use. To use this, you will need to add some logic to select the retriever to do. We will show a simple example (using mock data) of how to do that.

Setup​

Install dependencies​

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/core @langchain/community @langchain/openai zod chromadb

Set environment variables​

OPENAI_API_KEY=your-api-key

# Optional, use LangSmith for best-in-class observability
LANGSMITH_API_KEY=your-api-key
LANGCHAIN_TRACING_V2=true

Create Index​

We will create a vectorstore over fake information.

import { Chroma } from "@langchain/community/vectorstores/chroma";
import { OpenAIEmbeddings } from "@langchain/openai";
import "chromadb";

const texts = ["Harrison worked at Kensho"];
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const vectorstore = await Chroma.fromTexts(texts, {}, embeddings, {
  collectionName: "harrison",
});
const retrieverHarrison = vectorstore.asRetriever(1);
[Module: null prototype] {
  AdminClient: [class AdminClient],
  ChromaClient: [class ChromaClient],
  CloudClient: [class CloudClient extends ChromaClient],
  CohereEmbeddingFunction: [class CohereEmbeddingFunction],
  Collection: [class Collection],
  DefaultEmbeddingFunction: [class _DefaultEmbeddingFunction],
  GoogleGenerativeAiEmbeddingFunction: [class _GoogleGenerativeAiEmbeddingFunction],
  HuggingFaceEmbeddingServerFunction: [class HuggingFaceEmbeddingServerFunction],
  IncludeEnum: {
    Documents: "documents",
    Embeddings: "embeddings",
    Metadatas: "metadatas",
    Distances: "distances"
  },
  JinaEmbeddingFunction: [class JinaEmbeddingFunction],
  OpenAIEmbeddingFunction: [class _OpenAIEmbeddingFunction],
  TransformersEmbeddingFunction: [class _TransformersEmbeddingFunction]
}
const texts = ["Ankush worked at Facebook"];
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const vectorstore = await Chroma.fromTexts(texts, {}, embeddings, {
  collectionName: "ankush",
});
const retrieverAnkush = vectorstore.asRetriever(1);

Query analysis​

We will use function calling to structure the output. We will let it return multiple queries.

import { z } from "zod";

const searchSchema = z.object({
  query: z.string().describe("Query to look up"),
  person: z
    .string()
    .describe(
      "Person to look things up for. Should be `HARRISON` or `ANKUSH`."
    ),
});

Pick your chat model:

Install dependencies

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/openai

Add environment variables

OPENAI_API_KEY=your-api-key

Instantiate the model

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-3.5-turbo",
  temperature: 0
});
import { ChatPromptTemplate } from "@langchain/core/prompts";
import {
  RunnableSequence,
  RunnablePassthrough,
} from "@langchain/core/runnables";

const system = `You have the ability to issue search queries to get information to help answer user information.`;
const prompt = ChatPromptTemplate.fromMessages([
  ["system", system],
  ["human", "{question}"],
]);
const llmWithTools = llm.withStructuredOutput(searchSchema, {
  name: "Search",
});
const queryAnalyzer = RunnableSequence.from([
  {
    question: new RunnablePassthrough(),
  },
  prompt,
  llmWithTools,
]);

We can see that this allows for routing between retrievers

await queryAnalyzer.invoke("where did Harrison Work");
{ query: "workplace of Harrison", person: "HARRISON" }
await queryAnalyzer.invoke("where did ankush Work");
{ query: "Workplace of Ankush", person: "ANKUSH" }

Retrieval with query analysis​

So how would we include this in a chain? We just need some simple logic to select the retriever and pass in the search query

const retrievers = {
  HARRISON: retrieverHarrison,
  ANKUSH: retrieverAnkush,
};
import { RunnableConfig, RunnableLambda } from "@langchain/core/runnables";

const chain = async (question: string, config?: RunnableConfig) => {
  const response = await queryAnalyzer.invoke(question, config);
  const retriever = retrievers[response.person];
  return retriever.invoke(response.query, config);
};

const customChain = new RunnableLambda({ func: chain });
await customChain.invoke("where did Harrison Work");
[ Document { pageContent: "Harrison worked at Kensho", metadata: {} } ]
await customChain.invoke("where did ankush Work");
[ Document { pageContent: "Ankush worked at Facebook", metadata: {} } ]

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