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How to handle multiple queries

Sometimes, a query analysis technique may allow for multiple queries to be generated. In these cases, we need to remember to run all queries and then to combine the results. 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", "Ankush worked at Facebook"];
const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" });
const vectorstore = await Chroma.fromTexts(texts, {}, embeddings, {
  collectionName: "multi_query",
});
const retriever = 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]
}

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({
    queries: z.array(z.string()).describe("Distinct queries to search for"),
  })
  .describe("Search over a database of job records.");

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.

If you need to look up two distinct pieces of information, you are allowed to do that!`;

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 creating multiple queries

await queryAnalyzer.invoke("where did Harrison Work");
{ queries: [ "Harrison" ] }
await queryAnalyzer.invoke("where did Harrison and ankush Work");
{ queries: [ "Harrison work", "Ankush work" ] }

Retrieval with query analysis​

So how would we include this in a chain? One thing that will make this a lot easier is if we call our retriever asyncronously - this will let us loop over the queries and not get blocked on the response time.

import { RunnableConfig, RunnableLambda } from "@langchain/core/runnables";

const chain = async (question: string, config?: RunnableConfig) => {
  const response = await queryAnalyzer.invoke(question, config);
  const docs = [];
  for (const query of response.queries) {
    const newDocs = await retriever.invoke(query, config);
    docs.push(...newDocs);
  }
  // You probably want to think about reranking or deduplicating documents here
  // But that is a separate topic
  return docs;
};

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 Harrison and ankush Work");
[
  Document { pageContent: "Harrison worked at Kensho", metadata: {} },
  Document { pageContent: "Ankush worked at Facebook", metadata: {} }
]

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