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                *   [Chroma Self Query Retriever](/v0.1/docs/modules/data_connection/retrievers/self_query/chroma-self-query/)
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*   [Self-querying](/v0.1/docs/modules/data_connection/retrievers/self_query/)
*   Vectara Self Query Retriever

On this page

Vectara Self Query Retriever
============================

This example shows how to use a self query retriever with a [Vectara](https://vectara.com/) vector store.

If you haven't already set up Vectara, please [follow the instructions here](/v0.1/docs/integrations/vectorstores/vectara/).

Usage[​](#usage "Direct link to Usage")
---------------------------------------

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

This example shows how to intialize a `SelfQueryRetriever` with a vector store:

    import { AttributeInfo } from "langchain/schema/query_constructor";import { SelfQueryRetriever } from "langchain/retrievers/self_query";import { OpenAI } from "@langchain/openai";import { VectaraStore } from "@langchain/community/vectorstores/vectara";import { VectaraTranslator } from "langchain/retrievers/self_query/vectara";import { FakeEmbeddings } from "@langchain/core/utils/testing";import { Document } from "@langchain/core/documents";/** * First, we create a bunch of documents. You can load your own documents here instead. * Each document has a pageContent and a metadata field. Make sure your metadata matches the AttributeInfo below. */const docs = [  new Document({    pageContent:      "A bunch of scientists bring back dinosaurs and mayhem breaks loose",    metadata: { year: 1993, rating: 7.7, genre: "science fiction" },  }),  new Document({    pageContent:      "Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",    metadata: { year: 2010, director: "Christopher Nolan", rating: 8.2 },  }),  new Document({    pageContent:      "A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",    metadata: { year: 2006, director: "Satoshi Kon", rating: 8.6 },  }),  new Document({    pageContent:      "A bunch of normal-sized women are supremely wholesome and some men pine after them",    metadata: { year: 2019, director: "Greta Gerwig", rating: 8.3 },  }),  new Document({    pageContent: "Toys come alive and have a blast doing so",    metadata: { year: 1995, genre: "animated" },  }),  new Document({    pageContent: "Three men walk into the Zone, three men walk out of the Zone",    metadata: {      year: 1979,      rating: 9.9,      director: "Andrei Tarkovsky",      genre: "science fiction",    },  }),];/** * Next, we define the attributes we want to be able to query on. * in this case, we want to be able to query on the genre, year, director, rating, and length of the movie. * We also provide a description of each attribute and the type of the attribute. * This is used to generate the query prompts. * * We need to setup the filters in the vectara as well otherwise filter won't work. * To setup the filter in vectara, go to Data -> {your_created_corpus} -> overview * In the overview section edit the filters section and all the following attributes in * the filters. */const attributeInfo: AttributeInfo[] = [  {    name: "genre",    description: "The genre of the movie",    type: "string or array of strings",  },  {    name: "year",    description: "The year the movie was released",    type: "number",  },  {    name: "director",    description: "The director of the movie",    type: "string",  },  {    name: "rating",    description: "The rating of the movie (1-10)",    type: "number",  },];/** * Next, we instantiate a vector store. This is where we store the embeddings of the documents. * We also need to provide an embeddings object. This is used to embed the documents. */const config = {  customerId: Number(process.env.VECTARA_CUSTOMER_ID),  corpusId: Number(process.env.VECTARA_CORPUS_ID),  apiKey: String(process.env.VECTARA_API_KEY),  verbose: true,};const vectorStore = await VectaraStore.fromDocuments(  docs,  new FakeEmbeddings(),  config);const llm = new OpenAI();const documentContents = "Brief summary of a movie";const selfQueryRetriever = SelfQueryRetriever.fromLLM({  llm,  vectorStore,  documentContents,  attributeInfo,  /**   * We need to create a basic translator that translates the queries into a   * filter format that the vector store can understand. We provide a basic translator   * here, but you can create your own translator by extending BaseTranslator   * abstract class. Note that the vector store needs to support filtering on the metadata   * attributes you want to query on.   */  structuredQueryTranslator: new VectaraTranslator(),});/** * Now we can query the vector store. * We can ask questions like "Which movies are less than 90 minutes?" or "Which movies are rated higher than 8.5?". * We can also ask questions like "Which movies are either comedy or drama and are less than 90 minutes?". * The retriever will automatically convert these questions into queries that can be used to retrieve documents. */const query1 = await selfQueryRetriever.invoke(  "What are some movies about dinosaurs");const query2 = await selfQueryRetriever.invoke(  "I want to watch a movie rated higher than 8.5");const query3 = await selfQueryRetriever.invoke(  "Which movies are directed by Greta Gerwig?");const query4 = await selfQueryRetriever.invoke(  "Which movies are either comedy or science fiction and are rated higher than 8.5?");console.log(query1, query2, query3, query4);

#### API Reference:

*   [AttributeInfo](https://api.js.langchain.com/classes/langchain_schema_query_constructor.AttributeInfo.html) from `langchain/schema/query_constructor`
*   [SelfQueryRetriever](https://api.js.langchain.com/classes/langchain_retrievers_self_query.SelfQueryRetriever.html) from `langchain/retrievers/self_query`
*   [OpenAI](https://api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai`
*   [VectaraStore](https://api.js.langchain.com/classes/langchain_community_vectorstores_vectara.VectaraStore.html) from `@langchain/community/vectorstores/vectara`
*   [VectaraTranslator](https://api.js.langchain.com/classes/langchain_retrievers_self_query_vectara.VectaraTranslator.html) from `langchain/retrievers/self_query/vectara`
*   [FakeEmbeddings](https://api.js.langchain.com/classes/langchain_core_utils_testing.FakeEmbeddings.html) from `@langchain/core/utils/testing`
*   [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

You can also initialize the retriever with default search parameters that apply in addition to the generated query:

    const selfQueryRetriever = SelfQueryRetriever.fromLLM({  llm,  vectorStore,  documentContents,  attributeInfo,  /**   * We need to use a translator that translates the queries into a   * filter format that the vector store can understand. LangChain provides one here.   */  structuredQueryTranslator: new VectaraTranslator()(),  searchParams: {    filter: {      filter: "( doc.genre = 'science fiction' ) and ( doc.rating > 8.5 )",    },    mergeFiltersOperator: "and",  },});

See the [official docs](https://docs.vectara.com/) for more on how to construct metadata filters.

* * *

#### Help us out by providing feedback on this documentation page:

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