Class PineconeStore

Pinecone vector store integration.

Setup: Install @langchain/pinecone and @pinecone-database/pinecone to pass a client in.

npm install @langchain/pinecone @pinecone-database/pinecone

Constructor args

Instantiate 
import { PineconeStore } from '@langchain/pinecone';
// Or other embeddings
import { OpenAIEmbeddings } from '@langchain/openai';

import { Pinecone as PineconeClient } from "@pinecone-database/pinecone";

const pinecone = new PineconeClient();

// Will automatically read the PINECONE_API_KEY env var
const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
  pineconeIndex,
  // Maximum number of batch requests to allow at once. Each batch is 1000 vectors.
  maxConcurrency: 5,
  // You can pass a namespace here too
  // namespace: "foo",
});

Add documents 
import type { Document } from '@langchain/core/documents';

const document1 = { pageContent: "foo", metadata: { baz: "bar" } };
const document2 = { pageContent: "thud", metadata: { bar: "baz" } };
const document3 = { pageContent: "i will be deleted :(", metadata: {} };

const documents: Document[] = [document1, document2, document3];
const ids = ["1", "2", "3"];
await vectorStore.addDocuments(documents, { ids });

Delete documents 
await vectorStore.delete({ ids: ["3"] });

Similarity search 
const results = await vectorStore.similaritySearch("thud", 1);
for (const doc of results) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output: * thud [{"baz":"bar"}]

Similarity search with filter 
const resultsWithFilter = await vectorStore.similaritySearch("thud", 1, { baz: "bar" });

for (const doc of resultsWithFilter) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output: * foo [{"baz":"bar"}]

Similarity search with score 
const resultsWithScore = await vectorStore.similaritySearchWithScore("qux", 1);
for (const [doc, score] of resultsWithScore) {
  console.log(`* [SIM=${score.toFixed(6)}] ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
// Output: * [SIM=0.000000] qux [{"bar":"baz","baz":"bar"}]

As a retriever 
const retriever = vectorStore.asRetriever({
  searchType: "mmr", // Leave blank for standard similarity search
  k: 1,
});
const resultAsRetriever = await retriever.invoke("thud");
console.log(resultAsRetriever);

// Output: [Document({ metadata: { "baz":"bar" }, pageContent: "thud" })]

Hierarchy

  • VectorStore
    • PineconeStore

Constructors

constructor

Properties

FilterType caller embeddings filter? namespace? pineconeIndex textKey

Methods

addDocuments addVectors asRetriever delete maxMarginalRelevanceSearch similaritySearch similaritySearchVectorWithScore similaritySearchWithScore toJSON toJSONNotImplemented fromDocuments fromExistingIndex fromTexts

Constructors

constructor

Properties

FilterType

FilterType: PineconeMetadata

caller

caller: AsyncCaller

embeddings

embeddings: EmbeddingsInterface<number[]>

Embeddings interface for generating vector embeddings from text queries, enabling vector-based similarity searches.

Optionalfilter

filter?: PineconeMetadata

Optionalnamespace

namespace?: string

pineconeIndex

pineconeIndex: Index<RecordMetadata>

textKey

textKey: string

Methods

addDocuments

  • addDocuments(documents, options?): Promise<string[]>

  • Method that adds documents to the Pinecone database.

    Parameters

    • documents: Document<Record<string, any>>[]

      Array of documents to add to the Pinecone database.

    • Optionaloptions: string[] | {
          ids?: string[];
          namespace?: string;
      }

      Optional ids for the documents.

    Returns Promise<string[]>

    Promise that resolves with the ids of the added documents.

addVectors

  • addVectors(vectors, documents, options?): Promise<string[]>

  • Method that adds vectors to the Pinecone database.

    Parameters

    • vectors: number[][]

      Array of vectors to add to the Pinecone database.

    • documents: Document<Record<string, any>>[]

      Array of documents associated with the vectors.

    • Optionaloptions: string[] | {
          ids?: string[];
          namespace?: string;
      }

      Optional ids for the vectors.

    Returns Promise<string[]>

    Promise that resolves with the ids of the added vectors.

asRetriever

  • asRetriever(kOrFields?, filter?, callbacks?, tags?, metadata?, verbose?): VectorStoreRetriever<PineconeStore>

  • Creates a VectorStoreRetriever instance with flexible configuration options.

    Parameters

    • OptionalkOrFields: number | Partial<VectorStoreRetrieverInput<PineconeStore>>

      If a number is provided, it sets the k parameter (number of items to retrieve).

      • If an object is provided, it should contain various configuration options.
    • Optionalfilter: PineconeMetadata

      Optional filter criteria to limit the items retrieved based on the specified filter type.

    • Optionalcallbacks: Callbacks

      Optional callbacks that may be triggered at specific stages of the retrieval process.

    • Optionaltags: string[]

      Tags to categorize or label the VectorStoreRetriever. Defaults to an empty array if not provided.

    • Optionalmetadata: Record<string, unknown>

      Additional metadata as key-value pairs to add contextual information for the retrieval process.

    • Optionalverbose: boolean

      If true, enables detailed logging for the retrieval process. Defaults to false.

    Returns VectorStoreRetriever<PineconeStore>

    • A configured VectorStoreRetriever instance based on the provided parameters.

    Example

    Basic usage with a k value:

    const retriever = myVectorStore.asRetriever(5);

    Usage with a configuration object:

    const retriever = myVectorStore.asRetriever({
      k: 10,
      filter: myFilter,
      tags: ['example', 'test'],
      verbose: true,
      searchType: 'mmr',
      searchKwargs: { alpha: 0.5 },
    });

delete

maxMarginalRelevanceSearch

  • maxMarginalRelevanceSearch(query, options): Promise<DocumentInterface<Record<string, any>>[]>

  • Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.

    Parameters

    • query: string

      Text to look up documents similar to.

    • options: MaxMarginalRelevanceSearchOptions<PineconeMetadata>

    Returns Promise<DocumentInterface<Record<string, any>>[]>

    • List of documents selected by maximal marginal relevance.

similaritySearch

  • similaritySearch(query, k?, filter?, _callbacks?): Promise<DocumentInterface<Record<string, any>>[]>

  • Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.

    Parameters

    • query: string

      Text query for finding similar documents.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: PineconeMetadata

      Optional filter based on FilterType.

    • Optional_callbacks: Callbacks

      Optional callbacks for monitoring search progress

    Returns Promise<DocumentInterface<Record<string, any>>[]>

    A promise resolving to an array of DocumentInterface instances representing similar documents.

similaritySearchVectorWithScore

  • similaritySearchVectorWithScore(query, k, filter?): Promise<[Document<Record<string, any>>, number][]>

  • Method that performs a similarity search in the Pinecone database and returns the results along with their scores.

    Parameters

    • query: number[]

      Query vector for the similarity search.

    • k: number

      Number of top results to return.

    • Optionalfilter: PineconeMetadata

      Optional filter to apply to the search.

    Returns Promise<[Document<Record<string, any>>, number][]>

    Promise that resolves with an array of documents and their scores.

similaritySearchWithScore

  • similaritySearchWithScore(query, k?, filter?, _callbacks?): Promise<[DocumentInterface<Record<string, any>>, number][]>

  • Searches for documents similar to a text query by embedding the query, and returns results with similarity scores.

    Parameters

    • query: string

      Text query for finding similar documents.

    • Optionalk: number

      Number of similar results to return. Defaults to 4.

    • Optionalfilter: PineconeMetadata

      Optional filter based on FilterType.

    • Optional_callbacks: Callbacks

      Optional callbacks for monitoring search progress

    Returns Promise<[DocumentInterface<Record<string, any>>, number][]>

    A promise resolving to an array of tuples, each containing a document and its similarity score.

toJSON

  • toJSON(): Serialized

  • Returns Serialized

toJSONNotImplemented

StaticfromDocuments

  • fromDocuments(docs, embeddings, dbConfig): Promise<PineconeStore>

  • Static method that creates a new instance of the PineconeStore class from documents.

    Parameters

    • docs: Document<Record<string, any>>[]

      Array of documents to add to the Pinecone database.

    • embeddings: EmbeddingsInterface<number[]>

      Embeddings to use for the documents.

    • dbConfig: PineconeStoreParams

      Configuration for the Pinecone database.

    Returns Promise<PineconeStore>

    Promise that resolves with a new instance of the PineconeStore class.

StaticfromExistingIndex

  • fromExistingIndex(embeddings, dbConfig): Promise<PineconeStore>

  • Static method that creates a new instance of the PineconeStore class from an existing index.

    Parameters

    • embeddings: EmbeddingsInterface<number[]>

      Embeddings to use for the documents.

    • dbConfig: PineconeStoreParams

      Configuration for the Pinecone database.

    Returns Promise<PineconeStore>

    Promise that resolves with a new instance of the PineconeStore class.

StaticfromTexts

  • fromTexts(texts, metadatas, embeddings, dbConfig): Promise<PineconeStore>

  • Static method that creates a new instance of the PineconeStore class from texts.

    Parameters

    • texts: string[]

      Array of texts to add to the Pinecone database.

    • metadatas: object | object[]

      Metadata associated with the texts.

    • embeddings: EmbeddingsInterface<number[]>

      Embeddings to use for the texts.

    • dbConfig: PineconeStoreParams | {
          namespace?: string;
          pineconeIndex: Index<RecordMetadata>;
          textKey?: string;
      }

      Configuration for the Pinecone database.

    Returns Promise<PineconeStore>

    Promise that resolves with a new instance of the PineconeStore class.

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