Pinecone vector store integration.
Setup:
Install @langchain/pinecone and @pinecone-database/pinecone to pass a client in.
npm install @langchain/pinecone @pinecone-database/pineconeimport { 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",
});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 });await vectorStore.delete({ ids: ["3"] });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"}]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"}]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"}]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" })]Embeddings interface for generating vector embeddings from text queries, enabling vector-based similarity searches.
Returns a string representing the type of vector store, which subclasses must implement to identify their specific vector storage type.
Method that adds documents to the Pinecone database.
Method that adds vectors to the Pinecone database.
Creates a VectorStoreRetriever instance with flexible configuration options.
Method that deletes vectors from the Pinecone database.
Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Searches for documents similar to a text query by embedding the query and performing a similarity search on the resulting vector.
Method that performs a similarity search in the Pinecone database and returns the results along with their scores.
Searches for documents similar to a text query by embedding the query, and returns results with similarity scores.
Static method that creates a new instance of the PineconeStore class from documents.
Static method that creates a new instance of the PineconeStore class from an existing index.
Static method that creates a new instance of the PineconeStore class from texts.
The name of the serializable. Override to provide an alias or to preserve the serialized module name in minified environments.
Implemented as a static method to support loading logic.