am creating embedding using langchain the dimension is 1536
I can created the index using nodejs and its perfectly running fine but when I call PineconeStore.fromDocuments() its returning
PineconeError: PineconeClient: Error calling upsert: PineconeError: undefined
I even tried manually with await index.upsert({ upsertRequest });
manually to push vector to pinecone but its not working or the issue is only with asia-northeast1-gcp environment ?
here is the code with langchain:
import { PineconeClient } from "@pinecone-database/pinecone";
import * as dotenv from "dotenv";
import { Document } from "langchain/document";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { PineconeStore } from "langchain/vectorstores/pinecone";
dotenv.config();
const client = new PineconeClient();
await client.init({
apiKey: process.env.PINECONE_API_KEY,
environment: process.env.PINECONE_ENVIRONMENT,
});
const pineconeIndex = client.Index("new");
const docs = [
new Document({
metadata: { foo: "bar" },
pageContent: "pinecone is a vector db",
}),
new Document({
metadata: { foo: "bar" },
pageContent: "the quick brown fox jumped over the lazy dog",
})
];
await PineconeStore.fromDocuments(docs, new OpenAIEmbeddings(), { pineconeIndex,});
here is the code that I did manually
import { PineconeClient } from "@pinecone-database/pinecone";
import * as dotenv from "dotenv";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
dotenv.config();
const client = new PineconeClient();
await client.init({
apiKey: process.env.PINECONE_API_KEY,
environment: process.env.PINECONE_ENVIRONMENT,
});
const embedd = new OpenAIEmbeddings()
const data = await embedd.embedQuery('Hi')
console.log(data);
const index = client.Index("new");
// console.log(index);
const upsertRequest = {
vectors: [
{
id: "vec1",
values: data,
},
],
namespace: "give_me",
};
const upsertResponse = await index.upsert({ upsertRequest });