Pinecone
Compatibility
仅适用于 Node.js。
Langchain.js 将 @pinecone-database/pinecone 作为 Pinecone 向量存储的客户端。使用以下命令安装库：
- npm
- Yarn
- pnpm
npm install -S dotenv langchain @pinecone-database/pinecone
yarn add dotenv langchain @pinecone-database/pinecone
pnpm add dotenv langchain @pinecone-database/pinecone
索引文档
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(process.env.PINECONE_INDEX);
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",
}),
new Document({
metadata: { baz: "qux" },
pageContent: "lorem ipsum dolor sit amet",
}),
new Document({
metadata: { baz: "qux" },
pageContent: "pinecones are the woody fruiting body and of a pine tree",
}),
];
await PineconeStore.fromDocuments(docs, new OpenAIEmbeddings(), {
pineconeIndex,
});
查询文档
import { PineconeClient } from "@pinecone-database/pinecone";
import * as dotenv from "dotenv";
import { VectorDBQAChain } from "langchain/chains";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { OpenAI } from "langchain/llms/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(process.env.PINECONE_INDEX);
const vectorStore = await PineconeStore.fromExistingIndex(
new OpenAIEmbeddings(),
{ pineconeIndex }
);
/* Search the vector DB independently with meta filters */
const results = await vectorStore.similaritySearch("pinecone", 1, {
foo: "bar",
});
console.log(results);
/*
[
Document {
pageContent: 'pinecone is a vector db',
metadata: { foo: 'bar' }
}
]
*/
/* Use as part of a chain (currently no metadata filters) */
const model = new OpenAI();
const chain = VectorDBQAChain.fromLLM(model, vectorStore, {
k: 1,
returnSourceDocuments: true,
});
const response = await chain.call({ query: "What is pinecone?" });
console.log(response);
/*
{
text: ' A pinecone is the woody fruiting body of a pine tree.',
sourceDocuments: [
Document {
pageContent: 'pinecones are the woody fruiting body and of a pine tree',
metadata: [Object]
}
]
}
*/