How to get response from Pinecone using LangChain?

[GoldenDragon0710]

Checklist

• I added a very descriptive title to this issue.
• I included a link to the documentation page I am referring to (if applicable).

Issue with current documentation:

const getReply = async (messages, namespaceId) => {
  const pinecone = new Pinecone({
    apiKey: process.env.PINECONE_API_KEY,
  });
  const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX);
  const vectorStore = await PineconeStore.fromExistingIndex(
    new OpenAIEmbeddings(),
    { pineconeIndex, namespace: namespaceId }
  );

  const CONDENSE_PROMPT = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
        Chat History: {chat_history}
        Follow Up Input: {question}
        Standalone question:`;

  const QA_PROMPT = `
        ${systemPrompt}
        =========
        {context}
        =========
        Question: {question}
        
        Answer:`;
  const chat = new ChatOpenAI({
    modelName: "gpt-4",
    temperature: 0.2,
    apiKey: process.env.OPENAI_API_KEY,
  });

  const chain = ConversationalRetrievalQAChain.fromLLM(
    chat,
    vectorStore.asRetriever(),
    {
      qaTemplate: QA_PROMPT,
      questionGeneratorTemplate: CONDENSE_PROMPT,
      returnSourceDocuments: true,
    }
  );

  const result = await chain.call({
    chat_history: messages,
  });
  return result.text;
};

This is code for generating response from Pinecone by using Langchain v0.0.91.
But this code does not work in Langchain v0.1.36. How to update this code?

Idea or request for content:

No response

[dosubot]

To update your code for LangChain v0.1.36, follow these key steps:

1. Update Import Statements to match the latest module names and paths, for example:

   import { Pinecone } from "@pinecone-database/pinecone";
   import { Document } from "@langchain/core/documents";
   import { OpenAIEmbeddings } from "@langchain/openai";
   import { PineconeStore } from "@langchain/pinecone";

2. Initialize Pinecone Client more simply with:

   const pinecone = new Pinecone();
   const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);

3. Set Up Embeddings and PineconeStore with:

   const embeddings = new OpenAIEmbeddings();
   const pineconeStore = new PineconeStore(embeddings, { pineconeIndex });

4. Create Documents using the Document class:

   const docs = [
     new Document({
       metadata: { foo: "bar" },
       pageContent: "pinecone is a vector db",
     }),
     // Additional documents...
   ];

5. Add and Search Documents with updated API usage:

   const ids = await pineconeStore.addDocuments(docs);
   const results = await pineconeStore.similaritySearch(pageContent, 2, { foo: "bar" });

6. Delete Documents using:

   pineconeStore.delete({ ids: [ids[0], ids[1]], });

Ensure your code includes try-catch blocks around asynchronous operations for error handling. The ConversationalRetrievalQAChain class initialization has changed, so refer to the ConversationalRetrievalQAChain class for configuring parameters and options.

This should make your code compatible with LangChain v0.1.36. If you need further assistance, feel free to ask.

Details

Sources

• [TypeError: this.pineconeClient.upsert is not a function]
• use official pinecone package
• examples/src/indexes/vector_stores/pinecone/index_docs.ts
• examples/src/indexes/vector_stores/pinecone/delete_docs.ts
• langchain/src/util/entrypoint_deprecation.ts
• libs/langchain-scripts/src/migrations/0_1.ts
• langchain/src/vectorstores/pinecone.ts
• libs/langchain-community/src/vectorstores/pinecone.ts
• libs/langchain-pinecone/src/vectorstores.ts
• langchain/src/chains/conversational_retrieval_chain.ts

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