Why Does My LangChainJS Retrieval Return Generic Responses Instead of Querying Pinecone Data?

Below is a few things to check when you get a generic (ChatGPT-like) response instead of one that leverages your Pinecone data:

---

1. Ensure Your Query is Driving Retrieval

Your chain design expects the retriever to pull in context from your vector store via:

JSCopied
context: vectorStoreRetriever.pipe(formatDocumentsAsString),

Make sure that:

• The retriever actually returns relevant documents. Try manually calling:

  JSCopied
  const docs = await vectorStoreRetriever.getRelevantDocuments('Tell me a bit about the data you have stored in your vectors');
  console.log(docs);

• The formatDocumentsAsString function is correctly implemented so that it transforms the retrieved documents into a string for the prompt. If it fails (or returns an empty string), your chain ends up with no useful context.

---

2. Verify Data and Embeddings Consistency

• Data in Pinecone: Confirm that your Pinecone index has the documents you expect and that they were embedded using the same model (or at least similar) as used now. If your stored embeddings and query embeddings differ significantly, you might not get the expected retrieval.

• Embeddings Model: Your code instantiates embeddings with new OpenAIEmbeddings(). Make sure the same or a compatible model was used when initially embedding the data.

---

3. Prompt Template and Chain Setup

• The system prompt uses a placeholder {context} to inject the retrieved data. If no context is being provided (e.g., because the retriever returned nothing or the format function is off), the chat model falls back to its own generic capabilities.

• Double-check that your chain is set up correctly:

  JSCopied
  const chain = RunnableSequence.from([
    {
      context: vectorStoreRetriever.pipe(formatDocumentsAsString),
      question: new RunnablePassthrough(),
    },
    prompt,
    model,
    new StringOutputParser(),
  ]);

  Sometimes, the order of operations or a missing step (like an explicit retrieval call) can cause the chain to bypass your stored context.

---

4. Model and Query Considerations

• Query Specificity: The question "Tell me a bit about the data you have stored in your vectors" might be too generic. In many setups, retrieval chains work best when the query is clearly related to the specific contents of your documents. Try a question that references a detail you know is in your data.

• Model Selection: You're using a model labeled gpt-4o-mini. Ensure that this model is capable of following the prompt pattern (i.e., using the provided context) as expected. Some models might lean towards generating generic responses if not carefully prompted.

---

Summary

You’re likely not missing a major configuration step but rather encountering one (or more) of these issues:

• The retriever isn’t actually finding any or the right context (or the context isn’t being formatted correctly).
• Your query may not be specific enough, causing the language model to default to generic knowledge.
• Misalignment between the embeddings used for storage and those used at query time.

Check the retrieval flow independently and ensure that your formatDocumentsAsString returns a non-empty, meaningful context string. With these steps, you should be able to drill down to why your chain isn’t leveraging your Pinecone data as expected.