How to gracefully cache and update vector data in Pinecone?

Hello everyone, I encountered a problem while using the Pinecone vector database and would like to hear your thoughts and suggestions.

My requirement is to cache some vector data in Pinecone, so that when querying the same vector next time, it can directly return the cached result, avoid repeated calculations, and improve query efficiency. However, I now find that once a query has been made, even if I insert new vector data, subsequent queries will still return the old cached result, unable to obtain the newly inserted vectors.

I have thought of the following ideas:

  1. After inserting a new vector, manually delete the cache to ensure that subsequent queries can return the latest results.

  2. When querying, explicitly specify to ignore the cache and query the database directly every time.

  3. Adjust Pinecone’s global cache configuration to shorten the cache time.

  4. After inserting a new vector, call the API method to refresh the cache.

However, these solutions have some drawbacks, such as requiring additional cache deletion operations, or not being able to use caches to improve performance, etc.

I wonder if anyone has better ideas or suggestions? My ideal situation is that while the cache is still effective, if a new vector is inserted, the cache can be automatically updated, and the latest result can always be obtained when querying. I don’t know if this can be achieved in Pinecone? Or are there any other vector databases or solutions recommended?

Looking forward to everyone’s ideas and experience sharing. Thank you very much!

Hi and welcome to the Pinecone forums!

Thank you for your question. Here are some ideas that will hopefully assist you / bear some fruit with additional experimentation on your part:

  1. Hierarchical Naming for Vector IDs:
  • Use a hierarchical pattern like parentId-chunkId for vector IDs. This simplifies managing and retrieving related chunks.
  • Store metadata such as chunkCount to track the number of chunks per document. This facilitates easy updates and deletions.

Example Code for Upserting Vectors:

from pinecone import Pinecone

pc = Pinecone(api_key='YOUR_API_KEY')
index = pc.Index("your-index-name")

vectors = [
    {"id": "doc1-0", "values": [0.1, 0.2], "metadata": {"chunkCount": 2}},
    {"id": "doc1-1", "values": [0.2, 0.3]},
    # Add more vectors here

  1. Fetch Vectors:
  • Use the fetch operation to retrieve vectors by their IDs to ensure your cached data is up-to-date.
  • Fetch Data in Pinecone
  1. Delete and Update Vectors:
  1. Use Namespaces:
  • Organize vectors into namespaces to limit the scope of queries and operations, enhancing performance.
  • Use Namespaces
  1. Metadata Filtering:
  • Apply metadata filters in queries to limit the search space and improve relevance.
  • Metadata Filtering
  1. Consider a Semantic Cache:
  • Implementing a semantic cache can reduce latency and costs for language model queries. This approach uses cached responses for semantically similar queries, optimizing API call usage and improving response times.
  • Read about Semantic Cache

I hope these ideas are helpful - let me know how you get on and looking forward to your response.