How to efficiently store and query chatbot-specific embeddings in a shared Pinecone index

Question

With the help of code examples, can you tell me How to efficiently store and query chatbot-specific embeddings in a shared Pinecone index?

Answer 1

To efficiently store and query chatbot-specific embeddings in a shared Pinecone index:

• Set up Pinecone Index: Create an index with the required dimensionality and metadata support.
• Store Embeddings: Use unique IDs and metadata to distinguish chatbot-specific embeddings.
• Query Embeddings: Filter by chatbot-specific metadata during similarity searches.

Here is the code example you can refer to:

In the above code, we are using the following key points:

• Metadata: Use metadata like chatbot_id to separate embeddings for different chatbots.
• Filters: Apply filters during queries to retrieve chatbot-specific results.
• Efficiency: Leverage Pinecone's optimized indexing and search capabilities for fast lookups.

Hence, by referring to above, you can efficiently store and query chatbot-specific embeddings in a shared Pinecone index