Querying Vector Data in Pinecone

After setting up your index and adding data, the next crucial step is to retrieve relevant information. This is where querying comes in!

Querying in Pinecone means finding vectors in your index that are most similar to a given "query vector." It's how you perform semantic search, recommendations, and more.

Just like the data you stored, your search query also needs to be converted into a vector. This "query vector" is then compared against all vectors in your Pinecone index.

• Embedding Model: You use the same embedding model that generated your stored vectors to create your query vector.
• Similarity: Pinecone calculates the distance or similarity between your query vector and indexed vectors.