Deleting vectors

Deleting vectors isn't just about tidying up our databases; it's about optimizing performance. As your indexes grow, unnecessary or outdated vectors can clutter your storage and slow down query performance. By removing redundant data, you streamline your operations, leading to faster responses and better resource utilization.

In this exercise, you'll practice deleting vectors from the 'datacamp-index' Pinecone index. You'll check the index metrics to verify the deletion took place.

If you accidentally delete the vectors but don't pass the exercise for a different reason, add the following code before your .delete() code to re-upsert the vectors for deletion:

index.upsert(vectors=vectors)

Deze oefening maakt deel uit van de cursus

Vector Databases for Embeddings with Pinecone

Cursus bekijken

Oefeninstructies

Initialize the Pinecone connection using your API key.
Delete the vectors with IDs "3" and "4".
Retrieve the metrics of the datacamp-index Pinecone index to check that the number of stored vectors has decreased.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Initialize the Pinecone client using your API key
pc = Pinecone(api_key="____")

index = pc.Index('datacamp-index')

# Delete vectors
____

# Retrieve metrics of the connected Pinecone index
print(____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Vector Databases for Embeddings with Pinecone

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

Explore the mechanics behind Pinecone's vector database, from pods and indexes to comparing it with other databases. Learn to differentiate pod types, acquire API keys, and initialise Pinecone connection using python. Finally, you’ll learn how to create Pinecone indexes, exploring different parameters such as dimensionality, distance metrics, pod types, and others.

Exercise 1: Introduction to Pinecone indexes Exercise 2: Creating a Pinecone client Exercise 3: Your first Pinecone index Exercise 4: Managing indexes Exercise 5: Connecting to an index Exercise 6: Deleting an index Exercise 7: The Pinecone ecosystem Exercise 8: Vector ingestion Exercise 9: Checking dimensionality Exercise 10: Ingesting vectors with metadata

Get hands-on with Pinecone in Python, where we explore the practical side of using Pinecone for managing indexes, adding vectors with metadata, searching and retrieving vectors, and making updates or deletions. Gain a solid grasp of the key functions and ideas to smoothly handle data in the Pinecone vector database.

Exercise 1: Retrieving vectors Exercise 2: Querying vs. fetching Exercise 3: Fetching vectors Exercise 4: Querying vectors Exercise 5: Returning the most similar vectors Exercise 6: Changing distance metrics Exercise 7: Metadata filtering Exercise 8: Filtering queries Exercise 9: Multiple metadata filters Exercise 10: Updating and deleting vectors Exercise 11: Updating vector values Exercise 12: Updating vector metadata Exercise 13: Deleting vectors

Huidige oefening

In this chapter, learners delve into optimizing Pinecone index performance, leveraging multi-tenant namespaces for cost reduction, building semantic search engines, and creating retrieval-augmented question answering systems using Pinecone with the OpenAI API. Through these lessons, learners gain practical skills in performance tuning, semantic search, and retrieval-augmented question answering, empowering them to apply Pinecone effectively in real-world AI applications.

Exercise 1: Batching upserts Exercise 2: Defining a function for chunking Exercise 3: Batching upserts in chunks Exercise 4: Batching upserts in parallel Exercise 5: Multitenancy and namespaces Exercise 6: Namespaces Exercise 7: Querying namespaces Exercise 8: Semantic search with Pinecone Exercise 9: Creating and configuring a Pinecone index Exercise 10: Upserting vectors for semantic search Exercise 11: Querying vectors for semantic search Exercise 12: RAG chatbot with Pinecone and OpenAI Exercise 13: Upserting YouTube transcripts Exercise 14: Building a retrieval function Exercise 15: RAG questions answering function Exercise 16: Congratulations!