Vectors ophalen

In deze oefening krijg je een lijst met ids die ID's van verschillende records bevat in je 'datacamp-index'-index. Je gebruikt deze ID's om de bijbehorende records op te halen en hun metadata te verkennen.

Deze oefening maakt deel uit van de cursus

Vector-databases voor embeddings met Pinecone

Cursus bekijken

Oefeninstructies

Initialiseer de Pinecone-verbinding met je API-sleutel.
Haal de vectors op met ID's in de ids-lijst uit de verbonden index.
Maak een lijst van dictionaries met de metadata van elk record in fetched_vectors.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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

index = pc.Index('datacamp-index')
ids = ['2', '5', '8']

# Fetch the vectors from the connected Pinecone index
fetched_vectors = ____

# Extract the metadata from each result in fetched_vectors
metadatas = [fetched_vectors['____'][____]['____'] for id in ids]
print(metadatas)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Vector-databases voor embeddings met 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: Vectoren ophalen Exercise 2: Querying vs. fetching Exercise 3: Vectors ophalen

Huidige oefening

Exercise 4: Vectors opvragen Exercise 5: De meest vergelijkbare vectors retourneren Exercise 6: Afstandsmaat wijzigen Exercise 7: Metadata filteren Exercise 8: Queries filteren Exercise 9: Meerdere metadatafilters Exercise 10: Vectoren bijwerken en verwijderen Exercise 11: Vectorwaarden bijwerken Exercise 12: Metadata van vectors bijwerken Exercise 13: Vectoren verwijderen

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!