Recuperare vettori

In questo esercizio ti viene fornito un elenco di ids contenente gli ID di vari record nel tuo indice 'datacamp-index'. Userai questi ID per recuperare i record associati ed esplorarne i metadati.

Questo esercizio fa parte del corso

Database vettoriali per Embeddings con Pinecone

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Istruzioni dell'esercizio

Inizializza la connessione a Pinecone con la tua API key.
Recupera i vettori con gli ID nella lista ids dall'indice collegato.
Crea un elenco di dizionari che contenga i metadati di ciascun record in fetched_vectors.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Database vettoriali per Embeddings con Pinecone

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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: Recupero dei vettori Exercise 2: Querying vs. fetching Exercise 3: Recuperare vettori

Esercizio in corso

Exercise 4: Interrogare i vettori Exercise 5: Restituire i vettori più simili Exercise 6: Modificare le metriche di distanza Exercise 7: Filtraggio dei metadati Exercise 8: Query con filtro Exercise 9: Filtri multipli sulla metadata Exercise 10: Aggiornare ed eliminare vettori Exercise 11: Aggiornare i valori di un vettore Exercise 12: Aggiornare i metadati dei vettori Exercise 13: Eliminare vettori

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!