Vektoren abrufen

In dieser Übung hast du eine Liste mit „ ids “ bekommen, die IDs von verschiedenen Datensätzen in deinem Index „ 'datacamp-index' “ enthält. Du brauchst diese IDs, um die dazugehörigen Datensätze abzurufen und ihre Metadaten zu checken.

Diese Übung ist Teil des Kurses

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Übungsanweisungen

Starte die Pinecone-Verbindung mit deinem API-Schlüssel.
Hol die Vektoren mit den IDs in der Liste „ ids “ aus dem verbundenen Index.
Erstell eine Liste von Wörterbüchern mit den Metadaten aus jedem Datensatz in „ fetched_vectors “.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# 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 bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Vektordatenbanken für Einbettungen mit Pinecone</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

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: Vektoren abrufen Exercise 2: Abfragen vs. Abrufen Exercise 3: Vektoren abrufen

Aktuelle Übung

Exercise 4: Vektoren abfragen Exercise 5: Die ähnlichsten Vektoren zurückgeben Exercise 6: Ändern der Entfernungsmessung Exercise 7: Metadaten-Filterung Exercise 8: Abfragen filtern Exercise 9: Mehrere Metadatenfilter Exercise 10: Vektoren aktualisieren und löschen Exercise 11: Vektorwerte aktualisieren Exercise 12: Vektormetadaten aktualisieren Exercise 13: Vektoren löschen

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!