Namespace

Namespace digunakan untuk memisahkan vektor dalam satu indeks, memungkinkan kueri yang tertarget dan meminimalkan jumlah rekaman yang dipindai.

Dalam latihan ini, Anda akan berlatih mengimpor vektor ke namespace yang berbeda di dalam indeks Pinecone 'datacamp-index'. Anda perlu terhubung ke indeks, melakukan upsert vektor ke berbagai namespace, dan memeriksa metrik indeks yang telah diperbarui.

Latihan ini adalah bagian dari kursus

Database Vektor untuk Embeddings dengan Pinecone

Petunjuk latihan

Inisialisasi koneksi Pinecone dengan kunci API Anda.
Lakukan upsert vektor pada vector_set1 ke namespace namespace1.
Lakukan upsert vektor pada vector_set2 ke namespace namespace2.
Cetak statistik deskriptif indeks.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Initialize the Pinecone client with your API key
pc = Pinecone(api_key="____")
index = pc.Index('datacamp-index')

# Upsert vector_set1 to namespace1
____(
  vectors=vector_set1,
  ____
)

# Upsert vector_set2 to namespace2
____(
  vectors=vector_set2,
  ____
)

# Print the index statistics
____

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Database Vektor untuk Embeddings dengan Pinecone

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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

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: Mengelompokkan upsert Exercise 2: Mendefinisikan fungsi untuk pemecahan ke dalam potongan Exercise 3: Melakukan upsert bertumpuk dalam potongan (chunks)Exercise 4: Batch upsert secara paralel Exercise 5: Multitenancy dan namespace Exercise 6: Namespace

Latihan Saat Ini

Exercise 7: Melakukan kueri pada namespace Exercise 8: Pencarian semantik dengan Pinecone Exercise 9: Membuat dan mengonfigurasi indeks Pinecone Exercise 10: Upsert vektor untuk pencarian semantik Exercise 11: Melakukan kueri vektor untuk pencarian semantik Exercise 12: Chatbot RAG dengan Pinecone dan OpenAI Exercise 13: Meng-upsert transkrip YouTube Exercise 14: Membangun fungsi retrieval Exercise 15: Fungsi RAG untuk menjawab pertanyaan Exercise 16: Selamat!