Indeks Pinecone pertama Anda

Dengan klien Pinecone Anda yang sudah diinisialisasi, Anda siap untuk mulai membuat sebuah indeks! Indeks digunakan untuk menyimpan rekaman, termasuk vector dan metadata terkait, serta melayani query dan manipulasi lainnya. Seiring kemajuan Anda dalam kursus ini, Anda akan melihat bagaimana langkah-langkah berbeda ini membangun sebuah sistem AI modern yang bertumpu pada vector database.

Jika Anda tidak sengaja membuat indeks valid yang tidak memenuhi spesifikasi seperti dijelaskan pada instruksi, Anda perlu menambahkan kode berikut sebelum kode .create_index() Anda untuk menghapusnya dan membuatnya kembali:

pc.delete_index('my-first-index')

Kelas Pinecone sudah diimpor untuk Anda.

Latihan ini adalah bagian dari kursus

Database Vektor untuk Embeddings dengan Pinecone

Petunjuk latihan

Impor kelas ServerlessSpec dari pinecone.
Inisialisasi koneksi Pinecone menggunakan kunci API Anda.
Buat indeks serverless bernama "my-first-index" untuk menampung vektor berdimensi 256, dan konfigurasikan indeks untuk platform cloud 'aws' di region 'us-east-1'.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Import ServerlessSpec
from pinecone import ____

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

# Create your Pinecone index
pc.____(
    name="____",
    dimension=____,
    spec=____(
        cloud='____'
        region='____'
    )
)

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: Pengantar indeks Pinecone Exercise 2: Membuat klien Pinecone Exercise 3: Indeks Pinecone pertama Anda

Latihan Saat Ini

Exercise 4: Mengelola indeks Exercise 5: Menyambungkan ke sebuah indeks Exercise 6: Menghapus sebuah indeks Exercise 7: Ekosistem Pinecone Exercise 8: Pemasukan vektor Exercise 9: Memeriksa dimensi Exercise 10: Mengimpor vektor beserta 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: 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!