Mengubah metrik jarak

Secara bawaan, Pinecone menggunakan metrik jarak cosine similarity untuk menghitung skor kemiripan antar vektor, yang digunakan saat melakukan kueri untuk menemukan vektor yang paling mirip. Pinecone juga mendukung metrik jarak lain, termasuk Euclidean distance dan dot product.

Metrik jarak ditetapkan saat indeks dibuat, dan tidak dapat diubah setelahnya. Pada latihan ini, Anda akan berlatih membuat indeks yang menggunakan metrik jarak dot product.

Latihan ini adalah bagian dari kursus

Database Vektor untuk Embeddings dengan Pinecone

Petunjuk latihan

Inisialisasi koneksi Pinecone dengan API key Anda.
Buat indeks baru bernama "dotproduct-index" yang menggunakan metrik jarak dot product.
Daftarkan indeks Anda untuk memverifikasi bahwa indeks telah dibuat dan metriknya sudah benar.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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

# Create an index that uses the dot product distance metric
pc.create_index(
    name="____",
    dimension=1536,
    ____,
    spec=ServerlessSpec(
        cloud='aws',
        region='us-east-1'
    )
)

# Print a list of your indexes
print(____)

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: Mengambil vektor Exercise 2: Querying vs. fetching Exercise 3: Mengambil vektor Exercise 4: Melakukan kueri vektor Exercise 5: Mengembalikan vektor yang paling mirip Exercise 6: Mengubah metrik jarak

Latihan Saat Ini

Exercise 7: Penyaringan metadata Exercise 8: Memfilter kueri Exercise 9: Beberapa filter metadata Exercise 10: Memperbarui dan menghapus vektor Exercise 11: Memperbarui nilai vektor Exercise 12: Memperbarui metadata vektor Exercise 13: Menghapus vektor

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!