Mendefinisikan fungsi untuk pemecahan ke dalam potongan

Agar dapat melakukan upsert secara batch dengan cara yang dapat direproduksi, Anda perlu mendefinisikan fungsi untuk membagi daftar vektor Anda menjadi potongan-poton kecil.

Modul bawaan itertools telah diimpor untuk Anda.

Latihan ini adalah bagian dari kursus

Database Vektor untuk Embeddings dengan Pinecone

Petunjuk latihan

Ubah masukan iterable menjadi sebuah iterator.
Iris it menjadi potongan berukuran batch_size menggunakan modul itertools.
Hasilkan potongan saat ini.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

def chunks(iterable, batch_size=100):
    """A helper function to break an iterable into chunks of size batch_size."""
    # Convert the iterable into an iterator
    it = ____
    # Slice the iterator into chunks of size batch_size
    chunk = tuple(itertools.____(it, ____))
    while chunk:
        # Yield the chunk
        ____
        chunk = tuple(itertools.islice(it, batch_size))

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

Latihan Saat Ini

Exercise 3: Melakukan upsert bertumpuk dalam potongan (chunks)Exercise 4: Batch upsert secara paralel Exercise 5: Multitenancy dan namespace Exercise 6: Namespace 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!