Code Along กับเราได้ที่
Code Along ↗Semantic Search with Pinecone
Learn the fundamentals of text embedding and vector databases with Pinecone to build a simple search engine.
James Briggs
สร้างบัญชีฟรีของคุณ
ฝึกฝนและตรวจสอบความก้าวหน้าของคุณ
ดำเนินการต่อด้วย Googleเมื่อดำเนินการต่อ ถือว่าคุณยอมรับ ข้อกำหนดการใช้งานและ นโยบายความเป็นส่วนตัว และยอมรับว่าข้อมูลของคุณจะถูกจัดเก็บในสหรัฐอเมริกาที่เกี่ยวข้อง

podcast
The Power of Vector Databases and Semantic Search with Elan Dekel, VP of Product at Pinecone
RIchie and Elan explore LLMs, vector databases and the best use-cases for them, semantic search, the tech stack for AI applications, emerging roles within the AI space, the future of vector databases and AI, and much more.
บทแนะนำ
Semantic Search with Pinecone and OpenAI
A step-by-step guide to building semantic search applications using OpenAI and Pinecone in Python.

บทแนะนำ
Mastering Vector Databases with Pinecone Tutorial: A Comprehensive Guide
Dive into the world of vector databases with our in-depth tutorial on Pinecone. Discover how to efficiently handle high-dimensional data, understand unstructured data, and harness the power of vector embeddings for AI-driven applications.


Vector databases enable semantic search - searching on the meaning of the text - to provide higher quality results than traditional keyword search. In this project, you'll learn how to use Pinecone, a popular vector database platform, to do this. You'll start by setting up a developer account with Pinecone and integrating it with DataLab (fka Workspace), and learn how to "embed text" and import it into Pinecone, before using it for search. You'll also learn about embedding dimensions, distance metrics, and other concepts to control the performance of the results.