Главная
Study mode:
on
1
What are Cohere embeddings
2
Cohere v OpenAI on cost
3
Cohere v OpenAI on performance
4
Implementing Cohere multilingual model
5
Data prep and embedding
6
Creating a vector index with Pinecone
7
Embedding and indexing everything
8
Making multilingual queries
9
Final throughts on Cohere and OpenAI
Description:
Explore a multilingual semantic search example using Cohere's new multilingual model and compare its performance against OpenAI's GPT 3.5 text-embedding-ada-002 model. Learn about the cost differences, implementation process, and performance metrics of both models. Dive into data preparation, embedding techniques, and vector indexing with Pinecone. Discover how to make multilingual queries and gain insights on the strengths and weaknesses of Cohere and OpenAI embeddings for multilingual search applications.

Cohere vs. OpenAI Embeddings - Multilingual Search

James Briggs
Add to list
0:00 / 0:00