Cohere AI's LLM for Semantic Search in Python

Learn how to implement semantic search using Cohere AI's large language model (LLM) and Pinecone vector database in Python. Explore the process of generating language embeddings with Cohere's Embed API endpoint and indexing them in Pinecone for fast and scalable vector search. Discover the power of combining these services to build applications for semantic search, question-answering, and advanced sentiment analysis. Follow along as the video guides you through architecture overview, code setup, API key configuration, data embedding, vector index creation, and query testing. Gain insights into leveraging state-of-the-art NLP models and vector search techniques for processing large text datasets efficiently.

Semantic search with Cohere LLM and Pinecone
Architecture overview
Getting code and prerequisites install
Cohere and Pinecone API keys
Initialize Cohere, get data, create embeddings
Creating Pinecone vector index
Querying with Cohere and Pinecone
Testing a few queries
Final notes