In this video, we will learn how to use the Cohere Embed API endpoint to generate language embeddings using a large language model (LLM) and then index those embeddings in the Pinecone vector database for fast and scalable vector search.

Cohere is an AI company that allows us to use state-of-the-art large language models (LLMs) in NLP. The Cohere Embed endpoint we use in this video gives us access to models similar to other popular LLMs like OpenAI's GPT 3, particularly their recent offerings via OpenAI Embeddings like the text-embedding-ada-002 model.

Pinecone is a vector database company allowing us to use state-of-the-art vector search through millions or even billions of data points.

Both services together are a powerful and common combination for building semantic search, question-answering, advanced sentiment analysis, and other applications that rely on NLP and search over a large corpus of text data.

🌲 Pinecone docs:
https://docs.pinecone.io/docs/cohere

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00:00 Semantic search with Cohere LLM and Pinecone
00:45 Architecture overview
04:06 Getting code and prerequisites install
04:50 Cohere and Pinecone API keys
06:12 Initialize Cohere, get data, create embeddings
07:43 Creating Pinecone vector index
10:37 Querying with Cohere and Pinecone
12:56 Testing a few queries
14:35 Final notes

(From Youtube)