Vector Database for LLMs

This repository contains essential techniques in vector data embedding, indexing, and retrieval, along with real-world applications.

Key Highlights

• Providing core techniques required for vector data embedding, indexing, and retrieval.
• Exploring Semantic Search Use Cases in detail, including Named Entity Recognition.
• Exploring various vector database technologies, with a focus on Pinecone.
• Developing an AI Chat Bot for Cognitive Search on Private Data using LangChain.
• Some notes and insights about the role of vector databases in generative AI and large language models (LLMs).

Key Technologies

• Pinecone Vector Database
• LangChain
• Transformer Models for vector embedding
• OpenAI API

Feel free to explore the contents of this repository to enhance your knowledge of these vital technologies and their practical applications.