Semantic search using Elasticsearch and OpenAI

Elasticsearch and OpenAI Integration Source Code Bundle

Unlock the power of semantic search and retrieval augmented generation with our comprehensive source code bundle, meticulously crafted for seamless integration between Elasticsearch and OpenAI. This package includes two robust Jupyter notebooks, each serving as a step-by-step guide to implementing cutting-edge search and AI functionalities.

1. Semantic Search Notebook: Dive into the world of semantic search with our detailed guide. You'll learn how to efficiently index the OpenAI Wikipedia vector dataset into Elasticsearch, encode questions using the OpenAI embeddings endpoint, and execute semantic searches that leverage the full potential of vector embeddings. This notebook is your gateway to creating a sophisticated search engine that understands the nuances of human language.
2. Retrieval Augmented Generation Notebook: Building on the foundations of semantic search, this notebook takes you a step further by integrating retrieval augmented generation. You'll not only perform semantic searches but also enhance AI responses by sending the top search results to the OpenAI Chat Completions API. This advanced technique enriches AI-generated text, making it more relevant and contextually aware.

Both notebooks are thoughtfully structured and include Google Colab integration for an effortless start. They come equipped with detailed explanations and code snippets, ensuring a smooth and educational experience, whether you're a seasoned developer or a curious learner.

Elevate your search and AI capabilities today with our Elasticsearch and OpenAI Integration Source Code Bundle!