Project to serve Job and Career related queries

[Audio] This is a Retrieval-augmented generation (RAG) application which uses a private Job dataset The purpose of this app is to create chatbot using generative AI models which will answer any Job or Career related question using a Frontend developed in PHP.

[Audio] Artificial intelligence (AI) integration has become essential in our quickly changing technology landscape. Azure AI Studio proves to be a potent platform that makes it remarkably simple and effective to create, implement, and manage AI-based applications. Azure AI Studio was used to create this project, which in turn created all the required component like Resource group, Azure AI Service, Azure Open AI Search, Storage Account and their connections inside a Hub..

[Audio] Job related dataset was download from Hugging Face, this data set in CSV format. CSV was uploaded to the Azure blob storage provided in the project A data connection was created to point to the Blob Storage A Vector Index was created using the data, the embedding was created using the text-embedding-ada-002 model Prompt Flow was created to accept user prompt, this prompt was searched in the Vector index to retrieve the most closely related data by comparing the vector embedding of the prompt and the embedding of the data.

[Audio] User Prompt and the retrieved document are then sent to an Open AI LLM gpt-35-turbo, to return context related answers.

[Audio] Step to create an app using Azure AI Studio: Launch AI Studio Create a new project Load a private Job dataset in the Azure Storage Create a Vector Index of Dataset Deploy two AI models Gpt-35-turbo (this is used to generate context related answers Text-embedding-ada-002 (this used to generate embedding for query and dataset) Prompt Flow was created to generate a chatbot app and it was deployed to a end point.