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If you want to develop a ChatBot with Azure and OpenAi in a few simple steps. You can follow the steps below.
- Design and Requirements Gathering:
Define the purpose and functionalities of the chatbot.
Gather requirements for integration with Azure, OpenAI, Langchain, Promo Engineering, Document Intelligence System, KNN-based question similarities with Redis, vector database, and Langchain memory.
- Azure Setup:
Create an Azure account if you don't have one.
Set up Azure Functions for serverless architecture.
Request access to Azure OpenAI Service.
- OpenAI Integration:
Obtain API access to OpenAI.
Integrate OpenAI's GPT models for natural language understanding and generation into your chatbot.
- Langchain Integration:
Explore Langchain's capabilities for language processing and understanding.
Integrate Langchain into your chatbot for multilingual support or specialized language tasks.
Implement Langchain memory for retaining context across conversations.
- Promo Engineering Integration:
Understand Promo Engineering's features for promotional content generation and analysis.
Integrate Promo Engineering into your chatbot for creating and optimizing promotional messages.
- Document Intelligence System Integration:
Investigate the Document Intelligence System's functionalities for document processing and analysis.
Integrate Document Intelligence System for tasks such as extracting information from documents or providing insights.
- Development of Chatbot Logic:
Develop the core logic of your chatbot using Python.
Utilize Azure Functions for serverless execution of the chatbot logic.
Implement KNN-based question similarities using Redis for efficient retrieval and comparison of similar questions.
- Integration Testing:
- Test the integrated components of the chatbot together to ensure seamless functionality.
- Azure AI Studio Deployment:
Deploy LLM model in Azure AI Studio.
Create an Azure AI Search service.
Connect Azure AI Search service to Azure AI Studio.
Add data to the chatbot in the Playground.
Add data using various methods like uploading files or programmatically creating an index.
Use Azure AI Search service to index documents by creating an index and defining fields for document properties.
- Deployment and Monitoring:
Deploy the chatbot as an App.
Navigate to the App in Azure.
Set up monitoring and logging to track performance and user interactions.
- Continuous Improvement:
Collect user feedback and analyze chatbot interactions.
Iterate on the chatbot's design and functionality to enhance user experience and performance.
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