Presentation Azure Chat Bot Project.pptx

this presentation is regarding the Indian polity chatbot deployed on azure openAI cloud platform

1. Indian Polity Chatbot Presented by:Anupama

2. Project Requirement Objective – Easy to access terms and concepts of Indian polity standard reference book by M. Lakshmikant Use case- Content generation interactive QA chat User Interactions – Chat interface

3. Project overview

4. Prerequisite

5. Planning and Design

6. Project Workflow

7. Data Import and Data Creation Data Source/ Raw data Records Creation /Ground Truth

8. Set Up Infrastructure

9. Setup Azure Subscription Azure Services Azure OpenAI : Create an instance for using OpenAI models (GPT-4) Azure Blob Storage : Set up Blob Storage for storing documents, datasets, and configuration files. Azure Cognitive Search : Create a search index for document search functionality if needed.

10. Crete a resource group on azure fig1 fig3 fig2

11. Azure Storage Account fig1 fig3 fig2 Blob Storage Account

12. Function App

13. All Azure Services

14. Azure Search Service

15. Create index

16. Azure OpenAI service for model

17. Azure Infrastructure to VS Code Cloud Connect

18. Install Azure Development Workload Open Visual Studio Installer . Click on Modify next to your installed version of Visual Studio. Select Azure Development workload. Click Modify to install the workload. Sign in to Azure in Visual Studio Launch Visual Studio . Go to the top-right corner of Visual Studio, where your profile is displayed. Click on Sign In . In the pop-up window, sign in with your Azure account credentials. View Azure Resources in Cloud Explorer Go to View in the top menu. Select Cloud Explorer . In the Cloud Explorer, you should see all the resources associated with your Azure account once you're logged in. Manage Azure Resources Use Cloud Explorer to view, monitor, and manage your Azure resources like Web Apps, Virtual Machines, and more. You can deploy your applications directly to Azure by right-clicking your projects and selecting Publish . Create or Manage Azure Resources From Cloud Explorer , you can also manage existing resources or create new ones like App Services, SQL Databases, and Azure Functions.

19. Python Program

20. Ingest Backend S erver-side system or service responsible for collecting, processing, and storing data from various sources Data ingestion is the process of moving and replicating data from data sources to destination such as a  cloud data lake  or  cloud data warehouse . 

21. Listing Project Dependencies requirements.txt Python Packages : to install all necessary dependencies with a single command:

22. LangChain Framework

23. Ingest_AP.py Functions: Get_file_names () download_blob_to_tem_file () load_from_azure () ingest()

24. Role of LangChain in Azure Chatbot Project

25. Data_management.py Functions: Get_file_names () Download_blob ()

26. LLM Models for chatbot Projects

27. Why OpenAI GPT-4.0?

28. Config.py function: get_custom_field ()

29. Create a vector index :vector fields &their data types Vector fields are characterized by  their data type , a  dimensions  property based on the embedding model used to output the vectors, and a vector profile.

30. OpenAI GPT-4.0 Specifications

31. Local machine load function

32. Chat_read_retrieve.py function: openai_completion () vector_serach () get_final_results ()

33. Optimization Paraments in GPT-4 model via Azure Services openai.ChatCompletion.create ()

34. engine

35. messages

36. max_tokens

37. temperature

38. n

39. stop

40. Other paraments

41. APIs

42. Deployment process on Azure with APIs

43. While deploying on Azure

44. The role of APIs in deploying a chatbot on Azure

45. how your chatbot interacts with backend services in an Azure-based project?

46. flask._app.py functions: Home() chatbot()

47. Function_app.py function: chat_function ()

48. Role of function app in chatbot

49. Front end

50. Frontend code

51. Testing & Monitoring

52. Best practice to follow

53. Best Practice ?

54. Any Questions ?