Using ChatGPT to Search Enterprise Data with Pamela Fox
Mar 12, 2024
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Pamela Fox from Microsoft discusses using ChatGPT to query enterprise data with Azure services, best practices for ChatGPT apps, and interoperability of sample code across programming languages. The podcast also explores pricing considerations for enterprise AI solutions.
Utilize ChatGPT & Azure Cognitive Search for enterprise data queries with chat interface.
Extend support to diverse data formats like HTML & CSV for broader user data ingestion.
Optimize performance with Chat GPT-3.5 for cost efficiency & accuracy in app development.
Enhance security with role-based access controls & streamline integration tests for reliability.
Implement streaming for real-time response delivery & containerization for deployment efficiency.
Deep dives
Pamela Fox's Role at Microsoft and Services Provided
Pamela Fox, a Cloud Advocate in Python at Microsoft, assists Python developers in effectively using Microsoft products like Azure, VS Code, and GitHub Codespaces. Her role focuses on deploying Python web apps on Azure servers using Azure Python SDK while collaborating on projects like the Python and Excel feature.
Chat GPT Sample App and Retrieval Augmented Generation Approach
The chat GPT sample app, maintained by Pamela Fox, exemplifies retrieval augmented generation (RAG) by constraining chat GPT responses to specific data with a prompt. The app uses Azure Cognitive Search to retrieve relevant documents in chat GPT-sized chunks, enhancing response accuracy and clarity. Through maintaining and evolving this widely deployed app, users can harness its retrieval augmented generation approach.
Enterprise Data Ingested and Supported Formats
The chat GPT sample app currently supports ingesting enterprise PDFs utilizing Azure Document Intelligence for extraction. While tailored for PDFs due to their prevalence in enterprises, efforts are underway to extend support to other formats like HTML and CSV to cater to diverse data sources and user needs.
Streamlined Evaluation Pipeline and Language Model Choices
The development includes an evaluation pipeline utilizing ground truth data and Chat GPT-4 to assess prompt alterations' impact before production. The preference for Chat GPT-3.5 over 4 emphasizes performance optimization without compromising accuracy or incurring unnecessary costs, aligning the app's language model selection with operational efficiency.
Best Practices, Including Mocking and Security Strategies
The implementation incorporates mocking for efficient testing, safeguarding against inadvertent key exposure, and adopting roles-based access controls instead of API keys for enhanced security and user-specific permissions. Further, sharing insights on streamlining integration tests and encryption bolster application security and testing reliability.
Exploring Additional Features Like Streaming and Containerization
The application integrates streaming to modify response transmission speed and user experience, efficiently delivering responses in real-time increments to enhance engagements. Containerization and best practice guidelines underscore simultaneous advancements in deployment efficiency, service scalability, and resource optimization across language-specific versions of the app.
Pricing Considerations and Model Selection Impact
While emphasizing cost-effectiveness and optimal performance, considerations like per-page costs for Azure Document Intelligence and per-token expenses for OpenAI underscore strategic data handling and model selection processes. Balancing functionality, latency, and resource consumption, users are encouraged to scrutinize the impact of model selections on performance and budgetary constraints.
Multilanguage Porting Initiative and Accessibility
The cross-language initiative ensures functional parity and interoperability across Python, C#, Java, and JavaScript versions of the app, catering to developers' language preferences and diverse technical backgrounds. With shared protocol standards and ongoing enhancements, users can seamlessly transition between language-specific deployments while leveraging common functionalities.
Engagement Channels and Continuous Feedback Loop
By actively monitoring the app's repository issues and engaging with the AI for developers discord community, users can directly interact with Pamela Fox to address queries, provide feedback, and collaborate on refining app functionalities. These channels offer a direct avenue for user support, feature suggestions, and continuous improvement efforts.
Summarizing the Value and Collaborative Potential
The discussion showcases the app's utility, with insights on practical deployment strategies, cost considerations, security measures, and performance optimizations resonating with diverse audiences. By offering comprehensive access to resources, best practices, and technical insights, users are empowered to navigate app development complexities, foster innovation, and leverage advanced capabilities effectively.
This is a re-post from November 2023.
In this episode, Thomas Betts talks with Pamela Fox, a cloud advocate in Python at Microsoft. They discuss several ChatGPT sample apps that Pamela helps maintain. These include a very popular integration of ChatGPT with Azure OpenAI and Cognitive Search for querying enterprise data with a chat interface. Pamela also covers some best practices for getting started with ChatGPT apps.
Read a transcript of this interview: https://www.infoq.com/podcasts/chatgpt-enterprise-data-search/
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