This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo and gpt3), and vector store (Pinecone, Redis and others) or Azure cognitive search for data indexing and retrieval.
The repo provides a way to upload your own data so it's ready to try end to end.
- 3/16/2023 - Initial Release, Ask your Data and Chat with your Data
- 3/17/2023
- Support uploading Multiple documents
- Bug fix - Redis Vectorstore Implementation
- 3/18/2023 - API to generate summary on documents & Sample QA
- 3/19/2023 - Add GPT3 Chat Implementation
- 3/23/2023 - Add Cognitive Search as option to store documents
- 3/29/2023 - Automated Deployment script
- 4/8/2023 - Ask your SQL - Using SQL Database Agent or Using SQL Database Chain
- 4/13/2023 - Add new feature to support asking questions on multiple document using Vector QA Agent
- 4/17/2023 - Real-time Speech Analytics and Speech to Text and Text to Speech for Chat & Ask Features. (You can configure Text to Speech feature from the Developer settings. You will need Azure Speech Services)
- 4/21/2023 - Add SQL Query & SQL Data tab to SQL NLP and fix Citations & Follow-up questions for Chat & Ask features
- 4/25/2023 - Initial version of Power Virtual Agent
- 4/28/2023 - Fix Bugs, Citations & Follow-up questions across QA & Chat. Prompt bit more restrictive to limit responding from the document.
- 4/29/2023 - AWS S3 Process Integration using S3, AWS Lambda Function and Azure Data Factory (automated deployment not available yet, scripts are available in /Deployment/aws folder)
Application and Function App Configuration
- Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and Cognitive Search
- Azure Cognitive Search
- Azure OpenAI Service
Adapted from the Azure OpenAI Search repo at OpenAI-CogSearch