https://github.com/devanshu-17/onelasttime

Team OneLastTime project for the KLEOS 2.0 2024 hackathon
https://github.com/devanshu-17/onelasttime

Last synced: 7 days ago
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Team OneLastTime project for the KLEOS 2.0 2024 hackathon

• Host: GitHub
• URL: https://github.com/devanshu-17/onelasttime
• Owner: Devanshu-17
• License: mit
• Created: 2024-06-21T13:54:57.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-06-23T20:03:07.000Z (6 months ago)
• Last Synced: 2024-12-22T15:52:06.693Z (12 days ago)
• Language: CSS
• Size: 6.51 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# OneLastTime

Team OneLastTime project for the KLEOS 2.0 2024 hackathon



  


  Checkout the Demo here »

  


  




## Problem Statement:



## Tentative System Architecture:






* The User first sends a query to the LLM.

* The LLM takes the user's query and checks it against the vector database (Qdrant) - This database will contain verified and trusted health information

* If the data is present in database, then send the response back to the user.

* If the data is not present, then using AI Agent, we will do a web search on the user's query (we will use CrewAI and SerperAPI).

* This web scraped data will be then sent to a panel of Medical experts, who will verify the data.

* This result will also be shown to the user, with a warning saying

    > :warning: **This data was taken from the web with these sources [....]**: Please wait for our Medical experts to verify the data.

* When the Medical experts verify the data, the user will be informed.

* In case, the results from the web is wrong, the the Medical Expert can send their own trusted opinion to the user.

* This final trusted and verified source will also be added to the vector database, making an ever growing set of perfect data. 🤗

## Tasks:

### PART 1: Backend Pipeline

* [x] Create a Notebook in which we will experiment AI Agents

* [x] Create a AI Agent for searching the web and summarising the scraped results

* [x] Test different prompts to verify which gives the best results

* [x] Create a Notebook to push the dataset into the database (Qdrant)

* [x] Check the threshold and FAITHFUL score of the database to ensure that the LLM can retrieve the data correctly (RAG)

### PART 2: Backend

* [x] Integrate the various pipeline into a single backend file (We will use FastAPI)

* [x] Create a MongoDB database for storing logs and Medical Expert's details.

* [x] Develop the workflow for connecting the API calls between the Medical Experts, the LLM and the users.

* [x] Email Notification Service 

### PART 3: Frontend

* [x] Create a demo frontend to show working of backend.

* [x] Create a landing page.

* [x] Create a Chat Page for user to interact with.

* [x] Create a Medical Expert page where they can validate and verify the user's query.

## Sources:

* Dataset Used: https://www.huggingface.co/datasets/lavita/ChatDoctor-HealthCareMagic-100k?row=36

* CrewAI: https://docs.crewai.com/

* Serper API: https://www.serper.dev

* Qdrant: https://cloud.qdrant.io

* https://arxiv.org/html/2402.04620v1

* https://www.analyticsvidhya.com/blog/2024/06/agentic-workflow-with-crewai-and-groq/