hyperledger-labs/aifaq

AI FAQ Proof-of-Concept project: it provides a chatbot that replies to the questions on Hyperledger Ecosystem

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aifaq's Issues

LLM Infrence speed is slow want to improve it by Groq

• I noticed that the after asking it usually give answer very approx 2min so I was thinking to play around other LLM Infrence Model like Groq to make the output real-time.

• Does this project active and how I can contribute to this with other open source models

• I have setup the project on Google colab
  

It would be great if you can guide me in the process @gcapuzzi

Suggestion to implement latest models like llama-3 or Mistral 7B and should be deployed on a proper FrontEnd Project

I suggest that we use llama models or Mistral 7B foundation models to develop a comprehensive dashboard. We can implement it using React and then deploy it on a cloud server. This approach will ensure scalability and proper accessibility.

@gcapuzzi

Feature Request: Collapsible Slide Bar for Frontend Chatbot Interface

A feature to add a collapsible slide bar in the frontend chatbot interface. This will enhance user experience by allowing users to expand or collapse the side bar as needed, providing a cleaner and more focused interface.

Requirements

• A slide bar on the left side of the chatbot interface.
• The slide bar should be collapsible with a toggle button.
• When collapsed, the slide bar should hide completely or minimize to a small icon.
• Smooth animation for expanding and collapsing the slide bar.
• Ensure the feature is responsive and works well on both desktop and mobile devices.

Acceptance Criteria

• A collapsible slide bar is present in the frontend chatbot interface.
• The slide bar can be toggled open and closed by a button.
• Smooth animations for the slide bar's collapsing and expanding actions.
• The slide bar does not interfere with the chatbot's main functionality.
• The feature is fully responsive on various screen sizes.
• The UI/UX design follows the project's style guide.

Mockups / Examples

• Include any relevant mockups or examples here to visualize the feature.

feat: Containerize the applications

We need to containerize the following applications for an easy deployment:

• frontend
• backend

ui: Proposing a new theme for the application

This issue will track updates to the application theme. Please open pull requests here with your proposed changes, such as new colors, fonts, etc. Ensure that you use properly licensed assets only.

Feature Request : Implement JWT Authentication for Backend with SQL Database

Description:
We need to add user authentication to our backend API using JWT and store user credentials in a SQL database.

1. Set up a SQL database to store user information:

   • Create a 'users' table with fields for username, hashed password.

2. Implement user registration endpoint:

   • Create a /register route
   • Accept username and password in the request body
   • Hash the password before storing it in the database
   • Use SQL INSERT query to add new user to the 'users' table
   • Return appropriate success/error messages

3. Implement user login endpoint:

   • Create a /login route
   • Use SQL SELECT query to retrieve user credentials from the database
   • Verify provided password against the stored hashed password
   • Generate a JWT token upon successful authentication
   • Return the token in the response

4. Create JWT authentication middleware:

   • Implement a function to verify JWT tokens
   • Create a dependency that can be used to protect routes

5. Secure relevant API endpoints:

   • Apply the JWT authentication dependency to the /query route
   • Return appropriate error messages for unauthorized access attempts

Feature Request : Implement rate limiting

Implement rate limiting in the FastAPI backend to control the number of requests a client can make within a specific time period.

Task:

• Add rate limiting middleware to the FastAPI backend.
• Configure rate limiting rules.

EHN: Integrate TanStack Query and Axios for efficient and reliable frontend-to-backend API communication.

Description

TanStack Query offers auto caching, auto refetching, and dedicated devtools without requiring a backend. It facilitates real-time inquiries, dependent and concurrent queries, window focus refetching, and makes data modifications easier via the Mutations API. In addition to providing infinite scroll, request cancellation, suspense integration, prefetching, offline support, SSR optimization, and data pickers, it effectively manages big datasets.

Task:

• Set up TanStack Query for managing API calls and state.
• Use Axios as the HTTP client for making requests to the backend.

core: Connect the api to the frontend of the application

Tasks:

• Enable communication between the backend and the frontend.
• Display response on the frontend.

Feature : Add CONTRIBUTING.md and requirements.txt files

It would be better to move correct requirements to a requirements.txt file with following content:

transformers
torch
gradio
bs4
safetensors
accelerate
sentencepiece
bitsandbytes
sentence-transformers
chromadb
langchain

I'd also like to create a CONTRIBUTING.md file describing how to setup the environment.

Any other recommendations @gcapuzzi ?

frontend: Revamp UI components

The following UI components need to be revamped. Note that we are using shadcn/ui and radix for the base components so we can support any first-class dependencies from there.

• Responsive Navigation Bar
• Dropdown Menu
• Responsive Modals

Fixing prompt bug in API testing

While performing the API testing ,I have faced an internal error after asking 2-3 questions.