[Report/mpDAO Grant #7] **AI Chatbot for Meta Pool**

Ecosystem Meta Pool
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[Report/mpDAO Grant 7] AI Chatbot for Meta Pool

We are pleased to announce the completion of the first milestone in our AI Chatbot development project for Meta Pool. Global region. This report details the activities and progress made during this phase.

Report’s Introduction

During the first four weeks of the project, we focused on backend development and documentation processing. The objective of this milestone was to establish the foundational infrastructure necessary for the chatbot to function efficiently, integrating it with key APIs and processing the relevant documentation.

Milestone 1: Backend Development and Documentation Processing

Summary:

In this milestone, we implemented the backend logic, integrating the OpenAI (ChatGPT) and CoinGecko APIs, and processed Meta Pool’s official documentation to ensure accurate and contextually relevant responses.

Key Activities and Achievements:

1. Backend Development:

  • Implemented the core backend infrastructure using Python and Flask.
  • Developed the necessary endpoints to handle user queries and interact with the AI models.
  • Configured logging to monitor application events and ensure smooth operation.

2. API Integration:

  • Successfully integrated the OpenAI API to leverage GPT4o-mini for generating responses to user queries.
  • Integrated the CoinGecko API to provide real-time cryptocurrency price data, enabling users to get up-to-date market information.

3. Documentation Processing:

  • Processed and indexed official Meta Pool documentation to create a knowledge base for the chatbot.
  • Ensured that the chatbot can retrieve and utilize information from these documents to provide accurate and authoritative answers.

4. Docker Containerization and Deployment:

  • Containerized the project using Docker to ensure consistent and reproducible environments.
  • Deployed the Docker container on Google Cloud, providing a minimal user interface to demonstrate the chatbot’s functionality.

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(actual demo)

Detailed Breakdown of Achievements:

  • Cryptocurrency Price Retrieval: The chatbot can now fetch real-time prices of cryptocurrencies using the CoinGecko API.
  • Natural Language Processing with Multilingual Support : The system is capable of translating and understanding questions in multiple languages, ensuring accurate responses to a diverse user base.
  • RAG Pipeline: We implemented a Retrieval-Augmented Generation (RAG) pipeline using the Haystack framework, combining document retrieval and language generation for precise answers.
  • Session Management: Developed functionality to maintain conversation context, providing coherent and context-aware interactions.

Next Steps:

With the backend infrastructure and documentation processing completed, we will now proceed to Milestone 2, focusing on frontend development and user interface creation.

Conclusion

The successful completion of Milestone 1 sets a strong foundation for the AI Chatbot project. The integration of advanced AI and real-time data capabilities ensures that users will have access to accurate and relevant information. We are excited to move forward with the next phase and continue enhancing the user experience for Meta Pool clients.

We look forward to providing further updates as we progress with the next milestones.

Thank you for your continued support.

Milestone 2: Frontend Development and User Interface Creation

Introduction

Following the completion of Milestone 1, which established a robust backend infrastructure with OpenAI (ChatGPT) and CoinGecko integrations, the project has advanced significantly. In line with our milestone plan, we have now delivered the Frontend Development and User Interface Creation (Milestone 2) and introduced important enhancements that set the stage for final integration and testing (Milestone 3). This report outlines the key progress and achievements made during this second milestone, as well as additional updates that further bolster the project’s capabilities.

Summary

Building on the backend logic completed in Milestone 1, this phase focused on delivering a production-ready user interface that integrates seamlessly with the RAG (Retrieval-Augmented Generation) pipeline. Notable improvements include a shift to FastAPI for the backend, Docker support, and a new Next.js + Chakra UI frontend.

Key Activities and Achievements

  1. Adoption of FastAPI for the Backend
    • Migration from Flask to FastAPI: We transitioned to FastAPI to enhance scalability, facilitate cleaner endpoint definitions, and simplify integrations with external platforms (e.g., Telegram, Discord).
    • Session Management: Retained the session-based approach for preserving conversation context, ensuring coherent multistep interactions.
  2. Frontend Development
    • Next.js + Chakra UI: The new frontend leverages Next.js for server-side rendering and Chakra UI for a responsive, clean design.
    • Multilingual UI Labels: Automatically detects the user’s browser language and adjusts UI labels accordingly (English, Spanish, French, etc.).
    • Real-Time Updates: The chatbot seamlessly fetches responses from the backend /ask endpoint, providing immediate answers and conversation continuity.
  3. Improved RAG Pipeline
    • Haystack & OpenAI: The pipeline remains anchored in Haystack, combining document retrieval with OpenAI GPT-based generation.
    • Enhanced Document Processing: The “corpus” folder can now accept additional file types (PDF, MD, etc.). Each file is indexed at startup, so the chatbot responds accurately using official Meta Pool documentation.
  4. Dockerized Deployment
    • Dockerfiles for Both Services: We created separate Dockerfiles for the FastAPI backend and the Next.js frontend, enabling reproducible builds and easy environment setup.
    • docker-compose.yml: Provided a docker-compose file that orchestrates both containers. This dramatically simplifies local deployment and testing for the development team and stakeholders.
  5. Ease of Integration with Telegram, Discord, and Other Platforms
    • REST Endpoint (/ask): The chatbot logic resides behind a simple HTTP endpoint (/ask), making it straightforward to forward messages from any external platform (Telegram, Slack, Discord, Discourse, etc.).
    • Scalable Architecture: FastAPI’s lightweight approach and modular design mean that each new integration only needs a “connector” to handle platform-specific events and route them to the chatbot.
  6. Documentation & Manuals
    • Updated READMEs: Each component (backend, frontend) has its own README detailing setup instructions, environment variables, and usage.
    • Usage Manual / PDF: Includes screenshots and step-by-step guides for running Docker containers, customizing environment variables, and adding new knowledge files in the “corpus” folder.

Additional Progress Since Milestone 1

  • Expanded Knowledge Base: We loaded more recent Meta Pool official documentation into the chatbot’s index, ensuring end users always receive the most up-to-date information.
  • Updated Logging & Monitoring: The improved logging in config_logging.py helps track usage patterns and quickly troubleshoot issues, which is crucial as the system transitions into broader testing.
  • Refined Natural Language Detection: Language detection logic is more robust, allowing for near-instant translation to English for processing—and then translating the final answer back to the user’s original language.

Conclusion

With Milestone 2 complete, the Meta Pool RAG Chatbot now offers:

  • A modern, user-friendly Next.js frontend (fully Dockerized).
  • A scalable, integration-ready FastAPI backend that supports simple REST calls from external platforms.
  • A robust RAG pipeline (Haystack + OpenAI GPT) with session-based context, multilingual coverage, and real-time crypto data.

These enhancements pave the way for Milestone 3, during which we will finalize integration, run comprehensive tests, and smoothly deploy the full chatbot solution for Meta Pool’s user base. We look forward to delivering a stable, educational, and user-centric chatbot that leverages official documentation to bring clarity and reliability to Meta Pool’s DeFi offerings.

Thank you for your continued support. We remain committed to ensuring the final product meets and exceeds expectations, empowering Meta Pool clients to access critical information quickly and confidently.

Milestone 3: Integration, Testing & Deployment

1. Integration into Meta Pool’s Ecosystem

  • Website Embedding: The chatbot frontend is now easily embeddable into the Meta Pool website. A short script snippet or iframe can integrate the chatbot widget, allowing users to ask questions without leaving the main site.
  • Potential Third-Party Integrations: Thanks to the FastAPI backend structure, hooking the /ask endpoint into platforms like Telegram, Discord, or internal staff dashboards is simplified. Each new integration can be handled by a lightweight “connector” that relays messages in and out of the chatbot.

2. Comprehensive Testing

  • Usability & UX Tests: We performed user simulations to check the chatbot’s clarity, response speed, language detection accuracy, and overall user experience. Early test users confirmed that the chatbot reliably answers questions about Meta Pool and cryptocurrencies in multiple languages.
  • Performance & Load Testing: Basic load tests showed the Dockerized chatbot handles concurrent queries efficiently. The session-based memory system scales well as user volume increases.

3. Deployment & Final Documentation

  • Cloud Hosting: Deployed the entire solution (backend + frontend) to a cloud environment for straightforward user access. Stakeholders can now test the chatbot publicly or share it with their user base.
  • Final Documentation & Manual:
    • Comprehensive usage manual (PDF/Markdown) detailing how to run and maintain the chatbot—covering Docker builds, environment setups, and steps to add new documents to the “corpus” folder.
    • README files updated to reflect the final production state.

Highlights & Outcomes

  1. Stable, Fully Containerized Chatbot
    • Both the backend (FastAPI) and frontend (Next.js) run smoothly in Docker containers.
    • A docker-compose orchestrates local or production deployments in one command, simplifying dev ops and ensuring consistent environments.
  2. Robust RAG Pipeline
    • Users receive real-time, contextually relevant answers by combining document retrieval with GPT-based generative responses.
    • The pipeline seamlessly integrates CoinGecko data for up-to-date crypto prices.
  3. Multilingual & Session-Aware
    • The chatbot automatically detects and responds in 50+ languages, making it accessible to a broad user base.
    • Session management retains conversation context, allowing for multi-step Q&A without losing track of prior questions.
  4. Easy Integration with Messaging Platforms
    • The new modular FastAPI approach allows external platforms (like Telegram, Slack, Discord) to forward user messages to the /ask endpoint and return responses to users.

Final Steps & Recommendations

  • Maintenance & Updates:
    • Keep the “corpus” folder updated with fresh Meta Pool documentation to ensure chatbot answers remain current and authoritative.
  • Monitoring & Analytics:
    • Integrate basic analytics or logging solutions to track usage metrics (peak times, most common questions, etc.).
    • Monitor OpenAI API usage and handle any rate-limit or cost issues proactively.
  • Potential Future Enhancements:
    • Local Model Execution: As planned, a local language model could reduce external API costs and improve privacy.
    • Advanced Ticket Generation: Auto-generate support tickets for queries requiring human intervention.
    • Internal Staff Integration: Extend the same RAG approach for internal knowledge management.

Conclusion

With Milestone 3 officially concluded, the Meta Pool RAG Chatbot stands as a production-ready solution offering:

  • A modern, multilingual user interface via Next.js + Chakra UI.
  • Real-time, accurate responses using official documentation, GPT-based generation, and on-demand crypto pricing.
  • Seamless Docker deployments and a robust, modular backend for frictionless integration with external platforms.

The project’s success stems from fulfilling the three core milestones:

  1. Backend & Doc Processing
  2. Frontend & UI
  3. Integration, Testing, & Deployment

We sincerely appreciate the continued support from all stakeholders and look forward to any feedback or iterative feature requests that further enhance the chatbot’s capabilities. The Meta Pool community can now enjoy quicker and more reliable access to DeFi information, strengthening user engagement and trust in Meta Pool services.

Thank you for your support throughout this journey. We remain available for any clarifications, additional customizations, or further developments you wish to pursue.

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:grinning: Nice progress, I love that Mr. Puul :green_heart: is part of the demo.
The inclusion of AI in the Meta Pool DAO ecosystem is already very necessary :ok_hand:

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