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Hackathon Huggingface

Tracks:

  • Track 1: MCP Server Implementation
  • Track 2: Custom Agent Components
  • Track 3: Agentic Demo Showcase

Core Idea: Gradio [UI] + Agent

Proposed MVP Ideas:

MVP Idea 1: Intelligent Document Q&A System

Description: A Gradio-based application where users can upload documents (PDFs, text files, etc.), and the system will automatically process them to enable intelligent Q&A. This MVP focuses on robust data ingestion, indexing, and retrieval.

Key Features:

  • Document Upload: Users can upload various document types.
  • Automated Chunking & QA Generation: Documents are automatically chunked, and Q&A pairs are generated from each chunk.
  • Hybrid Search: Utilizes both vector embeddings and keyword indexing for efficient retrieval.
  • LLM Integration: Leverages an LLM for generating concise and accurate answers based on retrieved context.

Technology Stack:

  • Frontend: Gradio
  • Backend: Python, LangGraph (for orchestration), Convex DB (for indexing and persistence), LLM (e.g., Hugging Face models)
  • Data Sources: PDF, text files (extensible to others like Slack, Google Drive)

Placeholder Questions:

  • How will we handle different document formats and their specific parsing challenges?
  • What is the strategy for generating high-quality Q&A pairs from document chunks?
  • How will we ensure the hybrid search effectively combines vector and keyword results?
  • What metrics will define the success of the Q&A accuracy?

MVP Idea 2: Personalized Meeting Assistant

Description: An agentic system that assists users with personalized pre-meeting preparation and post-meeting documentation by leveraging audio data and transcripts.

Key Features:

  • Audio Transcription: Transcribes meeting audio into text.
  • Key Information Extraction: Identifies key discussion points, action items, and decisions.
  • Personalized Summaries: Generates concise summaries tailored to the user's role or interests.
  • Knowledge Graph Integration (Stretch Goal): Links meeting insights to a broader knowledge graph for richer context.

Technology Stack:

  • Frontend: Gradio (for interaction and display of summaries)
  • Backend: Python, Speech-to-Text models (e.g., Hugging Face ASR), LLM, Convex DB (for storing transcripts and extracted info)
  • Data Sources: Audio recordings, existing documentation (for context)

Placeholder Questions:

  • How will we handle speaker diarization for multi-person meetings?
  • What is the approach for extracting actionable insights from meeting transcripts?
  • How will the system personalize summaries effectively?
  • What are the privacy considerations for handling meeting audio and transcripts?

MVP Idea 3: Automated Research Agent

Description: An agent that accepts a research question, searches a knowledge base, summarizes findings, cites sources, and highlights key information.

Key Features:

  • Question Input: Users provide a research question.
  • Knowledge Base Search: The agent searches across various data sources (e.g., indexed documents, web).
  • Summarization & Citation: Generates a comprehensive summary with proper citations.
  • Key Information Highlighting: Identifies and highlights critical details in the summary.

Technology Stack:

  • Frontend: Gradio
  • Backend: Python, LangGraph, LLM, Convex DB, potentially web scraping tools or APIs for external data.
  • Data Sources: Indexed internal documents, potentially external web sources (e.g., Wikipedia, research papers)

Placeholder Questions:

  • How will the agent prioritize and select relevant information from diverse sources?
  • What mechanisms will ensure accurate citation and attribution?
  • How will the system handle conflicting information from different sources?
  • What is the strategy for evaluating the quality and comprehensiveness of the generated research summaries?

General Technology Stack & Concepts:

  • UI: Gradio
  • Agent Orchestration: LangGraph
  • Database: Convex DB (for hybrid search, knowledge graph, and persistence)
  • LLMs: Hugging Face models (for RAG, Q&A, summarization)
  • Retrieval: Hybrid search (vector embeddings + keyword indexing), RAG
  • Data Ingestion: PDF parsers, audio transcription, OCR (from Screenpipe)

Enterprise Knowledge Data:

  • Data Collection & Processing: PDF, docs, Wikipedia, Slack, Google Drive (extensible), WhatsApp, messaging channels.
  • Indexing: Data Ingestion -> Original doc -> Convert to chunks -> Generate Q/A pair from each chunk -> Index QA embeddings in a vector DB -> Index doc to keyword indexing -> Wrap inside Convex function.

Runtime Flow:

  1. User Interaction: User loads web app (Gradio), uploads a document or provides a query (text/audio).
  2. Agent Processing:
    • Query Classification/Routing: Determines the intent of the user's query.
    • Retrieval: Hybrid search on Q&A embeddings (returns parent documents/code).
    • Reranking: Reranks retrieved results for optimal relevance.
    • LLM Response: Generates a response based on the reranked context.
  3. **MCP Server Interaction (for advanced use cases): ** * Client-side business logic invokes MCP server.
    • MCP server returns a list of tools.
    • LLM selects and executes appropriate tools (e.g., retrieve function with query embedding).
    • Convex function returns chunks/context.
    • Context + prompt -> LLM Call -> Response.

Milestones:

  • Phase 1 (Core MVP): Gradio app where users can upload documents, generate chunks, and indexed chunks are persisted. Prototype an agentic interaction (e.g., "Why is X important in the company handbook?").
  • Phase 2 (Enhancements): Implement personalized features, expand data source integrations, refine hybrid search.
  • Phase 3 (Advanced): Integrate knowledge graph for richer RAG, explore advanced agentic behaviors.

Resources:

Existing Repositories:

Implementation Principles:

  • Distill: Convert arbitrary data into distilled documents.
  • Plan-and-Execute: Plan/revise a series of defined subtasks, then execute.
  • Act: Perform actions based on agentic decisions.