study-llama/README.md

Study-Llama

Study-Llama is a demo application for organizing, extracting, and searching study notes using LlamaAgents, LlamaClassify, and LlamaExtract. It features a Go-based web frontend and a Python backend for advanced note processing.

Overview

• Frontend (Go):
  Serves the web interface, handles user authentication, file uploads, and search requests. Renders pages using HTML templates and manages static assets.
• Backend (Python):
  Provides APIs for classifying notes, extracting information, storing/retrieving data, and performing metadata-filtered vector searches. Handles database operations and vector search.

How It Works

1. User Interaction:
   Users access the web UI to sign in, create categories for their study notes, upload notes, and search among them.

2. Frontend-to-Backend Communication:
   The Go server receives requests from the UI and communicates with the Python backend via HTTP API calls.

   • For example, when a user uploads a note, the frontend sends the file and metadata to the backend for processing.
   • Search queries are also forwarded to the python backend for vector search.

3. Backend Processing:
   The Python backend (deployed on LlamaCloud as a LlamaAgent) handles:

   • Classification & Extraction:
     Uses workflows in classify_and_extract/workflow.py to classify notes (with LlamaClassify) and extract structured information (with LlamaExtract).
   • Database Operations:
     • Files:
       Uploaded files and their metadata (name, category, owner) are stored.
     • Classification Rules:
       Custom rules for categorizing notes are stored and retrieved from the database.
   • Vector Search:
     Extracted summaries and FAQs are indexed for semantic search and retrieval.

Features

• Upload and categorize study notes.
• Extract structured information from notes.
• Search notes with metadata filters.
• User authentication and access control.
• Modern web UI with Go templates.

Project Structure

• frontend/
  • main.go: Web server entry point.
  • handlers/: HTTP request handlers.
  • auth/, files/, rules/: Business logic and DB operations.
  • static/: Images and assets.
  • templates/: HTML templates.
• src/
  • study_llama/: Python backend modules for classification, extraction, search, and database logic.