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> It’s a Mind with a custom Memory. <

MnemoMind: AI Assistant with Custom Memory

MnemoMind is an intelligent, conversational AI assistant designed to interact with your documents and code. It leverages the power of Google Gemini models for understanding and generation, combined with Elasticsearch (or client-side search) for efficient retrieval from your custom data sources. 🧠📚

Key Features:

Connect Multiple Data Sources: Upload local folders (like codebases), individual files (PDF, TXT, MD, etc.), or connect your Google Drive.
Context-Aware Responses: Ask questions, request summaries, or generate content based specifically on the documents you provide.
Retrieval-Augmented Generation (RAG): Finds relevant snippets from your data using vector search (via Elasticsearch or simulated locally) and provides them to the Gemini model for accurate answers.
Web & Maps Grounding: Optionally allows the AI to use Google Search or Google Maps (if configured) when information isn't found in your documents.
Code Generation & Editing: Suggests code modifications based on your requests and the context of your codebase (currently supports cloud-based sources). Review diffs and apply changes directly.
Citation Support: Clearly indicates which source document(s) were used to generate parts of the answer.
Rich UI: Markdown rendering, code highlighting, interactive tables (with export to Google Sheets), file browsing, diff viewing, and more.
Model Selection: Switch between different Gemini models (Flash Lite, Flash, Pro).
Configurable Tools: Toggle code generation and grounding sources (Cloud Search, Preloaded Files, Google Search, Google Maps).
Theme Switching: Light and Dark mode support.

🚀 Getting Started

Prerequisites

Node.js: (LTS version recommended) - Download from nodejs.org
npm: (Usually included with Node.js)
Python: (>= 3.9 recommended) - Required for the backend API and Elasticsearch indexing script.
pip: (Python package installer)

Setup

Clone the Repository:

git clone https://github.com/HeshamASH/mnemomind
cd mnemomind

Frontend Setup:
- Navigate to the root directory (mnemomind-main).
- Install dependencies: bash npm install
- Create a .env.local file in the root directory and add your Google Gemini API Key: dotenv # .env.local VITE_GEMINI_API_KEY=YOUR_GEMINI_API_KEY (Get your key from Google AI Studio).
Backend Setup:
- Navigate to the API directory: bash cd api
- (Recommended) Create and activate a Python virtual environment: bash python -m venv venv # On Windows: .\venv\Scripts\activate # On macOS/Linux: source venv/bin/activate
- Install Python dependencies: bash pip install -r requirements.txt
- Elasticsearch (Optional but Recommended):
  - If you want to use Elasticsearch Cloud for document indexing and search (required for code editing suggestions):
    - Sign up for a free trial or use an existing deployment on Elastic Cloud.
    - Create an API Key with appropriate permissions.
    - Find your Cloud ID.
    - Update the .env.local file (in the project root) with your Elastic credentials: dotenv # .env.local (add these) ELASTIC_CLOUD_ID=YOUR_ELASTIC_CLOUD_ID ELASTIC_API_KEY=YOUR_ELASTIC_API_KEY ELASTIC_INDEX=rag_documents # Or your preferred index name
    - (First Time) Run the indexing script to load documents into Elastic Cloud:
      - Place the documents you want to index into the fixed_documents folder (or update the path in the script).
      - Navigate to the Elastic Cloud HQ directory: cd ../"Elastic Cloud HQ"
      - Run the script (ensure your virtual environment is active): bash python embed-chunk-index-to-elastic-cloud.py
- Google Drive/Sheets Integration (Optional):
  - Follow Google Cloud guides to create OAuth 2.0 Credentials (Client ID and Secret) for a Web Application.
  - Enable the Google Drive API and Google Sheets API.
  - Add http://localhost:5173/api/auth/google/callback to the "Authorized redirect URIs" in your Google Cloud credentials settings. (Replace 5173 if you use a different port).
  - Add your Google credentials to the .env.local file: dotenv # .env.local (add these if using Google Drive/Sheets) GOOGLE_CLIENT_ID=YOUR_GOOGLE_CLIENT_ID GOOGLE_CLIENT_SECRET=YOUR_GOOGLE_CLIENT_SECRET SECRET_KEY=generate_a_strong_random_secret_key # For signing session cookies
Run the Application:
- Start the Backend API:
  - Navigate back to the api directory (cd ../api).
  - Ensure your Python virtual environment is active.
  - Run the FastAPI server using Uvicorn: bash uvicorn main:app --reload --port 8000 (The --reload flag automatically restarts the server when code changes.)
- Start the Frontend:
  - Open a new terminal.
  - Navigate to the project root directory (mnemomind-main).
  - Run the Vite development server: bash npm run dev
- Open your browser and navigate to http://localhost:5173 (or the port Vite indicates).