A personal memory embedding and search system that allows you to store text memories and retrieve them based on semantic similarity using vector embeddings.

• Add personal memories via text input
• Search memories using natural language queries
• View all stored memories
• Delete unwanted memories
• Vector-based similarity search with configurable threshold

• Backend: FastAPI, ChromaDB, Sentence Transformers
• Frontend: Streamlit
• Embeddings: all-MiniLM-L6-v2 model

• Python 3.11 or higher
• Git
• Docker and Docker Compose (for easy setup)

1. Install Docker and Docker Compose on your system.

2. Clone the repository:

   git clone https://github.com/ashangupta/echomind cd echomind 

3. Run the application:

   docker-compose up 

4. Open your browser and go to http://localhost:8501 to access the app.

That's it! The backend and frontend will start automatically.

1. Clone the repository:

   git clone https://github.com/ashankgupta/echomind cd echomind 

2. Create a virtual environment:

   python -m venv venv 

3. Activate the virtual environment:

   • On Linux/Mac: source venv/bin/activate
   • On Windows: venv\Scripts\activate

4. Install dependencies:

   pip install -r requirements.txt 

5. Run the FastAPI server:

   uvicorn main:app --reload 

6. In another terminal, run the Streamlit app:

   streamlit run streamlit_app.py 

• Open the Streamlit app in your browser (usually http://localhost:8501)
• Add memories in the "Add Memory" section
• Search for memories in the "Search Memories" section
• View and delete memories in the "Show all stored memories" section

• POST /ingest: Ingest a new memory (text)
• POST /context: Query similar memories (query, top_k)
• GET /all_memories: Retrieve all stored memories
• DELETE /delete: Delete a memory by ID

1. Build the backend image:

   docker build -t echomind-backend . 

2. Run the backend container:

   docker run -p 8000:8000 echomind-backend 

1. Build the frontend image:

   docker build -f Dockerfile.streamlit -t echomind-frontend . 

2. Run the frontend container:

   docker run -p 8501:8501 echomind-frontend 

Note: The Streamlit app is configured to connect to http://localhost:8000. When running in Docker, you may need to use Docker networking or adjust the API_URL in streamlit_app.py to point to the backend container.

This project is licensed under the MIT License.