DeepSeekRAGE

created in 48hrs as MVP to connect with ollama recognizing ollama from localhost if running
RAGE folder contains the as yet to be implented Retrieval Augmented Generative Engine
DeepSeekRAGE functions as a basic UI for chat response from localhost ollama model
with memory.py and logger.py

DeepSeekRAGE/
├── src/
│   ├── memory.py
│   ├── logger.py
│   ├── openmind.py
│   └── locallama.py
├── gfx/
│   └── styles.css
├── memory/
│   ├── sessions/
│   ├── knowledge/
│   └── long_term_memory.json
└── rage.py

DeepSeekRAGE

Retrieval Augmented Generative Engine
DeepSeek RAGE

RAGE Retrieval Augmented Generative Engine is a dynamic engine designed to learn from context, injest and memory over time. While I have had the idea for sometime no working expression of RAGE has been created, until this weekend.

Context-Aware Responses:

By leveraging the continuously updated data and learning from past interactions, RAGE can understand and respond to nuances in user queries. This ability makes it particularly effective in scenarios where context heavily influences the nature of the response.

Adaptive Response Generation:

As RAGE evolves, it becomes more adept at predicting user needs and adjusting its responses accordingly, ensuring high relevance and personalization.

git clone https://github.com/GATERAGE/DeepSeekRAGE

python3.11 -m venv rage

source rage/bin/activate

pip install --no-cache-dir -r requirements.txt

streamlit run rage.py