Chat Assist is an AI-powered chatbot built using streamlit
and ollama
, with a contextual search system leveraging FAISS
and sentence-transformers
for relevant response retrieval.
- Multiple AI Model Support: Supports models such as DeepSeek-R1 (1.5B, 8B, 14B), LLaMA 3.1 (8B), LLaMA 3.2 (3B), and Mistral 7B.
- Context-Aware Responses: Uses vectorized context matching to enhance response accuracy.
- Chat History Retention: Maintains conversation history to provide coherent interactions.
- Embedded Context Search: Uses FAISS to retrieve relevant stored context dynamically.
- File-Based Context Expansion: Allows users to upload text, JSON, or DOCX files for additional context.
- Python 3.8+
- Install required dependencies:
pip install -r requirements.txt
Ollama is required to run AI models locally. Follow the steps below to install it:
- macOS: Download the installer from the Ollama website and follow the instructions.
- Linux: Open a terminal and run:
Refer to the Ollama Linux guide for more details.
curl -fsSL https://ollama.com/install.sh | sh
- Windows: Download and install Ollama from the official website.
After installing Ollama, download and run the required AI models:
ollama run llama3.1
This command will automatically download and install the LLaMA 3.1 model. For more models, visit the Ollama model library.
-
Start the chatbot:
To guarantee that your app is only accessible from your machine, run Streamlit with the
--server.address
option set to localhost:streamlit run chat_assist.py --server.address localhost
-
Select the AI model from the dropdown menu.
-
Upload an optional file (TXT, DOCX) to provide additional context.
-
Enter your message in the chat input.
-
The bot retrieves relevant context and generates a response using an embedded context search system.
Chat Assist leverages FAISS-based vectorization to dynamically retrieve relevant context from a predefined dataset or uploaded files. The chatbot interface is built with streamlit
, while the AI model is hosted via ollama
.
User: "Tell me about autonomous vehicles in heavy rain."
Bot: "Autonomous vehicles face challenges in heavy rain due to sensor interference and reduced visibility. Relevant studies suggest..."
- Enhance model selection.
- Improve context matching and retrieval.
- Support for additional AI models.
- Optimize performance for large document uploads.
This project is licensed under the MIT License.