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https://github.com/saritaphd/end-to-end-qna-chatbot-using-ollama
This is a Q&A chatbot application built using Streamlit, Langchain, and Ollama. The chatbot leverages open-source models to generate responses based on user input.
https://github.com/saritaphd/end-to-end-qna-chatbot-using-ollama
Last synced: 3 days ago
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This is a Q&A chatbot application built using Streamlit, Langchain, and Ollama. The chatbot leverages open-source models to generate responses based on user input.
- Host: GitHub
- URL: https://github.com/saritaphd/end-to-end-qna-chatbot-using-ollama
- Owner: SaritaPhD
- License: mit
- Created: 2024-07-31T07:51:51.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-07-31T09:14:18.000Z (4 months ago)
- Last Synced: 2024-08-01T09:34:28.906Z (4 months ago)
- Language: Python
- Homepage:
- Size: 360 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# End-to-End-Q&A-ChatBot-Using-ollama
## Q&A Chatbot Using Ollama (Open Source Mistral)
This is a Q&A chatbot application built using Streamlit, Langchain, and Ollama. The chatbot leverages open-source models to generate responses based on user input.
## Features
- Select from open-source models for generating responses.
- Adjust response parameters such as temperature and maximum tokens.
- User-friendly interface for input and output.
## Requirements
- Python 3.10+
- Streamlit
- Mistal
- Langchain API Key
- Langchain
- Ollama
- dotenv
## Installation
1. **Clone the repository:**
```sh
git clone [email protected]:SaritaPhD/End-to-End-QnA-ChatBot-Using-ollama.git
cd End-to-End-QnA-ChatBot-Using-ollama
```
2. **Create and activate a virtual environment:**
```sh
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
3. **Install the required packages:**
```sh
pip install -r requirements.txt
```
4. **Set up environment variables:**
Create a `.env` file in the root directory and add your Langchain API key and Langchain API key:
```env
LANGCHAIN_API_KEY=your_langchain_api_key
```
## Usage
1. **Run the Streamlit app:**
```sh
streamlit run app.py
```
2. **Navigate to the local server:**
Open your web browser and go to `http://localhost:8501`.
3. **Interact with the chatbot:**
- Select the model from the sidebar.
- Adjust the response parameters (temperature and max tokens).
- Enter your question in the text input field and get the response.
## Getting the "mistral" Model from Ollama
1. **Install Ollama:**
```sh
pip install ollama
```
2. **Get the "mistral" Model:**
To get the "mistral" model, run the following command in the terminal:
```
ollama run mistral
```
## Project Structure
```plaintext
.
├── app.py
├── logger.py
├── exception.py
├── requirements.txt
├── .env
└── README.md
```
