Chatbot with Ollama Model

This is a Python-based chatbot using the Ollama language model from the langchain_community.llms package. The chatbot allows users to interact with an AI model in real-time, providing dynamic responses based on conversation history.

Features

• Interactive chatbot with real-time input/output
• Use of the Ollama model from LangChain for generating responses
• Conversation history maintained to provide coherent responses
• A "thinking" animation that runs in a separate thread while the model is processing
• Simple exit mechanism by typing exit

Prerequisites

To run this chatbot, you will need Python 3.7 or higher and the following libraries installed:

Required Libraries

• langchain_community: This library is required to load and use the Ollama model.
• threading and time: These are standard Python libraries used to handle the "thinking" animation and timed events.
• logging: Used to suppress unwanted logging output from some external libraries (like urllib3).

Ollama Model

The chatbot uses the Ollama language model, which must be installed separately.

To install the Ollama model:

1. Download the Ollama CLI tool from the Ollama official website.

   • You will need to follow the installation instructions for your operating system (Windows, macOS, or Linux).

2. Once installed, verify that the Ollama CLI is working by running the following command in your terminal: ollama --help

3. Install the desired Ollama model, for example llama2, using the following command:

ollama pull llama3.1

After installing the model, the chatbot will be able to load it via the Ollama interface in the langchain_community.llms library.

Python Library Installation

You can install the necessary Python libraries using pip. Run the following command in your terminal:

pip install langchain_community

Usage

Run the Chatbot: You can start the chatbot by running the main function in your terminal:

python chatbot.py Interacting with the Bot: Once the chatbot is running, you can type your messages after the You: prompt, and the bot will generate responses. You can exit the chat at any time by typing exit.

Thinking Animation: While the bot is generating a response, you'll see an animated "thinking" symbol that runs until the bot completes its response.

Code Explanation

-Thinking Animation: A separate thread is created to run the show_thinking_symbol function, which displays a rotating symbol to indicate the bot is thinking. This thread stops once the bot generates a response.

-Ollama Model: The chatbot uses the Ollama model from LangChain to generate responses. You can specify the model variant by changing the llm = Ollama(model="llama3.1") line.

-Conversation History: The bot keeps track of all messages in the conversation, ensuring that responses are contextual. If the conversation history becomes too long, it is truncated to prevent memory overflow.

Customization

Model Selection: You can customize the chatbot to use different Ollama models by modifying the llm = Ollama(model="llama3.1") line.

Conversation Management: You can adjust the conversation history limits by modifying the max_tokens variable to suit your memory requirements.

Error Handling

The code includes basic error handling for cases where the model initialization or response generation fails. Errors are printed to the console without breaking the program.