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language-model-toolkit
Advanced tools
A holisitic for interfacing with ollama and other llm hosts. Created mainly for private experimentation.
This Python module, built around the OllamaClient
, facilitates generating text completions and managing interactive chat sessions. It is designed to serve as a foundational tool for developers, researchers, and hobbyists who are exploring conversational AI technologies. The module provides a straightforward interface for sending prompts to a conversational AI model and receiving generated responses, suitable for a wide range of applications from chatbots to creative writing aids.
Ensure Python 3.6+ is installed. Clone this repository and install the required dependencies to get started:
git clone https://github.com/yourusername/conversational-ai-module.git
cd conversational-ai-module
pip install -r requirements.txt
Import the OllamaClient
in your Python script to begin interacting with the conversational AI model:
from interface.cls_ollama_client import OllamaClient
# Initialize the client
client = OllamaClient()
# Generate a single completion
response = client.generate_completion("Your prompt here.")
print(response)
To engage in an interactive chat session, you can use the following pattern in your script:
client = OllamaClient()
while True:
user_input = input("Enter your prompt: ")
response = client.generate_completion(user_input)
print(response)
The module includes functionality to set up a sandbox environment, isolating your interactions and data. This is particularly useful for testing and development purposes.
Call setup_sandbox()
before starting your session to prepare the environment:
from your_module import setup_sandbox
setup_sandbox()
Contributions are welcome! Please feel free to submit pull requests, report bugs, or suggest features.
This project is licensed under the MIT License - see the LICENSE file for more details.
FAQs
A holisitic for interfacing with ollama and other llm hosts. Created mainly for private experimentation.
We found that language-model-toolkit demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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Socket’s new Tier 1 Reachability filters out up to 80% of irrelevant CVEs, so security teams can focus on the vulnerabilities that matter.
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