llmstudio-core 1.1.1a1

LLMStudio core capabilities for routing llm calls for any vendor. No proxy server required. For that use llmstudio[proxy]

LLMstudio-core by TensorOps

Prompt Engineering at your fingertips

🌟 Features

• Custom and Local LLM Support: Use custom or local open-source LLMs through Ollama.
• Python SDK: Easily integrate LLMstudio into your existing workflows.
• LangChain Integration: LLMstudio integrates with your already existing LangChain projects.

🚀 Quickstart

Don't forget to check out https://docs.llmstudio.ai page.

Installation

Install the latest version of LLMstudio using pip. We suggest that you create and activate a new environment using conda

pip install llmstudio-core

Create a .env file at the same path you'll run LLMstudio

OPENAI_API_KEY="sk-api_key"
GOOGLE_API_KEY="sk-api_key"

Now you should be able to run LLMstudio Providers using the following code:

# You can set OPENAI_API_KEY environment variable, add it to .env, or pass directly as api_key
import os
from llmstudio_core.providers import LLMCore as LLM
llm = LLM("vertexai", api_key=os.environ["GOOGLE_API_KEY"])
response = llm.chat("How are you", model="gemini-1.5-pro-latest")
print(response.chat_output, response.metrics)

📖 Documentation

• Visit our docs to learn how the SDK works (coming soon)
• Checkout our notebook examples to follow along with interactive tutorials

👨‍💻 Contributing

• Head on to our Contribution Guide to see how you can help LLMstudio.
• Join our Discord to talk with other LLMstudio enthusiasts.

Training

---

Thank you for choosing LLMstudio. Your journey to perfecting AI interactions starts here.