intura-ai 0.0.3.3

Intelligent Research and Experimentation AI for LLM experimentation production.

Intura-AI: Intelligent Research and Experimentation AI

intura-ai is a Python package designed to streamline LLM experimentation and production. It provides tools for logging LLM usage and managing experiment predictions, with seamless LangChain compatibility.

Dashboard: dashboard.intura.co

Features

• Callbacks:
  • UsageTrackCallback: Log LLM usage details for analysis and monitoring.
• Experiment Prediction:
  • ChatModelExperiment: Facilitates the selection and execution of LangChain models based on experiment configurations.
• LangChain Compatibility:
  • Designed to integrate smoothly with LangChain workflows.

Installation

pip install intura-ai

Usage

Initialization

Before using intura-ai, you need to initialize the client with your API key.

import os
from intura_ai.client import intura_initialization

INTURA_API_KEY = "..."
intura_initialization(INTURA_API_KEY)

Experiment Prediction

Use ChatModelExperiment to fetch and execute pre-configured LangChain models.

from intura_ai.experiments import ChatModelExperiment

EXPERIMENT_ID = "..."
client = ChatModelExperiment(EXPERIMENT_ID)

choiced_model, model_config, chat_prompts = client.build(
    features={
        "user_id": "Rama12345", 
        "membership": "FREE", 
        "employment_type": "FULL_TIME",
        "feature_x": "your custom features"
    }
)
chat_prompts.append(('human', 'give me today quote for programmer'))

print(client.choiced_model) # Your choiced model for instance: claude-3-5-sonnet-20240620
model = choiced_model(**model_config, api_key="<YOUR_API_KEY | Set by Environment>")
model.invoke(chat_prompts)

Usage Tracking Callback

Integrate UsageTrackCallback to log LLM usage during execution.

from intura_ai.callbacks import UsageTrackCallback
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain.schema import HumanMessage

EXPERIMENT_ID = "..."
llm = ChatGoogleGenerativeAI(
    model="gemini-1.5-pro",
    max_tokens=300,
    timeout=None,
    max_retries=2,
    callbacks=[
        UsageTrackCallback(EXPERIMENT_ID)
    ]
)

messages = [HumanMessage(content="What is the capital of France?")]
llm.invoke(messages)

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bug reports or feature requests.

License

This project is licensed under the MIT License.