sonika-ai-toolkit 0.2.1

Toolkit para creación de agentes de IA y procesamiento de documentos

Sonika AI Toolkit

A robust Python library designed to build state-of-the-art conversational agents and AI tools. It leverages LangChain and LangGraph to create autonomous bots capable of complex reasoning and tool execution.

Installation

pip install sonika-ai-toolkit

Prerequisites

You'll need the following API keys depending on the model you wish to use:

• OpenAI API Key
• DeepSeek API Key (Optional)
• Google Gemini API Key (Optional)
• AWS Bedrock API Key (Optional, for Bedrock)

Create a .env file in the root of your project with the following variables:

OPENAI_API_KEY=your_openai_key_here
DEEPSEEK_API_KEY=your_deepseek_key_here
GOOGLE_API_KEY=your_gemini_key_here
AWS_BEARER_TOKEN_BEDROCK=your_bedrock_api_key_here
AWS_REGION=us-east-1

Key Features

• Multi-Model Support: Agnostic integration with OpenAI, DeepSeek, Google Gemini, and Amazon Bedrock.
• Conversational Agent: Robust agent (ReactBot) with native tool execution capabilities and LangGraph state management.
• Tasker Agent: Advanced planner-executor agent (TaskerBot) for complex multi-step tasks.
• Structured Classification: Text classification with strongly typed outputs.
• Document Processing: Utilities for processing PDFs, DOCX, and other formats with intelligent chunking.
• Custom Tools: Easy integration of custom tools via Pydantic and LangChain.

Basic Usage

Conversational Agent with Tools

import os
from dotenv import load_dotenv
from sonika_ai_toolkit.tools.integrations import EmailTool
from sonika_ai_toolkit.agents.react import ReactBot
from sonika_ai_toolkit.utilities.types import Message
from sonika_ai_toolkit.utilities.models import OpenAILanguageModel

# Load environment variables
load_dotenv()

# Configure model
api_key = os.getenv("OPENAI_API_KEY")
language_model = OpenAILanguageModel(api_key, model_name='gpt-4o-mini', temperature=0.7)

# Configure tools
tools = [EmailTool()]

# Create agent instance
bot = ReactBot(language_model, instructions="You are a helpful assistant", tools=tools)

# Get response
user_message = 'Send an email to [e-mail] saying hello'
messages = [Message(content="My name is Erley", is_bot=False)]
response = bot.get_response(user_message, messages, logs=[])

print(response["content"])

Text Classification

import os
from sonika_ai_toolkit.classifiers.text import TextClassifier
from sonika_ai_toolkit.utilities.models import OpenAILanguageModel
from pydantic import BaseModel, Field

# Define classification structure
class Classification(BaseModel):
    intention: str = Field()
    sentiment: str = Field(..., enum=["happy", "neutral", "sad", "excited"])

# Initialize classifier
model = OpenAILanguageModel(os.getenv("OPENAI_API_KEY"))
classifier = TextClassifier(llm=model, validation_class=Classification)

# Classify text
result = classifier.classify("I am very happy today!")
print(result.result)

Available Components

Agents

• ReactBot: Standard agent using LangGraph workflow.
• TaskerBot: Advanced planner agent for multi-step tasks.

Utilities

• ILanguageModel: Unified interface for LLM providers.
• DocumentProcessor: Text extraction and chunking utilities.

Project Structure

src/sonika_ai_toolkit/
├── agents/             # Bot implementations
├── classifiers/        # Text classification tools
├── document_processing/# PDF and document tools
├── tools/             # Tool definitions
└── utilities/         # Models and common types

License

This project is licensed under the MIT License.