Meez 0.3.4

AI Assistant and Copilot SDK for SaaS Applications

Meez

AI Assistant and Copilot SDK for SaaS Applications

Meez is a powerful Python library that provides AI-powered assistance capabilities for SaaS applications. It offers intention detection, contextual responses, and workflow automation using LangChain and LangGraph.

Features

• Intention Detection: Automatically detect user intentions from natural language input

• Contextual Responses: Generate intelligent responses based on provided context data

• Workflow Automation: Create complex AI workflows using LangGraph

• Multiple Data Sources: Support for text, JSON, and file-based data sources

• OpenAI Integration: Built on top of OpenAI’s GPT models for reliable AI responses

• Flexible Architecture: Modular design for easy integration and customization

Installation

Install Meez using pip:

pip install Meez

Quick Start

1. Set up your OpenAI API key:

import os
os.environ["OPENAI_API_KEY"] = "your-openai-api-key-here"

2. Basic intention detection:

from meez.core import Langchain, Intention

# Initialize LangChain
langchain = Langchain(openai_api_key="your-api-key")

# Define possible intentions
intentions = ["greeting", "weather", "help", "goodbye", "unknown"]

# Create intention detector
detector = Intention(langchain=langchain, intentions=intentions)

# Detect intention
user_input = "Hello there!"
intention = detector.detect(user_input)
print(f"Detected intention: {intention}")

3. Contextual responses:

from meez.core import Langchain, Respond
from meez.data import TextReader

# Initialize components
langchain = Langchain(openai_api_key="your-api-key")
respond = Respond(langchain)

# Create data source
context_data = "Python is a high-level programming language..."
data_reader = TextReader(context_data)

# Get contextual response
question = "What is Python?"
response = respond.run(question=question, data=data_reader)
print(f"Response: {response}")

Usage Examples

Intention Detection

Detect user intentions from natural language:

import os
from meez.core import Langchain, Intention

# Setup
api_key = os.getenv("OPENAI_API_KEY")
langchain = Langchain(openai_api_key=api_key)

# Define intentions
intentions = [
    "greeting",
    "weather",
    "joke",
    "help",
    "goodbye",
    "book_appointment",
    "cancel_appointment",
    "unknown"
]

# Create detector
detector = Intention(langchain=langchain, intentions=intentions)

# Test inputs
test_texts = [
    "Hello there!",
    "What's the weather like?",
    "I need to book an appointment",
    "Can you help me cancel my appointment?"
]

for text in test_texts:
    intention = detector.detect(text)
    print(f"'{text}' → {intention}")

Contextual Responses

Generate responses based on context data:

from meez.core import Langchain, Respond
from meez.data import TextReader, JsonReader, FileReader

# Initialize
langchain = Langchain(openai_api_key=api_key)
respond = Respond(langchain)

# Using text data
text_data = "Artificial Intelligence is a branch of computer science..."
text_reader = TextReader(text_data)

response = respond.run(
    question="What is AI?",
    data=text_reader
)

# Using JSON data
json_data = {"company": {"name": "TechCorp", "employees": 250}}
json_reader = JsonReader(json_data)

response = respond.run(
    question="How many employees does the company have?",
    data=json_reader
)

# Using file data
file_reader = FileReader("documentation.txt")
response = respond.run(
    question="What are the main features?",
    data=file_reader
)

General Respond

Respond to general user questions using AI knowledge:

from meez.core import Langchain, GeneralRespond

# Initialize
langchain = Langchain(openai_api_key=api_key)
respond = GeneralRespond(langchain)

# Get response
question = "What is the capital of France?"
response = respond.run(question=question)
print(f"Response: {response}")

Workflow Automation with LangGraph

Create complex AI workflows:

from meez.core.langgraph import LangGraph, MainState
from meez.core import Langchain, Intention

# Define workflow nodes
def get_intent(state: MainState) -> MainState:
    # Detect user intention
    detector = Intention(langchain, ["get_phone", "get_email", "unknown"])
    intent = detector.detect(state["messages"][-1]["content"])
    state["messages"].append({"role": "assistant", "content": intent, "internal": True})
    return state

def decide(state: MainState) -> str:
    # Return the detected intent to determine next step
    return state["messages"][-1]["content"]

def get_phone(state: MainState) -> MainState:
    state["messages"].append({"role": "assistant", "content": "Phone: [phone]"})
    return state

def get_email(state: MainState) -> MainState:
    state["messages"].append({"role": "assistant", "content": "Email: [e-mail]"})
    return state

def unknown(state: MainState) -> MainState:
    state["messages"].append({"role": "assistant", "content": "I'm sorry, I don't know that."})
    return state

# Create and configure graph
graph = LangGraph()
graph.add_node("get_intent", get_intent)
graph.add_node("decide", decide)
graph.add_node("get_phone", get_phone)
graph.add_node("get_email", get_email)
graph.add_node("unknown", unknown)

graph.set_entry_point("get_intent")
graph.add_conditional_edge("get_intent", decide)
graph.add_finish_point("get_phone")
graph.add_finish_point("get_email")
graph.add_finish_point("unknown")

# Run workflow
initial_state = {"messages": [{"role": "user", "content": "What's your phone number?"}]}
result = graph.run(initial_state)

Examples

See the examples/ directory for complete working examples:

• intention_detection.py - Basic intention detection

• respond_to_user.py - Contextual responses with different data sources

• complex_graph.py - Advanced workflow automation

• sample_graph.py - Simple graph workflow

• sample_assistant.py - Complete assistant implementation

Development

Setup Development Environment

# Clone the repository
git clone https://github.com/clivern/meez.git
cd meez

# Install development dependencies
pip install -r requirements.test.txt
pip install -e .

Running Tests

# Run tests
make ci

Support

• Documentation: https://github.com/clivern/meez/

• Issues: https://github.com/clivern/meez/issues

• Email: [e-mail]

Changelog

See CHANGELOG.rst for a detailed history of changes.