LangChain StackSpot AI

A Python library for seamless integration between StackSpot AI and LangChain, enabling developers to leverage StackSpot AI's capabilities within the LangChain ecosystem.

Features

• Easy-to-use StackSpot AI chat model implementation for LangChain
• Function calling support with StackSpot AI
• Agent executors that work with StackSpot AI
• Adapters for converting between LangChain and StackSpot AI formats
• Comprehensive documentation and examples

Installation

From PyPI

pip install langchain-stackspot-ai

Using Poetry

poetry add langchain-stackspot-ai

Quick Start

from langchain_stackspot_ai.models import ChatStackSpotAI
from langchain_core.messages import HumanMessage, SystemMessage

# Initialize the StackSpot AI chat model
chat = ChatStackSpotAI(
    client_id="your-client-id",
    client_secret="your-client-secret",
    slug="your-slug"
)

# Use the model
messages = [
    SystemMessage(content="You are a helpful assistant."),
    HumanMessage(content="What is LangChain?")
]

response = chat.invoke(messages)
print(response.content)

Using with Tools and Agents

from langchain_stackspot_ai.models import ChatStackSpotAI
from langchain_stackspot_ai.agents import create_stackspot_agent_executor
from langchain_core.tools import Tool

# Define tools
tools = [
    Tool(
        name="search",
        func=lambda query: f"Search results for: {query}",
        description="Search the web for information."
    )
]

# Initialize the StackSpot AI chat model
chat = ChatStackSpotAI(
    client_id="your-client-id",
    client_secret="your-client-secret",
    slug="your-slug"
)

# Create an agent executor
agent_executor = create_stackspot_agent_executor(
    llm=chat,
    tools=tools,
    system_message="You are a helpful assistant with access to tools."
)

# Use the agent
result = agent_executor.invoke({"input": "What's the weather in New York?"})
print(result["output"])

Documentation

For detailed documentation, visit https://langchain-stackspot-ai.readthedocs.io.

Development

This project uses Poetry for dependency management and packaging.

Setting up the development environment

# Clone the repository
git clone https://github.com/stackspot/langchain-stackspot-ai.git
cd langchain-stackspot-ai

# Install dependencies
poetry install

# Activate the virtual environment
poetry shell

Running tests

pytest

Code quality

# Format code
black langchain_stackspot_ai tests

# Sort imports
isort langchain_stackspot_ai tests

# Type checking
mypy langchain_stackspot_ai

# Linting
flake8 langchain_stackspot_ai tests

Building documentation

cd docs
make html

Publishing to PyPI

Test PyPI

poetry config repositories.testpypi https://test.pypi.org/legacy/
poetry publish --build --repository testpypi

Production PyPI

poetry publish --build

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request