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Swarmauri SDK

The Swarmauri SDK provides a powerful, extensible framework for building AI-powered applications. This repository includes core interfaces, standard abstract base classes, and concrete reference implementations.

Installation Options

Swarmauri offers multiple installation options to suit different needs:

Option 1: Complete SDK Installation

For a full-featured experience with all standard components:

# Install the main namespace package with standard components
pip install swarmauri

# Install the main namespace package with extra standard components
pip install swarmauri[full]

Option 2: Core Only

For a minimal installation with just the core interfaces:

# Install only the core components
pip install swarmauri_core

Option 3: Experimental Components

Add additional experimental components:

# Add experimental features (optional)
pip install swarmauri_experimental

Option 4: Standalone Packages

Install specific packages for targeted functionality:

# Install only the vector store implementations you need
pip install swarmauri_vectorstore_pinecone
pip install swarmauri_vectorstore_annoy

# Install specific tools
pip install swarmauri_tool_jupyterexportlatex

Development Installation

For contributors or those wanting the latest features:

# Clone the repository
git clone https://github.com/swarmauri/swarmauri-sdk.git
cd swarmauri-sdk

# Install in development mode
pip install -e .

# Or with UV for faster installation
pip install uv
uv pip install -e .

Using Swarmauri Components

Method 1: Access via Namespace (Recommended)

The namespace approach provides a clean, unified interface to all components:

# Import through the swarmauri namespace
from swarmauri.vectorstores import PineconeVectorStore, AnnoyVectorStore
from swarmauri.documents import Document
from swarmauri.tools import JupyterExportLatexTool

# Create a vector store
vector_store = PineconeVectorStore(
    api_key="your-api-key",
    environment="your-environment",
    index_name="your-index"
)

# Create a document
document = Document(
    content="Sample text content",
    metadata={"source": "example"}
)

# Add document to vector store
vector_store.add_document(document)

# Retrieve similar documents
results = vector_store.retrieve("query text", top_k=5)

Method 2: Direct Package Access

For more explicit imports or when working with specific packages:

# Import directly from individual packages
from swarmauri_vectorstore_pinecone import PineconeVectorStore
from swarmauri_standard.documents import Document
from swarmauri_tool_jupyterexportlatex import JupyterExportLatexTool

# Use components as before
vector_store = PineconeVectorStore(...)

Package Structure

The Swarmauri SDK is organized into several key packages:

• swarmauri_core: Core interfaces and constants
• swarmauri_base: Abstract base classes for extensibility
• swarmauri_standard: Standard implementations of common components
• swarmauri: Main namespace package that unifies all components
• swarmauri_experimental: Experimental features under development

Individual components follow these naming conventions:

• swarmauri_vectorstore_*: Vector database integrations
• swarmauri_embedding_*: Embedding model implementations
• swarmauri_tool_*: Task-specific tools
• swarmauri_parser_*: Text parsing utilities
• swarmauri_distance_*: Distance calculation methods

For Contributors

If you want to contribute to the Swarmauri SDK, please read our guidelines for contributing and style guide to get started.

Creating a New Plugin

Swarmauri uses Python's entry point system for plugin discovery. Here's how to register your component:

# In your pyproject.toml
[project.entry-points.'swarmauri.vectorstores']
YourVectorStore = "swarmauri_vectorstore_yourplugin:YourVectorStore"

# In your implementation file
@ComponentBase.register_type(VectorStoreBase, "YourVectorStore")
class YourVectorStore(VectorStoreBase):
    # Your implementation

License

The Swarmauri SDK is licensed under the Apache License 2.0. See the LICENSE file for details.