langchain-stardog 0.1.1

LangChain integrations for Stardog

Stardog LangChain Integration

LangChain integration for Stardog Voicebox - enabling natural language querying over your enterprise data using LangChain runnables and tools.

Features

• LangChain Tools: Ready-to-use tools for LangChain agents
• LCEL Runnables: Composable runnables for building chains
• Async & Sync: Full support for both async and synchronous operations

Table of Contents

• Requirements
• Installation
• Quick Start
  • Setup Environment Variables
  • Basic Usage with Tools
  • Using Runnables in LCEL Chains
• Class Reference
  • VoiceboxAskRunnable
  • VoiceboxAskTool
  • VoiceboxClient
• Examples
• Development
• Contributing
• General Support

Requirements

• Python 3.10+
• A Stardog Cloud account with a Voicebox application
• Voicebox API token (process to obtain explained below)
• uv: a python package manager for development and contributions (uv)

Installation

pip install langchain-stardog

Quick Start

Setup Environment Variables

The simplest way to get started is to set your API token as an environment variable:

export SD_VOICEBOX_API_TOKEN="your-voicebox-api-token"

Getting Your API Token:

1. Log in to Stardog Cloud
2. Click on your profile icon and select Manage API Keys.
3. Create a new application and generate a secret.
4. Copy the API token and keep it secure.
5. For more details, see Stardog Voicebox API access.

Optional Environment Variables:

export SD_VOICEBOX_CLIENT_ID="my-app"                      # Client identifier (default: VBX-LANGCHAIN)
export SD_CLOUD_ENDPOINT="https://cloud.stardog.com/api"  # Custom endpoint (optional)

Basic Usage with Tools

Tools are designed for agent workflows and automatically load credentials from environment variables:

from langchain_stardog.voicebox import VoiceboxAskTool

# Tools automatically load credentials from SD_VOICEBOX_API_TOKEN
ask_tool = VoiceboxAskTool()

# Ask a question
result = await ask_tool._arun(question="What flights are delayed?")
print(result["answer"])

Note: Tools only support environment variable initialization to ensure consistent, secure configuration in agent workflows.

Using Runnables in LCEL Chains

Runnables support two initialization patterns:

Pattern 1: Auto-load from Environment (Simple)

from langchain_core.runnables import RunnablePassthrough
from langchain_stardog.voicebox import VoiceboxAskRunnable

# Automatically loads from SD_VOICEBOX_API_TOKEN
chain = (
    RunnablePassthrough()
    | VoiceboxAskRunnable()
    | (lambda x: f"Answer: {x['answer']}")
)

result = await chain.ainvoke({"question": "Show me airports in Texas"})

Pattern 2: Explicit Client (Advanced)

from langchain_core.runnables import RunnablePassthrough
from langchain_stardog.voicebox import VoiceboxClient, VoiceboxAskRunnable

# Create client for custom configuration
client = VoiceboxClient(
    api_token="your-token",
    client_id="my-app"
)

chain = (
    RunnablePassthrough()
    | VoiceboxAskRunnable(client=client)
    | (lambda x: f"Answer: {x['answer']}")
)

result = await chain.ainvoke({"question": "Show me airports in Texas"})

Class Reference

The library provides the following main classes:

Runnables (for LCEL chains):

• VoiceboxSettingsRunnable - Retrieve app settings
• VoiceboxAskRunnable - Ask questions and get answers
• VoiceboxGenerateQueryRunnable - Generate SPARQL queries

Tools (for agent integration):

• VoiceboxSettingsTool
• VoiceboxAskTool
• VoiceboxGenerateQueryTool

Client:

• VoiceboxClient - Core client for Stardog Voicebox API

VoiceboxAskRunnable

Initialization:

# From environment variables (simple)
runnable = VoiceboxAskRunnable()

# With explicit client (advanced)
client = VoiceboxClient.from_env()
runnable = VoiceboxAskRunnable(client=client)

Usage:

# Async
result = await runnable.ainvoke({
    "question": "Your question here",
    "conversation_id": "optional-conv-id"
})

# Sync
result = runnable.invoke({
    "question": "Your question here",
    "conversation_id": "optional-conv-id"
})

Output:

{
    "answer": "Natural language answer",
    "sparql_query": "SELECT ...",
    "interpreted_question": "How Voicebox understood it",
    "conversation_id": "conv-123",
    "message_id": "msg-456"
}

VoiceboxAskTool

Initialization:

Tools only support environment variable initialization for consistent, secure agent workflows:

# Automatically loads from SD_VOICEBOX_API_TOKEN
tool = VoiceboxAskTool()

Usage:

# Async
result = await tool._arun(
    question="Your question",
    conversation_id="optional"  # Optional: for multi-turn conversations
)

# Sync
result = tool._run(
    question="Your question",
    conversation_id="optional"
)

VoiceboxClient

Initialization:

# From environment variables (recommended)
client = VoiceboxClient.from_env()

# With custom configuration
client = VoiceboxClient.from_env(
    client_id="my-app",           # Optional: override default client ID
    endpoint="custom-endpoint"     # Optional: custom API endpoint
)

# Direct initialization (advanced)
client = VoiceboxClient(
    api_token="your-token",           # Required: Voicebox API token
    client_id="my-app",                # Optional: Client identifier (default: VBX-LANGCHAIN)
    endpoint="https://...",            # Optional: API endpoint
    auth_token_override="sso-token"    # Optional: SSO auth token
)

Methods:

• async_get_settings() / get_settings() - Get Voicebox app settings
• async_ask(question, conversation_id=None) / ask(...) - Ask a question
• async_generate_query(question, conversation_id=None) / generate_query(...) - Generate SPARQL query

Examples

Check out the examples/ directory for basic examples on how to use the library:

• direct_tool_usage.py - Direct tool usage and multi-turn conversations
• agent_integration.py - Agent integration using AWS Bedrock
• runnable_chains.py - LCEL chains with runnables

Development

Setup

Clone the repository and install dependencies:

git clone https://github.com/stardog-union/stardog-langchain.git
cd stardog-langchain
make install-dev

Common development commands:

# Run tests
make test

# Run tests with coverage
make test-cov

# Format code
make format

# Type checking
make type-check

# Linting
make lint

# Run all CI checks (format, type-check, lint, test)
make ci

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes, add tests and run make test to verify
4. Run make ci to verify all static code quality checks pass
5. Submit a pull request

General Support

• Documentation: Stardog Documentation
• Community: Stardog Community