netra-sdk 0.1.87

A Python SDK for AI application observability that provides OpenTelemetry-based monitoring, tracing, and PII protection for LLM and vector database applications. Enables easy instrumentation, session tracking, and privacy-focused data collection for AI systems in production environments.

Netra SDK

🚀 Netra SDK is a comprehensive Python library for AI application observability that provides OpenTelemetry-based monitoring, and tracing for LLM applications. It enables easy instrumentation, session tracking, and privacy-focused data analysis for AI systems.

✨ Key Features

• 🔍 Comprehensive AI Observability: Monitor LLM calls, vector database operations, and HTTP requests
• 📊 OpenTelemetry Integration: Industry-standard tracing and metrics
• 🎯 Decorator Support: Easy instrumentation with @workflow, @agent, @task and @span decorators
• 🔧 Multi-Provider Support: Works with OpenAI, Cohere, Google GenAI, Mistral, and more
• 📈 Session Management: Track user sessions and custom attributes
• 🌐 HTTP Client Instrumentation: Automatic tracing for aiohttp and httpx
• 💾 Vector Database Support: Weaviate, Qdrant, and other vector DB instrumentation

📦 Installation

You can install the Netra SDK using pip:

pip install netra-sdk

Or, using Poetry:

poetry add netra-sdk

🚀 Quick Start

Basic Setup

Initialize the Netra SDK at the start of your application:

from netra import Netra
from netra.instrumentation.instruments import InstrumentSet

# Initialize with default settings
Netra.init(app_name="Your application name", instruments={InstrumentSet.OPENAI, InstrumentSet.ANTHROPIC})

# Or with custom configuration
api_key = "Your API key"
headers = f"x-api-key={api_key}"
Netra.init(
    app_name="Your application name",
    headers=headers,
    trace_content=True,
    environment="Your Application environment",
    instruments={InstrumentSet.OPENAI, InstrumentSet.ANTHROPIC},
)

🎯 Decorators for Easy Instrumentation

Use decorators to automatically trace your functions and classes:

from netra.decorators import workflow, agent, task, span

@workflow
def data_processing_workflow(data):
    """Main workflow for processing data"""
    cleaned_data = clean_data(data)
    return analyze_data(cleaned_data)

@agent
def ai_assistant(query):
    """AI agent that processes user queries"""
    return generate_response(query)

@task
def data_validation_task(data):
    """Task for validating input data"""
    return validate_schema(data)

@span
def data_processing_span(data):
    """Span for processing data"""
    return process_data(data)

# Works with async functions too
@workflow(name="Async Data Pipeline")
async def async_workflow(data):
    result = await process_data_async(data)
    return result

# Apply to classes to instrument all methods
@agent
class CustomerSupportAgent:
    def handle_query(self, query):
        return self.process_query(query)

    def escalate_issue(self, issue):
        return self.forward_to_human(issue)

@task
async def async_task(data):
    """Task for processing data"""
    return await process_data_async(data)

@span
async def async_span(data):
    """Span for processing data"""
    return await process_data_async(data)

🔍 Supported Instrumentations

🤖 LLM Providers

• OpenAI - GPT models and completions API
• Anthropic Claude - Claude 3 models and messaging API
• Cohere - Command models and generation API
• Google GenAI (Gemini) - Gemini Pro and other Google AI models
• Mistral AI - Mistral models and chat completions
• Aleph Alpha - Advanced European AI models
• AWS Bedrock - Amazon's managed AI service
• Groq - High-performance AI inference
• Ollama - Local LLM deployment and management
• Replicate - Cloud-based model hosting platform
• Together AI - Collaborative AI platform
• Transformers - Hugging Face transformers library
• Vertex AI - Google Cloud AI platform
• Watson X - IBM's enterprise AI platform

💾 Vector Databases

• Weaviate - Open-source vector database with GraphQL
• Qdrant - High-performance vector similarity search
• Pinecone - Managed vector database service
• Chroma - Open-source embedding database
• LanceDB - Fast vector database for AI applications
• Marqo - Tensor-based search engine
• Milvus - Open-source vector database at scale
• Redis - Vector search with Redis Stack

🌐 HTTP Clients & Web Frameworks

• HTTPX - Modern async HTTP client
• AIOHTTP - Asynchronous HTTP client/server
• FastAPI - Modern web framework for APIs
• Requests - Popular HTTP library for Python
• Django - High-level Python web framework
• Flask - Lightweight WSGI web application framework
• Falcon - High-performance Python web framework
• Starlette - Lightweight ASGI framework/toolkit
• Tornado - Asynchronous networking library and web framework
• gRPC - High-performance, open-source universal RPC framework
• Urllib - Standard Python HTTP client library
• Urllib3 - Powerful, user-friendly HTTP client for Python

🗄️ Database Clients

• PyMySQL - Pure Python MySQL client
• Redis - In-memory data structure store
• SQLAlchemy - SQL toolkit and Object-Relational Mapper
• Psycopg - Modern PostgreSQL database adapter for Python
• Pymongo - Python driver for MongoDB
• Elasticsearch - Distributed, RESTful search and analytics engine
• Cassandra - Distributed NoSQL database
• PyMSSQL - Simple Microsoft SQL Server client
• MySQL Connector - Official MySQL driver
• Sqlite3 - Built-in SQL database engine
• Aiopg - Asynchronous PostgreSQL client
• Asyncpg - Fast asynchronous PostgreSQL client
• Pymemcache - Comprehensive Memcached client
• Tortoise ORM - Easy-to-use asyncio ORM

📨 Messaging & Task Queues

• Celery - Distributed task queue
• Pika - Pure-Python implementation of the AMQP 0-9-1 protocol
• AIO Pika - Asynchronous AMQP client
• Kafka-Python - Python client for Apache Kafka
• AIOKafka - Asynchronous Python client for Kafka
• Confluent-Kafka - Confluent's Python client for Apache Kafka
• Boto3 SQS - Amazon SQS client via Boto3

🔧 AI Frameworks & Orchestration

• LangChain - Framework for developing LLM applications
• LangGraph - Modern framework for LLM applications
• LlamaIndex - Data framework for LLM applications
• Haystack - End-to-end NLP framework
• CrewAI - Multi-agent AI systems
• Pydantic AI - AI model communication standard
• MCP (Model Context Protocol) - AI model communication standard
• LiteLLM - LLM provider agnostic client

📊 Context and Event Logging

Track user sessions and add custom context:

from netra import Netra
from netra.instrumentation.instruments import InstrumentSet

# Initialize SDK
Netra.init(app_name="My App", instruments={InstrumentSet.OPENAI})

# Set session identification
Netra.set_session_id("unique-session-id")
Netra.set_user_id("user-123")
Netra.set_tenant_id("tenant-456")

# Add custom context attributes
Netra.set_custom_attributes(key="customer_tier", value="premium")
Netra.set_custom_attributes(key="region", value="us-east")

# Record custom events
Netra.set_custom_event(event_name="user_feedback", attributes={
    "rating": 5,
    "comment": "Great response!",
    "timestamp": "2024-01-15T10:30:00Z"
})

# Custom events for business metrics
Netra.set_custom_event(event_name="conversion", attributes={
    "type": "subscription",
    "plan": "premium",
    "value": 99.99
})

🔄 Custom Span Tracking

Use the custom span tracking utility to track external API calls with detailed observability:

from netra import Netra, UsageModel

# Start a new span
with Netra.start_span("image_generation") as span:
    # Set span attributes
    span.set_prompt("A beautiful sunset over mountains")
    span.set_negative_prompt("blurry, low quality")
    span.set_model("dall-e-3")
    span.set_llm_system("openai")

    # Set usage data with UsageModel
    usage_data = [
        UsageModel(
            model="dall-e-3",
            usage_type="image_generation",
            units_used=1,
            cost_in_usd=0.02
        )
    ]
    span.set_usage(usage_data)

    # Your API calls here
    # ...

    # Set custom attributes
    span.set_attribute("custom_key", "custom_value")

    # Add events
    span.add_event("generation_started", {"step": "1", "status": "processing"})
    span.add_event("processing_completed", {"step": "rendering"})

    # Get the current active open telemetry span
    current_span = span.get_current_span()

    # Track database operations and other actions
    action = ActionModel(
        start_time="1753857049844249088",  # timestamp in nanoseconds
        action="DB",
        action_type="INSERT",
        affected_records=[
            {"record_id": "user_123", "record_type": "user"},
            {"record_id": "profile_456", "record_type": "profile"}
        ],
        metadata={
            "table": "users",
            "operation_id": "tx_789",
            "duration_ms": "45"
        },
        success=True
    )
    span.set_action([action])

    # Record API calls
    api_action = ActionModel(
        start_time="1753857049844249088",  # timestamp in nanoseconds
        action="API",
        action_type="CALL",
        metadata={
            "endpoint": "/api/v1/process",
            "method": "POST",
            "status_code": 200,
            "duration_ms": "120"
        },
        success=True
    )
    span.set_action([api_action])

Action Tracking Schema

Action tracking follows this schema:

[
    {
        "start_time": str,            # Start time of the action in nanoseconds
        "action": str,                # Type of action (e.g., "DB", "API", "CACHE")
        "action_type": str,           # Action subtype (e.g., "INSERT", "SELECT", "CALL")
        "affected_records": [         # Optional: List of records affected
            {
                "record_id": str,     # ID of the affected record
                "record_type": str    # Type of the record
            }
        ],
        "metadata": Dict[str, str],   # Additional metadata as key-value pairs
        "success": bool              # Whether the action succeeded
    }
]

🔧 Advanced Configuration

Environment Variables

Netra SDK can be configured using the following environment variables:

Netra-specific Variables

Variable Name	Description	Default
NETRA_APP_NAME	Logical name for your service	Falls back to OTEL_SERVICE_NAME or llm_tracing_service
NETRA_OTLP_ENDPOINT	URL for OTLP collector	Falls back to OTEL_EXPORTER_OTLP_ENDPOINT
NETRA_API_KEY	API key for authentication	None
NETRA_HEADERS	Additional headers in W3C Correlation-Context format	None
NETRA_DISABLE_BATCH	Disable batch span processor (true/false)	false
NETRA_TRACE_CONTENT	Whether to capture prompt/completion content (true/false)	true
NETRA_ENV	Deployment environment (e.g., prod, staging, dev)	local
NETRA_RESOURCE_ATTRS	JSON string of custom resource attributes	{}

Standard OpenTelemetry Variables

Variable Name	Description	Used When
OTEL_SERVICE_NAME	Logical name for your service	When NETRA_APP_NAME is not set
OTEL_EXPORTER_OTLP_ENDPOINT	URL for OTLP collector	When NETRA_OTLP_ENDPOINT is not set
OTEL_EXPORTER_OTLP_HEADERS	Additional headers for OTLP exporter	When NETRA_HEADERS is not set
OTEL_RESOURCE_ATTRIBUTES	Additional resource attributes	When NETRA_RESOURCE_ATTRS is not set

Configuration Precedence

Configuration values are resolved in the following order (highest to lowest precedence):

1. Code Parameters: Values passed directly to Netra.init()
2. Netra Environment Variables: NETRA_* variables
3. OpenTelemetry Environment Variables: Standard OTEL_* variables
4. Default Values: Fallback values defined in the SDK

This allows you to:

• 🔄 Vendor Agnostic: Switch between observability platforms without code changes
• 📊 Standard Format: Consistent telemetry data across all tools
• 🔧 Flexible Integration: Works with existing observability infrastructure
• 🚀 Future Proof: Built on industry-standard protocols
• 📈 Rich Ecosystem: Leverage the entire OpenTelemetry ecosystem

🧪 Tests

Our test suite is built on pytest and is designed to ensure the reliability and stability of the Netra SDK. We follow comprehensive testing standards, including unit, integration, and thread-safety tests.

Running Tests

To run the complete test suite, use the following command from the root of the project:

poetry run pytest

Run Specific Test File

To run a specific test file, use the following command from the root of the project:

poetry run pytest tests/test_netra_init.py

Test Coverage

To generate a test coverage report, you can run:

poetry run pytest --cov=netra --cov-report=html

This will create an htmlcov directory with a detailed report.

Running Specific Test Categories

Tests are organized using pytest markers. You can run specific categories of tests as follows:

# Run only unit tests (default)
poetry run pytest -m unit

# Run only integration tests
poetry run pytest -m integration

# Run only thread-safety tests
poetry run pytest -m thread_safety

For more detailed information on our testing strategy, fixtures, and best practices, please refer to the README.md file in the tests directory.

🛠️ Development Setup

To set up your development environment for the Netra SDK, run the provided setup script:

./setup_dev.sh

This script will:

1. Install all Python dependencies in development mode
2. Set up pre-commit hooks for code quality
3. Configure commit message formatting

Manual Setup

If you prefer to set up manually:

# Install dependencies
pip install -e ".[dev,test]"

# Install pre-commit hooks
pip install pre-commit
pre-commit install --install-hooks
pre-commit install --hook-type commit-msg
pre-commit install --hook-type pre-push

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for detailed information on how to contribute to the project, including development setup, testing, and our commit message format.

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