NeoAPI SDK

The official Python SDK for integrating neoapi.ai LLM Analytics with your LLM pipelines. Track, analyze, and optimize your Language Model outputs with real-time analytics.

Installation

pip install neoapi-sdk

Quick Start Guide

First, set your API key as an environment variable:

export NEOAPI_API_KEY="your-api-key"

Basic Usage

from neoapi import NeoApiClientSync, track_llm_output

# The context manager handles client lifecycle automatically
with NeoApiClientSync() as client:
    # Decorate your LLM function to track its outputs
    @track_llm_output(client=client)
    def get_llm_response(prompt: str) -> str:
        # Your LLM logic here
        return "AI generated response"
    
    # Use your function normally
    response = get_llm_response("What is machine learning?")

Async Support

import asyncio
from neoapi import NeoApiClientAsync, track_llm_output

async def main():
    async with NeoApiClientAsync() as client:
        @track_llm_output(
            client=client,
            project="chatbot",
            need_analysis_response=True  # Get analytics feedback
        )
        async def get_llm_response(prompt: str) -> str:
            # Your async LLM logic here
            await asyncio.sleep(0.1)  # Simulated API call
            return "Async AI response"
        
        response = await get_llm_response("Explain async programming")

# Run your async code
asyncio.run(main())

OpenAI Integration Example

from openai import OpenAI
from neoapi import NeoApiClientSync, track_llm_output

def chat_with_gpt():
    openai_client = OpenAI()  # Uses OPENAI_API_KEY env variable
    
    with NeoApiClientSync() as neo_client:
        @track_llm_output(
            client=neo_client,
            project="gpt4_chat",
            need_analysis_response=True,  # Get quality metrics
            format_json_output=True       # Pretty-print analytics
        )
        def ask_gpt(prompt: str) -> str:
            response = openai_client.chat.completions.create(
                messages=[{"role": "user", "content": prompt}],
                model="gpt-4o-mini"
            )
            return response.choices[0].message.content

        # Use the tracked function
        response = ask_gpt("What are the key principles of clean code?")
        print(response)  # Analytics will be logged automatically

Key Features

• 🔄 Automatic Tracking: Decorator-based output monitoring
• ⚡ Async Support: Built for high-performance async applications
• 🔍 Real-time Analytics: Get immediate feedback on output quality
• 🛠 Flexible Integration: Works with any LLM provider
• 🔧 Configurable: Extensive customization options
• 🔐 Secure: Environment-based configuration

Configuration Options

Environment Variables

# Required
export NEOAPI_API_KEY="your-api-key"


### Client Configuration
```python
client = NeoApiClientSync(
    # Basic settings
    api_key="your-api-key",      # Optional if env var is set
    check_frequency=1,           # Process every Nth output
    
    # Performance tuning
    batch_size=10,               # Outputs per batch
    flush_interval=5.0,          # Seconds between flushes
    max_retries=3,              # Retry attempts on failure
    
    # Advanced options
    api_url="custom-url",        # Optional API endpoint
    max_batch_size=100,         # Maximum batch size
)

Decorator Options

@track_llm_output(
    client=client,
    
    # Organization
    project="my_project",        # Project identifier
    group="experiment_a",        # Subgroup within project
    analysis_slug="v1.2",        # Version or analysis identifier
    
    # Analytics
    need_analysis_response=True, # Get quality metrics
    format_json_output=True,     # Pretty-print analytics
    
    # Custom data
    metadata={                   # Additional tracking info
        "model": "gpt-4",
        "temperature": 0.7,
        "user_id": "user123"
    },
    save_text=True              # Store output text
)

Best Practices

1. Use Context Managers: They handle client lifecycle automatically

   with NeoApiClientSync() as client:
       # Your code here

2. Group Related Outputs: Use project and group parameters

   @track_llm_output(client=client, project="chatbot", group="user_support")

3. Add Relevant Metadata: Include context for better analysis

   @track_llm_output(
       client=client,
       metadata={"user_type": "premium", "session_id": "abc123"}
   )

Resources

• 📚 Full Documentation
• 💻 GitHub Repository
• 🤝 Support
• 📝 API Reference

License

Apache License 2.0 - See LICENSE for details