waveassist 0.3.1

WaveAssist Python SDK for storing and retrieving structured data, LLM integration, and credit management

WaveAssist SDK & CLI 🌊

WaveAssist SDK makes it simple to store and retrieve data in your automation workflows. Access your projects through our Python SDK or CLI.

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✨ Features

• 🔐 One-line init() to connect with your WaveAssist project
• ⚙️ Automatically works on local and WaveAssist Cloud (worker) environments
• 📦 Store and retrieve data (DataFrames, JSON, strings)
• 🧠 LLM-friendly function names (init, store_data, fetch_data)
• 📁 Auto-serialization for common Python objects
• 🤖 LLM integration with structured outputs via Instructor and OpenRouter
• 💳 Credit management and automatic email notifications
• 🖥️ Command-line interface for project management
• ✅ Built for automation workflows, cron jobs, and AI pipelines

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🚀 Getting Started

1. Install

pip install waveassist

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2. Initialize the SDK

import waveassist

# Option 1: Use no arguments (recommended)
waveassist.init()

# Option 2: With explicit parameters
waveassist.init(
    token="your-user-id",
    project_key="your-project-key",
    environment_key="your-env-key",  # optional
    run_id="run-123",  # optional
    check_credits=True  # optional: raises error if credits_available is "0"
)

# Will auto-resolve from:
# 1. Explicit args (if passed)
# 2. .env file (uid, project_key, environment_key)
# 3. Worker-injected credentials (on [WaveAssist Cloud](https://waveassist.io))

🛠 Setting up .env (for local runs)

uid=your-user-id
project_key=your-project-key

# optional
environment_key=your-env-key

This file will be ignored by Git if you use our default .gitignore.

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3. Store Data

🧾 Store a string

waveassist.store_data("welcome_message", "Hello, world!")

📊 Store a DataFrame

import pandas as pd

df = pd.DataFrame({"name": ["Alice", "Bob"], "score": [95, 88]})
waveassist.store_data("user_scores", df)

🧠 Store JSON/dict/array

profile = {"name": "Alice", "age": 30}
waveassist.store_data("profile_data", profile)

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4. Fetch Data

result = waveassist.fetch_data("user_scores")

# Will return:
# - A DataFrame (if stored as one)
# - A dict/list (if stored as JSON)
# - A string (if stored as text)

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5. Check Credits and Notify

Check OpenRouter credits and automatically send email notifications if insufficient credits are available:

# Check if you have enough credits for an operation
has_credits = waveassist.check_credits_and_notify(
    required_credits=10.5,
    assistant_name="WavePredict"
)

if has_credits:
    # Proceed with your operation
    print("Credits available, proceeding...")
else:
    # Credits insufficient - email notification sent (max 3 times)
    print("Insufficient credits, operation skipped")

Features:

• Automatically checks OpenRouter credit balance
• Sends email notification if credits are insufficient (max 3 times)
• Resets notification count when credits become sufficient
• Stores credit availability status for workflow control

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6. Call LLM with Structured Outputs

Use Instructor library to get structured responses from LLMs via OpenRouter:

from pydantic import BaseModel

# Define your response structure
class UserInfo(BaseModel):
    name: str
    age: int
    email: str

# Call LLM with structured output
result = waveassist.call_llm(
    model="gpt-4o",
    prompt="Extract user info: John Doe, 30, [e-mail]",
    response_model=UserInfo
)

print(result.name)  # "John Doe"
print(result.age)    # 30
print(result.email)  # "[e-mail]"

Setup:

1. Store your OpenRouter API key:

waveassist.store_data('open_router_key', 'your_openrouter_api_key')

2. Use call_llm() with any Pydantic model for structured outputs

Advanced Usage:

result = waveassist.call_llm(
    model="anthropic/claude-3-opus",
    prompt="Analyze this data...",
    response_model=MyModel,
    max_tokens=3000,
    extra_body={"web_search_options": {"search_context_size": "medium"}}
)

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🖥️ Command Line Interface

WaveAssist CLI comes bundled with the Python package. After installation, you can use the following commands:

🔑 Authentication

waveassist login

This will open your browser for authentication and store the token locally.

📤 Push Code

waveassist push PROJECT_KEY [--force]

Push your local Python code to a WaveAssist project.

📥 Pull Code

waveassist pull PROJECT_KEY [--force]

Pull Python code from a WaveAssist project to your local machine.

ℹ️ Version Info

waveassist version

Display CLI version and environment information.

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🧪 Running Tests

If you’re not using pytest, just run the test script directly:

python tests/run_tests.py

✅ Includes tests for:

• String roundtrip
• JSON/dict roundtrip
• DataFrame roundtrip
• Error if init() is not called

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🛠 Project Structure

WaveAssist/
├── waveassist/
│   ├── __init__.py          # init(), store_data(), fetch_data(), check_credits_and_notify(), call_llm()
│   ├── _config.py           # Global config vars
│   ├── constants.py         # Constants and email templates
│   ├── utils.py             # API utility functions
│   └── ...
├── tests/
│   └── run_tests.py         # Manual test runner

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📌 Notes

• Data is stored in your WaveAssist backend (e.g. MongoDB) as serialized content
• store_data() auto-detects the object type and serializes it (CSV/JSON/text)
• fetch_data() deserializes it back to the right Python object

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🧠 Example Use Cases

Basic Data Storage

import waveassist
waveassist.init()  # Auto-initialized from .env or worker

# Store GitHub PR data
waveassist.store_data("latest_pr", {
    "title": "Fix bug in auth",
    "author": "alice",
    "status": "open"
})

# Later, fetch it for further processing
pr = waveassist.fetch_data("latest_pr")
print(pr["title"])

LLM Integration with Credit Management

import waveassist
from pydantic import BaseModel

waveassist.init()

# Store OpenRouter API key
waveassist.store_data('open_router_key', 'your_api_key')

# Check credits before expensive operation
required_credits = 5.0
if waveassist.check_credits_and_notify(required_credits, "MyAssistant"):
    # Use LLM with structured output
    class AnalysisResult(BaseModel):
        summary: str
        confidence: float
        recommendations: list[str]

    result = waveassist.call_llm(
        model="gpt-4o",
        prompt="Analyze this data and provide recommendations...",
        response_model=AnalysisResult
    )

    # Store the structured result
    waveassist.store_data("analysis_result", result.dict())

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🤝 Contributing

Want to add formats, features, or cloud extensions? PRs welcome!

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📬 Contact

Need help or have feedback? Reach out at [e-mail], visit WaveAssist.io, or open an issue.

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© 2025 WaveAssist