# LLM Wrapper
A comprehensive Python wrapper for Azure OpenAI with built-in PostgreSQL integration and usage tracking. Provides detailed analytics for LLM usage with support for both text and JSON response formats.
## Features
- 🚀 **Easy Integration**: Simple API for interacting with Azure OpenAI services
- 📊 **Usage Tracking**: Comprehensive logging and analytics for all LLM requests
- 💾 **PostgreSQL Integration**: Built-in PostgreSQL database support with automatic table creation
- ⚡ **High Performance**: Optimized for concurrent requests and high throughput
- 🔒 **Secure**: Built-in security features and API key management
- 📈 **Analytics**: Detailed usage statistics and reporting
- 🎯 **Response Types**: Support for both text and JSON response formats
- 🐳 **Production Ready**: Robust error handling and logging
## Installation
### Basic Installation
```bash
pip install llm_wrapper_biz
```
## Quick Start
### Basic Usage
```python
from llm_wrapper_biz import LLMWrapper
# Initialize the wrapper (database connection is automatic)
wrapper = LLMWrapper(
service_url="https://your-azure-openai-instance.openai.azure.com",
api_key="your-azure-openai-api-key",
deployment_name="your-deployment-name",
api_version="2023-05-15",
default_model='gpt-4'
)
# Send a text request
response = wrapper.send_request(
input_text="What are the benefits of renewable energy?",
customer_id=1,
organization_id=1,
response_type="text", # "text" or "json"
temperature=0.7,
max_tokens=2000
)
print(f"Response: {response['processed_output']}")
print(f"Tokens used: {response['total_tokens']}")
print(f"Response type: {response['response_type']}")
# Send a JSON request
json_response = wrapper.send_request(
input_text="Create a JSON object with information about Python programming including name, creator, and year_created.",
customer_id=1,
organization_id=1,
response_type="json"
)
print(f"JSON Response: {json_response['processed_output']}")
print(f"Creator: {json_response['processed_output'].get('creator', 'N/A')}")
# Get usage statistics
stats = wrapper.get_usage_stats()
print(f"Total requests: {stats['total_requests']}")
print(f"Total tokens: {stats['total_tokens']}")
# Clean up
wrapper.close()
```
### Simplified Usage
For easier integration, use the simplified methods:
```python
# Get just the processed output (text)
text_result = wrapper.send_request(
input_text="Explain quantum computing",
customer_id=1,
organization_id=1,
response_type="text"
)
print(text_result)
# Get just the processed output (JSON)
json_result = wrapper.send_request(
input_text="Create JSON with weather data for London",
customer_id=1,
organization_id=1,
response_type="json"
)
```
## Response Types
### Text Response (Default)
```python
response = wrapper.send_request(
input_text="Explain artificial intelligence",
customer_id=1,
organization_id=1,
response_type="text"
)
# Response structure:
{
"output_text": "raw response from API",
"processed_output": "same as output_text for text responses",
"response_type": "text",
"input_tokens": 10,
"output_tokens": 150,
"total_tokens": 160,
"response_time_ms": 1200,
"model": "gpt-4",
"full_response": {...}
}
```
### JSON Response
```python
response = wrapper.send_request(
input_text="Create a JSON object with user information including name, age, and skills array",
customer_id=1,
organization_id=1,
response_type="json"
)
# Response structure:
{
"output_text": '{"name": "John", "age": 30, "skills": ["Python", "AI"]}',
"processed_output": {"name": "John", "age": 30, "skills": ["Python", "AI"]},
"response_type": "json",
"input_tokens": 15,
"output_tokens": 25,
"total_tokens": 40,
"response_time_ms": 1500,
"model": "gpt-4",
"full_response": {...}
}
```
## Database Schema
The wrapper automatically creates the following PostgreSQL table:
```sql
CREATE TABLE token_usage_log (
id SERIAL PRIMARY KEY,
customer_id INTEGER NOT NULL,
organization_id INTEGER NOT NULL,
model_name VARCHAR(255) NOT NULL,
request_params JSON,
response_params JSON,
input_tokens INTEGER NOT NULL,
output_tokens INTEGER NOT NULL,
total_tokens INTEGER NOT NULL,
request_timestamp TIMESTAMP DEFAULT NOW(),
response_time_ms INTEGER NOT NULL,
status VARCHAR(50) DEFAULT 'success'
);
```
## Usage Analytics
```python
# Get overall statistics
stats = wrapper.get_usage_stats()
# Get customer-specific statistics
customer_stats = wrapper.get_usage_stats(customer_id=1)
# Get organization-specific statistics
org_stats = wrapper.get_usage_stats(organization_id=1)
# Get statistics for a specific time period
period_stats = wrapper.get_usage_stats(
start_date="2024-01-01T00:00:00",
end_date="2024-01-31T23:59:59"
)
# Example stats output:
{
"total_requests": 150,
"total_tokens": 45000,
"models": [
{
"model_name": "gpt-4",
"requests": 100,
"input_tokens": 15000,
"output_tokens": 20000,
"total_tokens": 35000,
"avg_response_time_ms": 1200
},
{
"model_name": "gpt-3.5-turbo",
"requests": 50,
"input_tokens": 5000,
"output_tokens": 5000,
"total_tokens": 10000,
"avg_response_time_ms": 800
}
]
}
```
## Configuration Options
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| `service_url` | str | Required | Azure OpenAI service endpoint URL |
| `api_key` | str | Required | Azure OpenAI API key |
| `deployment_name` | str | Required | Azure OpenAI deployment name |
| `api_version` | str | Required | Azure OpenAI API version |
| `default_model` | str | 'gpt-4' | Default model identifier |
| `timeout` | int | 30 | Request timeout in seconds |
## API Reference
### Core Methods
#### `send_request(input_text, customer_id, organization_id, response_type="text", **kwargs)`
Send a request to the Azure OpenAI service.
**Parameters:**
- `input_text` (str): The prompt text
- `customer_id` (int): Customer identifier
- `organization_id` (int): Organization identifier
- `response_type` (str): Response format - "text" or "json"
- `model` (str, optional): Model to use for this request
- `temperature` (float, optional): Sampling temperature (0.0-1.0)
- `max_tokens` (int, optional): Maximum tokens in response
**Returns:**
- `dict`: Response containing output text, processed output, token counts, and metadata
#### `send_request_simple(input_text, customer_id, organization_id, response_type="text", **kwargs)`
Simplified method that returns only the processed output.
**Parameters:**
- Same as `send_request()`
**Returns:**
- `str` (for text) or `dict` (for JSON): Direct processed output
#### `get_usage_stats(**filters)`
Get usage statistics with optional filtering.
**Parameters:**
- `customer_id` (int, optional): Filter by customer
- `organization_id` (int, optional): Filter by organization
- `start_date` (str, optional): Start date in ISO format
- `end_date` (str, optional): End date in ISO format
**Returns:**
- `dict`: Usage statistics including request counts, token usage, and performance metrics
#### `close()`
Close database connections and clean up resources.
## Requirements
- Python 3.8+
## License
This project is licensed under the MIT License.
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## Acknowledgments
- Thanks to all contributors who have helped shape this project
- Built with love for the AI/ML community
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"description": "# LLM Wrapper\r\n\r\nA comprehensive Python wrapper for Azure OpenAI with built-in PostgreSQL integration and usage tracking. Provides detailed analytics for LLM usage with support for both text and JSON response formats.\r\n\r\n## Features\r\n\r\n- \ud83d\ude80 **Easy Integration**: Simple API for interacting with Azure OpenAI services\r\n- \ud83d\udcca **Usage Tracking**: Comprehensive logging and analytics for all LLM requests\r\n- \ud83d\udcbe **PostgreSQL Integration**: Built-in PostgreSQL database support with automatic table creation\r\n- \u26a1 **High Performance**: Optimized for concurrent requests and high throughput\r\n- \ud83d\udd12 **Secure**: Built-in security features and API key management\r\n- \ud83d\udcc8 **Analytics**: Detailed usage statistics and reporting\r\n- \ud83c\udfaf **Response Types**: Support for both text and JSON response formats\r\n- \ud83d\udc33 **Production Ready**: Robust error handling and logging\r\n\r\n## Installation\r\n\r\n### Basic Installation\r\n\r\n```bash\r\npip install llm_wrapper_biz\r\n```\r\n\r\n## Quick Start\r\n\r\n### Basic Usage\r\n\r\n```python\r\nfrom llm_wrapper_biz import LLMWrapper\r\n\r\n# Initialize the wrapper (database connection is automatic)\r\nwrapper = LLMWrapper(\r\n service_url=\"https://your-azure-openai-instance.openai.azure.com\",\r\n api_key=\"your-azure-openai-api-key\",\r\n deployment_name=\"your-deployment-name\",\r\n api_version=\"2023-05-15\",\r\n default_model='gpt-4'\r\n)\r\n\r\n# Send a text request\r\nresponse = wrapper.send_request(\r\n input_text=\"What are the benefits of renewable energy?\",\r\n customer_id=1,\r\n organization_id=1,\r\n response_type=\"text\", # \"text\" or \"json\"\r\n temperature=0.7,\r\n max_tokens=2000\r\n)\r\n\r\nprint(f\"Response: {response['processed_output']}\")\r\nprint(f\"Tokens used: {response['total_tokens']}\")\r\nprint(f\"Response type: {response['response_type']}\")\r\n\r\n# Send a JSON request\r\njson_response = wrapper.send_request(\r\n input_text=\"Create a JSON object with information about Python programming including name, creator, and year_created.\",\r\n customer_id=1,\r\n organization_id=1,\r\n response_type=\"json\"\r\n)\r\n\r\nprint(f\"JSON Response: {json_response['processed_output']}\")\r\nprint(f\"Creator: {json_response['processed_output'].get('creator', 'N/A')}\")\r\n\r\n# Get usage statistics\r\nstats = wrapper.get_usage_stats()\r\nprint(f\"Total requests: {stats['total_requests']}\")\r\nprint(f\"Total tokens: {stats['total_tokens']}\")\r\n\r\n# Clean up\r\nwrapper.close()\r\n```\r\n\r\n### Simplified Usage\r\n\r\nFor easier integration, use the simplified methods:\r\n\r\n```python\r\n# Get just the processed output (text)\r\ntext_result = wrapper.send_request(\r\n input_text=\"Explain quantum computing\",\r\n customer_id=1,\r\n organization_id=1,\r\n response_type=\"text\"\r\n)\r\nprint(text_result)\r\n\r\n# Get just the processed output (JSON)\r\njson_result = wrapper.send_request(\r\n input_text=\"Create JSON with weather data for London\",\r\n customer_id=1,\r\n organization_id=1,\r\n response_type=\"json\"\r\n)\r\n```\r\n\r\n## Response Types\r\n\r\n### Text Response (Default)\r\n\r\n```python\r\nresponse = wrapper.send_request(\r\n input_text=\"Explain artificial intelligence\",\r\n customer_id=1,\r\n organization_id=1,\r\n response_type=\"text\"\r\n)\r\n\r\n# Response structure:\r\n{\r\n \"output_text\": \"raw response from API\",\r\n \"processed_output\": \"same as output_text for text responses\",\r\n \"response_type\": \"text\",\r\n \"input_tokens\": 10,\r\n \"output_tokens\": 150,\r\n \"total_tokens\": 160,\r\n \"response_time_ms\": 1200,\r\n \"model\": \"gpt-4\",\r\n \"full_response\": {...}\r\n}\r\n```\r\n\r\n### JSON Response\r\n\r\n```python\r\nresponse = wrapper.send_request(\r\n input_text=\"Create a JSON object with user information including name, age, and skills array\",\r\n customer_id=1,\r\n organization_id=1,\r\n response_type=\"json\"\r\n)\r\n\r\n# Response structure:\r\n{\r\n \"output_text\": '{\"name\": \"John\", \"age\": 30, \"skills\": [\"Python\", \"AI\"]}',\r\n \"processed_output\": {\"name\": \"John\", \"age\": 30, \"skills\": [\"Python\", \"AI\"]},\r\n \"response_type\": \"json\",\r\n \"input_tokens\": 15,\r\n \"output_tokens\": 25,\r\n \"total_tokens\": 40,\r\n \"response_time_ms\": 1500,\r\n \"model\": \"gpt-4\",\r\n \"full_response\": {...}\r\n}\r\n```\r\n\r\n\r\n## Database Schema\r\n\r\nThe wrapper automatically creates the following PostgreSQL table:\r\n\r\n```sql\r\nCREATE TABLE token_usage_log (\r\n id SERIAL PRIMARY KEY,\r\n customer_id INTEGER NOT NULL,\r\n organization_id INTEGER NOT NULL,\r\n model_name VARCHAR(255) NOT NULL,\r\n request_params JSON,\r\n response_params JSON,\r\n input_tokens INTEGER NOT NULL,\r\n output_tokens INTEGER NOT NULL,\r\n total_tokens INTEGER NOT NULL,\r\n request_timestamp TIMESTAMP DEFAULT NOW(),\r\n response_time_ms INTEGER NOT NULL,\r\n status VARCHAR(50) DEFAULT 'success'\r\n);\r\n```\r\n\r\n## Usage Analytics\r\n\r\n```python\r\n# Get overall statistics\r\nstats = wrapper.get_usage_stats()\r\n\r\n# Get customer-specific statistics\r\ncustomer_stats = wrapper.get_usage_stats(customer_id=1)\r\n\r\n# Get organization-specific statistics\r\norg_stats = wrapper.get_usage_stats(organization_id=1)\r\n\r\n# Get statistics for a specific time period\r\nperiod_stats = wrapper.get_usage_stats(\r\n start_date=\"2024-01-01T00:00:00\",\r\n end_date=\"2024-01-31T23:59:59\"\r\n)\r\n\r\n# Example stats output:\r\n{\r\n \"total_requests\": 150,\r\n \"total_tokens\": 45000,\r\n \"models\": [\r\n {\r\n \"model_name\": \"gpt-4\",\r\n \"requests\": 100,\r\n \"input_tokens\": 15000,\r\n \"output_tokens\": 20000,\r\n \"total_tokens\": 35000,\r\n \"avg_response_time_ms\": 1200\r\n },\r\n {\r\n \"model_name\": \"gpt-3.5-turbo\",\r\n \"requests\": 50,\r\n \"input_tokens\": 5000,\r\n \"output_tokens\": 5000,\r\n \"total_tokens\": 10000,\r\n \"avg_response_time_ms\": 800\r\n }\r\n ]\r\n}\r\n```\r\n\r\n## Configuration Options\r\n\r\n| Parameter | Type | Default | Description |\r\n|-----------|------|---------|-------------|\r\n| `service_url` | str | Required | Azure OpenAI service endpoint URL |\r\n| `api_key` | str | Required | Azure OpenAI API key |\r\n| `deployment_name` | str | Required | Azure OpenAI deployment name |\r\n| `api_version` | str | Required | Azure OpenAI API version |\r\n| `default_model` | str | 'gpt-4' | Default model identifier |\r\n| `timeout` | int | 30 | Request timeout in seconds |\r\n\r\n## API Reference\r\n\r\n### Core Methods\r\n\r\n#### `send_request(input_text, customer_id, organization_id, response_type=\"text\", **kwargs)`\r\n\r\nSend a request to the Azure OpenAI service.\r\n\r\n**Parameters:**\r\n- `input_text` (str): The prompt text\r\n- `customer_id` (int): Customer identifier\r\n- `organization_id` (int): Organization identifier\r\n- `response_type` (str): Response format - \"text\" or \"json\"\r\n- `model` (str, optional): Model to use for this request\r\n- `temperature` (float, optional): Sampling temperature (0.0-1.0)\r\n- `max_tokens` (int, optional): Maximum tokens in response\r\n\r\n**Returns:**\r\n- `dict`: Response containing output text, processed output, token counts, and metadata\r\n\r\n#### `send_request_simple(input_text, customer_id, organization_id, response_type=\"text\", **kwargs)`\r\n\r\nSimplified method that returns only the processed output.\r\n\r\n**Parameters:**\r\n- Same as `send_request()`\r\n\r\n**Returns:**\r\n- `str` (for text) or `dict` (for JSON): Direct processed output\r\n\r\n#### `get_usage_stats(**filters)`\r\n\r\nGet usage statistics with optional filtering.\r\n\r\n**Parameters:**\r\n- `customer_id` (int, optional): Filter by customer\r\n- `organization_id` (int, optional): Filter by organization\r\n- `start_date` (str, optional): Start date in ISO format\r\n- `end_date` (str, optional): End date in ISO format\r\n\r\n**Returns:**\r\n- `dict`: Usage statistics including request counts, token usage, and performance metrics\r\n\r\n#### `close()`\r\n\r\nClose database connections and clean up resources.\r\n\r\n\r\n## Requirements\r\n\r\n- Python 3.8+\r\n\r\n## License\r\n\r\nThis project is licensed under the MIT License.\r\n\r\n## Contributing\r\n\r\nContributions are welcome! Please feel free to submit a Pull Request.\r\n\r\n## Acknowledgments\r\n\r\n- Thanks to all contributors who have helped shape this project\r\n- Built with love for the AI/ML community\r\n",
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