# Panoptica GenAI Protection SDK
A simple python client SDK for integration with Panoptica GenAI Protection.
___
GenAI Protection is part of Panoptica, a cloud native application protection platform (CNAPP), and provides protection for LLM-backed systems.
Specifically, the GenAI Protection SDK inspects both input and output prompts, flagging those it identifies as likely containing malicious content with a high degree of certainty.
The Python SDK is provided to programmatically integrate your system with our LLM protection software,
enabling you to verify the safety level of processing a user requested prompt before actually processing it.
Following this evaluation, the application can then determine the appropriate subsequent steps based on your policy.
## Installation
```shell
pip install panoptica_genai_protection
```
## Usage Example
Working assumptions:
- You have generated a key-pair for GenAI Protection in the Panoptica settings screen
* The access key is set in the GENAI_PROTECTION_ACCESS_KEY environment variable
* The secret key is set in the GENAI_PROTECTION_SECRET_KEY environment variable
- We denote the call to generating the LLM response as get_llm_response()
GenAIProtectionClient provides the check_llm_prompt method to determine the safety level of a given prompt.
### Sample Snippet
```python
from panoptica_genai_protection.client import GenAIProtectionClient
from panoptica_genai_protection.gen.models import Result as InspectionResult
# ... Other code in your module ...
# initialize the client
genai_protection_client = GenAIProtectionClient()
# Send the prompt for inspection BEFORE sending it to the LLM
inspection_result = genai_protection_client.check_llm_prompt(
chat_request.prompt,
api_name="chat_service", # Name of the service running the LLM
api_endpoint_name="/chat", # Name of the endpoint serving the LLM interaction
sequence_id=chat_id, # UUID of the chat, if you don't have one, provide `None`
actor="John Doe", # Name of the "actor" interacting with the LLM service.
actor_type="user", # Actor type, one of {"user", "ip", "bot"}
)
if inspection_result.result == InspectionResult.safe:
# Prompt is safe, generate an LLM response
llm_response = get_llm_response(
chat_request.prompt
)
# Call GenAI protection on LLM response (completion)
inspection_result = genai_protection_client.check_llm_response(
prompt=chat_request.prompt,
response=llm_response,
api_name="chat_service",
api_endpoint_name="/chat",
actor="John Doe",
actor_type="user",
request_id=inspection_result.reqId,
sequence_id=chat_id,
)
if inspection_result.result != InspectionResult.safe:
# LLM answer is flagged as unsafe, return a predefined error message to the user
answer_response = "Something went wrong."
else:
# Prompt is flagged as unsafe, return a predefined error message to the user
answer_response = "Something went wrong."
```
#### Async use:
You may use the client in async context in two ways:
```python
async def my_async_call_to_gen_ai_protection(prompt: str):
client = GenAIProtectionClient(as_async=True)
return await client.check_llm_prompt_async(
prompt=prompt,
api_name="test",
api_endpoint_name="/test",
actor="John Doe",
actor_type="user"
)
```
or
```python
async def my_other_async_call_to_gen_ai_protection(prompt: str):
async with GenAIProtectionClient() as client:
return await client.check_llm_prompt_async(
prompt=prompt,
api_name="test",
api_endpoint_name="/test",
actor="John Doe",
actor_type="user"
)
```
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"description": "# Panoptica GenAI Protection SDK\nA simple python client SDK for integration with Panoptica GenAI Protection.\n___\nGenAI Protection is part of Panoptica, a cloud native application protection platform (CNAPP), and provides protection for LLM-backed systems.\nSpecifically, the GenAI Protection SDK inspects both input and output prompts, flagging those it identifies as likely containing malicious content with a high degree of certainty. \n\nThe Python SDK is provided to programmatically integrate your system with our LLM protection software,\nenabling you to verify the safety level of processing a user requested prompt before actually processing it.\nFollowing this evaluation, the application can then determine the appropriate subsequent steps based on your policy.\n\n## Installation\n```shell\npip install panoptica_genai_protection\n```\n\n## Usage Example\nWorking assumptions:\n- You have generated a key-pair for GenAI Protection in the Panoptica settings screen\n * The access key is set in the GENAI_PROTECTION_ACCESS_KEY environment variable\n * The secret key is set in the GENAI_PROTECTION_SECRET_KEY environment variable\n- We denote the call to generating the LLM response as get_llm_response()\n\nGenAIProtectionClient provides the check_llm_prompt method to determine the safety level of a given prompt.\n\n\n### Sample Snippet\n\n```python\nfrom panoptica_genai_protection.client import GenAIProtectionClient\nfrom panoptica_genai_protection.gen.models import Result as InspectionResult\n\n# ... Other code in your module ...\n\n# initialize the client\ngenai_protection_client = GenAIProtectionClient()\n\n# Send the prompt for inspection BEFORE sending it to the LLM\ninspection_result = genai_protection_client.check_llm_prompt(\n chat_request.prompt,\n api_name=\"chat_service\", # Name of the service running the LLM\n api_endpoint_name=\"/chat\", # Name of the endpoint serving the LLM interaction\n sequence_id=chat_id, # UUID of the chat, if you don't have one, provide `None`\n actor=\"John Doe\", # Name of the \"actor\" interacting with the LLM service.\n actor_type=\"user\", # Actor type, one of {\"user\", \"ip\", \"bot\"}\n)\n\nif inspection_result.result == InspectionResult.safe:\n # Prompt is safe, generate an LLM response\n llm_response = get_llm_response(\n chat_request.prompt\n )\n\n # Call GenAI protection on LLM response (completion)\n inspection_result = genai_protection_client.check_llm_response(\n prompt=chat_request.prompt,\n response=llm_response,\n api_name=\"chat_service\",\n api_endpoint_name=\"/chat\",\n actor=\"John Doe\",\n actor_type=\"user\",\n request_id=inspection_result.reqId,\n sequence_id=chat_id,\n )\n if inspection_result.result != InspectionResult.safe:\n # LLM answer is flagged as unsafe, return a predefined error message to the user\n answer_response = \"Something went wrong.\"\nelse:\n # Prompt is flagged as unsafe, return a predefined error message to the user\n answer_response = \"Something went wrong.\"\n```\n\n#### Async use:\nYou may use the client in async context in two ways:\n```python\nasync def my_async_call_to_gen_ai_protection(prompt: str):\n client = GenAIProtectionClient(as_async=True)\n return await client.check_llm_prompt_async(\n prompt=prompt,\n api_name=\"test\",\n api_endpoint_name=\"/test\",\n actor=\"John Doe\",\n actor_type=\"user\"\n )\n```\nor\n```python\nasync def my_other_async_call_to_gen_ai_protection(prompt: str):\n async with GenAIProtectionClient() as client:\n return await client.check_llm_prompt_async(\n prompt=prompt,\n api_name=\"test\",\n api_endpoint_name=\"/test\",\n actor=\"John Doe\",\n actor_type=\"user\"\n )\n```\n",
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