panoptica-genai-protection 0.1.18
Protecting GenAI from Prompt Injection
Requires Python: >=3.9

# 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"
      )
```