Comprehend Moderation 0.2

[nikhilkjha]

This PR replaces the previous Intent check with the new Prompt Safety check. The logic and steps to enable chain moderation via the Amazon Comprehend service, allowing you to detect and redact PII, Toxic, and Prompt Safety information in the LLM prompt or answer remains unchanged.
This implementation updates the code and configuration types with respect to Prompt Safety.

Usage sample

from langchain_experimental.comprehend_moderation import (BaseModerationConfig, 
                                 ModerationPromptSafetyConfig, 
                                 ModerationPiiConfig, 
                                 ModerationToxicityConfig
)

pii_config = ModerationPiiConfig(
    labels=["SSN"],
    redact=True,
    mask_character="X"
)

toxicity_config = ModerationToxicityConfig(
    threshold=0.5
)

prompt_safety_config = ModerationPromptSafetyConfig(
    threshold=0.5
)

moderation_config = BaseModerationConfig(
    filters=[pii_config, toxicity_config, prompt_safety_config]
)

comp_moderation_with_config = AmazonComprehendModerationChain(
    moderation_config=moderation_config, #specify the configuration
    client=comprehend_client,            #optionally pass the Boto3 Client
    verbose=True
)

template = """Question: {question}

Answer:"""

prompt = PromptTemplate(template=template, input_variables=["question"])

responses = [
    "Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.", 
    "Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here."
]
llm = FakeListLLM(responses=responses)

llm_chain = LLMChain(prompt=prompt, llm=llm)

chain = ( 
    prompt 
    | comp_moderation_with_config 
    | {llm_chain.input_keys[0]: lambda x: x['output'] }  
    | llm_chain 
    | { "input": lambda x: x['text'] } 
    | comp_moderation_with_config 
)

try:
    response = chain.invoke({"question": "A sample SSN number looks like this 123-456-7890. Can you give me some more samples?"})
except Exception as e:
    print(str(e))
else:
    print(response['output'])

Output

> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running prompt safety Validation...

> Finished chain.


> Entering new AmazonComprehendModerationChain chain...
Running AmazonComprehendModerationChain...
Running pii Validation...
Running toxicity Validation...
Running prompt safety Validation...

> Finished chain.
Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like XXXXXXXXXXXX John Doe's phone number is (999)253-9876.

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Name	Status	Preview	Comments	Updated (UTC)
langchain	✅ Ready (Inspect)	Visit Preview	💬 Add feedback	Oct 19, 2023 7:15pm

[nikhilkjha]

@baskaryan need your help to move this PR forward. thanks.

[baskaryan]

cc @3coins

[iamwrick]

@baskaryan - can you help with this one please?
3coins - anything you would like us to look into or confirm you are good from your end to move forward ?

[3coins]

Other than some minor comments about the docs, nothing blocking here to merge. LGTM!

[3coins]

This sounds a bit vague. Would help to elaborate on what particular traits this class/function is supposed to check.

[3coins]

Is the response generic enough that could be encoded into a model?