Amazon Comprehend Moderation Chain

Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning to uncover valuable insights and connections in text.

This notebook shows how to use Amazon Comprehend to detect and handle Personally Identifiable Information (PII) and toxicity.

Setting up

%pip install boto3 nltk

%pip install -U langchain_experimental

%pip install -U langchain pydantic

import os

import boto3

comprehend_client = boto3.client("comprehend", region_name="us-east-1")

from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain

comprehend_moderation = AmazonComprehendModerationChain(
    client=comprehend_client,
    verbose=True,  # optional
)

Using AmazonComprehendModerationChain with LLM chain

Note: The example below uses the Fake LLM from LangChain, but the same concept could be applied to other LLMs.

from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
    ModerationPiiError,
)

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.",
    # replace with your own expletive
    "Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]
llm = FakeListLLM(responses=responses)

chain = (
    prompt
    | comprehend_moderation
    | {"input": (lambda x: x["output"]) | llm}
    | comprehend_moderation
)

try:
    response = chain.invoke(
        {
            "question": "A sample SSN number looks like this 123-22-3345. Can you give me some more samples?"
        }
    )
except ModerationPiiError as e:
    print(str(e))
else:
    print(response["output"])

Using moderation_config to customize your moderation

Use Amazon Comprehend Moderation with a configuration to control what moderations you wish to perform and what actions should be taken for each of them. There are three different moderations that happen when no configuration is passed as demonstrated above. These moderations are:

• PII (Personally Identifiable Information) checks
• Toxicity content detection
• Prompt Safety detection

Here is an example of a moderation config.

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

At the core of the the configuration there are three configuration models to be used

• ModerationPiiConfig used for configuring the behavior of the PII validations. Following are the parameters it can be initialized with
  • labels the PII entity labels. Defaults to an empty list which means that the PII validation will consider all PII entities.
  • threshold the confidence threshold for the detected entities, defaults to 0.5 or 50%
  • redact a boolean flag to enforce whether redaction should be performed on the text, defaults to False. When False, the PII validation will error out when it detects any PII entity, when set to True it simply redacts the PII values in the text.
  • mask_character the character used for masking, defaults to asterisk (*)
• ModerationToxicityConfig used for configuring the behavior of the toxicity validations. Following are the parameters it can be initialized with
  • labels the Toxic entity labels. Defaults to an empty list which means that the toxicity validation will consider all toxic entities. all
  • threshold the confidence threshold for the detected entities, defaults to 0.5 or 50%
• ModerationPromptSafetyConfig used for configuring the behavior of the prompt safety validation
  • threshold the confidence threshold for the the prompt safety classification, defaults to 0.5 or 50%

Finally, you use the BaseModerationConfig to define the order in which each of these checks are to be performed. The BaseModerationConfig takes an optional filters parameter which can be a list of one or more than one of the above validation checks, as seen in the previous code block. The BaseModerationConfig can also be initialized with any filters in which case it will use all the checks with default configuration (more on this explained later).

Using the configuration in the previous cell will perform PII checks and will allow the prompt to pass through however it will mask any SSN numbers present in either the prompt or the LLM output.

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

from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM

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.",
    # replace with your own expletive
    "Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]
llm = FakeListLLM(responses=responses)

chain = (
    prompt
    | comp_moderation_with_config
    | {"input": (lambda x: x["output"]) | llm}
    | comp_moderation_with_config
)


try:
    response = chain.invoke(
        {
            "question": "A sample SSN number looks like this 123-45-7890. Can you give me some more samples?"
        }
    )
except Exception as e:
    print(str(e))
else:
    print(response["output"])

Unique ID, and Moderation Callbacks

When Amazon Comprehend moderation action identifies any of the configugred entity, the chain will raise one of the following exceptions- - ModerationPiiError, for PII checks - ModerationToxicityError, for Toxicity checks - ModerationPromptSafetyError for Prompt Safety checks

In addition to the moderation configuration, the AmazonComprehendModerationChain can also be initialized with the following parameters

• unique_id [Optional] a string parameter. This parameter can be used to pass any string value or ID. For example, in a chat application, you may want to keep track of abusive users, in this case, you can pass the user’s username/email ID etc. This defaults to None.

• moderation_callback [Optional] the BaseModerationCallbackHandler that will be called asynchronously (non-blocking to the chain). Callback functions are useful when you want to perform additional actions when the moderation functions are executed, for example logging into a database, or writing a log file. You can override three functions by subclassing BaseModerationCallbackHandler - on_after_pii(), on_after_toxicity(), and on_after_prompt_safety(). Note that all three functions must be async functions. These callback functions receive two arguments:

  • moderation_beacon a dictionary that will contain information about the moderation function, the full response from Amazon Comprehend model, a unique chain id, the moderation status, and the input string which was validated. The dictionary is of the following schema-

  { 
      'moderation_chain_id': 'xxx-xxx-xxx', # Unique chain ID
      'moderation_type': 'Toxicity' | 'PII' | 'PromptSafety', 
      'moderation_status': 'LABELS_FOUND' | 'LABELS_NOT_FOUND',
      'moderation_input': 'A sample SSN number looks like this 123-456-7890. Can you give me some more samples?',
      'moderation_output': {...} #Full Amazon Comprehend PII, Toxicity, or Prompt Safety Model Output
  }
  • unique_id if passed to the AmazonComprehendModerationChain

NOTE: moderation_callback is different from LangChain Chain Callbacks. You can still use LangChain Chain callbacks with AmazonComprehendModerationChain via the callbacks parameter. Example:

from langchain.callbacks.stdout import StdOutCallbackHandler
comp_moderation_with_config = AmazonComprehendModerationChain(verbose=True, callbacks=[StdOutCallbackHandler()])

from langchain_experimental.comprehend_moderation import BaseModerationCallbackHandler

# Define callback handlers by subclassing BaseModerationCallbackHandler


class MyModCallback(BaseModerationCallbackHandler):
    async def on_after_pii(self, output_beacon, unique_id):
        import json

        moderation_type = output_beacon["moderation_type"]
        chain_id = output_beacon["moderation_chain_id"]
        with open(f"output-{moderation_type}-{chain_id}.json", "w") as file:
            data = {"beacon_data": output_beacon, "unique_id": unique_id}
            json.dump(data, file)

    """
    async def on_after_toxicity(self, output_beacon, unique_id):
        pass
    
    async def on_after_prompt_safety(self, output_beacon, unique_id):
        pass
    """


my_callback = MyModCallback()

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

toxicity_config = ModerationToxicityConfig(threshold=0.5)

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

comp_moderation_with_config = AmazonComprehendModerationChain(
    moderation_config=moderation_config,  # specify the configuration
    client=comprehend_client,  # optionally pass the Boto3 Client
    unique_id="john.doe@email.com",  # A unique ID
    moderation_callback=my_callback,  # BaseModerationCallbackHandler
    verbose=True,
)

from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM

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.",
    # replace with your own expletive
    "Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]

llm = FakeListLLM(responses=responses)

chain = (
    prompt
    | comp_moderation_with_config
    | {"input": (lambda x: x["output"]) | llm}
    | 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"])

moderation_config and moderation execution order

If AmazonComprehendModerationChain is not initialized with any moderation_config then it is initialized with the default values of BaseModerationConfig. If no filters are used then the sequence of moderation check is as follows.

AmazonComprehendModerationChain
│
└──Check PII with Stop Action
    ├── Callback (if available)
    ├── Label Found ⟶ [Error Stop]
    └── No Label Found 
        └──Check Toxicity with Stop Action
            ├── Callback (if available)
            ├── Label Found ⟶ [Error Stop]
            └── No Label Found
                └──Check Prompt Safety with Stop Action
                    ├── Callback (if available)
                    ├── Label Found ⟶ [Error Stop]
                    └── No Label Found
                        └── Return Prompt

If any of the check raises a validation exception then the subsequent checks will not be performed. If a callback is provided in this case, then it will be called for each of the checks that have been performed. For example, in the case above, if the Chain fails due to presence of PII then the Toxicity and Prompt Safety checks will not be performed.

You can override the execution order by passing moderation_config and simply specifying the desired order in the filters parameter of the BaseModerationConfig. In case you specify the filters, then the order of the checks as specified in the filters parameter will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Prompt Safety validation will be performed. In this case, AmazonComprehendModerationChain will perform the desired checks in the specified order with default values of each model kwargs.

pii_check = ModerationPiiConfig()
toxicity_check = ModerationToxicityConfig()
prompt_safety_check = ModerationPromptSafetyConfig()

moderation_config = BaseModerationConfig(filters=[toxicity_check, pii_check, prompt_safety_check])

You can have also use more than one configuration for a specific moderation check, for example in the sample below, two consecutive PII checks are performed. First the configuration checks for any SSN, if found it would raise an error. If any SSN isn’t found then it will next check if any NAME and CREDIT_DEBIT_NUMBER is present in the prompt and will mask it.

pii_check_1 = ModerationPiiConfig(labels=["SSN"])
pii_check_2 = ModerationPiiConfig(labels=["NAME", "CREDIT_DEBIT_NUMBER"], redact=True)

moderation_config = BaseModerationConfig(filters=[pii_check_1, pii_check_2])

1. For a list of PII labels see Amazon Comprehend Universal PII entity types - https://docs.aws.amazon.com/comprehend/latest/dg/how-pii.html#how-pii-types
2. Following are the list of available Toxicity labels-
   • HATE_SPEECH: Speech that criticizes, insults, denounces or dehumanizes a person or a group on the basis of an identity, be it race, ethnicity, gender identity, religion, sexual orientation, ability, national origin, or another identity-group.
   • GRAPHIC: Speech that uses visually descriptive, detailed and unpleasantly vivid imagery is considered as graphic. Such language is often made verbose so as to amplify an insult, discomfort or harm to the recipient.
   • HARASSMENT_OR_ABUSE: Speech that imposes disruptive power dynamics between the speaker and hearer, regardless of intent, seeks to affect the psychological well-being of the recipient, or objectifies a person should be classified as Harassment.
   • SEXUAL: Speech that indicates sexual interest, activity or arousal by using direct or indirect references to body parts or physical traits or sex is considered as toxic with toxicityType “sexual”.
   • VIOLENCE_OR_THREAT: Speech that includes threats which seek to inflict pain, injury or hostility towards a person or group.
   • INSULT: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language.
   • PROFANITY: Speech that contains words, phrases or acronyms that are impolite, vulgar, or offensive is considered as profane.
3. For a list of Prompt Safety labels refer to documentation [link here]

Examples

With Hugging Face Hub Models

Get your API Key from Hugging Face hub

%pip install huggingface_hub

import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = "<YOUR HF TOKEN HERE>"

# See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options
repo_id = "google/flan-t5-xxl"

from langchain.prompts import PromptTemplate
from langchain_community.llms import HuggingFaceHub

template = """{question}"""

prompt = PromptTemplate(template=template, input_variables=["question"])
llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 256}
)

Create a configuration and initialize an Amazon Comprehend Moderation chain

# define filter configs
pii_config = ModerationPiiConfig(
    labels=["SSN", "CREDIT_DEBIT_NUMBER"], redact=True, mask_character="X"
)

toxicity_config = ModerationToxicityConfig(threshold=0.5)

prompt_safety_config = ModerationPromptSafetyConfig(threshold=0.8)

# define different moderation configs using the filter configs above
moderation_config_1 = BaseModerationConfig(
    filters=[pii_config, toxicity_config, prompt_safety_config]
)

moderation_config_2 = BaseModerationConfig(filters=[pii_config])


# input prompt moderation chain with callback
amazon_comp_moderation = AmazonComprehendModerationChain(
    moderation_config=moderation_config_1,
    client=comprehend_client,
    moderation_callback=my_callback,
    verbose=True,
)

# Output from LLM moderation chain without callback
amazon_comp_moderation_out = AmazonComprehendModerationChain(
    moderation_config=moderation_config_2, client=comprehend_client, verbose=True
)

The moderation_config will now prevent any inputs containing obscene words or sentences, bad intent, or PII with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed. It will also mask any SSN or credit card numbers from the model’s response.

chain = (
    prompt
    | amazon_comp_moderation
    | {"input": (lambda x: x["output"]) | llm}
    | amazon_comp_moderation_out
)

try:
    response = chain.invoke(
        {
            "question": """What is John Doe's address, phone number and SSN from the following text?

John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.
"""
        }
    )
except Exception as e:
    print(str(e))
else:
    print(response["output"])

With Amazon SageMaker Jumpstart

The exmaple below shows how to use Amazon Comprehend Moderation chain with an Amazon SageMaker Jumpstart hosted LLM. You should have an Amazon SageMaker Jumpstart hosted LLM endpoint within your AWS Account. Refer to this notebook for more on how to deploy an LLM with Amazon SageMaker Jumpstart hosted endpoints.

endpoint_name = "<SAGEMAKER_ENDPOINT_NAME>"  # replace with your SageMaker Endpoint name
region = "<REGION>"  # replace with your SageMaker Endpoint region

import json

from langchain.prompts import PromptTemplate
from langchain_community.llms import SagemakerEndpoint
from langchain_community.llms.sagemaker_endpoint import LLMContentHandler


class ContentHandler(LLMContentHandler):
    content_type = "application/json"
    accepts = "application/json"

    def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:
        input_str = json.dumps({"text_inputs": prompt, **model_kwargs})
        return input_str.encode("utf-8")

    def transform_output(self, output: bytes) -> str:
        response_json = json.loads(output.read().decode("utf-8"))
        return response_json["generated_texts"][0]


content_handler = ContentHandler()

template = """From the following 'Document', precisely answer the 'Question'. Do not add any spurious information in your answer.

Document: John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.
Question: {question}
Answer:
"""

# prompt template for input text
llm_prompt = PromptTemplate(template=template, input_variables=["question"])

llm = SagemakerEndpoint(
    endpoint_name=endpoint_name,
    region_name=region,
    model_kwargs={
        "temperature": 0.95,
        "max_length": 200,
        "num_return_sequences": 3,
        "top_k": 50,
        "top_p": 0.95,
        "do_sample": True,
    },
    content_handler=content_handler,
)

Create a configuration and initialize an Amazon Comprehend Moderation chain

# define filter configs
pii_config = ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X")

toxicity_config = ModerationToxicityConfig(threshold=0.5)


# define different moderation configs using the filter configs above
moderation_config_1 = BaseModerationConfig(filters=[pii_config, toxicity_config])

moderation_config_2 = BaseModerationConfig(filters=[pii_config])


# input prompt moderation chain with callback
amazon_comp_moderation = AmazonComprehendModerationChain(
    moderation_config=moderation_config_1,
    client=comprehend_client,
    moderation_callback=my_callback,
    verbose=True,
)

# Output from LLM moderation chain without callback
amazon_comp_moderation_out = AmazonComprehendModerationChain(
    moderation_config=moderation_config_2, client=comprehend_client, verbose=True
)

The moderation_config will now prevent any inputs and model outputs containing obscene words or sentences, bad intent, or Pii with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed.

chain = (
    prompt
    | amazon_comp_moderation
    | {"input": (lambda x: x["output"]) | llm}
    | amazon_comp_moderation_out
)

try:
    response = chain.invoke(
        {"question": "What is John Doe's address, phone number and SSN?"}
    )
except Exception as e:
    print(str(e))
else:
    print(response["output"])