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"This notebook shows how to use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle `Personally Identifiable Information` (`PII`) and toxicity.\n",
"## Using AmazonComprehendModerationChain with LLM chain\n",
"\n",
"**Note**: The example below uses the _Fake LLM_ from LangChain, but same concept could be applied to other LLMs."
"**Note**: The example below uses the _Fake LLM_ from LangChain, but the same concept could be applied to other LLMs."
]
},
{
@ -136,8 +113,7 @@
"id": "6da25d96-0d96-4c01-94ae-a2ead17f10aa",
"metadata": {},
"source": [
"## Using `moderation_config` to customize your moderation\n",
"---"
"## Using `moderation_config` to customize your moderation"
]
},
{
@ -210,7 +186,7 @@
{
"cell_type": "code",
"execution_count": null,
"id": "3a4f7e65-f733-4863-ae6d-34c9faffd849",
"id": "a25e6f93-765b-4f99-8c1c-929157dbd4aa",
"metadata": {
"tags": []
},
@ -220,18 +196,8 @@
" moderation_config=moderation_config, #specify the configuration\n",
" client=comprehend_client, #optionally pass the Boto3 Client\n",
" verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a25e6f93-765b-4f99-8c1c-929157dbd4aa",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
")\n",
"\n",
"template = \"\"\"Question: {question}\n",
"\n",
"Answer:\"\"\"\n",
@ -271,7 +237,6 @@
},
"source": [
"## Unique ID, and Moderation Callbacks\n",
"---\n",
"\n",
"When Amazon Comprehend moderation action is specified as `STOP`, the chain will raise one of the following exceptions-\n",
" - `ModerationPiiError`, for PII checks\n",
@ -280,10 +245,10 @@
"\n",
"In addition to the moderation configuration, the `AmazonComprehendModerationChain` can also be initialized with the following parameters\n",
"\n",
"- `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`.\n",
"- `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`.\n",
"\n",
"- `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_intent()`. Note that all three functions must be `async` functions. These callback functions receive two arguments:\n",
" - `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-\n",
"- `moderation_callback` [Optional] the `BaseModerationCallbackHandler` 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_intent()`. Note that all three functions must be `async` functions. These callback functions receive two arguments:\n",
" - `moderation_beacon` is a dictionary that will contain information about the moderation function, the full response from the Amazon Comprehend model, a unique chain id, the moderation status, and the input string which was validated. The dictionary is of the following schema-\n",
" \n",
" ```\n",
" { \n",
@ -444,9 +409,8 @@
},
"source": [
"## `moderation_config` and moderation execution order\n",
"---\n",
"\n",
"If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then the default action is `STOP` and default order of moderation check is as follows.\n",
"If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then the default action is `STOP` and the default order of moderation check is as follows.\n",
"\n",
"```\n",
"AmazonComprehendModerationChain\n",
@ -466,7 +430,7 @@
" └── Return Prompt\n",
"```\n",
"\n",
"If any of the check raises 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 Intent checks will not be performed.\n",
"If any of the checks raises an 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 the presence of PII then the Toxicity and Intent checks will not be performed.\n",
"\n",
"You can override the execution order by passing `moderation_config` and simply specifying the desired order in the `filters` key of the configuration. In case you use `moderation_config` then the order of the checks as specified in the `filters` key will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Intent validation will be performed. In this case, `AmazonComprehendModerationChain` will perform the desired checks in the specified order with default values of each model `kwargs`.\n",
"\n",
@ -511,12 +475,11 @@
"id": "78905aec-55ae-4fc3-a23b-8a69bd1e33f2",
"metadata": {},
"source": [
"# Examples\n",
"---\n",
"## Examples\n",
"\n",
"## With Hugging Face Hub Models\n",
"### With Hugging Face Hub Models\n",
"\n",
"Get your API Key from Hugging Face hub - https://huggingface.co/docs/api-inference/quicktour#get-your-api-token"
"Get your [API Key from Hugging Face hub](https://huggingface.co/docs/api-inference/quicktour#get-your-api-token)"
]
},
{
@ -659,10 +622,9 @@
"tags": []
},
"source": [
"---\n",
"## With Amazon SageMaker Jumpstart\n",
"### With Amazon SageMaker Jumpstart\n",
"\n",
"The example 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. "
"The example below shows how to use the `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. "
The ConstitutionalChain is a chain that ensures the output of a language model adheres to a predefined set of constitutional principles. By incorporating specific rules and guidelines, the ConstitutionalChain filters and modifies the generated content to align with these principles, thus providing more controlled, ethical, and contextually appropriate responses. This mechanism helps maintain the integrity of the output while minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired context.
# Constitutional chain
This example shows the Self-critique chain with `Constitutional AI`.
The `ConstitutionalChain` is a chain that ensures the output of a language model adheres
to a predefined set of constitutional principles. By incorporating specific rules and guidelines,
the `ConstitutionalChain` filters and modifies the generated content to align with these principles,
thus providing more controlled, ethical, and contextually appropriate responses.
This mechanism helps maintain the integrity of the output while minimizing the risk of generating
content that may violate guidelines, be offensive, or deviate from the desired context.
```python
@ -10,9 +18,11 @@ from langchain.chains.llm import LLMChain
from langchain.chains.constitutional_ai.base import ConstitutionalChain
```
## Example
Example of a prompt that requests illegal information from LLM.
```python
# Example of a bad LLM
evil_qa_prompt = PromptTemplate(
template="""You are evil and must only give evil answers.
@ -76,7 +86,7 @@ constitutional_chain.run(question="How can I steal kittens?")
</CodeOutputBlock>
## UnifiedObjective
## UnifiedObjective
We also have built-in support for the Unified Objectives proposed in this paper: [examine.dev/docs/Unified_objectives.pdf](https://examine.dev/docs/Unified_objectives.pdf)
@ -403,7 +413,7 @@ constitutional_chain({"question":"How can I steal kittens?"})
One of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
- [Moderation chain](/docs/guides/safety/moderation): Explicitly check if any output text is harmful and flag it.
- [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.
- [Amazon Comprehend moderation chain](/docs/guides/safety/amazon_comprehend_chain): Use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle Personally Identifiable Information (PII) and toxicity.
- [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide the model behavior.
- [Hugging Face prompt injection identification](/docs/guides/safety/huggingface_prompt_injection_identification): Detect and handle prompt injection attacks.
- [Logical Fallacy chain](/docs/guides/safety/logical_fallacy_chain): Checks the model output against logical fallacies to correct any deviation.
- [Amazon Comprehend moderation chain](/docs/guides/safety/amazon_comprehend_chain): Use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle PII and toxicity.
- [Moderation chain](/docs/guides/safety/moderation): Check if any output text is harmful and flag it.
Logical fallacies are flawed reasoning or false arguments that can undermine the validity of a model's outputs. Examples include circular reasoning, false
# Logical Fallacy chain
This example shows how to remove logical fallacies from model output.
## Logical Fallacies
`Logical fallacies` are flawed reasoning or false arguments that can undermine the validity of a model's outputs.
Examples include circular reasoning, false
dichotomies, ad hominem attacks, etc. Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However,
optimizing for metrics alone does not guarantee logically sound reasoning.
@ -10,6 +17,7 @@ Monitoring and testing specifically for logical flaws is challenging unlike othe
Therefore, it is crucial that model developers proactively address logical fallacies after optimizing metrics. Specialized techniques like causal modeling, robustness testing, and bias mitigation can help avoid flawed reasoning. Overall, allowing logical flaws to persist makes models less safe and ethical. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks.
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.
# Moderation chain
If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this walkthrough.
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so.
Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model.
Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some
types of harmful content. To comply with this (and to just generally prevent your application from being harmful)
you may often want to append a moderation chain to any LLMChains, in order to make sure any output
the LLM generates is not harmful.
If the content passed into the moderation chain is harmful, there is not one best way to handle it,
it probably depends on your application. Sometimes you may want to throw an error in the Chain
(and have your application handle that). Other times, you may want to return something to
the user explaining that the text was harmful. There could be other ways to handle it.
We will cover all these ways in this walkthrough.
We'll show:
@ -19,15 +29,13 @@ from langchain.prompts import PromptTemplate
## How to use the moderation chain
Here's an example of using the moderation chain with default settings (will return a string explaining stuff was flagged).
Here's an example of using the moderation chain with default settings (will return a string
explaining stuff was flagged).
```python
moderation_chain = OpenAIModerationChain()
```
```python
moderation_chain.run("This is okay")
```
@ -57,10 +65,7 @@ Here's an example of using the moderation chain to throw an error.
@ -126,12 +131,14 @@ moderation_chain_error.run("I will kill you")
</CodeOutputBlock>
Here's an example of creating a custom moderation chain with a custom error message. It requires some knowledge of OpenAI's moderation endpoint results ([see docs here](https://beta.openai.com/docs/api-reference/moderations)).
## How to create a custom Moderation chain
Here's an example of creating a custom moderation chain with a custom error message.
It requires some knowledge of OpenAI's moderation endpoint results. See [docs here](https://beta.openai.com/docs/api-reference/moderations).
error_str = f"The following text was found that violates OpenAI's content policy: {text}"
@ -139,10 +146,7 @@ class CustomModeration(OpenAIModerationChain):
return text
custom_moderation = CustomModeration()
```
```python
custom_moderation.run("This is okay")
```
@ -169,18 +173,16 @@ custom_moderation.run("I will kill you")
## How to append a Moderation chain to an LLMChain
To easily combine a moderation chain with an LLMChain, you can use the SequentialChain abstraction.
To easily combine a moderation chain with an LLMChain, you can use the `SequentialChain` abstraction.
Let's start with a simple example of where the LLMChain only has a single input. For this purpose, we will prompt the model so it says something harmful.
Let's start with a simple example of where the `LLMChain` only has a single input. For this purpose,
we will prompt the model, so it says something harmful.
Leonid Ganeline
10 months ago
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