Ensuring Content Compliance with Amazon Bedrock and ConstitutionalChain

Introduction

Generative AI transforms content creation by streamlining workflows and improving efficiency across various domains, including marketing, image generation, and content moderation. However, ensuring that AI-generated content adheres to ethical guidelines and regulatory standards remains a critical challenge. Constitutional AI and LangGraph reflection mechanisms offer a structured approach to maintaining compliance. While Anthropic embeds ethical principles during model training, LangGraph reinforces them at runtime through self-correction and reflection. By leveraging these capabilities with Amazon Bedrock and ConstitutionalChain, content creators can generate high-quality, regulation-compliant content with minimal manual intervention. This approach enhances transparency, accountability, and efficiency, making it particularly valuable in highly regulated industries such as finance and healthcare. In this blog, we will explore strategies for implementing Constitutional AI to ensure compliance while optimizing content production.

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Solution overview

Create an Amazon Bedrock knowledge base

Make a new knowledge base on the Amazon Bedrock console.
Create a new AWS IAM service role and give your knowledge base a name.

step1

3. Select Amazon S3 as the data source and supply the Amazon S3 bucket where the data is stored.

4. Select OpenSearch Serverless as the vector storage and Amazon Titan Text Embeddings v2 as the embeddings model.

5. Select Create Knowledge Base

step5

Define Constitutional AI components

We define a criticism class to organize the results of the criticism process. Next, we draft templates for review and editing. Finally, we use LangChain to create chains that generate comments, criticisms, and changes.

# LangChain Constitutional chain migration to LangGraph

class Critique(TypedDict):
    """Generate a critique, if needed."""

    critique_needed: Annotated[bool, ..., "Whether or not a critique is needed."]
    critique: Annotated[str, ..., "If needed, the critique."]

critique_prompt = ChatPromptTemplate.from_template(
    "Critique this response according to the critique request. "
…
)

revision_prompt = ChatPromptTemplate.from_template(
    "Revise this response according to the critique and reivsion request.\n\n"
    ….
)
chain = llm | StrOutputParser()
critique_chain = critique_prompt | llm.with_structured_output(Critique)
revision_chain = revision_prompt | llm | StrOutputParser()

# LangChain Constitutional chain migration to LangGraph

class Critique(TypedDict):

"""Generate a critique, if needed."""

critique_needed: Annotated[bool, ..., "Whether or not a critique is needed."]

critique: Annotated[str, ..., "If needed, the critique."]

critique_prompt = ChatPromptTemplate.from_template(

"Critique this response according to the critique request. "

…

)

revision_prompt = ChatPromptTemplate.from_template(

"Revise this response according to the critique and reivsion request.\n\n"

….

)

chain = llm | StrOutputParser()

critique_chain = critique_prompt | llm.with_structured_output(Critique)

revision_chain = revision_prompt | llm | StrOutputParser()

Use the Amazon Bedrock Knowledge Bases retriever and define a State class

To handle the discussion state, which consists of the question, guiding principles, answers, and criticisms, we construct a LangGraph State class:

# LangGraph State

class State(TypedDict):
    query: str
    constitutional_principles: List[ConstitutionalPrinciple]

# LangGraph State

class State(TypedDict):

query: str

constitutional_principles: List[ConstitutionalPrinciple]

We configured an Amazon Bedrock Knowledge Bases retriever to extract the pertinent data. To write an essay based on mental health papers, we use the Amazon Bedrock knowledge base that we previously established. Be careful to use the knowledge base you built in the preceding stages to change the knowledge base ID in the following code:

#-----------------------------------------------------------------
# Amazon Bedrock KnowledgeBase

from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever

retriever = AmazonKnowledgeBasesRetriever(
knowledge_base_id="W3NMIJXLUE", # Change it to your Knowledge base ID
…
)

#-----------------------------------------------------------------

# Amazon Bedrock KnowledgeBase

from langchain_aws.retrievers import AmazonKnowledgeBasesRetriever

retriever = AmazonKnowledgeBasesRetriever(

knowledge_base_id="W3NMIJXLUE", # Change it to your Knowledge base ID

…

)

Construct a LangGraph graph and its nodes using constitutional principles

graph

It incorporates a unique DEI approach to direct the LLM’s answers and employs a StateGraph to control the flow between RAG and critique/revision nodes. The Streamlit program, which offers an interactive chat platform where people can submit questions and examine the LLM’s preliminary replies, criticisms, and updated responses, is used to showcase the method. Additionally, the program has a sidebar explaining the applicable ethical principle and a graph depicting the workflow. By employing adaptable constitutional principles that direct a reflection flow (critique and revise), this all-encompassing approach ensures that the LLM’s outputs are both knowledge-based and ethically aligned while preserving a user-friendly interface with features like chat history management and an obvious chat option.

Streamlit application

This code Streamlit application component gives the Constitutional AI model an engaging and interactive interface. It opens a side pane with a discussion of the DEI principle in use and a depiction of the LLM process graph. Users can enter their questions and see the LLM answers in the chat part of the main interface.

# ------------------------------------------------------------------------
# Streamlit App

# Clear Chat History fuction
def clear_screen():
    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

with st.sidebar:
    st.subheader('Constitutional AI Demo')
…..
    ConstitutionalPrinciple(
        name="DEI Principle",
        critique_request="Analyze the content for any lack of diversity, equity, or inclusion. Identify specific instances where the text could be more inclusive or representative of diverse perspectives.",
        revision_request="Rewrite the content by incorporating critiques to be more diverse, equitable, and inclusive. Ensure representation of various perspectives and use inclusive language throughout."
    )
    """)
    st.button('Clear Screen', on_click=clear_screen)

# Store LLM generated responses
if "messages" not in st.session_state.keys():
    st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

# Chat Input - User Prompt 
if prompt := st.chat_input():
….

    with st.spinner(f"Generating..."):
        ….
    with st.chat_message("assistant"):
        st.markdown("**[initial response]**")
….
        st.session_state.messages.append({"role": "assistant", "content": "[revised response] " + generation['response']})

# ------------------------------------------------------------------------

# Streamlit App

# Clear Chat History fuction

def clear_screen():

st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

with st.sidebar:

st.subheader('Constitutional AI Demo')

…..

ConstitutionalPrinciple(

name="DEI Principle",

critique_request="Analyze the content for any lack of diversity, equity, or inclusion. Identify specific instances where the text could be more inclusive or representative of diverse perspectives.",

revision_request="Rewrite the content by incorporating critiques to be more diverse, equitable, and inclusive. Ensure representation of various perspectives and use inclusive language throughout."

)

""")

st.button('Clear Screen', on_click=clear_screen)

# Store LLM generated responses

if "messages" not in st.session_state.keys():

st.session_state.messages = [{"role": "assistant", "content": "How may I assist you today?"}]

# Chat Input - User Prompt

if prompt := st.chat_input():

….

with st.spinner(f"Generating..."):

….

with st.chat_message("assistant"):

st.markdown("**[initial response]**")

….

st.session_state.messages.append({"role": "assistant", "content": "[revised response] " + generation['response']})

The tool keeps track of user inputs and LLM answers in the chat history, showing the original response, any criticisms created, and the final, updated response. The user is shown each step of the LLM procedure with a clear label. A Clear Screen option to clear the conversation history is also included in the UI. The application provides transparency into the LLM functioning by displaying the runtime and a loading spinner when processing a query. Users may engage with the LLM and see how constitutional principles are used to improve its outcomes due to its thorough user interface design.

Conclusion

We explored implementing a structured approach to generating compliant content using Amazon Bedrock and LangGraph.

By leveraging Constitutional AI, content creators can ensure ethical alignment, apply predefined principles, and refine AI-generated responses in real-time. This approach reduces the need for extensive manual oversight while enhancing transparency and regulatory adherence.

To further strengthen compliance, integrating Amazon Bedrock Guardrails with LangGraph Constitutional AI provides a multi-layered safety mechanism. While Amazon Bedrock enforces content filtering and policy constraints at the API level, LangGraph applies ethical reasoning and self-correction at runtime. These technologies create a framework for producing responsible AI-generated content, particularly in highly regulated industries such as finance and healthcare. As AI continues to evolve, businesses can build on these foundations to refine compliance strategies and enhance the trustworthiness of AI-driven content.

Drop a query if you have any questions regarding Amazon Bedrock or LangGraph and we will get back to you quickly.

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FAQs

1. How does Constitutional AI ensure content compliance?

ANS: – It applies predefined ethical principles, using LangGraph for critique and revision, ensuring transparency and regulatory adherence.

2. How does LangGraph improve AI content moderation?

ANS: – LangGraph enables real-time critique and revision, refining AI-generated content to align with ethical and compliance standards.

WRITTEN BY Aayushi Khandelwal

Aayushi, a dedicated Research Associate pursuing a Bachelor's degree in Computer Science, is passionate about technology and cloud computing. Her fascination with cloud technology led her to a career in AWS Consulting, where she finds satisfaction in helping clients overcome challenges and optimize their cloud infrastructure. Committed to continuous learning, Aayushi stays updated with evolving AWS technologies, aiming to impact the field significantly and contribute to the success of businesses leveraging AWS services.