How to build an AI DevSecOps Chatbot

Understood, the implementation of Retrieval Augmented Generation (RAG) for DevSecOps in a GenAI application involves integrating a powerful search service like Amazon Kendra with language models (LLMs) to enhance conversational experiences. Here's a step-by-step guide on how to implement this RAG workflow:

1. Content Indexing: Utilize Amazon Kendra to index your enterprise knowledge base, including documents, articles, manuals, and any other relevant content related to DevSecOps. Ensure that the indexing is comprehensive and covers all the necessary topics within DevSecOps.
2. Query Understanding: Develop mechanisms to understand user queries effectively. This involves parsing user input to identify the intent and extract key information or keywords. Natural Language Understanding (NLU) techniques can be employed here to enhance the accuracy of query understanding.
3. Search Query Generation: Generate search queries based on the user's input. This could involve refining the query, adding additional context, or structuring it in a way that maximizes relevance to the indexed content.
4. Search Execution: Send the generated search query to Amazon Kendra for execution. Amazon Kendra will return the most relevant documents and passages based on the query and the indexed content.
5. Context Bundling: Bundle the retrieved content along with the original user query as context. This context will serve as input to the language model, providing it with relevant information to generate accurate responses.
6. Response Generation: Feed the bundled context (user query + relevant content) to the language model for response generation. The language model, having access to both user queries and relevant content, can generate responses that are contextually appropriate and informative.
7. Response Ranking: Optionally, if multiple responses are generated, employ a ranking mechanism to prioritize the most relevant and accurate response based on factors such as coherence, informativeness, and relevance to the user query.
8. Response Delivery: Finally, deliver the generated response to the user interface or application frontend for presentation to the user.

By following this workflow, you can leverage the combined capabilities of a powerful search service like Amazon Kendra and language models to create a state-of-the-art GenAI application tailored for DevSecOps, providing accurate and informative conversational experiences over enterprise content.

EC2 Bootstrap Script:

export DEBIAN_FRONTEND=noninteractive 
apt update
apt install awscli python3-pip python3-venv unzip -y
aws s3 cp s3://genai-devsecops-chatbot-{account_id}/generative-ai-to-build-a-devsecops-chatbot.zip .
unzip generative-ai-to-build-a-devsecops-chatbot.zip
cd generative-ai-to-build-a-devsecops-chatbot
python3 -m venv .venv
source .venv/bin/activate
pip3 install boto3 langchain streamlit
export AWS_REGION=us-west-2
export KENDRA_INDEX_ID={kendra_index_id}
streamlit run app.py {model_name}        

References:

• https://www.youtube.com/watch?v=5YpKt7eS2ic
• https://github.com/aws-samples/generative-ai-to-build-a-devsecops-chatbot/
• https://catalog.us-east-1.prod.workshops.aws/workshops/75a20314-5e15-4246-9352-3643d9dafc43/en-US