Generative AI and large language models (LLMs) are often used for question answering systems based on external knowledge. Traditional systems struggle with vague or ambiguous questions without context. To address this, an interactive clarification component using LangChain is introduced, allowing a conversational dialogue with users to gather context and provide accurate answers. The solution is demonstrated using Amazon Kendra, Amazon Bedrock LLM, and Streamlit.
Improve LLM Responses in RAG Use Cases with Interactive User Interaction
One common application of generative AI and large language models (LLMs) is answering questions based on external knowledge. However, traditional systems often struggle with vague or ambiguous questions, leading to unhelpful or incorrect responses. In this post, we introduce a solution to enhance the quality of answers in such cases by incorporating interactive clarification using LangChain.
Solution Overview
To demonstrate the solution, we set up an Amazon Kendra index, a LangChain agent with an Amazon Bedrock LLM, and a Streamlit user interface.
Prerequisites
To run this demo, complete the following prerequisites:
- Clone the GitHub repository and follow the steps in the README.
- Deploy an Amazon Kendra index in your AWS account.
- Set up the LangChain agent with the necessary foundations.
- Use Amazon SageMaker Studio to run the Streamlit app.
Implement the Solution
Traditional RAG agents retrieve relevant documents and provide answers based on the retrieved context. In this solution, we enhance the agent by adding a custom tool called AskHumanTool. This tool allows the agent to ask the user for clarification when the initial question is unclear.
By incorporating this interactive dialogue, the agent can gather the necessary context to provide accurate and helpful answers, even with ambiguous queries.
Example Workflow
Here is an example workflow:
- The user asks a question: “How many GPUs does my EC2 instance have?”
- The agent uses the LLM to decide the next action.
- The agent retrieves information from the Amazon Kendra index.
- If the retrieved context is insufficient, the agent uses the AskHumanTool to ask the user for clarification.
- Once the user provides the necessary information, the agent incorporates it and retrieves the correct answer.
This interactive approach improves the reliability and accuracy of responses, leading to a better customer experience in various RAG applications.
Clean Up
To avoid unnecessary costs, delete the Amazon Kendra index and shut down the SageMaker Studio instance if not in use.
Conclusion
By adding interactive user interaction to RAG systems, we can enhance the customer experience and deliver more satisfactory answers. This approach can be applied to various generative AI use cases, not just RAG. To learn more about using Amazon Kendra with generative AI, refer to our resources.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Improve LLM responses in RAG use cases by interacting with the user
- AWS Machine Learning Blog
- Twitter – @itinaicom
