Multimodal RAG Chatbot for Accurate Energy Domain Answers
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Solution Overview
Problem
Conventional chatbots are not tailored to provide detailed answers in specific technology areas like the energy domain, such as oil and gas, leading to inadequate query responses.
Innovation Solution
A system and method utilizing a multimodal retrieval-augmented generation (RAG) agent to convert user queries into contextualized queries, retrieve relevant information from vector databases, and generate answers using large language models (LLM) and custom vision language models (VLM), with domain-specific guardrails and benchmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional chatbots are used to answer user queries, then the system is simple and easy to operate, but the chatbot cannot provide detailed answers in specific technology areas like the energy domain
Solution Approach 1:
The patent introduces a retrieval-augmented generation (RAG) system as an intermediary between the chatbot and the knowledge base. This RAG system retrieves relevant information from external sources (technical documents, manuals, and other domain-specific resources) and augments the chatbot's responses, enabling it to provide detailed and accurate answers in specific technology areas like the energy domain while maintaining ease of operation for users
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing domain-specific knowledge in structured formats (vector databases, knowledge graphs) before queries are submitted. This allows the chatbot to quickly retrieve and utilize relevant information during interactions, ensuring comprehensive and accurate responses without complicating the user interface or interaction process
2Reliability
If a multimodal RAG system with vector databases and multiple models is implemented, then the chatbot can provide accurate and detailed responses in the energy domain, but the system complexity increases
Solution Approach 1:
The patent segments the complex system into distinct modular components: a retrieval module that queries vector databases, a generation module that synthesizes responses using LLMs, a multimodal processing module that handles both text and images, and a guardrail module that ensures safe and compliant outputs. This segmentation allows each component to be optimized independently while working together to achieve high response accuracy in the energy domain
Solution Approach 2:
The system employs universal components that perform multiple functions. For example, the multimodal RAG agent both retrieves information from vector databases and generates synthesized responses, while also handling both text and image inputs. The guardrail service simultaneously performs safety checks, content filtering, and domain-specific validation. This multi-functionality reduces the need for separate specialized components, managing system complexity while maintaining high reliability
Data Source
AI summary
A method for generating a response to a user query includes receiving a user query that involves user query text and/or one or more user query images. The method also includes converting the user query into a contextualized query using a multimodal retrieval-augmented generation (RAG) agent. The method also includes retrieving paths from a vector database in response to the contextualized query. The method also includes retrieving contents from a storage in response to the paths. The method also includes generating an answer to the user query based upon the contents.