Case study
AI Assistant for Hydrogen and Renewable Energy
Stack
DURATION
TEAM SIZE
Project Overview:
The project aims to develop an AI-powered chatbot for the renewable energy and hydrogen domain, designed to answer user queries efficiently while maintaining chat history and filtering irrelevant questions. It incorporates a multi-agent AI approach to improve accuracy, manage different tasks, and optimize system performance. The chatbot uses advanced techniques like Retrieval-Augmented Generation (RAG) and integrates tools for SQL querying and data visualization.
FastAPI
OpenAI GPT-4
LangChain
ChromaDB
pyTesseract
Azure
Stack:
- Web Interface: FastAPI
- AI Brain: OpenAI GPT-4
- AI Workflow Management: LangChain
- Contextual Answers: RAG Pipeline
- Specialized AI Agents: For routing, document search (ChromaDB), SQL querying, and plotting.
- Data Storage: ChromaDB (documents), SQL Database (structured data).
- Data Extraction: pyTesseract (OCR for images/documents).
- Data Visualization: Integrated plotting tool.
- Cloud Hosting: Azure (scalable infrastructure).
Results the Client Got:
To address the challenges of accessing and leveraging information within the Renewable Energy and Hydrogen domain, we developed a comprehensive AI-powered assistant. The solution involved a structured seven-step implementation process:
- Strategic Planning and Design: We began by thoroughly understanding the client’s needs, defining key use cases like domain-specific Q&A, SQL data retrieval, data visualization, and document analysis. We then architected the system using LangChain, specifying input and output formats and selecting core technologies like ChromaDB for retrieval, FastAPI for communication, and Azure for hosting.

- Knowledge Base Construction: We established a robust data ingestion pipeline capable of processing various document types (text, PDFs, images via pyTesseract). Unstructured content was extracted, cleaned, and stored in ChromaDB with relevant metadata to ensure efficient and accurate retrieval.

- Intelligent Information Retrieval: We implemented a Retrieval-Augmented Generation (RAG) pipeline. Documents were divided into meaningful segments, and embeddings were generated. This pipeline enables the AI to fetch relevant context from the knowledge base during user interactions, leading to more informed and accurate responses.

- Core AI Integration: We integrated OpenAI’s GPT-4 as the central reasoning engine, utilizing LangChain to orchestrate the various tools and specialized AI agents. Careful prompt engineering was employed to ensure responses were structured, domain-specific, and aligned with the required tone. A memory component was added to maintain context throughout user conversations.

- Data Analysis and Visualization Capabilities: We built an interface allowing the AI to interpret user queries and generate corresponding SQL commands for data retrieval. Furthermore, we integrated a plotting library, enabling the AI to automatically create relevant visualizations based on the data, enhancing user understanding. Robust input sanitization and fallback mechanisms were implemented to handle unclear or ambiguous queries.
- Rigorous Testing and Optimization: We conducted extensive manual testing across different user scenarios and agent functionalities (question answering, SQL querying, plotting). Based on the evaluation results, we refined document chunking, retrieval strategies, and agent routing. Prompts were iteratively improved to maximize accuracy, response quality, and efficient tool utilization.
- Scalable and Reliable Deployment: The final solution was containerized using Docker and deployed on the Azure cloud platform, leveraging services optimized for scalability (VMs, App Services, or Kubernetes). We implemented API rate limiting, timeouts, and asynchronous request handling to ensure optimal performance and stability.
Results:
Instant Expert Support and Knowledge Access
Benefit: Users can receive accurate, domain-specific answers instantly, reducing the need to search technical documents or wait for human expert input.
Value: Minimizes downtime and improve chances that customers will move forward with the company by getting familiar with the domain.
Advanced Document Insights
Benefit: The RAG pipeline and embedded document analysis enable deeper insights from internal reports, research documents, and operational data.
Value: Empowers data-driven decisions by unlocking critical knowledge previously buried in text or images.
Automated Data Visualization
Benefit: Users receive intuitive plots and graphs automatically generated based on their queries and context.
Value: Facilitates quicker understanding of trends and outliers for business intelligence and presentations.
Domain-Optimized Responses
Benefit: Through prompt engineering and task-specific agents, the system avoids hallucinations and ensures responses are aligned with hydrogen and renewable energy terminology.
Value: Builds confidence in AI-driven answers, crucial for high-stakes decisions in technical or regulatory contexts.
Michael Babylon
German Representative
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