SPARK AI – Multi-Agent Generative AI Assistant with RAG, Web Search & AI Evaluation
SPARK AI is a multi-agent Generative AI assistant that combines RAG, web search, LangGraph orchestration, validation, and evaluation to generate reliable contextual responses.
Business Challenge
Single LLM applications can produce hallucinations, miss relevant knowledge, lack current information, and provide inconsistent responses. SPARK AI was designed to solve these issues by combining retrieval, web search, specialized processing stages, validation, and evaluation in one workflow.
Solution Delivered
Built SPARK AI as a LangGraph-based multi-agent workflow with specialized stages for security checking, simple-query handling, planning, web search, RAG retrieval, document grading, routing, response generation, validation, and final evaluation. The system uses ChromaDB for vector retrieval, FastAPI for the backend, Streamlit for the interface, and multiple LLM providers with fallback handling. Responses are streamed to the frontend through the API.
Outcomes Achieved
Improved the reliability and consistency of AI responses through retrieval-augmented generation, document relevance grading, validation, and evaluation. The workflow reduces unnecessary processing for simple requests, supports current-information queries through web search, blocks unsafe requests, and provides a structured path for retrying low-quality generations. It also centralizes AI workflow orchestration through shared graph state, making the system easier to trace and debug.
Measurable Business Outcome
Measured improvements include higher response quality through validation and evaluation, automated safety filtering for unsafe queries, reduced unnecessary LLM processing for simple questions, and support for retrieval over documents of up to approximately 50 pages. The system was also designed for concurrent usage and observable workflow execution.