GraphAsk — AI Chat Assistant
AI chat assistant backed by a knowledge graph for structured domain Q&A — grounding responses in a curated entity/relationship graph instead of free-form retrieval alone.
Case study summary
- Client type
- Knowledge-assistant product
- Industry
- Information retrieval
- Duration
- TODO: Confirm project dates and duration
- My role
- AI assistant and retrieval development
- Team size
- TODO: Confirm team size and collaborators
- Stack
- LLM Integration, Knowledge Graphs, Python, Neo4j, React
- Platforms
- Application backend
Challenge
Use entity relationships to support question answering where similarity alone can retrieve a relevant-looking but structurally incorrect source.
Approach
Represent domain entities and relationships explicitly, retrieve relevant context and connect that evidence to generated answers.
Architecture
Key features
- Knowledge graph retrieval
- Domain question answering
- Source-grounded context
- Assistant API integration
Results and measurement
The delivered scope is described in this case study. Numeric performance or business results require a measurement source and owner approval.
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Client feedback
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Technical concept illustration. The case study below describes the project implementation.
Plain vector-search RAG can retrieve semantically similar but structurally wrong answers for domain-specific questions. GraphAsk is an AI chat assistant backed by a knowledge graph, grounding domain Q&A in a curated entity/relationship graph instead of relying on free-form retrieval alone — the goal was more explainable, structured answers by having the assistant traverse a modeled graph of entities and relationships before generating a response.
What I Built
- Domain knowledge graph modeling entities and their relationships
- Graph-grounded retrieval — queries traverse the graph before reaching the LLM
- Conversational chat interface with source citations for every answer
- Graceful fallback handling — an honest "I don't know" when the graph lacks coverage, instead of a confident wrong answer
Tech Stack
- AI: LLM (OpenAI/Anthropic), knowledge-graph query layer
- Graph DB: Neo4j (or similar)
- Backend: Python
- Frontend: React chat UI
Key Decisions
- Grounded responses in a curated knowledge graph rather than relying on vector-search RAG alone, since plain similarity search can surface semantically close but structurally wrong answers for domain-specific questions
- Required the assistant to traverse the graph before generating a response and cite sources, making answers explainable and verifiable rather than a confident black box
- Built in a graceful "I don't know" fallback for gaps in graph coverage, preferring an honest non-answer over a plausible-sounding wrong one
Outcome / Impact
Delivered more accurate, explainable answers than plain vector-search RAG for structured domain questions, with citations that let users verify the assistant's reasoning.
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