The challenge
A mental-health startup needed a supportive assistant that could guide users with evidence-based techniques.
Safety was non-negotiable: the assistant had to recognize crisis signals and route people to real help instead of improvising.
- 01
ScreenCrisis-risk detection
- 02
RetrievePinecone CBT/mindfulness corpus
- 03
GenerateGPT-4o via LangChain
- 04
RememberSession memory and mood history
Architecture · 4 stages
- 01
Screen
Crisis-risk detection
- 02
Retrieve
Pinecone CBT/mindfulness corpus
- 03
Generate
GPT-4o via LangChain
- 04
Remember
Session memory and mood history
The solution
I built a FastAPI service with LangChain orchestration over GPT-4o, grounded in a Pinecone knowledge base of CBT and mindfulness material. The assistant runs mood assessments, tracks mood over time and keeps session memory for continuity.
A dedicated risk-detection layer screens messages and surfaces crisis helplines when warning signs appear. The MVP shipped production-ready with a 100% pass rate across 11 core automated tests and documented performance targets, including sub-5-second RAG responses.
Engineering highlights
- Automatic crisis-risk detection with helpline escalation
- Mood assessment and longitudinal mood tracking
- CBT and mindfulness guidance grounded in a curated corpus
- Session memory for continuity of care
Technology
- Python
- FastAPI
- LangChain
- Pinecone
- GPT-4o
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