Patient Support Chatbot With Persistent Conversation Memory

Added Jun 2025 3 design docs

Patients managing ongoing conditions interact with healthcare systems repeatedly, yet every phone line and web chat treats them as strangers: they re-explain their situation, repeat their medication list, and restate last week's question. Stateless AI chatbots reproduce this failure. For a health assistant to be genuinely supportive it must remember, both within a conversation and across sessions, while handling medical topics with appropriate caution. The intern builds a healthcare chatbot with exactly this dual memory architecture. The service is written in Python on FastAPI, with LangChain providing the conversation framework and OpenAI models generating responses. Temporary memory maintains context within a session so follow-up questions resolve naturally, while permanent memory persists key facts across sessions, such as topics previously discussed and preferences expressed, so a returning patient continues rather than restarts. The intern implements both memory layers, the retrieval logic that decides what past context is relevant to the current message, and the safety framing that keeps responses informational, encourages professional consultation, and avoids diagnostic overreach. The project demonstrates conversation-state engineering, a defining skill of production chatbot work: memory design, context-window management, and responsible handling of a sensitive domain.

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