agentic-customer-service-medical-clinic

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Basic Information

This repository provides an autonomous conversational agent configured for a medical or dental clinic to handle customer service interactions. It is built using LangGraph and LangChain and is designed to answer general clinic questions, manage appointments, check doctor availability, verify whether results are ready, and explain available services. The agent is intended to be deployed across messaging channels such as WhatsApp, Telegram, or Instagram. The project includes components for a retrieval-augmented generation (RAG) approach using a vector database and demonstrates error handling and realistic chat flows. Setup and operation are script-driven with instructions to create and populate the vector database and to run the agent locally or via LangGraph Studio.

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App Details

Features
The project uses LangGraph and LangChain to orchestrate autonomous agent behavior and a RAG workflow backed by a vector database. It includes scripts to create the vector database and to refresh availability data via provided synthetic data utilities. The agent supports common secretary tasks like booking, cancelling, and rescheduling appointments and checking provider availability. The README highlights demo recordings showing chat handling and resilience to incorrect identifiers. Environment-driven configuration is used for LLM provider keys and Pinecone API access. The agent can be launched with a main agent script or via LangGraph Studio for visual orchestration and debugging.
Use Cases
This repository helps clinics automate front-desk activities by providing an agent that handles routine customer interactions, reducing manual workload and improving response times. It supports appointment lifecycle actions and availability checks so patients can book or modify visits through messaging channels. The inclusion of a retrieval-augmented approach enables the agent to answer factual questions about services and patient results by consulting a vector database. Provided scripts for building and updating the vector store and for synthetic availability data simplify setup and maintenance. Demonstrations of error handling suggest the agent tolerates common input mistakes, which helps provide a smoother user experience during live deployments.

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