MedMind: AI Backend API (LangServe) 🤖

This repository contains the AI Backend API for the MedMind project. It is a high-performance, asynchronous service built with FastAPI and LangServe, designed to host and expose specialized LLM (Large Language Model) chains for the frontend application to consume.

🧠 AI Capabilities (LangServe Endpoints)

The API utilizes LangChain to orchestrate specialized AI chains, primarily using Ollama for local, open-source LLM inference (e.g., MedGemma).

Path	Description	Chain Type	Input Key
/chat	General Medical Chatbot with conversational memory.	chat_chain_with_memory	query
/symptom_checker	Analyzes symptoms to provide preliminary health information.	medgemma_symptoms_chain	query
/lab_report_analysis	Interprets and explains medical lab report results.	medgemma_lab_report_chain	Custom (LabReportInput)
/mental_health_support	Empathetic and supportive conversational agent with memory.	mental_health_chain_with_memory	query

🛠️ Tech Stack

• Package Manager: uv (Astral)
• API Framework: FastAPI
• LLM Orchestration: LangChain and LangServe
• LLM Inference: Ollama (local server)

🔗 Project Dependency (Frontend Web App)

This API is the intelligence core for the MedMind web application. It is crucial that this service is running before starting the frontend.

• Frontend Repository: https://github.com/smriti2805/MedMind

🚀 Setup & Installation

Prerequisites

1. uv: This project uses uv for fast package management.
   • Install uv (Official Guide)
2. Ollama: Must be installed and running on your system to serve the LLMs.
   • Download Ollama
   • Pull the required model (e.g., ollama pull medgemma or ollama pull gemma:2b).

Installation Steps

1. Clone this repository:

   git clone [https://github.com/kishandev2509/MedMind---Backend](https://github.com/kishandev2509/MedMind---Backend)
   cd MedMind---Backend

2. Sync Dependencies: Use uv to automatically create the environment and install dependencies:

   uv sync

3. Run the Server: Start the backend service using uv:

   uv run main.py

   The lifespan function will automatically check for and attempt to start the Ollama server before adding the LangServe routes.

   • API URL: http://localhost:8000
   • Interactive Docs: http://localhost:8000/docs