End-to-End Medical Chatbot Project by FIRAS TLILIEnd-to-End Medical Chatbot Project by FIRAS TLILI

End-to-End Medical Chatbot Project

FIRAS TLILI

AI Agent Engineer

ML Engineer

AI Engineer

AWS

LangChain

Python

Healthcare

End-To-End-Production-Ready-Medical-Chatbot-with-LLMOps

The "End-to-End Medical Chatbot Project" aims to build a sophisticated conversational AI for the healthcare domain, utilizing Large Language Models (LLMs) integrated with Pinecone and LangChain. The chatbot will provide accurate responses to health-related queries, assist in medical guidance, and retrieve relevant medical data using Pinecone's vector database for efficient and scalable search. The project involves setting up a retrieval-augmented generation (RAG) pipeline, configuring conversational flows, ensuring compliance with health information privacy standards, and validating outputs for medical accuracy. This project is ideal for creating intelligent virtual assistants for patient support, telemedicine, or healthcare research.

Demo

How to run?

STEPS:

Clone the repository

Project repo: https://github.com/

STEP 01- Create a conda environment after opening the repository

conda create -n medibot python=3.10 -y

conda activate medibot

STEP 02- install the requirements

pip install -r requirements.txt

Create a `.env` file in the root directory and add your Pinecone & openai credentials as follows:

PINECONE_API_KEY = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
OPENAI_API_KEY = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# run the following command to store embeddings to pinecone
python store_index.py

# Finally run the following command
python app.py

Now,

open up localhost:

Techstack Used:

Python

LangChain

Flask

GPT

Pinecone

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2 

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 970547337635.dkr.ecr.ap-south-1.amazonaws.com/medicalchatbot

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

AWS_ACCESS_KEY_ID

AWS_SECRET_ACCESS_KEY

AWS_DEFAULT_REGION

ECR_REPO

PINECONE_API_KEY

OPENAI_API_KEY

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Posted May 2, 2025

Built a medical chatbot using LLMs, Pinecone, and LangChain for healthcare.

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Timeline

Feb 4, 2025 - Feb 8, 2025

End-to-End Medical Chatbot Project

End-To-End-Production-Ready-Medical-Chatbot-with-LLMOps

Demo

How to run?

STEPS:

STEP 01- Create a conda environment after opening the repository

STEP 02- install the requirements

Create a .env file in the root directory and add your Pinecone & openai credentials as follows:

Techstack Used:

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

3. Create ECR repo to store/save docker image

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

6. Configure EC2 as self-hosted runner:

7. Setup github secrets:

End-to-End Medical Chatbot Project

End-To-End-Production-Ready-Medical-Chatbot-with-LLMOps

Demo

How to run?

STEPS:

STEP 01- Create a conda environment after opening the repository

STEP 02- install the requirements

Create a .env file in the root directory and add your Pinecone & openai credentials as follows:

Techstack Used:

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

3. Create ECR repo to store/save docker image

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

6. Configure EC2 as self-hosted runner:

7. Setup github secrets:

Create a `.env` file in the root directory and add your Pinecone & openai credentials as follows:

Create a `.env` file in the root directory and add your Pinecone & openai credentials as follows: