https://github.com/isfarbaset/dsan-assistant
This repository hosts an AI-powered Georgetown Course Finder, developed using LangChain and deployed as an AWS Lambda function.
https://github.com/isfarbaset/dsan-assistant
ai-assistant course-information-retrieval fastapi georgetown-university langchain python-development streamlit unified-search
Last synced: about 2 months ago
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This repository hosts an AI-powered Georgetown Course Finder, developed using LangChain and deployed as an AWS Lambda function.
- Host: GitHub
- URL: https://github.com/isfarbaset/dsan-assistant
- Owner: isfarbaset
- License: mit
- Created: 2025-03-26T23:05:24.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-31T21:33:55.000Z (about 2 months ago)
- Last Synced: 2025-03-31T22:28:27.114Z (about 2 months ago)
- Topics: ai-assistant, course-information-retrieval, fastapi, georgetown-university, langchain, python-development, streamlit, unified-search
- Language: Python
- Homepage:
- Size: 76.1 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DSAN Assistant
A LangGraph-based RAG (Retrieval-Augmented Generation) system for the Georgetown University Data Science and Analytics (DSAN) program. This repository showcases a design pattern for building and deploying LangGraph agents with a progression from local development to serverless deployment. We crawl the [DSAN](https://analytics.georgetown.edu/) website and use it to provide answers to general questions about different courses. Question such as _Is dsan 6000 a prereq for 6725?_, _is there a course on bioinformatics?_, _how many core courses are there?_ and so on and so forth.
We use the following tools and technologies:
1. [Firecrawl.dev](https://www.firecrawl.dev/) and ingest the [data](data/documents_1.json) in a local [`FAISS`](https://python.langchain.com/docs/integrations/vectorstores/faiss/) index.
1. [Amazon Bedrock](https://aws.amazon.com/bedrock/) for LLMs, Amazon API gateway and AWS Lambda for hosting.
1. [LangGraph](https://www.langchain.com/langgraph) for Agents and LangChain for RAG.
## Demo video
## Architecture Overview
```mermaid
graph LR
%% Agent Building section
A1["LangGraph"] --> D["FastAPI"]
A2["Streamlit"] --> D
A3["Amazon Bedrock"] --> D
%% API & Packaging section
D --> E["Docker Container"]
%% AWS Deployment section
E --> F["AWS Services"]
F --> G1["AWS Lambda & Amazon API Gateway"]
%% Subgraph definitions
subgraph "Agent Building"
A1
A2
A3
end
subgraph "API & Packaging"
D
E
end
subgraph "AWS Deployment"
F
G1
end
%% Styling
classDef dev fill:#d1f0ff,stroke:#0077b6
classDef mid fill:#ffe8d1,stroke:#b66300
classDef aws fill:#ffd1e8,stroke:#b6007a
class A,B,C1,C3 dev
class D,E mid
class F,G1 aws
```
This project demonstrates a complete workflow for developing and deploying AI agents:
1. **Local Development**: Build and test the agent locally
2. **FastAPI Server**: Convert the agent to a FastAPI application
3. **Docker Containerization**: Package the application in a Docker container
4. **AWS Lambda Deployment**: Deploy the containerized application to AWS Lambda with API Gateway
## Components
- **RAG System**: Uses LangChain, FAISS, and AWS Bedrock to provide information about Georgetown's DSAN program
- **LangGraph Agent**: ReAct agent pattern with tools for retrieving program information
- **Streamlit Frontend**: User-friendly chat interface for interacting with the agent
- **FastAPI Backend**: Serves the agent via HTTP endpoints
- **AWS Lambda Integration**: Serverless deployment with API Gateway
### Dev workflow
1. **Data Collection**:
- Crawl data using firecrawl.dev. Save the data as JSON and place it as [`documents_1.json`](data/documents_1.json) in the `data` folder.
- Place the crawled data in the data folder
2. **Index Building**:
- Run build_index.py to create the FAISS vector index
3. **Local Testing**:
- Run the FastAPI server with `langchain serve`
- Test the API endpoints with the local webserver
- Test the user interface with Streamlit
4. **Deployment**:
- Run python deploy.py to deploy to AWS Lambda and API Gateway
- Test the deployed application by running Streamlit with the API Gateway endpoint
```mermaid
graph TD
A[Crawl data with firecrawl.dev] --> B[Place data in data folder]
B --> C[Run build_index.py]
C --> D[Run langchain server]
D --> E[Test with FastAPI/uvicorn local webserver]
E --> F[Test with Streamlit]
F --> G[Run python deploy.py]
G --> H[Deploy to API Gateway and Lambda]
H --> I[Run Streamlit with API Gateway endpoint]
classDef data fill:#d1f0ff,stroke:#0077b6
classDef local fill:#ffe8d1,stroke:#b66300
classDef deploy fill:#ffd1e8,stroke:#b6007a
class A,B,C data
class D,E,F local
class G,H,I deploy
```
## Prerequisites
- Python 3.11+
- [uv](https://github.com/astral-sh/uv) for Python package management
- Docker (for containerization)
- AWS CLI configured with appropriate permissions
- AWS account with access to Bedrock and Lambda services
## Setup Instructions
### 1. Clone the Repository
```bash
git https://github.com/yourusername/dsan-assistant
cd dsan-assistant
```
### 2. Environment Setup
Create a `.env` file in the project root with your AWS credentials and configuration:
```
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-1
```
### 3. Install Dependencies with uv
This project uses `uv` for Python package management:
```bash
# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh
export PATH="$HOME/.local/bin:$PATH"
# Create a virtual environment and install dependencies
uv venv --python 3.11 && source .venv/bin/activate && uv pip install --requirement pyproject.toml
```
### 4. Build the Vector Index
Before running the application, you need to build the vector index from the source documents:
```bash
python build_index.py
```
This will create a FAISS index in the `indexes/dsan_index` directory.
## Running Locally
### Run the FastAPI Server
```bash
langchain serve
```
### Run the Streamlit Frontend
```bash
streamlit run chatbot.py -- --api-server-url http://localhost:8000/generate
```
## Setup LangSmith (Optional)
LangSmith will help us trace, monitor and debug LangChain applications.
You can sign up for LangSmith [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=
export LANGCHAIN_PROJECT= # if not specified, defaults to "default"
```
## Deployment Process
### 1. Build and Push Docker Image
The repository includes a script to build and push the Docker image to Amazon ECR:
```bash
chmod +x build_and_push.sh
./build_and_push.sh
```
### 2. Deploy to AWS Lambda
Use the deployment script to create or update the Lambda function and API Gateway:
```bash
python deploy.py --function-name dsan-assistant --role-arn YOUR_LAMBDA_ROLE_ARN --api-gateway
```
If you want to your Amazon Bedrock in a cross-account way i.e. the Lambda exists in say Account A but you want to use Amazon Bedrock in Account B then use the following command line
```bash
python deploy.py --function-name dsan-assistant --role-arn YOUR_LAMBDA_ROLE_ARN --bedrock-role-arn YOUR_ACCOUNT_B_BEDROCK_ROLE_ARN--api-gateway
```
The IAM role you need to use for the AWS Lambda needs to have Amazon Bedrock access (for example via [`AmazonBedrockFullAccess`](https://docs.aws.amazon.com/aws-managed-policy/latest/reference/AmazonBedrockFullAccess.html)) to use the models available via Amazon Bedrock and the models need to be enabled within your AWS account, see instructions available [here](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html).
This will:
1. Create/update a Lambda function using the Docker image
2. Set up an API Gateway with appropriate routes
3. Configure permissions and API keys
4. Output the deployed API URL
### 3. Connect Streamlit to Deployed API
Once deployed, you can connect the Streamlit frontend to the deployed API:
```bash
streamlit run chatbot.py -- --api-server-url https://YOUR_API_ID.execute-api.us-east-1.amazonaws.com/prod/generate
```
## Project Structure
```
gtown-course-finder/
├── app/ # FastAPI application
│ ├── __init__.py
│ └── server.py # FastAPI server implementation
├── data/ # Source data
│ └── documents_1.json # DSAN program information
├── indexes/ # Vector indexes
│ └── dsan_index/ # FAISS index for DSAN data
├── .env # Environment variables (not in repo)
├── .gitignore # Git ignore file
├── build_and_push.sh # Script to build and push Docker image
├── build_index.py # Script to build vector index
├── chatbot.py # Streamlit frontend
├── deploy.py # AWS Lambda deployment script
├── Dockerfile # Docker configuration
├── dsan_rag_setup.py # RAG system setup
├── pyproject.toml # Project configuration
├── README.md # Project documentation
└── requirements.txt # Python dependencies
```
## Key Features
- **Conversation Memory**: Maintains chat history for contextual responses
- **Vector Search**: FAISS-based retrieval for efficient document search
- **AWS Bedrock Integration**: Leverages AWS's foundation models
- **Cross-Account Access**: Supports cross-account access to AWS Bedrock
- **Streamlit UI**: User-friendly interface with Georgetown branding
## License
[LICENCE](./LICENSE)
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines.