https://github.com/aghoshpro/fullstack-rag-analytics
Fullstack AI + Data integration app to perform a “Natural Text-To-SQL” query using LangGraph & LLMs ChatGPT, Claude and Ollama
https://github.com/aghoshpro/fullstack-rag-analytics
Last synced: 3 months ago
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Fullstack AI + Data integration app to perform a “Natural Text-To-SQL” query using LangGraph & LLMs ChatGPT, Claude and Ollama
- Host: GitHub
- URL: https://github.com/aghoshpro/fullstack-rag-analytics
- Owner: aghoshpro
- License: apache-2.0
- Created: 2021-06-07T20:08:21.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-09-19T10:43:41.000Z (4 months ago)
- Last Synced: 2025-09-19T12:38:49.751Z (4 months ago)
- Language: TypeScript
- Homepage:
- Size: 3.84 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# RAG Analytics
Fullstack AI + Data integration project performs a “Text-To-SQL” query using LLMs ChatGPT, Claude and Ollama over a small dataset stored locally. More data can be added.
## Key Highlights
- **Fullstack Application**: Combines a React frontend with a LangGraph backend, representing each state of execution as a node.
- **Automatic Routing**: Identifies which action to perform (RAG-based or analytics), addresses knowledge gaps, and refines searches.
- The data files are located in the `backend/data` folder (including `products.csv` and `rules.md`), from which the `frontend/store` folder is created containing SQLite databases for both files and embeddings.
- The project is organized into two main directories:
- `frontend/`: Contains the React application built with Vite.
- `backend/`: Contains the LangGraph/FastAPI application, including the agent logic.
### Here's what the program does:
1. **Data Ingestion & Indexing**: Raw data is ingested and indexed into a vector store.
2. **Context Retrieval**: Retrieves relevant context for free-text questions.
3. **Ad-hoc Analytics**: Performs SQL-style queries for analytics.
4. **Natural Language Generation**: Generates natural answers via OpenAI, with a persona flag to adjust tone.
5. **HTTP API Exposure**: Exposes a simple HTTP API.
6. **Docker Containerization**: Containerizes the application for deployment.
## TechStack Used
- [React](https://reactjs.org/) (with [Vite](https://vitejs.dev/)) - For the frontend user interface.
- [Tailwind CSS](https://tailwindcss.com/) - For styling.
- [Shadcn UI](https://ui.shadcn.com/) - For ui components.
- [LangGraph](https://github.com/langchain-ai/langgraph) - For building the backend research agent.
- [Open AI](https://platform.openai.com/docs/models) - CHatGPT as LLM for query generation and answer synthesis.
- [Ollama](https://ollama.com/) - Use local LLM like llama3.2, llama3.3 for query generation so the data stays in your local
## Local Development and Testing
### Prerequisites
- **Node.js and npm** (or yarn/pnpm)
- **Python 3.11+**
- **API Keys**: The backend agent requires an OpenAI API key.
1. Navigate to the main directory.
2. navigate to the `backend\` folder
3. Add the followings to the `.env` file
- OpenAI API key: `OPENAI_API_KEY`=sk-proj-..........
- LangSmith API Key: `LANGSMITH_API_KEY`=lsv2_...........
## Frontend (ReactJs + TypeScript)
- Go to `frontend\` and use `npm` to download node modules
```bash
cd frontend
```
```bash
npm install
```
- To fix vulnerabilities if occurs
- To address issues that do not require attention, run:
```sh
npm audit fix
```
- To address all issues (including breaking changes), run:
```sh
npm audit fix --force
```
- Run the cmd to open frontend web app at _`http://localhost:5173/app`_
```sh
npm run dev
```
## Backend (Python + LangGraph)
- Navigate to `backend\`and use `uv` as the package manager to set up python env as stated in `pyproject.toml`.
```bash
cd backend
```
```bash
uv sync
```
- Run the **LangGraph development server** with the `--allow-blocking` flag, which enables blocking operations in the agent workflow during development.
```bash
uv run langgraph dev --allow-blocking
```
- The backend API will be available at `http://127.0.0.1:2024\docs`. It will also open a browser window to the FastAPI docs where request can be accessed. The core of the backend is a LangGraph agent defined in `backend/src/agent/graph.py` which follows the steps below:
## Queries
Click on `http://localhost:5173/app/` to see the web application.
- What is the average, maximum and minimum turnover of each country?
- What are the countries with the maximum turnover?
- List all the countries segment wise
## Deployment
**1. Build the Docker Image:**
Notice!: You might encounter some issue when building the image, I am currently fixing the error
Run the following command from the **project root directory**:
```bash
docker build -t hybrid-rag-service -f Dockerfile .
```
**2. Run the Production Server:**
```bash
docker-compose up
```
## Project Folder Structure
The project is organized into two main directories: The core of the backend is a LangGraph agent defined in backend/src/agent/graph.py. It follows these steps:
### Backend Structure
```
backend/
├── .langgraph_api/
├── src/
│ ├── agent/
│ │ ├── __init__.py
│ │ ├── app.py
│ │ ├── configuration.py
│ │ ├── graph.py
│ │ ├── router.py
│ │ └── state.py
│ ├── data/
│ │ ├── products.csv
│ │ └── rules.md
│ ├── store/
│ │ ├── analytic.sqlite
│ │ └── rag.sqlite
│ ├── wrangler/
│ │ ├── embedding/
│ │ │ ├── __init__.py
│ │ │ ├── base.py
│ │ │ └── openai.py
│ │ ├── model/
│ │ │ ├── __init__.py
│ │ │ ├── chunk.py
│ │ │ ├── document.py
│ │ │ └── product.py
│ │ ├── repository/
│ │ │ ├── __init__.py
│ │ │ ├── analytic.py
│ │ │ ├── base.py
│ │ │ ├── chunk.py
│ │ │ ├── document.py
│ │ │ └── store.py
│ │ ├── __init__.py
│ │ ├── ingest.py
│ │ ├── qa_agent.py
│ │ ├── queryTranslation.py
│ │ ├── ragUtil.py
│ │ └── repository.py
│ └── __init__.py
├── .venv/
├── test-agent.ipynb
├── uv.lock
├── .gitignore
├── langgraph.json
├── LICENSE
├── Makefile
└── pyproject.toml
```
### Frontend Structure
```
frontend/
├── node_modules/
├── public/
│ └── vite.svg
├── src/
│ ├── components/
│ │ ├── ui/
│ │ │ ├── badge.tsx
│ │ │ ├── button.tsx
│ │ │ ├── card.tsx
│ │ │ ├── input.tsx
│ │ │ ├── scroll-area.tsx
│ │ │ ├── select.tsx
│ │ │ ├── tabs.tsx
│ │ │ └── textarea.tsx
│ │ ├── ActivityTimeline.tsx
│ │ ├── ChatMessagesView.tsx
│ │ ├── InputForm.tsx
│ │ ├── TableView.tsx
│ │ └── WelcomeScreen.tsx
│ ├── lib/
│ │ └── utils.ts
│ ├── App.tsx
│ ├── global.css
│ ├── main.tsx
│ └── vite-env.d.ts
├── .gitignore
├── components.json
├── eslint.config.js
├── index.html
├── package-lock.json
├── package.json
├── tsconfig.json
├── tsconfig.node.json
└── vite.config.ts
```