https://github.com/renardeinside/chatten
RAG application (backend & frontend) with sources retriveal and highlighting on the Databricks Platform
https://github.com/renardeinside/chatten
dash databricks python rag vector-search
Last synced: about 1 month ago
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RAG application (backend & frontend) with sources retriveal and highlighting on the Databricks Platform
- Host: GitHub
- URL: https://github.com/renardeinside/chatten
- Owner: renardeinside
- License: mit
- Created: 2025-02-06T21:54:54.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-04-29T16:34:00.000Z (about 2 months ago)
- Last Synced: 2025-04-29T17:39:20.664Z (about 2 months ago)
- Topics: dash, databricks, python, rag, vector-search
- Language: Python
- Homepage: https://www.linkedin.com/pulse/end-to-end-rag-application-source-retriveal-platform-ivan-trusov-znvqf/
- Size: 13.6 MB
- Stars: 10
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🚀 Chatten
RAG with sources, built with **Dash**, **FastAPI**, and the **Databricks** platform.
---
## 💫 Showcase
Here is an example of the app in action:
## 🛠 Developer Setup
To install the project, ensure you have the following dependencies:
- 📦 [uv](https://docs.astral.sh/uv/): for managing the project
- 🚀 [Databricks CLI](https://docs.databricks.com/dev-tools/cli/index.html): for deploying the app
- 🌐 [Node.js](https://nodejs.org/en/): for building the UI
### 📥 Installation Steps
1. Clone the repo:
```bash
git clone
```
2. Run sync:
```bash
uv sync --all-packages
```
3. Configure environment variables in the `.env` file:
```bash
# Name of your Databricks profile
CHATTEN_PROFILE=...
# Optionally, you can include any bundle or Chatten variables:
CHATTEN_CATALOG=...
# Optionally, by check default values in databricks.yml
BUNDLE_VAR_vsi_endpoint=...
```
---
## 🏗 Development
1. Start the UI watcher in one terminal:
```bash
cd packages/chatten_ui && npm run watch
```
2. Run the server in another terminal:
```bash
uvicorn chatten_app.app:app --reload
```
---
## 🚀 Deployment
1. Authenticate with Databricks:
```bash
databricks auth login -p
```
2. Deploy the app:
```bash
# See Makefile for additional variables
make deploy profile=fe-az-ws catalog=
```
3. Run the RAG workflow:
```bash
# See Makefile for additional variables
make run-rag profile=fe-az-ws catalog=
```
4. Grant app principal access to the Volume.
5. Run the app:
```bash
make run-app profile=fe-az-ws catalog=
```
6. Open the app from **Databricks Workspace** 🎉
---
## 🤖 Agent Serving Endpoint Response Parsing
Check the [`api_app`](packages/chatten_app/chatten_app/api_app.py) source code for details on how the agent serving endpoint response is parsed.
Specifically, the `/chat` API endpoint handles:
- Parsing responses
- Sending messages to the chat interface
---
## 🏛 App Implementation Details
The implementation consists of a **FastAPI** backend with two sub-apps:
1. **Dash app (`/` route)**: Uses a custom component to render the chat UI.
2. **FastAPI app (`/api` route)**: Provides API endpoints for the chat.
The chat is implemented as a **custom Dash component**, which is a React-based UI element that communicates with the FastAPI backend.
The `/api` app interacts with the **Databricks Serving Endpoint** to handle chat requests and responses. Another route is responsible for serving PDF files from **Databricks Volume**.
---
## 📂 Code Structure
```
📦 chatten # Main package (FastAPI + Dash app)
┣ 📂 packages/chatten_ui # Dash UI & custom chat component
┣ 📂 packages/chatten_rag # RAG workflow implementation
┣ 📂 packages/chatten_app # FastAPI & Dash apps
┣ 📂 src/chatten # Common config & utilities
┣ 📂 src/chatten/app.py # Main entry point
```
---
## 🏗 Technologies Used
### 🔥 Core Platform
- [Databricks](https://databricks.com/)
- [Apps](https://www.databricks.com/product/databricks-apps) - App serving
- [Asset Bundles](https://docs.databricks.com/en/dev-tools/bundles/index.html) - Deployment
- [Mosaic AI Model Serving](https://docs.databricks.com/en/machine-learning/model-serving/index.html) - Model serving
- [Mosaic AI Vector Search](https://docs.databricks.com/en/generative-ai/vector-search.html) - Vector search
### 🏗 Frameworks & Libraries
- 🖥 [Dash](https://dash.plotly.com/) - UI framework
- ⚡ [FastAPI](https://fastapi.tiangolo.com/) - Backend API
- 🎨 [Tailwind CSS](https://tailwindcss.com/) - Styling
- 📄 [react-pdf](https://github.com/wojtekmaj/react-pdf) - PDF rendering
- 🔍 [Fuse.js](https://www.fusejs.io/) - Client-side fuzzy search
- 📜 [pypdf](https://pypdf.readthedocs.io/en/stable/) - Server-side PDF text extraction
- ⚡ [RapidFuzz](https://pypi.org/project/RapidFuzz/) - Fuzzy string matching
- 📦 [uv](https://docs.astral.sh/uv/) - Dependency management