https://github.com/manjushree08/langflow_rag
This project integrates LangFlow as a backend API with a Streamlit frontend for a chatbot interface. It also includes RAGAS evaluation for measuring the performance of RAG (Retrieval-Augmented Generation) pipelines.
https://github.com/manjushree08/langflow_rag
agentic fastapi langflow project-management ragas-evaluation streamlit uvicorn
Last synced: about 2 months ago
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This project integrates LangFlow as a backend API with a Streamlit frontend for a chatbot interface. It also includes RAGAS evaluation for measuring the performance of RAG (Retrieval-Augmented Generation) pipelines.
- Host: GitHub
- URL: https://github.com/manjushree08/langflow_rag
- Owner: manjushree08
- Created: 2025-03-03T11:52:18.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-03-03T23:07:45.000Z (about 2 months ago)
- Last Synced: 2025-03-04T00:20:12.599Z (about 2 months ago)
- Topics: agentic, fastapi, langflow, project-management, ragas-evaluation, streamlit, uvicorn
- Language: Python
- Homepage:
- Size: 3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LangFlow Streamlit Integration with RAGAS Evaluation
This project integrates LangFlow as a backend API with a Streamlit frontend for a chatbot interface. It also includes RAGAS evaluation for measuring the performance of RAG (Retrieval-Augmented Generation) pipelines.
## Project Structure
```
project/
├── api/ # FastAPI server that connects to LangFlow
├── chatbot/ # Streamlit application
├── evaluation/ # RAGAS evaluation tools
├── data/ # Data storage for evaluation
├── .env # Environment variables
├── requirements.txt # Project dependencies
├── docker-compose.yml # Docker configuration
```
## Getting Started
### Langflow Graph
### Prerequisites
- Docker and Docker Compose
- Python 3.10+
### Installation
1. Clone this repository
2. Create a virtual environment and install dependencies:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
```
3. Create a `.env` file with the following content:
```
LANGFLOW_API_URL=http://localhost:7860
API_PORT=8000
DEBUG=True
```
### Running the Application
#### Using Docker
The easiest way to run the entire stack is with Docker Compose:
```bash
docker-compose up -d
```
This will start:
- LangFlow on port 7860
- The API server on port 8000
- The Streamlit UI on port 8501
#### Running Locally
1. Start LangFlow:
```bash
docker run -p 7860:7860 logspace/langflow:latest
```
2. Start the API server:
```bash
uvicorn api.app:app --reload
```
```
```
3. Start the Streamlit application:
```bash
streamlit run chatbot/app.py
```
4. Start the evaluation dashboard:
```bash
streamlit run evaluation/metrics.py
```
## Using the Application
### Setting Up LangFlow
1. Access LangFlow at http://localhost:7860
2. Create a new flow using the drag-and-drop interface
3. Set up your RAG pipeline with appropriate components:
- Document loaders
- Vector stores
- LLM models
- Chain components
4. Deploy your flow
### Using the Chatbot
1. Access the Streamlit UI at http://localhost:8501
2. Select your flow from the dropdown in the sidebar
3. Start chatting with your LangFlow-powered application
### Running Evaluations
1. Access the evaluation dashboard at http://localhost:8501/evaluation
2. Select the flow you want to evaluate
3. Click "Run Evaluation" to test your flow with RAGAS metrics
4. Review the results and optimize your flow accordingly
## RAGAS Evaluation
This project uses RAGAS to evaluate the performance of your RAG pipelines with the following metrics:
1. **Faithfulness**: Measures how factually consistent the generated answer is with the retrieved context
2. **Answer Relevancy**: Evaluates whether the answer addresses the question
3. **Context Relevancy**: Assesses the quality of retrieval - how relevant the retrieved context is to the question
4. **Context Recall**: Measures how well the retrieved context covers the information needed to answer the question
5. **Harmfulness**: Evaluates the safety of the generated response
## Customizing the Application
### Adding Custom Evaluation Questions
Edit or replace the `data/questions.json` file with your domain-specific questions and ground truth answers.
### Extending the API
The API is built with FastAPI, making it easy to add new endpoints:
1. Open `api/app.py`
2. Add new route functions using the FastAPI decorator syntax
3. Implement your endpoint logic
### Customizing the UI
The Streamlit UI can be customized:
1. Edit `chatbot/app.py` to adjust the main application flow
2. Modify components in the `chatbot/components/` directory
3. Add new utility functions as needed
## Evaluation Insights
The evaluation dashboard provides valuable insights into your RAG pipeline performance:
- Metric comparison across different flows
- Historical performance tracking
- Detailed view of evaluation results
- Areas for improvement identification
Use these insights to iteratively improve your LangFlow pipelines:
1. Identify metrics with lower scores
2. Adjust relevant components in your flow
3. Re-run evaluations to measure improvement
4. Repeat until satisfactory performance is achieved