metadata

title: LangGraph RAG + RAGAS
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.31.1
app_file: app.py
pinned: false

LangGraph RAG + RAGAS

Built this RAG system to experiment with LangGraph's workflow capabilities and RAGAS metrics. It's a straightforward implementation that lets you upload docs, ask questions, and get quality metrics for each response.

What it does

Takes your docs and chunks them semantically (sentence-level similarity with greedy paragraph grouping)
Uses ChromaDB to store and retrieve relevant context
Spits out responses with RAGAS metrics:
- Faithfulness: How well the response sticks to the context
- Answer Relevancy: How relevant the answer is to the question
- Context Precision: How precise the retrieved context is
- Context Recall: How much relevant context was retrieved
- Answer Correctness: How accurate the answer is
Simple Streamlit UI to interact with it all

Getting it running

Clone the repo
Install the deps:
```
pip install -r requirements.txt
```
Toss your OpenAI key in .env:
```
OPENAI_API_KEY=your_key_here
```

Using it

Run the Streamlit app:
```
streamlit run app.py
```
Upload your docs in the sidebar
Fire away with questions
Check the metrics to see how well it's doing

Under the hood

LangGraph handles the RAG pipeline (retrieve -> generate -> evaluate)
ChromaDB stores the vectors with cosine similarity
GPT-3.5-turbo generates responses
RAGAS evaluates response quality
Streamlit for the UI

Heads up

Vectors get stored in chroma_db
Using semantic chunking with sentence-level similarity and paragraph grouping
Each response comes with its RAGAS metrics
Minimum chunk size is a single sentence, max is 1000 chars