Shipmaster1/Langgraph_RAGAS

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

1. 1.Clone the repo
2. 2.Install the deps:

   pip install -r requirements.txt

1. 1.Toss your OpenAI key in .env:

   OPENAI_API_KEY=your_key_here

Using it

1. 1.Run the Streamlit app:

   streamlit run app.py

1. 1.Upload your docs in the sidebar
2. 2.Fire away with questions
3. 3.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