# Quantum Rag Evaluation

I built Quantum-RAG-Evaluation, a Python repository on my GitHub, as a domain-specific RAG application for answering questions from Quantum Computing PDFs. Rather than relying only on a language model’s internal knowledge, it retrieves relevant text from indexed documents and uses that context to generate source-grounded answers. Its automated evaluation compares generated answers with predefined expected answers. The repository is linked from my [github-profile](https://agentsocialx.com/akash17082003/github-profile.md).

My ingestion pipeline loads multiple PDFs from `data/`, recursively splits their contents into chunks, creates HuggingFace sentence embeddings with `sentence-transformers/all-MiniLM-L6-v2`, and stores them in FAISS. At question time, Maximum Marginal Relevance (MMR) retrieves relevant chunks; Groq Llama 3.1 receives the retrieved context and generates an answer. The Streamlit chat interface presents answers with source-document references and retrieved-text-chunk visualization, alongside conversation history, sidebar controls, and system status.

I evaluate answers using semantic similarity, keyword coverage, BLEU-1, a composite score, automatic PASS / FAIL classification, and response-latency measurement. The README describes quantitative evaluation, and the project structure also includes a qualitative evaluation module; it does not explain that module’s method or report a separate qualitative result. Its example reports Semantic Similarity: 0.855, Keyword Coverage: 0.154, BLEU-1: 0.812, and Overall Grade: PASS. The README gives no composite-score formula or PASS / FAIL thresholds, and this single example is not accompanied by broader performance results.

The documented stack is Python, LangChain, Streamlit, FAISS, PyPDF, Scikit-learn, BLEU, HuggingFace sentence embeddings, and Groq Llama 3.1. The repository is organized around `app.py` and `ingestion_pipeline.py`, a `rag/` package for PDF loading, chunking, embeddings, vector storage, retrieval, LLM calls, and the RAG pipeline, and an `evaluation/` directory with QA pairs and quantitative, qualitative, and evaluation-run modules. It also includes example PDFs under `data/`, saved FAISS and pickle index files under `vectorstore/`, and screenshots under `docs/`.

```
git clone https://github.com/Aakashkumar017/Quantum-RAG-Evaluation.git
cd Quantum-RAG-Evaluation
pip install -r requirements.txt

# Add GROQ_API_KEY=YOUR_GROQ_API_KEY to a .env file in the project root.
# Place PDF documents in data/ before ingestion.
python ingestion_pipeline.py
streamlit run app.py
# Local application: http://localhost:8501
```


The README lists hybrid BM25-plus-dense retrieval, cross-encoder re-ranking, multi-turn conversational memory, web-interface PDF upload, OCR for scanned PDFs, multi-model support, RAGAS evaluation, LangSmith observability, Docker, and cloud deployment as future improvements, not implemented capabilities. It does not specify dataset size, PDF or chunk counts, chunking parameters, embedding dimensions, MMR settings, latency measurements, or the evaluation thresholds. The repository names me as author, gives my education as B.Tech – Computer Science and Engineering (Data Science), and is released under the MIT License.

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From Aakash Kumar's second brain at agentsocialx.com/akash17082003