---
title: SQL Helper RAG
emoji: 🧮
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: mit
short_description: RAG Q&A over SQL knowledge base (Jina + Qdrant + Groq)
---

# SQL Helper — RAG Q&A

A retrieval-augmented Q&A demo that answers SQL questions grounded in a curated SQL knowledge base.

## Architecture

```
User question
     │
     ▼
Jina embeddings (jina-embeddings-v3, retrieval.query)
     │
     ▼
Qdrant Cloud — top-5 similar chunks from collection "sql_kb"
     │
     ▼
Groq + openai/gpt-oss-20b — answer grounded in retrieved context
     │
     ▼
Answer + cited sources
```

## Stack

| Layer | Tool | Why |
|---|---|---|
| Embeddings | Jina `jina-embeddings-v3` (1024-dim, multilingual) | Free tier, supports Ukrainian, asymmetric query/passage encoding |
| Vector DB | Qdrant Cloud | Free 1 GB cluster, managed, low-latency |
| LLM | Groq `openai/gpt-oss-20b` | Free tier, very fast inference |
| UI | Gradio | Standard for HF Spaces, quick prototyping |

## Knowledge base

7 markdown documents covering:

- SELECT basics (WHERE, ORDER BY, NULL handling, DISTINCT, aliases)
- JOINs (INNER, LEFT, FULL, CROSS, self-join, common mistakes)
- Aggregations and GROUP BY (HAVING vs WHERE, NULL behavior, ROLLUP)
- Window functions (ranking, frames, LAG/LEAD, FIRST_VALUE)
- Subqueries and CTEs (EXISTS, recursive CTE, scalar/derived/correlated)
- Indexes and performance (composite indexes, EXPLAIN, common slow patterns)
- Common gotchas (NULL behavior, integer division, JOIN-explosion, deep pagination)

## Design choices worth noting

- **Asymmetric encoding** — documents use Jina `retrieval.passage`, questions use `retrieval.query`. More accurate than encoding both as one type.
- **Honest refusal on out-of-scope** — system prompt explicitly says "if not in context, say so". Tested with non-SQL questions (e.g. MongoDB) — model correctly refuses.
- **Multilingual** — Jina v3 handles Ukrainian/Russian/etc; ask in any language, get answer in same language.
- **Source citations** — every answer shows which chunks were retrieved and their similarity scores.
- **Low temperature (0.2)** — factual Q&A, not creative writing.

## Possible extensions

- Hybrid search (semantic + BM25) for better code/identifier matching
- Jina reranker v2 on top-20 → top-5 for higher precision
- LLM-as-judge eval set for measurable quality

## Author

Built as part of a portfolio for AI/LLM Engineer roles.

- Companion models on HF: [`llama-3.2-3b-text2sql-lora`](https://huggingface.co/notingemiu/llama-3.2-3b-text2sql-lora), [`llama-3.2-3b-ukrainian-alpaca-lora`](https://huggingface.co/notingemiu/llama-3.2-3b-ukrainian-alpaca-lora)