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ViFortune-AI/
Qwen3-0.6B-text2sql

Name: Qwen3-0.6B-text2sql
Rating: 2.5 (7 reviews)

safetensors

qwen3

text-generation-inference

Qwen/Qwen3-0.6B

finetune

apache-2.0

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Text-to-SQL Evaluation Pipeline

Overview

This repository implements a Text-to-SQL evaluation pipeline using the OpenAI API. The system is designed for the banking domain, where strict business rules and deterministic SQL generation are required.

Key goals:

Translate natural language questions into valid SQLite SQL queries
Enforce domain-specific constraints via prompt engineering
Benchmark model outputs using multiple evaluation metrics

Key Features

Important: The existing MyModel class and its formatting are kept exactly as-is.
This project does not modify, refactor, or reformat the demo code. The README only documents how the current implementation works.

The MyModel class structure, method names, and prompt formatting remain unchanged
No code auto-formatting or refactoring is applied
All behavior described below reflects the original demo code

Key Features

Deterministic SQL generation (temperature = 0)
Strong prompt constraints (no markdown, no explanations, SQL only)
Banking-specific metric grouping and unit conversion logic
Multi-metric evaluation for both syntactic and semantic quality

High-level Architecture

text

User Question
     │
     ▼
Prompt Builder (System + User)
     │
     ▼
OpenAI ChatCompletion API
     │
     ▼
Generated SQL
     │
     ▼
Evaluation Metrics

Suggested Project Structure

text

.
├── main.py              # Entry point, runs inference and prints metrics
├── model.py             # OpenAI client wrapper (MyModel)
├── evaluator.py         # Evaluation metrics implementation
├── prompts/
│   └── text2sql.txt     # System prompt with banking rules
├── README.md
└── requirements.txt

Prompt Design

System Prompt Responsibilities

The system prompt enforces the following rules:

Always perform an INNER JOIN between system_data and branch
Always SELECT the following columns:
- system_data.data_code
- system_data.year
- system_data.branch_id
- branch.name
- system_data.value
SQL keywords must be UPPERCASE
Text filters must use LIKE '%keyword%'
Vietnamese location names must use exact accents
Output SQL only (no markdown, no explanations)

Metric Grouping Logic

Metrics are classified by metric_code prefix:

Group	Description
A	Inbound metrics (`MET_A_%`)
B	Outbound metrics (`MET_B_%`)
C	Stock / snapshot metrics (`MET_C_%`)
D	Exposure / obligation metrics
E	Resource mobilization metrics
F	Ratio & efficiency metrics

Unit Conversion Rule

Stored unit: Million VND
If the question mentions "Billion VND", multiply value by 1000

Example Input (Schema)

sql

CREATE TABLE entity_a (
    id INTEGER,
    group_id INTEGER,
    org_id INTEGER,
    code VARCHAR(100),
    name VARCHAR(255),
    attr_1 VARCHAR(255),
    attr_2 VARCHAR(255),
    attr_3 TEXT
);

CREATE TABLE entity_b (
    id INTEGER,
    group_id INTEGER,
    entity_a_id INTEGER,
    time_key INTEGER,
    metric_name VARCHAR(255),
    metric_code VARCHAR(100),
    metric_value REAL,
    metric_unit VARCHAR(100)
);

Evaluation Metrics

Evaluation results are printed at the very top of the output:

Label	Value
rouge	0.9290708304
meteor	0.9191570862
binary	0.55
llm-as-a-judge	0.65

Metric Definitions

ROUGE: Token-level overlap between generated and reference SQL
METEOR: Semantic similarity with synonym awareness
Binary Match: Exact string match (0 or 1)
LLM-as-a-Judge: LLM-based holistic judgment of correctness

Full Demo Code (Kept Exactly As-Is)

The following is the original demo code, included verbatim for clarity and ease of understanding. No refactoring, no reformatting, no behavioral changes have been applied.

python

import argparse

from openai import OpenAI

DEFAULT_QUESTION = """CREATE TABLE entity_a (
    id INTEGER,
    group_id INTEGER,
    org_id INTEGER,
    code VARCHAR(100),
    name VARCHAR(255),
    attr_1 VARCHAR(255),
    attr_2 VARCHAR(255),
    attr_3 TEXT
);
CREATE TABLE entity_b (
    id INTEGER,
    group_id INTEGER,
    entity_a_id INTEGER,
    time_key INTEGER,
    metric_name VARCHAR(255),
    metric_code VARCHAR(100),
    metric_value REAL,
    metric_unit VARCHAR(100)
);
ENTITIES = {
    "metric": {
        "metric_code": "METRIC_X",
        "metric_unit": "UNIT_A"
    },
    "entity_a_field": {
        "attr_1": [],
        "attr_2": [],
        "attr_3": [],
        "id": []
    },
    "time_key": [year],
Query:
}


"""


class MyModel(object):
    def __init__(self, model_name: str, api_key: str):
        self.model_name = model_name
        self.client = OpenAI(base_url="", api_key=api_key)
    def get_prompt(
        self,
        question: str,
    ) -> list[dict[str, str]]:
        return [
            {
                "role": "system",
                "content": """
You are a problem solving model working on task_description XML block:
<task_description>You are a specialized Text-to-SQL assistant in the banking domain. Your objective is to translate natural language questions into valid SQLite queries using the provided schema and banking business logic.
### Input:
- Schema: Table definitions in SQL DDL format.
- Relationships: Key linking logic between tables (system_data.branch_id = branch.id).
- Data Content Context:
    Indicator_Categories:
      Group_A:
        description: Primary metrics – inbound type
        rule:
          - metric_code LIKE 'MET_A_%'
    
      Group_B:
        description: Primary metrics – outbound type
        rule:
          - metric_code LIKE 'MET_B_%'
    
      Group_C:
        description: Stock / snapshot metrics
        rule:
          - metric_code LIKE 'MET_C_%'
    
      Group_D:
        description: Exposure / obligation related metrics
        rule:
          - metric_code LIKE 'MET_D_%'
          - metric_code LIKE 'MET_D_TOTAL_%'
          - metric_code = 'MET_D_SPECIAL'
    
      Group_E:
        description: Resource mobilization metrics
        rule:
          - metric_code LIKE 'MET_E_%'
    
      Group_F:
        description: Ratio & efficiency indicators
        rule:
  - Unit Logic: {Which dmain} data is stored in 'Triệu VND'. If the Question mentions 'Tỷ', multiply the value by 1000.
- Entities: Extracted key information including data_code, year, and branch filtering criteria.
### Rules:
1. ALWAYS perform an INNER JOIN between system_data and branch on system_data.branch_id = branch.id.
2. ALWAYS SELECT system_data.data_code, system_data.year, system_data.branch_id, branch.name, system_data.value.
3. Use exact Vietnamese accents for location values.
4. Use LIKE '%keyword%' for text matching.
5. Use UPPERCASE for SQL keywords.
6. Output ONLY the SQL query. No explanations or markdown blocks.</task_description>
You will be given a single task in the question XML block
Solve only the task in question block.
Generate only the answer, do not generate anything else
""",
            },
            {
                "role": "user",
                "content": f"""
Now for the real task, solve the task in question block.
Generate only the solution, do not generate anything else
<question>{question}</question>
""",
            },
        ]
    def invoke(self, question: str) -> str:
        chat_response = self.client.chat.completions.create(
            model=self.model_name,
            messages=self.get_prompt(question),
            temperature=0,
            reasoning_effort="none",
        )
        return chat_response.choices[0].message.content

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--question", type=str, default=DEFAULT_QUESTION, required=False)
    parser.add_argument("--api-key", type=str, default="", required=False)
    parser.add_argument("--model", type=str, default="model", required=False)
    args = parser.parse_args()
    client = MyModel(model_name=args.model, api_key=args.api_key)
    print(client.invoke(args.question))

How to Run

bash

python main.py \
  --question "<QUESTION_TEXT>" \
  --api-key "YOUR_OPENAI_API_KEY" \
  --model "model"

Important Notes

temperature = 0 ensures reproducible results
Function calling is intentionally avoided to prevent JSON-wrapped SQL
The prompt is optimized for SQLite dialect

Possible Extensions

Multi-year queries using IN or ranges
Queries combining multiple metric groups
Execution-based evaluation (SQL result comparison)
Support for additional SQL dialects (PostgreSQL, MySQL)

ViFortune-AI/Qwen3-0.6B-text2sql

Text-to-SQL Evaluation Pipeline

Overview

Key Features

Key Features

High-level Architecture

Suggested Project Structure

Prompt Design

System Prompt Responsibilities

Metric Grouping Logic

Unit Conversion Rule

Example Input (Schema)

Evaluation Metrics

Metric Definitions

Full Demo Code (Kept Exactly As-Is)

How to Run

Important Notes

Possible Extensions

ViFortune-AI/
Qwen3-0.6B-text2sql