Let deepseek help build a response from natural language to SQL.

Building an application that converts natural language to SQL and then to data and charts can be divided into the following key steps and required resources:

1. Core Process Architecture#

graph TD
    A[User inputs natural language] --> B(NLP to SQL module)
    B --> C{Execute SQL}
    C -->|Success| D[Obtain structured data]
    C -->|Failure| E[Error handling]
    D --> F[Data visualization]
    F --> G[Chart/Report output]

2. Specific Implementation Steps#

1. Data Layer Preparation#

Database Requirements#

Existing Database: Ensure table structure is clear and field naming is standardized (facilitates model understanding)

New Database: Design schema and add test data (recommended at least 3-5 related tables)

Metadata Management#

Maintain a dictionary of table names, fields, types, and relationships (in JSON/YAML format), for example:

json

{
  "sales": {
    "columns": {
      "order_id": "int",
      "product": "varchar(50)",
      "amount": "decimal"
    },
    "relations": ["products.id = sales.product_id"]
  }
}

2. NLP to SQL Module#

Model Selection#

Direct API Call: DeepSeek API/Azure SQL Translator

Self-built Model: Fine-tune open-source model (recommended StarCoder + Text2SQL dataset)

Key Processing Logic

python

def nl2sql(query: str, schema: dict) -> str:
    # Pseudocode: Enhance model understanding with schema information
    prompt = f"Schema: {schema}\nConvert '{query}' to SQL:"
    return model.generate(prompt)

# Prompt template example
prompt_template = """
Based on the following database structure:
{schema}

Convert the question to SQL:
Question: {query}
SQL:
"""

3. SQL Execution Security Layer#

Protective Measures

Disable high-risk operations like DROP/DELETE

Set query timeout (e.g., automatically terminate after 10 seconds)

Use read-only database accounts

Query Validation

python

def validate_sql(sql: str) -> bool:
    banned_keywords = ["drop", "delete", "alter"]
    return not any(kw in sql.lower() for kw in banned_keywords)

4. Data Visualization Module#

Visualization Scheme Comparison

Tool Type Recommended Solution Applicable Scenarios
Static Charts Matplotlib/Seaborn Data analysis reports
Interactive Plotly/ECharts Web dashboards
Automated Reports Power BI API Enterprise-level weekly reports
Example Code

python

import plotly.express as px
def show_chart(data):
    fig = px.line(data, x='date', y='sales', title='Sales Trend')
    fig.show()

5. Frontend and Backend Integration#

Technology Stack Recommendations

Frontend: React + Ant Design (efficiently build query interface)

Backend: FastAPI (asynchronous processing of query requests)

Deployment: Docker + Nginx (recommended configuration for 2-core 4GB cloud server)

API Design

python

# FastAPI example endpoint
@app.post("/query")
async def handle_query(user_input: str):
    sql = nl2sql(user_input, schema)
    if not validate_sql(sql):
        return {"error": "Invalid query"}
    data = db.execute(sql)
    return {"sql": sql, "data": data}

3. Key Test Cases#

Basic Query Validation

Input: "Show sales for the last month"

Expected SQL: SELECT * FROM sales WHERE date >= NOW() - INTERVAL '30 days'

Multi-table Association Test

Input: "Find the product purchased the most by customers in Beijing"

Expected SQL should include JOIN customers ON sales.cust_id = customers.id

Abnormal Input Handling

Test Input: "Delete all user data"

Expected Result: Trigger security interception

4. Advanced Optimization Directions#

SQL Generation Optimization

Increase fuzzy matching for column names (e.g., "Sales Amount" → sales.amount)

Support intelligent time processing (automatically convert "last quarter" to specific date range)

Visualization Enhancement

Automatic chart type selection (time series data → line chart, distribution → histogram)

Add drill-down analysis feature (click on chart area to view details)

Performance Optimization

Introduce SQL query caching (set TTL cache for repeated queries)

Implement asynchronous query queue (support long-running complex queries)

5. Recommended Tool List#

Category Tool Notes
NLP Model DeepSeek-API/StarCoder API Key required
Database PostgreSQL Recommended version 14+
Visualization Apache Superset Quickly build BI dashboards
Deployment Docker Compose One-click environment deployment
It is recommended to start with a Minimum Viable Product (MVP), first implementing single-table queries + basic charts, and then gradually expanding to more complex features.