# Natural Language Queries

This guide explains how to use **Code-Llama** to convert natural language questions into SQL queries using a notebook on the E2E Network AI Platform.

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## Overview

* Convert natural language queries into valid SQL
* No SQL knowledge required
* Uses Code-Llama Instruct model from HuggingFace

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## Step 1: Launch a Instance

1. Sign in at **E2E Cloud**
2. Switch to **AI Platform**
3. Create and launch a Instance(Node)

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## Step 2: Install Dependencies

Run inside notebook:

```bash
!pip install transformers accelerate
```

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## Step 3: Run NL → SQL Conversion

Replace the token with your HuggingFace API Key:

```python
import requests
from IPython.display import Markdown, display

input_text = "Find the names of all employees who are from California."
API_URL = "https://api-inference.huggingface.co/models/codellama/CodeLlama-34b-Instruct-hf"
headers = {"Authorization": "Bearer hf_XXXXXXXXXXXXXXXXXXXXXXXX"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({"inputs": input_text})

while output[0]['generated_text'] != input_text:
    input_text = output[0]['generated_text']
    output = query({'inputs': input_text})

display(Markdown(output[0]['generated_text']))
```

### HuggingFace Token

Create your token here:

> [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)

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## Example Inputs & Outputs

🧠 **Example Query 1:**
Retrieve customers who spent more than $100 in the last month.

```sql
SELECT name, email
FROM customers
WHERE id IN (
  SELECT customer_id
  FROM orders
  WHERE total_amount > 100
  AND created_at > DATE_SUB(NOW(), INTERVAL 1 MONTH)
);
```

🧠 **Example Query 2:**
Average salary per department with >10 employees.

```sql
SELECT d.name AS department_name,
       AVG(e.salary) AS average_salary,
       COUNT(e.id) AS total_employees,
       MAX(e.salary) AS highest_salary
FROM departments d
JOIN employees e ON d.id = e.department_id
GROUP BY d.name
HAVING COUNT(e.id) > 10;
```

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## Best Practices for Better Results

✅ Provide full context (table + filters + timeframe)

✅ Use explicit SQL-style references (e.g., `hire_date > '2023-01-01'`)

✅ Avoid vague terms (“recent”, “large sales”, “active users”)

✅ Include entity names (departments, customers, etc.)

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## Challenges & Solutions

| Challenge               | Recommended Solution                           |
| ----------------------- | ---------------------------------------------- |
| SQL dialect differences | Match your DB engine (MySQL / Postgres / etc.) |
| Complex multi-joins     | Break query into smaller steps, refine output  |
| Missing schema context  | Mention table + field names explicitly         |
| Incorrect SQL syntax    | Validate + autorepair in application layer     |
| Data privacy risks      | Use authentication and restricted roles        |

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## Conclusion

Code-Llama helps:

* Non-SQL users query databases easily
* Speed up data-driven decisions
* Avoid manual SQL writing

It performs best when inputs are precise and unambiguous — treat it as a **power tool** that enhances human capability.

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