README.md

---
license: apache-2.0
language:
- en
metrics:
- accuracy
tags:
- api
- open-api
- swagger
- api doc
- api call
- code
- instruction_tuned
- basemodel
- pytorch
- RL Tuned
- text-generation-inferenc
library_name: transformers
pipeline_tag: text-generation
---
# pip-api-expert

[pipableAi](https://pipable.ai/)

[colab_notebook]()

## What have we built?
A 1.3 bn state of the art model for api calling , documentation, testing management.
The tasks that the model can accomplish are the following.

```javascript
1.  Convert any bad format text to open api
2.  Convert any bad format text to mark down.
3.  Given docs generate and execute the api call in python
```

## How we built it?

We used a simulator and a form of policy gradient to train the model to self instruct itself to make documents and then perform executable calls on the document.




## Benchmarking :
For benchmarking purposes we are using Semantic Evaluation for Text-to-SQL with 
Distilled Test Suites, an officially accepted evaluation framework for Spider, SParC, and CoSQL which was proposed by a research team of Yale and Berkeley. 
The benchmark contains 2200 test data points
Here is the link to run the evaluation:



## License
The model is open source under apache 2.0. License

## Usage


### Installation

```bash
pip install transformers

```

### Prompt
```python
prompt = f"""<schema>{schema}</schema>
<question>{question}</question>
<sql>"""
```

### PyTorch
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("PipableAI/pip-sql-1.3b")
tokenizer = AutoTokenizer.from_pretrained("PipableAI/pip-sql-1.3b")

inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split('<sql>')[1].split('</sql>')[0])
```


## Examples

### Schema
```sql
CREATE TABLE Products (
  product_id number,
  parent_product_id number,
  product_name text,
  product_price number,
  product_color text,
  product_size text,
  product_description text);

CREATE TABLE Customers (
  customer_id number,
  gender_code text,
  customer_first_name text,
  customer_middle_initial text,
  customer_last_name text,
  email_address text,
  login_name text,
  login_password text,
  phone_number text,
  address_line_1 text,
  town_city text,
  county text,
  country text);

CREATE TABLE Customer_Payment_Methods (
  customer_id number,
  payment_method_code text);

CREATE TABLE Invoices (
  invoice_number number,
  invoice_status_code text,
  invoice_date time);

CREATE TABLE Orders (
  order_id number,
  customer_id number,
  order_status_code text,
  date_order_placed time);

CREATE TABLE Order_Items (
  order_item_id number,
  product_id number,
  order_id number,
  order_item_status_code text);

CREATE TABLE Shipments (
  shipment_id number,
  order_id number,
  invoice_number number,
  shipment_tracking_number text,
  shipment_date time);

CREATE TABLE Shipment_Items (
  shipment_id number,
  order_item_id number);
```

### Questions
What are the email address, town and county of the customers who are of the least common gender?
```sql
SELECT email_address ,  town_city ,  county FROM customers GROUP BY gender_code ORDER BY count(*) ASC LIMIT 1
```

What are the product price and the product size of the products whose price is above average?
```sql
SELECT product_price ,  product_size FROM products WHERE product_price  > (SELECT avg(product_price) FROM products)
```

Which customers did not make any orders? List the first name, middle initial and last name.
```sql
SELECT T1.customer_first_name ,  T1.customer_middle_initial ,  T1.customer_last_name FROM Customers AS T1 WHERE T1.customer_id NOT IN (SELECT T2.customer_id FROM Orders AS T2)
```

### Team
Avi Kothari, Pratham Gupta, Ritvik Aryan Kalra, Rohan Bhatial, Soham Acharya