metadata

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

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.

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

pip install transformers

Prompt

prompt = f"""<schema>{schema}</schema>
<question>{question}</question>
<sql>"""

PyTorch

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

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?

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?

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.

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