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Library Parser
Parses Python Libraries/Packages
Generate & Execute Plans
Simulator
Simulates execution environment
Enables feedback for model training
Auto-Learn with feedback
Training Algorithm
1.3 billion parameters Matches GPT 3.5 on similar tasks
Usage Details
Git Stars
Model Downloads
Services
One OS Library, unlimited solutions
The library simplifies docstring creation, provides function calling capabilities, facilitates SQL query generation, and enhances development workflows with intuitive functionality from natural language input.
RPA Planning and Generation
Robotic Process Automation (RPA) involves the planning and generation of automated workflows to streamline business processes, reducing human intervention and increasing efficiency.
Code Synthesis Based on Strategic Plans
Code synthesis involves the generation of software code based on strategic plans and requirements, ensuring the alignment of software development with organizational objectives and goals.
Making API Calls Across Various Programming Languages
Making API calls involves invoking Application Programming Interfaces (APIs) across different programming languages, enabling interoperability and seamless communication between software components.
Automated Docstring Generation for Code Documentation
Automated docstring generation involves the creation of descriptive documentation strings within code files automatically, enhancing code readability, maintainability, and understanding.
Conversion of Textual Descriptions into SQL Queries
Text to SQL query generation involves the transformation of natural language textual descriptions into structured SQL queries, facilitating database interaction and query execution.
Function Invocation Using Natural Language Commands
Function calling using natural language commands enables developers to invoke functions within code using human-readable language, enhancing developer productivity and code accessibility.
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- Models:
- Libraries:
- All
- Plan
- SQL
- DOC
- Function Call
- Parse Data to Json
Plan:
- Make get request to https://colab.research.google.com/
- Create a Beautiful soup object.
- Find all the divs in it
Plan:
- Create a dataframe using dict {"a":[1,2,3], "b":[4,5,6]}
- Find shape of the dataframe and column names in it
Plan:
- read a.csv as dataframe.
- describe the .25, .50 and .75 percentiles in the dataframe.
SQL:
- SELECT Name FROM department WHERE Budget_in_Billions > 3;
SQL:
- SELECT d.Name AS Department_Name, h.age AS Head_Age, CASE WHEN m.temporary_acting IS NOT NULL THEN 'Yes' ELSE 'No' END AS Temporary_Acting_Head FROM department d JOIN management m ON d.Department_ID = m.department_ID JOIN head h ON m.head_ID = h.head_ID WHERE d.Budget_in_Billions > 3;
Docs:
- Docs for the method: requests.post
SQL:
- SELECT name FROM head WHERE born_state != 'California';
Function Call:
- make_get_req(path=variable_2, data=variable_1, params={'weight': variable_3, 'width': 10}, headers={}, not_json_response=True, absolute=True);
Docs:
- Docs for written code: def add_numbers(a: float, b:float):
return a + b
JSON:
{ "users_list": [ { "ids": ["id234", "id4532134"], "info": { "description": "some text", "isActive": true } } ] }