api_doc.md

Open-Creator API Documentation

Function: create

Generates a CodeSkill instance using different input sources.

Parameters:

• request: String detailing the skill functionality.
• messages or messages_json_path: Messages as a list of dictionaries or a path to a JSON file containing messages.
• file_content or file_path: String of file content or path to a code/API doc file.
• skill_path or skill_json_path: Directory path with skill name as stem or file path with skill.json as stem.
• huggingface_repo_id: Identifier for a Huggingface repository.
• huggingface_skill_path: Path to the skill within the Huggingface repository.

Returns:

• CodeSkill: The created skill.

Usage:

1. Creating Skill using a Request String:

skill = create(request="filter how many prime numbers are in 201")

2. Creating Skill using Messages:

• Directly:

skill = create(messages=[{"role": "user", "content": "write a program..."}])

• Via JSON Path:

skill = create(messages_json_path="./messages_example.json")

3. Creating Skill using File Content or File Path:

• Direct Content:

skill = create(file_content="def example_function(): pass")

• File Path:

skill = create(file_path="../creator/utils/example.py")

4. Creating Skill using Skill Path or Skill JSON Path:

• JSON Path:

skill = create(skill_json_path="~/.cache/open_creator/skill_library/create/skill.json")

• Skill Path:

skill = create(skill_path="~/.cache/open_creator/skill_library/create")

5. Creating Skill using Huggingface Repository ID and Skill Path: If a skill is hosted in a Huggingface repository, you can create it by specifying the repository ID and the skill path within the repository.

skill = create(huggingface_repo_id="YourRepo/skill-library", huggingface_skill_path="specific_skill")

Notes:

• Ensure to provide accurate and accessible file paths.
• At least one parameter must be specified to generate a skill.
• Parameters’ functionality does not overlap; specify the most relevant one for clarity.
• Use absolute paths where possible to avoid relative path issues.
• Ensure the repository ID and skill path are accurate and that you have the necessary access permissions to retrieve the skill from the repository.

Function: save

Stores a CodeSkill instance either to a local path or a Huggingface repository. In default just use save(skill) and it will store the skill into the default path. Only save the skill when the user asks to do so.

Parameters:

• skill (CodeSkill): The skill instance to be saved.
• huggingface_repo_id (Optional[str]): Identifier for a Huggingface repository.
• skill_path (Optional[str]): Local path where the skill should be saved.

Returns:

• None

Usage:

The save function allows for the persistent storage of a CodeSkill instance by saving it either locally or to a specified Huggingface repository.

1. Save to Huggingface Repository:

save(skill=skill, huggingface_repo_id="YourRepo/skill_library")

2. Save Locally:

save(skill=skill, skill_path="/path/to/save")

Notes:

• At least one of huggingface_repo_id or skill_path must be provided to execute the function, otherwise a ValueError will be raised.
• Ensure provided paths and repository identifiers are accurate and accessible.

Function: search

Retrieve skills related to a specified query from the available pool of skills.

Parameters:

• query (str): Search query string.
• top_k (Optional[int]): Maximum number of skills to return. Default is 1.
• threshold (Optional[float]): Minimum similarity score to return a skill. Default is 0.8.

Returns:

• List[CodeSkill]: A list of retrieved CodeSkill objects that match the query.

Usage:

The search function allows users to locate skills related to a particular query string. This is particularly useful for identifying pre-existing skills within a skill library that may fulfill a requirement or for exploring available functionalities.

1. Basic Search:

skills = search("extract pages from a pdf")

2. Refined Search:

skills = search("extract pages from a pdf", top_k=3, threshold=0.85)

Notes:

• The query should be descriptive to enhance the accuracy of retrieved results.
• Adjust top_k and threshold to balance between specificity and breadth of results.
• Ensure to check the length of the returned list to validate the presence of results before usage.

Skill Object Methods and Operator Overloading

Explore the functionalities and modifications of a skill object through methods and overloaded operators.

Method: run

Execute a skill with provided arguments or request.

• Example Usage:

skills = search("pdf extract section")
if skills:
  skill = skills[0]
  input_args = {
      "pdf_path": "creator.pdf",
      "start_page": 3,
      "end_page": 8,
      "output_path": "creator3-8.pdf"
  }
  print(skill.run(input_args))

Method: test

Validate a skill using a tester agent.

• Example Usage:

skill = create(request="filter prime numbers in a range, e.g., filter_prime_numbers(2, 201)")
test_summary = skill.test()
print(test_summary)
print(skill.conversation_history)

Overloaded Operators:

Modify and refine skills using operator overloading.

1. Combining Skills: Utilize the + operator to chain or execute skills in parallel, detailing the coordination with the > operator.

new_skill = skillA + skillB > "Explanation of how skills A and B operate together"

2. Refactoring Skills: Employ the > operator to enhance or modify existing skills.

   refactored_skill = skill > "Descriptive alterations or enhancements"

3. Decomposing Skills: Use the < operator to break down a skill into simpler components.

   simpler_skills = skill < "Description of how the skill should be decomposed"

Notes:

• Ensure accurate descriptions when using overloaded operators to ensure skill modifications are clear and understandable.
• Validate skills with test method to ensure functionality post-modification.