experimental AI mode

[sgfost]

experiment with adding an optional mode to sidestep encoded logic conversions between formats and use an LLM to convert software metadata in one format to another, receiving structured data that is de-serialized into codemeticulous/pydantic objects

benefits are twofold:

• avoid writing conversion logic by hand
• potentially infer or keep more information than would otherwise be feasible

there are 2 possible sub-modes:

1. converting from unstructured or an unknown format

• the data itself is the only context and we ask the LLM to extract anything it can

raw_detail_page = open("codebase-detail.html").read()

convert_ai(
  source_format="unstructured",
  target_format="codemeta",
  model="openai/gpt-4o",
  source_data=raw_detail_page
)
# returns a codemeticulous.codemeta.models.CodeMeta object supposedly describing the software record in codebase-detail.html

codemeticulous convert --ai --model=openai/gpt-4o --key=abc123 --to codemeta --unstructured codebase-detail.html > codemeta.json

2. converting from known formats (CodeMeta, citation file, etc)

• prompt can be contextualized with a known schema, term definitions/explanations, etc.

datacite_representation = DataCite(...)

convert_ai(
  source_format="datacite",
  target_format="codemeta",
  model="openai/gpt-4o",
  source_data=datacite_representation
)
# returns a codemeticulous.codemeta.models.CodeMeta object supposedly describing the software record in codebase-detail.html

codemeticulous convert --ai --model=openai/gpt-4o --key=abc123 --to codemeta --from datacite datacite.json > codemeta.json

Note

consider supporting multi-input ( [CITATION.cff, LICENSE, AUTHORS, github_repo] -> codemeta

[alee]

consider using ollama or vllm for local model execution

[asuworks]

1. no ai

codemeticulous convert --to codemeta --from datacite datacite.json > codemeta.json

2. with ai

codemeticulous convert --one-shot --model=openai/gpt-4o some_metadata.json > codemeta.json.llm

3. with "agentic ai" - REACT loop with some tools like: extract_metadata_from_file (non pdf), extract_metadata_from_github, etc. (maybe some more nuanced tools). "agentic" means that the LLM decides what tool to use next based on the agent prompt like:

"""
You are a careful scientific software metadata extractor agent. You will be given a directory tree as input. Decide which files might contain metadata information and use the extract_metadata_from_file tool to extract metadata information defined by following schema:

<codemeta schema> # or better split by metadata category and use partial scemas.

Additional tools:
- extract_metadata_from_github
- extract_metadata_from_url
- ...
"""

codemeticulous convert --agentic --max-turns=10 --model=openai/gpt-4o path_to_archive_dir > codemeta.llm.json

4. build pydantic objects from codemeta.json and codemeta.llm.json using codemeticulous and compare them

[asuworks]

here is a vibed poc for 3: https://github.com/asuworks/codemetawizard