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lock-version = "2.0" python-versions = "^3.9" -content-hash = "93a4598590c65422d6a1172c6247c4540bf2025afd9b6c3860628456e5a273ca" +content-hash = "80107a6e5a41177d90d4281a95fdb794ff2636f774f067057b273e7fe72bca11" diff --git a/pyproject.toml b/pyproject.toml index ef58861..ae68d89 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,7 @@ astropy = "^6.0.0" markdown-it-py = "^3.0.0" rich = "^13.7.0" lxml = "^5.1.0" +instructor = "^0.4.6" python-frontmatter = "^1.1.0" [tool.poetry.group.dev.dependencies] diff --git a/sdRDM/tools/__init__.py b/sdRDM/tools/__init__.py index e69de29..46ffe9d 100644 --- a/sdRDM/tools/__init__.py +++ b/sdRDM/tools/__init__.py @@ -0,0 +1 @@ +from .harvester import harvest_metadata diff --git a/sdRDM/tools/harvester.py b/sdRDM/tools/harvester.py new file mode 100644 index 0000000..5fbd43a --- /dev/null +++ b/sdRDM/tools/harvester.py @@ -0,0 +1,53 @@ +import rich +import instructor +from openai import OpenAI + + +def harvest_metadata( + model: "DataModel", + data: str, + openai_key: str, + prompt: str = "Extract metadata from the following document", + gpt_model: str = "gpt-4", + **openai_kwargs, +) -> "DataModel": + """ + Harvests metadata from a document using OpenAI GPT-4 model and adds it to the given data model. + + Args: + model (DataModel): The data model instance. + data (str): The document from which to extract metadata. + openai_key (str): The API key for OpenAI. + prompt (str, optional): The prompt for the OpenAI chat completion. Defaults to "Extract metadata from the following document". + gpt_model (str, optional): The GPT model to use. Defaults to "gpt-4". + **openai_kwargs: Additional keyword arguments to pass to the OpenAI API. + + Returns: + DataModel: The updated data model instance with extracted metadata. + """ + + assert hasattr(model, "model_fields"), "'model' must be an instance of DataModel" + + rich.print(f"[bold]\n\nšŸ‘Øā€šŸŒ¾ Harvesting metadata from text into {model.__name__}") + + client = instructor.patch( + OpenAI( + api_key=openai_key, + **openai_kwargs, + ) + ) + + result = client.chat.completions.create( + model=gpt_model, + response_model=model, + messages=[ + { + "role": "user", + "content": f"{prompt}: {data}", + }, + ], + ) + + rich.print(f"[bold]\n\nMetadata harvested successfully!\n") + + return result