Methods Metadata Extraction Tool
Langchain tools that execute zero-shot extraction over a local database of full text papers previously imported into our database.
BaseMetadataExtractionTool
BaseMetadataExtractionTool (db:alhazen.utils.ceifns_db.Ceifns_Literature Db, llm:Optional[langchain_core.language_mode ls.chat_models.BaseChatModel]=None, slm:Optio nal[langchain_core.language_models.chat_model s.BaseChatModel]=None, name:str='metadata_extraction', description:str='Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.', args_schema:Optional[Type[pydanti c.v1.main.BaseModel]]=None, return_direct:bool=True, verbose:bool=False, callbacks:Union[List[langchain_core.callbacks .base.BaseCallbackHandler],langchain_core.cal lbacks.base.BaseCallbackManager,NoneType]=Non e, callback_manager:Optional[langchain_core.c allbacks.base.BaseCallbackManager]=None, tags:Optional[List[str]]=None, metadata:Optional[Dict[str,Any]]=None, handle _tool_error:Union[bool,str,Callable[[langchai n_core.tools.ToolException],str],NoneType]=Fa lse, handle_validation_error:Union[bool,str,C allable[[pydantic.v1.error_wrappers.Validatio nError],str],NoneType]=False, examples:dict={})
Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.
MetadataExtractionToolSchema
MetadataExtractionToolSchema (paper_id:str, extraction_type:str, run_label:Optional[str]=None)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
MetadataExtraction_EverythingEverywhere_Tool
MetadataExtraction_EverythingEverywhere_Tool (db:alhazen.utils.ceifns_db .Ceifns_LiteratureDb, llm:O ptional[langchain_core.lang uage_models.chat_models.Bas eChatModel]=None, slm:Optio nal[langchain_core.language _models.chat_models.BaseCha tModel]=None, name:str='met adata_extraction', description:str='Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.', args _schema:Optional[Type[pydan tic.v1.main.BaseModel]]=Non e, return_direct:bool=True, verbose:bool=False, callbac ks:Union[List[langchain_cor e.callbacks.base.BaseCallba ckHandler],langchain_core.c allbacks.base.BaseCallbackM anager,NoneType]=None, call back_manager:Optional[langc hain_core.callbacks.base.Ba seCallbackManager]=None, ta gs:Optional[List[str]]=None , metadata:Optional[Dict[st r,Any]]=None, handle_tool_e rror:Union[bool,str,Callabl e[[langchain_core.tools.Too lException],str],NoneType]= False, handle_validation_er ror:Union[bool,str,Callable [[pydantic.v1.error_wrapper s.ValidationError],str],Non eType]=False, examples:dict={})
Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.
MetadataExtraction_MethodsSectionOnly_Tool
MetadataExtraction_MethodsSectionOnly_Tool (db:alhazen.utils.ceifns_db.C eifns_LiteratureDb, llm:Optio nal[langchain_core.language_m odels.chat_models.BaseChatMod el]=None, slm:Optional[langch ain_core.language_models.chat _models.BaseChatModel]=None, name:str='metadata_extraction ', description:str='Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.', args_schema:Optio nal[Type[pydantic.v1.main.Bas eModel]]=None, return_direct:bool=True, verbose:bool=False, callbacks :Union[List[langchain_core.ca llbacks.base.BaseCallbackHand ler],langchain_core.callbacks .base.BaseCallbackManager,Non eType]=None, callback_manager :Optional[langchain_core.call backs.base.BaseCallbackManage r]=None, tags:Optional[List[s tr]]=None, metadata:Optional[ Dict[str,Any]]=None, handle_t ool_error:Union[bool,str,Call able[[langchain_core.tools.To olException],str],NoneType]=F alse, handle_validation_error :Union[bool,str,Callable[[pyd antic.v1.error_wrappers.Valid ationError],str],NoneType]=Fa lse, examples:dict={})
Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.
MetadataExtraction_RAGOnSections_Tool
MetadataExtraction_RAGOnSections_Tool (db:alhazen.utils.ceifns_db.Ceifns _LiteratureDb, llm:Optional[langch ain_core.language_models.chat_mode ls.BaseChatModel]=None, slm:Option al[langchain_core.language_models. chat_models.BaseChatModel]=None, name:str='metadata_extraction', description:str='Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.', args_schema:Optional[T ype[pydantic.v1.main.BaseModel]]=N one, return_direct:bool=True, verbose:bool=False, callbacks:Unio n[List[langchain_core.callbacks.ba se.BaseCallbackHandler],langchain_ core.callbacks.base.BaseCallbackMa nager,NoneType]=None, callback_man ager:Optional[langchain_core.callb acks.base.BaseCallbackManager]=Non e, tags:Optional[List[str]]=None, metadata:Optional[Dict[str,Any]]=N one, handle_tool_error:Union[bool, str,Callable[[langchain_core.tools .ToolException],str],NoneType]=Fal se, handle_validation_error:Union[ bool,str,Callable[[pydantic.v1.err or_wrappers.ValidationError],str], NoneType]=False, examples:dict={})
Runs a specified metadata extraction pipeline over a research paper that has been loaded in the local literature database.
SimpleExtractionWithRAGTool
SimpleExtractionWithRAGTool (db:alhazen.utils.ceifns_db.Ceifns_Literatur eDb, llm:Optional[langchain_core.language_mo dels.chat_models.BaseChatModel]=None, slm:Op tional[langchain_core.language_models.chat_m odels.BaseChatModel]=None, name:str='simple_extraction', description:str='Performs simple information extraction from a specified research paper from the database.', args_schema:Optional[Ty pe[pydantic.v1.main.BaseModel]]=None, return_direct:bool=False, verbose:bool=False, callbacks:Union[List[lan gchain_core.callbacks.base.BaseCallbackHandl er],langchain_core.callbacks.base.BaseCallba ckManager,NoneType]=None, callback_manager:O ptional[langchain_core.callbacks.base.BaseCa llbackManager]=None, tags:Optional[List[str]]=None, metadata:Optional[Dict[str,Any]]=None, handl e_tool_error:Union[bool,str,Callable[[langch ain_core.tools.ToolException],str],NoneType] =False, handle_validation_error:Union[bool,s tr,Callable[[pydantic.v1.error_wrappers.Vali dationError],str],NoneType]=False)
Performs simple information extraction from a specified research paper from the database.
SimpleExtractionWithRAGToolSchema
SimpleExtractionWithRAGToolSchema (paper_id:str, variable_name:str, question:str)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.