Title / Abstract Extraction Tool
Langchain tools that execute zero-shot extraction tasks over a local database of title/abstracts from papers previously imported into our database.
BaseTitleAbstractExtractionTool
BaseTitleAbstractExtractionTool (db:alhazen.utils.ceifns_db.Ceifns_Liter atureDb, llm:Optional[langchain_core.lan guage_models.chat_models.BaseChatModel]= None, slm:Optional[langchain_core.langua ge_models.chat_models.BaseChatModel]=Non e, name:str='tiab_classification', description:str='Runs a specified document classification pipeline over papers in a collection.', args_schema:Op tional[Type[pydantic.v1.main.BaseModel]] =None, return_direct:bool=True, verbose:bool=False, callbacks:Union[List [langchain_core.callbacks.base.BaseCallb ackHandler],langchain_core.callbacks.bas e.BaseCallbackManager,NoneType]=None, ca llback_manager:Optional[langchain_core.c allbacks.base.BaseCallbackManager]=None, tags:Optional[List[str]]=None, metadata:Optional[Dict[str,Any]]=None, h andle_tool_error:Union[bool,str,Callable [[langchain_core.tools.ToolException],st r],NoneType]=False, handle_validation_er ror:Union[bool,str,Callable[[pydantic.v1 .error_wrappers.ValidationError],str],No neType]=False, prompt_name:str='binary methods paper', examples:dict={})
Runs a specified document classification pipeline over papers in a collection.
TitleAbstractExtractionToolSchema
TitleAbstractExtractionToolSchema (collection_id:str, domain:str, extraction_type:str, repeat_run:Optional[bool]=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.
TitleAbstractExtraction_OneDocAtATime_Tool
TitleAbstractExtraction_OneDocAtATime_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='tiab_one_doc_classi fication', description:str='Runs a specified document classification pipeline over papers in a collection by running a simple classifier over the text of each title + abstract.', 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, prompt_name:str='binary methods paper', examples:dict={})
Runs a specified document classification pipeline over papers in a collection.