o |!g@s*ddlZddlZddlmZddlmZddlmZm Z ddl m Z ddl m Z ddlmZdd lmZdd lmZdd lmZdd lmZdd lmZddlmZddlmZddlmZddl m!Z!ddl"m#Z#ddl$m%Z%ddl&m'Z'ddl(m)Z)ddl*m+Z+ddl,m-Z-ddl.m/Z/ddl0m1Z1ddl2m3Z3ddl4m5Z5ddl6m7Z7ddl8m9Z9ddl:m;Z;ddlm?Z?dd!l@mAZAdd"lBmCZCd#d$lDmEZEd#d%lFmGZGd#d&lHmIZIzddlJZJeJjKLd'reMddlNmOZJWn eMyddlJZJYnwePejQd(ZRGd)d*d*ZSGd+d,d,ZTdS)-N)JSONDecodeError)ApiError)AsyncClientWrapperSyncClientWrapper)jsonable_encoder)remove_none_from_dict)UnprocessableEntityError)ChatData) CloudDocument)CloudDocumentCreate)ConfiguredTransformationItem)DataSinkCreate)EvalExecutionParams)HttpValidationError) InputMessage)LlamaParseParameters)ManagedIngestionStatusResponse)MetadataFilters)#PaginatedListCloudDocumentsResponse)"PaginatedListPipelineFilesResponse)Pipeline)PipelineCreate)PipelineDataSource)PipelineDataSourceCreate)PipelineDeployment) PipelineFile)PipelineFileCreate) PipelineType)PlaygroundSession)PresetRetrievalParams) RetrievalMode)RetrieveResults)TextNode)%PipelineFileUpdateCustomMetadataValue)PipelineUpdateEmbeddingConfig)PipelineUpdateTransformConfigz1..c@ eZdZdefddZddddddejedejedejed ejed ej e f d d Z ddd dejedejede d e fddZ ddd dejedejede d e fddZded e fddZeeeeeeeeeeed dedejedejedejej edejedejedejedejedejed ejed!ejed"ejed e fd#d$Zded dfd%d&Zded efd'd(Zded e fd)d*Zded e fd+d,Zded e fd-d.Zddd/ded0ejed1ejed ej e fd2d3Z!dedej e"d ej e fd4d5Z#ddddd6ded0ejed1ejed7eje$d8eje$d e%f d9d:Z&d;eded efdd;eded?ejej(eeje)fd e fd@dAZ*d;eded dfdBdCZ+dedDej,d ej(eeffdEdFZ-ded dfdGdHZ.ded ej e/fdIdJZ0dedej e1d ej e/fdKdLZ2edMd0ededNeje3d e/fdOdPZ4d0eded dfdQdRZ5d0eded e fdSdTZ6d0eded efdUdVZ7eeeeeeeeeeedW dedXeje$dYeje3dZeje$d[ejed\eje$d]eje3d^eje8d_eje$d`eje9daejedbedcejed e:fdddeZ;ded ej e<fdfdgZ=dheded eded e?fdkdlZ@eeedmdednejej eAdoejeBdcejed ejCf dpdqZDddddddrdedseje$d7eje$d;ejedtejeduejed ej eEfdvdwZFdedej eGd ej eEfdxdyZHdedej eGd ej eEfdzd{ZIddddddrdedseje$d7eje$d;ejedtejeduejed eJfd|d}ZKd~eded eEfddZLd~eded dfddZMd~eded efddZNd~eded ej eOfddZPdS)PipelinesClientclient_wrappercC ||_dSN_client_wrapperselfr*r1h/mnt/skqttb/ctump_chatbot/chatbot/lib/python3.10/site-packages/llama_cloud/resources/pipelines/client.py__init__7 zPipelinesClient.__init__N project_id project_name pipeline_name pipeline_typer6r7r8r9returnc Cs|jjjdtj|jddt||||d|jdd}d|j kr,dkr9nn t t j t|S|j d krHtt t|z|}Wnty]t|j |jd wt|j |d ) aA Search for pipelines by various parameters. Parameters: - project_id: typing.Optional[str]. - project_name: typing.Optional[str]. - pipeline_name: typing.Optional[str]. - pipeline_type: typing.Optional[PipelineType]. --- from llama_cloud import PipelineType from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.search_pipelines( pipeline_type=PipelineType.PLAYGROUND, ) GET/api/v1/pipelinesr5<paramsheaderstimeout, status_codebodyr. httpx_clientrequesturllibparseurljoin get_base_urlr get_headersrGpydantic parse_obj_astypingListrjsonr rrrtextr0r6r7r8r9 _response_response_jsonr1r1r2search_pipelines:s.   z PipelinesClient.search_pipelinesr6organization_idr\rKcC|jjjdtj|jddt||dt||j dd}d|j kr-dkr7nnt t |S|j d krFtt t|z|}Wnty[t|j |jd wt|j |d ) Create a new pipeline for a project. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: PipelineCreate. POSTr<r=r[r>r@rUrArBrCrDrErFr.rJrKrLrMrNrOrrrPrGrQrRrrUr rrrrVr0r6r\rKrXrYr1r1r2create_pipelineps$    zPipelinesClient.create_pipelinecCr]) H Upsert a pipeline for a project. Updates if a pipeline with the same name and project_id already exists. Otherwise, creates a new pipeline. Parameters: - project_id: typing.Optional[str]. - organization_id: typing.Optional[str]. - request: PipelineCreate. PUTr<r=r[r>r`rCrDrErFrarbr1r1r2upsert_pipelines$    zPipelinesClient.upsert_pipeline pipeline_idcCs|jjjdtj|jdd||jdd}d|jkr'dkr1nnt t | S|jdkr@t t t| z| }WntyUt|j|jd wt|j|d ) p Get a pipeline by ID for a given project. Parameters: - pipeline_id: str. r;r<api/v1/pipelines/r>rArBrCrDrErFr.rJrKrLrMrNrOrPrGrQrRrrUr rrrrVr0rgrXrYr1r1r2 get_pipelines    zPipelinesClient.get_pipeline embedding_configtransform_configconfigured_transformations data_sink_idembedding_model_config_id data_sinkpreset_retrieval_parameterseval_parametersllama_parse_parametersnamemanaged_pipeline_idrorprqrrrsrtrurvrwrxryc Csti} |tur || d<|tur|| d<|tur|| d<|tur"|| d<|tur*|| d<|tur2|| d<|tur:|| d<| turB| | d<| turJ| | d <| turR| | d <| turZ| | d <|jjjd tj|jd d|t| |j dd}d|j krdkrnnt t |S|j dkrtt t|z|}Wntyt|j |jdwt|j |d) Update an existing pipeline for a project. Parameters: - pipeline_id: str. - embedding_config: typing.Optional[PipelineUpdateEmbeddingConfig]. - transform_config: typing.Optional[PipelineUpdateTransformConfig]. Configuration for the transformation. - configured_transformations: typing.Optional[typing.List[ConfiguredTransformationItem]]. - data_sink_id: typing.Optional[str]. - embedding_model_config_id: typing.Optional[str]. - data_sink: typing.Optional[DataSinkCreate]. - preset_retrieval_parameters: typing.Optional[PresetRetrievalParams]. - eval_parameters: typing.Optional[EvalExecutionParams]. - llama_parse_parameters: typing.Optional[LlamaParseParameters]. - name: typing.Optional[str]. - managed_pipeline_id: typing.Optional[str]. rorprqrrrsrtrurvrwrxryrer<rir>rUrArBrCrDrErFOMITr.rJrKrLrMrNrOrrPrGrQrRrrUr rrrrVr0rgrorprqrrrsrtrurvrwrxry_requestrXrYr1r1r2update_existing_pipelinesP,   z(PipelinesClient.update_existing_pipelinecCs|jjjdtj|jdd||jdd}d|jkr(dkr)dS|jd kr8t t t | z| }WntyMt|j|jd wt|j|d ) a; Delete a pipeline by ID. Parameters: - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.delete_pipeline( pipeline_id="string", ) DELETEr<rir>rjrCrDNrErFr.rJrKrLrMrNrOrPrGr rQrRrrUrrrVrlr1r1r2delete_pipeline"s"   zPipelinesClient.delete_pipelinecC|jjjdtj|jdd|d|jdd}d|jkr(dkr2nnt t | S|jd krAt t t| z| }WntyVt|j|jd wt|j|d ) aJ Get the status of a pipeline by ID. Parameters: - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.get_pipeline_status( pipeline_id="string", ) r;r<ri/statusr>rjrCrDrErFr.rJrKrLrMrNrOrPrGrQrRrrUr rrrrVrlr1r1r2get_pipeline_statusBs     z#PipelinesClient.get_pipeline_statuscCr) Run ingestion for the pipeline by incrementally updating the data-sink with upstream changes from data-sources & files. Parameters: - pipeline_id: str. r_r<ri/syncr>rjrCrDrErFrkrlr1r1r2 sync_pipelineb     zPipelinesClient.sync_pipelinecCr) = Parameters: - pipeline_id: str. r_r<ri /sync/cancelr>rjrCrDrErFrkrlr1r1r2cancel_pipeline_syncys$   z$PipelinesClient.cancel_pipeline_synccCr) ] Copy a pipeline by ID. Parameters: - pipeline_id: str. r_r<ri/copyr>rjrCrDrErFrkrlr1r1r2 copy_pipelinerzPipelinesClient.copy_pipelinedata_source_idonly_manually_uploadedrrcCs|jjjdtj|jdd|dt||d|jdd}d|j kr.d kr;nn t t j t|S|j d krJtt t|z|}Wnty_t|j |jd wt|j |d ) a Get files for a pipeline. Parameters: - pipeline_id: str. - data_source_id: typing.Optional[str]. - only_manually_uploaded: typing.Optional[bool]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.list_pipeline_files( pipeline_id="string", ) r;r<ri/filesrr>r?rCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrSrTrrUr rrrrVr0rgrrrXrYr1r1r2list_pipeline_filess&     z#PipelinesClient.list_pipeline_filescC|jjjdtj|jdd|dt||jdd}d|j kr+dkr8nn t t j t|S|j d krGtt t|z|}Wnty\t|j |jd wt|j |d ) a Add files to a pipeline. Parameters: - pipeline_id: str. - request: typing.List[PipelineFileCreate]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.add_files_to_pipeline( pipeline_id="string", request=[], ) rer<rirr>r{rCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrSrTrrUr rrrrVr0rgrKrXrYr1r1r2add_files_to_pipelines"    z%PipelinesClient.add_files_to_pipelinerrlimitoffsetrrc Cs|jjjdtj|jdd|dt||||d|jdd}d|j kr0d kr:nnt t | S|j d krItt t| z| }Wnty^t|j |jd wt|j |d ) a Get files for a pipeline. Parameters: - pipeline_id: str. - data_source_id: typing.Optional[str]. - only_manually_uploaded: typing.Optional[bool]. - limit: typing.Optional[int]. - offset: typing.Optional[int]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.list_pipeline_files_2( pipeline_id="string", ) r;r<ri/files2rr>r?rCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrrUr rrrrVr0rgrrrrrXrYr1r1r2list_pipeline_files_2s.     z%PipelinesClient.list_pipeline_files_2file_idc C|jjjdtj|jdd|d|d|jdd}d|jkr+d kr5nnt t | S|jd krDt t t| z| }WntyYt|j|jd wt|j|d ) a Get status of a file for a pipeline. Parameters: - file_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.get_pipeline_file_status( file_id="string", pipeline_id="string", ) r;r<ri/files/rr>rjrCrDrErFrr0rrgrXrYr1r1r2get_pipeline_file_status2s$   z(PipelinesClient.get_pipeline_file_statuscustom_metadatarc Ci}|tur ||d<|jjjdtj|jdd|d|t||j dd}d|j kr7d krAnnt t |S|j d krPtt t|z|}Wntyet|j |jd wt|j |d ) a Update a file for a pipeline. Parameters: - file_id: str. - pipeline_id: str. - custom_metadata: typing.Optional[typing.Dict[str, typing.Optional[PipelineFileUpdateCustomMetadataValue]]]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.update_pipeline_file( file_id="string", pipeline_id="string", ) rrer<rirr>r{rCrDrErFr}r.rJrKrLrMrNrOrrPrGrQrRrrUr rrrrVr0rrgrrrXrYr1r1r2update_pipeline_fileWs,    z$PipelinesClient.update_pipeline_filec C|jjjdtj|jdd|d||jdd}d|jkr+dkr,d S|jd kr;t t t | z| }WntyPt|j|jd wt|j|d ) a Delete a file from a pipeline. Parameters: - file_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.delete_pipeline_file( file_id="string", pipeline_id="string", ) rr<rirr>rjrCrDNrErFrrr1r1r2delete_pipeline_files&   z$PipelinesClient.delete_pipeline_file upload_filecCs|jjjdtj|jdd|dtid|i|jdd}d|j kr.d kr=nn t t j ttf|S|j d krLtt t|z|}Wntyat|j |jd wt|j |d ) Import metadata for a pipeline. Parameters: - pipeline_id: str. - upload_file: typing.IO. rer<ri /metadatarr>datafilesrArBrCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrSDictstrrUr rrrrVr0rgrrXrYr1r1r2import_pipeline_metadatas$     z(PipelinesClient.import_pipeline_metadatacCs|jjjdtj|jdd|d|jdd}d|jkr)dkr*d S|jd kr9t t t | z| }WntyNt|j|jd wt|j|d ) a^ Delete metadata for all files in a pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.delete_pipeline_files_metadata( pipeline_id="string", ) rr<rirr>rjrCrDNrErFrrlr1r1r2delete_pipeline_files_metadatas"    z.PipelinesClient.delete_pipeline_files_metadatacC|jjjdtj|jdd|d|jdd}d|jkr(dkr5nn t t j t |S|jd krDtt t|z|}WntyYt|j|jd wt|j|d ) aN Get data sources for a pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.list_pipeline_data_sources( pipeline_id="string", ) r;r<ri /data-sourcesr>rjrCrDrErFr.rJrKrLrMrNrOrPrGrQrRrSrTrrUr rrrrVrlr1r1r2list_pipeline_data_sourcess$   z*PipelinesClient.list_pipeline_data_sourcescCr) a Add data sources to a pipeline. Parameters: - pipeline_id: str. - request: typing.List[PipelineDataSourceCreate]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.add_data_sources_to_pipeline( pipeline_id="string", request=[], ) rer<rirr>r{rCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrSrTrrUr rrrrVrr1r1r2add_data_sources_to_pipeline &    z,PipelinesClient.add_data_sources_to_pipeline sync_intervalrc Cr) a Update the configuration of a data source in a pipeline. Parameters: - data_source_id: str. - pipeline_id: str. - sync_interval: typing.Optional[float]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.update_pipeline_data_source( data_source_id="string", pipeline_id="string", ) rrer<ri/data-sources/r>r{rCrDrErFr}r.rJrKrLrMrNrOrrPrGrQrRrrUr rrrrVr0rrgrrrXrYr1r1r2update_pipeline_data_source4s.    z+PipelinesClient.update_pipeline_data_sourcec Cr) a Delete a data source from a pipeline. Parameters: - data_source_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.delete_pipeline_data_source( data_source_id="string", pipeline_id="string", ) rr<rirr>rjrCrDNrErFrr0rrgrXrYr1r1r2delete_pipeline_data_sourcebs(    z+PipelinesClient.delete_pipeline_data_sourcec Cr) Run ingestion for the pipeline data source by incrementally updating the data-sink with upstream changes from data-source. Parameters: - data_source_id: str. - pipeline_id: str. r_r<rirrr>rjrCrDrErFrkrr1r1r2sync_pipeline_data_sources&     z)PipelinesClient.sync_pipeline_data_sourcec Cr) a Get the status of a data source for a pipeline. Parameters: - data_source_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.get_pipeline_data_source_status( data_source_id="string", pipeline_id="string", ) r;r<rirrr>rjrCrDrErFrrr1r1r2get_pipeline_data_source_status&    z/PipelinesClient.get_pipeline_data_source_status dense_similarity_top_kdense_similarity_cutoffsparse_similarity_top_kenable_reranking rerank_top_nalphasearch_filters files_top_kretrieval_moderetrieve_image_nodes class_namerrrrrrrrrrqueryrc Cszd| i}|tur ||d<|tur||d<|tur||d<|tur$||d<|tur,||d<|tur4||d<|tur<||d<| turD| |d <| turL| |d <| turT| |d <| tur\| |d <|jjjd tj|jdd|dt||j dd}d|j krdkrnnt t |S|j dkrtt t|z|}Wntyt|j |jdwt|j |d)ai Get retrieval results for a managed pipeline and a query Parameters: - pipeline_id: str. - dense_similarity_top_k: typing.Optional[int]. - dense_similarity_cutoff: typing.Optional[float]. - sparse_similarity_top_k: typing.Optional[int]. - enable_reranking: typing.Optional[bool]. - rerank_top_n: typing.Optional[int]. - alpha: typing.Optional[float]. - search_filters: typing.Optional[MetadataFilters]. - files_top_k: typing.Optional[int]. - retrieval_mode: typing.Optional[RetrievalMode]. The retrieval mode for the query. - retrieve_image_nodes: typing.Optional[bool]. Whether to retrieve image nodes. - query: str. The query to retrieve against. - class_name: typing.Optional[str]. --- from llama_cloud import FilterCondition, MetadataFilters, RetrievalMode from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.run_search( pipeline_id="string", search_filters=MetadataFilters( filters=[], condition=FilterCondition.AND, ), retrieval_mode=RetrievalMode.CHUNKS, query="string", ) rrrrrrrrrrrrr_r<ri /retriever>r{rCrDrErFr}r.rJrKrLrMrNrOrrPrGrQrRr"rUr rrrrVr0rgrrrrrrrrrrrrrrXrYr1r1r2 run_searchsP?    zPipelinesClient.run_searchcCr) a> Get jobs for a pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.list_pipeline_jobs( pipeline_id="string", ) r;r<ri/jobsr>rjrCrDrErFr.rJrKrLrMrNrOrPrGrQrRrSrTrrUr rrrrVrlr1r1r2list_pipeline_jobs1s     z"PipelinesClient.list_pipeline_jobsjob_idc C|jjjdtj|jdd|d||jdd}d|jkr*dkr4nnt t | S|jd krCt t t| z| }WntyXt|j|jd wt|j|d ) av Get a job for a pipeline. Parameters: - job_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.get_pipeline_job( job_id="string", pipeline_id="string", ) r;r<ri/jobs/r>rjrCrDrErFr.rJrKrLrMrNrOrPrGrQrRrrUr rrrrVr0rrgrXrYr1r1r2get_pipeline_jobQ$   z PipelinesClient.get_pipeline_jobcCr) a[ Get a playground session for a user and pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.get_playground_session( pipeline_id="string", ) r;r<ri/playground-sessionr>rjrCrDrErFr.rJrKrLrMrNrOrPrGrQrRrrUr rrrrVrlr1r1r2get_playground_sessionvs$   z&PipelinesClient.get_playground_sessionmessagesrrrrcCsi}|tur ||d<|tur||d<|tur||d<|jjjdtj|jdd|dt||j dd }d |j krEd krPnn t t j|S|j d kr_tt t|z|}Wntytt|j |jd wt|j |d )a Make a retrieval query + chat completion for a managed pipeline. Parameters: - pipeline_id: str. - messages: typing.Optional[typing.List[InputMessage]]. - data: typing.Optional[ChatData]. - class_name: typing.Optional[str]. --- from llama_cloud import ( ChatData, FilterCondition, LlmParameters, MetadataFilters, PresetRetrievalParams, RetrievalMode, SupportedLlmModelNames, ) from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.chat( pipeline_id="string", data=ChatData( retrieval_parameters=PresetRetrievalParams( search_filters=MetadataFilters( filters=[], condition=FilterCondition.AND, ), retrieval_mode=RetrievalMode.CHUNKS, ), llm_parameters=LlmParameters( model_name=SupportedLlmModelNames.GPT_3_5_TURBO, ), ), ) rrrr_r<ri/chatr>r{rCrDrErFr}r.rJrKrLrMrNrOrrPrGrQrRrSAnyrUr rrrrVr0rgrrrrrXrYr1r1r2chats02    zPipelinesClient.chatskiprronly_direct_uploadonly_api_data_source_documentsrrrc Cs|jjjdtj|jdd|dt|||||d|jdd}d|j kr1d kr>nn t t j t|S|j d krMtt t|z|}Wntybt|j |jd wt|j |d ) aZ Return a list of documents for a pipeline. Parameters: - pipeline_id: str. - skip: typing.Optional[int]. - limit: typing.Optional[int]. - file_id: typing.Optional[str]. - only_direct_upload: typing.Optional[bool]. - only_api_data_source_documents: typing.Optional[bool]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.list_pipeline_documents( pipeline_id="string", ) r;r<ri /documentsrr>r?rCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrSrTr rUr rrrrV r0rgrrrrrrXrYr1r1r2list_pipeline_documentss4#    z'PipelinesClient.list_pipeline_documentscCr) a Batch create documents for a pipeline. Parameters: - pipeline_id: str. - request: typing.List[CloudDocumentCreate]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.create_batch_pipeline_documents( pipeline_id="string", request=[], ) r_r<rirr>r{rCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrSrTr rUr rrrrVrr1r1r2create_batch_pipeline_documents rz/PipelinesClient.create_batch_pipeline_documentscCr) a Batch create or update a document for a pipeline. Parameters: - pipeline_id: str. - request: typing.List[CloudDocumentCreate]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.upsert_batch_pipeline_documents( pipeline_id="string", request=[], ) rer<rirr>r{rCrDrErFrrr1r1r2upsert_batch_pipeline_documentsHrz/PipelinesClient.upsert_batch_pipeline_documentsc Cs|jjjdtj|jdd|dt|||||d|jdd}d|j kr1d kr;nnt t | S|j d krJtt t| z| }Wnty_t|j |jd wt|j |d ) ad Return a list of documents for a pipeline. Parameters: - pipeline_id: str. - skip: typing.Optional[int]. - limit: typing.Optional[int]. - file_id: typing.Optional[str]. - only_direct_upload: typing.Optional[bool]. - only_api_data_source_documents: typing.Optional[bool]. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.paginated_list_pipeline_documents( pipeline_id="string", ) r;r<ri/documents/paginatedrr>r?rCrDrErFr.rJrKrLrMrNrOrrPrGrQrRrrUr rrrrVrr1r1r2!paginated_list_pipeline_documentsps4#    z1PipelinesClient.paginated_list_pipeline_documents document_idc Cr) a Return a single document for a pipeline. Parameters: - document_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.get_pipeline_document( document_id="string", pipeline_id="string", ) r;r<ri /documents/r>rjrCrDrErFr.rJrKrLrMrNrOrPrGrQrRr rUr rrrrVr0rrgrXrYr1r1r2get_pipeline_documentrz%PipelinesClient.get_pipeline_documentc Cr) a? Delete a document from a pipeline. Initiates an async job that will: 1. Delete vectors from the vector store 2. Delete the document from MongoDB after vectors are successfully deleted Parameters: - document_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.delete_pipeline_document( document_id="string", pipeline_id="string", ) rr<rirr>rjrCrDNrErFrrr1r1r2delete_pipeline_documents&   z(PipelinesClient.delete_pipeline_documentc Cr) a Return a single document for a pipeline. Parameters: - document_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.get_pipeline_document_status( document_id="string", pipeline_id="string", ) r;r<rirrr>rjrCrDrErFrrr1r1r2get_pipeline_document_statusrz,PipelinesClient.get_pipeline_document_statusc Cs|jjjdtj|jdd|d|d|jdd}d|jkr+d kr8nn t t j t |S|jd krGtt t|z|}Wnty\t|j|jd wt|j|d ) a Return a list of chunks for a pipeline document. Parameters: - document_id: str. - pipeline_id: str. --- from llama_cloud.client import LlamaCloud client = LlamaCloud( token="YOUR_TOKEN", ) client.pipelines.list_pipeline_document_chunks( document_id="string", pipeline_id="string", ) r;r<rir/chunksr>rjrCrDrErFr.rJrKrLrMrNrOrPrGrQrRrSrTr#rUr rrrrVrr1r1r2list_pipeline_document_chunks"s&    z-PipelinesClient.list_pipeline_document_chunks)Q__name__ __module__ __qualname__rr3rSOptionalrrrTrrZrrcrfrmr}r&r'r rr rrrrrrrrrboolrrrrintrrrrr%rrIOrrrrrrfloatrrrrrr!r"rrrrrrrr rrr rr rrrrrrr r#r r1r1r1r2r)6s 9 & $      T   - * 8, 3"% " ) .&*      g %& N > ( , >%) &r)c@r()AsyncPipelinesClientr*cCr+r,r-r/r1r1r2r3Jr4zAsyncPipelinesClient.__init__Nr5r6r7r8r9r:c s|jjjdtj|jddt||||d|jddIdH}d|j kr0d kr=nn t t j t|S|j d krLtt t|z|}Wntyat|j |jd wt|j |d ) aQ Search for pipelines by various parameters. Parameters: - project_id: typing.Optional[str]. - project_name: typing.Optional[str]. - pipeline_name: typing.Optional[str]. - pipeline_type: typing.Optional[PipelineType]. --- from llama_cloud import PipelineType from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.search_pipelines( pipeline_type=PipelineType.PLAYGROUND, ) r;r<r=r5r>r?NrCrDrErFrIrWr1r1r2rZMs0    z%AsyncPipelinesClient.search_pipelinesr[r\rKc|jjjdtj|jddt||dt||j ddIdH}d|j kr1d kr;nnt t |S|j d krJtt t|z|}Wnty_t|j |jd wt|j |d ) r^r_r<r=r[r>r`NrCrDrErFrarbr1r1r2rcs&     z$AsyncPipelinesClient.create_pipelinecr) rdrer<r=r[r>r`NrCrDrErFrarbr1r1r2rfs&     z$AsyncPipelinesClient.upsert_pipelinergcs|jjjdtj|jdd||jddIdH}d|jkr+dkr5nnt t | S|jd krDt t t| z| }WntyYt|j|jd wt|j|d ) rhr;r<rir>rjNrCrDrErFrkrlr1r1r2rms"    z!AsyncPipelinesClient.get_pipelinernrorprqrrrsrtrurvrwrxryc s|i} |tur || d<|tur|| d<|tur|| d<|tur#|| d<|tur+|| d<|tur3|| d<|tur;|| d<| turC| | d<| turK| | d <| turS| | d <| tur[| | d <|jjjd tj|jd d|t| |j ddIdH}d|j krdkrnnt t |S|j dkrtt t|z|}Wntyt|j |jdwt|j |d)rzrorprqrrrsrtrurvrwrxryrer<rir>r{NrCrDrErFr|r~r1r1r2rsR,    z-AsyncPipelinesClient.update_existing_pipelinecs|jjjdtj|jdd||jddIdH}d|jkr,dkr-dS|jd krrjNrCrDrErFrrlr1r1r2r5s$    z$AsyncPipelinesClient.delete_pipelinec|jjjdtj|jdd|d|jddIdH}d|jkr,d kr6nnt t | S|jd krEt t t| z| }WntyZt|j|jd wt|j|d ) aZ Get the status of a pipeline by ID. Parameters: - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.get_pipeline_status( pipeline_id="string", ) r;r<rirr>rjNrCrDrErFrrlr1r1r2rUs"     z(AsyncPipelinesClient.get_pipeline_statuscr) rr_r<rirr>rjNrCrDrErFrkrlr1r1r2ru"     z"AsyncPipelinesClient.sync_pipelinecr) rr_r<rirr>rjNrCrDrErFrkrlr1r1r2rs&    z)AsyncPipelinesClient.cancel_pipeline_synccr) rr_r<rirr>rjNrCrDrErFrkrlr1r1r2rrz"AsyncPipelinesClient.copy_pipelinerrrcs|jjjdtj|jdd|dt||d|jddIdH}d |j kr2d kr?nn t t j t|S|j d krNtt t|z|}Wntyct|j |jd wt|j |d ) a Get files for a pipeline. Parameters: - pipeline_id: str. - data_source_id: typing.Optional[str]. - only_manually_uploaded: typing.Optional[bool]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.list_pipeline_files( pipeline_id="string", ) r;r<rirrr>r?NrCrDrErFrrr1r1r2rs(      z(AsyncPipelinesClient.list_pipeline_filesc|jjjdtj|jdd|dt||jddIdH}d|j kr/d krr{NrCrDrErFrrr1r1r2rs$     z*AsyncPipelinesClient.add_files_to_pipelinerrrc s|jjjdtj|jdd|dt||||d|jddIdH}d |j kr4d kr>nnt t | S|j d krMtt t| z| }Wntybt|j |jd wt|j |d ) a Get files for a pipeline. Parameters: - pipeline_id: str. - data_source_id: typing.Optional[str]. - only_manually_uploaded: typing.Optional[bool]. - limit: typing.Optional[int]. - offset: typing.Optional[int]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.list_pipeline_files_2( pipeline_id="string", ) r;r<rirrr>r?NrCrDrErFrrr1r1r2r s0      z*AsyncPipelinesClient.list_pipeline_files_2rc |jjjdtj|jdd|d|d|jddIdH}d |jkr/d kr9nnt t | S|jd krHt t t| z| }Wnty]t|j|jd wt|j|d ) a Get status of a file for a pipeline. Parameters: - file_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.get_pipeline_file_status( file_id="string", pipeline_id="string", ) r;r<rirrr>rjNrCrDrErFrrr1r1r2rEs&    z-AsyncPipelinesClient.get_pipeline_file_statusrrc i}|tur ||d<|jjjdtj|jdd|d|t||j ddIdH}d |j kr;d krEnnt t |S|j d krTtt t|z|}Wntyit|j |jd wt|j |d ) a  Update a file for a pipeline. Parameters: - file_id: str. - pipeline_id: str. - custom_metadata: typing.Optional[typing.Dict[str, typing.Optional[PipelineFileUpdateCustomMetadataValue]]]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.update_pipeline_file( file_id="string", pipeline_id="string", ) rrer<rirr>r{NrCrDrErFrrr1r1r2rjs.     z)AsyncPipelinesClient.update_pipeline_filec |jjjdtj|jdd|d||jddIdH}d|jkr/d kr0dS|jd kr?t t t | z| }WntyTt|j|jd wt|j|d ) a Delete a file from a pipeline. Parameters: - file_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.delete_pipeline_file( file_id="string", pipeline_id="string", ) rr<rirr>rjNrCrDrErFrrr1r1r2rs(    z)AsyncPipelinesClient.delete_pipeline_filercs|jjjdtj|jdd|dtid|i|jddIdH}d |j kr2d krAnn t t j ttf|S|j d krPtt t|z|}Wntyet|j |jd wt|j |d ) rrer<rirrr>rNrCrDrErFrrr1r1r2rs&      z-AsyncPipelinesClient.import_pipeline_metadatacs|jjjdtj|jdd|d|jddIdH}d|jkr-d kr.dS|jd kr=t t t | z| }WntyRt|j|jd wt|j|d ) an Delete metadata for all files in a pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.delete_pipeline_files_metadata( pipeline_id="string", ) rr<rirr>rjNrCrDrErFrrlr1r1r2rs$     z3AsyncPipelinesClient.delete_pipeline_files_metadatac|jjjdtj|jdd|d|jddIdH}d|jkr,d kr9nn t t j t |S|jd krHtt t|z|}Wnty]t|j|jd wt|j|d ) a^ Get data sources for a pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.list_pipeline_data_sources( pipeline_id="string", ) r;r<rirr>rjNrCrDrErFrrlr1r1r2rs&    z/AsyncPipelinesClient.list_pipeline_data_sourcescr) a Add data sources to a pipeline. Parameters: - pipeline_id: str. - request: typing.List[PipelineDataSourceCreate]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.add_data_sources_to_pipeline( pipeline_id="string", request=[], ) rer<rirr>r{NrCrDrErFrrr1r1r2r (     z1AsyncPipelinesClient.add_data_sources_to_pipelinerrc r) a Update the configuration of a data source in a pipeline. Parameters: - data_source_id: str. - pipeline_id: str. - sync_interval: typing.Optional[float]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.update_pipeline_data_source( data_source_id="string", pipeline_id="string", ) rrer<rirr>r{NrCrDrErFrrr1r1r2rG s0     z0AsyncPipelinesClient.update_pipeline_data_sourcec r) a Delete a data source from a pipeline. Parameters: - data_source_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.delete_pipeline_data_source( data_source_id="string", pipeline_id="string", ) rr<rirr>rjNrCrDrErFrrr1r1r2ru s*     z0AsyncPipelinesClient.delete_pipeline_data_sourcec r) rr_r<rirrr>rjNrCrDrErFrkrr1r1r2r s(      z.AsyncPipelinesClient.sync_pipeline_data_sourcec r) a Get the status of a data source for a pipeline. Parameters: - data_source_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.get_pipeline_data_source_status( data_source_id="string", pipeline_id="string", ) r;r<rirrr>rjNrCrDrErFrrr1r1r2r s(     z4AsyncPipelinesClient.get_pipeline_data_source_statusrrrrrrrrrrrrrc sd| i}|tur ||d<|tur||d<|tur||d<|tur%||d<|tur-||d<|tur5||d<|tur=||d<| turE| |d <| turM| |d <| turU| |d <| tur]| |d <|jjjd tj|jdd|dt||j ddIdH}d|j krdkrnnt t |S|j dkrtt t|z|}Wntyt|j |jdwt|j |d)ay Get retrieval results for a managed pipeline and a query Parameters: - pipeline_id: str. - dense_similarity_top_k: typing.Optional[int]. - dense_similarity_cutoff: typing.Optional[float]. - sparse_similarity_top_k: typing.Optional[int]. - enable_reranking: typing.Optional[bool]. - rerank_top_n: typing.Optional[int]. - alpha: typing.Optional[float]. - search_filters: typing.Optional[MetadataFilters]. - files_top_k: typing.Optional[int]. - retrieval_mode: typing.Optional[RetrievalMode]. The retrieval mode for the query. - retrieve_image_nodes: typing.Optional[bool]. Whether to retrieve image nodes. - query: str. The query to retrieve against. - class_name: typing.Optional[str]. --- from llama_cloud import FilterCondition, MetadataFilters, RetrievalMode from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.run_search( pipeline_id="string", search_filters=MetadataFilters( filters=[], condition=FilterCondition.AND, ), retrieval_mode=RetrievalMode.CHUNKS, query="string", ) rrrrrrrrrrrrr_r<rirr>r{NrCrDrErFrrr1r1r2r sR?     zAsyncPipelinesClient.run_searchcr) aN Get jobs for a pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.list_pipeline_jobs( pipeline_id="string", ) r;r<rirr>rjNrCrDrErFrrlr1r1r2rF s"     z'AsyncPipelinesClient.list_pipeline_jobsrc |jjjdtj|jdd|d||jddIdH}d|jkr.d kr8nnt t | S|jd krGt t t| z| }Wnty\t|j|jd wt|j|d ) a Get a job for a pipeline. Parameters: - job_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.get_pipeline_job( job_id="string", pipeline_id="string", ) r;r<rirr>rjNrCrDrErFrrr1r1r2rf &    z%AsyncPipelinesClient.get_pipeline_jobcr) ak Get a playground session for a user and pipeline. Parameters: - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.get_playground_session( pipeline_id="string", ) r;r<rirr>rjNrCrDrErFrrlr1r1r2r s&    z+AsyncPipelinesClient.get_playground_sessionrrrcsi}|tur ||d<|tur||d<|tur||d<|jjjdtj|jdd|dt||j dd Id H}d |j krId krTnn t t j|S|j d krctt t|z|}Wntyxt|j |jdwt|j |d)a Make a retrieval query + chat completion for a managed pipeline. Parameters: - pipeline_id: str. - messages: typing.Optional[typing.List[InputMessage]]. - data: typing.Optional[ChatData]. - class_name: typing.Optional[str]. --- from llama_cloud import ( ChatData, FilterCondition, LlmParameters, MetadataFilters, PresetRetrievalParams, RetrievalMode, SupportedLlmModelNames, ) from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.chat( pipeline_id="string", data=ChatData( retrieval_parameters=PresetRetrievalParams( search_filters=MetadataFilters( filters=[], condition=FilterCondition.AND, ), retrieval_mode=RetrievalMode.CHUNKS, ), llm_parameters=LlmParameters( model_name=SupportedLlmModelNames.GPT_3_5_TURBO, ), ), ) rrrr_r<rirr>r{NrCrDrErFrrr1r1r2r s22     zAsyncPipelinesClient.chatrrrrc s|jjjdtj|jdd|dt|||||d|jddIdH}d |j kr5d krBnn t t j t|S|j d krQtt t|z|}Wntyft|j |jd wt|j |d ) aj Return a list of documents for a pipeline. Parameters: - pipeline_id: str. - skip: typing.Optional[int]. - limit: typing.Optional[int]. - file_id: typing.Optional[str]. - only_direct_upload: typing.Optional[bool]. - only_api_data_source_documents: typing.Optional[bool]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.list_pipeline_documents( pipeline_id="string", ) r;r<rirrr>r?NrCrDrErFrrr1r1r2r s6#     z,AsyncPipelinesClient.list_pipeline_documentscr) a Batch create documents for a pipeline. Parameters: - pipeline_id: str. - request: typing.List[CloudDocumentCreate]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.create_batch_pipeline_documents( pipeline_id="string", request=[], ) r_r<rirr>r{NrCrDrErFrrr1r1r2r5 rz4AsyncPipelinesClient.create_batch_pipeline_documentscr) a Batch create or update a document for a pipeline. Parameters: - pipeline_id: str. - request: typing.List[CloudDocumentCreate]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.upsert_batch_pipeline_documents( pipeline_id="string", request=[], ) rer<rirr>r{NrCrDrErFrrr1r1r2r] rz4AsyncPipelinesClient.upsert_batch_pipeline_documentsc s|jjjdtj|jdd|dt|||||d|jddIdH}d |j kr5d kr?nnt t | S|j d krNtt t| z| }Wntyct|j |jd wt|j |d ) at Return a list of documents for a pipeline. Parameters: - pipeline_id: str. - skip: typing.Optional[int]. - limit: typing.Optional[int]. - file_id: typing.Optional[str]. - only_direct_upload: typing.Optional[bool]. - only_api_data_source_documents: typing.Optional[bool]. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.paginated_list_pipeline_documents( pipeline_id="string", ) r;r<rirrr>r?NrCrDrErFrrr1r1r2r s6#     z6AsyncPipelinesClient.paginated_list_pipeline_documentsrc r) a Return a single document for a pipeline. Parameters: - document_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.get_pipeline_document( document_id="string", pipeline_id="string", ) r;r<rirr>rjNrCrDrErFrrr1r1r2r r z*AsyncPipelinesClient.get_pipeline_documentc r) aO Delete a document from a pipeline. Initiates an async job that will: 1. Delete vectors from the vector store 2. Delete the document from MongoDB after vectors are successfully deleted Parameters: - document_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.delete_pipeline_document( document_id="string", pipeline_id="string", ) rr<rirr>rjNrCrDrErFrrr1r1r2r s(    z-AsyncPipelinesClient.delete_pipeline_documentc r) a Return a single document for a pipeline. Parameters: - document_id: str. - pipeline_id: str. --- from llama_cloud.client import AsyncLlamaCloud client = AsyncLlamaCloud( token="YOUR_TOKEN", ) await client.pipelines.get_pipeline_document_status( document_id="string", pipeline_id="string", ) r;r<rirrr>rjNrCrDrErFrrr1r1r2r  s(     z1AsyncPipelinesClient.get_pipeline_document_statusc s|jjjdtj|jdd|d|d|jddIdH}d |jkr/d krrjNrCrDrErFr rr1r1r2r 7 s(     z2AsyncPipelinesClient.list_pipeline_document_chunks)Qr rrrr3rSrrrrTrrZrrcrfrmr}r&r'r rr rrrrrrrrrrrrrrrrrrrr%rrrrrrrrrrrrrrrr!r"rrrrrrrr rrr rr rrrrrrr r#r r1r1r1r2rIs( 9 & $      T   - * 8, 3"% " ) .& ,      g %& N > ( , >%) &r)UrS urllib.parserL json.decoderrcore.api_errorrcore.client_wrapperrrcore.jsonable_encoderrcore.remove_none_from_dictr!errors.unprocessable_entity_errorr types.chat_datar types.cloud_documentr types.cloud_document_creater $types.configured_transformation_itemr types.data_sink_creatertypes.eval_execution_paramsrtypes.http_validation_errorrtypes.input_messagertypes.llama_parse_parametersr'types.managed_ingestion_status_responsertypes.metadata_filtersr-types.paginated_list_cloud_documents_responser,types.paginated_list_pipeline_files_responsertypes.pipelinertypes.pipeline_creatertypes.pipeline_data_sourcer!types.pipeline_data_source_creatertypes.pipeline_deploymentrtypes.pipeline_filertypes.pipeline_file_creatertypes.pipeline_typertypes.playground_sessionrtypes.preset_retrieval_paramsr types.retrieval_moder!types.retrieve_resultsr"types.text_noder#0types.pipeline_file_update_custom_metadata_valuer%&types.pipeline_update_embedding_configr&&types.pipeline_update_transform_configr'rQ __version__ startswith ImportError pydantic.v1v1castrr}r)rr1r1r1r2sx