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refactor: port api/controllers/console/datasets/datasets_document.py api/controllers/service_api/app/annotation.py api/core/app/app_config/easy_ui_based_app/agent/manager.py api/core/app/apps/pipeline/pipeline_generator.py api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py to match case (#31832)
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Asuka Minato
GitHub
@ -303,33 +303,34 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
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elif str(node_data.retrieval_mode) == DatasetRetrieveConfigEntity.RetrieveStrategy.MULTIPLE:
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if node_data.multiple_retrieval_config is None:
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raise ValueError("multiple_retrieval_config is required")
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if node_data.multiple_retrieval_config.reranking_mode == "reranking_model":
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if node_data.multiple_retrieval_config.reranking_model:
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reranking_model = {
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"reranking_provider_name": node_data.multiple_retrieval_config.reranking_model.provider,
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"reranking_model_name": node_data.multiple_retrieval_config.reranking_model.model,
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}
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else:
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match node_data.multiple_retrieval_config.reranking_mode:
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case "reranking_model":
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if node_data.multiple_retrieval_config.reranking_model:
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reranking_model = {
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"reranking_provider_name": node_data.multiple_retrieval_config.reranking_model.provider,
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"reranking_model_name": node_data.multiple_retrieval_config.reranking_model.model,
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}
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else:
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reranking_model = None
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weights = None
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case "weighted_score":
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if node_data.multiple_retrieval_config.weights is None:
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raise ValueError("weights is required")
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reranking_model = None
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weights = None
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elif node_data.multiple_retrieval_config.reranking_mode == "weighted_score":
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if node_data.multiple_retrieval_config.weights is None:
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raise ValueError("weights is required")
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reranking_model = None
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vector_setting = node_data.multiple_retrieval_config.weights.vector_setting
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weights = {
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"vector_setting": {
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"vector_weight": vector_setting.vector_weight,
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"embedding_provider_name": vector_setting.embedding_provider_name,
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"embedding_model_name": vector_setting.embedding_model_name,
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},
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"keyword_setting": {
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"keyword_weight": node_data.multiple_retrieval_config.weights.keyword_setting.keyword_weight
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},
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}
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else:
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reranking_model = None
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weights = None
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vector_setting = node_data.multiple_retrieval_config.weights.vector_setting
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weights = {
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"vector_setting": {
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"vector_weight": vector_setting.vector_weight,
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"embedding_provider_name": vector_setting.embedding_provider_name,
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"embedding_model_name": vector_setting.embedding_model_name,
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},
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"keyword_setting": {
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"keyword_weight": node_data.multiple_retrieval_config.weights.keyword_setting.keyword_weight
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},
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}
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case _:
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reranking_model = None
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weights = None
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all_documents = dataset_retrieval.multiple_retrieve(
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app_id=self.app_id,
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tenant_id=self.tenant_id,
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@ -453,73 +454,74 @@ class KnowledgeRetrievalNode(LLMUsageTrackingMixin, Node[KnowledgeRetrievalNodeD
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)
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filters: list[Any] = []
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metadata_condition = None
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if node_data.metadata_filtering_mode == "disabled":
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return None, None, usage
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elif node_data.metadata_filtering_mode == "automatic":
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automatic_metadata_filters, automatic_usage = self._automatic_metadata_filter_func(
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dataset_ids, query, node_data
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)
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usage = self._merge_usage(usage, automatic_usage)
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if automatic_metadata_filters:
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conditions = []
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for sequence, filter in enumerate(automatic_metadata_filters):
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DatasetRetrieval.process_metadata_filter_func(
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sequence,
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filter.get("condition", ""),
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filter.get("metadata_name", ""),
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filter.get("value"),
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filters,
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)
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conditions.append(
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Condition(
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name=filter.get("metadata_name"), # type: ignore
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comparison_operator=filter.get("condition"), # type: ignore
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value=filter.get("value"),
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)
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)
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metadata_condition = MetadataCondition(
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logical_operator=node_data.metadata_filtering_conditions.logical_operator
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if node_data.metadata_filtering_conditions
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else "or",
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conditions=conditions,
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match node_data.metadata_filtering_mode:
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case "disabled":
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return None, None, usage
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case "automatic":
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automatic_metadata_filters, automatic_usage = self._automatic_metadata_filter_func(
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dataset_ids, query, node_data
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)
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elif node_data.metadata_filtering_mode == "manual":
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if node_data.metadata_filtering_conditions:
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conditions = []
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for sequence, condition in enumerate(node_data.metadata_filtering_conditions.conditions): # type: ignore
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metadata_name = condition.name
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expected_value = condition.value
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if expected_value is not None and condition.comparison_operator not in ("empty", "not empty"):
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if isinstance(expected_value, str):
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expected_value = self.graph_runtime_state.variable_pool.convert_template(
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expected_value
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).value[0]
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if expected_value.value_type in {"number", "integer", "float"}:
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expected_value = expected_value.value
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elif expected_value.value_type == "string":
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expected_value = re.sub(r"[\r\n\t]+", " ", expected_value.text).strip()
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else:
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raise ValueError("Invalid expected metadata value type")
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conditions.append(
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Condition(
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name=metadata_name,
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comparison_operator=condition.comparison_operator,
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value=expected_value,
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usage = self._merge_usage(usage, automatic_usage)
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if automatic_metadata_filters:
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conditions = []
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for sequence, filter in enumerate(automatic_metadata_filters):
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DatasetRetrieval.process_metadata_filter_func(
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sequence,
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filter.get("condition", ""),
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filter.get("metadata_name", ""),
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filter.get("value"),
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filters,
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)
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conditions.append(
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Condition(
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name=filter.get("metadata_name"), # type: ignore
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comparison_operator=filter.get("condition"), # type: ignore
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value=filter.get("value"),
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)
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)
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metadata_condition = MetadataCondition(
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logical_operator=node_data.metadata_filtering_conditions.logical_operator
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if node_data.metadata_filtering_conditions
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else "or",
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conditions=conditions,
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)
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filters = DatasetRetrieval.process_metadata_filter_func(
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sequence,
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condition.comparison_operator,
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metadata_name,
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expected_value,
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filters,
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case "manual":
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if node_data.metadata_filtering_conditions:
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conditions = []
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for sequence, condition in enumerate(node_data.metadata_filtering_conditions.conditions): # type: ignore
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metadata_name = condition.name
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expected_value = condition.value
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if expected_value is not None and condition.comparison_operator not in ("empty", "not empty"):
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if isinstance(expected_value, str):
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expected_value = self.graph_runtime_state.variable_pool.convert_template(
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expected_value
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).value[0]
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if expected_value.value_type in {"number", "integer", "float"}:
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expected_value = expected_value.value
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elif expected_value.value_type == "string":
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expected_value = re.sub(r"[\r\n\t]+", " ", expected_value.text).strip()
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else:
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raise ValueError("Invalid expected metadata value type")
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conditions.append(
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Condition(
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name=metadata_name,
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comparison_operator=condition.comparison_operator,
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value=expected_value,
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)
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)
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filters = DatasetRetrieval.process_metadata_filter_func(
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sequence,
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condition.comparison_operator,
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metadata_name,
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expected_value,
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filters,
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)
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metadata_condition = MetadataCondition(
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logical_operator=node_data.metadata_filtering_conditions.logical_operator,
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conditions=conditions,
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)
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metadata_condition = MetadataCondition(
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logical_operator=node_data.metadata_filtering_conditions.logical_operator,
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conditions=conditions,
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)
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else:
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raise ValueError("Invalid metadata filtering mode")
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case _:
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raise ValueError("Invalid metadata filtering mode")
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if filters:
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if (
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node_data.metadata_filtering_conditions
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@ -482,16 +482,17 @@ class ToolNode(Node[ToolNodeData]):
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result = {}
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for parameter_name in typed_node_data.tool_parameters:
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input = typed_node_data.tool_parameters[parameter_name]
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if input.type == "mixed":
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assert isinstance(input.value, str)
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selectors = VariableTemplateParser(input.value).extract_variable_selectors()
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for selector in selectors:
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result[selector.variable] = selector.value_selector
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elif input.type == "variable":
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selector_key = ".".join(input.value)
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result[f"#{selector_key}#"] = input.value
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elif input.type == "constant":
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pass
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match input.type:
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case "mixed":
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assert isinstance(input.value, str)
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selectors = VariableTemplateParser(input.value).extract_variable_selectors()
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for selector in selectors:
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result[selector.variable] = selector.value_selector
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case "variable":
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selector_key = ".".join(input.value)
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result[f"#{selector_key}#"] = input.value
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case "constant":
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pass
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result = {node_id + "." + key: value for key, value in result.items()}
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