Merge branch main into feat/rag-2

api/core/workflow/nodes/agent/agent_node.py

@@ -309,7 +309,7 @@ class AgentNode(BaseNode):
                             }
                         )
                     value = tool_value
-                if parameter.type == "model-selector":
+                if parameter.type == AgentStrategyParameter.AgentStrategyParameterType.MODEL_SELECTOR:
                     value = cast(dict[str, Any], value)
                     model_instance, model_schema = self._fetch_model(value)
                     # memory config

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -228,7 +228,7 @@ class KnowledgeRetrievalNode(BaseNode):
         # Subquery: Count the number of available documents for each dataset
         subquery = (
             db.session.query(Document.dataset_id, func.count(Document.id).label("available_document_count"))
-            .filter(
+            .where(
                 Document.indexing_status == "completed",
                 Document.enabled == True,
                 Document.archived == False,
@@ -242,8 +242,8 @@ class KnowledgeRetrievalNode(BaseNode):
         results = (
             db.session.query(Dataset)
             .outerjoin(subquery, Dataset.id == subquery.c.dataset_id)
-            .filter(Dataset.tenant_id == self.tenant_id, Dataset.id.in_(dataset_ids))
-            .filter((subquery.c.available_document_count > 0) | (Dataset.provider == "external"))
+            .where(Dataset.tenant_id == self.tenant_id, Dataset.id.in_(dataset_ids))
+            .where((subquery.c.available_document_count > 0) | (Dataset.provider == "external"))
             .all()
         )
 
@@ -370,7 +370,7 @@ class KnowledgeRetrievalNode(BaseNode):
                     dataset = db.session.query(Dataset).filter_by(id=segment.dataset_id).first()  # type: ignore
                     document = (
                         db.session.query(Document)
-                        .filter(
+                        .where(
                             Document.id == segment.document_id,
                             Document.enabled == True,
                             Document.archived == False,
@@ -415,7 +415,7 @@ class KnowledgeRetrievalNode(BaseNode):
     def _get_metadata_filter_condition(
         self, dataset_ids: list, query: str, node_data: KnowledgeRetrievalNodeData
     ) -> tuple[Optional[dict[str, list[str]]], Optional[MetadataCondition]]:
-        document_query = db.session.query(Document).filter(
+        document_query = db.session.query(Document).where(
             Document.dataset_id.in_(dataset_ids),
             Document.indexing_status == "completed",
             Document.enabled == True,
@@ -462,7 +462,7 @@ class KnowledgeRetrievalNode(BaseNode):
                                 expected_value = self.graph_runtime_state.variable_pool.convert_template(
                                     expected_value
                                 ).value[0]
-                                if expected_value.value_type == "number":  # type: ignore
+                                if expected_value.value_type in {"number", "integer", "float"}:  # type: ignore
                                     expected_value = expected_value.value  # type: ignore
                                 elif expected_value.value_type == "string":  # type: ignore
                                     expected_value = re.sub(r"[\r\n\t]+", " ", expected_value.text).strip()  # type: ignore
@@ -493,9 +493,9 @@ class KnowledgeRetrievalNode(BaseNode):
                 node_data.metadata_filtering_conditions
                 and node_data.metadata_filtering_conditions.logical_operator == "and"
             ):  # type: ignore
-                document_query = document_query.filter(and_(*filters))
+                document_query = document_query.where(and_(*filters))
             else:
-                document_query = document_query.filter(or_(*filters))
+                document_query = document_query.where(or_(*filters))
         documents = document_query.all()
         # group by dataset_id
         metadata_filter_document_ids = defaultdict(list) if documents else None  # type: ignore
@@ -507,7 +507,7 @@ class KnowledgeRetrievalNode(BaseNode):
         self, dataset_ids: list, query: str, node_data: KnowledgeRetrievalNodeData
     ) -> list[dict[str, Any]]:
         # get all metadata field
-        metadata_fields = db.session.query(DatasetMetadata).filter(DatasetMetadata.dataset_id.in_(dataset_ids)).all()
+        metadata_fields = db.session.query(DatasetMetadata).where(DatasetMetadata.dataset_id.in_(dataset_ids)).all()
         all_metadata_fields = [metadata_field.name for metadata_field in metadata_fields]
         if node_data.metadata_model_config is None:
             raise ValueError("metadata_model_config is required")

api/core/workflow/nodes/list_operator/node.py

@@ -184,11 +184,10 @@ class ListOperatorNode(BaseNode):
         value = int(self.graph_runtime_state.variable_pool.convert_template(self._node_data.extract_by.serial).text)
         if value < 1:
             raise ValueError(f"Invalid serial index: must be >= 1, got {value}")
+        if value > len(variable.value):
+            raise InvalidKeyError(f"Invalid serial index: must be <= {len(variable.value)}, got {value}")
         value -= 1
-        if len(variable.value) > int(value):
-            result = variable.value[value]
-        else:
-            result = ""
+        result = variable.value[value]
         return variable.model_copy(update={"value": [result]})
 
 

api/core/workflow/nodes/llm/node.py

@@ -565,7 +565,7 @@ class LLMNode(BaseNode):
                     retriever_resources=original_retriever_resource, context=context_str.strip()
                 )
 
-    def _convert_to_original_retriever_resource(self, context_dict: dict):
+    def _convert_to_original_retriever_resource(self, context_dict: dict) -> RetrievalSourceMetadata | None:
         if (
             "metadata" in context_dict
             and "_source" in context_dict["metadata"]

api/core/workflow/nodes/tool/entities.py

@@ -54,7 +54,7 @@ class ToolNodeData(BaseNodeData, ToolEntity):
                 for val in value:
                     if not isinstance(val, str):
                         raise ValueError("value must be a list of strings")
-            elif typ == "constant" and not isinstance(value, str | int | float | bool):
+            elif typ == "constant" and not isinstance(value, str | int | float | bool | dict):
                 raise ValueError("value must be a string, int, float, or bool")
             return typ
 

api/core/workflow/nodes/tool/tool_node.py

@@ -316,7 +316,14 @@ class ToolNode(BaseNode):
                     variables[variable_name] = variable_value
             elif message.type == ToolInvokeMessage.MessageType.FILE:
                 assert message.meta is not None
-                assert isinstance(message.meta, File)
+                assert isinstance(message.meta, dict)
+                # Validate that meta contains a 'file' key
+                if "file" not in message.meta:
+                    raise ToolNodeError("File message is missing 'file' key in meta")
+
+                # Validate that the file is an instance of File
+                if not isinstance(message.meta["file"], File):
+                    raise ToolNodeError(f"Expected File object but got {type(message.meta['file']).__name__}")
                 files.append(message.meta["file"])
             elif message.type == ToolInvokeMessage.MessageType.LOG:
                 assert isinstance(message.message, ToolInvokeMessage.LogMessage)