fix: improve schema detection for graph model nodes

samples/multi-agent-planner-researcher-coder-distributed/.agent/SDK_REFERENCE.md

@@ -221,6 +221,12 @@ sdk.context_grounding.delete_index(index: uipath.platform.context_grounding.cont
 # Asynchronously delete a context grounding index.
 sdk.context_grounding.delete_index_async(index: uipath.platform.context_grounding.context_grounding_index.ContextGroundingIndex, folder_key: Optional[str]=None, folder_path: Optional[str]=None) -> None
 
+# Downloads the Batch Transform result file to the specified path.
+sdk.context_grounding.download_batch_transform_result(id: str, destination_path: str, validate_status: bool=True, index_name: str | None=None) -> None
+
+# Asynchronously downloads the Batch Transform result file to the specified path.
+sdk.context_grounding.download_batch_transform_result_async(id: str, destination_path: str, validate_status: bool=True, index_name: str | None=None) -> None
+
 # Ingest data into the context grounding index.
 sdk.context_grounding.ingest_data(index: uipath.platform.context_grounding.context_grounding_index.ContextGroundingIndex, folder_key: Optional[str]=None, folder_path: Optional[str]=None) -> None
 
@@ -233,6 +239,12 @@ sdk.context_grounding.retrieve(name: str, folder_key: Optional[str]=None, folder
 # Asynchronously retrieve context grounding index information by its name.
 sdk.context_grounding.retrieve_async(name: str, folder_key: Optional[str]=None, folder_path: Optional[str]=None) -> uipath.platform.context_grounding.context_grounding_index.ContextGroundingIndex
 
+# Retrieves a Batch Transform task status.
+sdk.context_grounding.retrieve_batch_transform(id: str, index_name: str | None=None) -> uipath.platform.context_grounding.context_grounding.BatchTransformResponse
+
+# Asynchronously retrieves a Batch Transform task status.
+sdk.context_grounding.retrieve_batch_transform_async(id: str, index_name: str | None=None) -> uipath.platform.context_grounding.context_grounding.BatchTransformResponse
+
 # Retrieve context grounding index information by its ID.
 sdk.context_grounding.retrieve_by_id(id: str, folder_key: Optional[str]=None, folder_path: Optional[str]=None) -> typing.Any
 
@@ -251,11 +263,17 @@ sdk.context_grounding.search(name: str, query: str, number_of_results: int=10, f
 # Search asynchronously for contextual information within a specific index.
 sdk.context_grounding.search_async(name: str, query: str, number_of_results: int=10, folder_key: Optional[str]=None, folder_path: Optional[str]=None) -> typing.List[uipath.platform.context_grounding.context_grounding.ContextGroundingQueryResponse]
 
+# Starts a Batch Transform, task on the targeted index.
+sdk.context_grounding.start_batch_transform(name: str, index_name: str, prompt: Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=250000)])], output_columns: list[uipath.platform.context_grounding.context_grounding.BatchTransformOutputColumn], storage_bucket_folder_path_prefix: Annotated[str | None, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=512)])]=None, enable_web_search_grounding: bool=False, folder_key: str | None=None, folder_path: str | None=None) -> uipath.platform.context_grounding.context_grounding.BatchTransformCreationResponse
+
+# Asynchronously starts a Batch Transform, task on the targeted index.
+sdk.context_grounding.start_batch_transform_async(name: str, index_name: str, prompt: Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=250000)])], output_columns: list[uipath.platform.context_grounding.context_grounding.BatchTransformOutputColumn], storage_bucket_folder_path_prefix: Annotated[str | None, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=512)])]=None, enable_web_search_grounding: bool=False, folder_key: str | None=None, folder_path: str | None=None) -> uipath.platform.context_grounding.context_grounding.BatchTransformCreationResponse
+
 # Starts a Deep RAG task on the targeted index.
-sdk.context_grounding.start_deep_rag(name: str, index_name: str, prompt: str, glob_pattern: str="*", citation_mode: <enum 'CitationMode="CitationMode.SKIP", folder_key: str | None=None, folder_path: str | None=None) -> uipath.platform.context_grounding.context_grounding.DeepRagCreationResponse
+sdk.context_grounding.start_deep_rag(name: str, index_name: Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=512)])], prompt: Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=250000)])], glob_pattern: Annotated[str, FieldInfo(annotation=NoneType, required=False, default='*', metadata=[MaxLen(max_length=512)])]="**", citation_mode: <enum 'CitationMode="CitationMode.SKIP", folder_key: str | None=None, folder_path: str | None=None) -> uipath.platform.context_grounding.context_grounding.DeepRagCreationResponse
 
 # Asynchronously starts a Deep RAG task on the targeted index.
-sdk.context_grounding.start_deep_rag_async(name: str, index_name: str, prompt: str, glob_pattern: str="*", citation_mode: <enum 'CitationMode="CitationMode.SKIP", folder_key: str | None=None, folder_path: str | None=None) -> uipath.platform.context_grounding.context_grounding.DeepRagCreationResponse
+sdk.context_grounding.start_deep_rag_async(name: str, index_name: Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=512)])], prompt: Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=250000)])], glob_pattern: Annotated[str, FieldInfo(annotation=NoneType, required=False, default='*', metadata=[MaxLen(max_length=512)])]="**", citation_mode: <enum 'CitationMode="CitationMode.SKIP", folder_key: str | None=None, folder_path: str | None=None) -> uipath.platform.context_grounding.context_grounding.DeepRagCreationResponse
 
 ```
 
@@ -380,7 +398,7 @@ Guardrails service
 
 ```python
 # Validate input text using the provided guardrail.
-sdk.guardrails.evaluate_guardrail(input_data: str | dict[str, Any], guardrail: Annotated[Union[uipath.platform.guardrails.guardrails.DeterministicGuardrail, uipath.platform.guardrails.guardrails.BuiltInValidatorGuardrail], FieldInfo(annotation=NoneType, required=True, discriminator='guardrail_type')]) -> uipath.platform.guardrails.guardrails.GuardrailValidationResult
+sdk.guardrails.evaluate_guardrail(input_data: str | dict[str, Any], guardrail: uipath.platform.guardrails.guardrails.BuiltInValidatorGuardrail) -> uipath.core.guardrails.guardrails.GuardrailValidationResult
 
 ```
 

samples/multi-agent-planner-researcher-coder-distributed/entry-points.json

@@ -4,7 +4,7 @@
     "entryPoints": [
         {
             "filePath": "planner",
-            "uniqueId": "7ab9bf62-e4a4-4261-8b47-0223be930f45",
+            "uniqueId": "612002c9-74ee-4b66-b262-0dd0a524f3c8",
             "type": "agent",
             "input": {
                 "type": "object",
@@ -46,15 +46,18 @@
                         "metadata": {}
                     },
                     {
-                        "id": "create_plan",
-                        "name": "create_plan",
-                        "type": "node",
+                        "id": "planner_agent",
+                        "name": "planner_agent",
+                        "type": "model",
                         "subgraph": null,
-                        "metadata": {}
+                        "metadata": {
+                            "model_name": "claude-3-7-sonnet-latest",
+                            "max_tokens": 64000
+                        }
                     },
                     {
-                        "id": "supervisor",
-                        "name": "supervisor",
+                        "id": "router",
+                        "name": "router",
                         "type": "node",
                         "subgraph": null,
                         "metadata": {}
@@ -66,6 +69,13 @@
                         "subgraph": null,
                         "metadata": {}
                     },
+                    {
+                        "id": "output",
+                        "name": "output",
+                        "type": "node",
+                        "subgraph": null,
+                        "metadata": {}
+                    },
                     {
                         "id": "__end__",
                         "name": "__end__",
@@ -82,30 +92,45 @@
                     },
                     {
                         "source": "input",
-                        "target": "supervisor",
+                        "target": "router",
                         "label": null
                     },
                     {
-                        "source": "supervisor",
-                        "target": "__end__",
+                        "source": "invoke_agent",
+                        "target": "router",
                         "label": null
                     },
                     {
-                        "source": "create_plan",
-                        "target": "supervisor",
+                        "source": "planner_agent",
+                        "target": "router",
                         "label": null
                     },
                     {
-                        "source": "invoke_agent",
-                        "target": "supervisor",
+                        "source": "router",
+                        "target": "invoke_agent",
+                        "label": null
+                    },
+                    {
+                        "source": "router",
+                        "target": "output",
+                        "label": "__end__"
+                    },
+                    {
+                        "source": "router",
+                        "target": "planner_agent",
+                        "label": null
+                    },
+                    {
+                        "source": "output",
+                        "target": "__end__",
                         "label": null
                     }
                 ]
             }
         },
         {
             "filePath": "researcher",
-            "uniqueId": "834546c0-6741-4a84-9159-dcc40f5942e8",
+            "uniqueId": "84adaea3-48d6-4e92-a095-2e4f9d4f60eb",
             "type": "agent",
             "input": {
                 "type": "object",
@@ -1283,7 +1308,7 @@
         },
         {
             "filePath": "coder",
-            "uniqueId": "49f05858-56b9-412e-affb-c63048d689bc",
+            "uniqueId": "ecd1a7d1-3348-4d86-9cfe-69408e575d87",
             "type": "agent",
             "input": {
                 "type": "object",

samples/multi-agent-planner-researcher-coder-distributed/planner.mermaid

@@ -1,12 +1,16 @@
 flowchart TB
   __start__(__start__)
   input(input)
-  create_plan(create_plan)
-  supervisor(supervisor)
+  planner_agent(planner_agent)
+  router(router)
   invoke_agent(invoke_agent)
+  output(output)
   __end__(__end__)
   __start__ --> input
-  input --> supervisor
-  supervisor --> __end__
-  create_plan --> supervisor
-  invoke_agent --> supervisor
+  input --> router
+  invoke_agent --> router
+  planner_agent --> router
+  router --> invoke_agent
+  router --> |__end__|output
+  router --> planner_agent
+  output --> __end__

samples/multi-agent-planner-researcher-coder-distributed/pyproject.toml

@@ -9,7 +9,7 @@ dependencies = [
     "langchain-anthropic>=1.2.0",
     "langchain-experimental>=0.4.0",
     "langchain-tavily>=0.2.13",
-    "uipath-langchain>=0.1.22, <0.2.0",
+    "uipath-langchain>=0.1.28, <0.2.0",
     "uipath>=2.2.26, <2.3.0",
 ]