feat: add dump and dumps methods to Graph (#3202)

* feat(utils.py): add escape_json_dump function to escape JSON strings for Edge dictionaries

* refactor(Output): streamline add_types method to prevent duplicate entries in types list for improved type management

* feat(data.py): add classmethod decorator to validate_data for enhanced validation logic when checking data types

* feat(setup.py): implement retry logic for loading starter projects to enhance robustness against JSON decode errors

* fix(input_mixin.py): improve model_config formatting and update field_type alias for clarity and consistency in field definitions

* feat(types.py): refactor vertex constructors to use NodeData and add input/output methods for better component interaction

* feat(schema.py): add NodeData and Position TypedDicts for improved type safety and structure in vertex data handling

* feat(base.py): update Vertex to use NodeData type and add to_data method for better data management and access

* refactor(schema.py): update TargetHandle and SourceHandle models to include model_config attribute

* Add TypedDict classes for graph schema serialization in `schema.py`

* Refactor `Edge` class to improve handle validation and data handling

- Consolidated imports and removed redundant `BaseModel` definitions for `SourceHandle` and `TargetHandle`.
- Added `valid_handles`, `target_param`, and `_target_handle` attributes to `Edge` class.
- Enhanced handle validation logic to distinguish between dictionary and string types.
- Introduced `to_data` method to return edge data.
- Updated attribute names to follow consistent naming conventions (`base_classes`, `input_types`, `field_name`).

* Refactor `Edge` class to improve handle validation and data handling

* Refactor: Standardize attribute naming and add `to_data` method in Edge class

- Renamed attributes to use snake_case consistently (`baseClasses` to `base_classes`, `inputTypes` to `input_types`, `fieldName` to `field_name`).
- Added `to_data` method to return `_data` attribute.
- Updated validation methods to use new attribute names.

* Refactor: Update Edge class to consistently use snake_case for attributes and improve validation logic for handles

* Refactor: Change node argument type in add_node and _create_vertex methods to NodeData for better type safety and clarity

* Refactor: Implement JSON serialization for graph data with `dumps` and `dump` methods, enhancing data export capabilities

* Refactor: Add pytest fixtures for ingestion and RAG graphs, enhance test structure for better clarity and organization

* Refactor: Add pytest fixtures for memory_chatbot_graph tests and improve test structure

* Refactor: Remove unused methods in ComponentVertex class to streamline code and improve readability

* Refactor: Remove unnecessary line in ComponentVertex class to enhance code clarity and maintainability

* Refactor: Update import path for DefaultPromptField to improve code organization and maintainability in api_utils.py

* Refactor: Update import path for DefaultPromptField to enhance code organization and maintainability in prompt.py

* fix: Remove  fixture in test_memory_chatbot.py that blocked db setup

* Refactor: Add durations path for unit tests to improve test reporting

* Refactor: Add splitting algorithm option for unit tests

* Add async option to Makefile for unit tests and update GitHub Actions workflow

- Introduced `async` variable in Makefile to conditionally run unit tests with or without parallel execution.
- Updated `unit_tests` target in Makefile to handle `async` flag.
- Modified GitHub Actions workflow to set `async=false` for unit tests.

.github/workflows/python_test.yml

@@ -50,7 +50,7 @@ jobs:
         with:
           timeout_minutes: 12
           max_attempts: 2
-          command: make unit_tests args="--splits ${{ matrix.splitCount }} --group ${{ matrix.group }}"
+          command: make unit_tests async=false args="--splits ${{ matrix.splitCount }} --group ${{ matrix.group }}"
 
   test-cli:
     name: Test CLI - Python ${{ matrix.python-version }}

Makefile

@@ -18,7 +18,7 @@ env ?= .env
 open_browser ?= true
 path = src/backend/base/langflow/frontend
 workers ?= 1
-
+async ?= true
 all: help
 
 ######################
@@ -130,14 +130,25 @@ coverage: ## run the tests and generate a coverage report
 	@poetry run coverage erase
 
 unit_tests: ## run unit tests
+ifeq ($(async), true)
 	poetry run pytest src/backend/tests \
 		--ignore=src/backend/tests/integration \
-		--instafail -ra -n auto -m "not api_key_required" \
+		--instafail -n auto -ra -m "not api_key_required" \
+		--durations-path src/backend/tests/.test_durations \
+		--splitting-algorithm least_duration \
 		$(args)
+else
+	poetry run pytest src/backend/tests \
+		--ignore=src/backend/tests/integration \
+		--instafail -ra -m "not api_key_required" \
+		--durations-path src/backend/tests/.test_durations \
+		--splitting-algorithm least_duration \
+		$(args)
+endif
 
 integration_tests: ## run integration tests
 	poetry run pytest src/backend/tests/integration \
-		--instafail -ra -n auto \
+		--instafail -ra \
 		$(args)
 
 tests: ## run unit, integration, coverage tests

src/backend/base/langflow/base/prompts/api_utils.py

@@ -6,7 +6,7 @@ from langchain_core.prompts import PromptTemplate
 from loguru import logger
 
 from langflow.interface.utils import extract_input_variables_from_prompt
-from langflow.template.field.prompt import DefaultPromptField
+from langflow.inputs.inputs import DefaultPromptField
 
 
 _INVALID_CHARACTERS = {

src/backend/base/langflow/graph/edge/base.py

@@ -1,51 +1,31 @@
-from typing import TYPE_CHECKING, Any, List, Optional, cast
+from typing import TYPE_CHECKING, Any, cast
 
 from loguru import logger
-from pydantic import BaseModel, Field, field_validator
 
-from langflow.graph.edge.schema import EdgeData
+from langflow.graph.edge.schema import EdgeData, SourceHandle, TargetHandle, TargetHandleDict
 from langflow.schema.schema import INPUT_FIELD_NAME
 
 if TYPE_CHECKING:
     from langflow.graph.vertex.base import Vertex
 
 
-class SourceHandle(BaseModel):
-    baseClasses: list[str] = Field(default_factory=list, description="List of base classes for the source handle.")
-    dataType: str = Field(..., description="Data type for the source handle.")
-    id: str = Field(..., description="Unique identifier for the source handle.")
-    name: Optional[str] = Field(None, description="Name of the source handle.")
-    output_types: List[str] = Field(default_factory=list, description="List of output types for the source handle.")
-
-    @field_validator("name", mode="before")
-    @classmethod
-    def validate_name(cls, v, _info):
-        if _info.data["dataType"] == "GroupNode":
-            # 'OpenAIModel-u4iGV_text_output'
-            splits = v.split("_", 1)
-            if len(splits) != 2:
-                raise ValueError(f"Invalid source handle name {v}")
-            v = splits[1]
-        return v
-
-
-class TargetHandle(BaseModel):
-    fieldName: str = Field(..., description="Field name for the target handle.")
-    id: str = Field(..., description="Unique identifier for the target handle.")
-    inputTypes: Optional[List[str]] = Field(None, description="List of input types for the target handle.")
-    type: str = Field(..., description="Type of the target handle.")
-
-
 class Edge:
     def __init__(self, source: "Vertex", target: "Vertex", edge: EdgeData):
         self.source_id: str = source.id if source else ""
         self.target_id: str = target.id if target else ""
+        self.valid_handles: bool = False
+        self.target_param: str | None = None
+        self._target_handle: TargetHandleDict | str | None = None
+        self._data = edge.copy()
         if data := edge.get("data", {}):
             self._source_handle = data.get("sourceHandle", {})
-            self._target_handle = data.get("targetHandle", {})
+            self._target_handle = cast(TargetHandleDict, data.get("targetHandle", {}))
             self.source_handle: SourceHandle = SourceHandle(**self._source_handle)
+            if isinstance(self._target_handle, dict):
                 self.target_handle: TargetHandle = TargetHandle(**self._target_handle)
-            self.target_param = self.target_handle.fieldName
+            else:
+                raise ValueError("Target handle is not a dictionary")
+            self.target_param = self.target_handle.field_name
             # validate handles
             self.validate_handles(source, target)
         else:
@@ -55,23 +35,31 @@ class Edge:
             self._target_handle = edge.get("targetHandle", "")  # type: ignore
             # 'BaseLoader;BaseOutputParser|documents|PromptTemplate-zmTlD'
             # target_param is documents
-            self.target_param = cast(str, self._target_handle.split("|")[1])  # type: ignore
+            if isinstance(self._target_handle, str):
+                self.target_param = self._target_handle.split("|")[1]
+                self.source_handle = None
+                self.target_handle = None
+            else:
+                raise ValueError("Target handle is not a string")
         # Validate in __init__ to fail fast
         self.validate_edge(source, target)
 
+    def to_data(self):
+        return self._data
+
     def validate_handles(self, source, target) -> None:
-        if isinstance(self._source_handle, str) or self.source_handle.baseClasses:
+        if isinstance(self._source_handle, str) or self.source_handle.base_classes:
             self._legacy_validate_handles(source, target)
         else:
             self._validate_handles(source, target)
 
     def _validate_handles(self, source, target) -> None:
-        if self.target_handle.inputTypes is None:
+        if self.target_handle.input_types is None:
             self.valid_handles = self.target_handle.type in self.source_handle.output_types
 
         elif self.source_handle.output_types is not None:
             self.valid_handles = (
-                any(output_type in self.target_handle.inputTypes for output_type in self.source_handle.output_types)
+                any(output_type in self.target_handle.input_types for output_type in self.source_handle.output_types)
                 or self.target_handle.type in self.source_handle.output_types
             )
 
@@ -81,12 +69,12 @@ class Edge:
             raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has invalid handles")
 
     def _legacy_validate_handles(self, source, target) -> None:
-        if self.target_handle.inputTypes is None:
+        if self.target_handle.input_types is None:
-            self.valid_handles = self.target_handle.type in self.source_handle.baseClasses
+            self.valid_handles = self.target_handle.type in self.source_handle.base_classes
         else:
             self.valid_handles = (
-                any(baseClass in self.target_handle.inputTypes for baseClass in self.source_handle.baseClasses)
+                any(baseClass in self.target_handle.input_types for baseClass in self.source_handle.base_classes)
-                or self.target_handle.type in self.source_handle.baseClasses
+                or self.target_handle.type in self.source_handle.base_classes
             )
         if not self.valid_handles:
             logger.debug(self.source_handle)
@@ -101,9 +89,9 @@ class Edge:
         self.target_handle = state.get("target_handle")
 
     def validate_edge(self, source, target) -> None:
-        # If the self.source_handle has baseClasses, then we are using the legacy
+        # If the self.source_handle has base_classes, then we are using the legacy
         # way of defining the source and target handles
-        if isinstance(self._source_handle, str) or self.source_handle.baseClasses:
+        if isinstance(self._source_handle, str) or self.source_handle.base_classes:
             self._legacy_validate_edge(source, target)
         else:
             self._validate_edge(source, target)
@@ -230,5 +218,5 @@ class ContractEdge(Edge):
         if (hasattr(self, "source_handle") and self.source_handle) and (
             hasattr(self, "target_handle") and self.target_handle
         ):
-            return f"{self.source_id} -[{self.source_handle.name}->{self.target_handle.fieldName}]-> {self.target_id}"
+            return f"{self.source_id} -[{self.source_handle.name}->{self.target_handle.field_name}]-> {self.target_id}"
         return f"{self.source_id} -[{self.target_param}]-> {self.target_id}"

src/backend/base/langflow/graph/edge/schema.py

@@ -1,6 +1,6 @@
 from typing import Any, List, Optional
 
-from pydantic import Field, field_validator
+from pydantic import ConfigDict, Field, field_validator
 from typing_extensions import TypedDict
 
 from langflow.helpers.base_model import BaseModel
@@ -39,7 +39,8 @@ class Payload(BaseModel):
 
 
 class TargetHandle(BaseModel):
-    fieldName: str = Field(..., alias="fieldName", description="Field name for the target handle.")
+    model_config = ConfigDict(populate_by_name=True)
+    field_name: str = Field(..., alias="fieldName", description="Field name for the target handle.")
     id: str = Field(..., description="Unique identifier for the target handle.")
     input_types: List[str] = Field(
         default_factory=list, alias="inputTypes", description="List of input types for the target handle."
@@ -48,6 +49,7 @@ class TargetHandle(BaseModel):
 
 
 class SourceHandle(BaseModel):
+    model_config = ConfigDict(populate_by_name=True)
     base_classes: list[str] = Field(
         default_factory=list, alias="baseClasses", description="List of base classes for the source handle."
     )

src/backend/base/langflow/graph/graph/base.py

@@ -1,4 +1,5 @@
 import asyncio
+import json
 import uuid
 from collections import defaultdict, deque
 from datetime import datetime, timezone
@@ -14,11 +15,12 @@ from langflow.graph.edge.base import ContractEdge
 from langflow.graph.edge.schema import EdgeData
 from langflow.graph.graph.constants import Finish, lazy_load_vertex_dict
 from langflow.graph.graph.runnable_vertices_manager import RunnableVerticesManager
-from langflow.graph.graph.schema import VertexBuildResult
+from langflow.graph.graph.schema import GraphData, GraphDump, VertexBuildResult
 from langflow.graph.graph.state_manager import GraphStateManager
 from langflow.graph.graph.utils import find_start_component_id, process_flow, sort_up_to_vertex
 from langflow.graph.schema import InterfaceComponentTypes, RunOutputs
 from langflow.graph.vertex.base import Vertex, VertexStates
+from langflow.graph.vertex.schema import NodeData
 from langflow.graph.vertex.types import ComponentVertex, InterfaceVertex, StateVertex
 from langflow.schema import Data
 from langflow.schema.schema import INPUT_FIELD_NAME, InputType
@@ -75,7 +77,7 @@ class Graph:
         self.vertices: List[Vertex] = []
         self.run_manager = RunnableVerticesManager()
         self.state_manager = GraphStateManager()
-        self._vertices: List[dict] = []
+        self._vertices: List[NodeData] = []
         self._edges: List[EdgeData] = []
         self.top_level_vertices: List[str] = []
         self.vertex_map: Dict[str, Vertex] = {}
@@ -86,6 +88,7 @@ class Graph:
         self._run_queue: deque[str] = deque()
         self._first_layer: List[str] = []
         self._lock = asyncio.Lock()
+        self.raw_graph_data: GraphData = {"nodes": [], "edges": []}
         try:
             self.tracing_service: "TracingService" | None = get_tracing_service()
         except Exception as exc:
@@ -97,7 +100,39 @@ class Graph:
         if (start is not None and end is None) or (start is None and end is not None):
             raise ValueError("You must provide both input and output components")
 
-    def add_nodes_and_edges(self, nodes: List[Dict], edges: List[EdgeData]):
+    def dumps(
+        self,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        endpoint_name: Optional[str] = None,
+    ) -> str:
+        graph_dict = self.dump(name, description, endpoint_name)
+        return json.dumps(graph_dict, indent=4, sort_keys=True)
+
+    def dump(
+        self, name: Optional[str] = None, description: Optional[str] = None, endpoint_name: Optional[str] = None
+    ) -> GraphDump:
+        if self.raw_graph_data != {"nodes": [], "edges": []}:
+            data_dict = self.raw_graph_data
+        else:
+            # we need to convert the vertices and edges to json
+            nodes = [node.to_data() for node in self.vertices]
+            edges = [edge.to_data() for edge in self.edges]
+            self.raw_graph_data = {"nodes": nodes, "edges": edges}
+            data_dict = self.raw_graph_data
+        graph_dict: GraphDump = {
+            "data": data_dict,
+            "is_component": len(data_dict.get("nodes", [])) == 1 and data_dict["edges"] == [],
+        }
+        if name:
+            graph_dict["name"] = name
+        if description:
+            graph_dict["description"] = description
+        if endpoint_name:
+            graph_dict["endpoint_name"] = endpoint_name
+        return graph_dict
+
+    def add_nodes_and_edges(self, nodes: List[NodeData], edges: List[EdgeData]):
         self._vertices = nodes
         self._edges = edges
         self.raw_graph_data = {"nodes": nodes, "edges": edges}
@@ -183,7 +218,7 @@ class Graph:
                 return
 
     def start(self, inputs: Optional[List[dict]] = None) -> Generator:
-        #! Change this soon
+        #! Change this ASAP
         nest_asyncio.apply()
         loop = asyncio.get_event_loop()
         async_gen = self.async_start(inputs)
@@ -208,8 +243,7 @@ class Graph:
         self.in_degree_map[target_id] += 1
         self.parent_child_map[source_id].append(target_id)
 
-    # TODO: Create a TypedDict to represente the node
+    def add_node(self, node: NodeData):
-    def add_node(self, node: dict):
         self._vertices.append(node)
 
     def add_edge(self, edge: EdgeData):
@@ -1400,7 +1434,7 @@ class Graph:
 
         return vertices
 
-    def _create_vertex(self, frontend_data: dict):
+    def _create_vertex(self, frontend_data: NodeData):
         vertex_data = frontend_data["data"]
         vertex_type: str = vertex_data["type"]  # type: ignore
         vertex_base_type: str = vertex_data["node"]["template"]["_type"]  # type: ignore

src/backend/base/langflow/graph/graph/schema.py

@@ -1,10 +1,35 @@
 from typing import TYPE_CHECKING, NamedTuple
 
+from typing_extensions import NotRequired, TypedDict
+
+from langflow.graph.edge.schema import EdgeData
+from langflow.graph.vertex.schema import NodeData
+
 if TYPE_CHECKING:
     from langflow.graph.schema import ResultData
     from langflow.graph.vertex.base import Vertex
 
 
+class ViewPort(TypedDict):
+    x: float
+    y: float
+    zoom: float
+
+
+class GraphData(TypedDict):
+    nodes: list[NodeData]
+    edges: list[EdgeData]
+    viewport: NotRequired[ViewPort]
+
+
+class GraphDump(TypedDict, total=False):
+    data: GraphData
+    is_component: bool
+    name: str
+    description: str
+    endpoint_name: str
+
+
 class VertexBuildResult(NamedTuple):
     result_dict: "ResultData"
     params: str

src/backend/base/langflow/graph/vertex/base.py

@@ -13,6 +13,7 @@ from loguru import logger
 from langflow.exceptions.component import ComponentBuildException
 from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData
 from langflow.graph.utils import UnbuiltObject, UnbuiltResult, log_transaction
+from langflow.graph.vertex.schema import NodeData
 from langflow.interface.initialize import loading
 from langflow.interface.listing import lazy_load_dict
 from langflow.schema.artifact import ArtifactType
@@ -42,7 +43,7 @@ class VertexStates(str, Enum):
 class Vertex:
     def __init__(
         self,
-        data: Dict,
+        data: NodeData,
         graph: "Graph",
         base_type: Optional[str] = None,
         is_task: bool = False,
@@ -63,7 +64,7 @@ class Vertex:
         self.has_external_input = False
         self.has_external_output = False
         self.graph = graph
-        self._data = data
+        self._data = data.copy()
         self.base_type: Optional[str] = base_type
         self.outputs: List[Dict] = []
         self._parse_data()
@@ -101,6 +102,9 @@ class Vertex:
             raise ValueError(f"Vertex {self.id} does not have a component instance.")
         self._custom_component._set_input_value(name, value)
 
+    def to_data(self):
+        return self._data
+
     def add_component_instance(self, component_instance: "Component"):
         component_instance.set_vertex(self)
         self._custom_component = component_instance

src/backend/base/langflow/graph/vertex/schema.py

@@ -0,0 +1,21 @@
+from typing import Dict
+
+from typing_extensions import NotRequired, TypedDict
+
+
+class Position(TypedDict):
+    x: float
+    y: float
+
+
+class NodeData(TypedDict):
+    id: str
+    data: Dict
+    dragging: NotRequired[bool]
+    height: NotRequired[int]
+    width: NotRequired[int]
+    position: NotRequired[Position]
+    positionAbsolute: NotRequired[Position]
+    selected: NotRequired[bool]
+    parent_node_id: NotRequired[str]
+    type: str

src/backend/base/langflow/graph/vertex/types.py

@@ -9,6 +9,7 @@ from loguru import logger
 from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes, ResultData
 from langflow.graph.utils import UnbuiltObject, log_transaction, log_vertex_build, serialize_field
 from langflow.graph.vertex.base import Vertex
+from langflow.graph.vertex.schema import NodeData
 from langflow.inputs.inputs import InputTypes
 from langflow.schema import Data
 from langflow.schema.artifact import ArtifactType
@@ -23,7 +24,7 @@ if TYPE_CHECKING:
 
 
 class CustomComponentVertex(Vertex):
-    def __init__(self, data: Dict, graph):
+    def __init__(self, data: NodeData, graph):
         super().__init__(data, graph=graph, base_type="custom_components")
 
     def _built_object_repr(self):
@@ -32,9 +33,19 @@ class CustomComponentVertex(Vertex):
 
 
 class ComponentVertex(Vertex):
-    def __init__(self, data: Dict, graph):
+    def __init__(self, data: NodeData, graph):
         super().__init__(data, graph=graph, base_type="component")
 
+    def get_input(self, name: str) -> InputTypes:
+        if self._custom_component is None:
+            raise ValueError(f"Vertex {self.id} does not have a component instance.")
+        return self._custom_component.get_input(name)
+
+    def get_output(self, name: str) -> Output:
+        if self._custom_component is None:
+            raise ValueError(f"Vertex {self.id} does not have a component instance.")
+        return self._custom_component.get_output(name)
+
     def _built_object_repr(self):
         if self.artifacts and "repr" in self.artifacts:
             return self.artifacts["repr"] or super()._built_object_repr()
@@ -58,16 +69,6 @@ class ComponentVertex(Vertex):
         for key, value in self._built_object.items():
             self.add_result(key, value)
 
-    def get_input(self, name: str) -> InputTypes:
-        if self._custom_component is None:
-            raise ValueError(f"Vertex {self.id} does not have a component instance.")
-        return self._custom_component.get_input(name)
-
-    def get_output(self, name: str) -> Output:
-        if self._custom_component is None:
-            raise ValueError(f"Vertex {self.id} does not have a component instance.")
-        return self._custom_component.get_output(name)
-
     def get_edge_with_target(self, target_id: str) -> Generator["ContractEdge", None, None]:
         """
         Get the edge with the target id.
@@ -174,7 +175,7 @@ class ComponentVertex(Vertex):
 
 
 class InterfaceVertex(ComponentVertex):
-    def __init__(self, data: Dict, graph):
+    def __init__(self, data: NodeData, graph):
         super().__init__(data, graph=graph)
         self.steps = [self._build, self._run]
 
@@ -424,7 +425,7 @@ class InterfaceVertex(ComponentVertex):
 
 
 class StateVertex(ComponentVertex):
-    def __init__(self, data: Dict, graph):
+    def __init__(self, data: NodeData, graph):
         super().__init__(data, graph=graph)
         self.steps = [self._build]
         self.is_state = False

src/backend/base/langflow/initial_setup/setup.py

@@ -2,6 +2,7 @@ import copy
 import json
 import os
 import shutil
+import time
 from collections import defaultdict
 from copy import deepcopy
 from datetime import datetime, timezone
@@ -23,6 +24,7 @@ from langflow.services.database.models.folder.utils import create_default_folder
 from langflow.services.database.models.user.crud import get_user_by_username
 from langflow.services.deps import get_settings_service, get_storage_service, get_variable_service, session_scope
 from langflow.template.field.prompt import DEFAULT_PROMPT_INTUT_TYPES
+from langflow.utils.util import escape_json_dump
 
 STARTER_FOLDER_NAME = "Starter Projects"
 STARTER_FOLDER_DESCRIPTION = "Starter projects to help you get started in Langflow."
@@ -319,10 +321,6 @@ def update_edges_with_latest_component_versions(project_data):
     return project_data_copy
 
 
-def escape_json_dump(edge_dict):
-    return json.dumps(edge_dict).replace('"', "œ")
-
-
 def log_node_changes(node_changes_log):
     # The idea here is to log the changes that were made to the nodes in debug
     # Something like:
@@ -339,17 +337,23 @@ def log_node_changes(node_changes_log):
         logger.debug("\n".join(formatted_messages))
 
 
-def load_starter_projects() -> list[tuple[Path, dict]]:
+def load_starter_projects(retries=3, delay=1) -> list[tuple[Path, dict]]:
     starter_projects = []
     folder = Path(__file__).parent / "starter_projects"
     for file in folder.glob("*.json"):
+        attempt = 0
+        while attempt < retries:
             with open(file, "r", encoding="utf-8") as f:
                 try:
                     project = orjson.loads(f.read())
                     starter_projects.append((file, project))
                     logger.info(f"Loaded starter project {file}")
+                    break  # Break if load is successful
                 except orjson.JSONDecodeError as e:
+                    attempt += 1
+                    if attempt >= retries:
                         raise ValueError(f"Error loading starter project {file}: {e}")
+                    time.sleep(delay)  # Wait before retrying
     return starter_projects
 
 

src/backend/base/langflow/inputs/input_mixin.py

@@ -27,9 +27,13 @@ SerializableFieldTypes = Annotated[FieldTypes, PlainSerializer(lambda v: v.value
 
 # Base mixin for common input field attributes and methods
 class BaseInputMixin(BaseModel, validate_assignment=True):  # type: ignore
-    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid", populate_by_name=True)
+    model_config = ConfigDict(
+        arbitrary_types_allowed=True,
+        extra="forbid",
+        populate_by_name=True,
+    )
 
-    field_type: SerializableFieldTypes = Field(default=FieldTypes.TEXT)
+    field_type: SerializableFieldTypes = Field(default=FieldTypes.TEXT, alias="type")
 
     required: bool = False
     """Specifies if the field is required. Defaults to False."""

src/backend/base/langflow/schema/data.py

@@ -24,6 +24,7 @@ class Data(BaseModel):
     default_value: Optional[str] = ""
 
     @model_validator(mode="before")
+    @classmethod
     def validate_data(cls, values):
         if not isinstance(values, dict):
             raise ValueError("Data must be a dictionary")

src/backend/base/langflow/template/field/base.py

@@ -184,12 +184,9 @@ class Output(BaseModel):
         return self.model_dump(by_alias=True, exclude_none=True)
 
     def add_types(self, _type: list[Any]):
-        for type_ in _type:
-            if self.types and type_ in self.types:
-                continue
         if self.types is None:
             self.types = []
-            self.types.append(type_)
+        self.types.extend([t for t in _type if t not in self.types])
 
     def set_selected(self):
         if not self.selected and self.types:

src/backend/base/langflow/template/field/prompt.py

@@ -1,3 +1,3 @@
 # This file is for backwards compatibility
 from langflow.inputs.inputs import DEFAULT_PROMPT_INTUT_TYPES  # noqa
-from langflow.inputs import DefaultPromptField  # noqa
+from langflow.inputs.inputs import DefaultPromptField  # noqa

src/backend/base/langflow/utils/util.py

@@ -1,5 +1,6 @@
 import importlib
 import inspect
+import json
 import re
 from functools import wraps
 from pathlib import Path
@@ -456,3 +457,7 @@ def is_class_method(func, cls):
     Check if a function is a class method.
     """
     return inspect.ismethod(func) and func.__self__ is cls.__class__
+
+
+def escape_json_dump(edge_dict):
+    return json.dumps(edge_dict).replace('"', "œ")

src/backend/tests/.test_durations

@@ -0,0 +1,354 @@
+{
+    "src/backend/tests/test_endpoints.py::test_build_vertex_invalid_flow_id": 3.1494096249807626,
+    "src/backend/tests/test_endpoints.py::test_build_vertex_invalid_vertex_id": 3.0606157919974066,
+    "src/backend/tests/test_endpoints.py::test_get_all": 10.10167008501594,
+    "src/backend/tests/test_endpoints.py::test_get_vertices": 4.5017141660209745,
+    "src/backend/tests/test_endpoints.py::test_get_vertices_flow_not_found": 3.7886676250200253,
+    "src/backend/tests/test_endpoints.py::test_invalid_flow_id": 4.073716707964195,
+    "src/backend/tests/test_endpoints.py::test_invalid_prompt": 2.7002592499775346,
+    "src/backend/tests/test_endpoints.py::test_invalid_run_with_input_type_chat": 2.987766916019609,
+    "src/backend/tests/test_endpoints.py::test_post_validate_code": 3.0467621669813525,
+    "src/backend/tests/test_endpoints.py::test_successful_run_with_input_type_any": 14.8548604179814,
+    "src/backend/tests/test_endpoints.py::test_successful_run_with_input_type_chat": 6.242352208995726,
+    "src/backend/tests/test_endpoints.py::test_successful_run_with_input_type_text": 5.7594154170074034,
+    "src/backend/tests/test_endpoints.py::test_successful_run_with_output_type_any": 7.347130999987712,
+    "src/backend/tests/test_endpoints.py::test_successful_run_with_output_type_debug": 6.291947416990297,
+    "src/backend/tests/test_endpoints.py::test_successful_run_with_output_type_text": 14.872085083043203,
+    "src/backend/tests/test_endpoints.py::test_valid_prompt": 2.7850471249839757,
+    "src/backend/tests/test_endpoints.py::test_various_prompts[The weather is {weather} today.-expected_input_variables1]": 2.535564499994507,
+    "src/backend/tests/test_endpoints.py::test_various_prompts[This prompt has no variables.-expected_input_variables2]": 9.15231529099401,
+    "src/backend/tests/test_endpoints.py::test_various_prompts[{a}, {b}, and {c} are variables.-expected_input_variables3]": 2.640623040992068,
+    "src/backend/tests/test_endpoints.py::test_various_prompts[{color} is my favorite color.-expected_input_variables0]": 2.079908042011084,
+    "src/backend/tests/test_messages_endpoints.py::test_delete_messages": 2.515260499989381,
+    "src/backend/tests/test_messages_endpoints.py::test_delete_messages_session": 2.3651068749895785,
+    "src/backend/tests/test_messages_endpoints.py::test_update_message": 2.5627032090269495,
+    "src/backend/tests/test_messages_endpoints.py::test_update_message_not_found": 3.504595792008331,
+    "src/backend/tests/test_schema.py::TestInput::test_field_type_str": 0.0005162910092622042,
+    "src/backend/tests/test_schema.py::TestInput::test_field_type_type": 0.0002682080084923655,
+    "src/backend/tests/test_schema.py::TestInput::test_input_to_dict": 0.0003857500269077718,
+    "src/backend/tests/test_schema.py::TestInput::test_invalid_field_type": 0.00031291699269786477,
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+}

src/backend/tests/unit/initial_setup/starter_projects/test_memory_chatbot.py

@@ -1,5 +1,7 @@
 from collections import deque
 
+import pytest
+
 from langflow.components.helpers.Memory import MemoryComponent
 from langflow.components.inputs.ChatInput import ChatInput
 from langflow.components.models.OpenAIModel import OpenAIModelComponent
@@ -7,9 +9,11 @@ from langflow.components.outputs.ChatOutput import ChatOutput
 from langflow.components.prompts.Prompt import PromptComponent
 from langflow.graph import Graph
 from langflow.graph.graph.constants import Finish
+from langflow.graph.graph.schema import GraphDump
 
 
-def test_memory_chatbot():
+@pytest.fixture
+def memory_chatbot_graph():
     session_id = "test_session_id"
     template = """{context}
 
@@ -32,10 +36,87 @@ AI: """
     chat_output.set(input_value=openai_component.text_response)
 
     graph = Graph(chat_input, chat_output)
+    return graph
+
+
+def test_memory_chatbot(memory_chatbot_graph):
     # Now we run step by step
     expected_order = deque(["chat_input", "chat_memory", "prompt", "openai", "chat_output"])
     for step in expected_order:
-        result = graph.step()
+        result = memory_chatbot_graph.step()
         if isinstance(result, Finish):
             break
         assert step == result.vertex.id
+
+
+def test_memory_chatbot_dump_structure(memory_chatbot_graph: Graph):
+    # Now we run step by step
+    graph_dict = memory_chatbot_graph.dump(
+        name="Memory Chatbot", description="A memory chatbot", endpoint_name="membot"
+    )
+    assert isinstance(graph_dict, dict)
+    # Test structure
+    assert "data" in graph_dict
+    assert "is_component" in graph_dict
+
+    data_dict = graph_dict["data"]
+    assert "nodes" in data_dict
+    assert "edges" in data_dict
+    assert "description" in graph_dict
+    assert "endpoint_name" in graph_dict
+
+    # Test data
+    nodes = data_dict["nodes"]
+    edges = data_dict["edges"]
+    description = graph_dict["description"]
+    endpoint_name = graph_dict["endpoint_name"]
+
+    assert len(nodes) == 5
+    assert len(edges) == 4
+    assert description is not None
+    assert endpoint_name is not None
+
+
+def test_memory_chatbot_dump_components_and_edges(memory_chatbot_graph: Graph):
+    # Check all components and edges were dumped correctly
+    graph_dict: GraphDump = memory_chatbot_graph.dump(
+        name="Memory Chatbot", description="A memory chatbot", endpoint_name="membot"
+    )
+
+    data_dict = graph_dict["data"]
+    nodes = data_dict["nodes"]
+    edges = data_dict["edges"]
+
+    # sort the nodes by id
+    nodes = sorted(nodes, key=lambda x: x["id"])
+
+    # Check each node
+    assert nodes[0]["data"]["type"] == "ChatInput"
+    assert nodes[0]["id"] == "chat_input"
+
+    assert nodes[1]["data"]["type"] == "MemoryComponent"
+    assert nodes[1]["id"] == "chat_memory"
+
+    assert nodes[2]["data"]["type"] == "ChatOutput"
+    assert nodes[2]["id"] == "chat_output"
+
+    assert nodes[3]["data"]["type"] == "OpenAIModelComponent"
+    assert nodes[3]["id"] == "openai"
+
+    assert nodes[4]["data"]["type"] == "PromptComponent"
+    assert nodes[4]["id"] == "prompt"
+
+    # Check edges
+    expected_edges = [
+        ("chat_input", "prompt"),
+        ("chat_memory", "prompt"),
+        ("prompt", "openai"),
+        ("openai", "chat_output"),
+    ]
+
+    assert len(edges) == len(expected_edges)
+
+    for edge in edges:
+        source = edge["source"]
+        target = edge["target"]
+        assert (source, target) in expected_edges, edge

src/backend/tests/unit/initial_setup/starter_projects/test_vector_store_rag.py

@@ -1,5 +1,7 @@
 from textwrap import dedent
 
+import pytest
+
 from langflow.components.data.File import FileComponent
 from langflow.components.embeddings.OpenAIEmbeddings import OpenAIEmbeddingsComponent
 from langflow.components.helpers.ParseData import ParseDataComponent
@@ -14,10 +16,17 @@ from langflow.graph.graph.constants import Finish
 from langflow.schema.data import Data
 
 
-def test_vector_store_rag():
+@pytest.fixture
+def client():
+    pass
+
+
+@pytest.fixture
+def ingestion_graph():
     # Ingestion Graph
     file_component = FileComponent(_id="file-123")
     file_component.set(path="test.txt")
+    file_component.set_output_value("data", Data(text="This is a test file."))
     text_splitter = SplitTextComponent(_id="text-splitter-123")
     text_splitter.set(data_inputs=file_component.load_file)
     openai_embeddings = OpenAIEmbeddingsComponent(_id="openai-embeddings-123")
@@ -31,8 +40,18 @@ def test_vector_store_rag():
         api_endpoint="https://astra.example.com",
         token="token",
     )
+    vector_store.set_output_value("vector_store", "mock_vector_store")
+    vector_store.set_output_value("base_retriever", "mock_retriever")
+    vector_store.set_output_value("search_results", [Data(text="This is a test file.")])
 
+    ingestion_graph = Graph(file_component, vector_store)
+    return ingestion_graph
+
+
+@pytest.fixture
+def rag_graph():
     # RAG Graph
+    openai_embeddings = OpenAIEmbeddingsComponent(_id="openai-embeddings-124")
     chat_input = ChatInput(_id="chatinput-123")
     chat_input.get_output("message").value = "What is the meaning of life?"
     rag_vector_store = AstraVectorStoreComponent(_id="rag-vector-store-123")
@@ -69,21 +88,160 @@ def test_vector_store_rag():
     chat_output.set(input_value=openai_component.text_response)
 
     graph = Graph(start=chat_input, end=chat_output)
-    assert graph is not None
+    return graph
-    ids = [
+
+
+def test_vector_store_rag(ingestion_graph, rag_graph):
+    assert ingestion_graph is not None
+    ingestion_ids = [
+        "file-123",
+        "text-splitter-123",
+        "openai-embeddings-123",
+        "vector-store-123",
+    ]
+    assert rag_graph is not None
+    rag_ids = [
         "chatinput-123",
         "chatoutput-123",
         "openai-123",
         "parse-data-123",
         "prompt-123",
         "rag-vector-store-123",
-        "openai-embeddings-123",
+        "openai-embeddings-124",
     ]
+    for ids, graph, len_results in zip([ingestion_ids, rag_ids], [ingestion_graph, rag_graph], [5, 8]):
         results = []
         for result in graph.start():
             results.append(result)
 
-    assert len(results) == 8
+        assert len(results) == len_results
         vids = [result.vertex.id for result in results if hasattr(result, "vertex")]
         assert all(vid in ids for vid in vids), f"Diff: {set(vids) - set(ids)}"
         assert results[-1] == Finish()
+
+
+def test_vector_store_rag_dump_components_and_edges(ingestion_graph, rag_graph):
+    # Test ingestion graph components and edges
+    ingestion_graph_dump = ingestion_graph.dump(
+        name="Ingestion Graph", description="Graph for data ingestion", endpoint_name="ingestion"
+    )
+
+    ingestion_data = ingestion_graph_dump["data"]
+    ingestion_nodes = ingestion_data["nodes"]
+    ingestion_edges = ingestion_data["edges"]
+
+    # Sort nodes by id to check components
+    ingestion_nodes = sorted(ingestion_nodes, key=lambda x: x["id"])
+
+    # Check components in the ingestion graph
+    assert ingestion_nodes[0]["data"]["type"] == "FileComponent"
+    assert ingestion_nodes[0]["id"] == "file-123"
+
+    assert ingestion_nodes[1]["data"]["type"] == "OpenAIEmbeddingsComponent"
+    assert ingestion_nodes[1]["id"] == "openai-embeddings-123"
+
+    assert ingestion_nodes[2]["data"]["type"] == "SplitTextComponent"
+    assert ingestion_nodes[2]["id"] == "text-splitter-123"
+
+    assert ingestion_nodes[3]["data"]["type"] == "AstraVectorStoreComponent"
+    assert ingestion_nodes[3]["id"] == "vector-store-123"
+
+    # Check edges in the ingestion graph
+    expected_ingestion_edges = [
+        ("file-123", "text-splitter-123"),
+        ("text-splitter-123", "vector-store-123"),
+        ("openai-embeddings-123", "vector-store-123"),
+    ]
+    assert len(ingestion_edges) == len(expected_ingestion_edges)
+
+    for edge in ingestion_edges:
+        source = edge["source"]
+        target = edge["target"]
+        assert (source, target) in expected_ingestion_edges, edge
+
+    # Test RAG graph components and edges
+    rag_graph_dump = rag_graph.dump(
+        name="RAG Graph", description="Graph for Retrieval-Augmented Generation", endpoint_name="rag"
+    )
+
+    rag_data = rag_graph_dump["data"]
+    rag_nodes = rag_data["nodes"]
+    rag_edges = rag_data["edges"]
+
+    # Sort nodes by id to check components
+    rag_nodes = sorted(rag_nodes, key=lambda x: x["id"])
+
+    # Check components in the RAG graph
+    assert rag_nodes[0]["data"]["type"] == "ChatInput"
+    assert rag_nodes[0]["id"] == "chatinput-123"
+
+    assert rag_nodes[1]["data"]["type"] == "ChatOutput"
+    assert rag_nodes[1]["id"] == "chatoutput-123"
+
+    assert rag_nodes[2]["data"]["type"] == "OpenAIModelComponent"
+    assert rag_nodes[2]["id"] == "openai-123"
+
+    assert rag_nodes[3]["data"]["type"] == "OpenAIEmbeddingsComponent"
+    assert rag_nodes[3]["id"] == "openai-embeddings-124"
+
+    assert rag_nodes[4]["data"]["type"] == "ParseDataComponent"
+    assert rag_nodes[4]["id"] == "parse-data-123"
+
+    assert rag_nodes[5]["data"]["type"] == "PromptComponent"
+    assert rag_nodes[5]["id"] == "prompt-123"
+
+    assert rag_nodes[6]["data"]["type"] == "AstraVectorStoreComponent"
+    assert rag_nodes[6]["id"] == "rag-vector-store-123"
+
+    # Check edges in the RAG graph
+    expected_rag_edges = [
+        ("chatinput-123", "rag-vector-store-123"),
+        ("openai-embeddings-124", "rag-vector-store-123"),
+        ("chatinput-123", "prompt-123"),
+        ("rag-vector-store-123", "parse-data-123"),
+        ("parse-data-123", "prompt-123"),
+        ("prompt-123", "openai-123"),
+        ("openai-123", "chatoutput-123"),
+    ]
+    assert len(rag_edges) == len(expected_rag_edges), rag_edges
+
+    for edge in rag_edges:
+        source = edge["source"]
+        target = edge["target"]
+        assert (source, target) in expected_rag_edges, f"Edge {source} -> {target} not found"
+
+
+def test_vector_store_rag_dump(ingestion_graph, rag_graph):
+    # Test ingestion graph dump
+    ingestion_graph_dump = ingestion_graph.dump(
+        name="Ingestion Graph", description="Graph for data ingestion", endpoint_name="ingestion"
+    )
+    assert isinstance(ingestion_graph_dump, dict)
+
+    ingestion_data = ingestion_graph_dump["data"]
+    assert "nodes" in ingestion_data
+    assert "edges" in ingestion_data
+    assert "description" in ingestion_graph_dump
+    assert "endpoint_name" in ingestion_graph_dump
+
+    ingestion_nodes = ingestion_data["nodes"]
+    ingestion_edges = ingestion_data["edges"]
+    assert len(ingestion_nodes) == 4  # There are 4 components in the ingestion graph
+    assert len(ingestion_edges) == 3  # There are 3 connections between components
+
+    # Test RAG graph dump
+    rag_graph_dump = rag_graph.dump(
+        name="RAG Graph", description="Graph for Retrieval-Augmented Generation", endpoint_name="rag"
+    )
+    assert isinstance(rag_graph_dump, dict)
+
+    rag_data = rag_graph_dump["data"]
+    assert "nodes" in rag_data
+    assert "edges" in rag_data
+    assert "description" in rag_graph_dump
+    assert "endpoint_name" in rag_graph_dump
+
+    rag_nodes = rag_data["nodes"]
+    rag_edges = rag_data["edges"]
+    assert len(rag_nodes) == 7  # There are 7 components in the RAG graph
+    assert len(rag_edges) == 7  # There are 7 connections between components