Merge remote-tracking branch 'origin/cz/mergeAll' into fix/edited_component

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

@@ -86,9 +86,9 @@ def update_frontend_node_with_template_values(frontend_node, raw_frontend_node):
 
     update_template_values(frontend_node["template"], raw_frontend_node["template"])
 
-    old_code = raw_frontend_node['template']['code']['value']
-    new_code = frontend_node['template']['code']['value']
-    frontend_node['edited'] = old_code != new_code
+    old_code = raw_frontend_node["template"]["code"]["value"]
+    new_code = frontend_node["template"]["code"]["value"]
+    frontend_node["edited"] = old_code != new_code
 
     return frontend_node
 
@@ -208,16 +208,18 @@ def format_elapsed_time(elapsed_time: float) -> str:
         return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}"
 
 
-async def build_and_cache_graph_from_db(
-    flow_id: str,
-    session: Session,
-    chat_service: "ChatService",
-):
+async def build_and_cache_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
     """Build and cache the graph."""
     flow: Optional[Flow] = session.get(Flow, flow_id)
     if not flow or not flow.data:
         raise ValueError("Invalid flow ID")
     graph = Graph.from_payload(flow.data, flow_id)
+    for vertex_id in graph._has_session_id_vertices:
+        vertex = graph.get_vertex(vertex_id)
+        if vertex is None:
+            raise ValueError(f"Vertex {vertex_id} not found")
+        if not vertex._raw_params.get("session_id"):
+            vertex.update_raw_params({"session_id": flow_id})
     await chat_service.set_cache(flow_id, graph)
     return graph
 
@@ -321,3 +323,4 @@ def parse_exception(exc):
     if hasattr(exc, "body"):
         return exc.body["message"]
     return str(exc)
+    return str(exc)

src/backend/base/langflow/api/v1/chat.py

@@ -168,9 +168,9 @@ async def build_vertex(
                 next_runnable_vertices,
                 top_level_vertices,
                 result_dict,
-                log_message,
+                params,
                 valid,
-                log_type,
+                artifacts,
                 vertex,
             ) = await graph.build_vertex(
                 lock=lock,
@@ -180,22 +180,22 @@ async def build_vertex(
                 inputs_dict=inputs.model_dump() if inputs else {},
                 files=files,
             )
-
+            log_obj = Log(message=vertex.artifacts_raw, type=vertex.artifacts_type)
             result_data_response = ResultDataResponse(**result_dict.model_dump())
 
         except Exception as exc:
             logger.exception(f"Error building vertex: {exc}")
-            log_message = format_exception_message(exc)
-            log_type = type(exc).__name__
+            params = format_exception_message(exc)
             valid = False
+            log_obj = Log(message=params, type="error")
             result_data_response = ResultDataResponse(results={})
-            log_object = Log(message=log_message, type=log_type)
-
+            artifacts = {}
             # If there's an error building the vertex
             # we need to clear the cache
             await chat_service.clear_cache(flow_id_str)
 
-        result_data_response.logs.append(log_object)
+        result_data_response.message = artifacts
+        result_data_response.logs.append(log_obj)
 
         # Log the vertex build
         if not vertex.will_stream:
@@ -204,8 +204,9 @@ async def build_vertex(
                 flow_id=flow_id_str,
                 vertex_id=vertex_id,
                 valid=valid,
-                logs=result_data_response.logs,
+                params=params,
                 data=result_data_response,
+                artifacts=artifacts,
             )
 
         timedelta = time.perf_counter() - start_time
@@ -231,6 +232,7 @@ async def build_vertex(
             next_vertices_ids=next_runnable_vertices,
             top_level_vertices=top_level_vertices,
             valid=valid,
+            params=params,
             id=vertex.id,
             data=result_data_response,
         )

src/backend/base/langflow/api/v1/monitor.py

@@ -117,6 +117,21 @@ async def get_transactions(
         dicts = monitor_service.get_transactions(
             source=source, target=target, status=status, order_by=order_by, flow_id=flow_id
         )
-        return [TransactionModelResponse(**d) for d in dicts]
+        result = []
+        for d in dicts:
+            d = TransactionModelResponse(
+                index=d["index"],
+                timestamp=d["timestamp"],
+                vertex_id=d["vertex_id"],
+                inputs=d["inputs"],
+                outputs=d["outputs"],
+                status=d["status"],
+                error=d["error"],
+                flow_id=d["flow_id"],
+                source=d["vertex_id"],
+                target=d["target_id"],
+            )
+            result.append(d)
+        return result
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))

src/backend/base/langflow/api/v1/schemas.py

@@ -2,6 +2,7 @@ from datetime import datetime, timezone
 from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
+from typing_extensions import TypedDict
 from uuid import UUID
 
 from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serializer
@@ -243,11 +244,11 @@ class VerticesOrderResponse(BaseModel):
     run_id: UUID
     vertices_to_run: List[str]
 
-
 class ResultDataResponse(BaseModel):
     results: Optional[Any] = Field(default_factory=dict)
     logs: List[Log | None] = Field(default_factory=list)
-    messages: List[ChatOutputResponse | None] = Field(default_factory=list)
+    message: Optional[Any] = Field(default_factory=dict)
+    artifacts: Optional[Any] = Field(default_factory=dict)
     timedelta: Optional[float] = None
     duration: Optional[str] = None
     used_frozen_result: Optional[bool] = False
@@ -259,6 +260,8 @@ class VertexBuildResponse(BaseModel):
     next_vertices_ids: Optional[List[str]] = None
     top_level_vertices: Optional[List[str]] = None
     valid: bool
+    params: Optional[Any] = Field(default_factory=dict)
+    """JSON string of the params."""
     data: ResultDataResponse
     """Mapping of vertex ids to result dict containing the param name and result value."""
     timestamp: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc))

src/backend/base/langflow/base/models/model.py

@@ -1,10 +1,13 @@
+import warnings
 from typing import Optional, Union
 
 from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.language_models.llms import LLM
+from langchain_core.load import load
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 
 from langflow.custom import CustomComponent
+from langflow.schema.schema import Record
 
 
 class LCModelComponent(CustomComponent):
@@ -82,7 +85,7 @@ class LCModelComponent(CustomComponent):
         return status_message
 
     def get_chat_result(
-        self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
+        self, runnable: BaseChatModel, stream: bool, input_value: str | Record, system_message: Optional[str] = None
     ):
         messages: list[Union[HumanMessage, SystemMessage]] = []
         if not input_value and not system_message:
@@ -90,7 +93,16 @@ class LCModelComponent(CustomComponent):
         if system_message:
             messages.append(SystemMessage(content=system_message))
         if input_value:
-            messages.append(HumanMessage(content=input_value))
+            if isinstance(input_value, Record):
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore")
+                    if "prompt" in input_value:
+                        prompt = load(input_value.prompt)
+                        runnable = prompt | runnable
+                    else:
+                        messages.append(input_value.to_lc_message())
+            else:
+                messages.append(HumanMessage(content=input_value))
         if stream:
             return runnable.stream(messages)
         else:

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

@@ -1,9 +1,10 @@
+import base64
 from copy import deepcopy
 
-
 from langchain_core.documents import Document
 
 from langflow.schema import Record
+from langflow.services.deps import get_storage_service
 
 
 def record_to_string(record: Record) -> str:
@@ -19,7 +20,7 @@ def record_to_string(record: Record) -> str:
     return record.get_text()
 
 
-def dict_values_to_string(d: dict) -> dict:
+async def dict_values_to_string(d: dict) -> dict:
     """
     Converts the values of a dictionary to strings.
 
@@ -36,16 +37,43 @@ def dict_values_to_string(d: dict) -> dict:
         if isinstance(value, list):
             for i, item in enumerate(value):
                 if isinstance(item, Record):
-                    d_copy[key][i] = record_to_string(item)
+                    d_copy[key][i] = item.to_lc_message()
                 elif isinstance(item, Document):
                     d_copy[key][i] = document_to_string(item)
         elif isinstance(value, Record):
-            d_copy[key] = record_to_string(value)
+            if "files" in value and value.files:
+                files = await get_file_paths(value.files)
+                value.files = files
+            d_copy[key] = value.to_lc_message()
         elif isinstance(value, Document):
             d_copy[key] = document_to_string(value)
     return d_copy
 
 
+async def get_file_paths(files: list[str]):
+    storage_service = get_storage_service()
+    file_paths = []
+    for file in files:
+        flow_id, file_name = file.split("/")
+        file_paths.append(storage_service.build_full_path(flow_id=flow_id, file_name=file_name))
+    return file_paths
+
+
+async def get_files(
+    file_paths: str,
+    convert_to_base64: bool = False,
+):
+    storage_service = get_storage_service()
+    file_objects = []
+    for file_path in file_paths:
+        flow_id, file_name = file_path.split("/")
+        file_object = await storage_service.get_file(flow_id=flow_id, file_name=file_name)
+        if convert_to_base64:
+            file_object = base64.b64encode(file_object).decode("utf-8")
+        file_objects.append(file_object)
+    return file_objects
+
+
 def document_to_string(document: Document) -> str:
     """
     Convert a document to a string.

src/backend/base/langflow/components/inputs/Prompt.py

@@ -1,7 +1,9 @@
-from langchain_core.prompts import PromptTemplate
+from langchain_core.prompts import ChatPromptTemplate
 
+from langflow.base.prompts.utils import dict_values_to_string
 from langflow.custom import CustomComponent
 from langflow.field_typing import Prompt, TemplateField, Text
+from langflow.schema.schema import Record
 
 
 class PromptComponent(CustomComponent):
@@ -15,19 +17,14 @@ class PromptComponent(CustomComponent):
             "code": TemplateField(advanced=True),
         }
 
-    def build(
+    async def build(
         self,
         template: Prompt,
         **kwargs,
-    ) -> Text:
-        from langflow.base.prompts.utils import dict_values_to_string
-
-        prompt_template = PromptTemplate.from_template(Text(template))
-        kwargs = dict_values_to_string(kwargs)
-        kwargs = {k: "\n".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}
-        try:
-            formated_prompt = prompt_template.format(**kwargs)
-        except Exception as exc:
-            raise ValueError(f"Error formatting prompt: {exc}") from exc
-        self.status = f'Prompt:\n"{formated_prompt}"'
-        return formated_prompt
+    ) -> Record:
+        prompt_template = ChatPromptTemplate.from_template(Text(template))
+        kwargs = await dict_values_to_string(kwargs)
+        messages = list(kwargs.values())
+        prompt = prompt_template + messages
+        self.status = f'Prompt:\n"{template}"'
+        return Record(data={"prompt": prompt.to_json()})

src/backend/base/langflow/components/models/AmazonBedrockModel.py

@@ -58,7 +58,7 @@ class AmazonBedrockComponent(LCModelComponent):
                 "advanced": True,
             },
             "cache": {"display_name": "Cache"},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "system_message": {
                 "display_name": "System Message",
                 "info": "System message to pass to the model.",

src/backend/base/langflow/components/models/AnthropicModel.py

@@ -63,7 +63,7 @@ class AnthropicLLM(LCModelComponent):
                 "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "advanced": True,

src/backend/base/langflow/components/models/AzureOpenAIModel.py

@@ -78,7 +78,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
                 "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/BaiduQianfanChatModel.py

@@ -81,7 +81,7 @@ class QianfanChatEndpointComponent(LCModelComponent):
                 "info": "Endpoint of the Qianfan LLM, required if custom model used.",
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/ChatLiteLLMModel.py

@@ -111,7 +111,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
                 "required": False,
                 "default": False,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/CohereModel.py

@@ -1,10 +1,11 @@
 from typing import Optional
 
+from langchain_cohere import ChatCohere
 from pydantic.v1 import SecretStr
-from langflow.field_typing import Text
+
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langchain_cohere import ChatCohere
+from langflow.field_typing import Text
 
 
 class CohereComponent(LCModelComponent):
@@ -42,7 +43,7 @@ class CohereComponent(LCModelComponent):
                 "type": "float",
                 "show": True,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,
@@ -69,3 +70,4 @@ class CohereComponent(LCModelComponent):
             temperature=temperature,
         )
         return self.get_chat_result(output, stream, input_value, system_message)
+        return self.get_chat_result(output, stream, input_value, system_message)

src/backend/base/langflow/components/models/HuggingFaceModel.py

@@ -2,9 +2,10 @@ from typing import Optional
 
 from langchain_community.chat_models.huggingface import ChatHuggingFace
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
-from langflow.field_typing import Text
+
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Text
 
 
 class HuggingFaceEndpointsComponent(LCModelComponent):
@@ -36,7 +37,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
                 "advanced": True,
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,
@@ -72,3 +73,4 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
             raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
         output = ChatHuggingFace(llm=llm)
         return self.get_chat_result(output, stream, input_value, system_message)
+        return self.get_chat_result(output, stream, input_value, system_message)

src/backend/base/langflow/components/models/MistralModel.py

@@ -27,7 +27,7 @@ class MistralAIModelComponent(LCModelComponent):
 
     def build_config(self):
         return {
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "max_tokens": {
                 "display_name": "Max Tokens",
                 "advanced": True,

src/backend/base/langflow/components/models/OllamaModel.py

@@ -194,7 +194,7 @@ class ChatOllamaComponent(LCModelComponent):
                 "info": "Template to use for generating text.",
                 "advanced": True,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/OpenAIModel.py

@@ -28,7 +28,7 @@ class OpenAIModelComponent(LCModelComponent):
 
     def build_config(self):
         return {
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "max_tokens": {
                 "display_name": "Max Tokens",
                 "advanced": True,

src/backend/base/langflow/components/models/VertexAiModel.py

@@ -1,6 +1,5 @@
 from typing import Optional
 
-
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Text
@@ -74,7 +73,7 @@ class ChatVertexAIComponent(LCModelComponent):
                 "value": False,
                 "advanced": True,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

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

@@ -738,7 +738,9 @@ class Graph:
                 # Check the cache for the vertex
                 cached_result = await chat_service.get_cache(key=vertex.id)
                 if isinstance(cached_result, CacheMiss):
-                    await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
+                    await vertex.build(
+                        user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
+                    )
                     await chat_service.set_cache(key=vertex.id, data=vertex)
                 else:
                     cached_vertex = cached_result["result"]
@@ -752,7 +754,9 @@ class Graph:
                         vertex.result.used_frozen_result = True
 
             else:
-                await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
+                await vertex.build(
+                    user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
+                )
 
             if vertex.result is not None:
                 params = f"{vertex._built_object_repr()}{params}"
@@ -765,11 +769,13 @@ class Graph:
             next_runnable_vertices, top_level_vertices = await self.get_next_and_top_level_vertices(
                 lock, set_cache_coro, vertex
             )
-            log_transaction(vertex, status="success")
+            flow_id = self.flow_id
+            log_transaction(flow_id, vertex, status="success")
             return next_runnable_vertices, top_level_vertices, result_dict, params, valid, artifacts, vertex
         except Exception as exc:
             logger.exception(f"Error building vertex: {exc}")
-            log_transaction(vertex, status="failure", error=str(exc))
+            flow_id = self.flow_id
+            log_transaction(flow_id, vertex, status="failure", error=str(exc))
             raise exc
 
     async def get_next_and_top_level_vertices(

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

@@ -31,10 +31,10 @@ class ResultData(BaseModel):
         if not values.get("logs") and values.get("artifacts"):
             # Build the log from the artifacts
             message = values["artifacts"]
-            if "stream_url" in message:
+            if "stream_url" in message and "type" in message:
                 stream_url = StreamURL(location=message["stream_url"])
                 values["logs"] = [Log(message=stream_url, type=message["type"])]
-            else:
+            elif "type" in message:
                 values["logs"] = [Log(message=message, type=message["type"])]
         return values
 

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

@@ -2,6 +2,7 @@ from enum import Enum
 from typing import Any, Generator, Union
 
 from langchain_core.documents import Document
+from langflow.schema.schema import Record
 from pydantic import BaseModel
 
 from langflow.interface.utils import extract_input_variables_from_prompt

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

@@ -10,11 +10,11 @@ from loguru import logger
 
 from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData
 from langflow.graph.utils import ArtifactType, UnbuiltObject, UnbuiltResult
-from langflow.graph.vertex.utils import log_transaction
 from langflow.interface.initialize import loading
 from langflow.interface.listing import lazy_load_dict
 from langflow.schema.schema import INPUT_FIELD_NAME
 from langflow.services.deps import get_storage_service
+from langflow.services.monitor.utils import log_transaction
 from langflow.utils.constants import DIRECT_TYPES
 from langflow.utils.schemas import ChatOutputResponse
 from langflow.utils.util import sync_to_async, unescape_string
@@ -529,12 +529,13 @@ class Vertex:
         Returns:
             The built result if use_result is True, else the built object.
         """
+        flow_id = self.graph.flow_id
         if not self._built:
-            log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error")
+            log_transaction(flow_id, vertex=self, target=requester, status="error")
             raise ValueError(f"Component {self.display_name} has not been built yet")
 
         result = self._built_result if self.use_result else self._built_object
-        log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="success")
+        log_transaction(flow_id, vertex=self, target=requester, status="success")
         return result
 
     async def _build_vertex_and_update_params(self, key, vertex: "Vertex"):
@@ -628,9 +629,8 @@ class Vertex:
                 self._built_object, self.artifacts = result
             elif len(result) == 3:
                 self._custom_component, self._built_object, self.artifacts = result
-                self.artifacts_raw = self.artifacts.get("raw")
-                self.artifacts_type = self.artifacts.get("type") or ArtifactType.UNKNOWN.value
-
+                self.artifacts_raw = self.artifacts.get("raw", None)
+                self.artifacts_type = self.artifacts.get("type", None) or ArtifactType.UNKNOWN.value
         else:
             self._built_object = result
 

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

@@ -116,7 +116,7 @@ class InterfaceVertex(Vertex):
                 sender_name=sender_name,
                 stream_url=stream_url,
                 files=files,
-                type=artifact_type.value,
+                type=artifact_type,
             )
 
             self.will_stream = stream_url is not None
@@ -213,9 +213,9 @@ class InterfaceVertex(Vertex):
             flow_id=self.graph.flow_id,
             vertex_id=self.id,
             valid=True,
-            logs=self._built_object_repr(),
+            params=self._built_object_repr(),
             data=self.result,
-            messages=self.artifacts,
+            artifacts=self.artifacts,
         )
 
         self._validate_built_object()

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

@@ -1,9 +1,5 @@
 from typing import TYPE_CHECKING
 
-from loguru import logger
-
-from langflow.services.deps import get_monitor_service
-
 if TYPE_CHECKING:
     from langflow.graph.vertex.base import Vertex
 
@@ -21,34 +17,3 @@ def build_clean_params(target: "Vertex") -> dict:
         if isinstance(value, list):
             params[key] = [item for item in value if isinstance(item, (str, int, bool, float, list, dict))]
     return params
-
-
-def log_transaction(source: "Vertex", target: "Vertex", flow_id, status, error=None):
-    """
-    Logs a transaction between two vertices.
-
-    Args:
-        source (Vertex): The source vertex of the transaction.
-        target (Vertex): The target vertex of the transaction.
-        status: The status of the transaction.
-        error (Optional): Any error associated with the transaction.
-
-    Raises:
-        Exception: If there is an error while logging the transaction.
-
-    """
-    try:
-        monitor_service = get_monitor_service()
-        clean_params = build_clean_params(target)
-        data = {
-            "source": source.vertex_type,
-            "target": target.vertex_type,
-            "target_args": clean_params,
-            "timestamp": monitor_service.get_timestamp(),
-            "status": status,
-            "error": error,
-            "flow_id": flow_id,
-        }
-        monitor_service.add_row(table_name="transactions", data=data)
-    except Exception as e:
-        logger.error(f"Error logging transaction: {e}")

src/backend/base/langflow/helpers/flow.py

@@ -251,7 +251,7 @@ def get_flow_by_id_or_endpoint_name(
         flow = db.get(Flow, flow_id)
     except ValueError:
         endpoint_name = flow_id_or_name
-        stmt = select(Flow).where(Flow.name == endpoint_name)
+        stmt = select(Flow).where(Flow.endpoint_name == endpoint_name)
         if user_id:
             stmt = stmt.where(Flow.user_id == user_id)
         flow = db.exec(stmt).first()

src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json

@@ -20,7 +20,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                "value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Record:\n        prompt_template = ChatPromptTemplate.from_template(Text(template))\n        kwargs = await dict_values_to_string(kwargs)\n        messages = list(kwargs.values())\n        prompt = prompt_template + messages\n        self.status = f'Prompt:\\n\"{template}\"'\n        return Record(data={\"prompt\": prompt.to_json()})\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -140,7 +140,7 @@
                 "info": "",
                 "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "input_types": ["Text", "Record"]
               },
               "code": {
                 "type": "code",
@@ -149,7 +149,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json

@@ -20,7 +20,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                "value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Record:\n        prompt_template = ChatPromptTemplate.from_template(Text(template))\n        kwargs = await dict_values_to_string(kwargs)\n        messages = list(kwargs.values())\n        prompt = prompt_template + messages\n        self.status = f'Prompt:\\n\"{template}\"'\n        return Record(data={\"prompt\": prompt.to_json()})\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -444,7 +444,7 @@
                 "info": "",
                 "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "input_types": ["Text", "Record"]
               },
               "code": {
                 "type": "code",
@@ -453,7 +453,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json

@@ -20,7 +20,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                "value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Record:\n        prompt_template = ChatPromptTemplate.from_template(Text(template))\n        kwargs = await dict_values_to_string(kwargs)\n        messages = list(kwargs.values())\n        prompt = prompt_template + messages\n        self.status = f'Prompt:\\n\"{template}\"'\n        return Record(data={\"prompt\": prompt.to_json()})\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -589,7 +589,7 @@
                 "info": "",
                 "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "input_types": ["Text", "Record"]
               },
               "code": {
                 "type": "code",
@@ -598,7 +598,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json

@@ -524,7 +524,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                "value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Record:\n        prompt_template = ChatPromptTemplate.from_template(Text(template))\n        kwargs = await dict_values_to_string(kwargs)\n        messages = list(kwargs.values())\n        prompt = prompt_template + messages\n        self.status = f'Prompt:\\n\"{template}\"'\n        return Record(data={\"prompt\": prompt.to_json()})\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -670,7 +670,7 @@
                 "info": "",
                 "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "input_types": ["Text", "Record"]
               },
               "code": {
                 "type": "code",
@@ -679,7 +679,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,

src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json

@@ -20,7 +20,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                "value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Record:\n        prompt_template = ChatPromptTemplate.from_template(Text(template))\n        kwargs = await dict_values_to_string(kwargs)\n        messages = list(kwargs.values())\n        prompt = prompt_template + messages\n        self.status = f'Prompt:\\n\"{template}\"'\n        return Record(data={\"prompt\": prompt.to_json()})\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -130,7 +130,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                "value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Record:\n        prompt_template = ChatPromptTemplate.from_template(Text(template))\n        kwargs = await dict_values_to_string(kwargs)\n        messages = list(kwargs.values())\n        prompt = prompt_template + messages\n        self.status = f'Prompt:\\n\"{template}\"'\n        return Record(data={\"prompt\": prompt.to_json()})\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -789,7 +789,7 @@
                 "info": "",
                 "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "input_types": ["Text", "Record"]
               },
               "code": {
                 "type": "code",
@@ -798,7 +798,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -1146,7 +1146,7 @@
                 "info": "",
                 "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "input_types": ["Text", "Record"]
               },
               "code": {
                 "type": "code",
@@ -1155,7 +1155,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,

src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json

@@ -784,7 +784,7 @@
                 "info": "",
                 "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "input_types": ["Text", "Record"]
               },
               "code": {
                 "type": "code",
@@ -793,7 +793,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-4o\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -1034,7 +1034,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
+                "value": "from langchain_core.prompts import ChatPromptTemplate\n\nfrom langflow.base.prompts.utils import dict_values_to_string\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.schema.schema import Record\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Record:\n        prompt_template = ChatPromptTemplate.from_template(Text(template))\n        kwargs = await dict_values_to_string(kwargs)\n        messages = list(kwargs.values())\n        prompt = prompt_template + messages\n        self.status = f'Prompt:\\n\"{template}\"'\n        return Record(data={\"prompt\": prompt.to_json()})\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,

src/backend/base/langflow/interface/initialize/loading.py

@@ -125,7 +125,6 @@ async def instantiate_custom_component(params, user_id, vertex, fallback_to_env_
         custom_repr = build_result
     if not isinstance(custom_repr, str):
         custom_repr = str(custom_repr)
-
     raw = custom_component.repr_value
     if hasattr(raw, "data"):
         raw = raw.data

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

@@ -1,11 +1,12 @@
 import copy
 import json
 from typing import Literal, Optional, cast
+from typing_extensions import TypedDict
 
 from langchain_core.documents import Document
 from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
-from pydantic import BaseModel, model_validator
-from typing_extensions import TypedDict
+from langchain_core.prompts.image import ImagePromptTemplate
+from pydantic import BaseModel, model_serializer, model_validator
 
 
 class Record(BaseModel):
@@ -30,6 +31,11 @@ class Record(BaseModel):
                 values["data"][key] = values[key]
         return values
 
+    @model_serializer(mode="plain", when_used="json")
+    def serialize_model(self):
+        data = {k: v.to_json() if hasattr(v, "to_json") else v for k, v in self.data.items()}
+        return data
+
     def get_text(self):
         """
         Retrieves the text value from the data dictionary.
@@ -103,7 +109,9 @@ class Record(BaseModel):
         text = self.data.pop(self.text_key, self.default_value)
         return Document(page_content=text, metadata=self.data)
 
-    def to_lc_message(self) -> BaseMessage:
+    def to_lc_message(
+        self,
+    ) -> BaseMessage:
         """
         Converts the Record to a BaseMessage.
 
@@ -119,8 +127,22 @@ class Record(BaseModel):
             raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}")
         sender = self.data.get("sender", "Machine")
         text = self.data.get("text", "")
+        files = self.data.get("files", [])
         if sender == "User":
-            return HumanMessage(content=text)
+            if files:
+                contents = [{"type": "text", "text": text}]
+                for file_path in files:
+                    image_template = ImagePromptTemplate()
+                    image_prompt_value = image_template.invoke(input={"path": file_path})
+                    contents.append({"type": "image_url", "image_url": image_prompt_value.image_url})
+                human_message = HumanMessage(content=contents)
+            else:
+                human_message = HumanMessage(
+                    content=[{"type": "text", "text": text}],
+                )
+
+            return human_message
+
         return AIMessage(content=text)
 
     def __getattr__(self, key):
@@ -170,8 +192,14 @@ class Record(BaseModel):
 
     def __str__(self) -> str:
         # return a JSON string representation of the Record atributes
+        try:
+            data = {k: v.to_json() if hasattr(v, "to_json") else v for k, v in self.data.items()}
+            return json.dumps(data, indent=4)
+        except Exception:
+            return str(self.data)
 
-        return json.dumps(self.data)
+    def __contains__(self, key):
+        return key in self.data
 
 
 INPUT_FIELD_NAME = "input_value"

src/backend/base/langflow/services/cache/utils.py

@@ -23,6 +23,9 @@ class CacheMiss:
     def __repr__(self):
         return "<CACHE_MISS>"
 
+    def __bool__(self):
+        return False
+
 
 def create_cache_folder(func):
     def wrapper(*args, **kwargs):

src/backend/base/langflow/services/monitor/schema.py

@@ -11,25 +11,26 @@ if TYPE_CHECKING:
 class TransactionModel(BaseModel):
     index: Optional[int] = Field(default=None)
     timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp")
-    flow_id: str
-    source: str
-    target: str
-    target_args: dict
+    vertex_id: str
+    target_id: str | None = None
+    inputs: dict
+    outputs: Optional[dict] = None
     status: str
     error: Optional[str] = None
+    flow_id: Optional[str] = Field(default=None, alias="flow_id")
 
     class Config:
         from_attributes = True
         populate_by_name = True
 
     # validate target_args in case it is a JSON
-    @field_validator("target_args", mode="before")
+    @field_validator("outputs", "inputs", mode="before")
     def validate_target_args(cls, v):
         if isinstance(v, str):
             return json.loads(v)
         return v
 
-    @field_serializer("target_args")
+    @field_serializer("outputs", "inputs")
     def serialize_target_args(v):
         if isinstance(v, dict):
             return json.dumps(v)
@@ -39,19 +40,21 @@ class TransactionModel(BaseModel):
 class TransactionModelResponse(BaseModel):
     index: Optional[int] = Field(default=None)
     timestamp: Optional[datetime] = Field(default_factory=datetime.now, alias="timestamp")
-    flow_id: str
-    source: str
-    target: str
-    target_args: dict
+    vertex_id: str
+    inputs: dict
+    outputs: Optional[dict] = None
     status: str
     error: Optional[str] = None
+    flow_id: Optional[str] = Field(default=None, alias="flow_id")
+    source: Optional[str] = None
+    target: Optional[str] = None
 
     class Config:
         from_attributes = True
         populate_by_name = True
 
     # validate target_args in case it is a JSON
-    @field_validator("target_args", mode="before")
+    @field_validator("outputs", "inputs", mode="before")
     def validate_target_args(cls, v):
         if isinstance(v, str):
             return json.loads(v)
@@ -129,15 +132,16 @@ class VertexBuildModel(BaseModel):
     id: Optional[str] = Field(default=None, alias="id")
     flow_id: str
     valid: bool
-    logs: Any
+    params: Any
     data: dict
+    artifacts: dict
     timestamp: datetime = Field(default_factory=datetime.now)
 
     class Config:
         from_attributes = True
         populate_by_name = True
 
-    @field_serializer("data")
+    @field_serializer("data", "artifacts")
     def serialize_dict(v):
         if isinstance(v, dict):
             # check if the value of each key is a BaseModel or a list of BaseModels
@@ -151,8 +155,8 @@ class VertexBuildModel(BaseModel):
             return v.model_dump_json()
         return v
 
-    @field_validator("logs", mode="before")
-    def validate_logs(cls, v):
+    @field_validator("params", mode="before")
+    def validate_params(cls, v):
         if isinstance(v, str):
             try:
                 return json.loads(v)
@@ -160,7 +164,7 @@ class VertexBuildModel(BaseModel):
                 return v
         return v
 
-    @field_serializer("logs")
+    @field_serializer("params")
     def serialize_params(v):
         if isinstance(v, list) and all(isinstance(i, BaseModel) for i in v):
             return json.dumps([i.model_dump() for i in v])
@@ -172,11 +176,17 @@ class VertexBuildModel(BaseModel):
             return json.loads(v)
         return v
 
+    @field_validator("artifacts", mode="before")
+    def validate_artifacts(cls, v):
+        if isinstance(v, str):
+            return json.loads(v)
+        elif isinstance(v, BaseModel):
+            return v.model_dump()
+        return v
+
 
 class VertexBuildResponseModel(VertexBuildModel):
-    messages: list[MessageModel] = []
-
-    @field_serializer("data")
+    @field_serializer("data", "artifacts")
     def serialize_dict(v):
         return v
 

src/backend/base/langflow/services/monitor/service.py

@@ -168,7 +168,9 @@ class MonitorService(Service):
         order_by: Optional[str] = "timestamp",
         flow_id: Optional[str] = None,
     ):
-        query = "SELECT index,flow_id, source, target, target_args, status, error, timestamp FROM transactions"
+        query = (
+            "SELECT index,flow_id, status, error, timestamp, vertex_id, inputs, outputs, target_id FROM transactions"
+        )
         conditions = []
         if source:
             conditions.append(f"source = '{source}'")
@@ -183,7 +185,7 @@ class MonitorService(Service):
             query += " WHERE " + " AND ".join(conditions)
 
         if order_by:
-            query += f" ORDER BY {order_by}"
+            query += f" ORDER BY {order_by} DESC"
         with duckdb.connect(str(self.db_path)) as conn:
             df = conn.execute(query).df()
 

src/backend/base/langflow/services/monitor/utils.py

@@ -119,21 +119,16 @@ async def log_message(
     sender_name: str,
     message: str,
     session_id: str,
-    artifacts: Optional[dict] = None,
+    files: Optional[list] = None,
     flow_id: Optional[str] = None,
 ):
     try:
-        from langflow.graph.vertex.base import Vertex
-
-        if isinstance(session_id, Vertex):
-            session_id = await session_id.build()  # type: ignore
-
         monitor_service = get_monitor_service()
         row = {
             "sender": sender,
             "sender_name": sender_name,
             "message": message,
-            "artifacts": artifacts or {},
+            "files": files or [],
             "session_id": session_id,
             "timestamp": monitor_service.get_timestamp(),
             "flow_id": flow_id,
@@ -147,9 +142,9 @@ async def log_vertex_build(
     flow_id: str,
     vertex_id: str,
     valid: bool,
-    logs: Any,
+    params: Any,
     data: "ResultDataResponse",
-    messages: Optional[dict] = None,
+    artifacts: Optional[dict] = None,
 ):
     try:
         monitor_service = get_monitor_service()
@@ -158,9 +153,9 @@ async def log_vertex_build(
             "flow_id": flow_id,
             "id": vertex_id,
             "valid": valid,
-            "logs": logs,
+            "params": params,
             "data": data.model_dump(),
-            "messages": messages or {},
+            "artifacts": artifacts or {},
             "timestamp": monitor_service.get_timestamp(),
         }
         monitor_service.add_row(table_name="vertex_builds", data=row)
@@ -183,17 +178,19 @@ def build_clean_params(target: "Vertex") -> dict:
     return params
 
 
-def log_transaction(vertex: "Vertex", status, error=None):
+def log_transaction(flow_id, vertex: "Vertex", status, target: Optional["Vertex"] = None, error=None):
     try:
         monitor_service = get_monitor_service()
         clean_params = build_clean_params(vertex)
         data = {
-            "vertex_id": vertex.id,
+            "vertex_id": str(vertex.id),
+            "target_id": str(target.id) if target else None,
             "inputs": clean_params,
-            "output": str(vertex.result),
+            "outputs": vertex.result.model_dump_json() if vertex.result else None,
             "timestamp": monitor_service.get_timestamp(),
             "status": status,
             "error": error,
+            "flow_id": flow_id,
         }
         monitor_service.add_row(table_name="transactions", data=data)
     except Exception as e:

src/backend/base/langflow/services/socket/utils.py

@@ -90,9 +90,9 @@ async def build_vertex(
             flow_id=flow_id,
             vertex_id=vertex_id,
             valid=valid,
-            logs=params,
+            params=params,
             data=result_dict,
-            messages=artifacts,
+            artifacts=artifacts,
         )
 
         # Emit the vertex build response

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

@@ -159,7 +159,10 @@ def create_class(code, class_name):
 
     # Replace from langflow import CustomComponent with from langflow.custom import CustomComponent
     code = code.replace("from langflow import CustomComponent", "from langflow.custom import CustomComponent")
-
+    code = code.replace(
+        "from langflow.interface.custom.custom_component import CustomComponent",
+        "from langflow.custom import CustomComponent",
+    )
     module = ast.parse(code)
     exec_globals = prepare_global_scope(code, module)
 

src/backend/base/poetry.lock

@@ -1159,13 +1159,13 @@ files = [
 
 [[package]]
 name = "langchain"
-version = "0.2.2"
+version = "0.2.3"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain-0.2.2-py3-none-any.whl", hash = "sha256:58ca0c47bcdd156da66f50a0a4fcedc49bf6950827f4a6b06c8c4842d55805f3"},
-    {file = "langchain-0.2.2.tar.gz", hash = "sha256:9d61e50e9cdc2bea659bc5e6c03650ba048fda63a307490ae368e539f61a0d3a"},
+    {file = "langchain-0.2.3-py3-none-any.whl", hash = "sha256:5dc33cd9c8008693d328b7cb698df69073acecc89ad9c2a95f243b3314f8d834"},
+    {file = "langchain-0.2.3.tar.gz", hash = "sha256:81962cc72cce6515f7bd71e01542727870789bf8b666c6913d85559080c1a201"},
 ]
 
 [package.dependencies]
@@ -1181,29 +1181,15 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<9.0.0"
 
-[package.extras]
-azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (<2)"]
-clarifai = ["clarifai (>=9.1.0)"]
-cli = ["typer (>=0.9.0,<0.10.0)"]
-cohere = ["cohere (>=4,<6)"]
-docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
-embeddings = ["sentence-transformers (>=2,<3)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<6)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.1,<0.2)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
-javascript = ["esprima (>=4.0.1,<5.0.0)"]
-llms = ["clarifai (>=9.1.0)", "cohere (>=4,<6)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
-openai = ["openai (<2)", "tiktoken (>=0.7,<1.0)"]
-qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
-text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
-
 [[package]]
 name = "langchain-community"
-version = "0.2.3"
+version = "0.2.4"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.2.3-py3-none-any.whl", hash = "sha256:aa895545be2f3f4aa2fea36f6da2e3b4ec50ce61ec986e8f146901a1e9138138"},
-    {file = "langchain_community-0.2.3.tar.gz", hash = "sha256:a3c35af215e47b700e7cb4e548fa8b45c6d46d52b5a5a65af2577c5a0104fc9f"},
+    {file = "langchain_community-0.2.4-py3-none-any.whl", hash = "sha256:8582e9800f4837660dc297cccd2ee1ddc1d8c440d0fe8b64edb07620f0373b0e"},
+    {file = "langchain_community-0.2.4.tar.gz", hash = "sha256:2bb6a1a36b8500a564d25d76469c02457b1a7c3afea6d4a609a47c06b993e3e4"},
 ]
 
 [package.dependencies]
@@ -1218,19 +1204,15 @@ requests = ">=2,<3"
 SQLAlchemy = ">=1.4,<3"
 tenacity = ">=8.1.0,<9.0.0"
 
-[package.extras]
-cli = ["typer (>=0.9.0,<0.10.0)"]
-extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "azure-identity (>=1.15.0,<2.0.0)", "azure-search-documents (==11.4.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.6,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cloudpathlib (>=0.18,<0.19)", "cloudpickle (>=2.0.0)", "cohere (>=4,<5)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "friendli-client (>=1.2.4,<2.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "httpx-sse (>=0.4.0,<0.5.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.3,<6.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "nvidia-riva-client (>=2.14.0,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "oracledb (>=2.2.0,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "premai (>=0.3.25,<0.4.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pyjwt (>=2.8.0,<3.0.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "simsimd (>=4.3.1,<5.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "tidb-vector (>=0.0.3,<1.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "tree-sitter (>=0.20.2,<0.21.0)", "tree-sitter-languages (>=1.8.0,<2.0.0)", "upstash-redis (>=0.15.0,<0.16.0)", "vdms (>=0.0.20,<0.0.21)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
-
 [[package]]
 name = "langchain-core"
-version = "0.2.4"
+version = "0.2.5"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.4-py3-none-any.whl", hash = "sha256:5212f7ec78a525e88a178ed3aefe2fd7134b03fb92573dfbab9914f1d92d6ec5"},
-    {file = "langchain_core-0.2.4.tar.gz", hash = "sha256:82bdcc546eb0341cefcf1f4ecb3e49836fff003903afddda2d1312bb8491ef81"},
+    {file = "langchain_core-0.2.5-py3-none-any.whl", hash = "sha256:abe5138f22acff23a079ec538be5268bbf97cf023d51987a0dd474d2a16cae3e"},
+    {file = "langchain_core-0.2.5.tar.gz", hash = "sha256:4a5c2f56b22396a63ef4790043660e393adbfa6832b978f023ca996a04b8e752"},
 ]
 
 [package.dependencies]
@@ -1241,9 +1223,6 @@ pydantic = ">=1,<3"
 PyYAML = ">=5.3"
 tenacity = ">=8.1.0,<9.0.0"
 
-[package.extras]
-extended-testing = ["jinja2 (>=3,<4)"]
-
 [[package]]
 name = "langchain-experimental"
 version = "0.0.60"

src/backend/base/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow-base"
-version = "0.0.59"
+version = "0.0.60"
 description = "A Python package with a built-in web application"
 authors = ["Langflow <contact@langflow.org>"]
 maintainers = [