Merge cz/mergeAll into shortcuts_settings

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

@@ -204,16 +204,18 @@ 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
 
@@ -317,3 +319,4 @@ 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

@@ -22,6 +22,7 @@ from langflow.api.v1.schemas import (
     VertexBuildResponse,
     VerticesOrderResponse,
 )
+from langflow.schema.schema import Log
 from langflow.services.auth.utils import get_current_active_user
 from langflow.services.chat.service import ChatService
 from langflow.services.deps import get_chat_service, get_session, get_session_service
@@ -123,6 +124,7 @@ async def build_vertex(
     vertex_id: str,
     background_tasks: BackgroundTasks,
     inputs: Annotated[Optional[InputValueRequest], Body(embed=True)] = None,
+    files: Optional[list[str]] = None,
     chat_service: "ChatService" = Depends(get_chat_service),
     current_user=Depends(get_current_active_user),
 ):
@@ -159,6 +161,7 @@ async def build_vertex(
         else:
             graph = cache.get("result")
         vertex = graph.get_vertex(vertex_id)
+        log_object = None
         try:
             lock = chat_service._cache_locks[flow_id_str]
             (
@@ -175,19 +178,25 @@ async def build_vertex(
                 vertex_id=vertex_id,
                 user_id=current_user.id,
                 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}")
             params = format_exception_message(exc)
             valid = False
+            log_obj = Log(message=params, type="error")
             result_data_response = ResultDataResponse(results={})
             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.message = artifacts
+        result_data_response.logs.append(log_obj)
+
         # Log the vertex build
         if not vertex.will_stream:
             background_tasks.add_task(

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

@@ -1,5 +1,7 @@
 from typing import List
 
+from langflow.helpers.flow import generate_unique_flow_name
+from langflow.helpers.folders import generate_unique_folder_name
 import orjson
 from fastapi import APIRouter, Depends, File, HTTPException, Response, UploadFile, status
 from sqlalchemy import or_, update
@@ -203,16 +205,9 @@ async def upload_file(
     if not data:
         raise HTTPException(status_code=400, detail="No flows found in the file")
 
-    folder_results = session.exec(
-        select(Folder).where(
-            Folder.name == data["folder_name"],
-            Folder.user_id == current_user.id,
-        )
-    )
-    existing_folder_names = [folder.name for folder in folder_results]
+    folder_name = generate_unique_folder_name(data["folder_name"], current_user.id, session)
 
-    if existing_folder_names:
-        data["folder_name"] = f"{data['folder_name']} ({len(existing_folder_names) + 1})"
+    data["folder_name"] = folder_name
 
     folder = FolderCreate(name=data["folder_name"], description=data["folder_description"])
 
@@ -232,6 +227,8 @@ async def upload_file(
         raise HTTPException(status_code=400, detail="No flows found in the data")
     # Now we set the user_id for all flows
     for flow in flow_list.flows:
+        flow_name = generate_unique_flow_name(flow.name, current_user.id, session)
+        flow.name = flow_name
         flow.user_id = current_user.id
         flow.folder_id = new_folder.id
 

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

@@ -71,9 +71,7 @@ async def login_to_get_access_token(
 
 @router.get("/auto_login")
 async def auto_login(
-    response: Response,
-    db: Session = Depends(get_session),
-    settings_service=Depends(get_settings_service)
+    response: Response, db: Session = Depends(get_session), settings_service=Depends(get_settings_service)
 ):
     auth_settings = settings_service.auth_settings
     if settings_service.auth_settings.AUTO_LOGIN:

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

@@ -1,9 +1,9 @@
 from typing import List, Optional
-
 from fastapi import APIRouter, Depends, HTTPException, Query
 
 from langflow.services.deps import get_monitor_service
 from langflow.services.monitor.schema import (
+    MessageModelRequest,
     MessageModelResponse,
     TransactionModelResponse,
     VertexBuildMapModel,
@@ -66,6 +66,44 @@ async def get_messages(
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@router.delete("/messages", status_code=204)
+async def delete_messages(
+    message_ids: List[int],
+    monitor_service: MonitorService = Depends(get_monitor_service),
+):
+    try:
+        monitor_service.delete_messages(message_ids=message_ids)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.post("/messages/{message_id}", response_model=MessageModelResponse)
+async def update_message(
+    message_id: str,
+    message: MessageModelRequest,
+    monitor_service: MonitorService = Depends(get_monitor_service),
+):
+    try:
+        message_dict = message.model_dump(exclude_none=True)
+        message_dict.pop("index", None)
+        monitor_service.update_message(message_id=message_id, **message_dict)
+        return MessageModelResponse(index=message_id, **message_dict)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.delete("/messages/session/{session_id}", status_code=204)
+async def delete_messages_session(
+    session_id: str,
+    monitor_service: MonitorService = Depends(get_monitor_service),
+):
+    try:
+        monitor_service.delete_messages_session(session_id=session_id)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @router.get("/transactions", response_model=List[TransactionModelResponse])
 async def get_transactions(
     source: Optional[str] = Query(None),

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
@@ -9,11 +10,12 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serial
 from langflow.graph.schema import RunOutputs
 from langflow.schema import dotdict
 from langflow.schema.graph import Tweaks
-from langflow.schema.schema import InputType, OutputType
+from langflow.schema.schema import InputType, Log, OutputType
 from langflow.services.database.models.api_key.model import ApiKeyRead
 from langflow.services.database.models.base import orjson_dumps
 from langflow.services.database.models.flow import FlowCreate, FlowRead
 from langflow.services.database.models.user import UserRead
+from langflow.utils.schemas import ChatOutputResponse
 
 
 class BuildStatus(Enum):
@@ -242,9 +244,10 @@ 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)
+    message: Optional[Any] = Field(default_factory=dict)
     artifacts: Optional[Any] = Field(default_factory=dict)
     timedelta: Optional[float] = None
     duration: Optional[str] = None

src/backend/base/langflow/base/curl/parse.py

@@ -15,10 +15,25 @@ import shlex
 from collections import OrderedDict, namedtuple
 from http.cookies import SimpleCookie
 
-from uncurl.api import parser  # type: ignore
-
-parser.add_argument("-x", "--proxy", default={})
-parser.add_argument("-U", "--proxy-user", default="")
+ParsedArgs = namedtuple(
+    "ParsedContext",
+    [
+        "command",
+        "url",
+        "data",
+        "data_binary",
+        "method",
+        "headers",
+        "compressed",
+        "insecure",
+        "user",
+        "include",
+        "silent",
+        "proxy",
+        "proxy_user",
+        "cookies",
+    ],
+)
 
 ParsedContext = namedtuple("ParsedContext", ["method", "url", "data", "headers", "cookies", "verify", "auth", "proxy"])
 
@@ -27,24 +42,90 @@ def normalize_newlines(multiline_text):
     return multiline_text.replace(" \\\n", " ")
 
 
+def parse_curl_command(curl_command):
+    tokens = shlex.split(normalize_newlines(curl_command))
+    tokens = [token for token in tokens if token and token != " "]
+    if "curl" not in tokens[0]:
+        raise ValueError("Invalid curl command")
+    args_template = {
+        "command": None,
+        "url": None,
+        "data": None,
+        "data_binary": None,
+        "method": "get",
+        "headers": [],
+        "compressed": False,
+        "insecure": False,
+        "user": (),
+        "include": False,
+        "silent": False,
+        "proxy": None,
+        "proxy_user": None,
+        "cookies": {},
+    }
+    args = args_template.copy()
+    method_on_curl = None
+    i = 0
+    while i < len(tokens):
+        token = tokens[i]
+        if token == "-X":
+            i += 1
+            args["method"] = tokens[i].lower()
+            method_on_curl = tokens[i].lower()
+        elif token in ("-d", "--data"):
+            i += 1
+            args["data"] = tokens[i]
+        elif token in ("-b", "--data-binary", "--data-raw"):
+            i += 1
+            args["data_binary"] = tokens[i]
+        elif token in ("-H", "--header"):
+            i += 1
+            args["headers"].append(tokens[i])
+        elif token == "--compressed":
+            args["compressed"] = True
+        elif token in ("-k", "--insecure"):
+            args["insecure"] = True
+        elif token in ("-u", "--user"):
+            i += 1
+            args["user"] = tuple(tokens[i].split(":"))
+        elif token in ("-I", "--include"):
+            args["include"] = True
+        elif token in ("-s", "--silent"):
+            args["silent"] = True
+        elif token in ("-x", "--proxy"):
+            i += 1
+            args["proxy"] = tokens[i]
+        elif token in ("-U", "--proxy-user"):
+            i += 1
+            args["proxy_user"] = tokens[i]
+        elif not token.startswith("-"):
+            if args["command"] is None:
+                args["command"] = token
+            else:
+                args["url"] = token
+        i += 1
+
+    args["method"] = method_on_curl or args["method"]
+
+    return ParsedArgs(**args)
+
+
 def parse_context(curl_command):
     method = "get"
 
-    tokens = shlex.split(normalize_newlines(curl_command))
-    tokens = [token for token in tokens if token and token != " "]
-    parsed_args = parser.parse_args(tokens)
+    parsed_args: ParsedArgs = parse_curl_command(curl_command)
 
     post_data = parsed_args.data or parsed_args.data_binary
     if post_data:
         method = "post"
 
-    if parsed_args.X:
-        method = parsed_args.X.lower()
+    if parsed_args.method:
+        method = parsed_args.method.lower()
 
     cookie_dict = OrderedDict()
     quoted_headers = OrderedDict()
 
-    for curl_header in parsed_args.header:
+    for curl_header in parsed_args.headers:
         if curl_header.startswith(":"):
             occurrence = [m.start() for m in re.finditer(":", curl_header)]
             header_key, header_value = curl_header[: occurrence[1]], curl_header[occurrence[1] + 1 :]

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

@@ -3,6 +3,7 @@ import xml.etree.ElementTree as ET
 from concurrent import futures
 from pathlib import Path
 from typing import Callable, List, Optional, Text
+import unicodedata
 import chardet
 import yaml
 
@@ -31,6 +32,17 @@ TEXT_FILE_TYPES = [
     "tsx",
 ]
 
+IMG_FILE_TYPES = [
+    "jpg",
+    "jpeg",
+    "png",
+    "bmp",
+]
+
+
+def normalize_text(text):
+    return unicodedata.normalize("NFKD", text)
+
 
 def is_hidden(path: Path) -> bool:
     return path.name.startswith(".")
@@ -92,7 +104,10 @@ def read_text_file(file_path: str) -> str:
     with open(file_path, "rb") as f:
         raw_data = f.read()
         result = chardet.detect(raw_data)
-        encoding = result['encoding']
+        encoding = result["encoding"]
+
+        if encoding in ["Windows-1254", "MacRoman"]:
+            encoding = "utf-8"
 
     with open(file_path, "r", encoding=encoding) as f:
         return f.read()
@@ -121,9 +136,15 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R
             text = read_docx_file(file_path)
         else:
             text = read_text_file(file_path)
+
         # if file is json, yaml, or xml, we can parse it
         if file_path.endswith(".json"):
             text = json.loads(text)
+            if isinstance(text, dict):
+                text = {k: normalize_text(v) if isinstance(v, str) else v for k, v in text.items()}
+            elif isinstance(text, list):
+                text = [normalize_text(item) if isinstance(item, str) else item for item in text]
+
         elif file_path.endswith(".yaml") or file_path.endswith(".yml"):
             text = yaml.safe_load(text)
         elif file_path.endswith(".xml"):

src/backend/base/langflow/base/io/chat.py

@@ -1,5 +1,6 @@
 from typing import Optional, Union
 
+from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
 from langflow.custom import CustomComponent
 from langflow.field_typing import Text
 from langflow.helpers.record import records_to_text
@@ -40,6 +41,13 @@ class ChatComponent(CustomComponent):
                 "info": "In case of Message being a Record, this template will be used to convert it to text.",
                 "advanced": True,
             },
+            "files": {
+                "field_type": "file",
+                "display_name": "Files",
+                "file_types": TEXT_FILE_TYPES + IMG_FILE_TYPES,
+                "info": "Files to be sent with the message.",
+                "advanced": True,
+            },
         }
 
     def store_message(
@@ -65,6 +73,7 @@ class ChatComponent(CustomComponent):
         sender: Optional[str] = "User",
         sender_name: Optional[str] = "User",
         input_value: Optional[Union[str, Record]] = None,
+        files: Optional[list[str]] = None,
         session_id: Optional[str] = None,
         return_record: Optional[bool] = False,
         record_template: str = "Text: {text}\nData: {data}",
@@ -76,6 +85,7 @@ class ChatComponent(CustomComponent):
                 input_value.data["sender"] = sender
                 input_value.data["sender_name"] = sender_name
                 input_value.data["session_id"] = session_id
+                input_value.data["files"] = files
             else:
                 input_value_record = Record(
                     text=input_value,
@@ -83,6 +93,7 @@ class ChatComponent(CustomComponent):
                         "sender": sender,
                         "sender_name": sender_name,
                         "session_id": session_id,
+                        "files": files,
                     },
                 )
         elif isinstance(input_value, Record):
@@ -103,17 +114,21 @@ class ChatComponent(CustomComponent):
         sender: Optional[str] = "User",
         sender_name: Optional[str] = "User",
         input_value: Optional[str] = None,
+        files: Optional[list[str]] = None,
         session_id: Optional[str] = None,
         return_record: Optional[bool] = False,
         record_template: str = "Text: {text}\nData: {data}",
     ) -> Union[Text, Record]:
         input_value_record: Optional[Record] = None
+        if files and not return_record:
+            raise ValueError("Files can only be provided when Return Record is enabled.")
         if return_record:
             if isinstance(input_value, Record):
                 # Update the data of the record
                 input_value.data["sender"] = sender
                 input_value.data["sender_name"] = sender_name
                 input_value.data["session_id"] = session_id
+                input_value.data["files"] = files
             else:
                 input_value_record = Record(
                     text=input_value,
@@ -121,6 +136,7 @@ class ChatComponent(CustomComponent):
                         "sender": sender,
                         "sender_name": sender_name,
                         "session_id": session_id,
+                        "files": files,
                     },
                 )
         elif isinstance(input_value, Record):

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):
@@ -53,19 +56,28 @@ class LCModelComponent(CustomComponent):
                 key in response_metadata["token_usage"] for key in inner_openai_keys
             ):
                 token_usage = response_metadata["token_usage"]
-                completion_tokens = token_usage["completion_tokens"]
-                prompt_tokens = token_usage["prompt_tokens"]
-                total_tokens = token_usage["total_tokens"]
-                finish_reason = response_metadata["finish_reason"]
-                status_message = f"Tokens:\nInput: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}"
+                status_message = {
+                    "tokens": {
+                        "input": token_usage["prompt_tokens"],
+                        "output": token_usage["completion_tokens"],
+                        "total": token_usage["total_tokens"],
+                        "stop_reason": response_metadata["finish_reason"],
+                        "response": content,
+                    }
+                }
+
             elif all(key in response_metadata for key in anthropic_keys) and all(
                 key in response_metadata["usage"] for key in inner_anthropic_keys
             ):
                 usage = response_metadata["usage"]
-                input_tokens = usage["input_tokens"]
-                output_tokens = usage["output_tokens"]
-                stop_reason = response_metadata["stop_reason"]
-                status_message = f"Tokens:\nInput: {input_tokens}\nOutput: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}"
+                status_message = {
+                    "tokens": {
+                        "input": usage["input_tokens"],
+                        "output": usage["output_tokens"],
+                        "stop_reason": response_metadata["stop_reason"],
+                        "response": content,
+                    }
+                }
             else:
                 status_message = f"Response: {content}"
         else:
@@ -73,7 +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:
@@ -81,7 +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/ChatInput.py

@@ -25,6 +25,7 @@ class ChatInput(ChatComponent):
         sender: Optional[str] = "User",
         sender_name: Optional[str] = "User",
         input_value: Optional[str] = None,
+        files: Optional[list[str]] = None,
         session_id: Optional[str] = None,
         return_record: Optional[bool] = False,
     ) -> Union[Text, Record]:
@@ -32,6 +33,7 @@ class ChatInput(ChatComponent):
             sender=sender,
             sender_name=sender_name,
             input_value=input_value,
+            files=files,
             session_id=session_id,
             return_record=return_record,
         )

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/components/outputs/ChatOutput.py

@@ -18,6 +18,7 @@ class ChatOutput(ChatComponent):
         session_id: Optional[str] = None,
         return_record: Optional[bool] = False,
         record_template: Optional[str] = "{text}",
+        files: Optional[list[str]] = None,
     ) -> Union[Text, Record]:
         return super().build_with_record(
             sender=sender,
@@ -26,4 +27,5 @@ class ChatOutput(ChatComponent):
             session_id=session_id,
             return_record=return_record,
             record_template=record_template or "",
+            files=files,
         )

src/backend/base/langflow/components/vectorsearch/__init__.py

@@ -1,27 +0,0 @@
-from .AstraDBSearch import AstraDBSearchComponent
-from .ChromaSearch import ChromaSearchComponent
-from .FAISSSearch import FAISSSearchComponent
-from .MongoDBAtlasVectorSearch import MongoDBAtlasSearchComponent
-from .PineconeSearch import PineconeSearchComponent
-from .QdrantSearch import QdrantSearchComponent
-from .RedisSearch import RedisSearchComponent
-from .SupabaseVectorStoreSearch import SupabaseSearchComponent
-from .VectaraSearch import VectaraSearchComponent
-from .WeaviateSearch import WeaviateSearchVectorStore
-from .pgvectorSearch import PGVectorSearchComponent
-from .Couchbase import CouchbaseSearchComponent  # type: ignore
-
-__all__ = [
-    "AstraDBSearchComponent",
-    "ChromaSearchComponent",
-    "CouchbaseSearchComponent",
-    "FAISSSearchComponent",
-    "MongoDBAtlasSearchComponent",
-    "PineconeSearchComponent",
-    "QdrantSearchComponent",
-    "RedisSearchComponent",
-    "SupabaseSearchComponent",
-    "VectaraSearchComponent",
-    "WeaviateSearchVectorStore",
-    "PGVectorSearchComponent",
-]

src/backend/base/langflow/components/vectorstores/__init__.py

@@ -1,28 +0,0 @@
-from .AstraDB import AstraDBVectorStoreComponent
-from .Chroma import ChromaComponent
-from .FAISS import FAISSComponent
-from .MongoDBAtlasVector import MongoDBAtlasComponent
-from .Pinecone import PineconeComponent
-from .Qdrant import QdrantComponent
-from .Redis import RedisComponent
-from .SupabaseVectorStore import SupabaseComponent
-from .Vectara import VectaraComponent
-from .Weaviate import WeaviateVectorStoreComponent
-from .pgvector import PGVectorComponent
-from .Couchbase import CouchbaseComponent
-
-__all__ = [
-    "AstraDBVectorStoreComponent",
-    "ChromaComponent",
-    "CouchbaseComponent",
-    "FAISSComponent",
-    "MongoDBAtlasComponent",
-    "PineconeComponent",
-    "QdrantComponent",
-    "RedisComponent",
-    "SupabaseComponent",
-    "VectaraComponent",
-    "WeaviateVectorStoreComponent",
-    "base",
-    "PGVectorComponent",
-]

src/backend/base/langflow/custom/code_parser/code_parser.py

@@ -297,7 +297,7 @@ class CodeParser:
             bases = self.execute_and_inspect_classes(self.code)
         except Exception as e:
             # If the code cannot be executed, return an empty list
-            logger.exception(e)
+            logger.debug(e)
             bases = []
             raise e
         return bases

src/backend/base/langflow/custom/directory_reader/directory_reader.py

@@ -78,7 +78,8 @@ class DirectoryReader:
                         component_tuple = (*build_component(component), component)
                         components.append(component_tuple)
                 except Exception as e:
-                    logger.error(f"Error while loading component { component['name']}: {e}")
+                    logger.debug(f"Error while loading component { component['name']}")
+                    logger.debug(e)
                     continue
             items.append({"name": menu["name"], "path": menu["path"], "components": components})
         filtered = [menu for menu in items if menu["components"]]
@@ -266,8 +267,7 @@ class DirectoryReader:
             if validation_result:
                 try:
                     output_types = self.get_output_types_from_code(result_content)
-                except Exception as exc:
-                    logger.exception(f"Error while getting output types from code: {str(exc)}")
+                except Exception:
                     output_types = [component_name_camelcase]
             else:
                 output_types = [component_name_camelcase]

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

@@ -710,6 +710,7 @@ class Graph:
         chat_service: ChatService,
         vertex_id: str,
         inputs_dict: Optional[Dict[str, str]] = None,
+        files: Optional[list[str]] = None,
         user_id: Optional[str] = None,
         fallback_to_env_vars: bool = False,
     ):
@@ -737,7 +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"]
@@ -751,7 +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}"

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

@@ -1,15 +1,17 @@
 from enum import Enum
 from typing import Any, List, Optional
 
-from pydantic import BaseModel, Field, field_serializer
+from pydantic import BaseModel, Field, field_serializer, model_validator
 
 from langflow.graph.utils import serialize_field
+from langflow.schema.schema import Log, StreamURL
 from langflow.utils.schemas import ChatOutputResponse, ContainsEnumMeta
 
 
 class ResultData(BaseModel):
     results: Optional[Any] = Field(default_factory=dict)
     artifacts: Optional[Any] = Field(default_factory=dict)
+    logs: Optional[List[dict]] = Field(default_factory=list)
     messages: Optional[list[ChatOutputResponse]] = Field(default_factory=list)
     timedelta: Optional[float] = None
     duration: Optional[str] = None
@@ -23,6 +25,19 @@ class ResultData(BaseModel):
             return {key: serialize_field(val) for key, val in value.items()}
         return serialize_field(value)
 
+    @model_validator(mode="before")
+    @classmethod
+    def validate_model(cls, values):
+        if not values.get("logs") and values.get("artifacts"):
+            # Build the log from the artifacts
+            message = values["artifacts"]
+            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"])]
+            elif "type" in message:
+                values["logs"] = [Log(message=message, type=message["type"])]
+        return values
+
 
 class InterfaceComponentTypes(str, Enum, metaclass=ContainsEnumMeta):
     # ChatInput and ChatOutput are the only ones that are

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

@@ -1,9 +1,12 @@
-from typing import Any, Union
+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
+from langflow.schema.schema import Record
 
 
 class UnbuiltObject:
@@ -14,6 +17,15 @@ class UnbuiltResult:
     pass
 
 
+class ArtifactType(str, Enum):
+    TEXT = "text"
+    RECORD = "record"
+    OBJECT = "object"
+    ARRAY = "array"
+    STREAM = "stream"
+    UNKNOWN = "unknown"
+
+
 def validate_prompt(prompt: str):
     """Validate prompt."""
     if extract_input_variables_from_prompt(prompt):
@@ -50,3 +62,33 @@ def serialize_field(value):
     elif isinstance(value, str):
         return {"result": value}
     return value
+
+
+def get_artifact_type(custom_component, build_result) -> str:
+    result = ArtifactType.UNKNOWN
+    value = custom_component.repr_value
+    match value:
+        case Record():
+            result = ArtifactType.RECORD
+
+        case str():
+            result = ArtifactType.TEXT
+
+        case dict():
+            result = ArtifactType.OBJECT
+
+        case list():
+            result = ArtifactType.ARRAY
+
+    if result == ArtifactType.UNKNOWN:
+        if isinstance(build_result, Generator):
+            result = ArtifactType.STREAM
+
+    return result.value
+
+
+def post_process_raw(raw, artifact_type: str):
+    if artifact_type == ArtifactType.STREAM.value:
+        raw = ""
+
+    return raw

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

@@ -9,12 +9,12 @@ from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator,
 from loguru import logger
 
 from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData
-from langflow.graph.utils import UnbuiltObject, UnbuiltResult
-from langflow.graph.vertex.utils import log_transaction
+from langflow.graph.utils import ArtifactType, UnbuiltObject, UnbuiltResult
 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
@@ -63,6 +63,8 @@ class Vertex:
         self._built_result = None
         self._built = False
         self.artifacts: Dict[str, Any] = {}
+        self.artifacts_raw: Any = None
+        self.artifacts_type: Optional[str] = None
         self.steps: List[Callable] = [self._build]
         self.steps_ran: List[Callable] = []
         self.task_id: Optional[str] = None
@@ -371,7 +373,7 @@ class Vertex:
         self.load_from_db_fields = load_from_db_fields
         self._raw_params = params.copy()
 
-    def update_raw_params(self, new_params: Dict[str, str], overwrite: bool = False):
+    def update_raw_params(self, new_params: Dict[str, str | list[str]], overwrite: bool = False):
         """
         Update the raw parameters of the vertex with the given new parameters.
 
@@ -426,7 +428,10 @@ class Vertex:
                     sender=artifacts.get("sender"),
                     sender_name=artifacts.get("sender_name"),
                     session_id=artifacts.get("session_id"),
+                    stream_url=artifacts.get("stream_url"),
+                    files=[{"path": file} if isinstance(file, str) else file for file in artifacts.get("files", [])],
                     component_id=self.id,
+                    type=self.artifacts_type,
                 ).model_dump(exclude_none=True)
             ]
         except KeyError:
@@ -444,7 +449,6 @@ class Vertex:
             messages = self.extract_messages_from_artifacts(artifacts)
         else:
             messages = []
-
         result_dict = ResultData(
             results=result_dict,
             artifacts=artifacts,
@@ -526,11 +530,11 @@ class Vertex:
             The built result if use_result is True, else the built object.
         """
         if not self._built:
-            log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error")
+            log_transaction(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(vertex=self, target=requester, status="success")
         return result
 
     async def _build_vertex_and_update_params(self, key, vertex: "Vertex"):
@@ -624,6 +628,8 @@ 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", None)
+                self.artifacts_type = self.artifacts.get("type", None) or ArtifactType.UNKNOWN.value
         else:
             self._built_object = result
 
@@ -664,6 +670,7 @@ class Vertex:
         self,
         user_id=None,
         inputs: Optional[Dict[str, Any]] = None,
+        files: Optional[list[str]] = None,
         requester: Optional["Vertex"] = None,
         **kwargs,
     ) -> Any:
@@ -681,9 +688,14 @@ class Vertex:
                 return await self.get_requester_result(requester)
             self._reset()
 
-            if self._is_chat_input() and inputs:
-                inputs = {"input_value": inputs.get(INPUT_FIELD_NAME, "")}
-                self.update_raw_params(inputs, overwrite=True)
+            if self._is_chat_input() and (inputs or files):
+                chat_input = {}
+                if inputs:
+                    chat_input.update({"input_value": inputs.get(INPUT_FIELD_NAME, "")})
+                if files:
+                    chat_input.update({"files": files})
+
+                self.update_raw_params(chat_input, overwrite=True)
 
             # Run steps
             for step in self.steps:

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

@@ -2,11 +2,11 @@ import json
 from typing import AsyncIterator, Dict, Iterator, List
 
 import yaml
-from langchain_core.messages import AIMessage
+from langchain_core.messages import AIMessage, AIMessageChunk
 from loguru import logger
 
 from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes
-from langflow.graph.utils import UnbuiltObject, serialize_field
+from langflow.graph.utils import ArtifactType, UnbuiltObject, serialize_field
 from langflow.graph.vertex.base import Vertex
 from langflow.schema import Record
 from langflow.schema.schema import INPUT_FIELD_NAME
@@ -83,10 +83,11 @@ class InterfaceVertex(Vertex):
         sender = self.params.get("sender", None)
         sender_name = self.params.get("sender_name", None)
         message = self.params.get(INPUT_FIELD_NAME, None)
+        files = [{"path": file} if isinstance(file, str) else file for file in self.params.get("files", [])]
         if isinstance(message, str):
             message = unescape_string(message)
         stream_url = None
-        if isinstance(self._built_object, AIMessage):
+        if isinstance(self._built_object, (AIMessage, AIMessageChunk)):
             artifacts = ChatOutputResponse.from_message(
                 self._built_object,
                 sender=sender,
@@ -108,12 +109,14 @@ class InterfaceVertex(Vertex):
             # it means that it is a stream of messages
             else:
                 message = self._built_object
-
+            artifact_type = ArtifactType.STREAM if stream_url is not None else ArtifactType.OBJECT
             artifacts = ChatOutputResponse(
                 message=message,
                 sender=sender,
                 sender_name=sender_name,
                 stream_url=stream_url,
+                files=files,
+                type=artifact_type,
             )
 
             self.will_stream = stream_url is not None
@@ -195,6 +198,8 @@ class InterfaceVertex(Vertex):
             message=complete_message,
             sender=self.params.get("sender", ""),
             sender_name=self.params.get("sender_name", ""),
+            files=[{"path": file} if isinstance(file, str) else file for file in self.params.get("files", [])],
+            type=ArtifactType.OBJECT.value,
         ).model_dump()
         self.params[INPUT_FIELD_NAME] = complete_message
         self._built_object = Record(text=complete_message, data=self.artifacts)

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

@@ -90,7 +90,9 @@ async def run_flow(
 
     fallback_to_env_vars = get_settings_service().settings.fallback_to_env_var
 
-    return await graph.arun(inputs_list, inputs_components=inputs_components, types=types, fallback_to_env_vars=fallback_to_env_vars)
+    return await graph.arun(
+        inputs_list, inputs_components=inputs_components, types=types, fallback_to_env_vars=fallback_to_env_vars
+    )
 
 
 def generate_function_for_flow(
@@ -257,3 +259,24 @@ def get_flow_by_id_or_endpoint_name(
         raise HTTPException(status_code=404, detail=f"Flow identifier {flow_id_or_name} not found")
 
     return flow
+
+
+def generate_unique_flow_name(flow_name, user_id, session):
+    original_name = flow_name
+    n = 1
+    while True:
+        # Check if a flow with the given name exists
+        existing_flow = session.exec(
+            select(Flow).where(
+                Flow.name == flow_name,
+                Flow.user_id == user_id,
+            )
+        ).first()
+
+        # If no flow with the given name exists, return the name
+        if not existing_flow:
+            return flow_name
+
+        # If a flow with the name already exists, append (n) to the name and increment n
+        flow_name = f"{original_name} ({n})"
+        n += 1

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

@@ -0,0 +1,23 @@
+from langflow.services.database.models.folder.model import Folder
+from sqlalchemy import select
+
+
+def generate_unique_folder_name(folder_name, user_id, session):
+    original_name = folder_name
+    n = 1
+    while True:
+        # Check if a folder with the given name exists
+        existing_folder = session.exec(
+            select(Folder).where(
+                Folder.name == folder_name,
+                Folder.user_id == user_id,
+            )
+        ).first()
+
+        # If no folder with the given name exists, return the name
+        if not existing_folder:
+            return folder_name
+
+        # If a folder with the name already exists, append (n) to the name and increment n
+        folder_name = f"{original_name} ({n})"
+        n += 1

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

@@ -20,7 +20,7 @@ from langflow.services.database.models.user.crud import get_user_by_username
 from langflow.services.deps import get_settings_service, session_scope
 
 from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist
-from langflow.services.deps import get_settings_service, session_scope, get_variable_service
+from langflow.services.deps import get_variable_service
 
 
 STARTER_FOLDER_NAME = "Starter Projects"
@@ -221,6 +221,7 @@ def _is_valid_uuid(val):
         return False
     return str(uuid_obj) == val
 
+
 def load_flows_from_directory():
     settings_service = get_settings_service()
     flows_path = settings_service.settings.load_flows_path
@@ -262,6 +263,7 @@ def load_flows_from_directory():
                     session.add(flow)
                 session.commit()
 
+
 def find_existing_flow(session, flow_id, flow_endpoint_name):
     if flow_endpoint_name:
         stmt = select(Flow).where(Flow.endpoint_name == flow_endpoint_name)
@@ -271,6 +273,8 @@ def find_existing_flow(session, flow_id, flow_endpoint_name):
     if existing := session.exec(stmt).first():
         return existing
     return None
+
+
 def create_or_update_starter_projects():
     components_paths = get_settings_service().settings.components_path
     try:

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,
@@ -392,7 +392,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n        files: Optional[list[str]] = None,\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n            files=files,\n        )\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -571,7 +571,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_record=return_record,\n        )\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,
@@ -260,7 +260,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n        files: Optional[list[str]] = None,\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n            files=files,\n        )\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,
@@ -262,7 +262,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -421,7 +421,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n        files: Optional[list[str]] = None,\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n            files=files,\n        )\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

@@ -22,7 +22,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -182,7 +182,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n        files: Optional[list[str]] = None,\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n            files=files,\n        )\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -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,
@@ -236,7 +236,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n        files: Optional[list[str]] = None,\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n            files=files,\n        )\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -411,7 +411,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n        files: Optional[list[str]] = None,\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n            files=files,\n        )\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

@@ -20,7 +20,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build_no_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -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,
@@ -1170,7 +1170,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n        )\n",
+                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n        files: Optional[list[str]] = None,\n    ) -> Union[Text, Record]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template or \"\",\n            files=files,\n        )\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,

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

@@ -7,6 +7,7 @@ import orjson
 from loguru import logger
 
 from langflow.custom.eval import eval_custom_component_code
+from langflow.graph.utils import get_artifact_type, post_process_raw
 from langflow.schema.schema import Record
 
 if TYPE_CHECKING:
@@ -124,4 +125,14 @@ 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)
-    return custom_component, build_result, {"repr": custom_repr}
+    raw = custom_component.repr_value
+    if hasattr(raw, "data"):
+        raw = raw.data
+
+    elif hasattr(raw, "model_dump"):
+        raw = raw.model_dump()
+
+    artifact_type = get_artifact_type(custom_component, build_result)
+    raw = post_process_raw(raw, artifact_type)
+    artifact = {"repr": custom_repr, "raw": raw, "type": artifact_type}
+    return custom_component, build_result, artifact

src/backend/base/langflow/processing/process.py

@@ -59,7 +59,7 @@ async def run_graph_internal(
         outputs or [],
         stream=stream,
         session_id=session_id_str or "",
-        fallback_to_env_vars=fallback_to_env_vars
+        fallback_to_env_vars=fallback_to_env_vars,
     )
     if session_id_str and session_service:
         await session_service.update_session(session_id_str, (graph, artifacts))

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

@@ -1,10 +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 langchain_core.prompts.image import ImagePromptTemplate
+from pydantic import BaseModel, model_serializer, model_validator
 
 
 class Record(BaseModel):
@@ -29,6 +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.
@@ -102,7 +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.
 
@@ -118,8 +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):
@@ -169,11 +192,26 @@ 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"
 
 InputType = Literal["chat", "text", "any"]
 OutputType = Literal["chat", "text", "any", "debug"]
+
+
+class StreamURL(TypedDict):
+    location: str
+
+
+class Log(TypedDict):
+    message: str | dict | StreamURL
+    type: str

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

@@ -215,10 +215,7 @@ def create_user_longterm_token(db: Session = Depends(get_session)) -> tuple[UUID
     username = settings_service.auth_settings.SUPERUSER
     super_user = get_user_by_username(db, username)
     if not super_user:
-        raise HTTPException(
-            status_code=status.HTTP_400_BAD_REQUEST,
-            detail="Super user hasn't been created"
-        )
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Super user hasn't been created")
     access_token_expires_longterm = timedelta(days=365)
     access_token = create_token(
         data={"sub": str(super_user.id)},

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/database/models/api_key/model.py

@@ -55,6 +55,7 @@ class ApiKeyRead(ApiKeyBase):
     id: UUID
     api_key: str = Field(schema_extra={"validate_default": True})
     user_id: UUID = Field()
+    created_at: datetime = Field()
 
     @field_validator("api_key")
     @classmethod

src/backend/base/langflow/services/database/models/flow/model.py

@@ -29,6 +29,7 @@ class FlowBase(SQLModel):
     is_component: Optional[bool] = Field(default=False, nullable=True)
     updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc), nullable=True)
     webhook: Optional[bool] = Field(default=False, nullable=True, description="Can be used on the webhook endpoint")
+    folder_id: Optional[UUID] = Field(default=None, nullable=True)
     endpoint_name: Optional[str] = Field(default=None, nullable=True, index=True)
 
     @field_validator("endpoint_name")

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

@@ -11,10 +11,10 @@ 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: dict
     status: str
     error: Optional[str] = None
 
@@ -23,13 +23,13 @@ class TransactionModel(BaseModel):
         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)
@@ -39,10 +39,9 @@ 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: dict
     status: str
     error: Optional[str] = None
 
@@ -51,7 +50,7 @@ class TransactionModelResponse(BaseModel):
         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)
@@ -75,14 +74,14 @@ class MessageModel(BaseModel):
     sender_name: str
     session_id: str
     message: str
-    artifacts: dict
+    files: list[str] = []
 
     class Config:
         from_attributes = True
         populate_by_name = True
 
-    @field_validator("artifacts", mode="before")
-    def validate_target_args(cls, v):
+    @field_validator("files", mode="before")
+    def validate_files(cls, v):
         if isinstance(v, str):
             return json.loads(v)
         return v
@@ -97,6 +96,7 @@ class MessageModel(BaseModel):
             sender_name=record.sender_name,
             message=record.text,
             session_id=record.session_id,
+            files=record.files or [],
             artifacts=record.artifacts or {},
             timestamp=record.timestamp,
             flow_id=flow_id,
@@ -106,12 +106,6 @@ class MessageModel(BaseModel):
 class MessageModelResponse(MessageModel):
     index: Optional[int] = Field(default=None)
 
-    @field_validator("artifacts", mode="before")
-    def serialize_artifacts(v):
-        if isinstance(v, str):
-            return json.loads(v)
-        return v
-
     @field_validator("index", mode="before")
     def validate_id(cls, v):
         if isinstance(v, float):
@@ -122,6 +116,13 @@ class MessageModelResponse(MessageModel):
         return v
 
 
+class MessageModelRequest(MessageModel):
+    message: str = Field(default="")
+    sender: str = Field(default="")
+    sender_name: str = Field(default="")
+    session_id: str = Field(default="")
+
+
 class VertexBuildModel(BaseModel):
     index: Optional[int] = Field(default=None, alias="index", exclude=True)
     id: Optional[str] = Field(default=None, alias="id")

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

@@ -32,6 +32,10 @@ class MonitorService(Service):
         except Exception as e:
             logger.exception(f"Error initializing monitor service: {e}")
 
+    def exec_query(self, query: str):
+        with duckdb.connect(str(self.db_path)) as conn:
+            return conn.execute(query).df()
+
     def to_df(self, table_name):
         return self.load_table_as_dataframe(table_name)
 
@@ -69,7 +73,7 @@ class MonitorService(Service):
         valid: Optional[bool] = None,
         order_by: Optional[str] = "timestamp",
     ):
-        query = "SELECT index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds"
+        query = "SELECT id, index,flow_id, valid, params, data, artifacts, timestamp FROM vertex_builds"
         conditions = []
         if flow_id:
             conditions.append(f"flow_id = '{flow_id}'")
@@ -88,6 +92,8 @@ class MonitorService(Service):
         with duckdb.connect(str(self.db_path)) as conn:
             df = conn.execute(query).df()
 
+        print(query)
+
         return df.to_dict(orient="records")
 
     def delete_vertex_builds(self, flow_id: Optional[str] = None):
@@ -98,11 +104,22 @@ class MonitorService(Service):
         with duckdb.connect(str(self.db_path)) as conn:
             conn.execute(query)
 
-    def delete_messages(self, session_id: str):
+    def delete_messages_session(self, session_id: str):
         query = f"DELETE FROM messages WHERE session_id = '{session_id}'"
 
-        with duckdb.connect(str(self.db_path)) as conn:
-            conn.execute(query)
+        return self.exec_query(query)
+
+    def delete_messages(self, message_ids: list[int]):
+        query = f"DELETE FROM messages WHERE index IN ({','.join(map(str, message_ids))})"
+
+        return self.exec_query(query)
+
+    def update_message(self, message_id: int, **kwargs):
+        query = (
+            f"""UPDATE messages SET {', '.join(f"{k} = '{v}'" for k, v in kwargs.items())} WHERE index = {message_id}"""
+        )
+
+        return self.exec_query(query)
 
     def add_message(self, message: MessageModel):
         self.add_row("messages", message)
@@ -117,7 +134,7 @@ class MonitorService(Service):
         order: Optional[str] = "DESC",
         limit: Optional[int] = None,
     ):
-        query = "SELECT index, flow_id, sender_name, sender, session_id, message, artifacts, timestamp FROM messages"
+        query = "SELECT index, flow_id, sender_name, sender, session_id, message, timestamp FROM messages"
         conditions = []
         if sender:
             conditions.append(f"sender = '{sender}'")

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,
@@ -183,14 +178,15 @@ def build_clean_params(target: "Vertex") -> dict:
     return params
 
 
-def log_transaction(vertex: "Vertex", status, error=None):
+def log_transaction(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(),
             "timestamp": monitor_service.get_timestamp(),
             "status": status,
             "error": error,

src/backend/base/langflow/services/settings/base.py

@@ -70,7 +70,7 @@ class Settings(BaseSettings):
     """Database URL for Langflow. If not provided, Langflow will use a SQLite database."""
     pool_size: int = 10
     """The number of connections to keep open in the connection pool. If not provided, the default is 10."""
-    max_overflow: int = 10
+    max_overflow: int = 20
     """The number of connections to allow that can be opened beyond the pool size. If not provided, the default is 10."""
     cache_type: str = "async"
     remove_api_keys: bool = False
@@ -78,7 +78,6 @@ class Settings(BaseSettings):
     langchain_cache: str = "InMemoryCache"
     load_flows_path: Optional[str] = None
 
-
     # Redis
     redis_host: str = "localhost"
     redis_port: int = 6379

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

@@ -1,5 +1,4 @@
 import os
-from typing import Optional
 
 import yaml
 from loguru import logger
@@ -8,6 +7,7 @@ from langflow.services.base import Service
 from langflow.services.settings.auth import AuthSettings
 from langflow.services.settings.base import Settings
 
+
 class SettingsService(Service):
     name = "settings_service"
 

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

@@ -2,7 +2,18 @@ import enum
 from typing import Dict, List, Optional, Union
 
 from langchain_core.messages import BaseMessage
-from pydantic import BaseModel, model_validator
+from pydantic import BaseModel, field_validator, model_validator
+from typing_extensions import TypedDict
+
+from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
+
+
+class File(TypedDict):
+    """File schema."""
+
+    path: str
+    name: str
+    type: str
 
 
 class ChatOutputResponse(BaseModel):
@@ -14,6 +25,47 @@ class ChatOutputResponse(BaseModel):
     session_id: Optional[str] = None
     stream_url: Optional[str] = None
     component_id: Optional[str] = None
+    files: List[File] = []
+    type: str
+
+    @field_validator("files", mode="before")
+    def validate_files(cls, files):
+        """Validate files."""
+        if not files:
+            return files
+
+        for file in files:
+            if not isinstance(file, dict):
+                raise ValueError("Files must be a list of dictionaries.")
+
+            if not all(key in file for key in ["path", "name", "type"]):
+                # If any of the keys are missing, we should extract the
+                # values from the file path
+                path = file.get("path")
+                if not path:
+                    raise ValueError("File path is required.")
+
+                name = file.get("name")
+                if not name:
+                    name = path.split("/")[-1]
+                    file["name"] = name
+                _type = file.get("type")
+                if not _type:
+                    # get the file type from the path
+                    extension = path.split(".")[-1]
+                    file_types = set(TEXT_FILE_TYPES + IMG_FILE_TYPES)
+                    if extension and extension in file_types:
+                        _type = extension
+                    else:
+                        for file_type in file_types:
+                            if file_type in path:
+                                _type = file_type
+                                break
+                    if not _type:
+                        raise ValueError("File type is required.")
+                file["type"] = _type
+
+        return files
 
     @classmethod
     def from_message(

src/backend/base/poetry.lock

@@ -1296,13 +1296,13 @@ types-requests = ">=2.31.0.2,<3.0.0.0"
 
 [[package]]
 name = "langsmith"
-version = "0.1.72"
+version = "0.1.75"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.72-py3-none-any.whl", hash = "sha256:a4456707669521bd75b7431b9205a6b99579fb9ff01bd338f52d29df11a7662d"},
-    {file = "langsmith-0.1.72.tar.gz", hash = "sha256:262ae9e8aceaba50f3a0f5b6eb559d6110886f0afc6b0ed5270e7d3d3f1fd8d6"},
+    {file = "langsmith-0.1.75-py3-none-any.whl", hash = "sha256:d08b08dd6b3fa4da170377f95123d77122ef4c52999d10fff4ae08ff70d07aed"},
+    {file = "langsmith-0.1.75.tar.gz", hash = "sha256:61274e144ea94c297dd78ce03e6dfae18459fe9bd8ab5094d61a0c4816561279"},
 ]
 
 [package.dependencies]
@@ -1600,13 +1600,13 @@ files = [
 
 [[package]]
 name = "marshmallow"
-version = "3.21.2"
+version = "3.21.3"
 description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "marshmallow-3.21.2-py3-none-any.whl", hash = "sha256:70b54a6282f4704d12c0a41599682c5c5450e843b9ec406308653b47c59648a1"},
-    {file = "marshmallow-3.21.2.tar.gz", hash = "sha256:82408deadd8b33d56338d2182d455db632c6313aa2af61916672146bb32edc56"},
+    {file = "marshmallow-3.21.3-py3-none-any.whl", hash = "sha256:86ce7fb914aa865001a4b2092c4c2872d13bc347f3d42673272cabfdbad386f1"},
+    {file = "marshmallow-3.21.3.tar.gz", hash = "sha256:4f57c5e050a54d66361e826f94fba213eb10b67b2fdb02c3e0343ce207ba1662"},
 ]
 
 [package.dependencies]

src/backend/base/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow-base"
-version = "0.0.57"
+version = "0.0.59"
 description = "A Python package with a built-in web application"
 authors = ["Langflow <contact@langflow.org>"]
 maintainers = [

src/frontend/package.json

@@ -22,6 +22,7 @@
     "@radix-ui/react-slot": "^1.0.2",
     "@radix-ui/react-switch": "^1.0.3",
     "@radix-ui/react-tabs": "^1.0.4",
+    "@radix-ui/react-toggle": "^1.0.3",
     "@radix-ui/react-tooltip": "^1.0.6",
     "@tabler/icons-react": "^2.32.0",
     "@tailwindcss/forms": "^0.5.6",
@@ -36,9 +37,9 @@
     "class-variance-authority": "^0.6.1",
     "clsx": "^1.2.1",
     "cmdk": "^1.0.0",
-    "debounce-promise": "^3.1.2",
     "dompurify": "^3.0.5",
     "dotenv": "^16.4.5",
+    "emoji-regex": "^10.3.0",
     "esbuild": "^0.17.19",
     "file-saver": "^2.0.5",
     "framer-motion": "^11.0.6",
@@ -47,6 +48,7 @@
     "million": "^3.0.6",
     "moment": "^2.29.4",
     "openseadragon": "^4.1.1",
+    "p-debounce": "^4.0.0",
     "playwright": "^1.42.0",
     "react": "^18.2.21",
     "react-ace": "^10.1.0",

src/frontend/playwright.config.ts

@@ -45,6 +45,9 @@ export default defineConfig({
       name: "chromium",
       use: {
         ...devices["Desktop Chrome"],
+        launchOptions: {
+          // headless: false,
+        },
         contextOptions: {
           // chromium-specific permissions
           permissions: ["clipboard-read", "clipboard-write"],
@@ -57,6 +60,7 @@ export default defineConfig({
     //   use: {
     //     ...devices["Desktop Firefox"],
     //     launchOptions: {
+    //       headless: false,
     //       firefoxUserPrefs: {
     //         "dom.events.asyncClipboard.readText": true,
     //         "dom.events.testing.asyncClipboard": true,

src/frontend/src/App.css

@@ -164,3 +164,13 @@ body {
 .ag-body-vertical-scroll-viewport::-webkit-scrollbar-thumb:hover {
   background-color: #bbb;
 }
+
+/* This CSS is to not apply the border for the column having 'no-border' class */
+.no-border.ag-cell:focus {
+  border: none !important;
+  outline: none;
+}
+.no-border.ag-cell {
+  border: none !important;
+  outline: none;
+}

src/frontend/src/App.tsx

@@ -1,4 +1,3 @@
-import axios from "axios";
 import { useContext, useEffect, useState } from "react";
 import { ErrorBoundary } from "react-error-boundary";
 import { useNavigate } from "react-router-dom";

src/frontend/src/CustomNodes/GenericNode/components/outputModal/components/switchOutputView/components/index.tsx

@@ -0,0 +1,12 @@
+import { Textarea } from "../../../../../../../components/ui/textarea";
+
+export default function ErrorOutput({ value }: { value: string }) {
+  return (
+    <Textarea
+      className={`h-full w-full text-destructive custom-scroll`}
+      placeholder={"Empty"}
+      value={value}
+      readOnly
+    />
+  );
+}

src/frontend/src/CustomNodes/GenericNode/components/outputModal/components/switchOutputView/helpers/convert-to-table-rows.ts

@@ -0,0 +1,4 @@
+export const convertToTableRows = (obj: Object) => {
+  const tokensArray = [Object.values(obj)[0]];
+  return tokensArray;
+};

src/frontend/src/CustomNodes/GenericNode/components/outputModal/components/switchOutputView/index.tsx

@@ -0,0 +1,90 @@
+import ForwardedIconComponent from "../../../../../../components/genericIconComponent";
+import RecordsOutputComponent from "../../../../../../components/recordsOutputComponent";
+import {
+  Alert,
+  AlertDescription,
+  AlertTitle,
+} from "../../../../../../components/ui/alert";
+import { Case } from "../../../../../../shared/components/caseComponent";
+import TextOutputView from "../../../../../../shared/components/textOutputView";
+import useFlowStore from "../../../../../../stores/flowStore";
+import ErrorOutput from "./components";
+
+export default function SwitchOutputView(nodeId): JSX.Element {
+  const nodeIdentity = nodeId.nodeId;
+
+  const nodes = useFlowStore((state) => state.nodes);
+  const flowPool = useFlowStore((state) => state.flowPool);
+  const node = nodes.find((node) => node?.id === nodeIdentity);
+
+  const flowPoolNode = (flowPool[nodeIdentity] ?? [])[
+    (flowPool[nodeIdentity]?.length ?? 1) - 1
+  ];
+
+  const results = flowPoolNode?.data?.logs[0] ?? "";
+  const resultType = results?.type;
+  let resultMessage = results?.message;
+  if (resultMessage.raw) {
+    resultMessage = resultMessage.raw;
+  }
+  console.log("resultType", results);
+  return (
+    <>
+      <Case condition={!resultType || resultType === "unknown"}>
+        <div>NO OUTPUT</div>
+      </Case>
+      <Case condition={resultType === "ValueError"}>
+        <ErrorOutput value={resultMessage}></ErrorOutput>
+      </Case>
+
+      <Case condition={node && resultType === "text"}>
+        <TextOutputView left={false} value={resultMessage} />
+      </Case>
+
+      <Case condition={resultType === "record"}>
+        <RecordsOutputComponent
+          rows={[resultMessage] ?? []}
+          pagination={true}
+          columnMode="union"
+        />
+      </Case>
+
+      <Case condition={resultType === "object"}>
+        <RecordsOutputComponent
+          rows={[resultMessage]}
+          pagination={true}
+          columnMode="union"
+        />
+      </Case>
+      {Array.isArray(resultMessage) && (
+        <Case condition={resultType === "array"}>
+          <RecordsOutputComponent
+            rows={
+              (resultMessage as Array<any>).every((item) => item.data)
+                ? (resultMessage as Array<any>).map((item) => item.data)
+                : resultMessage
+            }
+            pagination={true}
+            columnMode="union"
+          />
+        </Case>
+      )}
+      <Case condition={resultType === "stream"}>
+        <div className="flex h-full w-full items-center justify-center align-middle">
+          <Alert variant={"default"} className="w-fit">
+            <ForwardedIconComponent
+              name="AlertCircle"
+              className="h-5 w-5 text-primary"
+            />
+            <AlertTitle>{"Streaming is not supported"}</AlertTitle>
+            <AlertDescription>
+              {
+                "Use the playground to interact with components that stream data"
+              }
+            </AlertDescription>
+          </Alert>
+        </div>
+      </Case>
+    </>
+  );
+}

src/frontend/src/CustomNodes/GenericNode/components/outputModal/index.tsx

@@ -0,0 +1,25 @@
+import { Button } from "../../../../components/ui/button";
+import BaseModal from "../../../../modals/baseModal";
+import SwitchOutputView from "./components/switchOutputView";
+
+export default function OutputModal({ open, setOpen, nodeId }): JSX.Element {
+  return (
+    <BaseModal open={open} setOpen={setOpen} size="medium">
+      <BaseModal.Header description="Inspect the output of the component below.">
+        <div className="flex items-center">
+          <span className="pr-2">Component Output</span>
+        </div>
+      </BaseModal.Header>
+      <BaseModal.Content>
+        <SwitchOutputView nodeId={nodeId} />
+      </BaseModal.Content>
+      <BaseModal.Footer>
+        <div className="flex w-full justify-end  pt-2">
+          <Button className="flex gap-2 px-3" onClick={() => setOpen(false)}>
+            Close
+          </Button>
+        </div>
+      </BaseModal.Footer>
+    </BaseModal>
+  );
+}

src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx

@@ -22,7 +22,6 @@ import {
   TOOLTIP_EMPTY,
 } from "../../../../constants/constants";
 import { Case } from "../../../../shared/components/caseComponent";
-import useAlertStore from "../../../../stores/alertStore";
 import useFlowStore from "../../../../stores/flowStore";
 import useFlowsManagerStore from "../../../../stores/flowsManagerStore";
 import { useTypesStore } from "../../../../stores/typesStore";
@@ -45,6 +44,7 @@ import useFetchDataOnMount from "../../../hooks/use-fetch-data-on-mount";
 import useHandleOnNewValue from "../../../hooks/use-handle-new-value";
 import useHandleNodeClass from "../../../hooks/use-handle-node-class";
 import useHandleRefreshButtonPress from "../../../hooks/use-handle-refresh-buttons";
+import OutputModal from "../outputModal";
 import TooltipRenderComponent from "../tooltipRenderComponent";
 import { TEXT_FIELD_TYPES } from "./constants";
 
@@ -67,7 +67,6 @@ export default function ParameterComponent({
   const ref = useRef<HTMLDivElement>(null);
   const refHtml = useRef<HTMLDivElement & ReactNode>(null);
   const infoHtml = useRef<HTMLDivElement & ReactNode>(null);
-  const setErrorData = useAlertStore((state) => state.setErrorData);
   const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
   const nodes = useFlowStore((state) => state.nodes);
   const edges = useFlowStore((state) => state.edges);
@@ -80,6 +79,16 @@ export default function ParameterComponent({
   const flow = currentFlow?.data?.nodes ?? null;
   const groupedEdge = useRef(null);
   const setFilterEdge = useFlowStore((state) => state.setFilterEdge);
+  const [openOutputModal, setOpenOutputModal] = useState(false);
+  const flowPool = useFlowStore((state) => state.flowPool);
+
+  const displayOutputPreview = !!flowPool[data.id];
+
+  const unknownOutput = !!(
+    flowPool[data.id] &&
+    flowPool[data.id][flowPool[data.id].length - 1]?.data?.logs[0]?.type ===
+      "unknown"
+  );
 
   const { handleOnNewValue: handleOnNewValueHook } = useHandleOnNewValue(
     data,
@@ -251,9 +260,38 @@ export default function ParameterComponent({
               </span>
             </ShadTooltip>
           ) : (
-            <span className={!left && data.node?.frozen ? " text-ice" : ""}>
-              {title}
-            </span>
+            <div className="flex gap-2">
+              <span className={!left && data.node?.frozen ? " text-ice" : ""}>
+                {title}
+              </span>
+              {!left && (
+                <ShadTooltip
+                  content={
+                    displayOutputPreview
+                      ? unknownOutput
+                        ? "Output can't be displayed"
+                        : "Inspect Output"
+                      : "Please build the component first"
+                  }
+                >
+                  <button
+                    disabled={!displayOutputPreview || unknownOutput}
+                    onClick={() => setOpenOutputModal(true)}
+                    data-testid={`output-inspection-${title.toLowerCase()}`}
+                  >
+                    <IconComponent
+                      className={classNames(
+                        "h-5 w-5 rounded-md",
+                        displayOutputPreview && !unknownOutput
+                          ? " hover:bg-secondary-foreground/5 hover:text-medium-indigo"
+                          : " cursor-not-allowed text-muted-foreground",
+                      )}
+                      name={"ScanEye"}
+                    />
+                  </button>
+                </ShadTooltip>
+              )}
+            </div>
           )}
           <span className={(required ? "ml-2 " : "") + "text-status-red"}>
             {required ? "*" : ""}
@@ -392,7 +430,7 @@ export default function ParameterComponent({
                       });
                     }}
                     name={name}
-                    data={data}
+                    data={data.node?.template[name]!}
                   />
                 </div>
                 {data.node?.template[name]?.refresh_button && (
@@ -448,8 +486,8 @@ export default function ParameterComponent({
               data.node?.template[name]?.real_time_refresh)
           }
         >
-          <div className="mt-2 flex w-full items-center">
-            <div className="w-5/6 flex-grow">
+          <div className="mt-2 flex w-full items-center gap-2">
+            <div className="flex-1">
               <Dropdown
                 disabled={disabled}
                 isLoading={isLoading}
@@ -467,7 +505,6 @@ export default function ParameterComponent({
                   name={name}
                   data={data}
                   button_text={data.node?.template[name]?.refresh_button_text}
-                  className="extra-side-bar-buttons ml-2 mt-1"
                   handleUpdateValues={handleRefreshButtonPress}
                   id={"refresh-button-" + name}
                 />
@@ -580,6 +617,13 @@ export default function ParameterComponent({
             />
           </div>
         </Case>
+        {openOutputModal && (
+          <OutputModal
+            open={openOutputModal}
+            nodeId={data.id}
+            setOpen={setOpenOutputModal}
+          />
+        )}
       </>
     </div>
   );

src/frontend/src/CustomNodes/GenericNode/index.tsx

@@ -1,32 +1,36 @@
-import { cloneDeep } from "lodash";
-import { useCallback, useEffect, useMemo, useState } from "react";
+import emojiRegex from "emoji-regex";
+import { useEffect, useMemo, useState } from "react";
 import { NodeToolbar, useUpdateNodeInternals } from "reactflow";
 import IconComponent from "../../components/genericIconComponent";
 import InputComponent from "../../components/inputComponent";
 import ShadTooltip from "../../components/shadTooltipComponent";
 import { Button } from "../../components/ui/button";
-import Checkmark from "../../components/ui/checkmark";
-import Loading from "../../components/ui/loading";
 import { Textarea } from "../../components/ui/textarea";
-import Xmark from "../../components/ui/xmark";
 import {
   RUN_TIMESTAMP_PREFIX,
   STATUS_BUILD,
   STATUS_BUILDING,
 } from "../../constants/constants";
 import { BuildStatus } from "../../constants/enums";
+import { countHandlesFn } from "../helpers/count-handles";
+import { getSpecificClassFromBuildStatus } from "../helpers/get-class-from-build-status";
 import NodeToolbarComponent from "../../pages/FlowPage/components/nodeToolbarComponent";
 import useAlertStore from "../../stores/alertStore";
 import { useDarkStore } from "../../stores/darkStore";
 import useFlowStore from "../../stores/flowStore";
 import useFlowsManagerStore from "../../stores/flowsManagerStore";
 import { useTypesStore } from "../../stores/typesStore";
-import { APIClassType } from "../../types/api";
-import { validationStatusType } from "../../types/components";
+import { VertexBuildTypeAPI } from "../../types/api";
 import { NodeDataType } from "../../types/flow";
 import { handleKeyDown, scapedJSONStringfy } from "../../utils/reactflowUtils";
 import { nodeColors, nodeIconsLucide } from "../../utils/styleUtils";
 import { classNames, cn } from "../../utils/utils";
+import useCheckCodeValidity from "../hooks/use-check-code-validity";
+import useIconNodeRender from "../hooks/use-icon-render";
+import useIconStatus from "../hooks/use-icons-status";
+import useUpdateNodeCode from "../hooks/use-update-node-code";
+import useUpdateValidationStatus from "../hooks/use-update-validation-status";
+import useValidationStatusString from "../hooks/use-validation-status-string";
 import getFieldTitle from "../utils/get-field-title";
 import sortFields from "../utils/sort-fields";
 import isWrappedWithClass from "../../pages/FlowPage/components/PageComponent/utils/is-wrapped-with-class";
@@ -34,14 +38,13 @@ import ParameterComponent from "./components/parameterComponent";
 
 export default function GenericNode({
   data,
-  xPos,
-  yPos,
+
   selected,
 }: {
   data: NodeDataType;
   selected: boolean;
-  xPos: number;
-  yPos: number;
+  xPos?: number;
+  yPos?: number;
 }): JSX.Element {
   const types = useTypesStore((state) => state.types);
   const templates = useTypesStore((state) => state.templates);
@@ -51,7 +54,15 @@ export default function GenericNode({
   const setNode = useFlowStore((state) => state.setNode);
   const updateNodeInternals = useUpdateNodeInternals();
   const setErrorData = useAlertStore((state) => state.setErrorData);
-  const name = nodeIconsLucide[data.type] ? data.type : types[data.type];
+  const isDark = useDarkStore((state) => state.dark);
+  const buildStatus = useFlowStore(
+    (state) => state.flowBuildStatus[data.id]?.status,
+  );
+  const lastRunTime = useFlowStore(
+    (state) => state.flowBuildStatus[data.id]?.timestamp,
+  );
+  const takeSnapshot = useFlowsManagerStore((state) => state.takeSnapshot);
+
   const [inputName, setInputName] = useState(false);
   const [nodeName, setNodeName] = useState(data.node!.display_name);
   const [inputDescription, setInputDescription] = useState(false);
@@ -59,185 +70,25 @@ export default function GenericNode({
     data.node?.description!,
   );
   const [isOutdated, setIsOutdated] = useState(false);
-  const buildStatus = useFlowStore(
-    (state) => state.flowBuildStatus[data.id]?.status,
-  );
-  const lastRunTime = useFlowStore(
-    (state) => state.flowBuildStatus[data.id]?.timestamp,
-  );
   const [validationStatus, setValidationStatus] =
-    useState<validationStatusType | null>(null);
+    useState<VertexBuildTypeAPI | null>(null);
   const [handles, setHandles] = useState<number>(0);
-
   const [validationString, setValidationString] = useState<string>("");
 
-  const takeSnapshot = useFlowsManagerStore((state) => state.takeSnapshot);
-
-  useEffect(() => {
-    // This one should run only once
-    // first check if data.type in NATIVE_CATEGORIES
-    // if not return
-    if (!data.node?.template?.code?.value) return;
-    const thisNodeTemplate = templates[data.type]?.template;
-    // if the template does not have a code key
-    // return
-    if (!thisNodeTemplate?.code) return;
-    const currentCode = thisNodeTemplate.code?.value;
-    const thisNodesCode = data.node!.template?.code?.value;
-    const componentsToIgnore = ["Custom Component"];
-    if (
-      currentCode !== thisNodesCode &&
-      !componentsToIgnore.includes(data.node!.display_name)
-    ) {
-      setIsOutdated(true);
-    } else {
-      setIsOutdated(false);
-    }
-    // template.code can be undefined
-  }, [data.node?.template?.code?.value]);
-
-  const updateNodeCode = useCallback(
-    (newNodeClass: APIClassType, code: string, name: string) => {
-      setNode(data.id, (oldNode) => {
-        let newNode = cloneDeep(oldNode);
-
-        newNode.data = {
-          ...newNode.data,
-          node: newNodeClass,
-          description: newNodeClass.description ?? data.node!.description,
-          display_name: newNodeClass.display_name ?? data.node!.display_name,
-        };
-
-        newNode.data.node.template[name].value = code;
-        setIsOutdated(false);
-
-        return newNode;
-      });
-
-      updateNodeInternals(data.id);
-    },
-    [data.id, data.node, setNode, setIsOutdated],
-  );
-
-  if (!data.node!.template) {
-    setErrorData({
-      title: `Error in component ${data.node!.display_name}`,
-      list: [
-        `The component ${data.node!.display_name} has no template.`,
-        `Please contact the developer of the component to fix this issue.`,
-      ],
-    });
-    takeSnapshot();
-    deleteNode(data.id);
-  }
-
-  function countHandles(): void {
-    let count = Object.keys(data.node!.template)
-      .filter((templateField) => templateField.charAt(0) !== "_")
-      .map((templateCamp) => {
-        const { template } = data.node!;
-        if (template[templateCamp].input_types) return true;
-        if (!template[templateCamp].show) return false;
-        switch (template[templateCamp].type) {
-          case "str":
-          case "bool":
-          case "float":
-          case "code":
-          case "prompt":
-          case "file":
-          case "int":
-            return false;
-          default:
-            return true;
-        }
-      })
-      .reduce((total, value) => total + (value ? 1 : 0), 0);
-
-    setHandles(count);
-  }
-  useEffect(() => {
-    countHandles();
-  }, [data, data.node]);
-
-  useEffect(() => {
-    if (!selected) {
-      setInputName(false);
-      setInputDescription(false);
-    }
-  }, [selected]);
-
+  const iconStatus = useIconStatus(buildStatus, validationStatus);
+  const [showNode, setShowNode] = useState(data.showNode ?? true);
   // State for outline color
   const isBuilding = useFlowStore((state) => state.isBuilding);
 
-  // should be empty string if no duration
-  // else should be `Duration: ${duration}`
-  const getDurationString = (duration: number | undefined): string => {
-    if (duration === undefined) {
-      return "";
-    } else {
-      return `${duration}`;
-    }
-  };
-  const durationString = getDurationString(validationStatus?.data.duration);
+  const updateNodeCode = useUpdateNodeCode(
+    data?.id,
+    data.node!,
+    setNode,
+    setIsOutdated,
+    updateNodeInternals,
+  );
 
-  useEffect(() => {
-    setNodeDescription(data.node!.description);
-  }, [data.node!.description]);
-
-  useEffect(() => {
-    setNodeName(data.node!.display_name);
-  }, [data.node!.display_name]);
-
-  useEffect(() => {
-    const relevantData =
-      flowPool[data.id] && flowPool[data.id]?.length > 0
-        ? flowPool[data.id][flowPool[data.id].length - 1]
-        : null;
-    if (relevantData) {
-      // Extract validation information from relevantData and update the validationStatus state
-      setValidationStatus(relevantData);
-    } else {
-      setValidationStatus(null);
-    }
-  }, [flowPool[data.id], data.id]);
-
-  useEffect(() => {
-    if (validationStatus?.params) {
-      // if it is not a string turn it into a string
-      let newValidationString = validationStatus.params;
-      if (typeof newValidationString !== "string") {
-        newValidationString = JSON.stringify(validationStatus.params);
-      }
-
-      setValidationString(newValidationString);
-    }
-  }, [validationStatus, validationStatus?.params]);
-
-  const [showNode, setShowNode] = useState(data.showNode ?? true);
-
-  useEffect(() => {
-    setShowNode(data.showNode ?? true);
-  }, [data.showNode]);
-
-  const nameEditable = true;
-
-  const emojiRegex = /\p{Emoji}/u;
-  const isEmoji = emojiRegex.test(data?.node?.icon!);
-
-  const iconNodeRender = useCallback(() => {
-    const iconElement = data?.node?.icon;
-    const iconColor = nodeColors[types[data.type]];
-    const iconName =
-      iconElement || (data.node?.flow ? "group_components" : name);
-    const iconClassName = `generic-node-icon ${
-      !showNode ? " absolute inset-x-6 h-12 w-12 " : ""
-    }`;
-    if (iconElement && isEmoji) {
-      return nodeIconFragment(iconElement);
-    } else {
-      return checkNodeIconFragment(iconColor, iconName, iconClassName);
-    }
-  }, [data, isEmoji, name, showNode]);
+  const name = nodeIconsLucide[data.type] ? data.type : types[data.type];
 
   const nodeIconFragment = (icon) => {
     return <span className="text-lg">{icon}</span>;
@@ -253,79 +104,24 @@ export default function GenericNode({
     );
   };
 
-  const isDark = useDarkStore((state) => state.dark);
-  const renderIconStatus = (
-    buildStatus: BuildStatus | undefined,
-    validationStatus: validationStatusType | null,
-  ) => {
-    if (buildStatus === BuildStatus.BUILDING) {
-      return <Loading className="text-medium-indigo" />;
-    } else {
-      return (
-        <>
-          <IconComponent
-            name="Play"
-            className="absolute ml-0.5 h-5 fill-current stroke-2 text-medium-indigo opacity-0 transition-all group-hover:opacity-100"
-          />
-          {validationStatus && validationStatus.valid ? (
-            <Checkmark
-              className="absolute ml-0.5 h-5 stroke-2 text-status-green opacity-100 transition-all group-hover:opacity-0"
-              isVisible={true}
-            />
-          ) : validationStatus &&
-            !validationStatus.valid &&
-            buildStatus === BuildStatus.INACTIVE ? (
-            <IconComponent
-              name="Play"
-              className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-green opacity-30 transition-all group-hover:opacity-0"
-            />
-          ) : buildStatus === BuildStatus.ERROR ||
-            (validationStatus && !validationStatus.valid) ? (
-            <Xmark
-              isVisible={true}
-              className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-red opacity-100 transition-all group-hover:opacity-0"
-            />
-          ) : (
-            <IconComponent
-              name="Play"
-              className="absolute ml-0.5 h-5 fill-current stroke-2 text-muted-foreground opacity-100 transition-all group-hover:opacity-0"
-            />
-          )}
-        </>
-      );
-    }
-  };
-  const getSpecificClassFromBuildStatus = (
-    buildStatus: BuildStatus | undefined,
-    validationStatus: validationStatusType | null,
-  ) => {
-    let isInvalid = validationStatus && !validationStatus.valid;
-
-    if (buildStatus === BuildStatus.INACTIVE) {
-      // INACTIVE should have its own class
-      return "inactive-status";
-    }
-    if (
-      (buildStatus === BuildStatus.BUILT && isInvalid) ||
-      buildStatus === BuildStatus.ERROR
-    ) {
-      return isDark ? "built-invalid-status-dark" : "built-invalid-status";
-    } else if (buildStatus === BuildStatus.BUILDING) {
-      return "building-status";
-    } else {
-      return "";
-    }
+  const renderIconStatus = () => {
+    return (
+      <div className="generic-node-status-position flex items-center justify-center">
+        {iconStatus}
+      </div>
+    );
   };
 
   const getNodeBorderClassName = (
     selected: boolean,
     showNode: boolean,
     buildStatus: BuildStatus | undefined,
-    validationStatus: validationStatusType | null,
+    validationStatus: VertexBuildTypeAPI | null,
   ) => {
     const specificClassFromBuildStatus = getSpecificClassFromBuildStatus(
       buildStatus,
       validationStatus,
+      isDark,
     );
 
     const baseBorderClass = getBaseBorderClass(selected);
@@ -333,7 +129,7 @@ export default function GenericNode({
     const names = classNames(
       baseBorderClass,
       nodeSizeClass,
-      "generic-node-div",
+      "generic-node-div group/node",
       specificClassFromBuildStatus,
     );
     return names;
@@ -350,6 +146,64 @@ export default function GenericNode({
   const getNodeSizeClass = (showNode) =>
     showNode ? "w-96 rounded-lg" : "w-26 h-26 rounded-full";
 
+  const nameEditable = true;
+  const isEmoji = emojiRegex().test(data?.node?.icon!);
+
+  if (!data.node!.template) {
+    setErrorData({
+      title: `Error in component ${data.node!.display_name}`,
+      list: [
+        `The component ${data.node!.display_name} has no template.`,
+        `Please contact the developer of the component to fix this issue.`,
+      ],
+    });
+    takeSnapshot();
+    deleteNode(data.id);
+  }
+
+  useCheckCodeValidity(data, templates, setIsOutdated, types);
+  useValidationStatusString(validationStatus, setValidationString);
+  useUpdateValidationStatus(data?.id, flowPool, setValidationStatus);
+
+  const iconNodeRender = useIconNodeRender(
+    data,
+    types,
+    nodeColors,
+    name,
+    showNode,
+    isEmoji,
+    nodeIconFragment,
+    checkNodeIconFragment,
+  );
+
+  function countHandles(): void {
+    const count = countHandlesFn(data);
+    setHandles(count);
+  }
+
+  useEffect(() => {
+    countHandles();
+  }, [data, data.node]);
+
+  useEffect(() => {
+    if (!selected) {
+      setInputName(false);
+      setInputDescription(false);
+    }
+  }, [selected]);
+
+  useEffect(() => {
+    setNodeDescription(data.node!.description);
+  }, [data.node!.description]);
+
+  useEffect(() => {
+    setNodeName(data.node!.display_name);
+  }, [data.node!.display_name]);
+
+  useEffect(() => {
+    setShowNode(data.showNode ?? true);
+  }, [data.showNode]);
+
   const memoizedNodeToolbarComponent = useMemo(() => {
     return (
       <NodeToolbar>
@@ -593,67 +447,56 @@ export default function GenericNode({
               )}
             </div>
             {showNode && (
-              <ShadTooltip
-                content={
-                  buildStatus === BuildStatus.BUILDING ? (
-                    <span> {STATUS_BUILDING} </span>
-                  ) : !validationStatus ? (
-                    <span className="flex">{STATUS_BUILD}</span>
-                  ) : (
-                    <div className="max-h-100 p-2">
-                      <div>
-                        {lastRunTime && (
-                          <div className="justify-left flex font-normal text-muted-foreground">
-                            <div>{RUN_TIMESTAMP_PREFIX}</div>
-                            <div className="ml-1 text-status-blue">
-                              {lastRunTime}
+              <>
+                <ShadTooltip
+                  content={
+                    buildStatus === BuildStatus.BUILDING ? (
+                      <span> {STATUS_BUILDING} </span>
+                    ) : !validationStatus ? (
+                      <span className="flex">{STATUS_BUILD}</span>
+                    ) : (
+                      <div className="max-h-100 p-2">
+                        <div>
+                          {lastRunTime && (
+                            <div className="justify-left flex font-normal text-muted-foreground">
+                              <div>{RUN_TIMESTAMP_PREFIX}</div>
+                              <div className="ml-1 text-status-blue">
+                                {lastRunTime}
+                              </div>
                             </div>
+                          )}
+                        </div>
+                        <div className="justify-left flex font-normal text-muted-foreground">
+                          <div>Duration:</div>
+                          <div className="ml-1 text-status-blue">
+                            {validationStatus?.data.duration}
                           </div>
-                        )}
-                      </div>
-                      <div className="justify-left flex font-normal text-muted-foreground">
-                        <div>Duration:</div>
-                        <div className="mb-3 ml-1 text-status-blue">
-                          {validationStatus?.data.duration}
                         </div>
                       </div>
-                      <hr />
-                      <span className="mb-2 mt-2   flex justify-center font-semibold text-muted-foreground">
-                        Output
-                      </span>
-                      <div className="max-h-96 overflow-auto font-normal custom-scroll">
-                        {validationString.split("\n").map((line, index) => (
-                          <div className="font-normal" key={index}>
-                            {line}
-                          </div>
-                        ))}
-                      </div>
-                    </div>
-                  )
-                }
-                side="bottom"
-              >
-                <Button
-                  onClick={() => {
-                    if (buildStatus === BuildStatus.BUILDING || isBuilding)
-                      return;
-                    setValidationStatus(null);
-                    buildFlow({ stopNodeId: data.id });
-                  }}
-                  variant="secondary"
-                  className={"group h-9 px-1.5"}
+                    )
+                  }
+                  side="bottom"
                 >
-                  <div
-                    data-testid={
-                      `button_run_` + data?.node?.display_name.toLowerCase()
-                    }
+                  <Button
+                    onClick={() => {
+                      if (buildStatus === BuildStatus.BUILDING || isBuilding)
+                        return;
+                      setValidationStatus(null);
+                      buildFlow({ stopNodeId: data.id });
+                    }}
+                    variant="secondary"
+                    className={"group h-9 px-1.5"}
                   >
-                    <div className="generic-node-status-position flex items-center justify-center">
-                      {renderIconStatus(buildStatus, validationStatus)}
+                    <div
+                      data-testid={
+                        `button_run_` + data?.node?.display_name.toLowerCase()
+                      }
+                    >
+                      {renderIconStatus()}
                     </div>
-                  </div>
-                </Button>
-              </ShadTooltip>
+                  </Button>
+                </ShadTooltip>
+              </>
             )}
           </div>
         </div>

src/frontend/src/CustomNodes/helpers/count-handles.ts

@@ -0,0 +1,26 @@
+import { NodeDataType } from "../../types/flow";
+
+export function countHandlesFn(data: NodeDataType): number {
+  let count = Object.keys(data.node!.template)
+    .filter((templateField) => templateField.charAt(0) !== "_")
+    .map((templateCamp) => {
+      const { template } = data.node!;
+      if (template[templateCamp].input_types) return true;
+      if (!template[templateCamp].show) return false;
+      switch (template[templateCamp].type) {
+        case "str":
+        case "bool":
+        case "float":
+        case "code":
+        case "prompt":
+        case "file":
+        case "int":
+          return false;
+        default:
+          return true;
+      }
+    })
+    .reduce((total, value) => total + (value ? 1 : 0), 0);
+
+  return count;
+}

src/frontend/src/CustomNodes/helpers/get-class-from-build-status.ts

@@ -0,0 +1,25 @@
+import { BuildStatus } from "../../constants/enums";
+import { VertexBuildTypeAPI } from "../../types/api";
+
+export const getSpecificClassFromBuildStatus = (
+  buildStatus: BuildStatus | undefined,
+  validationStatus: VertexBuildTypeAPI | null,
+  isDark: boolean,
+) => {
+  let isInvalid = validationStatus && !validationStatus.valid;
+
+  if (buildStatus === BuildStatus.INACTIVE) {
+    // INACTIVE should have its own class
+    return "inactive-status";
+  }
+  if (
+    (buildStatus === BuildStatus.BUILT && isInvalid) ||
+    buildStatus === BuildStatus.ERROR
+  ) {
+    return isDark ? "built-invalid-status-dark" : "built-invalid-status";
+  } else if (buildStatus === BuildStatus.BUILDING) {
+    return "building-status";
+  } else {
+    return "";
+  }
+};

src/frontend/src/CustomNodes/hooks/use-check-code-validity.tsx

@@ -0,0 +1,39 @@
+import { useEffect } from "react";
+import { NATIVE_CATEGORIES } from "../../constants/constants";
+import { NodeDataType } from "../../types/flow";
+
+const useCheckCodeValidity = (
+  data: NodeDataType,
+  templates: { [key: string]: any },
+  setIsOutdated: (value: boolean) => void,
+  types,
+) => {
+  useEffect(() => {
+    // This one should run only once
+    // first check if data.type in NATIVE_CATEGORIES
+    // if not return
+    if (
+      !NATIVE_CATEGORIES.includes(types[data.type]) ||
+      !data.node?.template?.code?.value
+    )
+      return;
+    const thisNodeTemplate = templates[data.type].template;
+    // if the template does not have a code key
+    // return
+    if (!thisNodeTemplate.code) return;
+    const currentCode = thisNodeTemplate.code?.value;
+    const thisNodesCode = data.node!.template?.code?.value;
+    const componentsToIgnore = ["Custom Component", "Prompt"];
+    if (
+      currentCode !== thisNodesCode &&
+      !componentsToIgnore.includes(data.node!.display_name)
+    ) {
+      setIsOutdated(true);
+    } else {
+      setIsOutdated(false);
+    }
+    // template.code can be undefined
+  }, [data.node?.template?.code?.value, templates, setIsOutdated]);
+};
+
+export default useCheckCodeValidity;

src/frontend/src/CustomNodes/hooks/use-icon-render.tsx

@@ -0,0 +1,45 @@
+import { useCallback } from "react";
+import { NodeDataType } from "../../types/flow";
+
+const useIconNodeRender = (
+  data: NodeDataType,
+  types: { [key: string]: string },
+  nodeColors: { [key: string]: string },
+  name: string,
+  showNode: boolean,
+  isEmoji: boolean,
+  nodeIconFragment: (iconElement: string) => JSX.Element,
+  checkNodeIconFragment: (
+    iconColor: string,
+    iconName: string,
+    iconClassName: string,
+  ) => JSX.Element,
+) => {
+  const iconNodeRender = useCallback(() => {
+    const iconElement = data?.node?.icon;
+    const iconColor = nodeColors[types[data.type]];
+    const iconName =
+      iconElement || (data.node?.flow ? "group_components" : name);
+    const iconClassName = `generic-node-icon ${
+      !showNode ? " absolute inset-x-6 h-12 w-12 " : ""
+    }`;
+    if (iconElement && isEmoji) {
+      return nodeIconFragment(iconElement);
+    } else {
+      return checkNodeIconFragment(iconColor, iconName, iconClassName);
+    }
+  }, [
+    data,
+    types,
+    nodeColors,
+    name,
+    showNode,
+    isEmoji,
+    nodeIconFragment,
+    checkNodeIconFragment,
+  ]);
+
+  return iconNodeRender;
+};
+
+export default useIconNodeRender;

src/frontend/src/CustomNodes/hooks/use-icons-status.tsx

@@ -0,0 +1,54 @@
+import IconComponent from "../../components/genericIconComponent";
+import Checkmark from "../../components/ui/checkmark";
+import Loading from "../../components/ui/loading";
+import Xmark from "../../components/ui/xmark";
+import { BuildStatus } from "../../constants/enums";
+import { VertexBuildTypeAPI } from "../../types/api";
+
+const useIconStatus = (
+  buildStatus: BuildStatus | undefined,
+  validationStatus: VertexBuildTypeAPI | null,
+) => {
+  const renderIconStatus = () => {
+    if (buildStatus === BuildStatus.BUILDING) {
+      return <Loading className="text-medium-indigo" />;
+    } else {
+      return (
+        <>
+          <IconComponent
+            name="Play"
+            className="absolute ml-0.5 h-5 fill-current stroke-2 text-medium-indigo opacity-0 transition-all group-hover:opacity-100"
+          />
+          {validationStatus && validationStatus.valid ? (
+            <Checkmark
+              className="absolute ml-0.5 h-5 stroke-2 text-status-green opacity-100 transition-all group-hover:opacity-0"
+              isVisible={true}
+            />
+          ) : validationStatus &&
+            !validationStatus.valid &&
+            buildStatus === BuildStatus.INACTIVE ? (
+            <IconComponent
+              name="Play"
+              className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-green opacity-30 transition-all group-hover:opacity-0"
+            />
+          ) : buildStatus === BuildStatus.ERROR ||
+            (validationStatus && !validationStatus.valid) ? (
+            <Xmark
+              isVisible={true}
+              className="absolute ml-0.5 h-5 fill-current stroke-2 text-status-red opacity-100 transition-all group-hover:opacity-0"
+            />
+          ) : (
+            <IconComponent
+              name="Play"
+              className="absolute ml-0.5 h-5 fill-current stroke-2 text-muted-foreground opacity-100 transition-all group-hover:opacity-0"
+            />
+          )}
+        </>
+      );
+    }
+  };
+
+  return renderIconStatus();
+};
+
+export default useIconStatus;

src/frontend/src/CustomNodes/hooks/use-update-node-code.tsx

@@ -0,0 +1,38 @@
+import { cloneDeep } from "lodash"; // or any other deep cloning library you prefer
+import { useCallback } from "react";
+import { APIClassType } from "../../types/api";
+
+const useUpdateNodeCode = (
+  dataId: string,
+  dataNode: APIClassType, // Define YourNodeType according to your data structure
+  setNode: (id: string, callback: (oldNode) => any) => void,
+  setIsOutdated: (value: boolean) => void,
+  updateNodeInternals: (id: string) => void,
+) => {
+  const updateNodeCode = useCallback(
+    (newNodeClass: APIClassType, code: string, name: string) => {
+      setNode(dataId, (oldNode) => {
+        let newNode = cloneDeep(oldNode);
+
+        newNode.data = {
+          ...newNode.data,
+          node: newNodeClass,
+          description: newNodeClass.description ?? dataNode.description,
+          display_name: newNodeClass.display_name ?? dataNode.display_name,
+        };
+
+        newNode.data.node.template[name].value = code;
+        setIsOutdated(false);
+
+        return newNode;
+      });
+
+      updateNodeInternals(dataId);
+    },
+    [dataId, dataNode, setNode, setIsOutdated, updateNodeInternals],
+  );
+
+  return updateNodeCode;
+};
+
+export default useUpdateNodeCode;

src/frontend/src/CustomNodes/hooks/use-update-validation-status.tsx

@@ -0,0 +1,18 @@
+import { useEffect } from "react";
+
+const useUpdateValidationStatus = (dataId, flowPool, setValidationStatus) => {
+  useEffect(() => {
+    const relevantData =
+      flowPool[dataId] && flowPool[dataId]?.length > 0
+        ? flowPool[dataId][flowPool[dataId].length - 1]
+        : null;
+    if (relevantData) {
+      // Extract validation information from relevantData and update the validationStatus state
+      setValidationStatus(relevantData);
+    } else {
+      setValidationStatus(null);
+    }
+  }, [flowPool[dataId], dataId, setValidationStatus]);
+};
+
+export default useUpdateValidationStatus;

src/frontend/src/CustomNodes/hooks/use-validation-status-string.tsx

@@ -0,0 +1,22 @@
+import { useEffect } from "react";
+
+const useValidationStatusString = (validationStatus, setValidationString) => {
+  useEffect(() => {
+    if (validationStatus?.data.logs) {
+      // if it is not a string turn it into a string
+      let newValidationString = "";
+      if (Array.isArray(validationStatus.data.logs)) {
+        newValidationString = validationStatus.data.logs
+          .map((log) => (log?.message ? log.message : JSON.stringify(log)))
+          .join("\n");
+      }
+      if (typeof newValidationString !== "string") {
+        newValidationString = JSON.stringify(validationStatus.data.logs);
+      }
+
+      setValidationString(newValidationString);
+    }
+  }, [validationStatus, validationStatus?.data.logs, setValidationString]);
+};
+
+export default useValidationStatusString;

src/frontend/src/alerts/error/index.tsx

@@ -51,13 +51,15 @@ export default function ErrorAlert({
             />
           </div>
           <div className="ml-3">
-            <h3 className="error-build-foreground">{title}</h3>
+            <h3 className="error-build-foreground line-clamp-2">{title}</h3>
             {list?.length !== 0 &&
             list?.some((item) => item !== null && item !== undefined) ? (
               <div className="error-build-message-div">
                 <ul className="error-build-message-list">
                   {list.map((item, index) => (
-                    <li key={index}>{item}</li>
+                    <li key={index} className="line-clamp-5">
+                      {item}
+                    </li>
                   ))}
                 </ul>
               </div>

src/frontend/src/alerts/notice/index.tsx

@@ -47,7 +47,7 @@ export default function NoticeAlert({
             />
           </div>
           <div className="ml-3 flex-1 md:flex md:justify-between">
-            <p className="text-sm text-info-foreground word-break-break-word">
+            <p className="line-clamp-2 text-sm text-info-foreground word-break-break-word">
               {title}
             </p>
             <p className="mt-3 text-sm md:ml-6 md:mt-0">

src/frontend/src/alerts/success/index.tsx

@@ -45,7 +45,7 @@ export default function SuccessAlert({
             />
           </div>
           <div className="ml-3">
-            <p className="success-alert-message">{title}</p>
+            <p className="success-alert-message line-clamp-2">{title}</p>
           </div>
         </div>
       </div>

src/frontend/src/components/accordionComponent/index.tsx

@@ -7,7 +7,6 @@ import {
 } from "../../components/ui/accordion";
 import { AccordionComponentType } from "../../types/components";
 import { cn } from "../../utils/utils";
-import ShadTooltip from "../shadTooltipComponent";
 
 export default function AccordionComponent({
   trigger,

src/frontend/src/components/cardComponent/index.tsx

@@ -27,8 +27,8 @@ import {
 import { Checkbox } from "../ui/checkbox";
 import { FormControl, FormField } from "../ui/form";
 import Loading from "../ui/loading";
-import { convertTestName } from "./utils/convert-test-name";
 import DragCardComponent from "./components/dragCardComponent";
+import { convertTestName } from "./utils/convert-test-name";
 
 export default function CollectionCardComponent({
   data,

src/frontend/src/components/csvOutputComponent/index.tsx

@@ -115,6 +115,7 @@ function CsvOutputComponent({
           style={{ height: "100%", width: "100%" }}
         >
           <TableComponent
+            key={"csv-output"}
             rowData={rowData}
             columnDefs={colDefs}
             defaultColDef={defaultColDef}

src/frontend/src/components/dropdownComponent/index.tsx

@@ -33,9 +33,8 @@ export default function Dropdown({
 
   const refButton = useRef<HTMLButtonElement>(null);
 
-  const PopoverContentDropdown = children
-    ? PopoverContent
-    : PopoverContentWithoutPortal;
+  const PopoverContentDropdown =
+    children || editNode ? PopoverContent : PopoverContentWithoutPortal;
 
   return (
     <>

src/frontend/src/components/headerComponent/index.tsx

@@ -81,14 +81,16 @@ export default function Header(): JSX.Element {
           <span className="ml-4 text-2xl">⛓️</span>
         </Link>
         {showArrowReturnIcon && (
-          <button
+          <Button
+            variant="none"
+            size="none"
             onClick={() => {
               checkForChanges();
               redirectToLastLocation();
             }}
           >
             <IconComponent name="ChevronLeft" className="w-4" />
-          </button>
+          </Button>
         )}
 
         <MenuBar />
@@ -181,24 +183,14 @@ export default function Header(): JSX.Element {
               />
             </div>
           </AlertDropdown>
-          {autoLogin && (
-            <button
-              onClick={() => {
-                navigate("/account/api-keys");
-              }}
-            >
-              <IconComponent
-                name="Key"
-                className="side-bar-button-size text-muted-foreground hover:text-accent-foreground"
-              />
-            </button>
-          )}
 
           <>
             <Separator orientation="vertical" />
             <DropdownMenu>
               <DropdownMenuTrigger asChild>
-                <button
+                <Button
+                  variant="none"
+                  size="none"
                   data-testid="user-profile-settings"
                   className={
                     "h-7 w-7 rounded-full focus-visible:outline-0 " +
@@ -212,6 +204,28 @@ export default function Header(): JSX.Element {
                 />
               </DropdownMenuTrigger>
               <DropdownMenuContent>
+                {!autoLogin && (
+                  <>
+                    <DropdownMenuLabel>
+                      <div className="flex items-center gap-3">
+                        <div
+                          className={
+                            "h-5 w-5 rounded-full focus-visible:outline-0 " +
+                            (userData?.profile_image ??
+                              (userData?.id
+                                ? gradients[
+                                    parseInt(userData?.id ?? "", 30) %
+                                      gradients.length
+                                  ]
+                                : "bg-gray-500"))
+                          }
+                        />
+                        {userData?.username ?? "User"}
+                      </div>
+                    </DropdownMenuLabel>
+                    <DropdownMenuSeparator />
+                  </>
+                )}
                 <DropdownMenuLabel>General</DropdownMenuLabel>
                 <DropdownMenuItem
                   className="cursor-pointer"

src/frontend/src/components/inputComponent/components/popover/index.tsx

@@ -10,7 +10,11 @@ import {
   CommandList,
 } from "../../../ui/command";
 import { Input } from "../../../ui/input";
-import { Popover, PopoverContentWithoutPortal } from "../../../ui/popover";
+import {
+  Popover,
+  PopoverContent,
+  PopoverContentWithoutPortal,
+} from "../../../ui/popover";
 const CustomInputPopover = ({
   id,
   refInput,
@@ -39,6 +43,9 @@ const CustomInputPopover = ({
   showOptions,
 }) => {
   const setErrorData = useAlertStore.getState().setErrorData;
+  const PopoverContentInput = editNode
+    ? PopoverContent
+    : PopoverContentWithoutPortal;
 
   const handleInputChange = (e) => {
     if (password) {
@@ -107,7 +114,7 @@ const CustomInputPopover = ({
           data-testid={editNode ? id + "-edit" : id}
         />
       </PopoverAnchor>
-      <PopoverContentWithoutPortal
+      <PopoverContentInput
         className="nocopy nowheel nopan nodelete nodrag noundo p-0"
         style={{ minWidth: refInput?.current?.clientWidth ?? "200px" }}
         side="bottom"
@@ -184,7 +191,7 @@ const CustomInputPopover = ({
             </CommandGroup>
           </CommandList>
         </Command>
-      </PopoverContentWithoutPortal>
+      </PopoverContentInput>
     </Popover>
   );
 };

src/frontend/src/components/inputComponent/components/popoverObject/index.tsx

@@ -9,7 +9,11 @@ import {
   CommandList,
 } from "../../../ui/command";
 import { Input } from "../../../ui/input";
-import { Popover, PopoverContentWithoutPortal } from "../../../ui/popover";
+import {
+  Popover,
+  PopoverContent,
+  PopoverContentWithoutPortal,
+} from "../../../ui/popover";
 const CustomInputPopoverObject = ({
   id,
   refInput,
@@ -23,6 +27,7 @@ const CustomInputPopoverObject = ({
   disabled,
   setShowOptions,
   required,
+  editNode,
   className,
   placeholder,
   onChange,
@@ -34,6 +39,10 @@ const CustomInputPopoverObject = ({
   handleKeyDown,
   showOptions,
 }) => {
+  const PopoverContentInput = editNode
+    ? PopoverContent
+    : PopoverContentWithoutPortal;
+
   const handleInputChange = (e) => {
     onChange && onChange(e.target.value);
   };
@@ -79,7 +88,7 @@ const CustomInputPopoverObject = ({
           data-testid={id}
         />
       </PopoverAnchor>
-      <PopoverContentWithoutPortal
+      <PopoverContentInput
         className="nocopy nowheel nopan nodelete nodrag noundo p-0"
         style={{ minWidth: refInput?.current?.clientWidth ?? "200px" }}
         side="bottom"
@@ -159,7 +168,7 @@ const CustomInputPopoverObject = ({
             </CommandGroup>
           </CommandList>
         </Command>
-      </PopoverContentWithoutPortal>
+      </PopoverContentInput>
     </Popover>
   );
 };

src/frontend/src/components/inputComponent/index.tsx

@@ -108,6 +108,7 @@ export default function InputComponent({
               setSelectedOptions={setSelectedOptions}
               options={objectOptions}
               value={value}
+              editNode={editNode}
               autoFocus={autoFocus}
               disabled={disabled}
               setShowOptions={setShowOptions}

src/frontend/src/components/inputFileComponent/index.tsx

@@ -1,7 +1,6 @@
 import { useEffect, useState } from "react";
 import {
   CONSOLE_ERROR_MSG,
-  CONSOLE_SUCCESS_MSG,
   INVALID_FILE_ALERT,
 } from "../../constants/alerts_constants";
 import { uploadFile } from "../../controllers/API";

src/frontend/src/components/inputGlobalComponent/index.tsx

@@ -32,11 +32,11 @@ export default function InputGlobalComponent({
   const setErrorData = useAlertStore((state) => state.setErrorData);
 
   useEffect(() => {
-    if (data.node?.template[name])
+    if (data)
       if (
         globalVariablesEntries &&
-        !globalVariablesEntries.includes(data.node?.template[name].value) &&
-        data.node?.template[name].load_from_db
+        !globalVariablesEntries.includes(data.value) &&
+        data.load_from_db
       ) {
         setTimeout(() => {
           onChange("", true);
@@ -46,17 +46,11 @@ export default function InputGlobalComponent({
   }, [globalVariablesEntries]);
 
   useEffect(() => {
-    if (
-      !data.node?.template[name].value &&
-      data.node?.template[name].display_name
-    ) {
-      if (
-        unavaliableFields[data.node?.template[name].display_name!] &&
-        !disabled
-      ) {
+    if (!data.value && data.display_name) {
+      if (unavaliableFields[data.display_name!] && !disabled) {
         setTimeout(() => {
           setDb(true);
-          onChange(unavaliableFields[data.node?.template[name].display_name!]);
+          onChange(unavaliableFields[data.display_name!]);
         }, 100);
       }
     }
@@ -68,10 +62,7 @@ export default function InputGlobalComponent({
       await deleteGlobalVariable(id)
         .then(() => {
           removeGlobalVariable(key);
-          if (
-            data?.node?.template[name].value === key &&
-            data?.node?.template[name].load_from_db
-          ) {
+          if (data?.value === key && data?.load_from_db) {
             onChange("");
             setDb(false);
           }
@@ -94,8 +85,8 @@ export default function InputGlobalComponent({
       id={"input-" + name}
       editNode={editNode}
       disabled={disabled}
-      password={data.node?.template[name].password ?? false}
-      value={data.node?.template[name].value ?? ""}
+      password={data.password ?? false}
+      value={data.value ?? ""}
       options={globalVariablesEntries}
       optionsPlaceholder={"Global Variables"}
       optionsIcon="Globe"
@@ -138,10 +129,10 @@ export default function InputGlobalComponent({
         </DeleteConfirmationModal>
       )}
       selectedOption={
-        data?.node?.template[name].load_from_db &&
+        data?.load_from_db &&
         globalVariablesEntries &&
-        globalVariablesEntries.includes(data?.node?.template[name].value ?? "")
-          ? data?.node?.template[name].value
+        globalVariablesEntries.includes(data?.value ?? "")
+          ? data?.value
           : ""
       }
       setSelectedOption={(value) => {

src/frontend/src/components/recordsOutputComponent/index.tsx

@@ -1,19 +1,20 @@
 import { ColDef, ColGroupDef } from "ag-grid-community";
 import "ag-grid-community/styles/ag-grid.css"; // Mandatory CSS required by the grid
 import "ag-grid-community/styles/ag-theme-balham.css"; // Optional Theme applied to the grid
-import { FlowPoolObjectType } from "../../types/chat";
 import { extractColumnsFromRows } from "../../utils/utils";
 import TableComponent from "../tableComponent";
 
 function RecordsOutputComponent({
-  flowPool,
   pagination,
+  rows,
+  columnMode = "union",
 }: {
-  flowPool: FlowPoolObjectType;
   pagination: boolean;
+  rows: any;
+  columnMode?: "intersection" | "union";
 }) {
-  const rows = flowPool?.data?.artifacts?.records ?? [];
-  const columns = extractColumnsFromRows(rows, "union");
+  const columns = extractColumnsFromRows(rows, columnMode);
+
   const columnDefs = columns.map((col, idx) => ({
     ...col,
     resizable: idx !== columns.length - 1,
@@ -22,6 +23,7 @@ function RecordsOutputComponent({
 
   return (
     <TableComponent
+      key={"recordsOutputComponent"}
       overlayNoRowsTemplate="No data available"
       suppressRowClickSelection={true}
       pagination={pagination}