Merge remote-tracking branch 'origin/dev' into node-shortcuts-refactor

src/backend/Dockerfile

@@ -1,4 +1,4 @@
-FROM logspace/backend_build as backend_build
+FROM langflowai/backend_build as backend_build
 
 FROM python:3.10-slim
 WORKDIR /app

src/backend/base/langflow/alembic/versions/1a110b568907_replace_credential_table_with_variable.py

@@ -5,6 +5,7 @@ Revises: 63b9c451fd30
 Create Date: 2024-03-25 09:40:02.743453
 
 """
+
 from typing import Sequence, Union
 
 import sqlalchemy as sa

src/backend/base/langflow/alembic/versions/4e5980a44eaa_fix_date_times_again.py

@@ -0,0 +1,130 @@
+"""Fix date times again
+
+Revision ID: 4e5980a44eaa
+Revises: 79e675cb6752
+Create Date: 2024-04-12 18:11:06.454037
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from loguru import logger
+from sqlalchemy.dialects import postgresql
+from sqlalchemy.engine.reflection import Inspector
+
+# revision identifiers, used by Alembic.
+revision: str = "4e5980a44eaa"
+down_revision: Union[str, None] = "79e675cb6752"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)  # type: ignore
+    table_names = inspector.get_table_names()
+    # ### commands auto generated by Alembic - please adjust! ###
+    if "apikey" in table_names:
+        columns = inspector.get_columns("apikey")
+        created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
+        if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
+            with op.batch_alter_table("apikey", schema=None) as batch_op:
+                batch_op.alter_column(
+                    "created_at",
+                    existing_type=postgresql.TIMESTAMP(),
+                    type_=sa.DateTime(timezone=True),
+                    existing_nullable=False,
+                )
+        else:
+            if created_at_column is None:
+                logger.warning("Column 'created_at' not found in table 'apikey'")
+            else:
+                logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
+    if "variable" in table_names:
+        columns = inspector.get_columns("variable")
+        created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
+        updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
+        with op.batch_alter_table("variable", schema=None) as batch_op:
+            if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
+                batch_op.alter_column(
+                    "created_at",
+                    existing_type=postgresql.TIMESTAMP(),
+                    type_=sa.DateTime(timezone=True),
+                    existing_nullable=True,
+                )
+            else:
+                if created_at_column is None:
+                    logger.warning("Column 'created_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
+            if updated_at_column is not None and isinstance(updated_at_column["type"], postgresql.TIMESTAMP):
+                batch_op.alter_column(
+                    "updated_at",
+                    existing_type=postgresql.TIMESTAMP(),
+                    type_=sa.DateTime(timezone=True),
+                    existing_nullable=True,
+                )
+            else:
+                if updated_at_column is None:
+                    logger.warning("Column 'updated_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
+
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)  # type: ignore
+    table_names = inspector.get_table_names()
+    # ### commands auto generated by Alembic - please adjust! ###
+    if "variable" in table_names:
+        columns = inspector.get_columns("variable")
+        created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
+        updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
+        with op.batch_alter_table("variable", schema=None) as batch_op:
+            if updated_at_column is not None and isinstance(updated_at_column["type"], sa.DateTime):
+                batch_op.alter_column(
+                    "updated_at",
+                    existing_type=sa.DateTime(timezone=True),
+                    type_=postgresql.TIMESTAMP(),
+                    existing_nullable=True,
+                )
+            else:
+                if updated_at_column is None:
+                    logger.warning("Column 'updated_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
+            if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
+                batch_op.alter_column(
+                    "created_at",
+                    existing_type=sa.DateTime(timezone=True),
+                    type_=postgresql.TIMESTAMP(),
+                    existing_nullable=True,
+                )
+            else:
+                if created_at_column is None:
+                    logger.warning("Column 'created_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
+
+    if "apikey" in table_names:
+        columns = inspector.get_columns("apikey")
+        created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
+        if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
+            with op.batch_alter_table("apikey", schema=None) as batch_op:
+                batch_op.alter_column(
+                    "created_at",
+                    existing_type=sa.DateTime(timezone=True),
+                    type_=postgresql.TIMESTAMP(),
+                    existing_nullable=False,
+                )
+        else:
+            if created_at_column is None:
+                logger.warning("Column 'created_at' not found in table 'apikey'")
+            else:
+                logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
+
+    # ### end Alembic commands ###

src/backend/base/langflow/alembic/versions/58b28437a398_modify_nullable.py

@@ -0,0 +1,66 @@
+"""Modify nullable
+
+Revision ID: 58b28437a398
+Revises: 4e5980a44eaa
+Create Date: 2024-04-13 10:57:23.061709
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from loguru import logger
+from sqlalchemy.engine.reflection import Inspector
+
+down_revision: Union[str, None] = "4e5980a44eaa"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+# Revision identifiers, used by Alembic.
+revision = "58b28437a398"
+down_revision = "4e5980a44eaa"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    tables = ["apikey", "variable"]  # List of tables to modify
+
+    for table_name in tables:
+        modify_nullable(conn, inspector, table_name, upgrade=True)
+
+
+def downgrade():
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    tables = ["apikey", "variable"]  # List of tables to revert
+
+    for table_name in tables:
+        modify_nullable(conn, inspector, table_name, upgrade=False)
+
+
+def modify_nullable(conn, inspector, table_name, upgrade=True):
+    columns = inspector.get_columns(table_name)
+    nullable_changes = {"apikey": {"created_at": False}, "variable": {"created_at": True, "updated_at": True}}
+
+    if table_name in columns:
+        with op.batch_alter_table(table_name, schema=None) as batch_op:
+            for column_name, nullable_setting in nullable_changes.get(table_name, {}).items():
+                column_info = next((col for col in columns if col["name"] == column_name), None)
+                if column_info:
+                    current_nullable = column_info["nullable"]
+                    target_nullable = nullable_setting if upgrade else not nullable_setting
+
+                    if current_nullable != target_nullable:
+                        batch_op.alter_column(
+                            column_name, existing_type=sa.DateTime(timezone=True), nullable=target_nullable
+                        )
+                    else:
+                        logger.info(
+                            f"Column '{column_name}' in table '{table_name}' already has nullable={target_nullable}"
+                        )
+                else:
+                    logger.warning(f"Column '{column_name}' not found in table '{table_name}'")

src/backend/base/langflow/alembic/versions/79e675cb6752_change_datetime_type.py

@@ -5,13 +5,14 @@ Revises: e3bc869fa272
 Create Date: 2024-04-11 19:23:10.697335
 
 """
-from calendar import c
+
 from typing import Sequence, Union
 
 import sqlalchemy as sa
 from alembic import op
 from sqlalchemy.dialects import postgresql
 from sqlalchemy.engine.reflection import Inspector
+from loguru import logger
 
 # revision identifiers, used by Alembic.
 revision: str = "79e675cb6752"
@@ -28,7 +29,7 @@ def upgrade() -> None:
     if "apikey" in table_names:
         columns = inspector.get_columns("apikey")
         created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
-        if created_at_column is not None and created_at_column["type"] == postgresql.TIMESTAMP():
+        if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
             with op.batch_alter_table("apikey", schema=None) as batch_op:
                 batch_op.alter_column(
                     "created_at",
@@ -36,25 +37,40 @@ def upgrade() -> None:
                     type_=sa.DateTime(timezone=True),
                     existing_nullable=False,
                 )
+        else:
+            if created_at_column is None:
+                logger.warning("Column 'created_at' not found in table 'apikey'")
+            else:
+                logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
     if "variable" in table_names:
         columns = inspector.get_columns("variable")
         created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
         updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
         with op.batch_alter_table("variable", schema=None) as batch_op:
-            if created_at_column is not None and created_at_column["type"] == postgresql.TIMESTAMP():
+            if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP):
                 batch_op.alter_column(
                     "created_at",
                     existing_type=postgresql.TIMESTAMP(),
                     type_=sa.DateTime(timezone=True),
                     existing_nullable=True,
                 )
-            if updated_at_column is not None and updated_at_column["type"] == postgresql.TIMESTAMP():
+            else:
+                if created_at_column is None:
+                    logger.warning("Column 'created_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
+            if updated_at_column is not None and isinstance(updated_at_column["type"], postgresql.TIMESTAMP):
                 batch_op.alter_column(
                     "updated_at",
                     existing_type=postgresql.TIMESTAMP(),
                     type_=sa.DateTime(timezone=True),
                     existing_nullable=True,
                 )
+            else:
+                if updated_at_column is None:
+                    logger.warning("Column 'updated_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
 
     # ### end Alembic commands ###
 
@@ -69,25 +85,35 @@ def downgrade() -> None:
         created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
         updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None)
         with op.batch_alter_table("variable", schema=None) as batch_op:
-            if updated_at_column is not None and updated_at_column["type"] == sa.DateTime(timezone=True):
+            if updated_at_column is not None and isinstance(updated_at_column["type"], sa.DateTime):
                 batch_op.alter_column(
                     "updated_at",
                     existing_type=sa.DateTime(timezone=True),
                     type_=postgresql.TIMESTAMP(),
                     existing_nullable=True,
                 )
-            if created_at_column is not None and created_at_column["type"] == sa.DateTime(timezone=True):
+            else:
+                if updated_at_column is None:
+                    logger.warning("Column 'updated_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'")
+            if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
                 batch_op.alter_column(
                     "created_at",
                     existing_type=sa.DateTime(timezone=True),
                     type_=postgresql.TIMESTAMP(),
                     existing_nullable=True,
                 )
+            else:
+                if created_at_column is None:
+                    logger.warning("Column 'created_at' not found in table 'variable'")
+                else:
+                    logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'")
 
     if "apikey" in table_names:
         columns = inspector.get_columns("apikey")
         created_at_column = next((column for column in columns if column["name"] == "created_at"), None)
-        if created_at_column is not None and created_at_column["type"] == sa.DateTime(timezone=True):
+        if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime):
             with op.batch_alter_table("apikey", schema=None) as batch_op:
                 batch_op.alter_column(
                     "created_at",
@@ -95,5 +121,10 @@ def downgrade() -> None:
                     type_=postgresql.TIMESTAMP(),
                     existing_nullable=False,
                 )
+        else:
+            if created_at_column is None:
+                logger.warning("Column 'created_at' not found in table 'apikey'")
+            else:
+                logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'")
 
     # ### end Alembic commands ###

src/backend/base/langflow/alembic/versions/e3bc869fa272_fix_nullable.py

@@ -26,19 +26,21 @@ def upgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     if "variable" not in table_names:
         return
-
-    column_names = [column["name"] for column in inspector.get_columns("variable")]
+    columns = [column for column in inspector.get_columns("variable")]
+    column_names = [column["name"] for column in columns]
 
     with op.batch_alter_table("variable", schema=None) as batch_op:
         if "created_at" in column_names:
-            batch_op.alter_column(
-                "created_at",
-                existing_type=sa.TIMESTAMP(timezone=True),
-                nullable=True,
-                # existing_server_default expects str | bool | Identity | Computed | None
-                # sa.text("now()") is not a valid value for existing_server_default
-                existing_server_default=False,
-            )
+            created_at_colunmn = next(column for column in columns if column["name"] == "created_at")
+            if created_at_colunmn["nullable"] is False:
+                batch_op.alter_column(
+                    "created_at",
+                    existing_type=sa.TIMESTAMP(timezone=True),
+                    nullable=True,
+                    # existing_server_default expects str | bool | Identity | Computed | None
+                    # sa.text("now()") is not a valid value for existing_server_default
+                    existing_server_default=False,
+                )
 
     # ### end Alembic commands ###
 
@@ -50,13 +52,17 @@ def downgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     if "variable" not in table_names:
         return
+    columns = [column for column in inspector.get_columns("variable")]
+    column_names = [column["name"] for column in columns]
     with op.batch_alter_table("variable", schema=None) as batch_op:
-        if "created_at" in inspector.get_columns("variable"):
-            batch_op.alter_column(
-                "created_at",
-                existing_type=sa.TIMESTAMP(timezone=True),
-                nullable=False,
-                existing_server_default=False,
-            )
+        if "created_at" in column_names:
+            created_at_colunmn = next(column for column in columns if column["name"] == "created_at")
+            if created_at_colunmn["nullable"] is True:
+                batch_op.alter_column(
+                    "created_at",
+                    existing_type=sa.TIMESTAMP(timezone=True),
+                    nullable=False,
+                    existing_server_default=False,
+                )
 
     # ### end Alembic commands ###

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

@@ -109,6 +109,7 @@ async def simplified_run_flow(
     This endpoint provides a powerful interface for executing flows with enhanced flexibility and efficiency, supporting a wide range of applications by allowing for dynamic input and output configuration along with performance optimizations through session management and caching.
     """
     session_id = input_request.session_id
+
     try:
         task_result: List[RunOutputs] = []
         artifacts = {}
@@ -127,8 +128,9 @@ async def simplified_run_flow(
             if flow.data is None:
                 raise ValueError(f"Flow {flow_id} has no data")
             graph_data = flow.data
-            graph_data = process_tweaks(graph_data, input_request.tweaks or {})
-            graph = Graph.from_payload(graph_data, flow_id=flow_id)
+
+            graph_data = process_tweaks(graph_data, input_request.tweaks or {}, stream=stream)
+            graph = Graph.from_payload(graph_data, flow_id=flow_id, user_id=str(api_key_user.id))
         inputs = [
             InputValueRequest(components=[], input_value=input_request.input_value, type=input_request.input_type)
         ]

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

@@ -26,7 +26,7 @@ class BuildStatus(Enum):
 
 
 class TweaksRequest(BaseModel):
-    tweaks: Optional[Dict[str, Dict[str, str]]] = Field(default_factory=dict)
+    tweaks: Optional[Dict[str, Dict[str, Any]]] = Field(default_factory=dict)
 
 
 class UpdateTemplateRequest(BaseModel):

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

@@ -2,6 +2,7 @@ from typing import Annotated, List, Optional, Union
 from uuid import UUID
 
 from fastapi import APIRouter, Depends, HTTPException, Query
+from loguru import logger
 
 from langflow.api.utils import check_langflow_version
 from langflow.services.auth import utils as auth_utils
@@ -27,8 +28,11 @@ def get_user_store_api_key(
 ):
     if not user.store_api_key:
         raise HTTPException(status_code=400, detail="You must have a store API key set.")
-    decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
-    return decrypted
+    try:
+        decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
+        return decrypted
+    except Exception as e:
+        raise HTTPException(status_code=500, detail="Failed to decrypt API key. Please set a new one.") from e
 
 
 def get_optional_user_store_api_key(
@@ -37,8 +41,12 @@ def get_optional_user_store_api_key(
 ):
     if not user.store_api_key:
         return None
-    decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
-    return decrypted
+    try:
+        decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service)
+        return decrypted
+    except Exception as e:
+        logger.error(f"Failed to decrypt API key: {e}")
+        return user.store_api_key
 
 
 @router.get("/check/")

src/backend/base/langflow/base/agents/agent.py

@@ -1,13 +1,19 @@
 from typing import List, Optional, Union, cast
 
 from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
+from langchain_core.messages import BaseMessage
 from langchain_core.runnables import Runnable
 
+from langflow.base.agents.utils import get_agents_list, records_to_messages
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseMemory, Text, Tool
+from langflow.field_typing import Text, Tool
+from langflow.schema.schema import Record
 
 
 class LCAgentComponent(CustomComponent):
+    def get_agents_list(self):
+        return get_agents_list()
+
     def build_config(self):
         return {
             "lc": {
@@ -42,9 +48,8 @@ class LCAgentComponent(CustomComponent):
         self,
         agent: Union[Runnable, BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
         inputs: str,
-        input_variables: list[str],
         tools: List[Tool],
-        memory: Optional[BaseMemory] = None,
+        message_history: Optional[List[Record]] = None,
         handle_parsing_errors: bool = True,
         output_key: str = "output",
     ) -> Text:
@@ -55,13 +60,11 @@ class LCAgentComponent(CustomComponent):
                 agent=agent,  # type: ignore
                 tools=tools,
                 verbose=True,
-                memory=memory,
                 handle_parsing_errors=handle_parsing_errors,
             )
-        input_dict = {"input": inputs}
-        for var in input_variables:
-            if var not in ["agent_scratchpad", "input"]:
-                input_dict[var] = ""
+        input_dict: dict[str, str | list[BaseMessage]] = {"input": inputs}
+        if message_history:
+            input_dict["chat_history"] = records_to_messages(message_history)
         result = await runnable.ainvoke(input_dict)
         self.status = result
         if output_key in result:

src/backend/base/langflow/base/agents/default_prompts.py

@@ -0,0 +1,23 @@
+XML_AGENT_PROMPT = """You are a helpful assistant. Help the user answer any questions.
+
+            You have access to the following tools:
+
+            {tools}
+
+            In order to use a tool, you can use <tool></tool> and <tool_input></tool_input> tags. You will then get back a response in the form <observation></observation>
+            For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond:
+
+            <tool>search</tool><tool_input>weather in SF</tool_input>
+            <observation>64 degrees</observation>
+
+            When you are done, respond with a final answer between <final_answer></final_answer>. For example:
+
+            <final_answer>The weather in SF is 64 degrees</final_answer>
+
+            Begin!
+
+            Previous Conversation:
+            {chat_history}
+
+            Question: {input}
+            {agent_scratchpad}"""

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

@@ -0,0 +1,143 @@
+from typing import Any, Callable, Dict, List, Optional, Sequence, Union
+
+from langchain.agents import (
+    create_json_chat_agent,
+    create_openai_tools_agent,
+    create_tool_calling_agent,
+    create_xml_agent,
+)
+from langchain.agents.xml.base import render_text_description
+from langchain_core.language_models import BaseLanguageModel
+from langchain_core.messages import BaseMessage
+from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
+from langchain_core.tools import BaseTool
+from pydantic import BaseModel
+
+from langflow.schema.schema import Record
+
+from .default_prompts import XML_AGENT_PROMPT
+
+
+class AgentSpec(BaseModel):
+    func: Callable[
+        [
+            BaseLanguageModel,
+            Sequence[BaseTool],
+            BasePromptTemplate | ChatPromptTemplate,
+            Optional[Callable[[List[BaseTool]], str]],
+            Optional[Union[bool, List[str]]],
+        ],
+        Any,
+    ]
+    prompt: Optional[Any] = None
+    fields: List[str]
+    hub_repo: Optional[str] = None
+
+
+def records_to_messages(records: List[Record]) -> List[BaseMessage]:
+    """
+    Convert a list of records to a list of messages.
+
+    Args:
+        records (List[Record]): The records to convert.
+
+    Returns:
+        List[Message]: The records as messages.
+    """
+    return [record.to_lc_message() for record in records]
+
+
+def validate_and_create_xml_agent(
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: BasePromptTemplate,
+    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    *,
+    stop_sequence: Union[bool, List[str]] = True,
+):
+    return create_xml_agent(
+        llm=llm,
+        tools=tools,
+        prompt=prompt,
+        tools_renderer=tools_renderer,
+        stop_sequence=stop_sequence,
+    )
+
+
+def validate_and_create_openai_tools_agent(
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: ChatPromptTemplate,
+    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    *,
+    stop_sequence: Union[bool, List[str]] = True,
+):
+    return create_openai_tools_agent(
+        llm=llm,
+        tools=tools,
+        prompt=prompt,
+    )
+
+
+def validate_and_create_tool_calling_agent(
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: ChatPromptTemplate,
+    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    *,
+    stop_sequence: Union[bool, List[str]] = True,
+):
+    return create_tool_calling_agent(
+        llm=llm,
+        tools=tools,
+        prompt=prompt,
+    )
+
+
+def validate_and_create_json_chat_agent(
+    llm: BaseLanguageModel,
+    tools: Sequence[BaseTool],
+    prompt: ChatPromptTemplate,
+    tools_renderer: Callable[[List[BaseTool]], str] = render_text_description,
+    *,
+    stop_sequence: Union[bool, List[str]] = True,
+):
+    return create_json_chat_agent(
+        llm=llm,
+        tools=tools,
+        prompt=prompt,
+        tools_renderer=tools_renderer,
+        stop_sequence=stop_sequence,
+    )
+
+
+AGENTS: Dict[str, AgentSpec] = {
+    "Tool Calling Agent": AgentSpec(
+        func=validate_and_create_tool_calling_agent,
+        prompt=None,
+        fields=["llm", "tools", "prompt"],
+        hub_repo=None,
+    ),
+    "XML Agent": AgentSpec(
+        func=validate_and_create_xml_agent,
+        prompt=XML_AGENT_PROMPT,  # Ensure XML_AGENT_PROMPT is properly defined and typed.
+        fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"],
+        hub_repo="hwchase17/xml-agent-convo",
+    ),
+    "OpenAI Tools Agent": AgentSpec(
+        func=validate_and_create_openai_tools_agent,
+        prompt=None,
+        fields=["llm", "tools", "prompt"],
+        hub_repo=None,
+    ),
+    "JSON Chat Agent": AgentSpec(
+        func=validate_and_create_json_chat_agent,
+        prompt=None,
+        fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"],
+        hub_repo="hwchase17/react-chat-json",
+    ),
+}
+
+
+def get_agents_list():
+    return list(AGENTS.keys())

src/backend/base/langflow/base/constants.py

@@ -6,6 +6,7 @@ Constants:
 - NODE_FORMAT_ATTRIBUTES: A list of attributes used for formatting nodes.
 - FIELD_FORMAT_ATTRIBUTES: A list of attributes used for formatting fields.
 """
+
 STREAM_INFO_TEXT = "Stream the response from the model. Streaming works only in Chat."
 
 NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description"]

src/backend/base/langflow/base/memory/memory.py

@@ -0,0 +1,51 @@
+from typing import Optional
+
+from langflow.field_typing import Text
+from langflow.helpers.record import records_to_text
+from langflow.interface.custom.custom_component import CustomComponent
+from langflow.schema.schema import Record
+
+
+class BaseMemoryComponent(CustomComponent):
+    display_name = "Chat Memory"
+    description = "Retrieves stored chat messages given a specific Session ID."
+    beta: bool = True
+    icon = "history"
+
+    def build_config(self):
+        return {
+            "sender": {
+                "options": ["Machine", "User", "Machine and User"],
+                "display_name": "Sender Type",
+            },
+            "sender_name": {"display_name": "Sender Name", "advanced": True},
+            "n_messages": {
+                "display_name": "Number of Messages",
+                "info": "Number of messages to retrieve.",
+            },
+            "session_id": {
+                "display_name": "Session ID",
+                "info": "Session ID of the chat history.",
+                "input_types": ["Text"],
+            },
+            "order": {
+                "options": ["Ascending", "Descending"],
+                "display_name": "Order",
+                "info": "Order of the messages.",
+                "advanced": True,
+            },
+            "record_template": {
+                "display_name": "Record Template",
+                "multiline": True,
+                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+                "advanced": True,
+            },
+        }
+
+    def get_messages(self, **kwargs) -> list[Record]:
+        raise NotImplementedError
+
+    def add_message(
+        self, sender: str, sender_name: str, text: str, session_id: str, metadata: Optional[dict] = None, **kwargs
+    ):
+        raise NotImplementedError

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

@@ -2,7 +2,7 @@ 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.messages import HumanMessage, SystemMessage
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 
 from langflow.custom import CustomComponent
 
@@ -31,6 +31,47 @@ class LCModelComponent(CustomComponent):
             self.status = result
         return result
 
+    def build_status_message(self, message: AIMessage):
+        """
+        Builds a status message from an AIMessage object.
+
+        Args:
+            message (AIMessage): The AIMessage object to build the status message from.
+
+        Returns:
+            The status message.
+        """
+        if message.response_metadata:
+            # Build a well formatted status message
+            content = message.content
+            response_metadata = message.response_metadata
+            openai_keys = ["token_usage", "model_name", "finish_reason"]
+            inner_openai_keys = ["completion_tokens", "prompt_tokens", "total_tokens"]
+            anthropic_keys = ["model", "usage", "stop_reason"]
+            inner_anthropic_keys = ["input_tokens", "output_tokens"]
+            if all(key in response_metadata for key in openai_keys) and all(
+                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:\n- Input: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {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:\n- Input: {input_tokens}\n- Output: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}"
+            else:
+                status_message = f"Response: {content}"
+        else:
+            status_message = f"Response: {message.content}"
+        return status_message
+
     def get_chat_result(
         self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
     ):
@@ -46,5 +87,9 @@ class LCModelComponent(CustomComponent):
         else:
             message = runnable.invoke(messages)
             result = message.content
-            self.status = result
+            if isinstance(message, AIMessage):
+                status_message = self.build_status_message(message)
+                self.status = status_message
+            else:
+                self.status = result
             return result

src/backend/base/langflow/components/agents/ToolCallingAgent.py

@@ -0,0 +1,64 @@
+from typing import List, Optional
+
+from langchain.agents.tool_calling_agent.base import create_tool_calling_agent
+from langchain_core.prompts import ChatPromptTemplate
+
+from langflow.base.agents.agent import LCAgentComponent
+from langflow.field_typing import BaseLanguageModel, Text, Tool
+from langflow.schema.schema import Record
+
+
+class ToolCallingAgentComponent(LCAgentComponent):
+    display_name: str = "Tool Calling Agent"
+    description: str = "Agent that uses tools. Only models that are compatible with function calling are supported."
+
+    def build_config(self):
+        return {
+            "llm": {"display_name": "LLM"},
+            "tools": {"display_name": "Tools"},
+            "user_prompt": {
+                "display_name": "Prompt",
+                "multiline": True,
+                "info": "This prompt must contain 'input' key.",
+            },
+            "handle_parsing_errors": {
+                "display_name": "Handle Parsing Errors",
+                "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
+                "advanced": True,
+            },
+            "memory": {
+                "display_name": "Memory",
+                "info": "Memory to use for the agent.",
+            },
+            "input_value": {
+                "display_name": "Inputs",
+                "info": "Input text to pass to the agent.",
+            },
+        }
+
+    async def build(
+        self,
+        input_value: str,
+        llm: BaseLanguageModel,
+        tools: List[Tool],
+        user_prompt: str = "{input}",
+        message_history: Optional[List[Record]] = None,
+        system_message: str = "You are a helpful assistant",
+        handle_parsing_errors: bool = True,
+    ) -> Text:
+        if "input" not in user_prompt:
+            raise ValueError("Prompt must contain 'input' key.")
+        messages = [
+            ("system", system_message),
+            (
+                "placeholder",
+                "{chat_history}",
+            ),
+            ("human", user_prompt),
+            ("placeholder", "{agent_scratchpad}"),
+        ]
+        prompt = ChatPromptTemplate.from_messages(messages)
+        agent = create_tool_calling_agent(llm, tools, prompt)
+        result = await self.run_agent(agent, input_value, tools, message_history, handle_parsing_errors)
+        self.status = result
+        return result

src/backend/base/langflow/components/agents/XMLAgent.py

@@ -1,10 +1,12 @@
 from typing import List, Optional
 
 from langchain.agents import create_xml_agent
-from langchain_core.prompts import PromptTemplate
+from langchain_core.prompts import ChatPromptTemplate
+
 
 from langflow.base.agents.agent import LCAgentComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, Text, Tool
+from langflow.field_typing import BaseLanguageModel, Text, Tool
+from langflow.schema.schema import Record
 
 
 class XMLAgentComponent(LCAgentComponent):
@@ -15,7 +17,7 @@ class XMLAgentComponent(LCAgentComponent):
         return {
             "llm": {"display_name": "LLM"},
             "tools": {"display_name": "Tools"},
-            "prompt": {
+            "user_prompt": {
                 "display_name": "Prompt",
                 "multiline": True,
                 "info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.",
@@ -43,6 +45,11 @@ class XMLAgentComponent(LCAgentComponent):
             Question: {input}
             {agent_scratchpad}""",
             },
+            "system_message": {
+                "display_name": "System Message",
+                "info": "System message to be passed to the LLM.",
+                "advanced": True,
+            },
             "tool_template": {
                 "display_name": "Tool Template",
                 "info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.",
@@ -53,9 +60,9 @@ class XMLAgentComponent(LCAgentComponent):
                 "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
                 "advanced": True,
             },
-            "memory": {
-                "display_name": "Memory",
-                "info": "Memory to use for the agent.",
+            "message_history": {
+                "display_name": "Message History",
+                "info": "Message history to pass to the agent.",
             },
             "input_value": {
                 "display_name": "Inputs",
@@ -68,12 +75,13 @@ class XMLAgentComponent(LCAgentComponent):
         input_value: str,
         llm: BaseLanguageModel,
         tools: List[Tool],
-        prompt: str,
-        memory: Optional[BaseMemory] = None,
+        user_prompt: str = "{input}",
+        system_message: str = "You are a helpful assistant",
+        message_history: Optional[List[Record]] = None,
         tool_template: str = "{name}: {description}",
         handle_parsing_errors: bool = True,
     ) -> Text:
-        if "input" not in prompt:
+        if "input" not in user_prompt:
             raise ValueError("Prompt must contain 'input' key.")
 
         def render_tool_description(tools):
@@ -81,9 +89,23 @@ class XMLAgentComponent(LCAgentComponent):
                 [tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools]
             )
 
-        prompt_template = PromptTemplate.from_template(prompt)
-        input_variables = prompt_template.input_variables
-        agent = create_xml_agent(llm, tools, prompt_template, tools_renderer=render_tool_description)
-        result = await self.run_agent(agent, input_value, input_variables, tools, memory, handle_parsing_errors)
+        messages = [
+            ("system", system_message),
+            (
+                "placeholder",
+                "{chat_history}",
+            ),
+            ("human", user_prompt),
+            ("placeholder", "{agent_scratchpad}"),
+        ]
+        prompt = ChatPromptTemplate.from_messages(messages)
+        agent = create_xml_agent(llm, tools, prompt, tools_renderer=render_tool_description)
+        result = await self.run_agent(
+            agent=agent,
+            inputs=input_value,
+            tools=tools,
+            message_history=message_history,
+            handle_parsing_errors=handle_parsing_errors,
+        )
         self.status = result
         return result

src/backend/base/langflow/components/experimental/AgentComponent.py

@@ -0,0 +1,185 @@
+from typing import Any, List, Optional, cast
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessagePromptTemplate
+
+from langflow.base.agents.agent import LCAgentComponent
+from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list
+from langflow.field_typing import BaseLanguageModel, Text, Tool
+from langflow.schema.dotdict import dotdict
+from langflow.schema.schema import Record
+
+
+class AgentComponent(LCAgentComponent):
+    display_name = "Agent"
+    description = "Run any LangChain agent using a simplified interface."
+    field_order = [
+        "agent_name",
+        "llm",
+        "tools",
+        "prompt",
+        "tool_template",
+        "handle_parsing_errors",
+        "memory",
+        "input_value",
+    ]
+
+    def build_config(self):
+        return {
+            "agent_name": {
+                "display_name": "Agent",
+                "info": "The agent to use.",
+                "refresh_button": True,
+                "real_time_refresh": True,
+                "options": get_agents_list(),
+            },
+            "llm": {"display_name": "LLM"},
+            "tools": {"display_name": "Tools"},
+            "user_prompt": {
+                "display_name": "Prompt",
+                "multiline": True,
+                "info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.",
+            },
+            "system_message": {
+                "display_name": "System Message",
+                "info": "System message to be passed to the LLM.",
+                "advanced": True,
+            },
+            "tool_template": {
+                "display_name": "Tool Template",
+                "info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.",
+                "advanced": True,
+            },
+            "handle_parsing_errors": {
+                "display_name": "Handle Parsing Errors",
+                "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
+                "advanced": True,
+            },
+            "message_history": {
+                "display_name": "Message History",
+                "info": "Message history to pass to the agent.",
+            },
+            "input_value": {
+                "display_name": "Input",
+                "info": "Input text to pass to the agent.",
+            },
+            "langchain_hub_api_key": {
+                "display_name": "LangChain Hub API Key",
+                "info": "API key to use for LangChain Hub. If provided, prompts will be fetched from LangChain Hub.",
+                "advanced": True,
+            },
+        }
+
+    def get_system_and_user_message_from_prompt(self, prompt: Any):
+        """
+        Extracts the system message and user prompt from a given prompt object.
+
+        Args:
+            prompt (Any): The prompt object from which to extract the system message and user prompt.
+
+        Returns:
+            Tuple[Optional[str], Optional[str]]: A tuple containing the system message and user prompt.
+                If the prompt object does not have any messages, both values will be None.
+        """
+        if hasattr(prompt, "messages"):
+            system_message = None
+            user_prompt = None
+            for message in prompt.messages:
+                if isinstance(message, SystemMessagePromptTemplate):
+                    s_prompt = message.prompt
+                    if isinstance(s_prompt, list):
+                        s_template = " ".join([cast(str, s.template) for s in s_prompt if hasattr(s, "template")])
+                    elif hasattr(s_prompt, "template"):
+                        s_template = s_prompt.template
+                    system_message = s_template
+                elif isinstance(message, HumanMessagePromptTemplate):
+                    h_prompt = message.prompt
+                    if isinstance(h_prompt, list):
+                        h_template = " ".join([cast(str, h.template) for h in h_prompt if hasattr(h, "template")])
+                    elif hasattr(h_prompt, "template"):
+                        h_template = h_prompt.template
+                    user_prompt = h_template
+            return system_message, user_prompt
+        return None, None
+
+    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: Text | None = None):
+        """
+        Updates the build configuration based on the provided field value and field name.
+
+        Args:
+            build_config (dotdict): The build configuration to be updated.
+            field_value (Any): The value of the field being updated.
+            field_name (Text | None, optional): The name of the field being updated. Defaults to None.
+
+        Returns:
+            dotdict: The updated build configuration.
+        """
+        if field_name == "agent":
+            build_config["agent"]["options"] = get_agents_list()
+            if field_value in AGENTS:
+                # if langchain_hub_api_key is provided, fetch the prompt from LangChain Hub
+                if build_config["langchain_hub_api_key"]["value"] and AGENTS[field_value].hub_repo:
+                    from langchain import hub
+
+                    hub_repo: str | None = AGENTS[field_value].hub_repo
+                    if hub_repo:
+                        hub_api_key: str = build_config["langchain_hub_api_key"]["value"]
+                        prompt = hub.pull(hub_repo, api_key=hub_api_key)
+                        system_message, user_prompt = self.get_system_and_user_message_from_prompt(prompt)
+                        if system_message:
+                            build_config["system_message"]["value"] = system_message
+                        if user_prompt:
+                            build_config["user_prompt"]["value"] = user_prompt
+
+                if AGENTS[field_value].prompt:
+                    build_config["user_prompt"]["value"] = AGENTS[field_value].prompt
+                else:
+                    build_config["user_prompt"]["value"] = "{input}"
+            fields = AGENTS[field_value].fields
+            for field in ["llm", "tools", "prompt", "tools_renderer"]:
+                if field not in fields:
+                    build_config[field]["show"] = False
+        return build_config
+
+    async def build(
+        self,
+        agent_name: str,
+        input_value: str,
+        llm: BaseLanguageModel,
+        tools: List[Tool],
+        system_message: str = "You are a helpful assistant. Help the user answer any questions.",
+        user_prompt: str = "{input}",
+        message_history: Optional[List[Record]] = None,
+        tool_template: str = "{name}: {description}",
+        handle_parsing_errors: bool = True,
+    ) -> Text:
+        agent_spec: Optional[AgentSpec] = AGENTS.get(agent_name)
+        if agent_spec is None:
+            raise ValueError(f"{agent_name} not found.")
+
+        def render_tool_description(tools):
+            return "\n".join(
+                [tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools]
+            )
+
+        messages = [
+            ("system", system_message),
+            (
+                "placeholder",
+                "{chat_history}",
+            ),
+            ("human", user_prompt),
+            ("placeholder", "{agent_scratchpad}"),
+        ]
+        prompt = ChatPromptTemplate.from_messages(messages)
+        agent_func = agent_spec.func
+        agent = agent_func(llm, tools, prompt, render_tool_description, True)
+        result = await self.run_agent(
+            agent=agent,
+            inputs=input_value,
+            tools=tools,
+            message_history=message_history,
+            handle_parsing_errors=handle_parsing_errors,
+        )
+        self.status = result
+        return result

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

@@ -10,8 +10,10 @@ from .RunFlow import RunFlowComponent
 from .RunnableExecutor import RunnableExecComponent
 from .SQLExecutor import SQLExecutorComponent
 from .SubFlow import SubFlowComponent
+from .AgentComponent import AgentComponent
 
 __all__ = [
+    "AgentComponent",
     "ClearMessageHistoryComponent",
     "ExtractKeyFromRecordComponent",
     "FlowToolComponent",

src/backend/base/langflow/components/helpers/CombineTextsUnsorted.py

@@ -0,0 +1,25 @@
+from langflow.interface.custom.custom_component import CustomComponent
+from langflow.field_typing import Text
+
+
+class CombineTextsUnsortedComponent(CustomComponent):
+    display_name = "Combine Texts (Unsorted)"
+    description = "Concatenate text sources into a single text chunk using a specified delimiter."
+    icon = "merge"
+
+    def build_config(self):
+        return {
+            "texts": {
+                "display_name": "Texts",
+                "info": "The first text input to concatenate.",
+            },
+            "delimiter": {
+                "display_name": "Delimiter",
+                "info": "A string used to separate the two text inputs. Defaults to a whitespace.",
+            },
+        }
+
+    def build(self, texts: list[str], delimiter: str = " ") -> Text:
+        combined = delimiter.join(texts)
+        self.status = combined
+        return combined

src/backend/base/langflow/components/helpers/MemoryComponent.py

@@ -1,12 +1,13 @@
 from typing import Optional
 
+from langflow.base.memory.memory import BaseMemoryComponent
 from langflow.field_typing import Text
 from langflow.helpers.record import records_to_text
-from langflow.interface.custom.custom_component import CustomComponent
 from langflow.memory import get_messages
+from langflow.schema.schema import Record
 
 
-class MemoryComponent(CustomComponent):
+class MemoryComponent(BaseMemoryComponent):
     display_name = "Chat Memory"
     description = "Retrieves stored chat messages given a specific Session ID."
     beta: bool = True
@@ -42,6 +43,24 @@ class MemoryComponent(CustomComponent):
             },
         }
 
+    def get_messages(self, **kwargs) -> list[Record]:
+        # Validate kwargs by checking if it contains the correct keys
+        if "sender" not in kwargs:
+            kwargs["sender"] = None
+        if "sender_name" not in kwargs:
+            kwargs["sender_name"] = None
+        if "session_id" not in kwargs:
+            kwargs["session_id"] = None
+        if "limit" not in kwargs:
+            kwargs["limit"] = 5
+        if "order" not in kwargs:
+            kwargs["order"] = "Descending"
+
+        kwargs["order"] = "DESC" if kwargs["order"] == "Descending" else "ASC"
+        if kwargs["sender"] == "Machine and User":
+            kwargs["sender"] = None
+        return get_messages(**kwargs)
+
     def build(
         self,
         sender: Optional[str] = "Machine and User",
@@ -51,10 +70,7 @@ class MemoryComponent(CustomComponent):
         order: Optional[str] = "Descending",
         record_template: Optional[str] = "{sender_name}: {text}",
     ) -> Text:
-        order = "DESC" if order == "Descending" else "ASC"
-        if sender == "Machine and User":
-            sender = None
-        messages = get_messages(
+        messages = self.get_messages(
             sender=sender,
             sender_name=sender_name,
             session_id=session_id,

src/backend/base/langflow/components/memories/ZepMessageReader.py

@@ -0,0 +1,137 @@
+from typing import Optional, cast
+
+from langchain_community.chat_message_histories.zep import SearchScope, SearchType, ZepChatMessageHistory
+
+from langflow.base.memory.memory import BaseMemoryComponent
+from langflow.field_typing import Text
+from langflow.schema.schema import Record
+
+
+class ZepMessageReaderComponent(BaseMemoryComponent):
+    display_name = "Zep Message Reader"
+    description = "Retrieves stored chat messages from Zep."
+
+    def build_config(self):
+        return {
+            "session_id": {
+                "display_name": "Session ID",
+                "info": "Session ID of the chat history.",
+                "input_types": ["Text"],
+            },
+            "url": {
+                "display_name": "Zep URL",
+                "info": "URL of the Zep instance.",
+                "input_types": ["Text"],
+            },
+            "api_key": {
+                "display_name": "Zep API Key",
+                "info": "API Key for the Zep instance.",
+                "password": True,
+            },
+            "query": {
+                "display_name": "Query",
+                "info": "Query to search for in the chat history.",
+            },
+            "metadata": {
+                "display_name": "Metadata",
+                "info": "Optional metadata to attach to the message.",
+                "advanced": True,
+            },
+            "search_scope": {
+                "options": ["Messages", "Summary"],
+                "display_name": "Search Scope",
+                "info": "Scope of the search.",
+                "advanced": True,
+            },
+            "search_type": {
+                "options": ["Similarity", "MMR"],
+                "display_name": "Search Type",
+                "info": "Type of search.",
+                "advanced": True,
+            },
+            "limit": {
+                "display_name": "Limit",
+                "info": "Limit of search results.",
+                "advanced": True,
+            },
+        }
+
+    def get_messages(self, **kwargs) -> list[Record]:
+        """
+        Retrieves messages from the ZepChatMessageHistory memory.
+
+        If a query is provided, the search method is used to search for messages in the memory, otherwise all messages are returned.
+
+        Args:
+            memory (ZepChatMessageHistory): The ZepChatMessageHistory instance to retrieve messages from.
+            query (str, optional): The query string to search for messages. Defaults to None.
+            metadata (dict, optional): Additional metadata to filter the search results. Defaults to None.
+            search_scope (str, optional): The scope of the search. Can be 'messages' or 'summary'. Defaults to 'messages'.
+            search_type (str, optional): The type of search. Can be 'similarity' or 'exact'. Defaults to 'similarity'.
+            limit (int, optional): The maximum number of search results to return. Defaults to None.
+
+        Returns:
+            list[Record]: A list of Record objects representing the search results.
+        """
+        memory: ZepChatMessageHistory = cast(ZepChatMessageHistory, kwargs.get("memory"))
+        if not memory:
+            raise ValueError("ZepChatMessageHistory instance is required.")
+        query = kwargs.get("query")
+        search_scope = kwargs.get("search_scope", SearchScope.messages).lower()
+        search_type = kwargs.get("search_type", SearchType.similarity).lower()
+        limit = kwargs.get("limit")
+
+        if query:
+            memory_search_results = memory.search(
+                query,
+                search_scope=search_scope,
+                search_type=search_type,
+                limit=limit,
+            )
+            # Get the messages from the search results if the search scope is messages
+            result_dicts = []
+            for result in memory_search_results:
+                result_dict = {}
+                if search_scope == SearchScope.messages:
+                    result_dict["text"] = result.message
+                else:
+                    result_dict["text"] = result.summary
+                result_dict["metadata"] = result.metadata
+                result_dict["score"] = result.score
+                result_dicts.append(result_dict)
+            results = [Record(data=result_dict) for result_dict in result_dicts]
+        else:
+            messages = memory.messages
+            results = [Record.from_lc_message(message) for message in messages]
+        return results
+
+    def build(
+        self,
+        session_id: Text,
+        url: Optional[Text] = None,
+        api_key: Optional[Text] = None,
+        query: Optional[Text] = None,
+        search_scope: SearchScope = SearchScope.messages,
+        search_type: SearchType = SearchType.similarity,
+        limit: Optional[int] = None,
+    ) -> list[Record]:
+        try:
+            from zep_python import ZepClient
+            from zep_python.langchain import ZepChatMessageHistory
+        except ImportError:
+            raise ImportError(
+                "Could not import zep-python package. " "Please install it with `pip install zep-python`."
+            )
+        if url == "":
+            url = None
+        zep_client = ZepClient(api_url=url, api_key=api_key)
+        memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
+        records = self.get_messages(
+            memory=memory,
+            query=query,
+            search_scope=search_scope,
+            search_type=search_type,
+            limit=limit,
+        )
+        self.status = records
+        return records

src/backend/base/langflow/components/memories/ZepMessageWriter.py

@@ -0,0 +1,96 @@
+from typing import Optional, TYPE_CHECKING
+
+
+from langflow.base.memory.memory import BaseMemoryComponent
+from langflow.field_typing import Text
+from langflow.schema.schema import Record
+
+if TYPE_CHECKING:
+    from zep_python.langchain import ZepChatMessageHistory
+
+
+class ZepMessageWriterComponent(BaseMemoryComponent):
+    display_name = "Zep Message Writer"
+    description = "Writes a message to Zep."
+
+    def build_config(self):
+        return {
+            "session_id": {
+                "display_name": "Session ID",
+                "info": "Session ID of the chat history.",
+                "input_types": ["Text"],
+            },
+            "url": {
+                "display_name": "Zep URL",
+                "info": "URL of the Zep instance.",
+                "input_types": ["Text"],
+            },
+            "api_key": {
+                "display_name": "Zep API Key",
+                "info": "API Key for the Zep instance.",
+                "password": True,
+            },
+            "limit": {
+                "display_name": "Limit",
+                "info": "Limit of search results.",
+                "advanced": True,
+            },
+            "input_value": {
+                "display_name": "Input Record",
+                "info": "Record to write to Zep.",
+            },
+        }
+
+    def add_message(
+        self, sender: Text, sender_name: Text, text: Text, session_id: Text, metadata: dict | None = None, **kwargs
+    ):
+        """
+        Adds a message to the ZepChatMessageHistory memory.
+
+        Args:
+            sender (Text): The type of the message sender. Valid values are "Machine" or "User".
+            sender_name (Text): The name of the message sender.
+            text (Text): The content of the message.
+            session_id (Text): The session ID associated with the message.
+            metadata (dict | None, optional): Additional metadata for the message. Defaults to None.
+            **kwargs: Additional keyword arguments.
+
+        Raises:
+            ValueError: If the ZepChatMessageHistory instance is not provided.
+
+        """
+        memory: ZepChatMessageHistory | None = kwargs.pop("memory", None)
+        if memory is None:
+            raise ValueError("ZepChatMessageHistory instance is required.")
+        if metadata is None:
+            metadata = {}
+        metadata["sender_name"] = sender_name
+        metadata.update(kwargs)
+        if sender == "Machine":
+            memory.add_ai_message(text, metadata=metadata)
+        elif sender == "User":
+            memory.add_user_message(text, metadata=metadata)
+        else:
+            raise ValueError(f"Invalid sender type: {sender}")
+
+    def build(
+        self,
+        input_value: Record,
+        session_id: Text,
+        url: Optional[Text] = None,
+        api_key: Optional[Text] = None,
+    ) -> Record:
+        try:
+            from zep_python import ZepClient
+            from zep_python.langchain import ZepChatMessageHistory
+        except ImportError:
+            raise ImportError(
+                "Could not import zep-python package. " "Please install it with `pip install zep-python`."
+            )
+        if url == "":
+            url = None
+        zep_client = ZepClient(api_url=url, api_key=api_key)
+        memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
+        self.add_message(**input_value.data, memory=memory)
+        self.status = f"Added message to Zep memory for session {session_id}"
+        return input_value

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

@@ -105,7 +105,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
         system_message: Optional[str] = None,
         max_tokens: Optional[int] = 1000,
         stream: bool = False,
-    ) -> BaseLanguageModel:
+    ) -> Text:
         if api_key:
             secret_api_key = SecretStr(api_key)
         else:

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

@@ -142,7 +142,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
         max_retries: int = 6,
         verbose: bool = False,
         system_message: Optional[str] = None,
-    ) -> BaseLanguageModel:
+    ) -> Text:
         try:
             import litellm  # type: ignore
 

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

@@ -40,6 +40,7 @@ class OpenAIModelComponent(LCModelComponent):
                 "display_name": "Model Name",
                 "advanced": False,
                 "options": [
+                    "gpt-4-turbo-2024-04-09",
                     "gpt-4-turbo-preview",
                     "gpt-3.5-turbo",
                     "gpt-4-0125-preview",

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

@@ -61,10 +61,10 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
         input_value: Text,
         search_type: str,
         url: str,
+        index_name: str,
         number_of_results: int = 4,
         search_by_text: bool = False,
         api_key: Optional[str] = None,
-        index_name: Optional[str] = None,
         text_key: str = "text",
         embedding: Optional[Embeddings] = None,
         attributes: Optional[list] = None,

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

@@ -1,5 +1,6 @@
-from typing import List, Optional
+from typing import List, Optional, Union
 
+from langchain.schema import BaseRetriever
 from langchain_astradb import AstraDBVectorStore
 from langchain_astradb.utils.astradb import SetupMode
 
@@ -110,7 +111,7 @@ class AstraDBVectorStoreComponent(CustomComponent):
         metadata_indexing_include: Optional[List[str]] = None,
         metadata_indexing_exclude: Optional[List[str]] = None,
         collection_indexing_policy: Optional[dict] = None,
-    ) -> VectorStore:
+    ) -> Union[VectorStore, BaseRetriever]:
         try:
             setup_mode_value = SetupMode[setup_mode.upper()]
         except KeyError:

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

@@ -4,6 +4,7 @@ import weaviate  # type: ignore
 from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore, Weaviate
+from langchain_core.documents import Document
 
 from langflow.interface.custom.custom_component import CustomComponent
 from langflow.schema.schema import Record
@@ -50,9 +51,9 @@ class WeaviateVectorStoreComponent(CustomComponent):
     def build(
         self,
         url: str,
+        index_name: str,
         search_by_text: bool = False,
         api_key: Optional[str] = None,
-        index_name: Optional[str] = None,
         text_key: str = "text",
         embedding: Optional[Embeddings] = None,
         inputs: Optional[Record] = None,
@@ -78,11 +79,13 @@ class WeaviateVectorStoreComponent(CustomComponent):
             return pascal_case_word
 
         index_name = _to_pascal_case(index_name) if index_name else None
-        documents = []
+        if not index_name:
+            raise ValueError("Index name is required")
+        documents: list[Document] = []
         for _input in inputs or []:
             if isinstance(_input, Record):
                 documents.append(_input.to_lc_document())
-            else:
+            elif isinstance(_input, Document):
                 documents.append(_input)
 
         if documents and embedding is not None:

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

@@ -1,5 +1,7 @@
 import asyncio
+import uuid
 from collections import defaultdict, deque
+from functools import partial
 from itertools import chain
 from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Optional, Type, Union
 
@@ -16,6 +18,7 @@ from langflow.graph.vertex.types import ChatVertex, FileToolVertex, LLMVertex, R
 from langflow.interface.tools.constants import FILE_TOOLS
 from langflow.schema import Record
 from langflow.schema.schema import INPUT_FIELD_NAME, InputType
+from langflow.services.deps import get_chat_service
 
 if TYPE_CHECKING:
     from langflow.graph.schema import ResultData
@@ -29,6 +32,7 @@ class Graph:
         nodes: List[Dict],
         edges: List[Dict[str, str]],
         flow_id: Optional[str] = None,
+        user_id: Optional[str] = None,
     ) -> None:
         """
         Initializes a new instance of the Graph class.
@@ -44,6 +48,7 @@ class Graph:
         self._runs = 0
         self._updates = 0
         self.flow_id = flow_id
+        self.user_id = user_id
         self._is_input_vertices: List[str] = []
         self._is_output_vertices: List[str] = []
         self._is_state_vertices: List[str] = []
@@ -164,13 +169,14 @@ class Graph:
             raise ValueError("Run ID not set")
         return self._run_id
 
-    def set_run_id(self, run_id: str):
+    def set_run_id(self, run_id: str | uuid.UUID):
         """
         Sets the ID of the current run.
 
         Args:
             run_id (str): The run ID.
         """
+        run_id = str(run_id)
         for vertex in self.vertices:
             self.state_manager.subscribe(run_id, vertex.update_graph_state)
         self._run_id = run_id
@@ -446,7 +452,7 @@ class Graph:
         self.__init__(**state)
 
     @classmethod
-    def from_payload(cls, payload: Dict, flow_id: Optional[str] = None) -> "Graph":
+    def from_payload(cls, payload: Dict, flow_id: Optional[str] = None, user_id: Optional[str] = None) -> "Graph":
         """
         Creates a graph from a payload.
 
@@ -461,7 +467,7 @@ class Graph:
         try:
             vertices = payload["nodes"]
             edges = payload["edges"]
-            return cls(vertices, edges, flow_id)
+            return cls(vertices, edges, flow_id, user_id)
         except KeyError as exc:
             logger.exception(exc)
             if "nodes" not in payload and "edges" not in payload:
@@ -748,31 +754,53 @@ class Graph:
     async def process(self, start_component_id: Optional[str] = None) -> "Graph":
         """Processes the graph with vertices in each layer run in parallel."""
 
-        self.sort_vertices(start_component_id=start_component_id)
-        vertices_layers = self.sorted_vertices_layers
+        first_layer = self.sort_vertices(start_component_id=start_component_id)
         vertex_task_run_count: Dict[str, int] = {}
-        for layer_index, layer in enumerate(vertices_layers):
+        to_process = deque(first_layer)
+        layer_index = 0
+        chat_service = get_chat_service()
+        run_id = uuid.uuid4()
+        self.set_run_id(run_id)
+        while to_process:
+            current_batch = list(to_process)  # Copy current deque items to a list
+            to_process.clear()  # Clear the deque for new items
             tasks = []
-            for vertex_id in layer:
+            for vertex_id in current_batch:
                 vertex = self.get_vertex(vertex_id)
+                lock = chat_service._cache_locks[self.run_id]
+                set_cache_coro = partial(chat_service.set_cache, flow_id=self.run_id)
                 task = asyncio.create_task(
-                    vertex.build(),
+                    self.build_vertex(
+                        lock=lock,
+                        set_cache_coro=set_cache_coro,
+                        vertex_id=vertex_id,
+                        user_id=self.user_id,
+                        inputs_dict={},
+                    ),
                     name=f"{vertex.display_name} Run {vertex_task_run_count.get(vertex_id, 0)}",
                 )
                 tasks.append(task)
                 vertex_task_run_count[vertex_id] = vertex_task_run_count.get(vertex_id, 0) + 1
+
             logger.debug(f"Running layer {layer_index} with {len(tasks)} tasks")
-            await self._execute_tasks(tasks)
+            next_runnable_vertices = await self._execute_tasks(tasks)
+            to_process.extend(next_runnable_vertices)
+
         logger.debug("Graph processing complete")
         return self
 
-    async def _execute_tasks(self, tasks):
+    async def _execute_tasks(self, tasks: List[asyncio.Task]) -> List[str]:
         """Executes tasks in parallel, handling exceptions for each task."""
         results = []
         for i, task in enumerate(asyncio.as_completed(tasks)):
             try:
                 result = await task
-                results.append(result)
+                if isinstance(result, tuple) and len(result) == 7:
+                    # Get the next runnable vertices
+                    next_runnable_vertices = result[0]
+                    results.extend(next_runnable_vertices)
+                else:
+                    raise ValueError(f"Invalid result: {result}")
             except Exception as e:
                 # Log the exception along with the task name for easier debugging
                 # task_name = task.get_name()

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

@@ -161,7 +161,7 @@
                 "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.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            },\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\": [\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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.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            },\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\": [\n                    \"gpt-4-turbo-2024-04-09\",\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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,
@@ -222,6 +222,7 @@
                 "file_path": "",
                 "password": false,
                 "options": [
+                  "gpt-4-turbo-2024-04-09",
                   "gpt-4-turbo-preview",
                   "gpt-3.5-turbo",
                   "gpt-4-0125-preview",

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

@@ -494,7 +494,7 @@
                 "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.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            },\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\": [\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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.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            },\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\": [\n                    \"gpt-4-turbo-2024-04-09\",\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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,
@@ -555,6 +555,7 @@
                 "file_path": "",
                 "password": false,
                 "options": [
+                  "gpt-4-turbo-2024-04-09",
                   "gpt-4-turbo-preview",
                   "gpt-3.5-turbo",
                   "gpt-4-0125-preview",

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

@@ -651,7 +651,7 @@
                 "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.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            },\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\": [\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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.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            },\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\": [\n                    \"gpt-4-turbo-2024-04-09\",\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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,
@@ -712,6 +712,7 @@
                 "file_path": "",
                 "password": false,
                 "options": [
+                  "gpt-4-turbo-2024-04-09",
                   "gpt-4-turbo-preview",
                   "gpt-3.5-turbo",
                   "gpt-4-0125-preview",

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

@@ -377,7 +377,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.memory import get_messages\n\n\nclass MemoryComponent(CustomComponent):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages given a specific Session ID.\"\n    beta: bool = True\n    icon = \"history\"\n\n    def build_config(self):\n        return {\n            \"sender\": {\n                \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n                \"display_name\": \"Sender Type\",\n            },\n            \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n            \"n_messages\": {\n                \"display_name\": \"Number of Messages\",\n                \"info\": \"Number of messages to retrieve.\",\n            },\n            \"session_id\": {\n                \"display_name\": \"Session ID\",\n                \"info\": \"Session ID of the chat history.\",\n                \"input_types\": [\"Text\"],\n            },\n            \"order\": {\n                \"options\": [\"Ascending\", \"Descending\"],\n                \"display_name\": \"Order\",\n                \"info\": \"Order of the messages.\",\n                \"advanced\": True,\n            },\n            \"record_template\": {\n                \"display_name\": \"Record Template\",\n                \"multiline\": True,\n                \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine and User\",\n        sender_name: Optional[str] = None,\n        session_id: Optional[str] = None,\n        n_messages: int = 5,\n        order: Optional[str] = \"Descending\",\n        record_template: Optional[str] = \"{sender_name}: {text}\",\n    ) -> Text:\n        order = \"DESC\" if order == \"Descending\" else \"ASC\"\n        if sender == \"Machine and User\":\n            sender = None\n        messages = get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        messages_str = records_to_text(template=record_template or \"\", records=messages)\n        self.status = messages_str\n        return messages_str\n",
+                "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages given a specific Session ID.\"\n    beta: bool = True\n    icon = \"history\"\n\n    def build_config(self):\n        return {\n            \"sender\": {\n                \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n                \"display_name\": \"Sender Type\",\n            },\n            \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n            \"n_messages\": {\n                \"display_name\": \"Number of Messages\",\n                \"info\": \"Number of messages to retrieve.\",\n            },\n            \"session_id\": {\n                \"display_name\": \"Session ID\",\n                \"info\": \"Session ID of the chat history.\",\n                \"input_types\": [\"Text\"],\n            },\n            \"order\": {\n                \"options\": [\"Ascending\", \"Descending\"],\n                \"display_name\": \"Order\",\n                \"info\": \"Order of the messages.\",\n                \"advanced\": True,\n            },\n            \"record_template\": {\n                \"display_name\": \"Record Template\",\n                \"multiline\": True,\n                \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n                \"advanced\": True,\n            },\n        }\n\n    def get_messages(self, **kwargs) -> list[Record]:\n        # Validate kwargs by checking if it contains the correct keys\n        if \"sender\" not in kwargs:\n            kwargs[\"sender\"] = None\n        if \"sender_name\" not in kwargs:\n            kwargs[\"sender_name\"] = None\n        if \"session_id\" not in kwargs:\n            kwargs[\"session_id\"] = None\n        if \"limit\" not in kwargs:\n            kwargs[\"limit\"] = 5\n        if \"order\" not in kwargs:\n            kwargs[\"order\"] = \"Descending\"\n\n        kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n        if kwargs[\"sender\"] == \"Machine and User\":\n            kwargs[\"sender\"] = None\n        return get_messages(**kwargs)\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine and User\",\n        sender_name: Optional[str] = None,\n        session_id: Optional[str] = None,\n        n_messages: int = 5,\n        order: Optional[str] = \"Descending\",\n        record_template: Optional[str] = \"{sender_name}: {text}\",\n    ) -> Text:\n        messages = self.get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        messages_str = records_to_text(template=record_template or \"\", records=messages)\n        self.status = messages_str\n        return messages_str\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -751,7 +751,7 @@
                 "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.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            },\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\": [\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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.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            },\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\": [\n                    \"gpt-4-turbo-2024-04-09\",\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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,
@@ -812,6 +812,7 @@
                 "file_path": "",
                 "password": false,
                 "options": [
+                  "gpt-4-turbo-2024-04-09",
                   "gpt-4-turbo-preview",
                   "gpt-3.5-turbo",
                   "gpt-4-0125-preview",

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

@@ -884,7 +884,7 @@
                 "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.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            },\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\": [\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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.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            },\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\": [\n                    \"gpt-4-turbo-2024-04-09\",\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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,
@@ -945,6 +945,7 @@
                 "file_path": "",
                 "password": false,
                 "options": [
+                  "gpt-4-turbo-2024-04-09",
                   "gpt-4-turbo-preview",
                   "gpt-3.5-turbo",
                   "gpt-4-0125-preview",
@@ -1270,7 +1271,7 @@
                 "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.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            },\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\": [\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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.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            },\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\": [\n                    \"gpt-4-turbo-2024-04-09\",\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\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,\n        model_name: str,\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,\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,
@@ -1331,6 +1332,7 @@
                 "file_path": "",
                 "password": false,
                 "options": [
+                  "gpt-4-turbo-2024-04-09",
                   "gpt-4-turbo-preview",
                   "gpt-3.5-turbo",
                   "gpt-4-0125-preview",

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

@@ -227,34 +227,11 @@ def initialize_qdrant(class_object: Type[Qdrant], params: dict):
     return class_object.from_documents(**params)
 
 
-def initialize_elasticsearch(class_object: Type[ElasticsearchStore], params: dict):
-    """Initialize elastic and return the class object"""
-    if "index_name" not in params:
-        raise ValueError("Elasticsearch Index must be provided in the params")
-    if "es_url" not in params:
-        raise ValueError("Elasticsearch URL must be provided in the params")
-    if not docs_in_params(params):
-        existing_index_params = {
-            "embedding": params.pop("embedding"),
-        }
-        if "index_name" in params:
-            existing_index_params["index_name"] = params.pop("index_name")
-        if "es_url" in params:
-            existing_index_params["es_url"] = params.pop("es_url")
-
-        return class_object.from_existing_index(**existing_index_params)
-    # If there are docs in the params, create a new index
-    if "texts" in params:
-        params["documents"] = params.pop("texts")
-    return class_object.from_documents(**params)
-
-
 vecstore_initializer: Dict[str, Callable[[Type[Any], dict], Any]] = {
     "Pinecone": initialize_pinecone,
     "Chroma": initialize_chroma,
     "Qdrant": initialize_qdrant,
     "Weaviate": initialize_weaviate,
-    "ElasticsearchStore": initialize_elasticsearch,
     "FAISS": initialize_faiss,
     "SupabaseVectorStore": initialize_supabase,
     "MongoDBAtlasVectorSearch": initialize_mongodb,

src/backend/base/langflow/interface/run.py

@@ -1,9 +1,5 @@
-from typing import Dict, Tuple
-
 from loguru import logger
 
-from langflow.graph import Graph
-
 
 def get_memory_key(langchain_object):
     """

src/backend/base/langflow/main.py

@@ -11,6 +11,7 @@ from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from loguru import logger
 from rich import print as rprint
+from starlette.middleware.base import BaseHTTPMiddleware
 
 from langflow.api import router
 from langflow.initial_setup.setup import create_or_update_starter_projects
@@ -20,15 +21,38 @@ from langflow.services.utils import initialize_services, teardown_services
 from langflow.utils.logger import configure
 
 
+class JavaScriptMIMETypeMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        try:
+            response = await call_next(request)
+        except Exception as exc:
+            logger.error(exc)
+            raise exc
+        if "files/" not in request.url.path and request.url.path.endswith(".js") and response.status_code == 200:
+            response.headers["Content-Type"] = "text/javascript"
+        return response
+
+
 def get_lifespan(fix_migration=False, socketio_server=None):
+    from langflow.version import __version__  # type: ignore
+
     @asynccontextmanager
     async def lifespan(app: FastAPI):
         nest_asyncio.apply()
-        initialize_services(fix_migration=fix_migration, socketio_server=socketio_server)
-        setup_llm_caching()
-        LangfuseInstance.update()
-        create_or_update_starter_projects()
-        yield
+        # Startup message
+        if __version__:
+            rprint(f"[bold green]Starting Langflow v{__version__}...[/bold green]")
+        else:
+            rprint("[bold green]Starting Langflow...[/bold green]")
+        try:
+            initialize_services(fix_migration=fix_migration, socketio_server=socketio_server)
+            setup_llm_caching()
+            LangfuseInstance.update()
+            create_or_update_starter_projects()
+            yield
+        except Exception as exc:
+            if "langflow migration --fix" not in str(exc):
+                logger.error(exc)
         # Shutdown message
         rprint("[bold red]Shutting down Langflow...[/bold red]")
         teardown_services()
@@ -52,6 +76,7 @@ def create_app():
         allow_methods=["*"],
         allow_headers=["*"],
     )
+    app.add_middleware(JavaScriptMIMETypeMiddleware)
 
     @app.middleware("http")
     async def flatten_query_string_lists(request: Request, call_next):

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

@@ -3,6 +3,8 @@ from pathlib import Path
 from typing import List, Optional, Union
 
 from dotenv import load_dotenv
+from loguru import logger
+
 from langflow.graph import Graph
 from langflow.graph.schema import RunOutputs
 from langflow.processing.process import process_tweaks, run_graph
@@ -101,6 +103,12 @@ def run_flow_from_json(
         List[RunOutputs]: A list of RunOutputs objects representing the results of running the flow.
     """
     # Set all streaming to false
+    try:
+        import nest_asyncio  # type: ignore
+
+        nest_asyncio.apply()
+    except Exception as e:
+        logger.warning(f"Could not apply nest_asyncio: {e}")
     if tweaks is None:
         tweaks = {}
     tweaks["stream"] = False

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

@@ -1,5 +1,6 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
+
 from langchain.agents import AgentExecutor
 from langchain.schema import AgentAction
 from loguru import logger
@@ -13,6 +14,7 @@ from langflow.schema.graph import InputValue, Tweaks
 from langflow.schema.schema import INPUT_FIELD_NAME
 from langflow.services.session.service import SessionService
 
+
 if TYPE_CHECKING:
     from langflow.api.v1.schemas import InputValueRequest
 
@@ -243,7 +245,6 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None:
 
     for tweak_name, tweak_value in node_tweaks.items():
         if tweak_name not in template_data:
-            logger.warning(f"Node {node.get('id')} does not have a tweak named {tweak_name}")
             continue
         if tweak_name in template_data:
             key = "file_path" if template_data[tweak_name]["type"] == "file" else "value"
@@ -256,27 +257,33 @@ def apply_tweaks_on_vertex(vertex: Vertex, node_tweaks: Dict[str, Any]) -> None:
             vertex.params[tweak_name] = tweak_value
 
 
-def process_tweaks(graph_data: Dict[str, Any], tweaks: Union["Tweaks", Dict[str, Dict[str, Any]]]) -> Dict[str, Any]:
+def process_tweaks(
+    graph_data: Dict[str, Any], tweaks: Union["Tweaks", Dict[str, Dict[str, Any]]], stream: bool = False
+) -> Dict[str, Any]:
     """
     This function is used to tweak the graph data using the node id and the tweaks dict.
 
     :param graph_data: The dictionary containing the graph data. It must contain a 'data' key with
                        'nodes' as its child or directly contain 'nodes' key. Each node should have an 'id' and 'data'.
     :param tweaks: The dictionary containing the tweaks. The keys can be the node id or the name of the tweak.
-                     The values can be a dictionary containing the tweaks for the node or the value of the tweak.
+                   The values can be a dictionary containing the tweaks for the node or the value of the tweak.
+    :param stream: A boolean flag indicating whether streaming should be deactivated across all components or not. Default is False.
     :return: The modified graph_data dictionary.
-
     :raises ValueError: If the input is not in the expected format.
     """
+    tweaks_dict = {}
     if not isinstance(tweaks, dict):
-        tweaks = tweaks.model_dump()
-
-    nodes = validate_input(graph_data, tweaks)
+        tweaks_dict = tweaks.model_dump()
+    else:
+        tweaks_dict = tweaks
+    if "stream" not in tweaks_dict:
+        tweaks_dict["stream"] = stream
+    nodes = validate_input(graph_data, tweaks_dict)
     nodes_map = {node.get("id"): node for node in nodes}
     nodes_display_name_map = {node.get("data", {}).get("node", {}).get("display_name"): node for node in nodes}
 
     all_nodes_tweaks = {}
-    for key, value in tweaks.items():
+    for key, value in tweaks_dict.items():
         if isinstance(value, dict):
             if node := nodes_map.get(key):
                 apply_tweaks(node, value)

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

@@ -1,7 +1,8 @@
-from typing import List, Optional, Union
+from typing import Any, List, Optional, Union
+
+from pydantic import BaseModel, Field, RootModel
 
 from langflow.schema.schema import InputType
-from pydantic import BaseModel, Field, RootModel
 
 
 class InputValue(BaseModel):
@@ -14,7 +15,7 @@ class InputValue(BaseModel):
 
 
 class Tweaks(RootModel):
-    root: dict[str, Union[str, dict[str, str]]] = Field(
+    root: dict[str, Union[str, dict[str, Any]]] = Field(
         description="A dictionary of tweaks to adjust the flow's execution. Allows customizing flow behavior dynamically. All tweaks are overridden by the input values.",
     )
     model_config = {

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

@@ -1,7 +1,8 @@
 import copy
-from typing import Literal, Optional
+from typing import Literal, Optional, cast
 
 from langchain_core.documents import Document
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
 from pydantic import BaseModel, model_validator
 
 
@@ -54,6 +55,21 @@ class Record(BaseModel):
         data["text"] = document.page_content
         return cls(data=data, text_key="text")
 
+    @classmethod
+    def from_lc_message(cls, message: BaseMessage) -> "Record":
+        """
+        Converts a BaseMessage to a Record.
+
+        Args:
+            message (BaseMessage): The BaseMessage to convert.
+
+        Returns:
+            Record: The converted Record.
+        """
+        data: dict = {"text": message.content}
+        data["metadata"] = cast(dict, message.to_json())
+        return cls(data=data, text_key="text")
+
     def __add__(self, other: "Record") -> "Record":
         """
         Combines the data of two records by attempting to add values for overlapping keys
@@ -85,6 +101,26 @@ 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:
+        """
+        Converts the Record to a BaseMessage.
+
+        Returns:
+            BaseMessage: The converted BaseMessage.
+        """
+        # The idea of this function is to be a helper to convert a Record to a BaseMessage
+        # It will use the "sender" key to determine if the message is Human or AI
+        # If the key is not present, it will default to AI
+        # But first we check if all required keys are present in the data dictionary
+        # they are: "text", "sender"
+        if not all(key in self.data for key in ["text", "sender"]):
+            raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}")
+        sender = self.data.get("sender", "Machine")
+        text = self.data.get("text", "")
+        if sender == "User":
+            return HumanMessage(content=text)
+        return AIMessage(content=text)
+
     def __getattr__(self, key):
         """
         Allows attribute-like access to the data dictionary.

src/backend/base/langflow/services/database/models/api_key/crud.py

@@ -24,6 +24,7 @@ def create_api_key(session: Session, api_key_create: ApiKeyCreate, user_id: UUID
         api_key=generated_api_key,
         name=api_key_create.name,
         user_id=user_id,
+        created_at=api_key_create.created_at or datetime.datetime.now(datetime.timezone.utc),
     )
 
     session.add(api_key)

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

@@ -3,17 +3,18 @@ from typing import TYPE_CHECKING, Optional
 from uuid import UUID, uuid4
 
 from pydantic import field_validator, validator
-from sqlmodel import Field, Relationship, SQLModel, Column, func, DateTime
+from sqlmodel import Column, DateTime, Field, Relationship, SQLModel, func
 
 if TYPE_CHECKING:
     from langflow.services.database.models.user import User
 
 
+def utc_now():
+    return datetime.now(timezone.utc)
+
+
 class ApiKeyBase(SQLModel):
     name: Optional[str] = Field(index=True, nullable=True, default=None)
-    created_at: datetime = Field(
-        default=None, sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
-    )
     last_used_at: Optional[datetime] = Field(default=None, nullable=True)
     total_uses: int = Field(default=0)
     is_active: bool = Field(default=True)
@@ -21,7 +22,9 @@ class ApiKeyBase(SQLModel):
 
 class ApiKey(ApiKeyBase, table=True):
     id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
-
+    created_at: Optional[datetime] = Field(
+        default=None, sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=False)
+    )
     api_key: str = Field(index=True, unique=True)
     # User relationship
     # Delete API keys when user is deleted
@@ -34,10 +37,11 @@ class ApiKey(ApiKeyBase, table=True):
 class ApiKeyCreate(ApiKeyBase):
     api_key: Optional[str] = None
     user_id: Optional[UUID] = None
+    created_at: Optional[datetime] = Field(default_factory=utc_now)
 
     @field_validator("created_at", mode="before")
     def set_created_at(cls, v):
-        return v or datetime.now(timezone.utc)
+        return v or utc_now()
 
 
 class UnmaskedApiKeyRead(ApiKeyBase):

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

@@ -25,7 +25,7 @@ class Variable(VariableBase, table=True):
         description="Unique ID for the variable",
     )
     # name is unique per user
-    created_at: datetime = Field(
+    created_at: Optional[datetime] = Field(
         default=None,
         sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=True),
         description="Creation time of the variable",

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

@@ -133,7 +133,7 @@ class DatabaseService(Service):
         alembic_cfg = Config(stdout=buffer)
         # alembic_cfg.attributes["connection"] = session
         alembic_cfg.set_main_option("script_location", str(self.script_location))
-        alembic_cfg.set_main_option("sqlalchemy.url", self.database_url)
+        alembic_cfg.set_main_option("sqlalchemy.url", self.database_url.replace('%', '%%'))
 
         should_initialize_alembic = False
         with Session(self.engine) as session:

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

@@ -101,10 +101,16 @@ def add_row_to_table(
         conn.execute(insert_sql, values)
     except Exception as e:
         # Log values types
+        column_error_message = ""
         for key, value in validated_dict.items():
             logger.error(f"{key}: {type(value)}")
+            if value in str(e):
+                column_error_message = f"Column: {key} Value: {value} Error: {e}"
 
-        logger.error(f"Error adding row to table: {e}")
+        if column_error_message:
+            logger.error(f"Error adding row to {table_name}: {column_error_message}")
+        else:
+            logger.error(f"Error adding row to {table_name}: {e}")
 
 
 async def log_message(

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

@@ -121,7 +121,7 @@ class Settings(BaseSettings):
 
             # Define the app name and author
             app_name = "langflow"
-            app_author = "logspace"
+            app_author = "langflow"
 
             # Get the cache directory for the application
             cache_dir = user_cache_dir(app_name, app_author)

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

@@ -163,7 +163,6 @@ def initialize_services(fix_migration: bool = False, socketio_server=None):
     try:
         initialize_database(fix_migration=fix_migration)
     except Exception as exc:
-        logger.error(exc)
         raise exc
     setup_superuser(get_service(ServiceType.SETTINGS_SERVICE), next(get_session()))
     try:

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

@@ -54,22 +54,24 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None,
 
     if not log_file:
         cache_dir = Path(user_cache_dir("langflow"))
+        logger.debug(f"Cache directory: {cache_dir}")
         log_file = cache_dir / "langflow.log"
+        logger.debug(f"Log file: {log_file}")
+    try:
+        log_file = Path(log_file)
+        log_file.parent.mkdir(parents=True, exist_ok=True)
 
-    log_file = Path(log_file)
-    log_file.parent.mkdir(parents=True, exist_ok=True)
-
-    logger.add(
-        sink=str(log_file),
-        level=log_level.upper(),
-        format=log_format,
-        rotation="10 MB",  # Log rotation based on file size
-        serialize=True,
-    )
+        logger.add(
+            sink=str(log_file),
+            level=log_level.upper(),
+            format=log_format,
+            rotation="10 MB",  # Log rotation based on file size
+            serialize=True,
+        )
+    except Exception as exc:
+        logger.error(f"Error setting up log file: {exc}")
 
     logger.debug(f"Logger set up with log level: {log_level}")
-    if log_file:
-        logger.debug(f"Log file: {log_file}")
 
     setup_uvicorn_logger()
     setup_gunicorn_logger()

src/backend/base/poetry.lock

@@ -2,87 +2,87 @@
 
 [[package]]
 name = "aiohttp"
-version = "3.9.4"
+version = "3.9.5"
 description = "Async http client/server framework (asyncio)"
 optional = false
 python-versions = ">=3.8"
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+    {file = "aiohttp-3.9.5.tar.gz", hash = "sha256:edea7d15772ceeb29db4aff55e482d4bcfb6ae160ce144f2682de02f6d693551"},
 ]
 
 [package.dependencies]
@@ -517,13 +517,13 @@ test-randomorder = ["pytest-randomly"]
 
 [[package]]
 name = "dataclasses-json"
-version = "0.6.4"
+version = "0.6.5"
 description = "Easily serialize dataclasses to and from JSON."
 optional = false
-python-versions = ">=3.7,<4.0"
+python-versions = "<4.0,>=3.7"
 files = [
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+    {file = "dataclasses_json-0.6.5-py3-none-any.whl", hash = "sha256:f49c77aa3a85cac5bf5b7f65f4790ca0d2be8ef4d92c75e91ba0103072788a39"},
+    {file = "dataclasses_json-0.6.5.tar.gz", hash = "sha256:1c287594d9fcea72dc42d6d3836cf14848c2dc5ce88f65ed61b36b57f515fe26"},
 ]
 
 [package.dependencies]
@@ -624,13 +624,13 @@ gmpy2 = ["gmpy2"]
 
 [[package]]
 name = "emoji"
-version = "2.11.0"
+version = "2.11.1"
 description = "Emoji for Python"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
 files = [
-    {file = "emoji-2.11.0-py2.py3-none-any.whl", hash = "sha256:63fc9107f06c6c2e48e5078ce9575cef98518f5ac09474f6148a43e989989582"},
-    {file = "emoji-2.11.0.tar.gz", hash = "sha256:772eaa30f4e0b1ce95148a092df4c7dc97644532c03225326b0fd05e8a9f72a3"},
+    {file = "emoji-2.11.1-py2.py3-none-any.whl", hash = "sha256:b7ba25299bbf520cc8727848ae66b986da32aee27dc2887eaea2bff07226ce49"},
+    {file = "emoji-2.11.1.tar.gz", hash = "sha256:062ff0b3154b6219143f8b9f4b3e5c64c35bc2b146e6e2349ab5f29e218ce1ee"},
 ]
 
 [package.extras]
@@ -638,13 +638,13 @@ dev = ["coverage", "coveralls", "pytest"]
 
 [[package]]
 name = "exceptiongroup"
-version = "1.2.0"
+version = "1.2.1"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
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+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -652,13 +652,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "fastapi"
-version = "0.110.1"
+version = "0.110.2"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -828,22 +828,23 @@ test = ["objgraph", "psutil"]
 
 [[package]]
 name = "gunicorn"
-version = "21.2.0"
+version = "22.0.0"
 description = "WSGI HTTP Server for UNIX"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.7"
 files = [
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+    {file = "gunicorn-22.0.0-py3-none-any.whl", hash = "sha256:350679f91b24062c86e386e198a15438d53a7a8207235a78ba1b53df4c4378d9"},
+    {file = "gunicorn-22.0.0.tar.gz", hash = "sha256:4a0b436239ff76fb33f11c07a16482c521a7e09c1ce3cc293c2330afe01bec63"},
 ]
 
 [package.dependencies]
 packaging = "*"
 
 [package.extras]
-eventlet = ["eventlet (>=0.24.1)"]
+eventlet = ["eventlet (>=0.24.1,!=0.36.0)"]
 gevent = ["gevent (>=1.4.0)"]
 setproctitle = ["setproctitle"]
+testing = ["coverage", "eventlet", "gevent", "pytest", "pytest-cov"]
 tornado = ["tornado (>=0.2)"]
 
 [[package]]
@@ -1063,19 +1064,19 @@ text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
 
 [[package]]
 name = "langchain-community"
-version = "0.0.32"
+version = "0.0.34"
 description = "Community contributed LangChain integrations."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_community-0.0.32-py3-none-any.whl", hash = "sha256:406977009999952d0705de3806de2b4867e9bb8eda8ca154a59c7a8ed58da38d"},
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+    {file = "langchain_community-0.0.34-py3-none-any.whl", hash = "sha256:bc13b21a44bbfca01bff8b35c10a26d71485b57c1d284f499b577ba6e1a5d84a"},
+    {file = "langchain_community-0.0.34.tar.gz", hash = "sha256:96e9a807d9b4777820df5a970996f6bf3ad5632137bf0f4d863bd832bdeb2b0f"},
 ]
 
 [package.dependencies]
 aiohttp = ">=3.8.3,<4.0.0"
 dataclasses-json = ">=0.5.7,<0.7"
-langchain-core = ">=0.1.41,<0.2.0"
+langchain-core = ">=0.1.45,<0.2.0"
 langsmith = ">=0.1.0,<0.2.0"
 numpy = ">=1,<2"
 PyYAML = ">=5.3"
@@ -1089,13 +1090,13 @@ extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.
 
 [[package]]
 name = "langchain-core"
-version = "0.1.42"
+version = "0.1.46"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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+    {file = "langchain_core-0.1.46-py3-none-any.whl", hash = "sha256:1c0befcd2665dd4aa153318aa9bf729071644b4c179e491769b8e583b4bf7441"},
+    {file = "langchain_core-0.1.46.tar.gz", hash = "sha256:17c416349f5c7a9808e70e3725749a3a2df5088f1ecca045c883871aa95f9c9e"},
 ]
 
 [package.dependencies]
@@ -1144,15 +1145,30 @@ langchain-core = ">=0.1.28,<0.2.0"
 [package.extras]
 extended-testing = ["lxml (>=5.1.0,<6.0.0)"]
 
+[[package]]
+name = "langchainhub"
+version = "0.1.15"
+description = "The LangChain Hub API client"
+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = [
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+    {file = "langchainhub-0.1.15.tar.gz", hash = "sha256:fa3ff81a31946860f84c119f1e2f6b7c7707e2bd7ed2394a7313b286d59f3bda"},
+]
+
+[package.dependencies]
+requests = ">=2,<3"
+types-requests = ">=2.31.0.2,<3.0.0.0"
+
 [[package]]
 name = "langsmith"
-version = "0.1.45"
+version = "0.1.51"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -1927,18 +1943,19 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "platformdirs"
-version = "4.2.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+version = "4.2.1"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
 docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
 test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
 
 [[package]]
 name = "pyasn1"
@@ -1964,18 +1981,18 @@ files = [
 
 [[package]]
 name = "pydantic"
-version = "2.7.0"
+version = "2.7.1"
 description = "Data validation using Python type hints"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 annotated-types = ">=0.4.0"
-pydantic-core = "2.18.1"
+pydantic-core = "2.18.2"
 typing-extensions = ">=4.6.1"
 
 [package.extras]
@@ -1983,90 +2000,90 @@ email = ["email-validator (>=2.0.0)"]
 
 [[package]]
 name = "pydantic-core"
-version = "2.18.1"
+version = "2.18.2"
 description = "Core functionality for Pydantic validation and serialization"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "pydantic_core-2.18.2.tar.gz", hash = "sha256:2e29d20810dfc3043ee13ac7d9e25105799817683348823f305ab3f349b9386e"},
 ]
 
 [package.dependencies]
@@ -2568,6 +2585,20 @@ rich = ">=10.11.0"
 shellingham = ">=1.3.0"
 typing-extensions = ">=3.7.4.3"
 
+[[package]]
+name = "types-requests"
+version = "2.31.0.20240406"
+description = "Typing stubs for requests"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "types-requests-2.31.0.20240406.tar.gz", hash = "sha256:4428df33c5503945c74b3f42e82b181e86ec7b724620419a2966e2de604ce1a1"},
+    {file = "types_requests-2.31.0.20240406-py3-none-any.whl", hash = "sha256:6216cdac377c6b9a040ac1c0404f7284bd13199c0e1bb235f4324627e8898cf5"},
+]
+
+[package.dependencies]
+urllib3 = ">=2"
+
 [[package]]
 name = "typing-extensions"
 version = "4.11.0"
@@ -2861,4 +2892,4 @@ local = []
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.12"
-content-hash = "4f3f355cb54985a10ab577f0f2b495c7e6d9e7a8e21838b1742c43de927aba88"
+content-hash = "cd3479e6f463fcdce1bef948ca71952b0650d1d7f4891ba1bc873368cd4b095d"

src/backend/base/pyproject.toml

@@ -1,16 +1,16 @@
 [tool.poetry]
 name = "langflow-base"
-version = "0.0.30"
+version = "0.0.39"
 description = "A Python package with a built-in web application"
-authors = ["Logspace <contact@logspace.ai>"]
+authors = ["Langflow <contact@langflow.org>"]
 maintainers = [
-    "Carlos Coelho <carlos@logspace.ai>",
+    "Carlos Coelho <carlos@langflow.org>",
     "Cristhian Zanforlin <cristhian.lousa@gmail.com>",
-    "Gabriel Almeida <gabriel@logspace.ai>",
+    "Gabriel Almeida <gabriel@langflow.org>",
     "Igor Carvalho <igorr.ackerman@gmail.com>",
     "Lucas Eduoli <lucaseduoli@gmail.com>",
     "Otávio Anovazzi <otavio2204@gmail.com>",
-    "Rodrigo Nader <rodrigo@logspace.ai>",
+    "Rodrigo Nader <rodrigo@langflow.org>",
 ]
 repository = "https://github.com/langflow-ai/langflow"
 license = "MIT"
@@ -29,8 +29,9 @@ python = ">=3.10,<3.12"
 fastapi = "^0.110.1"
 httpx = "*"
 uvicorn = "^0.29.0"
-gunicorn = "^21.2.0"
-langchain = "~0.1.14"
+gunicorn = "^22.0.0"
+langchain = "~0.1.16"
+langchainhub = "~0.1.15"
 sqlmodel = "^0.0.16"
 loguru = "^0.7.1"
 rich = "^13.7.0"

src/frontend/.gitignore

@@ -28,3 +28,7 @@ yarn-error.log*
 /playwright-report/
 /blob-report/
 /playwright/.cache/
+/test-results/
+/playwright-report/
+/blob-report/
+/playwright/.cache/

src/frontend/cdk.Dockerfile

@@ -1,5 +1,5 @@
 #baseline
-FROM --platform=linux/amd64 node:19-bullseye-slim AS base
+FROM --platform=linux/amd64 node:21-bookworm-slim AS base
 RUN mkdir -p /home/node/app
 RUN chown -R node:node /home/node && chmod -R 770 /home/node
 RUN apt-get update && apt-get install -y jq curl

src/frontend/dev.Dockerfile

@@ -1,5 +1,5 @@
 #baseline
-FROM node:19-bullseye-slim AS base
+FROM node:21-bookworm-slim AS base
 RUN mkdir -p /home/node/app
 RUN chown -R node:node /home/node && chmod -R 770 /home/node
 RUN apt-get update && apt-get install -y jq

src/frontend/package-lock.json

@@ -31,6 +31,7 @@
         "@tailwindcss/line-clamp": "^0.4.4",
         "@types/axios": "^0.14.0",
         "ace-builds": "^1.24.1",
+        "ag-grid-react": "^31.2.1",
         "ansi-to-html": "^0.7.2",
         "axios": "^1.5.0",
         "base64-js": "^1.5.1",
@@ -38,12 +39,15 @@
         "clsx": "^1.2.1",
         "cmdk": "^1.0.0",
         "dompurify": "^3.0.5",
+        "dotenv": "^16.4.5",
         "esbuild": "^0.17.19",
+        "file-saver": "^2.0.5",
         "framer-motion": "^11.0.6",
         "lodash": "^4.17.21",
         "lucide-react": "^0.331.0",
         "million": "^3.0.6",
         "moment": "^2.29.4",
+        "openseadragon": "^4.1.1",
         "playwright": "^1.42.0",
         "react": "^18.2.0",
         "react-ace": "^10.1.0",
@@ -54,6 +58,7 @@
         "react-icons": "^5.0.1",
         "react-laag": "^2.0.5",
         "react-markdown": "^8.0.7",
+        "react-pdf": "^7.7.1",
         "react-router-dom": "^6.15.0",
         "react-syntax-highlighter": "^15.5.0",
         "react18-json-view": "^0.2.3",
@@ -71,7 +76,7 @@
         "zustand": "^4.4.7"
       },
       "devDependencies": {
-        "@playwright/test": "^1.42.0",
+        "@playwright/test": "^1.43.1",
         "@swc/cli": "^0.1.62",
         "@swc/core": "^1.3.80",
         "@tailwindcss/typography": "^0.5.9",
@@ -1059,6 +1064,59 @@
         "@jridgewell/sourcemap-codec": "^1.4.14"
       }
     },
+    "node_modules/@mapbox/node-pre-gyp": {
+      "version": "1.0.11",
+      "resolved": "https://registry.npmjs.org/@mapbox/node-pre-gyp/-/node-pre-gyp-1.0.11.tgz",
+      "integrity": "sha512-Yhlar6v9WQgUp/He7BdgzOz8lqMQ8sU+jkCq7Wx8Myc5YFJLbEe7lgui/V7G1qB1DJykHSGwreceSaD60Y0PUQ==",
+      "optional": true,
+      "dependencies": {
+        "detect-libc": "^2.0.0",
+        "https-proxy-agent": "^5.0.0",
+        "make-dir": "^3.1.0",
+        "node-fetch": "^2.6.7",
+        "nopt": "^5.0.0",
+        "npmlog": "^5.0.1",
+        "rimraf": "^3.0.2",
+        "semver": "^7.3.5",
+        "tar": "^6.1.11"
+      },
+      "bin": {
+        "node-pre-gyp": "bin/node-pre-gyp"
+      }
+    },
+    "node_modules/@mapbox/node-pre-gyp/node_modules/lru-cache": {
+      "version": "6.0.0",
+      "resolved": "https://registry.npmjs.org/lru-cache/-/lru-cache-6.0.0.tgz",
+      "integrity": "sha512-Jo6dJ04CmSjuznwJSS3pUeWmd/H0ffTlkXXgwZi+eq1UCmqQwCh+eLsYOYCwY991i2Fah4h1BEMCx4qThGbsiA==",
+      "optional": true,
+      "dependencies": {
+        "yallist": "^4.0.0"
+      },
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/@mapbox/node-pre-gyp/node_modules/semver": {
+      "version": "7.6.0",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-7.6.0.tgz",
+      "integrity": "sha512-EnwXhrlwXMk9gKu5/flx5sv/an57AkRplG3hTK68W7FRDN+k+OWBj65M7719OkA82XLBxrcX0KSHj+X5COhOVg==",
+      "optional": true,
+      "dependencies": {
+        "lru-cache": "^6.0.0"
+      },
+      "bin": {
+        "semver": "bin/semver.js"
+      },
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/@mapbox/node-pre-gyp/node_modules/yallist": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-4.0.0.tgz",
+      "integrity": "sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==",
+      "optional": true
+    },
     "node_modules/@million/lint": {
       "version": "0.0.73",
       "resolved": "https://registry.npmjs.org/@million/lint/-/lint-0.0.73.tgz",
@@ -1759,12 +1817,12 @@
       }
     },
     "node_modules/@playwright/test": {
-      "version": "1.42.1",
-      "resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.42.1.tgz",
-      "integrity": "sha512-Gq9rmS54mjBL/7/MvBaNOBwbfnh7beHvS6oS4srqXFcQHpQCV1+c8JXWE8VLPyRDhgS3H8x8A7hztqI9VnwrAQ==",
+      "version": "1.43.1",
+      "resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.43.1.tgz",
+      "integrity": "sha512-HgtQzFgNEEo4TE22K/X7sYTYNqEMMTZmFS8kTq6m8hXj+m1D8TgwgIbumHddJa9h4yl4GkKb8/bgAl2+g7eDgA==",
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       "dependencies": {
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+          "url": "https://github.com/sponsors/feross"
+        },
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+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
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+          "url": "https://feross.org/support"
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@@ -11873,6 +12358,50 @@
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+        "minipass": "^5.0.0",
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+        "mkdirp": "^1.0.3",
+        "yallist": "^4.0.0"
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+      "engines": {
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+    "node_modules/tar/node_modules/minipass": {
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+      "resolved": "https://registry.npmjs.org/minipass/-/minipass-5.0.0.tgz",
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+      "optional": true,
+      "bin": {
+        "mkdirp": "bin/cmd.js"
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+      "engines": {
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+    "node_modules/tar/node_modules/yallist": {
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@@ -11898,6 +12427,11 @@
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@@ -12855,6 +13389,14 @@
         "node": ">=14"
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+    "node_modules/warning": {
+      "version": "4.0.3",
+      "resolved": "https://registry.npmjs.org/warning/-/warning-4.0.3.tgz",
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@@ -13016,6 +13558,35 @@
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+    "node_modules/wide-align": {
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+      "optional": true,
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+    "node_modules/wide-align/node_modules/emoji-regex": {
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+      "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz",
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+      "optional": true
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+    "node_modules/wide-align/node_modules/string-width": {
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-      "dev": true
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src/frontend/package.json

@@ -26,6 +26,7 @@
     "@tailwindcss/line-clamp": "^0.4.4",
     "@types/axios": "^0.14.0",
     "ace-builds": "^1.24.1",
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     "ansi-to-html": "^0.7.2",
     "axios": "^1.5.0",
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@@ -33,12 +34,15 @@
     "clsx": "^1.2.1",
     "cmdk": "^1.0.0",
     "dompurify": "^3.0.5",
+    "dotenv": "^16.4.5",
     "esbuild": "^0.17.19",
+    "file-saver": "^2.0.5",
     "framer-motion": "^11.0.6",
     "lodash": "^4.17.21",
     "lucide-react": "^0.331.0",
     "million": "^3.0.6",
     "moment": "^2.29.4",
+    "openseadragon": "^4.1.1",
     "playwright": "^1.42.0",
     "react": "^18.2.0",
     "react-ace": "^10.1.0",
@@ -49,6 +53,7 @@
     "react-icons": "^5.0.1",
     "react-laag": "^2.0.5",
     "react-markdown": "^8.0.7",
+    "react-pdf": "^7.7.1",
     "react-router-dom": "^6.15.0",
     "react-syntax-highlighter": "^15.5.0",
     "react18-json-view": "^0.2.3",
@@ -93,7 +98,7 @@
   },
   "proxy": "http://127.0.0.1:7860",
   "devDependencies": {
-    "@playwright/test": "^1.42.0",
+    "@playwright/test": "^1.43.1",
     "@swc/cli": "^0.1.62",
     "@swc/core": "^1.3.80",
     "@tailwindcss/typography": "^0.5.9",

src/frontend/playwright.config.ts

@@ -1,14 +1,17 @@
 import { defineConfig, devices } from "@playwright/test";
+import * as dotenv from "dotenv";
+import path from "path";
+dotenv.config();
+dotenv.config({ path: path.resolve(__dirname, "../../.env") });
 
 /**
  * Read environment variables from file.
  * https://github.com/motdotla/dotenv
  */
-// require('dotenv').config();
-
 /**
  * See https://playwright.dev/docs/test-configuration.
  */
+
 export default defineConfig({
   testDir: "./tests",
   /* Run tests in files in parallel */
@@ -18,7 +21,7 @@ export default defineConfig({
   /* Retry on CI only */
   retries: process.env.CI ? 2 : 0,
   /* Opt out of parallel tests on CI. */
-  workers: 3,
+  workers: 1,
   /* Reporter to use. See https://playwright.dev/docs/test-reporters */
   timeout: 120 * 1000,
   // reporter: [
@@ -40,24 +43,32 @@ export default defineConfig({
   projects: [
     {
       name: "chromium",
-      use: { ...devices["Desktop Chrome"] },
+      use: {
+        ...devices["Desktop Chrome"],
+        contextOptions: {
+          // chromium-specific permissions
+          permissions: ["clipboard-read", "clipboard-write"],
+        },
+      },
     },
 
     {
       name: "firefox",
-      use: { ...devices["Desktop Firefox"] },
+      use: {
+        ...devices["Desktop Firefox"],
+        launchOptions: {
+          firefoxUserPrefs: {
+            "dom.events.asyncClipboard.readText": true,
+            "dom.events.testing.asyncClipboard": true,
+          },
+        },
+      },
     },
-
-    // {
-    //   name: "webkit",
-    //   use: { ...devices["Desktop Safari"] },
-    // },
   ],
-  /* Run your local dev server before starting the tests */
   webServer: [
     {
       command:
-        "poetry run uvicorn --factory langflow.main:create_app --host 127.0.0.1 --port 7860",
+        "poetry run uvicorn --factory langflow.main:create_app --host 127.0.0.1 --port 7860 --loop asyncio",
       port: 7860,
       env: {
         LANGFLOW_DATABASE_URL: "sqlite:///./temp",
@@ -65,7 +76,7 @@ export default defineConfig({
       },
       stdout: "ignore",
 
-      reuseExistingServer: !process.env.CI,
+      reuseExistingServer: true,
       timeout: 120 * 1000,
     },
     {

src/frontend/src/components/ImageViewer/index.tsx

@@ -0,0 +1,141 @@
+import { useEffect, useRef, useState } from "react";
+import ForwardedIconComponent from "../genericIconComponent";
+import useFlowStore from "../../stores/flowStore";
+import OpenSeadragon from 'openseadragon';
+import { Separator } from "../ui/separator";
+import { saveAs } from 'file-saver'
+import useAlertStore from "../../stores/alertStore";
+import { IMGViewErrorMSG, IMGViewErrorTitle } from "../../constants/constants";
+
+export default function ImageViewer({image }) {
+  const viewerRef = useRef(null);
+  const [errorDownloading, setErrordownloading] = useState(false)
+  const setErrorList = useAlertStore(state => state.setErrorData);
+  const [initialMsg, setInicialMsg] = useState("Please build your flow");
+
+
+    useEffect(() => {
+        try {
+          if (viewerRef.current) {
+            // Initialize OpenSeadragon viewer
+            const viewer = OpenSeadragon({
+              element: viewerRef.current,
+              prefixUrl: 'https://cdnjs.cloudflare.com/ajax/libs/openseadragon/2.4.2/images/', // Optional: Set the path to OpenSeadragon images
+              tileSources: {type: 'image', url: image},
+              defaultZoomLevel: 1,
+              maxZoomPixelRatio: 4,
+              showNavigationControl: false,
+            });
+            const zoomInButton = document.getElementById('zoom-in-button');
+        const zoomOutButton = document.getElementById('zoom-out-button');
+        const homeButton = document.getElementById('home-button');
+        const fullPageButton = document.getElementById('full-page-button');
+    
+        zoomInButton!.addEventListener('click', () => viewer.viewport.zoomBy(1.2));
+        zoomOutButton!.addEventListener('click', () => viewer.viewport.zoomBy(0.8));
+        homeButton!.addEventListener('click', () => viewer.viewport.goHome());
+        fullPageButton!.addEventListener('click', () => viewer.setFullScreen(true));
+      
+            // Optionally, you can set additional viewer options here
+      
+            // Cleanup function
+            return () => {
+              viewer.destroy();
+              zoomInButton!.removeEventListener('click', () => viewer.viewport.zoomBy(1.2));
+              zoomOutButton!.removeEventListener('click', () => viewer.viewport.zoomBy(0.8));
+              homeButton!.removeEventListener('click', () => viewer.viewport.goHome());
+              fullPageButton!.removeEventListener('click', () => viewer.setFullScreen(true));
+            };
+          }
+        } catch (error) {
+          console.error('Error initializing OpenSeadragon:', error);
+        }
+      }, [image]);
+
+      function download() {
+        const imageUrl = image;
+        // Fetch the image data
+        fetch(imageUrl)
+            .then(response => response.blob())
+            .then(blob => {
+                // Save the image using FileSaver.js
+                saveAs(blob, 'image.jpg');
+            })
+            .catch(error => {
+              setErrorList({title: "There was an error downloading your image"})
+              console.error('Error downloading image:', error)
+            });
+      }
+
+    return (
+        image === "" ? (
+          <div className="w-full h-full bg-muted rounded-md flex align-center justify-center flex-col  gap-5 border border-border">
+              <div className="flex gap-2 align-center justify-center ">
+                <ForwardedIconComponent
+                  name="Image"
+                />
+                {IMGViewErrorTitle}
+              </div>
+              <div className="flex align-center justify-center">
+                <div className="langflow-chat-desc flex align-center justify-center">
+                  <div className="langflow-chat-desc-span">
+                    {IMGViewErrorMSG}
+                  </div>
+                </div>
+              </div>
+          </div>
+        ) : (
+          <>
+          <div className="w-full flex align-center justify-center my-2 mb-4">
+            <div className="shadow-round-btn-shadow hover:shadow-round-btn-shadow flex items-center justify-center rounded-sm  border bg-muted shadow-md transition-all w-[50%]">
+              <button id="zoom-in-button" className="relative inline-flex w-full items-center justify-center px-3 py-3 text-sm font-semibold transition-all w-full transition-all duration-500 ease-in-out ease-in-out hover:bg-hover">
+                <ForwardedIconComponent
+                  name="ZoomIn"
+                  className={"text-secondary-foreground w-5 h-5"}
+                />
+              </button>
+              <div>
+                <Separator orientation="vertical" />
+              </div>
+              <button id="zoom-out-button" className="relative inline-flex w-full items-center justify-center px-3 py-3 text-sm font-semibold transition-all transition-all duration-500 ease-in-out ease-in-out hover:bg-hover">
+                <ForwardedIconComponent
+                  name="ZoomOut"
+                  className={"text-secondary-foreground w-5 h-5"}
+                />
+              </button>
+              <div>
+                <Separator orientation="vertical" />
+              </div>
+              <button id="home-button" className="relative inline-flex w-full items-center justify-center px-3 py-3 text-sm font-semibold transition-all transition-all duration-500 ease-in-out ease-in-out hover:bg-hover">
+                <ForwardedIconComponent
+                  name="RotateCcw"
+                  className={"text-secondary-foreground w-5 h-5"}
+                />
+              </button>
+              <div>
+                <Separator orientation="vertical" />
+              </div>
+              <button id="full-page-button" className="relative inline-flex w-full items-center justify-center px-3 py-3 text-sm font-semibold transition-all transition-all duration-500 ease-in-out ease-in-out hover:bg-hover">
+                <ForwardedIconComponent
+                  name="Maximize2"
+                  className={"text-secondary-foreground w-5 h-5"}
+                />
+              </button>
+              <div>
+                <Separator orientation="vertical" />
+              </div>
+              
+                <button onClick={download} className="relative inline-flex w-full items-center justify-center px-3 py-3 text-sm font-semibold transition-all transition-all duration-500 ease-in-out ease-in-out hover:bg-hover">
+                    <ForwardedIconComponent
+                    name="ArrowDownToLine"
+                    className={"text-secondary-foreground w-5 h-5"}
+                    />
+                </button>
+              
+            </div>
+          </div>
+          <div id="canvas" ref={viewerRef} className={`w-full h-[90%] `} />
+          </>
+        )
+    );
+}

src/frontend/src/components/csvOutputComponent/helpers/convert-data-function.ts

@@ -0,0 +1,27 @@
+export const convertCSVToData = (csvFile, csvSeparator: string) => {
+  const lines = csvFile.data.trim().split("\n");
+  const headers = lines[0].trim().split(csvSeparator);
+  
+
+  const initialRowData: any = [];
+  const initialColDefs = headers.map((header) => ({
+    field: header.trim(),
+    wrapText: true,
+    autoHeight: true,
+    height: "100%",
+  }));
+
+  for (let i = 1; i < lines.length; i++) {
+    const data = lines[i].trim().split(csvSeparator);
+    const rowDataEntry: any = {};
+
+    for (let j = 0; j < headers.length; j++) {
+      const value = isNaN(data[j]) ? data[j] : parseFloat(data[j]);
+      rowDataEntry[headers[j].trim()] = value;
+    }
+
+    initialRowData.push(rowDataEntry);
+  }
+
+  return { rowData: initialRowData, colDefs: initialColDefs };
+};

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

@@ -0,0 +1,182 @@
+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 { AgGridReact } from "ag-grid-react";
+import { useCallback, useEffect, useMemo, useState } from "react";
+import {
+  CSVError,
+  CSVNoDataError,
+  CSVViewErrorTitle,
+} from "../../constants/constants";
+import { useDarkStore } from "../../stores/darkStore";
+import { FlowPoolObjectType } from "../../types/chat";
+import { NodeType } from "../../types/flow";
+import ForwardedIconComponent from "../genericIconComponent";
+import Loading from "../ui/loading";
+import { convertCSVToData } from "./helpers/convert-data-function";
+
+function CsvOutputComponent({
+  csvNode,
+  flowPool,
+}: {
+  csvNode: NodeType;
+  flowPool: FlowPoolObjectType;
+}) {
+  const csvNodeArtifacts = flowPool?.data?.artifacts?.repr;
+  const jsonString = csvNodeArtifacts?.replace(/'/g, '"');
+  let file = null;
+  try {
+    file = JSON?.parse(jsonString) || "";
+  } catch (e) {
+    console.log("Error parsing JSON");
+  }
+
+  if (!file) {
+    return (
+      <div className=" align-center flex h-full w-full flex-col items-center justify-center gap-5">
+        <div className="align-center flex w-full justify-center gap-2">
+          <ForwardedIconComponent name="Table" />
+          {CSVViewErrorTitle}
+        </div>
+        <div className="align-center flex w-full justify-center">
+          <div className="langflow-chat-desc align-center flex justify-center px-6 py-8">
+            <div className="langflow-chat-desc-span">{CSVError}</div>
+          </div>
+        </div>
+      </div>
+    );
+  }
+
+  const separator = csvNode?.data?.node?.template?.separator?.value || ",";
+
+  const dark = useDarkStore.getState().dark;
+
+  const [rowData, setRowData] = useState([]);
+  const [colDefs, setColDefs] = useState([]);
+
+  const [status, setStatus] = useState("loading");
+  var currentRowHeight: number;
+  var minRowHeight = 25;
+  const defaultColDef = useMemo(() => {
+    return {
+      width: 200,
+      editable: true,
+      filter: true,
+    };
+  }, []);
+
+  useEffect(() => {
+    setStatus("loading");
+    if (file) {
+      const { rowData: data, colDefs: columns } = convertCSVToData(
+        file,
+        separator
+      );
+      setRowData(data);
+      setColDefs(columns);
+
+      setTimeout(() => {
+        setStatus("loaded");
+      }, 1000);
+    } else {
+      setStatus("nodata");
+    }
+  }, [separator]);
+
+  const getRowHeight = useCallback(() => {
+    return currentRowHeight;
+  }, []);
+
+  const onGridReady = useCallback((params: any) => {
+    minRowHeight = params.api.getSizesForCurrentTheme().rowHeight;
+    currentRowHeight = minRowHeight;
+  }, []);
+
+  const updateRowHeight = (params: { api: any }) => {
+    const bodyViewport = document.querySelector(".ag-body-viewport");
+    if (!bodyViewport) {
+      return;
+    }
+    var gridHeight = bodyViewport.clientHeight;
+    var renderedRowCount = params.api.getDisplayedRowCount();
+
+    if (renderedRowCount * minRowHeight >= gridHeight) {
+      if (currentRowHeight !== minRowHeight) {
+        currentRowHeight = minRowHeight;
+        params.api.resetRowHeights();
+      }
+    } else {
+      currentRowHeight = Math.floor(gridHeight / renderedRowCount);
+      params.api.resetRowHeights();
+    }
+  };
+
+  const onFirstDataRendered = useCallback(
+    (params: any) => {
+      updateRowHeight(params);
+    },
+    [updateRowHeight]
+  );
+
+  const onGridSizeChanged = useCallback(
+    (params: any) => {
+      updateRowHeight(params);
+    },
+    [updateRowHeight]
+  );
+
+  return (
+    <div className=" h-full rounded-md border bg-muted">
+      {status === "nodata" && (
+        <div className=" align-center flex h-full w-full flex-col items-center justify-center gap-5">
+          <div className="align-center flex w-full justify-center gap-2">
+            <ForwardedIconComponent name="Table" />
+            {CSVViewErrorTitle}
+          </div>
+          <div className="align-center flex w-full justify-center">
+            <div className="langflow-chat-desc align-center flex justify-center px-6 py-8">
+              <div className="langflow-chat-desc-span">{CSVNoDataError}</div>
+            </div>
+          </div>
+        </div>
+      )}
+      {status === "error" && (
+        <div className=" align-center flex h-full w-full flex-col items-center justify-center gap-5">
+          <div className="align-center flex w-full justify-center gap-2">
+            <ForwardedIconComponent name="Table" />
+            {CSVViewErrorTitle}
+          </div>
+          <div className="align-center flex w-full justify-center">
+            <div className="langflow-chat-desc align-center flex justify-center px-6 py-8">
+              <div className="langflow-chat-desc-span">{CSVError}</div>
+            </div>
+          </div>
+        </div>
+      )}
+
+      {status === "loaded" && (
+        <div
+          className={`${dark ? "ag-theme-balham-dark" : "ag-theme-balham"}`}
+          style={{ height: "100%", width: "100%" }}
+        >
+          <AgGridReact
+            rowData={rowData}
+            columnDefs={colDefs}
+            defaultColDef={defaultColDef}
+            getRowHeight={getRowHeight}
+            onGridReady={onGridReady}
+            onFirstDataRendered={onFirstDataRendered}
+            onGridSizeChanged={onGridSizeChanged}
+            scrollbarWidth={8}
+          />
+        </div>
+      )}
+      {status === "loading" && (
+        <div className="  flex h-full w-full items-center justify-center align-middle">
+          <Loading />
+        </div>
+      )}
+    </div>
+  );
+}
+
+export default CsvOutputComponent;

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

@@ -83,7 +83,9 @@ export const MenuBar = ({
           <DropdownMenuTrigger asChild>
             <Button asChild variant="primary" size="sm">
               <div className="header-menu-bar-display">
-                <div className="header-menu-flow-name">{currentFlow.name}</div>
+                <div className="header-menu-flow-name" data-testid="flow_name">
+                  {currentFlow.name}
+                </div>
                 <IconComponent name="ChevronDown" className="h-4 w-4" />
               </div>
             </Button>

src/frontend/src/components/pdfViewer/Error/index.tsx

@@ -0,0 +1,23 @@
+import { CHAT_FIRST_INITIAL_TEXT, CHAT_SECOND_INITIAL_TEXT, PDFCheckFlow, PDFLoadErrorTitle } from "../../../constants/constants";
+import IconComponent from "../../genericIconComponent";
+
+
+export default function Error(): JSX.Element {
+    return (
+        <div className="flex flex-col items-center justify-center h-full w-full bg-muted">
+            <div className="chat-alert-box">
+                <span className="flex gap-2">
+                    <IconComponent name="FileX2" />
+                    <span className="langflow-chat-span">{PDFLoadErrorTitle}</span>
+                </span>
+                <br />
+                <div className="langflow-chat-desc">
+                    <span className="langflow-chat-desc-span">
+                        {PDFCheckFlow}{" "}
+                    </span>
+                </div>
+            </div>
+
+        </div>
+    );
+}

src/frontend/src/components/pdfViewer/index.tsx

@@ -0,0 +1,155 @@
+import { useEffect, useRef, useState } from "react";
+import { Document, Page, pdfjs } from "react-pdf";
+import "react-pdf/dist/esm/Page/AnnotationLayer.css";
+import "react-pdf/dist/esm/Page/TextLayer.css";
+import IconComponent from "../genericIconComponent";
+import Loading from "../ui/loading";
+import Error from "./Error";
+import NoDataPdf from "./noData";
+
+pdfjs.GlobalWorkerOptions.workerSrc = `//unpkg.com/pdfjs-dist@${pdfjs.version}/build/pdf.worker.min.js`;
+
+export default function PdfViewer({ pdf }: { pdf: string }): JSX.Element {
+  const [numPages, setNumPages] = useState(-1);
+  const [pageNumber, setPageNumber] = useState(1);
+  const [scale, setScale] = useState(1);
+  const [width, setWidth] = useState<number | undefined>(undefined);
+  const [showControl, setShowControl] = useState(false);
+  const container = useRef<null | HTMLDivElement>(null);
+
+  //shortcuts to change page
+  useEffect(() => {
+    function handleKeyDown(event: KeyboardEvent) {
+      if (event.key === "ArrowLeft") {
+        if (pageNumber > 1) previousPage();
+      } else if (event.key === "ArrowRight") {
+        if (pageNumber < numPages) nextPage();
+      }
+    }
+    document.addEventListener("keydown", handleKeyDown);
+    return () => {
+      document.removeEventListener("keydown", handleKeyDown);
+    };
+  }, [pageNumber]);
+
+  function onDocumentLoadSuccess({ numPages }) {
+    setNumPages(numPages);
+    setPageNumber(1);
+  }
+
+  function changePage(offset) {
+    setPageNumber((prevPageNumber) => prevPageNumber + offset);
+  }
+
+  function previousPage() {
+    changePage(-1);
+  }
+
+  function nextPage() {
+    changePage(1);
+  }
+
+  //set handle scale in % to real number
+  function handleScaleChange(e) {
+    //check if e is a number
+    if (isNaN(e) || e < 0.1) return;
+    // round to 2 decimal places
+    e = Math.round(e * 10) / 10;
+
+    setScale(e);
+  }
+
+  function zoomIn() {
+    handleScaleChange(scale + 0.1);
+  }
+  function zoomOut() {
+    if (scale > 0.1) handleScaleChange(scale - 0.1);
+  }
+
+  function handlePageLoad(page) {
+    if (!container.current) return;
+    const containerWidth = container.current.clientWidth;
+    const pageWidth = page.width;
+    if (containerWidth > pageWidth) {
+      setWidth(containerWidth - 10);
+    }
+  }
+
+  return (
+    <div
+      ref={container}
+      onMouseEnter={(_) => setShowControl(true)}
+      onMouseLeave={(_) => setShowControl(false)}
+      className="flex h-full w-full flex-col items-center justify-end overflow-clip rounded-lg border border-border"
+    >
+      <div className={"h-full min-h-0 w-full overflow-auto custom-scroll"}>
+        <Document
+          loading={
+            <div className="flex h-full w-full items-center justify-center align-middle">
+              <Loading />
+            </div>
+          }
+          onLoadSuccess={onDocumentLoadSuccess}
+          file={pdf}
+          noData={<NoDataPdf />}
+          error={<Error />}
+          className="h-full w-full"
+        >
+          <Page
+            width={width}
+            onLoadSuccess={handlePageLoad}
+            scale={scale}
+            renderTextLayer
+            pageNumber={pageNumber}
+            className={"h-full max-h-0 w-full"}
+          />
+        </Document>
+      </div>
+      <div
+        className={
+          "absolute z-50 pb-5 " + (showControl && numPages > 0 ? "" : " hidden")
+        }
+      >
+        <div className=" flex w-min items-center justify-center gap-0.5 rounded-xl bg-secondary px-2 align-middle">
+          <button
+            type="button"
+            disabled={pageNumber <= 1}
+            onClick={previousPage}
+          >
+            <IconComponent
+              name={"ChevronLeft"}
+              className="h-6 w-6"
+            ></IconComponent>
+          </button>
+          <p>
+            {pageNumber || (numPages ? 1 : "--")}/{numPages || "--"}
+          </p>
+          <button
+            type="button"
+            disabled={pageNumber >= numPages}
+            onClick={nextPage}
+          >
+            <IconComponent
+              name={"ChevronRight"}
+              className="h-6 w-6"
+            ></IconComponent>
+          </button>
+          <p className="px-2">|</p>
+          <button type="button" onClick={zoomOut}>
+            <IconComponent name={"ZoomOut"} className="h-6 w-6"></IconComponent>
+          </button>
+          <input
+            type="number"
+            step={0.1}
+            className="w-6 border-b bg-transparent text-center arrow-hide"
+            onChange={(e) => handleScaleChange(e.target.value)}
+            value={scale}
+          />
+          <button type="button" onClick={zoomIn}>
+            <IconComponent name={"ZoomIn"} className="h-6 w-6"></IconComponent>
+          </button>
+        </div>
+      </div>
+    </div>
+  );
+}

src/frontend/src/components/pdfViewer/noData/index.tsx

@@ -0,0 +1,17 @@
+import { PDFErrorTitle, PDFLoadError } from "../../../constants/constants";
+
+export default function NoDataPdf(): JSX.Element {
+  return (
+    <div className="flex h-full w-full flex-col items-center justify-center bg-muted">
+      <div className="chat-alert-box">
+        <span>
+          📄 <span className="langflow-chat-span">{PDFErrorTitle}</span>
+        </span>
+        <br />
+        <div className="langflow-chat-desc">
+          <span className="langflow-chat-desc-span">{PDFLoadError} </span>
+        </div>
+      </div>
+    </div>
+  );
+}

src/frontend/src/constants/constants.ts

@@ -161,6 +161,29 @@ export const IMPORT_DIALOG_SUBTITLE =
  */
 export const TOOLTIP_EMPTY = "No compatible components found.";
 
+export const CSVViewErrorTitle = "CSV output";
+
+export const CSVNoDataError = "No data available";
+
+export const PDFViewConstant = "Expand the ouptut to see the PDF";
+
+export const CSVError = "Error loading CSV";
+
+export const PDFLoadErrorTitle = "Error loading PDF";
+
+export const PDFCheckFlow = "Please check your flow and try again";
+
+export const PDFErrorTitle = "PDF Output";
+
+export const PDFLoadError = "Run the flow to see the pdf";
+
+export const IMGViewConstant = "Expand the view to see the image";
+
+export const IMGViewErrorMSG =
+  "Run the flow or inform a valid url to see your image";
+
+export const IMGViewErrorTitle = "Image output";
+
 /**
  * The base text for subtitle of code dialog
  * @constant
@@ -688,8 +711,14 @@ export const LANGFLOW_SUPPORTED_TYPES = new Set([
 
 export const priorityFields = new Set(["code", "template"]);
 
-export const INPUT_TYPES = new Set(["ChatInput", "TextInput"]);
-export const OUTPUT_TYPES = new Set(["ChatOutput", "TextOutput"]);
+export const INPUT_TYPES = new Set(["ChatInput", "TextInput", "KeyPairInput"]);
+export const OUTPUT_TYPES = new Set([
+  "ChatOutput",
+  "TextOutput",
+  "PDFOutput",
+  "ImageOutput",
+  "CSVOutput",
+]);
 
 export const CHAT_FIRST_INITIAL_TEXT =
   "Start a conversation and click the agent's thoughts";

src/frontend/src/controllers/API/api.tsx

@@ -24,6 +24,9 @@ function ApiInterceptor() {
       async (error: AxiosError) => {
         if (error.response?.status === 403 || error.response?.status === 401) {
           if (!autoLogin) {
+            if (error?.config?.url?.includes("github")) {
+              return Promise.reject(error);
+            }
             const stillRefresh = checkErrorCount();
             if (!stillRefresh) {
               return Promise.reject(error);

src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx

@@ -1,5 +1,17 @@
 import { cloneDeep } from "lodash";
+import ImageViewer from "../../../../components/ImageViewer";
+import CsvOutputComponent from "../../../../components/csvOutputComponent";
+import PdfViewer from "../../../../components/pdfViewer";
+import {
+  Select,
+  SelectContent,
+  SelectGroup,
+  SelectItem,
+  SelectTrigger,
+  SelectValue,
+} from "../../../../components/ui/select";
 import { Textarea } from "../../../../components/ui/textarea";
+import { PDFViewConstant } from "../../../../constants/constants";
 import { InputOutput } from "../../../../constants/enums";
 import useFlowStore from "../../../../stores/flowStore";
 import { IOFieldViewProps } from "../../../../types/components";
@@ -15,6 +27,19 @@ export default function IOFieldView({
   const setNode = useFlowStore((state) => state.setNode);
   const flowPool = useFlowStore((state) => state.flowPool);
   const node = nodes.find((node) => node.id === fieldId);
+  const flowPoolNode = (flowPool[node!.id] ?? [])[
+    (flowPool[node!.id]?.length ?? 1) - 1
+  ];
+  const handleChangeSelect = (e) => {
+    if (node) {
+      let newNode = cloneDeep(node);
+      if (newNode.data.node.template.separator) {
+        newNode.data.node.template.separator.value = e;
+        setNode(newNode.id, newNode);
+      }
+    }
+  };
+
   function handleOutputType() {
     if (!node) return <>"No node found!"</>;
     switch (type) {
@@ -91,6 +116,58 @@ export default function IOFieldView({
                 readOnly
               />
             );
+          case "PDFOutput":
+            return left ? (
+              <div>{PDFViewConstant}</div>
+            ) : (
+              <PdfViewer pdf={flowPoolNode?.params ?? ""} />
+            );
+          case "CSVOutput":
+            return left ? (
+              <>
+                <div className="flex justify-between">
+                  Expand the ouptut to see the CSV
+                </div>
+                <div className="flex items-center justify-between pt-5">
+                  <span>CSV separator </span>
+                  <Select
+                    value={node.data.node.template.separator.value}
+                    onValueChange={(e) => handleChangeSelect(e)}
+                  >
+                    <SelectTrigger className="w-[70px]">
+                      <SelectValue />
+                    </SelectTrigger>
+                    <SelectContent>
+                      <SelectGroup>
+                        {node?.data?.node?.template?.separator?.options.map(
+                          (separator) => (
+                            <SelectItem key={separator} value={separator}>
+                              {separator}
+                            </SelectItem>
+                          )
+                        )}
+                      </SelectGroup>
+                    </SelectContent>
+                  </Select>
+                </div>
+              </>
+            ) : (
+              <>
+                <CsvOutputComponent csvNode={node} flowPool={flowPoolNode} />
+              </>
+            );
+          case "ImageOutput":
+            return left ? (
+              <div>Expand the view to see the image</div>
+            ) : (
+              <ImageViewer
+                image={
+                  (flowPool[node.id] ?? [])[
+                    (flowPool[node.id]?.length ?? 1) - 1
+                  ]?.params ?? ""
+                }
+              />
+            );
 
           default:
             return (

src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx

@@ -135,7 +135,12 @@ export default function ChatMessage({
                 alt={!chat.isSend ? "robot_image" : "male_technology"}
               />
             </div>
-            <span className="max-w-24 truncate text-xs">
+            <span
+              className="max-w-24 truncate text-xs"
+              data-testid={
+                "sender_name_" + chat.sender_name?.toLocaleLowerCase()
+              }
+            >
               {chat.sender_name}
             </span>
           </div>

src/frontend/src/modals/shareModal/index.tsx

@@ -26,6 +26,7 @@ import { getTagsIds } from "../../utils/storeUtils";
 import ConfirmationModal from "../ConfirmationModal";
 import BaseModal from "../baseModal";
 import { useHotkeys } from "react-hotkeys-hook";
+import ExportModal from "../exportModal";
 
 export default function ShareModal({
   component,
@@ -215,9 +216,8 @@ export default function ShareModal({
           {children ? children : <></>}
         </BaseModal.Trigger>
         <BaseModal.Header
-          description={`Publish ${
-            is_component ? "your component" : "workflow"
-          } to the Langflow Store.`}
+          description={`Publish ${is_component ? "your component" : "workflow"
+            } to the Langflow Store.`}
         >
           <span className="pr-2">Share</span>
           <IconComponent
@@ -246,6 +246,7 @@ export default function ShareModal({
               onCheckedChange={(event: boolean) => {
                 setSharePublic(event);
               }}
+              data-testid="public-checkbox"
             />
             <label htmlFor="public" className="export-modal-save-api text-sm ">
               Set {nameComponent} status to public
@@ -259,18 +260,34 @@ export default function ShareModal({
 
         <BaseModal.Footer>
           <div className="flex w-full justify-between gap-2">
-            <Button
+            {!is_component && <ExportModal>
+              <Button
+                type="button"
+                variant="outline"
+                className="gap-2"
+                onClick={() => {
+                  // (setOpen || internalSetOpen)(false);
+                }}
+              >
+                <IconComponent name="Download" className="h-4 w-4" />
+                Export
+              </Button>
+            </ExportModal>
+            }
+            {is_component && <Button
               type="button"
               variant="outline"
               className="gap-2"
               onClick={() => {
-                handleExportComponent();
                 (setOpen || internalSetOpen)(false);
+                handleExportComponent();
               }}
             >
               <IconComponent name="Download" className="h-4 w-4" />
               Export
             </Button>
+
+            }
             <Button
               disabled={loadingNames}
               type="button"

src/frontend/src/pages/FlowPage/index.tsx

@@ -38,7 +38,7 @@ export default function FlowPage({ view }: { view?: boolean }): JSX.Element {
         <a
           target={"_blank"}
           href="https://medium.com/logspace/langflow-datastax-better-together-1b7462cebc4d"
-          className="logspace-page-icon"
+          className="langflow-page-icon"
         >
           {version && <div className="mt-1">Langflow 🤝 DataStax</div>}
           <div className={version ? "mt-2" : "mt-1"}>⛓️ v{version}</div>

src/frontend/src/stores/darkStore.tsx

@@ -18,6 +18,11 @@ export const useDarkStore = create<DarkStoreType>((set, get) => ({
     });
   },
   refreshStars: () => {
+    if (import.meta.env.CI) {
+      window.localStorage.setItem("githubStars", "0");
+      set(() => ({ stars: 0, lastUpdated: new Date() }));
+      return;
+    }
     let lastUpdated = window.localStorage.getItem("githubStarsLastUpdated");
     let diff = 0;
     // check if lastUpdated actually exists
@@ -27,7 +32,7 @@ export const useDarkStore = create<DarkStoreType>((set, get) => ({
 
     // if lastUpdated is null or the difference is greater than 2 hours
     if (lastUpdated === null || diff > 7200000) {
-      getRepoStars("logspace-ai", "langflow").then((res) => {
+      getRepoStars("langflow-ai", "langflow").then((res) => {
         window.localStorage.setItem("githubStars", res.toString());
         window.localStorage.setItem(
           "githubStarsLastUpdated",

src/frontend/src/style/applies.css

@@ -205,11 +205,11 @@
   .flow-page-positioning {
     @apply h-full w-full overflow-hidden;
   }
-  .logspace-page-icon {
+  .langflow-page-icon {
     @apply absolute bottom-2 left-7 flex h-6 cursor-pointer flex-col items-center justify-start overflow-hidden rounded-lg bg-foreground px-2 text-center font-sans text-xs tracking-wide text-secondary transition-all duration-500 ease-in-out;
   }
 
-  .logspace-page-icon:hover {
+  .langflow-page-icon:hover {
     @apply hover:h-12;
   }
 

src/frontend/tests/end-to-end/assets/test_file.txt

@@ -0,0 +1 @@
+this is a test file

src/frontend/tests/end-to-end/auto_login.spec.ts

@@ -2,12 +2,12 @@ import { test } from "@playwright/test";
 
 test.describe("Auto_login tests", () => {
   test("auto_login sign in", async ({ page }) => {
-    await page.goto("http:localhost:3000/");
+    await page.goto("/");
     await page.locator('//*[@id="new-project-btn"]').click();
   });
 
   test("auto_login block_admin", async ({ page }) => {
-    await page.goto("http:localhost:3000/");
+    await page.goto("/");
     await page.locator('//*[@id="new-project-btn"]').click();
     await page.waitForTimeout(5000);
 

src/frontend/tests/end-to-end/chatInputOutput.spec.ts

@@ -0,0 +1,149 @@
+import { expect, test } from "@playwright/test";
+import * as dotenv from "dotenv";
+import { readFileSync } from "fs";
+import path from "path";
+
+test("user must interact with chat with Input/Output", async ({ page }) => {
+  if (!process.env.CI) {
+    dotenv.config();
+    dotenv.config({ path: path.resolve(__dirname, "../../.env") });
+  }
+
+  await page.goto("/");
+
+  await page.waitForTimeout(1000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
+  await page.getByRole("heading", { name: "Basic Prompting" }).click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  if (!process.env.OPENAI_API_KEY) {
+    //You must set the OPENAI_API_KEY on .env file to run this test
+    expect(false).toBe(true);
+  }
+
+  await page
+    .getByTestId("input-openai_api_key")
+    .fill(process.env.OPENAI_API_KEY ?? "");
+  await page.getByText("Run", { exact: true }).click();
+  await page.getByPlaceholder("Send a message...").fill("Hello, how are you?");
+  await page.getByTestId("icon-LucideSend").click();
+  let valueUser = await page.getByTestId("sender_name_user").textContent();
+  let valueAI = await page.getByTestId("sender_name_ai").textContent();
+
+  expect(valueUser).toBe("User");
+  expect(valueAI).toBe("AI");
+
+  await page.keyboard.press("Escape");
+
+  await page
+    .getByTestId("textarea-input_value")
+    .nth(1)
+    .fill(
+      "testtesttesttesttesttestte;.;.,;,.;,.;.,;,..,;;;;;;;;;;;;;;;;;;;;;,;.;,.;,.,;.,;.;.,~~çççççççççççççççççççççççççççççççççççççççisdajfdasiopjfaodisjhvoicxjiovjcxizopjviopasjioasfhjaiohf23432432432423423sttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttestççççççççççççççççççççççççççççççççç,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,!"
+    );
+  await page.getByTestId("input-sender_name").nth(1).fill("TestSenderNameUser");
+  await page.getByTestId("input-sender_name").nth(0).fill("TestSenderNameAI");
+
+  await page.getByText("Run", { exact: true }).click();
+  await page.getByTestId("icon-LucideSend").click();
+
+  valueUser = await page
+    .getByTestId("sender_name_testsendernameuser")
+    .textContent();
+  valueAI = await page
+    .getByTestId("sender_name_testsendernameai")
+    .textContent();
+
+  expect(valueUser).toBe("TestSenderNameUser");
+  expect(valueAI).toBe("TestSenderNameAI");
+
+  expect(
+    await page
+      .getByText(
+        "testtesttesttesttesttestte;.;.,;,.;,.;.,;,..,;;;;;;;;;;;;;;;;;;;;;,;.;,.;,.,;.,;.;.,~~çççççççççççççççççççççççççççççççççççççççisdajfdasiopjfaodisjhvoicxjiovjcxizopjviopasjioasfhjaiohf23432432432423423sttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttestççççççççççççççççççççççççççççççççç,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,!",
+        { exact: true }
+      )
+      .isVisible()
+  );
+});
+
+test("chat_io_teste", async ({ page }) => {
+  await page.goto("/");
+  await page.locator("span").filter({ hasText: "My Collection" }).isVisible();
+  // Read your file into a buffer.
+  const jsonContent = readFileSync(
+    "tests/end-to-end/assets/ChatTest.json",
+    "utf-8"
+  );
+
+  await page.waitForTimeout(3000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
+  await page.getByTestId("blank-flow").click();
+  await page.waitForTimeout(2000);
+
+  // Create the DataTransfer and File
+  const dataTransfer = await page.evaluateHandle((data) => {
+    const dt = new DataTransfer();
+    // Convert the buffer to a hex array
+    const file = new File([data], "ChatTest.json", {
+      type: "application/json",
+    });
+    dt.items.add(file);
+    return dt;
+  }, jsonContent);
+
+  // Now dispatch
+  await page.dispatchEvent(
+    '//*[@id="react-flow-id"]/div[1]/div[1]/div',
+    "drop",
+    {
+      dataTransfer,
+    }
+  );
+  await page.getByLabel("fit view").click();
+  await page.getByText("Run", { exact: true }).click();
+  await page.getByPlaceholder("Send a message...").click();
+  await page.getByPlaceholder("Send a message...").fill("teste");
+  await page.getByRole("button").nth(1).click();
+  const chat_output = page.getByTestId("chat-message-AI-teste");
+  const chat_input = page.getByTestId("chat-message-User-teste");
+  await expect(chat_output).toHaveText("teste");
+  await expect(chat_input).toHaveText("teste");
+});

src/frontend/tests/end-to-end/chat_io.spec.ts

@@ -1,49 +0,0 @@
-import { expect, test } from "@playwright/test";
-import { readFileSync } from "fs";
-
-test("chat_io_teste", async ({ page }) => {
-  await page.goto("/");
-  await page.locator("span").filter({ hasText: "My Collection" }).isVisible();
-  // Read your file into a buffer.
-  const jsonContent = readFileSync(
-    "tests/end-to-end/assets/ChatTest.json",
-    "utf-8"
-  );
-
-  await page.waitForTimeout(3000);
-
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await page.locator('//*[@id="new-project-btn"]').click();
-
-  await page.getByTestId("blank-flow").click();
-  await page.waitForTimeout(2000);
-
-  // Create the DataTransfer and File
-  const dataTransfer = await page.evaluateHandle((data) => {
-    const dt = new DataTransfer();
-    // Convert the buffer to a hex array
-    const file = new File([data], "ChatTest.json", {
-      type: "application/json",
-    });
-    dt.items.add(file);
-    return dt;
-  }, jsonContent);
-
-  // Now dispatch
-  await page.dispatchEvent(
-    '//*[@id="react-flow-id"]/div[1]/div[1]/div',
-    "drop",
-    {
-      dataTransfer,
-    }
-  );
-  await page.getByLabel("fit view").click();
-  await page.getByText("Run", { exact: true }).click();
-  await page.getByPlaceholder("Send a message...").click();
-  await page.getByPlaceholder("Send a message...").fill("teste");
-  await page.getByRole("button").nth(1).click();
-  const chat_output = page.getByTestId("chat-message-AI-teste");
-  const chat_input = page.getByTestId("chat-message-User-teste");
-  await expect(chat_output).toHaveText("teste");
-  await expect(chat_input).toHaveText("teste");
-});

src/frontend/tests/end-to-end/codeAreaModalComponent.spec.ts

@@ -1,67 +1,82 @@
-import { expect, test } from "@playwright/test";
+import { test } from "@playwright/test";
 
 test("CodeAreaModalComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
   await page.waitForTimeout(1000);
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);
   await page.getByTestId("extended-disclosure").click();
   await page.getByPlaceholder("Search").click();
-  await page.getByPlaceholder("Search").fill("pythonfunctiontool");
+  await page.getByPlaceholder("Search").fill("python function");
 
   await page.waitForTimeout(1000);
 
   await page
-    .getByTestId("toolsPythonFunctionTool")
+    .getByTestId("experimentalPython Function")
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.getByTestId("div-generic-node").click();
   await page.getByTestId("code-button-modal").click();
 
-  let value = await page.locator('//*[@id="codeValue"]').inputValue();
-  const code =
-    'def python_function(text: str) -> str:\n    """This is a default python function that returns the input text"""\n    return text';
   const wCode =
     'def python_function(text: str) -> st:    """This is a default python function that returns the input text"""    return text';
-  const assertCode =
-    'def python_function(text: str) -> str:    """This is a default python function that returns the input text"""    return text';
+
+  const customComponentCode = `from typing import Callable
+from langflow.field_typing import Code
+from langflow.interface.custom.custom_component import CustomComponent
+from langflow.interface.custom.utils import get_function
+
+class PythonFunctionComponent(CustomComponent):
+    def python_function(text: str) -> str:
+        """This is a default python function that returns the input text"""
+        return text`;
+
   await page
     .locator("#CodeEditor div")
-    .filter({ hasText: "def python_function(text: str" })
+    .filter({ hasText: "PythonFunctionComponent" })
     .nth(1)
     .click();
   await page.locator("textarea").press("Control+a");
   await page.locator("textarea").fill(wCode);
   await page.locator('//*[@id="checkAndSaveBtn"]').click();
   await page.waitForTimeout(1000);
-  expect(
-    await page.getByText("invalid syntax (<unknown>, line 1)").isVisible()
-  ).toBeTruthy();
+  // expect(
+  //   await page.getByText("invalid syntax (<unknown>, line 1)").isVisible()
+  // ).toBeTruthy();
   await page.locator("textarea").press("Control+a");
   await page.locator("textarea").fill(wCode);
-  await page.locator("textarea").fill(code);
+  await page.locator("textarea").fill(customComponentCode);
   await page.locator('//*[@id="checkAndSaveBtn"]').click();
   await page.waitForTimeout(1000);
 
-  expect(await page.getByText("Code is ready to run").isVisible()).toBeTruthy();
-  await page.getByTestId("code-button-modal").click();
-  expect(await page.locator('//*[@id="codeValue"]').inputValue()).toBe(
-    assertCode
-  );
+  // await page.getByTestId("code-button-modal").click();
+  // const inputCodeValue = await page
+  //   .locator('//*[@id="codeValue"]')
+  //   .inputValue();
+
+  // expect(inputCodeValue).toContain("def python_function(text: str) -> str");
 });

src/frontend/tests/end-to-end/curl_api_generation.spec.ts

@@ -1,9 +1,22 @@
 import { expect, test } from "@playwright/test";
 
 test("curl_api_generation", async ({ page, context }) => {
-  await page.goto("http:localhost:3000/");
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await context.grantPermissions(["clipboard-read", "clipboard-write"]);
+  await page.goto("/");
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.getByRole("heading", { name: "Basic Prompting" }).click();
   await page.waitForTimeout(2000);
   await page.getByText("API", { exact: true }).click();

src/frontend/tests/end-to-end/dragAndDrop.spec.ts

@@ -4,7 +4,25 @@ import { readFileSync } from "fs";
 test.describe("drag and drop test", () => {
   /// <reference lib="dom"/>
   test("drop collection", async ({ page }) => {
-    await page.goto("http:localhost:3000/");
+    await page.goto("/");
+
+    let modalCount = 0;
+    try {
+      const modalTitleElement = await page?.getByTestId("modal-title");
+      if (modalTitleElement) {
+        modalCount = await modalTitleElement.count();
+      }
+    } catch (error) {
+      modalCount = 0;
+    }
+
+    while (modalCount === 0) {
+      await page.locator('//*[@id="new-project-btn"]').click();
+      await page.waitForTimeout(5000);
+      modalCount = await page.getByTestId("modal-title")?.count();
+    }
+    await page.locator("span").filter({ hasText: "Close" }).first().click();
+
     await page.locator("span").filter({ hasText: "My Collection" }).isVisible();
     // Read your file into a buffer.
     const jsonContent = readFileSync(
@@ -36,7 +54,7 @@ test.describe("drag and drop test", () => {
     await page.waitForTimeout(1000);
 
     const genericNoda = page.getByTestId("div-generic-node");
-    const elementCount = await genericNoda.count();
+    const elementCount = await genericNoda?.count();
     if (elementCount > 0) {
       expect(true).toBeTruthy();
     }

src/frontend/tests/end-to-end/dropdownComponent.spec.ts

@@ -1,10 +1,24 @@
 import { expect, test } from "@playwright/test";
 
 test("dropDownComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.waitForTimeout(1000);
 
   await page.getByTestId("blank-flow").click();
@@ -22,17 +36,10 @@ test("dropDownComponent", async ({ page }) => {
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.getByTestId("title-Amazon Bedrock").click();
 
   await page.getByTestId("dropdown-model_id").click();

src/frontend/tests/end-to-end/fileUploadComponent.spec.ts

@@ -0,0 +1,88 @@
+import { expect, test } from "@playwright/test";
+import path from "path";
+
+test("dropDownComponent", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+  await page.waitForTimeout(1000);
+
+  await page.getByTestId("blank-flow").click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTestId("extended-disclosure").click();
+  await page.getByPlaceholder("Search").click();
+  await page.getByPlaceholder("Search").fill("file");
+
+  await page.waitForTimeout(1000);
+
+  await page
+    .getByTestId("dataFile")
+    .first()
+    .dragTo(page.locator('//*[@id="react-flow-id"]'));
+  await page.mouse.up();
+  await page.mouse.down();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  const fileChooserPromise = page.waitForEvent("filechooser");
+  await page.getByTestId("icon-FileSearch2").click();
+  const fileChooser = await fileChooserPromise;
+  await fileChooser.setFiles(path.join(__dirname, "/assets/test_file.txt"));
+  await page.getByText("test_file.txt").isVisible();
+
+  await page.getByPlaceholder("Search").click();
+  await page.getByPlaceholder("Search").fill("text output");
+
+  await page
+    .getByTestId("outputsText Output")
+    .first()
+    .dragTo(page.locator('//*[@id="react-flow-id"]'));
+  await page.mouse.up();
+  await page.mouse.down();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  // Click and hold on the first element
+  await page
+    .locator(
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[1]/div/div[2]/div[6]/button/div/div'
+    )
+    .hover();
+  await page.mouse.down();
+
+  // Move to the second element
+  await page
+    .locator(
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[2]/div/div[2]/div[3]/div/button/div/div'
+    )
+    .hover();
+
+  // Release the mouse
+  await page.mouse.up();
+
+  await page.getByText("Run", { exact: true }).click();
+  await page.getByText("Run Flow", { exact: true }).click();
+  await page.waitForTimeout(3000);
+  const textOutput = await page.getByPlaceholder("Empty").first().inputValue();
+
+  expect(textOutput).toContain("this is a test file");
+});

src/frontend/tests/end-to-end/filterEdge.spec.ts

@@ -1,11 +1,24 @@
 import { expect, test } from "@playwright/test";
 
 test("LLMChain - Tooltip", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await page.waitForTimeout(1000);
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);
@@ -20,18 +33,10 @@ test("LLMChain - Tooltip", async ({ page }) => {
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
   await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
 
   await page
     .locator(
@@ -39,26 +44,20 @@ test("LLMChain - Tooltip", async ({ page }) => {
     )
     .hover()
     .then(async () => {
-      await expect(
-        page.getByTestId("available-input-model_specs").first()
-      ).toBeVisible();
+      await expect(page.getByTestId("tooltip-Chains").first()).toBeVisible();
 
-      await expect(page.getByTestId("tooltip-Models").first()).toBeVisible();
+      await expect(page.getByTestId("tooltip-Inputs").first()).toBeVisible();
 
-      await expect(
-        page.getByTestId("tooltip-AzureOpenAIModel").first()
-      ).toBeVisible();
-
-      await expect(
-        page.getByTestId("tooltip-Model Specs").first()
-      ).toBeVisible();
+      await expect(page.getByTestId("tooltip-Outputs").first()).toBeVisible();
 
       await page.getByTestId("icon-X").click();
       await page.waitForTimeout(500);
     });
 
   await page.getByTitle("fit view").click();
-
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page
     .locator(
       '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[4]/div/button/div/div'
@@ -66,18 +65,14 @@ test("LLMChain - Tooltip", async ({ page }) => {
     .hover()
     .then(async () => {
       await expect(
-        page.getByTestId("available-input-memories").first()
+        page.getByTestId("tooltip-Model Specs").first()
       ).toBeVisible();
-
-      await expect(page.getByTestId("tooltip-Memories").first()).toBeVisible();
+      await page.waitForTimeout(2000);
 
       await expect(
-        page
-          .getByTestId(
-            "tooltip-ConversationBufferMemory, ConversationBufferWindowMemory, ConversationEntityMemory, ConversationKGMemory, ConversationSummaryMemory, MotorheadMemory, VectorStoreRetrieverMemory"
-          )
-          .first()
+        page.getByTestId("tooltip-Model Specs").first()
       ).toBeVisible();
+
       await page.getByTestId("icon-Search").click();
 
       await page.waitForTimeout(500);
@@ -97,11 +92,29 @@ test("LLMChain - Tooltip", async ({ page }) => {
 });
 
 test("LLMChain - Filter", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.waitForTimeout(1000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await page.waitForTimeout(1000);
+  await page.getByTestId(
+    "input-list-plus-btn-edit_metadata_indexing_include-2"
+  );
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);
@@ -115,43 +128,22 @@ test("LLMChain - Filter", async ({ page }) => {
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
   await page.getByTitle("fit view").click();
-
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.waitForTimeout(500);
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[3]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[4]/div/button/div/div'
     )
     .click();
 
-  await page
-    .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[3]/div/button/div/div'
-    )
-    .click();
-  await page.getByTestId("icon-Search").click();
-
-  await expect(page.getByTestId("disclosure-models")).toBeVisible();
   await expect(page.getByTestId("disclosure-model specs")).toBeVisible();
-
-  await expect(page.getByTestId("modelsAzure OpenAI")).toBeVisible();
+  await expect(page.getByTestId("model_specsAnthropic").first()).toBeVisible();
   await expect(page.getByTestId("model_specsAmazon Bedrock")).toBeVisible();
-  await expect(page.getByTestId("model_specsAnthropic")).toBeVisible();
-  await expect(page.getByTestId("model_specsAnthropicLLM")).toBeVisible();
   await expect(page.getByTestId("model_specsAzureChatOpenAI")).toBeVisible();
-  await expect(page.getByTestId("model_specsChatAnthropic")).toBeVisible();
   await expect(page.getByTestId("model_specsChatLiteLLM")).toBeVisible();
   await expect(page.getByTestId("model_specsChatOllama")).toBeVisible();
   await expect(page.getByTestId("model_specsChatOpenAI")).toBeVisible();
@@ -176,8 +168,6 @@ test("LLMChain - Filter", async ({ page }) => {
   await expect(page.getByTestId("model_specsCTransformers")).not.toBeVisible();
   await expect(page.getByTestId("model_specsAmazon Bedrock")).not.toBeVisible();
   await expect(page.getByTestId("modelsAzure OpenAI")).not.toBeVisible();
-  await expect(page.getByTestId("model_specsAnthropic")).not.toBeVisible();
-  await expect(page.getByTestId("model_specsAnthropicLLM")).not.toBeVisible();
   await expect(
     page.getByTestId("model_specsAzureChatOpenAI")
   ).not.toBeVisible();
@@ -190,52 +180,19 @@ test("LLMChain - Filter", async ({ page }) => {
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[4]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[7]/button/div/div'
     )
     .click();
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[4]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[7]/button/div/div'
     )
     .click();
 
-  await expect(page.getByTestId("disclosure-memories")).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesConversationBufferMemory")
-  ).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesConversationBufferWindowMemory")
-  ).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesConversationEntityMemory")
-  ).toBeVisible();
-  await expect(page.getByTestId("memoriesConversationKGMemory")).toBeVisible();
-  await expect(page.getByTestId("memoriesConversationKGMemory")).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesConversationSummaryMemory")
-  ).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesVectorStoreRetrieverMemory")
-  ).toBeVisible();
-
-  await page.getByTestId("rf__wrapper").click();
-
-  await expect(
-    page.getByTestId("memoriesConversationBufferMemory")
-  ).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesConversationBufferWindowMemory")
-  ).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesConversationEntityMemory")
-  ).toBeVisible();
-  await expect(page.getByTestId("memoriesConversationKGMemory")).toBeVisible();
-  await expect(page.getByTestId("memoriesConversationKGMemory")).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesConversationSummaryMemory")
-  ).toBeVisible();
-  await expect(
-    page.getByTestId("memoriesVectorStoreRetrieverMemory")
-  ).toBeVisible();
+  await expect(page.getByTestId("inputsChat Input")).toBeVisible();
+  await expect(page.getByTestId("outputsChat Output")).toBeVisible();
+  await expect(page.getByTestId("helpersID Generator")).toBeVisible();
+  await expect(page.getByTestId("vectorstoresChroma")).toBeVisible();
+  await expect(page.getByTestId("disclosure-vector stores")).toBeVisible();
 });

src/frontend/tests/end-to-end/floatComponent.spec.ts

@@ -1,10 +1,24 @@
 import { expect, test } from "@playwright/test";
 
 test("FloatComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.waitForTimeout(1000);
 
   await page.getByTestId("blank-flow").click();
@@ -20,28 +34,10 @@ test("FloatComponent", async ({ page }) => {
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
 
   await page.locator('//*[@id="float-input"]').click();
   await page.locator('//*[@id="float-input"]').fill("3");
@@ -145,7 +141,7 @@ test("FloatComponent", async ({ page }) => {
   await page.locator('//*[@id="saveChangesBtn"]').click();
 
   const plusButtonLocator = page.locator('//*[@id="float-input"]');
-  const elementCount = await plusButtonLocator.count();
+  const elementCount = await plusButtonLocator?.count();
   if (elementCount === 0) {
     expect(true).toBeTruthy();
 

src/frontend/tests/end-to-end/flowPage.spec.ts

@@ -1,17 +1,25 @@
-import { Page, test } from "@playwright/test";
+import { test } from "@playwright/test";
 
 test.describe("Flow Page tests", () => {
-  async function goToFlowPage(page: Page) {
-    await page.goto("http:localhost:3000/");
-    await page.getByRole("button", { name: "New Project" }).click();
-  }
-
   test("save", async ({ page }) => {
-    await page.goto("http:localhost:3000/");
+    await page.goto("/");
     await page.waitForTimeout(2000);
 
-    await page.locator('//*[@id="new-project-btn"]').click();
-    await page.waitForTimeout(1000);
+    let modalCount = 0;
+    try {
+      const modalTitleElement = await page?.getByTestId("modal-title");
+      if (modalTitleElement) {
+        modalCount = await modalTitleElement.count();
+      }
+    } catch (error) {
+      modalCount = 0;
+    }
+
+    while (modalCount === 0) {
+      await page.locator('//*[@id="new-project-btn"]').click();
+      await page.waitForTimeout(5000);
+      modalCount = await page.getByTestId("modal-title")?.count();
+    }
 
     await page.getByTestId("blank-flow").click();
     await page.waitForTimeout(1000);
@@ -26,18 +34,9 @@ test.describe("Flow Page tests", () => {
       .dragTo(page.locator('//*[@id="react-flow-id"]'));
     await page.mouse.up();
     await page.mouse.down();
-    await page
-      .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-      .click();
-
-    await page
-      .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-      .click();
-
-    await page
-      .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-      .click();
-    // await page.getByTestId("icon-ExternalLink").click();
-    // await page.locator('//*[@id="checkAndSaveBtn"]').click();
+    await page.getByTitle("fit view").click();
+    await page.getByTitle("zoom out").click();
+    await page.getByTitle("zoom out").click();
+    await page.getByTitle("zoom out").click();
   });
 });

src/frontend/tests/end-to-end/flowSettings.spec.ts

@@ -0,0 +1,73 @@
+import { expect, test } from "@playwright/test";
+
+test("flowSettings", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+  await page.waitForTimeout(1000);
+
+  await page.getByTestId("blank-flow").click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTestId("flow_name").click();
+  await page.getByText("Settings").first().click();
+  await page
+    .getByPlaceholder("Flow name")
+    .fill(
+      "Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test"
+    );
+
+  await page.getByText("Character limit reached").isVisible();
+
+  await page.getByPlaceholder("Flow name").click();
+  const randomName = Math.random().toString(36).substring(2);
+  await page.getByPlaceholder("Flow name").fill(randomName);
+  await page.getByPlaceholder("Flow name").click();
+  await page
+    .getByPlaceholder("Flow description")
+    .fill(
+      "Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test"
+    );
+
+  await page.getByText("Save").last().click();
+
+  await page.waitForTimeout(1000);
+
+  await page.getByText("Changes saved successfully").isVisible();
+
+  await page.getByTestId("flow_name").click();
+  await page.getByText("Settings").first().click();
+
+  const flowName = await page.getByPlaceholder("Flow name").inputValue();
+  const flowDescription = await page
+    .getByPlaceholder("Flow description")
+    .inputValue();
+
+  if (flowName != randomName) {
+    expect(false).toBeTruthy();
+  }
+
+  if (
+    flowDescription !=
+    "Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test"
+  ) {
+    expect(false).toBeTruthy();
+  }
+  await page.getByText("Saved").first().isVisible();
+  await page.getByTestId("icon-CheckCircle2").first().isVisible();
+});

src/frontend/tests/end-to-end/globalVariables.spec.ts

@@ -0,0 +1,79 @@
+import { expect, test } from "@playwright/test";
+
+test("GlobalVariables", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+  await page.waitForTimeout(1000);
+
+  await page.getByTestId("blank-flow").click();
+  await page.waitForTimeout(1000);
+  await page.getByTestId("extended-disclosure").click();
+  await page.getByPlaceholder("Search").click();
+  await page.getByPlaceholder("Search").fill("openai");
+
+  await page.waitForTimeout(1000);
+
+  await page
+    .getByTestId("modelsOpenAI")
+    .dragTo(page.locator('//*[@id="react-flow-id"]'));
+  await page.mouse.up();
+  await page.mouse.down();
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  const genericName = Math.random().toString();
+  const credentialName = Math.random().toString();
+
+  await page.getByTestId("icon-Globe").nth(1).click();
+  await page.getByText("Add New Variable", { exact: true }).click();
+  await page
+    .getByPlaceholder("Insert a name for the variable...")
+    .fill(genericName);
+  await page.getByTestId("icon-ChevronsUpDown").nth(1).click();
+  await page.getByText("Generic", { exact: true }).click();
+  await page
+    .getByPlaceholder("Insert a value for the variable...")
+    .fill("This is a test of generic variable value");
+  await page.getByText("Save Variable", { exact: true }).click();
+  expect(page.getByText(genericName, { exact: true })).not.toBeNull();
+  await page.getByText(genericName, { exact: true }).isVisible();
+
+  await page.getByText("Add New Variable", { exact: true }).click();
+  await page
+    .getByPlaceholder("Insert a name for the variable...")
+    .fill(credentialName);
+  await page.getByTestId("icon-ChevronsUpDown").nth(1).click();
+  await page.getByText("Credential", { exact: true }).click();
+  await page
+    .getByPlaceholder("Insert a value for the variable...")
+    .fill("This is a test of credential variable value");
+  await page.getByText("Save Variable", { exact: true }).click();
+  expect(page.getByText(credentialName, { exact: true })).not.toBeNull();
+  await page.getByText(credentialName, { exact: true }).isVisible();
+  await page
+    .getByText(credentialName, { exact: true })
+    .hover()
+    .then(async () => {
+      await page.getByTestId("icon-Trash2").last().click();
+      await page.getByText("Delete", { exact: true }).nth(1).click();
+    });
+});

src/frontend/tests/end-to-end/group.spec.ts

@@ -4,7 +4,21 @@ test.describe("group node test", () => {
   /// <reference lib="dom"/>
   test("group and ungroup updating values", async ({ page }) => {
     await page.goto("/");
-    await page.locator('//*[@id="new-project-btn"]').click();
+
+    let modalCount = 0;
+    try {
+      const modalTitleElement = await page?.getByTestId("modal-title");
+      if (modalTitleElement) {
+        modalCount = await modalTitleElement.count();
+      }
+    } catch (error) {
+      modalCount = 0;
+    }
+
+    while (modalCount === 0) {
+      await page.locator('//*[@id="new-project-btn"]').click();
+      modalCount = await page.getByTestId("modal-title")?.count();
+    }
 
     await page
       .getByRole("heading", { name: "Basic Prompting" })
@@ -13,8 +27,6 @@ test.describe("group node test", () => {
     await page.waitForTimeout(2000);
     await page.getByLabel("fit view").first().click();
     await page.getByTestId("title-OpenAI").click({ modifiers: ["Control"] });
-    await page.getByTestId("title-Prompt").click({ modifiers: ["Control"] });
-    await page.getByTestId("title-OpenAI").click({ modifiers: ["Control"] });
 
     await page.getByRole("button", { name: "Group" }).click();
     await page.getByTestId("title-Group").dblclick();

src/frontend/tests/end-to-end/inputComponent.spec.ts

@@ -1,10 +1,24 @@
 import { expect, test } from "@playwright/test";
 
 test("InputComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.waitForTimeout(1000);
 
   await page.getByTestId("blank-flow").click();
@@ -20,17 +34,10 @@ test("InputComponent", async ({ page }) => {
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.getByTestId("input-collection_name").click();
   await page
     .getByTestId("input-collection_name")
@@ -132,7 +139,7 @@ test("InputComponent", async ({ page }) => {
   await page.locator('//*[@id="saveChangesBtn"]').click();
 
   const plusButtonLocator = page.getByTestId("input-collection_name");
-  const elementCount = await plusButtonLocator.count();
+  const elementCount = await plusButtonLocator?.count();
   if (elementCount === 0) {
     expect(true).toBeTruthy();
 

src/frontend/tests/end-to-end/inputListComponent.spec.ts

@@ -1,36 +1,41 @@
 import { expect, test } from "@playwright/test";
 
 test("InputListComponent", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await page.waitForTimeout(1000);
-  await page.locator('//*[@id="new-project-btn"]').click();
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);
   await page.getByTestId("extended-disclosure").click();
   await page.getByPlaceholder("Search").click();
-  await page.getByPlaceholder("Search").fill("astradb search");
+  await page.getByPlaceholder("Search").fill("astradb");
 
   await page.waitForTimeout(1000);
   await page
-    .getByTestId("vectorsearchAstraDB Search")
+    .getByTestId("vectorsearchAstra DB Search")
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.getByTestId("div-generic-node").click();
   await page.getByTestId("more-options-modal").click();
   await page.getByTestId("edit-button-modal").click();
@@ -90,7 +95,7 @@ test("InputListComponent", async ({ page }) => {
   const plusButtonLocator = page.getByTestId(
     "input-list-plus-btn_metadata_indexing_include-1"
   );
-  const elementCount = await plusButtonLocator.count();
+  const elementCount = await plusButtonLocator?.count();
 
   if (elementCount > 0) {
     expect(false).toBeTruthy();
@@ -161,12 +166,12 @@ test("InputListComponent", async ({ page }) => {
   const plusButtonLocatorEdit0 = await page.getByTestId(
     "input-list-plus-btn-edit_metadata_indexing_include-0"
   );
-  const elementCountEdit0 = await plusButtonLocatorEdit0.count();
+  const elementCountEdit0 = await plusButtonLocatorEdit0?.count();
 
   const plusButtonLocatorEdit2 = await page.getByTestId(
     "input-list-plus-btn-edit_metadata_indexing_include-2"
   );
-  const elementCountEdit2 = await plusButtonLocatorEdit2.count();
+  const elementCountEdit2 = await plusButtonLocatorEdit2?.count();
 
   if (elementCountEdit0 > 0 || elementCountEdit2 > 0) {
     expect(false).toBeTruthy();
@@ -176,13 +181,13 @@ test("InputListComponent", async ({ page }) => {
     "input-list-minus-btn-edit_metadata_indexing_include-1"
   );
 
-  const elementCountMinusEdit1 = await minusButtonLocatorEdit1.count();
+  const elementCountMinusEdit1 = await minusButtonLocatorEdit1?.count();
 
   const minusButtonLocatorEdit2 = await page.getByTestId(
     "input-list-minus-btn-edit_metadata_indexing_include-2"
   );
 
-  const elementCountMinusEdit2 = await minusButtonLocatorEdit2.count();
+  const elementCountMinusEdit2 = await minusButtonLocatorEdit2?.count();
 
   if (elementCountMinusEdit1 > 0 || elementCountMinusEdit2 > 0) {
     expect(false).toBeTruthy();

src/frontend/tests/end-to-end/intComponent.spec.ts

@@ -1,10 +1,24 @@
 import { expect, test } from "@playwright/test";
 
 test("IntComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.waitForTimeout(1000);
 
   await page.getByTestId("blank-flow").click();
@@ -21,17 +35,10 @@ test("IntComponent", async ({ page }) => {
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.getByTestId("int-input-max_tokens").click();
   await page
     .getByTestId("int-input-max_tokens")
@@ -53,17 +60,10 @@ test("IntComponent", async ({ page }) => {
   }
 
   await page.getByTestId("title-ChatOpenAI").click();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
 
   await page.getByTestId("more-options-modal").click();
   await page.getByTestId("edit-button-modal").click();
@@ -157,7 +157,7 @@ test("IntComponent", async ({ page }) => {
   await page.locator('//*[@id="saveChangesBtn"]').click();
 
   const plusButtonLocator = page.getByTestId("int-input-max_tokens");
-  const elementCount = await plusButtonLocator.count();
+  const elementCount = await plusButtonLocator?.count();
   if (elementCount === 0) {
     expect(true).toBeTruthy();
 

src/frontend/tests/end-to-end/keyPairListComponent.spec.ts

@@ -1,36 +1,43 @@
 import { expect, test } from "@playwright/test";
 
 test("KeypairListComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.waitForTimeout(1000);
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);
   await page.getByTestId("extended-disclosure").click();
   await page.getByPlaceholder("Search").click();
-  await page.getByPlaceholder("Search").fill("csv");
+  await page.getByPlaceholder("Search").fill("amazon bedrock");
 
   await page.waitForTimeout(1000);
 
   await page
-    .getByTestId("documentloadersCSVLoader")
+    .getByTestId("model_specsAmazon Bedrock")
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.locator('//*[@id="keypair0"]').click();
   await page.locator('//*[@id="keypair0"]').fill("testtesttesttest");
   await page.locator('//*[@id="keypair100"]').click();
@@ -48,7 +55,7 @@ test("KeypairListComponent", async ({ page }) => {
   }
 
   const plusButtonLocatorNode = page.locator('//*[@id="plusbtn0"]');
-  const elementCountNode = await plusButtonLocatorNode.count();
+  const elementCountNode = await plusButtonLocatorNode?.count();
   if (elementCountNode > 0) {
     await plusButtonLocatorNode.click();
   }
@@ -59,7 +66,7 @@ test("KeypairListComponent", async ({ page }) => {
   await page.getByTestId("div-generic-node").click();
 
   const keyPairVerification = page.locator('//*[@id="keypair100"]');
-  const elementKeyCount = await keyPairVerification.count();
+  const elementKeyCount = await keyPairVerification?.count();
 
   if (elementKeyCount === 1) {
     expect(true).toBeTruthy();
@@ -70,16 +77,16 @@ test("KeypairListComponent", async ({ page }) => {
   await page.getByTestId("more-options-modal").click();
   await page.getByTestId("edit-button-modal").click();
 
-  await page.locator('//*[@id="showfile_path"]').click();
+  await page.locator('//*[@id="showcache"]').click();
+  expect(await page.locator('//*[@id="showcache"]').isChecked()).toBeFalsy();
+  await page.locator('//*[@id="showcredentials_profile_name"]').click();
   expect(
-    await page.locator('//*[@id="showfile_path"]').isChecked()
+    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
   ).toBeFalsy();
-  await page.locator('//*[@id="showmetadata"]').click();
-  expect(await page.locator('//*[@id="showmetadata"]').isChecked()).toBeFalsy();
   await page.locator('//*[@id="saveChangesBtn"]').click();
 
   const plusButtonLocator = page.locator('//*[@id="plusbtn0"]');
-  const elementCount = await plusButtonLocator.count();
+  const elementCount = await plusButtonLocator?.count();
   if (elementCount === 0) {
     expect(true).toBeTruthy();
     await page.getByTestId("div-generic-node").click();
@@ -87,20 +94,18 @@ test("KeypairListComponent", async ({ page }) => {
     await page.getByTestId("more-options-modal").click();
     await page.getByTestId("edit-button-modal").click();
 
-    await page.locator('//*[@id="showfile_path"]').click();
+    await page.locator('//*[@id="showcredentials_profile_name"]').click();
     expect(
-      await page.locator('//*[@id="showfile_path"]').isChecked()
-    ).toBeTruthy();
-    await page.locator('//*[@id="showmetadata"]').click();
-    expect(
-      await page.locator('//*[@id="showmetadata"]').isChecked()
+      await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
     ).toBeTruthy();
+    await page.locator('//*[@id="showcache"]').click();
+    expect(await page.locator('//*[@id="showcache"]').isChecked()).toBeTruthy();
 
     await page.locator('//*[@id="editNodekeypair0"]').click();
     await page.locator('//*[@id="editNodekeypair0"]').fill("testtesttesttest");
 
     const keyPairVerification = page.locator('//*[@id="editNodekeypair0"]');
-    const elementKeyCount = await keyPairVerification.count();
+    const elementKeyCount = await keyPairVerification?.count();
 
     if (elementKeyCount === 1) {
       await page.locator('//*[@id="saveChangesBtn"]').click();

src/frontend/tests/end-to-end/langflowShortcuts.spec.ts

@@ -1,6 +1,7 @@
 import { expect, test } from "@playwright/test";
 import uaParser from "ua-parser-js";
 test("LangflowShortcuts", async ({ page }) => {
+  await page.goto("/");
   const getUA = await page.evaluate(() => navigator.userAgent);
   const userAgentInfo = uaParser(getUA);
   let control = "Control";
@@ -9,11 +10,23 @@ test("LangflowShortcuts", async ({ page }) => {
     control = "Meta";
   }
 
-  await page.goto("http:localhost:3000/");
   await page.waitForTimeout(1000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await page.waitForTimeout(1000);
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);
@@ -30,17 +43,10 @@ test("LangflowShortcuts", async ({ page }) => {
   await page.mouse.down();
 
   await page.locator('//*[@id="react-flow-id"]/div/div[2]/button[3]').click();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.getByTestId("title-Ollama").click();
   await page.keyboard.press(`${control}+Shift+A`);
   await page.locator('//*[@id="saveChangesBtn"]').click();
@@ -48,7 +54,7 @@ test("LangflowShortcuts", async ({ page }) => {
   await page.getByTestId("title-Ollama").click();
   await page.keyboard.press(`${control}+d`);
 
-  let numberOfNodes = await page.getByTestId("title-Ollama").count();
+  let numberOfNodes = await page.getByTestId("title-Ollama")?.count();
   if (numberOfNodes != 2) {
     expect(false).toBeTruthy();
   }
@@ -60,7 +66,7 @@ test("LangflowShortcuts", async ({ page }) => {
     .click();
   await page.keyboard.press("Backspace");
 
-  numberOfNodes = await page.getByTestId("title-Ollama").count();
+  numberOfNodes = await page.getByTestId("title-Ollama")?.count();
   if (numberOfNodes != 1) {
     expect(false).toBeTruthy();
   }
@@ -71,7 +77,7 @@ test("LangflowShortcuts", async ({ page }) => {
   await page.getByTestId("title-Ollama").click();
   await page.keyboard.press(`${control}+v`);
 
-  numberOfNodes = await page.getByTestId("title-Ollama").count();
+  numberOfNodes = await page.getByTestId("title-Ollama")?.count();
   if (numberOfNodes != 2) {
     expect(false).toBeTruthy();
   }
@@ -86,12 +92,12 @@ test("LangflowShortcuts", async ({ page }) => {
   await page.getByTestId("title-Ollama").click();
   await page.keyboard.press(`${control}+x`);
 
-  numberOfNodes = await page.getByTestId("title-Ollama").count();
+  numberOfNodes = await page.getByTestId("title-Ollama")?.count();
   if (numberOfNodes != 0) {
     expect(false).toBeTruthy();
   }
   await page.keyboard.press(`${control}+v`);
-  numberOfNodes = await page.getByTestId("title-Ollama").count();
+  numberOfNodes = await page.getByTestId("title-Ollama")?.count();
   if (numberOfNodes != 1) {
     expect(false).toBeTruthy();
   }

src/frontend/tests/end-to-end/nestedComponent.spec.ts

@@ -1,11 +1,24 @@
 import { expect, test } from "@playwright/test";
 
 test("NestedComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await page.waitForTimeout(1000);
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);

src/frontend/tests/end-to-end/promptModalComponent.spec.ts

@@ -1,11 +1,24 @@
 import { expect, test } from "@playwright/test";
 
 test("PromptTemplateComponent", async ({ page }) => {
-  await page.goto("http:localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(2000);
 
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await page.waitForTimeout(1000);
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
 
   await page.getByTestId("blank-flow").click();
   await page.waitForTimeout(1000);
@@ -20,17 +33,10 @@ test("PromptTemplateComponent", async ({ page }) => {
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
-
-  await page
-    .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-    .click();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
   await page.getByTestId("prompt-input-template").click();
 
   await page

src/frontend/tests/end-to-end/python_api_generation.spec.ts

@@ -2,8 +2,21 @@ import { expect, test } from "@playwright/test";
 
 test("python_api_generation", async ({ page, context }) => {
   await page.goto("/");
-  await page.locator('//*[@id="new-project-btn"]').click();
-  await context.grantPermissions(["clipboard-read", "clipboard-write"]);
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
   await page.getByRole("heading", { name: "Basic Prompting" }).click();
   await page.waitForTimeout(2000);
   await page.getByText("API", { exact: true }).click();

src/frontend/tests/end-to-end/saveComponents.spec.ts

@@ -1,19 +1,26 @@
-import { Page, expect, test } from "@playwright/test";
+import { expect, test } from "@playwright/test";
 import { readFileSync } from "fs";
 
 test.describe("save component tests", () => {
-  async function saveComponent(page: Page, pattern: RegExp, n: number) {
-    for (let i = 0; i < n; i++) {
-      await page.getByTestId(pattern).click();
-      await page.getByLabel("Save").click();
-    }
-  }
-
   /// <reference lib="dom"/>
   test("save group component tests", async ({ page }) => {
-    await page.goto("http:localhost:3000/");
-    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.goto("/");
+    let modalCount = 0;
+    try {
+      const modalTitleElement = await page?.getByTestId("modal-title");
+      if (modalTitleElement) {
+        modalCount = await modalTitleElement.count();
+      }
+    } catch (error) {
+      modalCount = 0;
+    }
 
+    while (modalCount === 0) {
+      await page.locator('//*[@id="new-project-btn"]').click();
+      await page.waitForTimeout(5000);
+      modalCount = await page.getByTestId("modal-title")?.count();
+    }
+    await page.waitForTimeout(1000);
     await page.getByTestId("blank-flow").click();
     await page.waitForTimeout(1000);
 
@@ -46,7 +53,7 @@ test.describe("save component tests", () => {
     );
 
     const genericNoda = page.getByTestId("div-generic-node");
-    const elementCount = await genericNoda.count();
+    const elementCount = await genericNoda?.count();
     if (elementCount > 0) {
       expect(true).toBeTruthy();
     }
@@ -68,13 +75,13 @@ test.describe("save component tests", () => {
     await page.getByRole("button", { name: "Group" }).click();
 
     let textArea = page.getByTestId("div-textarea-description");
-    let elementCountText = await textArea.count();
+    let elementCountText = await textArea?.count();
     if (elementCountText > 0) {
       expect(true).toBeTruthy();
     }
 
     let groupNode = page.getByTestId("title-Group");
-    let elementGroup = await groupNode.count();
+    let elementGroup = await groupNode?.count();
     if (elementGroup > 0) {
       expect(true).toBeTruthy();
     }
@@ -98,25 +105,18 @@ test.describe("save component tests", () => {
       .dragTo(page.locator('//*[@id="react-flow-id"]'));
     await page.mouse.up();
     await page.mouse.down();
-    await page
-      .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-      .click();
-
-    await page
-      .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-      .click();
-
-    await page
-      .locator('//*[@id="react-flow-id"]/div[1]/div[2]/button[2]')
-      .click();
+    await page.getByTitle("fit view").click();
+    await page.getByTitle("zoom out").click();
+    await page.getByTitle("zoom out").click();
+    await page.getByTitle("zoom out").click();
     textArea = page.getByTestId("div-textarea-description");
-    elementCountText = await textArea.count();
+    elementCountText = await textArea?.count();
     if (elementCountText > 0) {
       expect(true).toBeTruthy();
     }
 
     groupNode = page.getByTestId("title-Group");
-    elementGroup = await groupNode.count();
+    elementGroup = await groupNode?.count();
     if (elementGroup > 0) {
       expect(true).toBeTruthy();
     }

src/frontend/tests/end-to-end/store.spec.ts

@@ -1,7 +1,7 @@
 import { expect, test } from "@playwright/test";
 
 test("should exists Store", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").isVisible();
@@ -9,7 +9,7 @@ test("should exists Store", async ({ page }) => {
 });
 
 test("should not have an API key", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").click();
@@ -19,7 +19,7 @@ test("should not have an API key", async ({ page }) => {
 });
 
 test("should find a searched Component on Store", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").click();
@@ -39,7 +39,7 @@ test("should find a searched Component on Store", async ({ page }) => {
 });
 
 test("should filter by tag", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").click();
@@ -66,7 +66,7 @@ test("should filter by tag", async ({ page }) => {
 });
 
 test("should order the visualization", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").click();
@@ -75,6 +75,7 @@ test("should order the visualization", async ({ page }) => {
   await page.getByText("Basic RAG").isVisible();
 
   await page.getByTestId("select-order-store").click();
+  await page.waitForTimeout(2000);
   await page.getByText("Alphabetical").click();
 
   await page.getByText("Album Cover Builder").isVisible();
@@ -86,7 +87,7 @@ test("should order the visualization", async ({ page }) => {
 });
 
 test("should filter by type", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").click();
@@ -97,7 +98,7 @@ test("should filter by type", async ({ page }) => {
   await page.getByTestId("flows-button-store").click();
   await page.waitForTimeout(3000);
 
-  let iconGroup = await page.getByTestId("icon-Group").count();
+  let iconGroup = await page.getByTestId("icon-Group")?.count();
   expect(iconGroup).not.toBe(0);
 
   await page.getByText("icon-ToyBrick").isHidden();
@@ -106,14 +107,14 @@ test("should filter by type", async ({ page }) => {
   await page.waitForTimeout(3000);
 
   await page.getByTestId("icon-Group").isHidden();
-  let toyBrick = await page.getByTestId("icon-ToyBrick").count();
+  let toyBrick = await page.getByTestId("icon-ToyBrick")?.count();
   expect(toyBrick).not.toBe(0);
 
   await page.getByTestId("all-button-store").click();
   await page.waitForTimeout(3000);
 
-  iconGroup = await page.getByTestId("icon-Group").count();
-  toyBrick = await page.getByTestId("icon-ToyBrick").count();
+  iconGroup = await page.getByTestId("icon-Group")?.count();
+  toyBrick = await page.getByTestId("icon-ToyBrick")?.count();
 
   if (iconGroup === 0 || toyBrick === 0) {
     expect(false).toBe(true);
@@ -121,7 +122,7 @@ test("should filter by type", async ({ page }) => {
 });
 
 test("should add API-KEY", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").click();
@@ -154,7 +155,7 @@ test("should add API-KEY", async ({ page }) => {
 });
 
 test("should like and add components and flows", async ({ page }) => {
-  await page.goto("http://localhost:3000/");
+  await page.goto("/");
   await page.waitForTimeout(1000);
 
   await page.getByTestId("button-store").click();
@@ -216,3 +217,60 @@ test("should like and add components and flows", async ({ page }) => {
   await page.getByTestId("sidebar-nav-Components").click();
   await page.getByText("Basic RAG").first().isVisible();
 });
+
+test("should share component with share button", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.locator('//*[@id="new-project-btn"]').click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+  await page.waitForTimeout(1000);
+
+  await page.getByRole("heading", { name: "Basic Prompting" }).click();
+  await page.waitForTimeout(1000);
+  const flowName = await page.getByTestId("flow_name").innerText();
+  await page.getByTestId("flow_name").click();
+  await page.getByText("Settings").click();
+  const flowDescription = await page
+    .getByPlaceholder("Flow description")
+    .inputValue();
+  await page.getByText("Save").last().click();
+
+  await page.getByTestId("icon-Share3").first().click();
+  await page.getByText("Name:").isVisible();
+  await page.getByText("Description:").isVisible();
+  await page.getByText("Set workflow status to public").isVisible();
+  await page
+    .getByText(
+      "Attention: API keys in specified fields are automatically removed upon sharing."
+    )
+    .isVisible();
+  await page.getByText("Export").first().isVisible();
+  await page.getByText("Share Flow").first().isVisible();
+
+  await page.waitForTimeout(5000);
+
+  await page.getByText("Agent").first().isVisible();
+  await page.getByText("Memory").first().isVisible();
+  await page.getByText("Chain").first().isVisible();
+  await page.getByText("Vector Store").first().isVisible();
+  await page.getByText("Prompt").last().isVisible();
+  await page.getByTestId("public-checkbox").isChecked();
+  await page.getByText(flowName).last().isVisible();
+  await page.getByText(flowDescription).last().isVisible();
+  await page.waitForTimeout(1000);
+  await page.getByText("Flow shared successfully").last().isVisible();
+});