Update docs (#1567)

* Add new documentation files and update package dependencies

* Refactor tweak application logic in process_tweaks function

* Add dynamic function creation and execution helpers

* Refactor build method to be asynchronous

* Add FlowToolComponent to handle flows as tools

* Update RunFlowComponent to include a method for updating build config

* Fix duplicated first layer results

* Refactor vertex building and streaming endpoints

* Add base_name attribute to Vertex class

* Refactor flow.py to generate dynamic flow functions and build schemas

* Refactor FlowToolComponent in FlowTool.py

* Add JSONInputComponent to load JSON object as input

* Update render_tool_description method in XMLAgent.py

* Refactor XMLAgentComponent.render_tool_description() method

* Refactor SearchApi.py to include typing and handle empty records

* Refactor SearchApi class to simplify code

* Add SearchApi and SearchApiTool components

* Refactor ServiceFactory and Dependencies (#1560)

* Update dependencies for OpenTelemetry

* Update service dependency logic and add first version of telemetry service

* Remove telemetry service and related code

* Update cache service references

* Refactor imports in env.py

* Refactor code for initializing services and socketio server

* Refactor parameterComponent to use inline button_text

* Refactor build_vertex method and add RunnableVerticesManager class

* Add import statement and update build_vertex function

* Add import statement for SettingsService in MonitorServiceFactory.create() method

* Refactor build_schema_from_inputs to use display_name and description for field names and descriptions respectively

* Refactor graph building and running logic

* Update input type mappings and function arguments

* Update default values for input types in flow.py

* Remove console.log statement in flowStore.ts

* Add vertices_to_run field to VerticesOrderResponse

* Add input_value parameter to chain components

* Refactor CSVAgent build method to include handle_parse_errors parameter

* Add agent_type parameter to CSVAgent build method

* Update model imports in component files

* Add LCAgentComponent and XMLAgentComponent

* Add "agents" category to NATIVE_CATEGORIES

* Refactor model.py to support chat models

* Add system_message parameter to model components

* Update CSVAgent.py: handle_parsing_errors and agent_type options

* Add ping animation to update button

* Fix encryption and decryption of API keys

* Update CSVAgentComponent constructor

* Refactor inputs parameter to inputs_dict in build_vertex function

* Removes "component" table and drops "flowstyle" table

* Delete component model and init files

* Removes "flowstyle" table and drops "user" table index

* Add typing import to CohereModel.py

* Fix ShareModal rendering issue

* Update models docs

* Changed vector-stores docs

* Update component documentation

* Add AstraDB and AstraDBSearch components for AstraDB Vector Store docs

* Rename GetNotified to Listen

* Update GetNotifiedComponent import

* Remove unused imports in flow-runner.mdx and features.mdx

* Add new documentation files and update existing files

* Update package versions in package-lock.json

* Remove unused files

* Delete run-flow.mdx file

* Update topics

* Add new file run-flow.mdx

---------

Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Co-authored-by: anovazzi1 <otavio2204@gmail.com>

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

@@ -2,9 +2,9 @@ from langflow.interface.custom.custom_component import CustomComponent
 from langflow.schema import Record
 
 
-class GetNotifiedComponent(CustomComponent):
-    display_name = "Get Notified"
-    description = "A component to get notified by Notify component."
+class ListenComponent(CustomComponent):
+    display_name = "Listen"
+    description = "A component to listen for a notification."
     beta: bool = True
 
     def build_config(self):

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

@@ -1,6 +1,6 @@
 from .ClearMessageHistory import ClearMessageHistoryComponent
 from .ExtractDataFromRecord import ExtractKeyFromRecordComponent
-from .GetNotified import GetNotifiedComponent
+from .Listen import GetNotifiedComponent
 from .ListFlows import ListFlowsComponent
 from .MergeRecords import MergeRecordsComponent
 from .Notify import NotifyComponent

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

@@ -106,7 +106,9 @@ def initialize_session_service():
     Initialize the session manager.
     """
     from langflow.services.cache import factory as cache_factory
-    from langflow.services.session import factory as session_service_factory  # type: ignore
+    from langflow.services.session import (
+        factory as session_service_factory,
+    )  # type: ignore
 
     initialize_settings_service()
 

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

@@ -0,0 +1,70 @@
+from typing import List, Union
+
+from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent
+
+from langflow import CustomComponent
+from langflow.field_typing import BaseMemory, Text, Tool
+
+
+class LCAgentComponent(CustomComponent):
+    def build_config(self):
+        return {
+            "lc": {
+                "display_name": "LangChain",
+                "info": "The LangChain to interact with.",
+            },
+            "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,
+            },
+            "output_key": {
+                "display_name": "Output Key",
+                "info": "The key to use to get the output from the agent.",
+                "advanced": True,
+            },
+            "memory": {
+                "display_name": "Memory",
+                "info": "Memory to use for the agent.",
+            },
+            "tools": {
+                "display_name": "Tools",
+                "info": "Tools the agent can use.",
+            },
+            "input_value": {
+                "display_name": "Input",
+                "info": "Input text to pass to the agent.",
+            },
+        }
+
+    async def run_agent(
+        self,
+        agent: Union[BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
+        inputs: str,
+        input_variables: list[str],
+        tools: List[Tool],
+        memory: BaseMemory = None,
+        handle_parsing_errors: bool = True,
+        output_key: str = "output",
+    ) -> Text:
+        if isinstance(agent, AgentExecutor):
+            runnable = agent
+        else:
+            runnable = AgentExecutor.from_agent_and_tools(
+                agent=agent, 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] = ""
+        result = await runnable.ainvoke(input_dict)
+        self.status = result
+        if output_key in result:
+            return result.get(output_key)
+        elif "output" not in result:
+            if output_key != "output":
+                raise ValueError(f"Output key not found in result. Tried '{output_key}' and 'output'.")
+            else:
+                raise ValueError("Output key not found in result. Tried 'output'.")
+
+        return result.get("output")

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

@@ -0,0 +1,3 @@
+from .model import LCModelComponent
+
+__all__ = ["LCModelComponent"]

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

@@ -0,0 +1,48 @@
+from typing import Optional
+
+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 langflow import CustomComponent
+
+
+class LCModelComponent(CustomComponent):
+    display_name: str = "Model Name"
+    description: str = "Model Description"
+
+    def get_result(self, runnable: LLM, stream: bool, input_value: str):
+        """
+        Retrieves the result from the output of a Runnable object.
+
+        Args:
+            output (Runnable): The output object to retrieve the result from.
+            stream (bool): Indicates whether to use streaming or invocation mode.
+            input_value (str): The input value to pass to the output object.
+
+        Returns:
+            The result obtained from the output object.
+        """
+        if stream:
+            result = runnable.stream(input_value)
+        else:
+            message = runnable.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
+        return result
+
+    def get_chat_result(
+        self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
+    ):
+        messages = []
+        if input_value:
+            messages.append(HumanMessage(input_value))
+        if system_message:
+            messages.append(SystemMessage(system_message))
+        if stream:
+            result = runnable.stream(messages)
+        else:
+            message = runnable.invoke(messages)
+            result = message.content
+            self.status = result
+        return result

src/backend/langflow/components/experimental/FlowTool.py

@@ -0,0 +1,85 @@
+from typing import Any, List, Optional, Text
+
+from langchain_core.tools import StructuredTool
+from loguru import logger
+
+from langflow import CustomComponent
+from langflow.field_typing import Tool
+from langflow.graph.graph.base import Graph
+from langflow.helpers.flow import build_function_and_schema
+from langflow.schema.dotdict import dotdict
+
+
+class FlowToolComponent(CustomComponent):
+    display_name = "Flow as Tool"
+    description = "Construct a Tool from a function that runs the loaded Flow."
+    field_order = ["flow_name", "name", "description", "return_direct"]
+
+    def get_flow_names(self) -> List[str]:
+        flow_records = self.list_flows()
+        return [flow_record.data["name"] for flow_record in flow_records]
+
+    def get_flow(self, flow_name: str) -> Optional[Text]:
+        """
+        Retrieves a flow by its name.
+
+        Args:
+            flow_name (str): The name of the flow to retrieve.
+
+        Returns:
+            Optional[Text]: The flow record if found, None otherwise.
+        """
+        flow_records = self.list_flows()
+        for flow_record in flow_records:
+            if flow_record.data["name"] == flow_name:
+                return flow_record
+        return None
+
+    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
+        logger.debug(f"Updating build config with field value {field_value} and field name {field_name}")
+        if field_name == "flow_name":
+            build_config["flow_name"]["options"] = self.get_flow_names()
+
+        return build_config
+
+    def build_config(self):
+        return {
+            "flow_name": {
+                "display_name": "Flow Name",
+                "info": "The name of the flow to run.",
+                "options": [],
+                "real_time_refresh": True,
+                "refresh_button": True,
+            },
+            "name": {
+                "display_name": "Name",
+                "description": "The name of the tool.",
+            },
+            "description": {
+                "display_name": "Description",
+                "description": "The description of the tool.",
+            },
+            "return_direct": {
+                "display_name": "Return Direct",
+                "description": "Return the result directly from the Tool.",
+                "advanced": True,
+            },
+        }
+
+    async def build(self, flow_name: str, name: str, description: str, return_direct: bool = False) -> Tool:
+        flow_record = self.get_flow(flow_name)
+        if not flow_record:
+            raise ValueError("Flow not found.")
+        graph = Graph.from_payload(flow_record.data["data"])
+        dynamic_flow_function, schema = build_function_and_schema(flow_record, graph)
+        tool = StructuredTool.from_function(
+            coroutine=dynamic_flow_function,
+            name=name,
+            description=description,
+            return_direct=return_direct,
+            args_schema=schema,
+        )
+        description_repr = repr(tool.description).strip("'")
+        args_str = "\n".join([f"- {arg_name}: {arg_data['description']}" for arg_name, arg_data in tool.args.items()])
+        self.status = f"{description_repr}\nArguments:\n{args_str}"
+        return tool

src/backend/langflow/components/tools/SearchAPITool.py

@@ -0,0 +1,37 @@
+from langchain_community.tools.searchapi import SearchAPIRun
+from langchain_community.utilities.searchapi import SearchApiAPIWrapper
+
+from langflow import CustomComponent
+from langflow.field_typing import Tool
+
+
+class SearchApiToolComponent(CustomComponent):
+    display_name: str = "SearchApi Tool"
+    description: str = "Real-time search engine results API."
+    documentation: str = "https://www.searchapi.io/docs/google"
+    field_config = {
+        "engine": {
+            "display_name": "Engine",
+            "field_type": "str",
+            "info": "The search engine to use.",
+        },
+        "api_key": {
+            "display_name": "API Key",
+            "field_type": "str",
+            "required": True,
+            "password": True,
+            "info": "The API key to use SearchApi.",
+        },
+    }
+
+    def build(
+        self,
+        engine: str,
+        api_key: str,
+    ) -> Tool:
+        search_api_wrapper = SearchApiAPIWrapper(engine=engine, searchapi_api_key=api_key)
+
+        tool = SearchAPIRun(api_wrapper=search_api_wrapper)
+
+        self.status = tool
+        return tool