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true, "justMyCode": true, + "env": { + "LANGFLOW_LOG_LEVEL": "debug" + }, "envFile": "${workspaceFolder}/.env" }, { diff --git a/poetry.lock b/poetry.lock index a7ed6a921..9e3d5480d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -418,17 +418,17 @@ files = [ [[package]] name = "boto3" -version = "1.34.49" +version = "1.34.50" description = "The AWS SDK for Python" optional = false python-versions = ">= 3.8" files = [ - {file = "boto3-1.34.49-py3-none-any.whl", hash = "sha256:ce8d1de03024f52a1810e8d71ad4dba3a5b9bb48b35567191500e3432a9130b4"}, - {file = "boto3-1.34.49.tar.gz", hash = "sha256:96b9dc85ce8d52619b56ca7b1ac1423eaf0af5ce132904bcc8aa81396eec2abf"}, + {file = "boto3-1.34.50-py3-none-any.whl", hash = "sha256:8d709365231234bc4f0ca98fdf33a25eeebf78072853c6aa3d259f0f5cf09877"}, + {file = "boto3-1.34.50.tar.gz", hash = "sha256:290952be7899560039cb0042e8a2354f61a7dead0d0ca8bea6ba901930df0468"}, ] [package.dependencies] -botocore = ">=1.34.49,<1.35.0" +botocore = ">=1.34.50,<1.35.0" 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import TYPE_CHECKING, List, Optional from fastapi import HTTPException -from langchain_core.documents import Document from platformdirs import user_cache_dir -from pydantic import BaseModel from sqlmodel import Session from langflow.graph.graph.base import Graph @@ -22,7 +20,9 @@ API_WORDS = ["api", "key", "token"] def has_api_terms(word: str): - return "api" in word and ("key" in word or ("token" in word and "tokens" not in word)) + return "api" in word and ( + "key" in word or ("token" in word and "tokens" not in word) + ) def remove_api_keys(flow: dict): @@ -32,7 +32,11 @@ def remove_api_keys(flow: dict): node_data = node.get("data").get("node") template = node_data.get("template") for value in template.values(): - if isinstance(value, dict) and has_api_terms(value["name"]) and value.get("password"): + if ( + isinstance(value, dict) + and has_api_terms(value["name"]) + and value.get("password") + ): value["value"] = None return flow @@ -53,7 +57,9 @@ def build_input_keys_response(langchain_object, artifacts): input_keys_response["input_keys"][key] = value # If the object has memory, that memory will have a memory_variables attribute # memory variables should be removed from the input keys - if hasattr(langchain_object, "memory") and hasattr(langchain_object.memory, "memory_variables"): + if hasattr(langchain_object, "memory") and hasattr( + langchain_object.memory, "memory_variables" + ): # Remove memory variables from input keys input_keys_response["input_keys"] = { key: value @@ -63,7 +69,9 @@ def build_input_keys_response(langchain_object, artifacts): # Add memory variables to memory_keys input_keys_response["memory_keys"] = langchain_object.memory.memory_variables - if hasattr(langchain_object, "prompt") and hasattr(langchain_object.prompt, "template"): + if hasattr(langchain_object, "prompt") and hasattr( + langchain_object.prompt, "template" + ): input_keys_response["template"] = langchain_object.prompt.template return input_keys_response @@ -98,7 +106,11 @@ def raw_frontend_data_is_valid(raw_frontend_data): def is_valid_data(frontend_node, raw_frontend_data): """Check if the data is valid for processing.""" - return frontend_node and "template" in frontend_node and raw_frontend_data_is_valid(raw_frontend_data) + return ( + frontend_node + and "template" in frontend_node + and raw_frontend_data_is_valid(raw_frontend_data) + ) def update_template_values(frontend_template, raw_template): @@ -138,7 +150,9 @@ def get_file_path_value(file_path): # If the path is not in the cache dir, return empty string # This is to prevent access to files outside the cache dir # If the path is not a file, return empty string - if not path.exists() or not str(path).startswith(user_cache_dir("langflow", "langflow")): + if not path.exists() or not str(path).startswith( + user_cache_dir("langflow", "langflow") + ): return "" return file_path @@ -169,7 +183,9 @@ async def check_langflow_version(component: StoreComponentCreate): langflow_version = get_lf_version_from_pypi() if langflow_version is None: - raise HTTPException(status_code=500, detail="Unable to verify the latest version of Langflow") + raise HTTPException( + status_code=500, detail="Unable to verify the latest version of Langflow" + ) elif langflow_version != component.last_tested_version: warnings.warn( f"Your version of Langflow ({component.last_tested_version}) is outdated. " @@ -199,20 +215,6 @@ def format_elapsed_time(elapsed_time: float) -> str: return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}" -def serialize_field(value): - """Unified serialization function for handling both BaseModel and Document types, - including handling lists of these types.""" - if isinstance(value, (list, tuple)): - return [serialize_field(v) for v in value] - elif isinstance(value, Document): - return value.to_json() - elif isinstance(value, BaseModel): - return value.model_dump() - elif isinstance(value, str): - return {"result": value} - return value - - def build_and_cache_graph( flow_id: str, session: Session, @@ -230,3 +232,25 @@ def build_and_cache_graph( graph = graph.update(other_graph) chat_service.set_cache(flow_id, graph) return graph + + +def format_syntax_error_message(exc: SyntaxError) -> str: + """Format a SyntaxError message for returning to the frontend.""" + return f"Syntax error in code. Error on line {exc.lineno}: {exc.text.strip()}" + + +def get_causing_exception(exc: Exception) -> Exception: + """Get the causing exception from an exception.""" + if hasattr(exc, "__cause__") and exc.__cause__: + return get_causing_exception(exc.__cause__) + return exc + + +def format_exception_message(exc: Exception) -> str: + """Format an exception message for returning to the frontend.""" + # We need to check if the __cause__ is a SyntaxError + # If it is, we need to return the message of the SyntaxError + causing_exception = get_causing_exception(exc) + if isinstance(causing_exception, SyntaxError): + return format_syntax_error_message(causing_exception) + return str(exc) diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py index d04588cba..91b4bcd65 100644 --- a/src/backend/langflow/api/v1/chat.py +++ b/src/backend/langflow/api/v1/chat.py @@ -1,5 +1,6 @@ import time -from typing import Optional +import uuid +from typing import TYPE_CHECKING, Optional from fastapi import ( APIRouter, @@ -14,9 +15,13 @@ from fastapi.responses import StreamingResponse from loguru import logger from sqlmodel import Session -from langflow.api.utils import build_and_cache_graph, format_elapsed_time +from langflow.api.utils import ( + build_and_cache_graph, + format_elapsed_time, + format_exception_message, +) from langflow.api.v1.schemas import ( - ResultData, + ResultDataResponse, StreamData, VertexBuildResponse, VerticesOrderResponse, @@ -27,8 +32,12 @@ from langflow.services.auth.utils import ( get_current_user_for_websocket, ) from langflow.services.chat.service import ChatService -from langflow.services.deps import get_chat_service, get_session +from langflow.services.deps import get_chat_service, get_session, get_session_service from langflow.services.monitor.utils import log_vertex_build +from langflow.services.session.service import SessionService + +if TYPE_CHECKING: + from langflow.graph.vertex.types import ChatVertex router = APIRouter(tags=["Chat"]) @@ -45,9 +54,13 @@ async def chat( user = await get_current_user_for_websocket(websocket, db) await websocket.accept() if not user: - await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized") + await websocket.close( + code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized" + ) elif not user.is_active: - await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized") + await websocket.close( + code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized" + ) if client_id in chat_service.cache_service: await chat_service.handle_websocket(client_id, websocket) @@ -63,7 +76,9 @@ async def chat( logger.error(f"Error in chat websocket: {exc}") messsage = exc.detail if isinstance(exc, HTTPException) else str(exc) if "Could not validate credentials" in str(exc): - await websocket.close(code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized") + await websocket.close( + code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized" + ) else: await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=messsage) @@ -110,7 +125,8 @@ async def get_vertices( # Now vertices is a list of lists # We need to get the id of each vertex # and return the same structure but only with the ids - return VerticesOrderResponse(ids=vertices) + run_id = uuid.uuid4() + return VerticesOrderResponse(ids=vertices, run_id=run_id) except Exception as exc: logger.error(f"Error checking build status: {exc}") @@ -133,30 +149,23 @@ async def build_vertex( cache = chat_service.get_cache(flow_id) if not cache: # If there's no cache - logger.warning(f"No cache found for {flow_id}. Building graph starting at {vertex_id}") - graph = build_and_cache_graph(flow_id=flow_id, session=next(get_session()), chat_service=chat_service) + logger.warning( + f"No cache found for {flow_id}. Building graph starting at {vertex_id}" + ) + graph = build_and_cache_graph( + flow_id=flow_id, session=next(get_session()), chat_service=chat_service + ) else: graph = cache.get("result") - result_dict = {} + result_data_response = {} duration = "" vertex = graph.get_vertex(vertex_id) try: if not vertex.pinned or not vertex._built: await vertex.build(user_id=current_user.id) - params = vertex._built_object_repr() - valid = True - result_dict = vertex.get_built_result() - # We need to set the artifacts to pass information - # to the frontend - vertex.set_artifacts() - artifacts = vertex.artifacts - result_dict = ResultData( - results=result_dict, - artifacts=artifacts, - ) - vertex.set_result(result_dict) - elif vertex.result is not None: + + if vertex.result is not None: params = vertex._built_object_repr() valid = True result_dict = vertex.result @@ -164,30 +173,34 @@ async def build_vertex( else: raise ValueError(f"No result found for vertex {vertex_id}") + result_data_response = ResultDataResponse(**result_dict.model_dump()) + except Exception as exc: - params = str(exc) + logger.error(f"Error building vertex: {exc}") + params = format_exception_message(exc) valid = False - result_dict = ResultData(results={}) + result_data_response = ResultDataResponse(results={}) artifacts = {} # If there's an error building the vertex # we need to clear the cache chat_service.clear_cache(flow_id) # Log the vertex build - background_tasks.add_task( - log_vertex_build, - flow_id=flow_id, - vertex_id=vertex_id, - valid=valid, - params=params, - data=result_dict, - artifacts=artifacts, - ) + if not vertex.will_stream: + background_tasks.add_task( + log_vertex_build, + flow_id=flow_id, + vertex_id=vertex_id, + valid=valid, + params=params, + data=result_data_response, + artifacts=artifacts, + ) timedelta = time.perf_counter() - start_time duration = format_elapsed_time(timedelta) - result_dict.duration = duration - result_dict.timedelta = timedelta + result_data_response.duration = duration + result_data_response.timedelta = timedelta vertex.add_build_time(timedelta) inactive_vertices = None if graph.inactive_vertices: @@ -200,7 +213,7 @@ async def build_vertex( valid=valid, params=params, id=vertex.id, - data=result_dict, + data=result_data_response, ) except Exception as exc: logger.error(f"Error building vertex: {exc}") @@ -215,41 +228,71 @@ async def build_vertex( async def build_vertex_stream( flow_id: str, vertex_id: str, + session_id: Optional[str] = None, chat_service: "ChatService" = Depends(get_chat_service), + session_service: "SessionService" = Depends(get_session_service), ): """Build a vertex instead of the entire graph.""" try: async def stream_vertex(): try: - cache = chat_service.get_cache(flow_id) - if not cache: - # If there's no cache - raise ValueError(f"No cache found for {flow_id}.") + if not session_id: + cache = chat_service.get_cache(flow_id) + if not cache: + # If there's no cache + raise ValueError(f"No cache found for {flow_id}.") + else: + graph = cache.get("result") else: - graph = cache.get("result") + session_data = await session_service.load_session(session_id) + graph, artifacts = session_data if session_data else (None, None) + if not graph: + raise ValueError(f"No graph found for {flow_id}.") - vertex = graph.get_vertex(vertex_id) - if not vertex.pinned or not vertex._built: + vertex: "ChatVertex" = graph.get_vertex(vertex_id) + if not hasattr(vertex, "stream"): + raise ValueError(f"Vertex {vertex_id} does not support streaming") + if isinstance(vertex._built_result, str) and vertex._built_result: stream_data = StreamData( event="message", - data={"message": "Building vertex"}, + data={"message": f"Streaming vertex {vertex_id}"}, + ) + yield str(stream_data) + stream_data = StreamData( + event="message", + data={"chunk": vertex._built_result}, ) yield str(stream_data) + elif not vertex.pinned or not vertex._built: + logger.debug(f"Streaming vertex {vertex_id}") + stream_data = StreamData( + event="message", + data={"message": f"Streaming vertex {vertex_id}"}, + ) + yield str(stream_data) async for chunk in vertex.stream(): stream_data = StreamData( event="message", data={"chunk": chunk}, ) yield str(stream_data) + elif vertex.result is not None: + stream_data = StreamData( + event="message", + data={"chunk": vertex._built_result}, + ) + yield str(stream_data) else: raise ValueError(f"No result found for vertex {vertex_id}") except Exception as exc: + logger.error(f"Error building vertex: {exc}") yield str(StreamData(event="error", data={"error": str(exc)})) - - yield str(StreamData(event="close", data={"message": "Stream closed"})) + finally: + logger.debug("Closing stream") + yield str(StreamData(event="close", data={"message": "Stream closed"})) return StreamingResponse(stream_vertex(), media_type="text/event-stream") except Exception as exc: diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py index 8a3f67ddf..116c63b2c 100644 --- a/src/backend/langflow/api/v1/endpoints.py +++ b/src/backend/langflow/api/v1/endpoints.py @@ -3,11 +3,15 @@ from typing import Annotated, Any, List, Optional, Union import sqlalchemy as sa from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, status +from loguru import logger +from sqlmodel import select + from langflow.api.utils import update_frontend_node_with_template_values from langflow.api.v1.schemas import ( CustomComponentCode, PreloadResponse, ProcessResponse, + RunResponse, TaskResponse, TaskStatusResponse, UploadFileResponse, @@ -15,15 +19,23 @@ from langflow.api.v1.schemas import ( from langflow.interface.custom.custom_component import CustomComponent from langflow.interface.custom.directory_reader import DirectoryReader from langflow.interface.custom.utils import build_custom_component_template -from langflow.processing.process import build_graph_and_generate_result, process_graph_cached, process_tweaks +from langflow.processing.process import ( + build_graph_and_generate_result, + process_graph_cached, + process_tweaks, + run_graph, +) from langflow.services.auth.utils import api_key_security, get_current_active_user from langflow.services.cache.utils import save_uploaded_file from langflow.services.database.models.flow import Flow from langflow.services.database.models.user.model import User -from langflow.services.deps import get_session, get_session_service, get_settings_service, get_task_service +from langflow.services.deps import ( + get_session, + get_session_service, + get_settings_service, + get_task_service, +) from langflow.services.session.service import SessionService -from loguru import logger -from sqlmodel import select try: from langflow.worker import process_graph_cached_task @@ -33,9 +45,10 @@ except ImportError: raise NotImplementedError("Celery is not installed") -from langflow.services.task.service import TaskService from sqlmodel import Session +from langflow.services.task.service import TaskService + # build router router = APIRouter(tags=["Base"]) @@ -80,9 +93,15 @@ async def process_graph_data( ) if session_id is None: # Generate a session ID - session_id = get_session_service().generate_key(session_id=session_id, data_graph=graph_data) + session_id = get_session_service().generate_key( + session_id=session_id, data_graph=graph_data + ) task_id, task = await task_service.launch_task( - process_graph_cached_task if task_service.use_celery else process_graph_cached, + ( + process_graph_cached_task + if task_service.use_celery + else process_graph_cached + ), graph_data, inputs, clear_cache, @@ -176,7 +195,11 @@ async def preload_flow( else: if session_id is None: session_id = flow_id - flow = session.exec(select(Flow).where(Flow.id == flow_id).where(Flow.user_id == api_key_user.id)).first() + flow = session.exec( + select(Flow) + .where(Flow.id == flow_id) + .where(Flow.user_id == api_key_user.id) + ).first() if flow is None: raise ValueError(f"Flow {flow_id} not found") @@ -197,6 +220,80 @@ async def preload_flow( raise HTTPException(status_code=500, detail=str(exc)) from exc +@router.post( + "/run/{flow_id}", response_model=RunResponse, response_model_exclude_none=True +) +async def run_flow_with_caching( + session: Annotated[Session, Depends(get_session)], + flow_id: str, + inputs: Optional[Union[List[dict], dict]] = None, + tweaks: Optional[dict] = None, + stream: Annotated[bool, Body(embed=True)] = False, # noqa: F821 + session_id: Annotated[Union[None, str], Body(embed=True)] = None, # noqa: F821 + api_key_user: User = Depends(api_key_security), + session_service: SessionService = Depends(get_session_service), +): + try: + if session_id: + session_data = await session_service.load_session(session_id) + graph, artifacts = session_data if session_data else (None, None) + task_result: Any = None + if not graph: + raise ValueError("Graph not found in the session") + task_result, session_id = await run_graph( + graph=graph, + flow_id=flow_id, + session_id=session_id, + inputs=inputs, + artifacts=artifacts, + session_service=session_service, + stream=stream, + ) + + else: + # Get the flow that matches the flow_id and belongs to the user + # flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first() + flow = session.exec( + select(Flow) + .where(Flow.id == flow_id) + .where(Flow.user_id == api_key_user.id) + ).first() + if flow is None: + raise ValueError(f"Flow {flow_id} not found") + + if flow.data is None: + raise ValueError(f"Flow {flow_id} has no data") + graph_data = flow.data + graph_data = process_tweaks(graph_data, tweaks) + task_result, session_id = await run_graph( + graph=graph_data, + flow_id=flow_id, + session_id=session_id, + inputs=inputs, + artifacts={}, + session_service=session_service, + stream=stream, + ) + + return RunResponse(outputs=task_result, session_id=session_id) + except sa.exc.StatementError as exc: + # StatementError('(builtins.ValueError) badly formed hexadecimal UUID string') + if "badly formed hexadecimal UUID string" in str(exc): + # This means the Flow ID is not a valid UUID which means it can't find the flow + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, detail=str(exc) + ) from exc + except ValueError as exc: + if f"Flow {flow_id} not found" in str(exc): + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, detail=str(exc) + ) from exc + else: + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc) + ) from exc + + @router.post( "/predict/{flow_id}", response_model=ProcessResponse, @@ -269,7 +366,11 @@ async def process( # Get the flow that matches the flow_id and belongs to the user # flow = session.query(Flow).filter(Flow.id == flow_id).filter(Flow.user_id == api_key_user.id).first() - flow = session.exec(select(Flow).where(Flow.id == flow_id).where(Flow.user_id == api_key_user.id)).first() + flow = session.exec( + select(Flow) + .where(Flow.id == flow_id) + .where(Flow.user_id == api_key_user.id) + ).first() if flow is None: raise ValueError(f"Flow {flow_id} not found") @@ -289,12 +390,18 @@ async def process( # StatementError('(builtins.ValueError) badly formed hexadecimal UUID string') if "badly formed hexadecimal UUID string" in str(exc): # This means the Flow ID is not a valid UUID which means it can't find the flow - raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, detail=str(exc) + ) from exc except ValueError as exc: if f"Flow {flow_id} not found" in str(exc): - raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)) from exc + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, detail=str(exc) + ) from exc else: - raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc)) from exc + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc) + ) from exc except Exception as e: # Log stack trace logger.exception(e) @@ -364,12 +471,16 @@ async def custom_component( built_frontend_node = build_custom_component_template(component, user_id=user.id) - built_frontend_node = update_frontend_node_with_template_values(built_frontend_node, raw_code.frontend_node) + built_frontend_node = update_frontend_node_with_template_values( + built_frontend_node, raw_code.frontend_node + ) return built_frontend_node @router.post("/custom_component/reload", status_code=HTTPStatus.OK) -async def reload_custom_component(path: str, user: User = Depends(get_current_active_user)): +async def reload_custom_component( + path: str, user: User = Depends(get_current_active_user) +): from langflow.interface.custom.utils import build_custom_component_template try: @@ -391,6 +502,8 @@ async def custom_component_update( ): component = CustomComponent(code=raw_code.code) - component_node = build_custom_component_template(component, user_id=user.id, update_field=raw_code.field) + component_node = build_custom_component_template( + component, user_id=user.id, update_field=raw_code.field + ) # Update the field return component_node diff --git a/src/backend/langflow/api/v1/schemas.py b/src/backend/langflow/api/v1/schemas.py index 23d8ddf9b..0092efa4e 100644 --- a/src/backend/langflow/api/v1/schemas.py +++ b/src/backend/langflow/api/v1/schemas.py @@ -4,12 +4,12 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Union from uuid import UUID -from langflow.api.utils import serialize_field +from pydantic import BaseModel, Field, field_validator, model_serializer + from langflow.services.database.models.api_key.model import ApiKeyRead from langflow.services.database.models.base import orjson_dumps from langflow.services.database.models.flow import FlowCreate, FlowRead from langflow.services.database.models.user import UserRead -from pydantic import BaseModel, Field, field_serializer, field_validator class BuildStatus(Enum): @@ -66,6 +66,26 @@ class ProcessResponse(BaseModel): backend: Optional[str] = None +class RunResponse(BaseModel): + """Run response schema.""" + + outputs: Optional[List[Any]] = None + session_id: Optional[str] = None + + @model_serializer(mode="wrap") + def serialize(self, handler): + # Serialize all the outputs if they are base models + if self.outputs: + serialized_outputs = [] + for output in self.outputs: + if isinstance(output, BaseModel): + serialized_outputs.append(output.model_dump(exclude_none=True)) + else: + serialized_outputs.append(output) + self.outputs = serialized_outputs + return handler(self) + + class PreloadResponse(BaseModel): """Preload response schema.""" @@ -73,9 +93,6 @@ class PreloadResponse(BaseModel): is_clear: Optional[bool] = None -# TaskStatusResponse( -# status=task.status, result=task.result if task.ready() else None -# ) class TaskStatusResponse(BaseModel): """Task status response schema.""" @@ -161,7 +178,9 @@ class StreamData(BaseModel): data: dict def __str__(self) -> str: - return f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n" + return ( + f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n" + ) class CustomComponentCode(BaseModel): @@ -218,20 +237,15 @@ class ApiKeyCreateRequest(BaseModel): class VerticesOrderResponse(BaseModel): ids: List[List[str]] + run_id: UUID -class ResultData(BaseModel): +class ResultDataResponse(BaseModel): results: Optional[Any] = Field(default_factory=dict) artifacts: Optional[Any] = Field(default_factory=dict) timedelta: Optional[float] = None duration: Optional[str] = None - @field_serializer("results") - def serialize_results(self, value): - if isinstance(value, dict): - return {key: serialize_field(val) for key, val in value.items()} - return serialize_field(value) - class VertexBuildResponse(BaseModel): id: Optional[str] = None @@ -239,7 +253,7 @@ class VertexBuildResponse(BaseModel): valid: bool params: Optional[str] """JSON string of the params.""" - data: ResultData + data: ResultDataResponse """Mapping of vertex ids to result dict containing the param name and result value.""" timestamp: Optional[datetime] = Field(default_factory=datetime.utcnow) """Timestamp of the build.""" diff --git a/src/backend/langflow/components/chains/ConversationChain.py b/src/backend/langflow/components/chains/ConversationChain.py index 43f71e67b..726056138 100644 --- a/src/backend/langflow/components/chains/ConversationChain.py +++ b/src/backend/langflow/components/chains/ConversationChain.py @@ -1,9 +1,9 @@ -from typing import Callable, Optional, Union +from typing import Optional from langchain.chains import ConversationChain from langflow import CustomComponent -from langflow.field_typing import BaseLanguageModel, BaseMemory, Chain, Text +from langflow.field_typing import BaseLanguageModel, BaseMemory, Text class ConversationChainComponent(CustomComponent): @@ -23,15 +23,15 @@ class ConversationChainComponent(CustomComponent): def build( self, - inputs: str, + input_value: str, llm: BaseLanguageModel, memory: Optional[BaseMemory] = None, - ) -> Union[Chain, Callable, Text]: + ) -> Text: if memory is None: chain = ConversationChain(llm=llm) else: chain = ConversationChain(llm=llm, memory=memory) - result = chain.invoke(inputs) + result = chain.invoke(input_value) # result is an AIMessage which is a subclass of BaseMessage # We need to check if it is a string or a BaseMessage if hasattr(result, "content") and isinstance(result.content, str): diff --git a/src/backend/langflow/components/chains/LLMCheckerChain.py b/src/backend/langflow/components/chains/LLMCheckerChain.py index 527cafbb7..15a540311 100644 --- a/src/backend/langflow/components/chains/LLMCheckerChain.py +++ b/src/backend/langflow/components/chains/LLMCheckerChain.py @@ -1,14 +1,15 @@ -from typing import Callable, Union - from langchain.chains import LLMCheckerChain + from langflow import CustomComponent -from langflow.field_typing import BaseLanguageModel, Chain +from langflow.field_typing import BaseLanguageModel, Text class LLMCheckerChainComponent(CustomComponent): display_name = "LLMCheckerChain" description = "" - documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_checker" + documentation = ( + "https://python.langchain.com/docs/modules/chains/additional/llm_checker" + ) def build_config(self): return { @@ -17,6 +18,12 @@ class LLMCheckerChainComponent(CustomComponent): def build( self, + input_value: str, llm: BaseLanguageModel, - ) -> Union[Chain, Callable]: - return LLMCheckerChain.from_llm(llm=llm) + ) -> Text: + + chain = LLMCheckerChain.from_llm(llm=llm) + response = chain.invoke({chain.input_key: inputs}) + result = response.get(chain.output_key) + self.status = result + return result diff --git a/src/backend/langflow/components/chains/LLMMathChain.py b/src/backend/langflow/components/chains/LLMMathChain.py index 28f430e6d..7fb253b83 100644 --- a/src/backend/langflow/components/chains/LLMMathChain.py +++ b/src/backend/langflow/components/chains/LLMMathChain.py @@ -1,15 +1,17 @@ -from typing import Callable, Optional, Union +from typing import Optional from langchain.chains import LLMChain, LLMMathChain from langflow import CustomComponent -from langflow.field_typing import BaseLanguageModel, BaseMemory, Chain +from langflow.field_typing import BaseLanguageModel, BaseMemory, Text class LLMMathChainComponent(CustomComponent): display_name = "LLMMathChain" description = "Chain that interprets a prompt and executes python code to do math." - documentation = "https://python.langchain.com/docs/modules/chains/additional/llm_math" + documentation = ( + "https://python.langchain.com/docs/modules/chains/additional/llm_math" + ) def build_config(self): return { @@ -22,10 +24,21 @@ class LLMMathChainComponent(CustomComponent): def build( self, + input_value: Text, llm: BaseLanguageModel, llm_chain: LLMChain, input_key: str = "question", output_key: str = "answer", memory: Optional[BaseMemory] = None, - ) -> Union[LLMMathChain, Callable, Chain]: - return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory) + ) -> Text: + chain = LLMMathChain( + llm=llm, + llm_chain=llm_chain, + input_key=input_key, + output_key=output_key, + memory=memory, + ) + response = chain.invoke({input_key: inputs}) + result = response.get(output_key) + self.status = result + return result diff --git a/src/backend/langflow/components/chains/RetrievalQA.py b/src/backend/langflow/components/chains/RetrievalQA.py index 2fe31353e..53fa24f15 100644 --- a/src/backend/langflow/components/chains/RetrievalQA.py +++ b/src/backend/langflow/components/chains/RetrievalQA.py @@ -20,14 +20,17 @@ class RetrievalQAComponent(CustomComponent): "input_key": {"display_name": "Input Key", "advanced": True}, "output_key": {"display_name": "Output Key", "advanced": True}, "return_source_documents": {"display_name": "Return Source Documents"}, - "inputs": {"display_name": "Input", "input_types": ["Text", "Document"]}, + "input_value": { + "display_name": "Input", + "input_types": ["Text", "Document"], + }, } def build( self, combine_documents_chain: BaseCombineDocumentsChain, retriever: BaseRetriever, - inputs: str = "", + input_value: str = "", memory: Optional[BaseMemory] = None, input_key: str = "query", output_key: str = "result", diff --git a/src/backend/langflow/components/chains/RetrievalQAWithSourcesChain.py b/src/backend/langflow/components/chains/RetrievalQAWithSourcesChain.py index faf3ab7dd..8be64c631 100644 --- a/src/backend/langflow/components/chains/RetrievalQAWithSourcesChain.py +++ b/src/backend/langflow/components/chains/RetrievalQAWithSourcesChain.py @@ -26,7 +26,7 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent): def build( self, - inputs: str, + input_value: str, retriever: BaseRetriever, llm: BaseLanguageModel, chain_type: str, diff --git a/src/backend/langflow/components/chains/SQLGenerator.py b/src/backend/langflow/components/chains/SQLGenerator.py index 5efb0f738..39b8fe394 100644 --- a/src/backend/langflow/components/chains/SQLGenerator.py +++ b/src/backend/langflow/components/chains/SQLGenerator.py @@ -28,25 +28,43 @@ class SQLGeneratorComponent(CustomComponent): def build( self, - inputs: Text, + input_value: Text, db: SQLDatabase, llm: BaseLanguageModel, top_k: int = 5, - prompt: Optional[PromptTemplate] = None, + prompt: Optional[Text] = None, ) -> Text: + if prompt: + prompt_template = PromptTemplate.from_template(template=prompt) + else: + prompt_template = None + if top_k > 0: kwargs = { "k": top_k, } - if not prompt: + if not prompt_template: sql_query_chain = create_sql_query_chain(llm=llm, db=db, **kwargs) else: - template = prompt.template if hasattr(prompt, "template") else prompt + template = ( + prompt_template.template + if hasattr(prompt, "template") + else prompt_template + ) # Check if {question} is in the prompt - if "{question}" not in template or "question" not in template.input_variables: - raise ValueError("Prompt must contain `{question}` to be used with Natural Language to SQL.") - sql_query_chain = create_sql_query_chain(llm=llm, db=db, prompt=prompt, **kwargs) - query_writer = sql_query_chain | {"query": lambda x: x.replace("SQLQuery:", "").strip()} + if ( + "{question}" not in template + or "question" not in template.input_variables + ): + raise ValueError( + "Prompt must contain `{question}` to be used with Natural Language to SQL." + ) + sql_query_chain = create_sql_query_chain( + llm=llm, db=db, prompt=prompt_template, **kwargs + ) + query_writer = sql_query_chain | { + "query": lambda x: x.replace("SQLQuery:", "").strip() + } response = query_writer.invoke({"question": inputs}) query = response.get("query") self.status = query diff --git a/src/backend/langflow/components/io/ChatInput.py b/src/backend/langflow/components/io/ChatInput.py index 4d8fc509c..de8ce14cb 100644 --- a/src/backend/langflow/components/io/ChatInput.py +++ b/src/backend/langflow/components/io/ChatInput.py @@ -1,59 +1,26 @@ -from typing import Optional +from typing import Optional, Union -from langflow import CustomComponent +from langflow.components.io.base.chat import ChatComponent +from langflow.field_typing import Text from langflow.schema import Record -class ChatInput(CustomComponent): +class ChatInput(ChatComponent): display_name = "Chat Input" description = "Used to get user input from the chat." - def build_config(self): - return { - "message": { - "input_types": ["Text"], - "display_name": "Message", - "multiline": True, - }, - "sender": { - "options": ["Machine", "User"], - "display_name": "Sender Type", - }, - "sender_name": {"display_name": "Sender Name"}, - "session_id": { - "display_name": "Session ID", - "info": "Session ID of the chat history.", - }, - "return_record": { - "display_name": "Return Record", - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - }, - } - def build( self, sender: Optional[str] = "User", sender_name: Optional[str] = "User", - message: Optional[str] = None, + input_value: Optional[str] = None, session_id: Optional[str] = None, return_record: Optional[bool] = False, - ) -> Record: - if return_record: - if isinstance(message, Record): - # Update the data of the record - message.data["sender"] = sender - message.data["sender_name"] = sender_name - message.data["session_id"] = session_id - else: - message = Record( - text=message, - data={ - "sender": sender, - "sender_name": sender_name, - "session_id": session_id, - }, - ) - if not message: - message = "" - self.status = message - return message + ) -> Union[Text, Record]: + return super().build( + sender=sender, + sender_name=sender_name, + input_value=input_value, + session_id=session_id, + return_record=return_record, + ) diff --git a/src/backend/langflow/components/io/ChatOutput.py b/src/backend/langflow/components/io/ChatOutput.py index 05639cdb2..0cd51f663 100644 --- a/src/backend/langflow/components/io/ChatOutput.py +++ b/src/backend/langflow/components/io/ChatOutput.py @@ -1,63 +1,26 @@ from typing import Optional, Union -from langflow import CustomComponent +from langflow.components.io.base.chat import ChatComponent from langflow.field_typing import Text from langflow.schema import Record -class ChatOutput(CustomComponent): +class ChatOutput(ChatComponent): display_name = "Chat Output" description = "Used to send a message to the chat." - field_config = { - "code": { - "show": True, - } - } - - def build_config(self): - return { - "message": {"input_types": ["Text"], "display_name": "Message"}, - "sender": { - "options": ["Machine", "User"], - "display_name": "Sender Type", - }, - "sender_name": {"display_name": "Sender Name"}, - "session_id": { - "display_name": "Session ID", - "info": "Session ID of the chat history.", - "input_types": ["Text"], - }, - "return_record": { - "display_name": "Return Record", - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - }, - } - def build( self, - sender: Optional[str] = "Machine", - sender_name: Optional[str] = "AI", + sender: Optional[str] = "User", + sender_name: Optional[str] = "User", + input_value: Optional[str] = None, session_id: Optional[str] = None, - message: Optional[str] = None, return_record: Optional[bool] = False, ) -> Union[Text, Record]: - if return_record: - if isinstance(message, Record): - # Update the data of the record - message.data["sender"] = sender - message.data["sender_name"] = sender_name - message.data["session_id"] = session_id - else: - message = Record( - text=message, - data={ - "sender": sender, - "sender_name": sender_name, - "session_id": session_id, - }, - ) - if not message: - message = "" - self.status = message - return message + return super().build( + sender=sender, + sender_name=sender_name, + input_value=input_value, + session_id=session_id, + return_record=return_record, + ) diff --git a/src/backend/langflow/components/io/TextInput.py b/src/backend/langflow/components/io/TextInput.py index b0d2eb0a7..f8c1ad606 100644 --- a/src/backend/langflow/components/io/TextInput.py +++ b/src/backend/langflow/components/io/TextInput.py @@ -9,11 +9,11 @@ class TextInput(CustomComponent): description = "Used to pass text input to the next component." field_config = { - "value": {"display_name": "Value"}, + "input_value": {"display_name": "Value", "multiline": True}, } - def build(self, value: Optional[str] = "") -> Text: - self.status = value - if not value: - value = "" - return value + def build(self, input_value: Optional[str] = "") -> Text: + self.status = input_value + if not input_value: + input_value = "" + return input_value diff --git a/src/backend/langflow/components/io/base/__init__.py b/src/backend/langflow/components/io/base/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/components/io/base/chat.py b/src/backend/langflow/components/io/base/chat.py new file mode 100644 index 000000000..4660b4276 --- /dev/null +++ b/src/backend/langflow/components/io/base/chat.py @@ -0,0 +1,102 @@ +import warnings +from typing import Optional, Union + +from langflow import CustomComponent +from langflow.field_typing import Text +from langflow.memory import add_messages +from langflow.schema import Record + + +class ChatComponent(CustomComponent): + display_name = "Chat Component" + description = "Use as base for chat components." + + def build_config(self): + return { + "input_value": { + "input_types": ["Text"], + "display_name": "Message", + "multiline": True, + }, + "sender": { + "options": ["Machine", "User"], + "display_name": "Sender Type", + }, + "sender_name": {"display_name": "Sender Name"}, + "session_id": { + "display_name": "Session ID", + "info": "If provided, the message will be stored in the memory.", + }, + "return_record": { + "display_name": "Return Record", + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + }, + } + + def store_message( + self, + message: Union[Text, Record], + session_id: Optional[str] = None, + sender: Optional[str] = None, + sender_name: Optional[str] = None, + ) -> list[Record]: + if not message: + warnings.warn("No message provided.") + return [] + + if not session_id or not sender or not sender_name: + raise ValueError( + "All of session_id, sender, and sender_name must be provided." + ) + if isinstance(message, Record): + record = message + record.data.update( + { + "session_id": session_id, + "sender": sender, + "sender_name": sender_name, + } + ) + else: + record = Record( + text=message, + data={ + "session_id": session_id, + "sender": sender, + "sender_name": sender_name, + }, + ) + + self.status = record + records = add_messages([record]) + return records[0] + + def build( + self, + sender: Optional[str] = "User", + sender_name: Optional[str] = "User", + input_value: Optional[str] = None, + session_id: Optional[str] = None, + return_record: Optional[bool] = False, + ) -> Union[Text, Record]: + if return_record: + if isinstance(input_value, Record): + # Update the data of the record + input_value.data["sender"] = sender + input_value.data["sender_name"] = sender_name + input_value.data["session_id"] = session_id + else: + input_value = Record( + text=input_value, + data={ + "sender": sender, + "sender_name": sender_name, + "session_id": session_id, + }, + ) + if not input_value: + input_value = "" + self.status = input_value + if session_id: + self.store_message(input_value, session_id, sender, sender_name) + return input_value diff --git a/src/backend/langflow/components/models/AmazonBedrockModel.py b/src/backend/langflow/components/models/AmazonBedrockModel.py index a2e008e2e..4ae28e70c 100644 --- a/src/backend/langflow/components/models/AmazonBedrockModel.py +++ b/src/backend/langflow/components/models/AmazonBedrockModel.py @@ -2,13 +2,14 @@ from typing import Optional from langchain_community.chat_models.bedrock import BedrockChat -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class AmazonBedrockComponent(CustomComponent): +class AmazonBedrockComponent(LCModelComponent): display_name: str = "Amazon Bedrock Model" description: str = "Generate text using LLM model from Amazon Bedrock." + icon = "AmazonBedrock" def build_config(self): return { @@ -34,12 +35,16 @@ class AmazonBedrockComponent(CustomComponent): "model_kwargs": {"display_name": "Model Kwargs"}, "cache": {"display_name": "Cache"}, "code": {"advanced": True}, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, - inputs: str, + input_value: str, model_id: str = "anthropic.claude-instant-v1", credentials_profile_name: Optional[str] = None, region_name: Optional[str] = None, @@ -47,6 +52,7 @@ class AmazonBedrockComponent(CustomComponent): endpoint_url: Optional[str] = None, streaming: bool = False, cache: Optional[bool] = None, + stream: bool = False, ) -> Text: try: output = BedrockChat( @@ -60,7 +66,5 @@ class AmazonBedrockComponent(CustomComponent): ) # type: ignore except Exception as e: raise ValueError("Could not connect to AmazonBedrock API.") from e - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/AnthropicModel.py b/src/backend/langflow/components/models/AnthropicModel.py index 793bec46a..a3ba510a4 100644 --- a/src/backend/langflow/components/models/AnthropicModel.py +++ b/src/backend/langflow/components/models/AnthropicModel.py @@ -3,13 +3,16 @@ from typing import Optional from langchain_community.chat_models.anthropic import ChatAnthropic from pydantic.v1 import SecretStr -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class AnthropicLLM(CustomComponent): +class AnthropicLLM(LCModelComponent): display_name: str = "AnthropicModel" - description: str = "Generate text using Anthropic Chat&Completion large language models." + description: str = ( + "Generate text using Anthropic Chat&Completion large language models." + ) + icon = "Anthropic" def build_config(self): return { @@ -47,17 +50,22 @@ class AnthropicLLM(CustomComponent): "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.", }, "code": {"show": False}, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, model: str, - inputs: str, + input_value: str, anthropic_api_key: Optional[str] = None, max_tokens: Optional[int] = None, temperature: Optional[float] = None, api_endpoint: Optional[str] = None, + stream: bool = False, ) -> Text: # Set default API endpoint if not provided if not api_endpoint: @@ -66,14 +74,14 @@ class AnthropicLLM(CustomComponent): try: output = ChatAnthropic( model_name=model, - anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None), + anthropic_api_key=( + SecretStr(anthropic_api_key) if anthropic_api_key else None + ), max_tokens_to_sample=max_tokens, # type: ignore temperature=temperature, anthropic_api_url=api_endpoint, ) except Exception as e: raise ValueError("Could not connect to Anthropic API.") from e - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/AzureOpenAIModel.py b/src/backend/langflow/components/models/AzureOpenAIModel.py index 1e646e43a..392f390c4 100644 --- a/src/backend/langflow/components/models/AzureOpenAIModel.py +++ b/src/backend/langflow/components/models/AzureOpenAIModel.py @@ -3,14 +3,17 @@ from typing import Optional from langchain.llms.base import BaseLanguageModel from langchain_openai import AzureChatOpenAI -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent -class AzureChatOpenAIComponent(CustomComponent): +class AzureChatOpenAIComponent(LCModelComponent): display_name: str = "AzureOpenAI Model" description: str = "Generate text using LLM model from Azure OpenAI." - documentation: str = "https://python.langchain.com/docs/integrations/llms/azure_openai" + documentation: str = ( + "https://python.langchain.com/docs/integrations/llms/azure_openai" + ) beta = False + icon = "Azure" AZURE_OPENAI_MODELS = [ "gpt-35-turbo", @@ -71,19 +74,24 @@ class AzureChatOpenAIComponent(CustomComponent): "info": "Maximum number of tokens to generate.", }, "code": {"show": False}, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, model: str, azure_endpoint: str, - inputs: str, + input_value: str, azure_deployment: str, api_key: str, api_version: str, temperature: float = 0.7, max_tokens: Optional[int] = 1000, + stream: bool = False, ) -> BaseLanguageModel: try: output = AzureChatOpenAI( @@ -97,7 +105,5 @@ class AzureChatOpenAIComponent(CustomComponent): ) except Exception as e: raise ValueError("Could not connect to AzureOpenAI API.") from e - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/BaiduQianfanChatModel.py b/src/backend/langflow/components/models/BaiduQianfanChatModel.py index 88051d0e9..f0815603f 100644 --- a/src/backend/langflow/components/models/BaiduQianfanChatModel.py +++ b/src/backend/langflow/components/models/BaiduQianfanChatModel.py @@ -3,16 +3,17 @@ from typing import Optional from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint from pydantic.v1 import SecretStr -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class QianfanChatEndpointComponent(CustomComponent): +class QianfanChatEndpointComponent(LCModelComponent): display_name: str = "QianfanChat Model" description: str = ( "Generate text using Baidu Qianfan chat models. Get more detail from " "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint." ) + icon = "BaiduQianfan" def build_config(self): return { @@ -68,12 +69,16 @@ class QianfanChatEndpointComponent(CustomComponent): "info": "Endpoint of the Qianfan LLM, required if custom model used.", }, "code": {"show": False}, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, - inputs: str, + input_value: str, model: str = "ERNIE-Bot-turbo", qianfan_ak: Optional[str] = None, qianfan_sk: Optional[str] = None, @@ -81,6 +86,7 @@ class QianfanChatEndpointComponent(CustomComponent): temperature: Optional[float] = None, penalty_score: Optional[float] = None, endpoint: Optional[str] = None, + stream: bool = False, ) -> Text: try: output = QianfanChatEndpoint( # type: ignore @@ -94,7 +100,5 @@ class QianfanChatEndpointComponent(CustomComponent): ) except Exception as e: raise ValueError("Could not connect to Baidu Qianfan API.") from e - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/CTransformersModel.py b/src/backend/langflow/components/models/CTransformersModel.py index 932b1b351..31123ad7e 100644 --- a/src/backend/langflow/components/models/CTransformersModel.py +++ b/src/backend/langflow/components/models/CTransformersModel.py @@ -2,11 +2,11 @@ from typing import Dict, Optional from langchain_community.llms.ctransformers import CTransformers -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class CTransformersComponent(CustomComponent): +class CTransformersComponent(LCModelComponent): display_name = "CTransformersModel" description = "Generate text using CTransformers LLM models" documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers" @@ -28,19 +28,24 @@ class CTransformersComponent(CustomComponent): "field_type": "dict", "value": '{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}', }, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, model: str, model_file: str, - inputs: str, + input_value: str, model_type: str, config: Optional[Dict] = None, + stream: Optional[bool] = False, ) -> Text: - output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config) - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + output = CTransformers( + model=model, model_file=model_file, model_type=model_type, config=config + ) + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/CohereModel.py b/src/backend/langflow/components/models/CohereModel.py index 3912cb855..a32fb9b4b 100644 --- a/src/backend/langflow/components/models/CohereModel.py +++ b/src/backend/langflow/components/models/CohereModel.py @@ -1,14 +1,16 @@ from langchain_community.chat_models.cohere import ChatCohere -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class CohereComponent(CustomComponent): +class CohereComponent(LCModelComponent): display_name = "CohereModel" description = "Generate text using Cohere large language models." documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere" + icon = "Cohere" + def build_config(self): return { "cohere_api_key": { @@ -28,23 +30,24 @@ class CohereComponent(CustomComponent): "type": "float", "show": True, }, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, cohere_api_key: str, - inputs: str, + input_value: str, max_tokens: int = 256, temperature: float = 0.75, + stream: bool = False, ) -> Text: output = ChatCohere( cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature, ) - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result - return result + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py index ce967bd57..423a66df6 100644 --- a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py +++ b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py @@ -1,16 +1,16 @@ from typing import Optional -from langchain_google_genai import ChatGoogleGenerativeAI # type: ignore -from pydantic.v1.types import SecretStr +from langchain_google_genai import ChatGoogleGenerativeAI +from pydantic.v1 import SecretStr -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import RangeSpec, Text -class GoogleGenerativeAIComponent(CustomComponent): +class GoogleGenerativeAIComponent(LCModelComponent): display_name: str = "Google Generative AIModel" description: str = "Generate text using Google Generative AI to generate text." - documentation: str = "http://docs.langflow.org/components/custom" + icon = "GoogleGenerativeAI" def build_config(self): return { @@ -50,19 +50,24 @@ class GoogleGenerativeAIComponent(CustomComponent): "code": { "advanced": True, }, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input", "info": "The input to the model."}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, google_api_key: str, model: str, - inputs: str, + input_value: str, max_output_tokens: Optional[int] = None, temperature: float = 0.1, top_k: Optional[int] = None, top_p: Optional[float] = None, n: Optional[int] = 1, + stream: bool = False, ) -> Text: output = ChatGoogleGenerativeAI( model=model, @@ -73,7 +78,4 @@ class GoogleGenerativeAIComponent(CustomComponent): n=n or 1, google_api_key=SecretStr(google_api_key), ) - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/HuggingFaceModel.py b/src/backend/langflow/components/models/HuggingFaceModel.py index 4357ede61..3d92272e6 100644 --- a/src/backend/langflow/components/models/HuggingFaceModel.py +++ b/src/backend/langflow/components/models/HuggingFaceModel.py @@ -3,14 +3,14 @@ from typing import Optional from langchain_community.chat_models.huggingface import ChatHuggingFace from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint -from langflow import CustomComponent - +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class HuggingFaceEndpointsComponent(CustomComponent): +class HuggingFaceEndpointsComponent(LCModelComponent): display_name: str = "Hugging Face Inference API models" description: str = "Generate text using LLM model from Hugging Face Inference API." + icon = "HuggingFace" def build_config(self): return { @@ -25,16 +25,21 @@ class HuggingFaceEndpointsComponent(CustomComponent): "field_type": "code", }, "code": {"show": False}, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, - inputs: str, + input_value: str, endpoint_url: str, task: str = "text2text-generation", huggingfacehub_api_token: Optional[str] = None, model_kwargs: Optional[dict] = None, + stream: bool = False, ) -> Text: try: llm = HuggingFaceEndpoint( @@ -46,7 +51,4 @@ class HuggingFaceEndpointsComponent(CustomComponent): except Exception as e: raise ValueError("Could not connect to HuggingFace Endpoints API.") from e output = ChatHuggingFace(llm=llm) - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/LlamaCppModel.py b/src/backend/langflow/components/models/LlamaCppModel.py index af0de5159..1ca2cd3c1 100644 --- a/src/backend/langflow/components/models/LlamaCppModel.py +++ b/src/backend/langflow/components/models/LlamaCppModel.py @@ -2,11 +2,11 @@ from typing import Any, Dict, List, Optional from langchain_community.llms.llamacpp import LlamaCpp -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class LlamaCppComponent(CustomComponent): +class LlamaCppComponent(LCModelComponent): display_name = "LlamaCppModel" description = "Generate text using llama.cpp model." documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp" @@ -56,13 +56,17 @@ class LlamaCppComponent(CustomComponent): "use_mmap": {"display_name": "Use Mmap", "advanced": True}, "verbose": {"display_name": "Verbose", "advanced": True}, "vocab_only": {"display_name": "Vocab Only", "advanced": True}, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, model_path: str, - inputs: str, + input_value: str, grammar: Optional[str] = None, cache: Optional[bool] = None, client: Optional[Any] = None, @@ -97,6 +101,7 @@ class LlamaCppComponent(CustomComponent): use_mmap: Optional[bool] = True, verbose: bool = True, vocab_only: bool = False, + stream: bool = False, ) -> Text: output = LlamaCpp( model_path=model_path, @@ -135,9 +140,5 @@ class LlamaCppComponent(CustomComponent): verbose=verbose, vocab_only=vocab_only, ) - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result - self.status = result - return result + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/OllamaModel.py b/src/backend/langflow/components/models/OllamaModel.py index 129f96482..7929c2b43 100644 --- a/src/backend/langflow/components/models/OllamaModel.py +++ b/src/backend/langflow/components/models/OllamaModel.py @@ -3,17 +3,19 @@ from typing import Any, Dict, List, Optional # from langchain_community.chat_models import ChatOllama from langchain_community.chat_models import ChatOllama +from langflow.components.models.base.model import LCModelComponent + # from langchain.chat_models import ChatOllama -from langflow import CustomComponent from langflow.field_typing import Text # whe When a callback component is added to Langflow, the comment must be uncommented. # from langchain.callbacks.manager import CallbackManager -class ChatOllamaComponent(CustomComponent): +class ChatOllamaComponent(LCModelComponent): display_name = "ChatOllamaModel" description = "Generate text using Local LLM for chat with Ollama." + icon = "Ollama" def build_config(self) -> dict: return { @@ -164,14 +166,18 @@ class ChatOllamaComponent(CustomComponent): "info": "Template to use for generating text.", "advanced": True, }, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, base_url: Optional[str], model: str, - inputs: str, + input_value: str, mirostat: Optional[str], mirostat_eta: Optional[float] = None, mirostat_tau: Optional[float] = None, @@ -197,6 +203,7 @@ class ChatOllamaComponent(CustomComponent): timeout: Optional[int] = None, top_k: Optional[int] = None, top_p: Optional[int] = None, + stream: Optional[bool] = False, ) -> Text: if not base_url: base_url = "http://localhost:11434" @@ -250,7 +257,5 @@ class ChatOllamaComponent(CustomComponent): output = ChatOllama(**llm_params) # type: ignore except Exception as e: raise ValueError("Could not initialize Ollama LLM.") from e - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/OpenAIModel.py b/src/backend/langflow/components/models/OpenAIModel.py index 1cc352b20..7a28acee6 100644 --- a/src/backend/langflow/components/models/OpenAIModel.py +++ b/src/backend/langflow/components/models/OpenAIModel.py @@ -1,17 +1,19 @@ from typing import Optional from langchain_openai import ChatOpenAI -from langflow import CustomComponent + +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import NestedDict, Text -class OpenAIModelComponent(CustomComponent): +class OpenAIModelComponent(LCModelComponent): display_name = "OpenAI Model" description = "Generates text using OpenAI's models." + icon = "OpenAI" def build_config(self): return { - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, "max_tokens": { "display_name": "Max Tokens", "advanced": False, @@ -56,21 +58,26 @@ class OpenAIModelComponent(CustomComponent): "required": False, "value": 0.7, }, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, - inputs: Text, + input_value: Text, max_tokens: Optional[int] = 256, model_kwargs: NestedDict = {}, model_name: str = "gpt-4-1106-preview", openai_api_base: Optional[str] = None, openai_api_key: Optional[str] = None, temperature: float = 0.7, + stream: Optional[bool] = False, ) -> Text: if not openai_api_base: openai_api_base = "https://api.openai.com/v1" - model = ChatOpenAI( + output = ChatOpenAI( max_tokens=max_tokens, model_kwargs=model_kwargs, model=model_name, @@ -79,7 +86,4 @@ class OpenAIModelComponent(CustomComponent): temperature=temperature, ) - message = model.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/VertexAiModel.py b/src/backend/langflow/components/models/VertexAiModel.py index eee804e02..d7eab71ed 100644 --- a/src/backend/langflow/components/models/VertexAiModel.py +++ b/src/backend/langflow/components/models/VertexAiModel.py @@ -2,11 +2,11 @@ from typing import List, Optional from langchain_core.messages.base import BaseMessage -from langflow import CustomComponent +from langflow.components.models.base.model import LCModelComponent from langflow.field_typing import Text -class ChatVertexAIComponent(CustomComponent): +class ChatVertexAIComponent(LCModelComponent): display_name = "ChatVertexAIModel" description = "Generate text using Vertex AI Chat large language models API." @@ -57,12 +57,16 @@ class ChatVertexAIComponent(CustomComponent): "value": False, "advanced": True, }, - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, + "stream": { + "display_name": "Stream", + "info": "Stream the response from the model.", + }, } def build( self, - inputs: str, + input_value: str, credentials: Optional[str], project: str, examples: Optional[List[BaseMessage]] = [], @@ -73,6 +77,7 @@ class ChatVertexAIComponent(CustomComponent): top_k: int = 40, top_p: float = 0.95, verbose: bool = False, + stream: bool = False, ) -> Text: try: from langchain_google_vertexai import ChatVertexAI @@ -92,7 +97,5 @@ class ChatVertexAIComponent(CustomComponent): top_p=top_p, verbose=verbose, ) - message = output.invoke(inputs) - result = message.content if hasattr(message, "content") else message - self.status = result - return result + + return self.get_result(output=output, stream=stream, input_value=input_value) diff --git a/src/backend/langflow/components/models/base/__init__.py b/src/backend/langflow/components/models/base/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/components/models/base/model.py b/src/backend/langflow/components/models/base/model.py new file mode 100644 index 000000000..9f9ca7b36 --- /dev/null +++ b/src/backend/langflow/components/models/base/model.py @@ -0,0 +1,28 @@ +from langchain_core.runnables import Runnable + +from langflow import CustomComponent + + +class LCModelComponent(CustomComponent): + display_name: str = "Model Name" + description: str = "Model Description" + + def get_result(self, output: Runnable, 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 = output.stream(input_value) + else: + message = output.invoke(input_value) + result = message.content if hasattr(message, "content") else message + self.status = result + return result diff --git a/src/backend/langflow/components/utilities/RunnableExecutor.py b/src/backend/langflow/components/utilities/RunnableExecutor.py index f83f352b4..502e1eec6 100644 --- a/src/backend/langflow/components/utilities/RunnableExecutor.py +++ b/src/backend/langflow/components/utilities/RunnableExecutor.py @@ -15,7 +15,7 @@ class RunnableExecComponent(CustomComponent): "display_name": "Input Key", "info": "The key to use for the input.", }, - "inputs": { + "input_value": { "display_name": "Inputs", "info": "The inputs to pass to the runnable.", }, @@ -32,7 +32,7 @@ class RunnableExecComponent(CustomComponent): def build( self, input_key: str, - inputs: str, + input_value: str, runnable: Runnable, output_key: str = "output", ) -> Text: diff --git a/src/backend/langflow/components/vectorstores/ChromaSearch.py b/src/backend/langflow/components/vectorstores/ChromaSearch.py index c6eb1ebac..e3f37108c 100644 --- a/src/backend/langflow/components/vectorstores/ChromaSearch.py +++ b/src/backend/langflow/components/vectorstores/ChromaSearch.py @@ -2,12 +2,13 @@ from typing import List, Optional import chromadb # type: ignore from langchain_community.vectorstores.chroma import Chroma -from langflow import CustomComponent + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent from langflow.field_typing import Embeddings, Text -from langflow.schema import Record, docs_to_records +from langflow.schema import Record -class ChromaSearchComponent(CustomComponent): +class ChromaSearchComponent(LCVectorStoreComponent): """ A custom component for implementing a Vector Store using Chroma. """ @@ -25,7 +26,7 @@ class ChromaSearchComponent(CustomComponent): - dict: A dictionary containing the configuration options for the component. """ return { - "inputs": {"display_name": "Input"}, + "input_value": {"display_name": "Input"}, "search_type": { "display_name": "Search Type", "options": ["Similarity", "MMR"], @@ -57,7 +58,7 @@ class ChromaSearchComponent(CustomComponent): def build( self, - inputs: Text, + input_value: Text, search_type: str, collection_name: str, embedding: Embeddings, @@ -92,24 +93,19 @@ class ChromaSearchComponent(CustomComponent): if chroma_server_host is not None: chroma_settings = chromadb.config.Settings( - chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None, + chroma_server_cors_allow_origins=chroma_server_cors_allow_origins + or None, chroma_server_host=chroma_server_host, chroma_server_port=chroma_server_port or None, chroma_server_grpc_port=chroma_server_grpc_port or None, chroma_server_ssl_enabled=chroma_server_ssl_enabled, ) index_directory = self.resolve_path(index_directory) - chroma = Chroma( + vector_store = Chroma( embedding_function=embedding, collection_name=collection_name, persist_directory=index_directory, client_settings=chroma_settings, ) - # Validate the inputs - docs = [] - if inputs and isinstance(inputs, str): - docs = chroma.search(query=inputs, search_type=search_type.lower()) - else: - raise ValueError("Invalid inputs provided.") - return docs_to_records(docs) + return self.search_with_vector_store(input_value, search_type, vector_store) diff --git a/src/backend/langflow/components/vectorstores/FAISS.py b/src/backend/langflow/components/vectorstores/FAISS.py index dec14f6db..0cecab8e7 100644 --- a/src/backend/langflow/components/vectorstores/FAISS.py +++ b/src/backend/langflow/components/vectorstores/FAISS.py @@ -3,24 +3,36 @@ from typing import List, Union from langchain.schema import BaseRetriever from langchain_community.vectorstores import VectorStore from langchain_community.vectorstores.faiss import FAISS + from langflow import CustomComponent from langflow.field_typing import Document, Embeddings class FAISSComponent(CustomComponent): display_name = "FAISS" - description = "Construct FAISS wrapper from raw documents." + description = "Ingest documents into FAISS Vector Store." documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss" def build_config(self): return { "documents": {"display_name": "Documents"}, "embedding": {"display_name": "Embedding"}, + "folder_path": { + "display_name": "Folder Path", + "info": "Path to save the FAISS index. It will be relative to where Langflow is running.", + }, + "index_name": {"display_name": "Index Name"}, } def build( self, embedding: Embeddings, documents: List[Document], + folder_path: str, + index_name: str = "langflow_index", ) -> Union[VectorStore, FAISS, BaseRetriever]: - return FAISS.from_documents(documents=documents, embedding=embedding) + vector_store = FAISS.from_documents(documents=documents, embedding=embedding) + if not folder_path: + raise ValueError("Folder path is required to save the FAISS index.") + path = self.resolve_path(folder_path) + vector_store.save_local(str(path), index_name) diff --git a/src/backend/langflow/components/vectorstores/FAISSSearch.py b/src/backend/langflow/components/vectorstores/FAISSSearch.py new file mode 100644 index 000000000..dbc63faac --- /dev/null +++ b/src/backend/langflow/components/vectorstores/FAISSSearch.py @@ -0,0 +1,45 @@ +from typing import List + +from langchain_community.vectorstores.faiss import FAISS + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.field_typing import Embeddings +from langflow.schema import Record + + +class FAISSSearchComponent(LCVectorStoreComponent): + display_name = "FAISS Search" + description = "Search a FAISS Vector Store for similar documents." + documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss" + + def build_config(self): + return { + "documents": {"display_name": "Documents"}, + "embedding": {"display_name": "Embedding"}, + "folder_path": { + "display_name": "Folder Path", + "info": "Path to save the FAISS index. It will be relative to where Langflow is running.", + }, + "input_value": {"display_name": "Input"}, + "index_name": {"display_name": "Index Name"}, + } + + def build( + self, + input_value: str, + embedding: Embeddings, + folder_path: str, + index_name: str = "langflow_index", + ) -> List[Record]: + if not folder_path: + raise ValueError("Folder path is required to save the FAISS index.") + path = self.resolve_path(folder_path) + vector_store = FAISS.load_local( + folder_path=str(path), embeddings=embedding, index_name=index_name + ) + if not vector_store: + raise ValueError("Failed to load the FAISS index.") + + return self.search_with_vector_store( + vector_store=vector_store, input_value=input_value, search_type="similarity" + ) diff --git a/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py b/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py new file mode 100644 index 000000000..de5533b0a --- /dev/null +++ b/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py @@ -0,0 +1,56 @@ +from typing import List, Optional + +from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSearch + +from langflow import CustomComponent +from langflow.field_typing import Document, Embeddings, NestedDict + + +class MongoDBAtlasComponent(CustomComponent): + display_name = "MongoDB Atlas" + description = ( + "Construct a `MongoDB Atlas Vector Search` vector store from raw documents." + ) + + def build_config(self): + return { + "documents": {"display_name": "Documents"}, + "embedding": {"display_name": "Embedding"}, + "collection_name": {"display_name": "Collection Name"}, + "db_name": {"display_name": "Database Name"}, + "index_name": {"display_name": "Index Name"}, + "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"}, + "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, + } + + def build( + self, + embedding: Embeddings, + documents: List[Document] = None, + collection_name: str = "", + db_name: str = "", + index_name: str = "", + mongodb_atlas_cluster_uri: str = "", + search_kwargs: Optional[NestedDict] = None, + ) -> MongoDBAtlasVectorSearch: + search_kwargs = search_kwargs or {} + if documents: + vector_store = MongoDBAtlasVectorSearch.from_documents( + documents=documents, + embedding=embedding, + collection_name=collection_name, + db_name=db_name, + index_name=index_name, + mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri, + search_kwargs=search_kwargs, + ) + else: + vector_store = MongoDBAtlasVectorSearch( + embedding=embedding, + collection_name=collection_name, + db_name=db_name, + index_name=index_name, + mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri, + search_kwargs=search_kwargs, + ) + return vector_store diff --git a/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py b/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py index d2d215f2b..6393c2a7b 100644 --- a/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py +++ b/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py @@ -1,22 +1,22 @@ from typing import List, Optional -from langchain_community.vectorstores import MongoDBAtlasVectorSearch - -from langflow import CustomComponent -from langflow.field_typing import ( - Document, - Embeddings, - NestedDict, -) +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.components.vectorstores.MongoDBAtlasVector import MongoDBAtlasComponent +from langflow.field_typing import Embeddings, NestedDict +from langflow.schema import Record -class MongoDBAtlasComponent(CustomComponent): - display_name = "MongoDB Atlas" - description = "Construct a `MongoDB Atlas Vector Search` vector store from raw documents." +class MongoDBAtlasSearchComponent(MongoDBAtlasComponent, LCVectorStoreComponent): + display_name = "MongoDB Atlas Search" + description = "Search a MongoDB Atlas Vector Store for similar documents." def build_config(self): return { - "documents": {"display_name": "Documents"}, + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "input_value": {"display_name": "Input"}, "embedding": {"display_name": "Embedding"}, "collection_name": {"display_name": "Collection Name"}, "db_name": {"display_name": "Database Name"}, @@ -27,17 +27,16 @@ class MongoDBAtlasComponent(CustomComponent): def build( self, - documents: List[Document], + input_value: str, + search_type: str, embedding: Embeddings, collection_name: str = "", db_name: str = "", index_name: str = "", mongodb_atlas_cluster_uri: str = "", search_kwargs: Optional[NestedDict] = None, - ) -> MongoDBAtlasVectorSearch: - search_kwargs = search_kwargs or {} - return MongoDBAtlasVectorSearch( - documents=documents, + ) -> List[Record]: + vector_store = super().build( embedding=embedding, collection_name=collection_name, db_name=db_name, @@ -45,3 +44,8 @@ class MongoDBAtlasComponent(CustomComponent): mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri, search_kwargs=search_kwargs, ) + if not vector_store: + raise ValueError("Failed to create MongoDB Atlas Vector Store") + return self.search_with_vector_store( + vector_store=vector_store, input_value=input_value, search_type=search_type + ) diff --git a/src/backend/langflow/components/vectorstores/Pinecone.py b/src/backend/langflow/components/vectorstores/Pinecone.py index 147af1df8..54222b133 100644 --- a/src/backend/langflow/components/vectorstores/Pinecone.py +++ b/src/backend/langflow/components/vectorstores/Pinecone.py @@ -5,6 +5,7 @@ import pinecone # type: ignore from langchain.schema import BaseRetriever from langchain_community.vectorstores import VectorStore from langchain_community.vectorstores.pinecone import Pinecone + from langflow import CustomComponent from langflow.field_typing import Document, Embeddings @@ -12,6 +13,7 @@ from langflow.field_typing import Document, Embeddings class PineconeComponent(CustomComponent): display_name = "Pinecone" description = "Construct Pinecone wrapper from raw documents." + icon = "Pinecone" def build_config(self): return { @@ -19,10 +21,23 @@ class PineconeComponent(CustomComponent): "embedding": {"display_name": "Embedding"}, "index_name": {"display_name": "Index Name"}, "namespace": {"display_name": "Namespace"}, - "pinecone_api_key": {"display_name": "Pinecone API Key", "default": "", "password": True, "required": True}, - "pinecone_env": {"display_name": "Pinecone Environment", "default": "", "required": True}, + "pinecone_api_key": { + "display_name": "Pinecone API Key", + "default": "", + "password": True, + "required": True, + }, + "pinecone_env": { + "display_name": "Pinecone Environment", + "default": "", + "required": True, + }, "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"}, - "pool_threads": {"display_name": "Pool Threads", "default": 1, "advanced": True}, + "pool_threads": { + "display_name": "Pool Threads", + "default": 1, + "advanced": True, + }, } def build( diff --git a/src/backend/langflow/components/vectorstores/PineconeSearch.py b/src/backend/langflow/components/vectorstores/PineconeSearch.py new file mode 100644 index 000000000..7af7f627f --- /dev/null +++ b/src/backend/langflow/components/vectorstores/PineconeSearch.py @@ -0,0 +1,70 @@ +from typing import List, Optional + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.components.vectorstores.Pinecone import PineconeComponent +from langflow.field_typing import Embeddings +from langflow.schema import Record + + +class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent): + display_name = "Pinecone Search" + description = "Search a Pinecone Vector Store for similar documents." + icon = "Pinecone" + + def build_config(self): + return { + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "input_value": {"display_name": "Input"}, + "embedding": {"display_name": "Embedding"}, + "index_name": {"display_name": "Index Name"}, + "namespace": {"display_name": "Namespace"}, + "pinecone_api_key": { + "display_name": "Pinecone API Key", + "default": "", + "password": True, + "required": True, + }, + "pinecone_env": { + "display_name": "Pinecone Environment", + "default": "", + "required": True, + }, + "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"}, + "pool_threads": { + "display_name": "Pool Threads", + "default": 1, + "advanced": True, + }, + } + + def build( + self, + input_value: str, + embedding: Embeddings, + pinecone_env: str, + text_key: str = "text", + pool_threads: int = 4, + index_name: Optional[str] = None, + pinecone_api_key: Optional[str] = None, + namespace: Optional[str] = "default", + search_type: str = "similarity", + ) -> List[Record]: + vector_store = super().build( + embedding=embedding, + pinecone_env=pinecone_env, + documents=[], + text_key=text_key, + pool_threads=pool_threads, + index_name=index_name, + pinecone_api_key=pinecone_api_key, + namespace=namespace, + ) + if not vector_store: + raise ValueError("Failed to load the Pinecone index.") + + return self.search_with_vector_store( + vector_store=vector_store, input_value=input_value, search_type=search_type + ) diff --git a/src/backend/langflow/components/vectorstores/QdrantSearch.py b/src/backend/langflow/components/vectorstores/QdrantSearch.py new file mode 100644 index 000000000..46e2766fd --- /dev/null +++ b/src/backend/langflow/components/vectorstores/QdrantSearch.py @@ -0,0 +1,91 @@ +from typing import List, Optional + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.components.vectorstores.Qdrant import QdrantComponent +from langflow.field_typing import Embeddings, NestedDict +from langflow.schema import Record + + +class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent): + display_name = "Qdrant" + description = "Construct Qdrant wrapper from a list of texts." + + def build_config(self): + return { + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "input_value": {"display_name": "Input"}, + "embedding": {"display_name": "Embedding"}, + "api_key": {"display_name": "API Key", "password": True, "advanced": True}, + "collection_name": {"display_name": "Collection Name"}, + "content_payload_key": { + "display_name": "Content Payload Key", + "advanced": True, + }, + "distance_func": {"display_name": "Distance Function", "advanced": True}, + "grpc_port": {"display_name": "gRPC Port", "advanced": True}, + "host": {"display_name": "Host", "advanced": True}, + "https": {"display_name": "HTTPS", "advanced": True}, + "location": {"display_name": "Location", "advanced": True}, + "metadata_payload_key": { + "display_name": "Metadata Payload Key", + "advanced": True, + }, + "path": {"display_name": "Path", "advanced": True}, + "port": {"display_name": "Port", "advanced": True}, + "prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True}, + "prefix": {"display_name": "Prefix", "advanced": True}, + "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, + "timeout": {"display_name": "Timeout", "advanced": True}, + "url": {"display_name": "URL", "advanced": True}, + } + + def build( + self, + input_value: str, + embedding: Embeddings, + collection_name: str, + search_type: str = "similarity", + api_key: Optional[str] = None, + content_payload_key: str = "page_content", + distance_func: str = "Cosine", + grpc_port: int = 6334, + https: bool = False, + host: Optional[str] = None, + location: Optional[str] = None, + metadata_payload_key: str = "metadata", + path: Optional[str] = None, + port: Optional[int] = 6333, + prefer_grpc: bool = False, + prefix: Optional[str] = None, + search_kwargs: Optional[NestedDict] = None, + timeout: Optional[int] = None, + url: Optional[str] = None, + ) -> List[Record]: + vector_store = super().build( + embedding=embedding, + collection_name=collection_name, + api_key=api_key, + content_payload_key=content_payload_key, + distance_func=distance_func, + grpc_port=grpc_port, + https=https, + host=host, + location=location, + metadata_payload_key=metadata_payload_key, + path=path, + port=port, + prefer_grpc=prefer_grpc, + prefix=prefix, + search_kwargs=search_kwargs, + timeout=timeout, + url=url, + ) + if not vector_store: + raise ValueError("Failed to load the Qdrant index.") + + return self.search_with_vector_store( + vector_store=vector_store, input_value=input_value, search_type=search_type + ) diff --git a/src/backend/langflow/components/vectorstores/RedisSearch.py b/src/backend/langflow/components/vectorstores/RedisSearch.py new file mode 100644 index 000000000..71022de1d --- /dev/null +++ b/src/backend/langflow/components/vectorstores/RedisSearch.py @@ -0,0 +1,77 @@ +from typing import List, Optional + +from langchain.embeddings.base import Embeddings + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.components.vectorstores.Redis import RedisComponent +from langflow.schema import Record + + +class RedisSearchComponent(RedisComponent, LCVectorStoreComponent): + """ + A custom component for implementing a Vector Store using Redis. + """ + + display_name: str = "Redis Search" + description: str = "Search a Redis Vector Store for similar documents." + documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis" + beta = True + + def build_config(self): + """ + Builds the configuration for the component. + + Returns: + - dict: A dictionary containing the configuration options for the component. + """ + return { + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "input_value": {"display_name": "Input"}, + "index_name": {"display_name": "Index Name", "value": "your_index"}, + "code": {"show": False, "display_name": "Code"}, + "documents": {"display_name": "Documents", "is_list": True}, + "embedding": {"display_name": "Embedding"}, + "schema": {"display_name": "Schema", "file_types": [".yaml"]}, + "redis_server_url": { + "display_name": "Redis Server Connection String", + "advanced": False, + }, + "redis_index_name": {"display_name": "Redis Index", "advanced": False}, + } + + def build( + self, + input_value: str, + search_type: str, + embedding: Embeddings, + redis_server_url: str, + redis_index_name: str, + schema: Optional[str] = None, + ) -> List[Record]: + """ + Builds the Vector Store or BaseRetriever object. + + Args: + - embedding (Embeddings): The embeddings to use for the Vector Store. + - documents (Optional[Document]): The documents to use for the Vector Store. + - redis_index_name (str): The name of the Redis index. + - redis_server_url (str): The URL for the Redis server. + + Returns: + - VectorStore: The Vector Store object. + """ + vector_store = super().build( + embedding=embedding, + redis_server_url=redis_server_url, + redis_index_name=redis_index_name, + schema=schema, + ) + if not vector_store: + raise ValueError("Failed to load the Redis index.") + + return self.search_with_vector_store( + input_value=input_value, search_type=search_type, vector_store=vector_store + ) diff --git a/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py b/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py new file mode 100644 index 000000000..493632fe9 --- /dev/null +++ b/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py @@ -0,0 +1,49 @@ +from typing import List + +from langchain_community.vectorstores.supabase import SupabaseVectorStore +from supabase.client import Client, create_client + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.field_typing import Embeddings +from langflow.schema import Record + + +class SupabaseSearchComponent(LCVectorStoreComponent): + display_name = "Supabase Search" + description = "Search a Supabase Vector Store for similar documents." + + def build_config(self): + return { + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "input_value": {"display_name": "Input"}, + "embedding": {"display_name": "Embedding"}, + "query_name": {"display_name": "Query Name"}, + "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, + "supabase_service_key": {"display_name": "Supabase Service Key"}, + "supabase_url": {"display_name": "Supabase URL"}, + "table_name": {"display_name": "Table Name", "advanced": True}, + } + + def build( + self, + input_value: str, + search_type: str, + embedding: Embeddings, + query_name: str = "", + supabase_service_key: str = "", + supabase_url: str = "", + table_name: str = "", + ) -> List[Record]: + supabase: Client = create_client( + supabase_url, supabase_key=supabase_service_key + ) + vector_store = SupabaseVectorStore( + client=supabase, + embedding=embedding, + table_name=table_name, + query_name=query_name, + ) + return self.search_with_vector_store(input_value, search_type, vector_store) diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py index 31615fe7f..b5360ffd2 100644 --- a/src/backend/langflow/components/vectorstores/Vectara.py +++ b/src/backend/langflow/components/vectorstores/Vectara.py @@ -8,12 +8,15 @@ from langchain_community.vectorstores.vectara import Vectara from langchain_core.vectorstores import VectorStore from langflow import CustomComponent from langflow.field_typing import BaseRetriever, Document +from langchain_community.vectorstores.vectara import Vectara class VectaraComponent(CustomComponent): display_name: str = "Vectara" description: str = "Implementation of Vector Store using Vectara" - documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara" + documentation = ( + "https://python.langchain.com/docs/integrations/vectorstores/vectara" + ) beta = True field_config = { "vectara_customer_id": { @@ -26,7 +29,10 @@ class VectaraComponent(CustomComponent): "display_name": "Vectara API Key", "password": True, }, - "documents": {"display_name": "Documents", "info": "If provided, will be upserted to corpus (optional)"}, + "documents": { + "display_name": "Documents", + "info": "If provided, will be upserted to corpus (optional)", + }, "files_url": { "display_name": "Files Url", "info": "Make vectara object using url of files (optional)", diff --git a/src/backend/langflow/components/vectorstores/VectaraSearch.py b/src/backend/langflow/components/vectorstores/VectaraSearch.py new file mode 100644 index 000000000..c90148d1e --- /dev/null +++ b/src/backend/langflow/components/vectorstores/VectaraSearch.py @@ -0,0 +1,64 @@ +from typing import List + +from langchain_community.vectorstores.vectara import Vectara + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.components.vectorstores.Vectara import VectaraComponent +from langflow.schema import Record + + +class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent): + display_name: str = "Vectara Search" + description: str = "Search a Vectara Vector Store for similar documents." + documentation = ( + "https://python.langchain.com/docs/integrations/vectorstores/vectara" + ) + beta = True + field_config = { + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "input_value": {"display_name": "Input"}, + "vectara_customer_id": { + "display_name": "Vectara Customer ID", + }, + "vectara_corpus_id": { + "display_name": "Vectara Corpus ID", + }, + "vectara_api_key": { + "display_name": "Vectara API Key", + "password": True, + }, + "documents": { + "display_name": "Documents", + "info": "If provided, will be upserted to corpus (optional)", + }, + "files_url": { + "display_name": "Files Url", + "info": "Make vectara object using url of files (optional)", + }, + } + + def build( + self, + input_value: str, + search_type: str, + vectara_customer_id: str, + vectara_corpus_id: str, + vectara_api_key: str, + ) -> List[Record]: + source = "Langflow" + vector_store = Vectara( + vectara_customer_id=vectara_customer_id, + vectara_corpus_id=vectara_corpus_id, + vectara_api_key=vectara_api_key, + source=source, + ) + + if not vector_store: + raise ValueError("Failed to create Vectara Vector Store") + + return self.search_with_vector_store( + vector_store=vector_store, input_value=input_value, search_type=search_type + ) diff --git a/src/backend/langflow/components/vectorstores/Weaviate.py b/src/backend/langflow/components/vectorstores/Weaviate.py index 9b4967c36..59bbf4fef 100644 --- a/src/backend/langflow/components/vectorstores/Weaviate.py +++ b/src/backend/langflow/components/vectorstores/Weaviate.py @@ -8,10 +8,12 @@ from langchain_community.vectorstores import VectorStore, Weaviate from langflow import CustomComponent -class WeaviateVectorStore(CustomComponent): +class WeaviateVectorStoreComponent(CustomComponent): display_name: str = "Weaviate" description: str = "Implementation of Vector Store using Weaviate" - documentation = "https://python.langchain.com/docs/integrations/vectorstores/weaviate" + documentation = ( + "https://python.langchain.com/docs/integrations/vectorstores/weaviate" + ) beta = True field_config = { "url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"}, @@ -24,7 +26,12 @@ class WeaviateVectorStore(CustomComponent): "display_name": "Index name", "required": False, }, - "text_key": {"display_name": "Text Key", "required": False, "advanced": True, "value": "text"}, + "text_key": { + "display_name": "Text Key", + "required": False, + "advanced": True, + "value": "text", + }, "documents": {"display_name": "Documents", "is_list": True}, "embedding": {"display_name": "Embedding"}, "attributes": { @@ -34,7 +41,11 @@ class WeaviateVectorStore(CustomComponent): "field_type": "str", "advanced": True, }, - "search_by_text": {"display_name": "Search By Text", "field_type": "bool", "advanced": True}, + "search_by_text": { + "display_name": "Search By Text", + "field_type": "bool", + "advanced": True, + }, "code": {"show": False}, } diff --git a/src/backend/langflow/components/vectorstores/WeaviateSearch.py b/src/backend/langflow/components/vectorstores/WeaviateSearch.py new file mode 100644 index 000000000..2d7001074 --- /dev/null +++ b/src/backend/langflow/components/vectorstores/WeaviateSearch.py @@ -0,0 +1,82 @@ +from typing import List, Optional + +from langchain.embeddings.base import Embeddings + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent +from langflow.schema import Record + + +class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent): + display_name: str = "Weaviate Search" + description: str = "Search a Weaviate Vector Store for similar documents." + documentation = ( + "https://python.langchain.com/docs/integrations/vectorstores/weaviate" + ) + beta = True + field_config = { + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "input_value": {"display_name": "Input"}, + "url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"}, + "api_key": { + "display_name": "API Key", + "password": True, + "required": False, + }, + "index_name": { + "display_name": "Index name", + "required": False, + }, + "text_key": { + "display_name": "Text Key", + "required": False, + "advanced": True, + "value": "text", + }, + "documents": {"display_name": "Documents", "is_list": True}, + "embedding": {"display_name": "Embedding"}, + "attributes": { + "display_name": "Attributes", + "required": False, + "is_list": True, + "field_type": "str", + "advanced": True, + }, + "search_by_text": { + "display_name": "Search By Text", + "field_type": "bool", + "advanced": True, + }, + "code": {"show": False}, + } + + def build( + self, + input_value: str, + search_type: str, + url: str, + 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, + ) -> List[Record]: + vector_store = super().build( + url=url, + api_key=api_key, + index_name=index_name, + text_key=text_key, + embedding=embedding, + attributes=attributes, + search_by_text=search_by_text, + ) + if not vector_store: + raise ValueError("Failed to load the Weaviate index.") + + return self.search_with_vector_store( + vector_store=vector_store, input_value=input_value, search_type=search_type + ) diff --git a/src/backend/langflow/components/vectorstores/base/__init__.py b/src/backend/langflow/components/vectorstores/base/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/langflow/components/vectorstores/base/model.py b/src/backend/langflow/components/vectorstores/base/model.py new file mode 100644 index 000000000..1cc8b9d88 --- /dev/null +++ b/src/backend/langflow/components/vectorstores/base/model.py @@ -0,0 +1,41 @@ +from typing import List + +from langchain_core.vectorstores import VectorStore + +from langflow import CustomComponent +from langflow.field_typing import Text +from langflow.schema import Record, docs_to_records + + +class LCVectorStoreComponent(CustomComponent): + + display_name: str = "LC Vector Store" + description: str = "Search a LC Vector Store for similar documents." + beta: bool = True + + def search_with_vector_store( + self, input_value: Text, search_type: str, vector_store: VectorStore + ) -> List[Record]: + """ + Search for records in the vector store based on the input value and search type. + + Args: + input_value (Text): The input value to search for. + search_type (str): The type of search to perform. + vector_store (VectorStore): The vector store to search in. + + Returns: + List[Record]: A list of records matching the search criteria. + + Raises: + ValueError: If invalid inputs are provided. + """ + + docs = [] + if input_value and isinstance(input_value, str): + docs = vector_store.search( + query=input_value, search_type=search_type.lower() + ) + else: + raise ValueError("Invalid inputs provided.") + return docs_to_records(docs) diff --git a/src/backend/langflow/components/vectorstores/pgvectorSearch.py b/src/backend/langflow/components/vectorstores/pgvectorSearch.py new file mode 100644 index 000000000..00e291e76 --- /dev/null +++ b/src/backend/langflow/components/vectorstores/pgvectorSearch.py @@ -0,0 +1,73 @@ +from typing import List, Optional + +from langchain.embeddings.base import Embeddings + +from langflow.components.vectorstores.base.model import LCVectorStoreComponent +from langflow.components.vectorstores.pgvector import PGVectorComponent +from langflow.schema import Record + + +class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent): + """ + A custom component for implementing a Vector Store using PostgreSQL. + """ + + display_name: str = "PGVector Search" + description: str = "Search a PGVector Store for similar documents." + documentation = ( + "https://python.langchain.com/docs/integrations/vectorstores/pgvector" + ) + + def build_config(self): + """ + Builds the configuration for the component. + + Returns: + - dict: A dictionary containing the configuration options for the component. + """ + return { + "code": {"show": False}, + "embedding": {"display_name": "Embedding"}, + "search_type": { + "display_name": "Search Type", + "options": ["Similarity", "MMR"], + }, + "pg_server_url": { + "display_name": "PostgreSQL Server Connection String", + "advanced": False, + }, + "collection_name": {"display_name": "Table", "advanced": False}, + "input_value": {"display_name": "Input"}, + } + + def build( + self, + input_value: str, + embedding: Embeddings, + pg_server_url: str, + collection_name: str, + search_type: Optional[str] = None, + ) -> List[Record]: + """ + Builds the Vector Store or BaseRetriever object. + + Args: + - input_value (str): The input value to search for. + - embedding (Embeddings): The embeddings to use for the Vector Store. + - collection_name (str): The name of the PG table. + - pg_server_url (str): The URL for the PG server. + + Returns: + - VectorStore: The Vector Store object. + """ + try: + vector_store = super().build( + embedding=embedding, + pg_server_url=pg_server_url, + collection_name=collection_name, + ) + except Exception as e: + raise RuntimeError(f"Failed to build PGVector: {e}") + return self.search_with_vector_store( + input_value=input_value, search_type=search_type, vector_store=vector_store + ) diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml index 85ac1785d..102cb9016 100644 --- a/src/backend/langflow/config.yaml +++ b/src/backend/langflow/config.yaml @@ -11,31 +11,6 @@ agents: documentation: "" SQLAgent: documentation: "" -chains: - # LLMChain: - # documentation: "https://python.langchain.com/docs/modules/chains/foundational/llm_chain" - LLMMathChain: - documentation: "https://python.langchain.com/docs/modules/chains/additional/llm_math" - LLMCheckerChain: - documentation: "https://python.langchain.com/docs/modules/chains/additional/llm_checker" - # ConversationChain: - # documentation: "" - SeriesCharacterChain: - documentation: "" - MidJourneyPromptChain: - documentation: "" - TimeTravelGuideChain: - documentation: "" - SQLDatabaseChain: - documentation: "" - RetrievalQA: - documentation: "https://python.langchain.com/docs/modules/chains/popular/vector_db_qa" - RetrievalQAWithSourcesChain: - documentation: "" - ConversationalRetrievalChain: - documentation: "https://python.langchain.com/docs/modules/chains/popular/chat_vector_db" - CombineDocsChain: - documentation: "" documentloaders: AirbyteJSONLoader: documentation: "https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json" @@ -243,24 +218,7 @@ retrievers: # https://github.com/supabase-community/supabase-py/issues/482 # ZepRetriever: # documentation: "https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/zep_memorystore" -vectorstores: - # Chroma: - # documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma" - Qdrant: - documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant" - FAISS: - documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss" - Pinecone: - documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone" - ElasticsearchStore: - documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/elasticsearch" - SupabaseVectorStore: - documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase" - MongoDBAtlasVectorSearch: - documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/mongodb_atlas" - # Requires docarray >=0.32.0 but langchain-serve requires jina 3.15.2 which doesn't support docarray >=0.32.0 - # DocArrayInMemorySearch: - # documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/docarray_in_memory" + wrappers: RequestsWrapper: documentation: "" diff --git a/src/backend/langflow/graph/edge/base.py b/src/backend/langflow/graph/edge/base.py index 99b24e2da..cfcd33dd1 100644 --- a/src/backend/langflow/graph/edge/base.py +++ b/src/backend/langflow/graph/edge/base.py @@ -4,6 +4,7 @@ from loguru import logger from pydantic import BaseModel, Field from langflow.graph.edge.utils import build_clean_params +from langflow.graph.schema import INPUT_FIELD_NAME from langflow.services.deps import get_monitor_service from langflow.services.monitor.utils import log_message @@ -12,7 +13,9 @@ if TYPE_CHECKING: class SourceHandle(BaseModel): - baseClasses: List[str] = Field(..., description="List of base classes for the source handle.") + baseClasses: List[str] = Field( + ..., description="List of base classes for the source handle." + ) dataType: str = Field(..., description="Data type for the source handle.") id: str = Field(..., description="Unique identifier for the source handle.") @@ -20,7 +23,9 @@ class SourceHandle(BaseModel): class TargetHandle(BaseModel): fieldName: str = Field(..., description="Field name for the target handle.") id: str = Field(..., description="Unique identifier for the target handle.") - inputTypes: Optional[List[str]] = Field(None, description="List of input types for the target handle.") + inputTypes: Optional[List[str]] = Field( + None, description="List of input types for the target handle." + ) type: str = Field(..., description="Type of the target handle.") @@ -49,16 +54,24 @@ class Edge: def validate_handles(self, source, target) -> None: if self.target_handle.inputTypes is None: - self.valid_handles = self.target_handle.type in self.source_handle.baseClasses + self.valid_handles = ( + self.target_handle.type in self.source_handle.baseClasses + ) else: self.valid_handles = ( - any(baseClass in self.target_handle.inputTypes for baseClass in self.source_handle.baseClasses) + any( + baseClass in self.target_handle.inputTypes + for baseClass in self.source_handle.baseClasses + ) or self.target_handle.type in self.source_handle.baseClasses ) if not self.valid_handles: logger.debug(self.source_handle) logger.debug(self.target_handle) - raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has invalid handles") + raise ValueError( + f"Edge between {source.vertex_type} and {target.vertex_type} " + f"has invalid handles" + ) def __setstate__(self, state): self.source_id = state["source_id"] @@ -75,7 +88,11 @@ class Edge: # Both lists contain strings and sometimes a string contains the value we are # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"] # so we need to check if any of the strings in source_types is in target_reqs - self.valid = any(output in target_req for output in self.source_types for target_req in self.target_reqs) + self.valid = any( + output in target_req + for output in self.source_types + for target_req in self.target_reqs + ) # Get what type of input the target node is expecting self.matched_type = next( @@ -86,7 +103,10 @@ class Edge: if no_matched_type: logger.debug(self.source_types) logger.debug(self.target_reqs) - raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has no matched type") + raise ValueError( + f"Edge between {source.vertex_type} and {target.vertex_type} " + f"has no matched type" + ) def __repr__(self) -> str: return ( @@ -98,7 +118,11 @@ class Edge: return hash(self.__repr__()) def __eq__(self, __value: object) -> bool: - return self.__repr__() == __value.__repr__() if isinstance(__value, Edge) else False + return ( + self.__repr__() == __value.__repr__() + if isinstance(__value, Edge) + else False + ) class ContractEdge(Edge): @@ -137,14 +161,15 @@ class ContractEdge(Edge): log_transaction(self, source, target, "success") # If the target vertex is a power component we log messages if target.vertex_type == "ChatOutput" and ( - isinstance(target.params.get("message"), str) or isinstance(target.params.get("message"), dict) + isinstance(target.params.get(INPUT_FIELD_NAME), str) + or isinstance(target.params.get(INPUT_FIELD_NAME), dict) ): if target.params.get("message") == "": return self.result await log_message( sender=target.params.get("sender", ""), sender_name=target.params.get("sender_name", ""), - message=target.params.get("message", {}), + message=target.params.get(INPUT_FIELD_NAME, {}), session_id=target.params.get("session_id", ""), artifacts=target.artifacts, ) @@ -154,7 +179,9 @@ class ContractEdge(Edge): return f"{self.source_id} -[{self.target_param}]-> {self.target_id}" -def log_transaction(edge: ContractEdge, source: "Vertex", target: "Vertex", status, error=None): +def log_transaction( + edge: ContractEdge, source: "Vertex", target: "Vertex", status, error=None +): try: monitor_service = get_monitor_service() clean_params = build_clean_params(target) diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py index 1ea34fd51..051fc6b3b 100644 --- a/src/backend/langflow/graph/graph/base.py +++ b/src/backend/langflow/graph/graph/base.py @@ -1,5 +1,6 @@ +import asyncio from collections import defaultdict, deque -from typing import Dict, Generator, List, Optional, Type, Union +from typing import TYPE_CHECKING, Dict, Generator, List, Optional, Type, Union from langchain.chains.base import Chain from loguru import logger @@ -7,13 +8,21 @@ from loguru import logger from langflow.graph.edge.base import ContractEdge from langflow.graph.graph.constants import lazy_load_vertex_dict from langflow.graph.graph.utils import process_flow -from langflow.graph.schema import InterfaceComponentTypes +from langflow.graph.schema import INPUT_FIELD_NAME, InterfaceComponentTypes from langflow.graph.vertex.base import Vertex -from langflow.graph.vertex.types import (ChatVertex, FileToolVertex, LLMVertex, - RoutingVertex, ToolkitVertex) +from langflow.graph.vertex.types import ( + ChatVertex, + FileToolVertex, + LLMVertex, + RoutingVertex, + ToolkitVertex, +) from langflow.interface.tools.constants import FILE_TOOLS from langflow.utils import payload +if TYPE_CHECKING: + from langflow.graph.schema import ResultData + class Graph: """A class representing a graph of vertices and edges.""" @@ -24,14 +33,16 @@ class Graph: edges: List[Dict[str, str]], flow_id: Optional[str] = None, ) -> None: - self.inputs = [] - self.outputs = [] self._vertices = nodes self._edges = edges self.raw_graph_data = {"nodes": nodes, "edges": edges} self._runs = 0 self._updates = 0 self.flow_id = flow_id + self._is_input_vertices = [] + self._is_output_vertices = [] + self._has_session_id_vertices = [] + self._sorted_vertices_layers = [] self.top_level_vertices = [] for vertex in self._vertices: @@ -44,6 +55,67 @@ class Graph: self.inactive_vertices = set() self._build_graph() self.build_graph_maps() + self.define_vertices_lists() + + @property + def sorted_vertices_layers(self): + if not self._sorted_vertices_layers: + self.sort_vertices() + return self._sorted_vertices_layers + + def define_vertices_lists(self): + """ + Defines the lists of vertices that are inputs, outputs, and have session_id. + """ + attributes = ["is_input", "is_output", "has_session_id"] + for vertex in self.vertices: + for attribute in attributes: + if getattr(vertex, attribute): + getattr(self, f"_{attribute}_vertices").append(vertex.id) + + async def _run(self, inputs: Dict[str, str], stream: bool) -> List["ResultData"]: + """Runs the graph with the given inputs.""" + for vertex_id in self._is_input_vertices: + vertex = self.get_vertex(vertex_id) + if vertex is None: + raise ValueError(f"Vertex {vertex_id} not found") + vertex.update_raw_params(inputs) + try: + await self.process() + self.increment_run_count() + except Exception as exc: + logger.exception(exc) + raise ValueError(f"Error running graph: {exc}") from exc + outputs = [] + for vertex_id in self._is_output_vertices: + vertex = self.get_vertex(vertex_id) + if vertex is None: + raise ValueError(f"Vertex {vertex_id} not found") + if not stream and hasattr(vertex, "consume_async_generator"): + await vertex.consume_async_generator() + outputs.append(vertex.result) + return outputs + + async def run( + self, inputs: Dict[str, Union[str, list[str]]], stream: bool + ) -> List["ResultData"]: + """Runs the graph with the given inputs.""" + + # inputs is {"message": "Hello, world!"} + # we need to go through self.inputs and update the self._raw_params + # of the vertices that are inputs + # if the value is a list, we need to run multiple times + outputs = [] + inputs_values = inputs.get(INPUT_FIELD_NAME) + if not isinstance(inputs_values, list): + inputs_values = [inputs_values] + for input_value in inputs_values: + run_outputs = await self._run( + {INPUT_FIELD_NAME: input_value}, stream=stream + ) + logger.debug(f"Run outputs: {run_outputs}") + outputs.extend(run_outputs) + return outputs @property def metadata(self): @@ -157,7 +229,10 @@ class Graph: self.edges = new_edges def vertex_data_is_identical(self, vertex: Vertex, other_vertex: Vertex) -> bool: - return vertex.__repr__() == other_vertex.__repr__() + data_is_equivalent = vertex.__repr__() == other_vertex.__repr__() + if not data_is_equivalent: + return False + return self.vertex_edges_are_identical(vertex, other_vertex) def vertex_edges_are_identical(self, vertex: Vertex, other_vertex: Vertex) -> bool: same_length = len(vertex.edges) == len(other_vertex.edges) @@ -246,28 +321,6 @@ class Graph: # Now that we have the vertices and edges # We need to map the vertices that are connected to # to ChatVertex instances - self._map_chat_vertices() - - def _map_chat_vertices(self) -> None: - """Maps the vertices that are connected to ChatVertex instances.""" - # For each edge, we need to check if the source or target vertex is a ChatVertex - # If it is, we need to update the other vertex `is_external` attribute - # and store the id of the ChatVertex in the attributes self.inputs and self.outputs - for edge in self.edges: - source_vertex = self.get_vertex(edge.source_id) - target_vertex = self.get_vertex(edge.target_id) - if isinstance(source_vertex, ChatVertex): - # The source vertex is a ChatVertex - # thus the target vertex is an external vertex - # and the source vertex is an input - target_vertex.has_external_input = True - self.inputs.append(source_vertex.id) - if isinstance(target_vertex, ChatVertex): - # The target vertex is a ChatVertex - # thus the source vertex is an external vertex - # and the target vertex is an output - source_vertex.has_external_output = True - self.outputs.append(target_vertex.id) def remove_vertex(self, vertex_id: str) -> None: """Removes a vertex from the graph.""" @@ -317,12 +370,20 @@ class Graph: except KeyError: raise ValueError(f"Vertex {vertex_id} not found") - def get_vertex_edges(self, vertex_id: str) -> List[ContractEdge]: + def get_vertex_edges( + self, + vertex_id: str, + is_target: Optional[bool] = None, + is_source: Optional[bool] = None, + ) -> List[ContractEdge]: """Returns a list of edges for a given vertex.""" + # The idea here is to return the edges that have the vertex_id as source or target + # or both return [ edge for edge in self.edges - if edge.source_id == vertex_id or edge.target_id == vertex_id + if (edge.source_id == vertex_id and is_source is not False) + or (edge.target_id == vertex_id and is_target is not False) ] def get_vertices_with_target(self, vertex_id: str) -> List[Vertex]: @@ -344,6 +405,38 @@ class Graph: raise ValueError("No root vertex found") return await root_vertex.build() + async def process(self) -> "Graph": + """Processes the graph with vertices in each layer run in parallel.""" + vertices_layers = self.sorted_vertices_layers + + for layer_index, layer in enumerate(vertices_layers): + tasks = [] + for vertex_id in layer: + vertex = self.get_vertex(vertex_id) + task = asyncio.create_task( + vertex.build(), name=f"layer-{layer_index}-vertex-{vertex_id}" + ) + tasks.append(task) + logger.debug(f"Running layer {layer_index} with {len(tasks)} tasks") + await self._execute_tasks(tasks) + logger.debug("Graph processing complete") + return self + + async def _execute_tasks(self, tasks): + """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) + except Exception as e: + # Log the exception along with the task name for easier debugging + # task_name = task.get_name() + # coroutine has not attribute get_name + task_name = tasks[i].get_name() + logger.error(f"Task {task_name} failed with exception: {e}") + return results + def topological_sort(self) -> List[Vertex]: """ Performs a topological sort of the vertices in the graph. @@ -611,6 +704,7 @@ class Graph: vertices_layers = self.sort_by_avg_build_time(vertices_layers) vertices_layers = self.sort_chat_inputs_first(vertices_layers) self.increment_run_count() + self._sorted_vertices_layers = vertices_layers return vertices_layers def sort_interface_components_first( diff --git a/src/backend/langflow/graph/schema.py b/src/backend/langflow/graph/schema.py index a3a2822e1..028b8db9f 100644 --- a/src/backend/langflow/graph/schema.py +++ b/src/backend/langflow/graph/schema.py @@ -1,8 +1,39 @@ from enum import Enum +from typing import Any, Optional +from langflow.graph.utils import serialize_field + +from pydantic import BaseModel, Field, field_serializer -class InterfaceComponentTypes(Enum): +class ResultData(BaseModel): + results: Optional[Any] = Field(default_factory=dict) + artifacts: Optional[Any] = Field(default_factory=dict) + timedelta: Optional[float] = None + duration: Optional[str] = None + + @field_serializer("results") + def serialize_results(self, value): + if isinstance(value, dict): + return {key: serialize_field(val) for key, val in value.items()} + return serialize_field(value) + + +class InterfaceComponentTypes(str, Enum): # ChatInput and ChatOutput are the only ones that are # power components ChatInput = "ChatInput" ChatOutput = "ChatOutput" + TextInput = "TextInput" + TextOutput = "TextOutput" + + +INPUT_COMPONENTS = [ + InterfaceComponentTypes.ChatInput, + InterfaceComponentTypes.TextInput, +] +OUTPUT_COMPONENTS = [ + InterfaceComponentTypes.ChatOutput, + InterfaceComponentTypes.TextOutput, +] + +INPUT_FIELD_NAME = "input_value" diff --git a/src/backend/langflow/graph/utils.py b/src/backend/langflow/graph/utils.py index 4bfd71fc5..83e2177b1 100644 --- a/src/backend/langflow/graph/utils.py +++ b/src/backend/langflow/graph/utils.py @@ -1,5 +1,8 @@ from typing import Any, Union +from langchain_core.documents import Document +from pydantic import BaseModel + from langflow.interface.utils import extract_input_variables_from_prompt @@ -33,3 +36,17 @@ def flatten_list(list_of_lists: list[Union[list, Any]]) -> list: else: new_list.append(item) return new_list + + +def serialize_field(value): + """Unified serialization function for handling both BaseModel and Document types, + including handling lists of these types.""" + if isinstance(value, (list, tuple)): + return [serialize_field(v) for v in value] + elif isinstance(value, Document): + return value.to_json() + elif isinstance(value, BaseModel): + return value.model_dump() + elif isinstance(value, str): + return {"result": value} + return value diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py index d31edfbce..b917889a4 100644 --- a/src/backend/langflow/graph/vertex/base.py +++ b/src/backend/langflow/graph/vertex/base.py @@ -7,7 +7,8 @@ from typing import (TYPE_CHECKING, Any, Callable, Coroutine, Dict, List, from loguru import logger -from langflow.graph.schema import InterfaceComponentTypes +from langflow.graph.schema import (INPUT_COMPONENTS, OUTPUT_COMPONENTS, + InterfaceComponentTypes, ResultData) from langflow.graph.utils import UnbuiltObject, UnbuiltResult from langflow.graph.vertex.utils import generate_result from langflow.interface.initialize import loading @@ -17,12 +18,11 @@ from langflow.utils.constants import DIRECT_TYPES from langflow.utils.util import sync_to_async if TYPE_CHECKING: - from langflow.api.v1.schemas import ResultData from langflow.graph.edge.base import ContractEdge from langflow.graph.graph.base import Graph -class VertexStates(Enum): +class VertexStates(str, Enum): """Vertex are related to it being active, inactive, or in an error state.""" ACTIVE = "active" @@ -41,10 +41,21 @@ class Vertex: ) -> None: # is_external means that the Vertex send or receives data from # an external source (e.g the chat) + self.will_stream = False + self.updated_raw_params = False + self.id: str = data["id"] + self.is_input = any( + input_component_name in self.id for input_component_name in INPUT_COMPONENTS + ) + self.is_output = any( + output_component_name in self.id + for output_component_name in OUTPUT_COMPONENTS + ) + self.has_session_id = None + self._custom_component = None self.has_external_input = False self.has_external_output = False self.graph = graph - self.id: str = data["id"] self._data = data self.base_type: Optional[str] = base_type self._parse_data() @@ -61,7 +72,7 @@ class Vertex: self.parent_is_top_level = False self.layer = None self.should_run = True - self.result: Optional["ResultData"] = None + self.result: Optional[ResultData] = None try: self.is_interface_component = InterfaceComponentTypes(self.vertex_type) except ValueError: @@ -116,7 +127,7 @@ class Vertex: ) return edge_results - def set_result(self, result: "ResultData") -> None: + def set_result(self, result: ResultData) -> None: self.result = result def get_built_result(self): @@ -202,12 +213,17 @@ class Vertex: self.output = self.data["node"]["base_classes"] self.display_name = self.data["node"]["display_name"] self.pinned = self.data["node"].get("pinned", False) + self.selected_output_type = self.data["node"].get("selected_output_type") + self.is_input = self.data["node"].get("is_input") or self.is_input + self.is_output = self.data["node"].get("is_output") or self.is_output template_dicts = { key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict) } + self.has_session_id = "session_id" in template_dicts + self.required_inputs = [ template_dicts[key]["type"] for key, value in template_dicts.items() @@ -267,6 +283,10 @@ class Vertex: if self.graph is None: raise ValueError("Graph not found") + if self.updated_raw_params: + self.updated_raw_params = False + return + template_dict = { key: value for key, value in self.data["node"]["template"].items() @@ -358,6 +378,22 @@ class Vertex: self.params = params self._raw_params = params.copy() + def update_raw_params(self, new_params: Dict[str, str]): + """ + Update the raw parameters of the vertex with the given new parameters. + + Args: + new_params (Dict[str, Any]): The new parameters to update. + + Raises: + ValueError: If any key in new_params is not found in self._raw_params. + """ + # First check if the input_value in _raw_params is not a vertex + if any(isinstance(self._raw_params.get(key), Vertex) for key in new_params): + return + self._raw_params.update(new_params) + self.updated_raw_params = True + async def _build(self, user_id=None): """ Initiate the build process. @@ -369,6 +405,18 @@ class Vertex: self._built = True + def _finalize_build(self): + result_dict = self.get_built_result() + # We need to set the artifacts to pass information + # to the frontend + self.set_artifacts() + artifacts = self.artifacts + result_dict = ResultData( + results=result_dict, + artifacts=artifacts, + ) + self.set_result(result_dict) + async def _run( self, user_id: str, @@ -406,6 +454,8 @@ class Vertex: await self._build_node_and_update_params(key, value, user_id) elif isinstance(value, list) and self._is_list_of_nodes(value): await self._build_list_of_nodes_and_update_params(key, value, user_id) + elif key not in self.params: + self.params[key] = value def _is_node(self, value): """ @@ -500,15 +550,18 @@ class Vertex: if self.base_type is None: raise ValueError(f"Base type for node {self.display_name} not found") try: + result = await loading.instantiate_class( node_type=self.vertex_type, base_type=self.base_type, params=self.params, user_id=user_id, + vertex=self, ) self._update_built_object_and_artifacts(result) except Exception as exc: logger.exception(exc) + raise ValueError( f"Error building node {self.display_name}: {str(exc)}" ) from exc @@ -518,7 +571,10 @@ class Vertex: Updates the built object and its artifacts. """ if isinstance(result, tuple): - self._built_object, self.artifacts = result + if len(result) == 2: + self._built_object, self.artifacts = result + elif len(result) == 3: + self._custom_component, self._built_object, self.artifacts = result else: self._built_object = result @@ -535,7 +591,7 @@ class Vertex: logger.warning(message) - def _reset(self): + def _reset(self, params_update: Optional[Dict[str, Any]] = None): self._built = False self._built_object = UnbuiltObject() self._built_result = UnbuiltResult() @@ -573,6 +629,8 @@ class Vertex: step(user_id=user_id, **kwargs) self.steps_ran.append(step) + self._finalize_build() + return await self.get_requester_result(requester) async def get_requester_result(self, requester: Optional["Vertex"]): diff --git a/src/backend/langflow/graph/vertex/types.py b/src/backend/langflow/graph/vertex/types.py index b7746b6a1..721a7ccc8 100644 --- a/src/backend/langflow/graph/vertex/types.py +++ b/src/backend/langflow/graph/vertex/types.py @@ -6,11 +6,12 @@ import yaml from langchain_core.messages import AIMessage from loguru import logger +from langflow.graph.schema import INPUT_FIELD_NAME from langflow.graph.utils import UnbuiltObject, flatten_list from langflow.graph.vertex.base import StatefulVertex, StatelessVertex from langflow.interface.utils import extract_input_variables_from_prompt from langflow.schema import Record -from langflow.services.monitor.utils import log_message +from langflow.services.monitor.utils import log_vertex_build from langflow.utils.schemas import ChatOutputResponse @@ -344,17 +345,6 @@ class ChatVertex(StatelessVertex): def build_stream_url(self): return f"/api/v1/build/{self.graph.flow_id}/{self.id}/stream" - async def _build(self, user_id=None): - """ - Initiate the build process. - """ - logger.debug(f"Building {self.vertex_type}") - await self._build_each_node_in_params_dict(user_id) - await self._get_and_instantiate_class(user_id) - self._validate_built_object() - - self._built = True - def _built_object_repr(self): if self.task_id and self.is_task: if task := self.get_task(): @@ -373,7 +363,7 @@ class ChatVertex(StatelessVertex): artifacts = None sender = self.params.get("sender", None) sender_name = self.params.get("sender_name", None) - message = self.params.get("message", None) + message = self.params.get(INPUT_FIELD_NAME, None) stream_url = None if isinstance(self._built_object, AIMessage): artifacts = ChatOutputResponse.from_message( @@ -404,10 +394,12 @@ class ChatVertex(StatelessVertex): sender_name=sender_name, stream_url=stream_url, ) + + self.will_stream = stream_url is not None if artifacts: self.artifacts = artifacts.model_dump() if isinstance(self._built_object, (AsyncIterator, Iterator)): - if self.params["as_record"]: + if self.params["return_record"]: self._built_object = Record(text=message, data=self.artifacts) else: self._built_object = message @@ -417,7 +409,7 @@ class ChatVertex(StatelessVertex): await super()._run(*args, **kwargs) async def stream(self): - iterator = self.params.get("message", None) + iterator = self.params.get(INPUT_FIELD_NAME, None) if not isinstance(iterator, (AsyncIterator, Iterator)): raise ValueError("The message must be an iterator or an async iterator.") is_async = isinstance(iterator, AsyncIterator) @@ -434,24 +426,35 @@ class ChatVertex(StatelessVertex): message = message.text if hasattr(message, "text") else message yield message complete_message += message - self._built_object = Record(text=complete_message, data=self.artifacts) - self._built_result = complete_message - # Update artifacts with the message - # and remove the stream_url self.artifacts = ChatOutputResponse( message=complete_message, sender=self.params.get("sender", ""), sender_name=self.params.get("sender_name", ""), ).model_dump() + self.params[INPUT_FIELD_NAME] = complete_message + self._built_object = Record(text=complete_message, data=self.artifacts) + self._built_result = complete_message + # Update artifacts with the message + # and remove the stream_url + self._finalize_build() + logger.debug(f"Streamed message: {complete_message}") - await log_message( - sender=self.params.get("sender", ""), - sender_name=self.params.get("sender_name", ""), - message=complete_message, - session_id=self.params.get("session_id", ""), + await log_vertex_build( + flow_id=self.graph.flow_id, + vertex_id=self.id, + valid=True, + params=self._built_object_repr(), + data=self.result, artifacts=self.artifacts, ) + self._validate_built_object() + self._built = True + + async def consume_async_generator(self): + async for _ in self.stream(): + pass + class RoutingVertex(StatelessVertex): def __init__(self, data: Dict, graph): diff --git a/src/backend/langflow/interface/custom/attributes.py b/src/backend/langflow/interface/custom/attributes.py index 8ec2a0daa..d3119cd3d 100644 --- a/src/backend/langflow/interface/custom/attributes.py +++ b/src/backend/langflow/interface/custom/attributes.py @@ -37,4 +37,6 @@ ATTR_FUNC_MAPPING = { "documentation": getattr_return_str, "icon": validate_icon, "pinned": getattr_return_bool, + "is_input": getattr_return_bool, + "is_output": getattr_return_bool, } diff --git a/src/backend/langflow/interface/custom/code_parser/code_parser.py b/src/backend/langflow/interface/custom/code_parser/code_parser.py index 7a102e33f..e54051a5c 100644 --- a/src/backend/langflow/interface/custom/code_parser/code_parser.py +++ b/src/backend/langflow/interface/custom/code_parser/code_parser.py @@ -6,6 +6,9 @@ from typing import Any, Dict, List, Type, Union from cachetools import TTLCache, cachedmethod, keys from fastapi import HTTPException +from loguru import logger + +from langflow.interface.custom.eval import eval_custom_component_code from langflow.interface.custom.schema import CallableCodeDetails, ClassCodeDetails @@ -92,7 +95,9 @@ class CodeParser: elif isinstance(node, ast.ImportFrom): for alias in node.names: if alias.asname: - self.data["imports"].append((node.module, f"{alias.name} as {alias.asname}")) + self.data["imports"].append( + (node.module, f"{alias.name} as {alias.asname}") + ) else: self.data["imports"].append((node.module, alias.name)) @@ -141,7 +146,9 @@ class CodeParser: return_type = None if node.returns: return_type_str = ast.unparse(node.returns) - eval_env = self.construct_eval_env(return_type_str, tuple(self.data["imports"])) + eval_env = self.construct_eval_env( + return_type_str, tuple(self.data["imports"]) + ) try: return_type = eval(return_type_str, eval_env) @@ -183,14 +190,22 @@ class CodeParser: num_defaults = len(node.args.defaults) num_missing_defaults = num_args - num_defaults missing_defaults = [None] * num_missing_defaults - default_values = [ast.unparse(default).strip("'") if default else None for default in node.args.defaults] + default_values = [ + ast.unparse(default).strip("'") if default else None + for default in node.args.defaults + ] # Now check all default values to see if there # are any "None" values in the middle - default_values = [None if value == "None" else value for value in default_values] + default_values = [ + None if value == "None" else value for value in default_values + ] defaults = missing_defaults + default_values - args = [self.parse_arg(arg, default) for arg, default in zip(node.args.args, defaults)] + args = [ + self.parse_arg(arg, default) + for arg, default in zip(node.args.args, defaults) + ] return args def parse_varargs(self, node: ast.FunctionDef) -> List[Dict[str, Any]]: @@ -208,11 +223,17 @@ class CodeParser: """ Parses the keyword-only arguments of a function or method node. """ - kw_defaults = [None] * (len(node.args.kwonlyargs) - len(node.args.kw_defaults)) + [ - ast.unparse(default) if default else None for default in node.args.kw_defaults + kw_defaults = [None] * ( + len(node.args.kwonlyargs) - len(node.args.kw_defaults) + ) + [ + ast.unparse(default) if default else None + for default in node.args.kw_defaults ] - args = [self.parse_arg(arg, default) for arg, default in zip(node.args.kwonlyargs, kw_defaults)] + args = [ + self.parse_arg(arg, default) + for arg, default in zip(node.args.kwonlyargs, kw_defaults) + ] return args def parse_kwargs(self, node: ast.FunctionDef) -> List[Dict[str, Any]]: @@ -268,15 +289,28 @@ class CodeParser: method = self.parse_callable_details(stmt) return (method, True) if stmt.name == "__init__" else (method, False) + def get_base_classes(self): + """ + Returns the base classes of the custom component class. + """ + try: + bases = self.execute_and_inspect_classes(self.code) + except Exception as e: + # If the code cannot be executed, return an empty list + logger.exception(e) + bases = [] + raise e + return bases + def parse_classes(self, node: ast.ClassDef) -> None: """ Extracts "classes" from the code, including inheritance and init methods. """ - + bases = self.get_base_classes() or [ast.unparse(b) for b in node.bases] class_details = ClassCodeDetails( name=node.name, doc=ast.get_docstring(node), - bases=[ast.unparse(base) for base in node.bases], + bases=bases, attributes=[], methods=[], init=None, @@ -303,11 +337,25 @@ class CodeParser: Extracts global variables from the code. """ global_var = { - "targets": [t.id if hasattr(t, "id") else ast.dump(t) for t in node.targets], + "targets": [ + t.id if hasattr(t, "id") else ast.dump(t) for t in node.targets + ], "value": ast.unparse(node.value), } self.data["global_vars"].append(global_var) + def execute_and_inspect_classes(self, code: str): + custom_component_class = eval_custom_component_code(code) + custom_component = custom_component_class() + dunder_class = custom_component.__class__ + # Get the base classes at two levels of inheritance + bases = [] + for base in dunder_class.__bases__: + bases.append(base.__name__) + for bases_base in base.__bases__: + bases.append(bases_base.__name__) + return bases + def parse_code(self) -> Dict[str, Any]: """ Runs all parsing operations and returns the resulting data. diff --git a/src/backend/langflow/interface/custom/custom_component/custom_component.py b/src/backend/langflow/interface/custom/custom_component/custom_component.py index 3534baf2a..a8c81f041 100644 --- a/src/backend/langflow/interface/custom/custom_component/custom_component.py +++ b/src/backend/langflow/interface/custom/custom_component/custom_component.py @@ -1,6 +1,15 @@ import operator from pathlib import Path -from typing import Any, Callable, ClassVar, List, Optional, Sequence, Union +from typing import ( + TYPE_CHECKING, + Any, + Callable, + ClassVar, + List, + Optional, + Sequence, + Union, +) from uuid import UUID import yaml @@ -24,6 +33,10 @@ from langflow.services.deps import ( from langflow.services.storage.service import StorageService from langflow.utils import validate +if TYPE_CHECKING: + from langflow.graph.edge.base import ContractEdge + from langflow.graph.vertex.base import Vertex + class CustomComponent(Component): display_name: Optional[str] = None @@ -32,6 +45,12 @@ class CustomComponent(Component): """The description of the component. Defaults to None.""" icon: Optional[str] = None """The icon of the component. It should be an emoji. Defaults to None.""" + is_input: Optional[bool] = None + """The input state of the component. Defaults to None. + If True, the component must have a field named 'input_value'.""" + is_output: Optional[bool] = None + """The output state of the component. Defaults to None. + If True, the component must have a field named 'input_value'.""" code: Optional[str] = None """The code of the component. Defaults to None.""" field_config: dict = {} @@ -40,6 +59,12 @@ class CustomComponent(Component): """The field order of the component. Defaults to an empty list.""" pinned: Optional[bool] = False """The default pinned state of the component. Defaults to False.""" + build_parameters: Optional[dict] = None + """The build parameters of the component. Defaults to None.""" + selected_output_type: Optional[str] = None + """The selected output type of the component. Defaults to None.""" + vertex: Optional["Vertex"] = None + """The edge target parameter of the component. Defaults to None.""" code_class_base_inheritance: ClassVar[str] = "CustomComponent" function_entrypoint_name: ClassVar[str] = "build" function: Optional[Callable] = None @@ -47,6 +72,7 @@ class CustomComponent(Component): user_id: Optional[Union[UUID, str]] = None status: Optional[Any] = None """The status of the component. This is displayed on the frontend. Defaults to None.""" + _tree: Optional[dict] = None def __init__(self, **data): @@ -88,7 +114,9 @@ class CustomComponent(Component): def tree(self): return self.get_code_tree(self.code or "") - def to_records(self, data: Any, text_key: str = "text", data_key: str = "data") -> List[dict]: + def to_records( + self, data: Any, text_key: str = "text", data_key: str = "data" + ) -> List[dict]: """ Convert data into a list of records. @@ -115,7 +143,9 @@ class CustomComponent(Component): return records - def create_references_from_records(self, records: List[dict], include_data: bool = False) -> str: + def create_references_from_records( + self, records: List[dict], include_data: bool = False + ) -> str: """ Create references from a list of records. @@ -150,7 +180,8 @@ class CustomComponent(Component): detail={ "error": "Type hint Error", "traceback": ( - "Prompt type is not supported in the build method." " Try using PromptTemplate instead." + "Prompt type is not supported in the build method." + " Try using PromptTemplate instead." ), }, ) @@ -164,14 +195,20 @@ class CustomComponent(Component): if not self.code: return {} - component_classes = [cls for cls in self.tree["classes"] if self.code_class_base_inheritance in cls["bases"]] + component_classes = [ + cls + for cls in self.tree["classes"] + if self.code_class_base_inheritance in cls["bases"] + ] if not component_classes: return {} # Assume the first Component class is the one we're interested in component_class = component_classes[0] build_methods = [ - method for method in component_class["methods"] if method["name"] == self.function_entrypoint_name + method + for method in component_class["methods"] + if method["name"] == self.function_entrypoint_name ] return build_methods[0] if build_methods else {} @@ -228,7 +265,9 @@ class CustomComponent(Component): # Retrieve and decrypt the credential by name for the current user db_service = get_db_service() with session_getter(db_service) as session: - return credential_service.get_credential(user_id=self._user_id or "", name=name, session=session) + return credential_service.get_credential( + user_id=self._user_id or "", name=name, session=session + ) return get_credential @@ -238,7 +277,9 @@ class CustomComponent(Component): credential_service = get_credential_service() db_service = get_db_service() with session_getter(db_service) as session: - return credential_service.list_credentials(user_id=self._user_id, session=session) + return credential_service.list_credentials( + user_id=self._user_id, session=session + ) def index(self, value: int = 0): """Returns a function that returns the value at the given index in the iterable.""" @@ -289,7 +330,11 @@ class CustomComponent(Component): if flow_id: flow = session.query(Flow).get(flow_id) elif flow_name: - flow = (session.query(Flow).filter(Flow.name == flow_name).filter(Flow.user_id == self.user_id)).first() + flow = ( + session.query(Flow) + .filter(Flow.name == flow_name) + .filter(Flow.user_id == self.user_id) + ).first() else: raise ValueError("Either flow_name or flow_id must be provided") diff --git a/src/backend/langflow/interface/custom/utils.py b/src/backend/langflow/interface/custom/utils.py index d2be47333..381193116 100644 --- a/src/backend/langflow/interface/custom/utils.py +++ b/src/backend/langflow/interface/custom/utils.py @@ -27,14 +27,18 @@ from langflow.utils import validate from langflow.utils.util import get_base_classes -def add_output_types(frontend_node: CustomComponentFrontendNode, return_types: List[str]): +def add_output_types( + frontend_node: CustomComponentFrontendNode, return_types: List[str] +): """Add output types to the frontend node""" for return_type in return_types: if return_type is None: raise HTTPException( status_code=400, detail={ - "error": ("Invalid return type. Please check your code and try again."), + "error": ( + "Invalid return type. Please check your code and try again." + ), "traceback": traceback.format_exc(), }, ) @@ -63,14 +67,18 @@ def reorder_fields(frontend_node: CustomComponentFrontendNode, field_order: List frontend_node.template.fields = reordered_fields -def add_base_classes(frontend_node: CustomComponentFrontendNode, return_types: List[str]): +def add_base_classes( + frontend_node: CustomComponentFrontendNode, return_types: List[str] +): """Add base classes to the frontend node""" for return_type_instance in return_types: if return_type_instance is None: raise HTTPException( status_code=400, detail={ - "error": ("Invalid return type. Please check your code and try again."), + "error": ( + "Invalid return type. Please check your code and try again." + ), "traceback": traceback.format_exc(), }, ) @@ -145,10 +153,14 @@ def add_new_custom_field( # If options is a list, then it's a dropdown # If options is None, then it's a list of strings is_list = isinstance(field_config.get("options"), list) - field_config["is_list"] = is_list or field_config.get("is_list", False) or field_contains_list + field_config["is_list"] = ( + is_list or field_config.get("is_list", False) or field_contains_list + ) if "name" in field_config: - warnings.warn("The 'name' key in field_config is used to build the object and can't be changed.") + warnings.warn( + "The 'name' key in field_config is used to build the object and can't be changed." + ) required = field_config.pop("required", field_required) placeholder = field_config.pop("placeholder", "") @@ -179,7 +191,9 @@ def add_extra_fields(frontend_node, field_config, function_args): if "name" not in extra_field or extra_field["name"] == "self": continue - field_name, field_type, field_value, field_required = get_field_properties(extra_field) + field_name, field_type, field_value, field_required = get_field_properties( + extra_field + ) config = field_config.get(field_name, {}) frontend_node = add_new_custom_field( frontend_node, @@ -217,7 +231,9 @@ def run_build_config( raise HTTPException( status_code=400, detail={ - "error": ("Invalid type convertion. Please check your code and try again."), + "error": ( + "Invalid type convertion. Please check your code and try again." + ), "traceback": traceback.format_exc(), }, ) from exc @@ -245,7 +261,9 @@ def run_build_config( raise HTTPException( status_code=400, detail={ - "error": ("Invalid type convertion. Please check your code and try again."), + "error": ( + "Invalid type convertion. Please check your code and try again." + ), "traceback": traceback.format_exc(), }, ) from exc @@ -300,16 +318,24 @@ def build_custom_component_template( frontend_node = build_frontend_node(custom_component.template_config) logger.debug("Updated attributes") - field_config, custom_instance = run_build_config(custom_component, user_id=user_id, update_field=update_field) + field_config, custom_instance = run_build_config( + custom_component, user_id=user_id, update_field=update_field + ) logger.debug("Built field config") entrypoint_args = custom_component.get_function_entrypoint_args add_extra_fields(frontend_node, field_config, entrypoint_args) - frontend_node = add_code_field(frontend_node, custom_component.code, field_config.get("code", {})) + frontend_node = add_code_field( + frontend_node, custom_component.code, field_config.get("code", {}) + ) - add_base_classes(frontend_node, custom_component.get_function_entrypoint_return_type) - add_output_types(frontend_node, custom_component.get_function_entrypoint_return_type) + add_base_classes( + frontend_node, custom_component.get_function_entrypoint_return_type + ) + add_output_types( + frontend_node, custom_component.get_function_entrypoint_return_type + ) logger.debug("Added base classes") reorder_fields(frontend_node, custom_instance._get_field_order()) @@ -321,7 +347,9 @@ def build_custom_component_template( raise HTTPException( status_code=400, detail={ - "error": ("Invalid type convertion. Please check your code and try again."), + "error": ( + "Invalid type convertion. Please check your code and try again." + ), "traceback": traceback.format_exc(), }, ) from exc @@ -345,7 +373,9 @@ def build_custom_components(settings_service): if not settings_service.settings.COMPONENTS_PATH: return {} - logger.info(f"Building custom components from {settings_service.settings.COMPONENTS_PATH}") + logger.info( + f"Building custom components from {settings_service.settings.COMPONENTS_PATH}" + ) custom_components_from_file = {} processed_paths = set() for path in settings_service.settings.COMPONENTS_PATH: @@ -356,7 +386,9 @@ def build_custom_components(settings_service): custom_component_dict = build_custom_component_list_from_path(path_str) if custom_component_dict: category = next(iter(custom_component_dict)) - logger.info(f"Loading {len(custom_component_dict[category])} component(s) from category {category}") + logger.info( + f"Loading {len(custom_component_dict[category])} component(s) from category {category}" + ) custom_components_from_file = merge_nested_dicts_with_renaming( custom_components_from_file, custom_component_dict ) @@ -400,8 +432,9 @@ def sanitize_field_config(field_config: Dict): def build_component(component): """Build a single component.""" component_name = determine_component_name(component) + logger.debug(f"Building component: {component_name}") component_template = create_component_template(component) - logger.debug(f"Building component: {component_name, component.get('output_types')}") + return component_name, component_template diff --git a/src/backend/langflow/interface/initialize/loading.py b/src/backend/langflow/interface/initialize/loading.py index 83bd67321..b87923c7f 100644 --- a/src/backend/langflow/interface/initialize/loading.py +++ b/src/backend/langflow/interface/initialize/loading.py @@ -1,6 +1,6 @@ import inspect import json -from typing import TYPE_CHECKING, Any, Callable, Dict, Sequence, Type +from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Sequence, Type import orjson from langchain.agents import agent as agent_module @@ -34,16 +34,17 @@ from langflow.utils import validate if TYPE_CHECKING: from langflow import CustomComponent - - -def build_vertex_in_params(params: Dict) -> Dict: + from langflow.graph.edge.base import ContractEdge from langflow.graph.vertex.base import Vertex - # If any of the values in params is a Vertex, we will build it - return {key: value.build() if isinstance(value, Vertex) else value for key, value in params.items()} - -async def instantiate_class(node_type: str, base_type: str, params: Dict, user_id=None) -> Any: +async def instantiate_class( + node_type: str, + base_type: str, + params: Dict, + user_id=None, + vertex: Optional["Vertex"] = None, +) -> Any: """Instantiate class from module type and key, and params""" params = convert_params_to_sets(params) params = convert_kwargs(params) @@ -55,7 +56,14 @@ async def instantiate_class(node_type: str, base_type: str, params: Dict, user_i return custom_node(**params) logger.debug(f"Instantiating {node_type} of type {base_type}") class_object = import_by_type(_type=base_type, name=node_type) - return await instantiate_based_on_type(class_object, base_type, node_type, params, user_id=user_id) + return await instantiate_based_on_type( + class_object=class_object, + base_type=base_type, + node_type=node_type, + params=params, + user_id=user_id, + vertex=vertex, + ) def convert_params_to_sets(params): @@ -82,7 +90,14 @@ def convert_kwargs(params): return params -async def instantiate_based_on_type(class_object, base_type, node_type, params, user_id): +async def instantiate_based_on_type( + class_object, + base_type, + node_type, + params, + user_id, + vertex, +): if base_type == "agents": return instantiate_agent(node_type, class_object, params) elif base_type == "prompts": @@ -116,17 +131,32 @@ async def instantiate_based_on_type(class_object, base_type, node_type, params, elif base_type == "memory": return instantiate_memory(node_type, class_object, params) elif base_type == "custom_components": - return await instantiate_custom_component(node_type, class_object, params, user_id) + return await instantiate_custom_component( + node_type, + class_object, + params, + user_id, + vertex, + ) elif base_type == "wrappers": return instantiate_wrapper(node_type, class_object, params) else: return class_object(**params) -async def instantiate_custom_component(node_type, class_object, params, user_id): +async def instantiate_custom_component( + node_type, class_object, params, user_id, vertex +): params_copy = params.copy() - class_object: Type["CustomComponent"] = eval_custom_component_code(params_copy.pop("code")) - custom_component: "CustomComponent" = class_object(user_id=user_id) + class_object: Type["CustomComponent"] = eval_custom_component_code( + params_copy.pop("code") + ) + custom_component: "CustomComponent" = class_object( + user_id=user_id, + parameters=params_copy, + vertex=vertex, + selected_output_type=vertex.selected_output_type, + ) if "retriever" in params_copy and hasattr(params_copy["retriever"], "as_retriever"): params_copy["retriever"] = params_copy["retriever"].as_retriever() @@ -141,7 +171,7 @@ async def instantiate_custom_component(node_type, class_object, params, user_id) # Call the build method directly if it's sync build_result = custom_component.build(**params_copy) - return build_result, {"repr": custom_component.custom_repr()} + return custom_component, build_result, {"repr": custom_component.custom_repr()} def instantiate_wrapper(node_type, class_object, params): @@ -194,7 +224,9 @@ def instantiate_memory(node_type, class_object, params): # I want to catch a specific attribute error that happens # when the object does not have a cursor attribute except Exception as exc: - if "object has no attribute 'cursor'" in str(exc) or 'object has no field "conn"' in str(exc): + if "object has no attribute 'cursor'" in str( + exc + ) or 'object has no field "conn"' in str(exc): raise AttributeError( ( "Failed to build connection to database." @@ -237,7 +269,9 @@ def instantiate_agent(node_type, class_object: Type[agent_module.Agent], params: if class_method := getattr(class_object, method, None): agent = class_method(**params) tools = params.get("tools", []) - return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, handle_parsing_errors=True) + return AgentExecutor.from_agent_and_tools( + agent=agent, tools=tools, handle_parsing_errors=True + ) return load_agent_executor(class_object, params) @@ -293,7 +327,11 @@ def instantiate_embedding(node_type, class_object, params: Dict): try: return class_object(**params) except ValidationError: - params = {key: value for key, value in params.items() if key in class_object.model_fields} + params = { + key: value + for key, value in params.items() + if key in class_object.model_fields + } return class_object(**params) @@ -305,7 +343,9 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict): if "texts" in params: params["documents"] = params.pop("texts") if "documents" in params: - params["documents"] = [doc for doc in params["documents"] if isinstance(doc, Document)] + params["documents"] = [ + doc for doc in params["documents"] if isinstance(doc, Document) + ] if initializer := vecstore_initializer.get(class_object.__name__): vecstore = initializer(class_object, params) else: @@ -320,7 +360,9 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict): return vecstore -def instantiate_documentloader(node_type: str, class_object: Type[BaseLoader], params: Dict): +def instantiate_documentloader( + node_type: str, class_object: Type[BaseLoader], params: Dict +): if "file_filter" in params: # file_filter will be a string but we need a function # that will be used to filter the files using file_filter @@ -329,13 +371,17 @@ def instantiate_documentloader(node_type: str, class_object: Type[BaseLoader], p # in x and if it is, we will return True file_filter = params.pop("file_filter") extensions = file_filter.split(",") - params["file_filter"] = lambda x: any(extension.strip() in x for extension in extensions) + params["file_filter"] = lambda x: any( + extension.strip() in x for extension in extensions + ) metadata = params.pop("metadata", None) if metadata and isinstance(metadata, str): try: metadata = orjson.loads(metadata) except json.JSONDecodeError as exc: - raise ValueError("The metadata you provided is not a valid JSON string.") from exc + raise ValueError( + "The metadata you provided is not a valid JSON string." + ) from exc if node_type == "WebBaseLoader": if web_path := params.pop("web_path", None): @@ -368,12 +414,16 @@ def instantiate_textsplitter( "Try changing the chunk_size of the Text Splitter." ) from exc - if ("separator_type" in params and params["separator_type"] == "Text") or "separator_type" not in params: + if ( + "separator_type" in params and params["separator_type"] == "Text" + ) or "separator_type" not in params: params.pop("separator_type", None) # separators might come in as an escaped string like \\n # so we need to convert it to a string if "separators" in params: - params["separators"] = params["separators"].encode().decode("unicode-escape") + params["separators"] = ( + params["separators"].encode().decode("unicode-escape") + ) text_splitter = class_object(**params) else: from langchain.text_splitter import Language @@ -400,7 +450,8 @@ def replace_zero_shot_prompt_with_prompt_template(nodes): tools = [ tool for tool in nodes - if tool["type"] != "chatOutputNode" and "Tool" in tool["data"]["node"]["base_classes"] + if tool["type"] != "chatOutputNode" + and "Tool" in tool["data"]["node"]["base_classes"] ] node["data"] = build_prompt_template(prompt=node["data"], tools=tools) break @@ -414,7 +465,9 @@ def load_agent_executor(agent_class: type[agent_module.Agent], params, **kwargs) # agent has hidden args for memory. might need to be support # memory = params["memory"] # if allowed_tools is not a list or set, make it a list - if not isinstance(allowed_tools, (list, set)) and isinstance(allowed_tools, BaseTool): + if not isinstance(allowed_tools, (list, set)) and isinstance( + allowed_tools, BaseTool + ): allowed_tools = [allowed_tools] tool_names = [tool.name for tool in allowed_tools] # Agent class requires an output_parser but Agent classes @@ -442,7 +495,10 @@ def build_prompt_template(prompt, tools): format_instructions = prompt["node"]["template"]["format_instructions"]["value"] tool_strings = "\n".join( - [f"{tool['data']['node']['name']}: {tool['data']['node']['description']}" for tool in tools] + [ + f"{tool['data']['node']['name']}: {tool['data']['node']['description']}" + for tool in tools + ] ) tool_names = ", ".join([tool["data"]["node"]["name"] for tool in tools]) format_instructions = format_instructions.format(tool_names=tool_names) diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py index ad4f8fb78..d7cf09a6f 100644 --- a/src/backend/langflow/processing/process.py +++ b/src/backend/langflow/processing/process.py @@ -7,6 +7,9 @@ from langchain.schema import AgentAction, Document from langchain_community.vectorstores import VectorStore from langchain_core.messages import AIMessage from langchain_core.runnables.base import Runnable +from loguru import logger +from pydantic import BaseModel + from langflow.graph.graph.base import Graph from langflow.graph.vertex.base import Vertex from langflow.interface.custom.custom_component import CustomComponent @@ -17,8 +20,6 @@ from langflow.interface.run import ( ) from langflow.services.deps import get_session_service from langflow.services.session.service import SessionService -from loguru import logger -from pydantic import BaseModel def fix_memory_inputs(langchain_object): @@ -146,7 +147,9 @@ async def process_runnable(runnable: Runnable, inputs: Union[dict, List[dict]]): elif isinstance(inputs, dict) and hasattr(runnable, "ainvoke"): result = await runnable.ainvoke(inputs) else: - raise ValueError(f"Runnable {runnable} does not support inputs of type {type(inputs)}") + raise ValueError( + f"Runnable {runnable} does not support inputs of type {type(inputs)}" + ) # Check if the result is a list of AIMessages if isinstance(result, list) and all(isinstance(r, AIMessage) for r in result): result = [r.content for r in result] @@ -155,7 +158,9 @@ async def process_runnable(runnable: Runnable, inputs: Union[dict, List[dict]]): return result -async def process_inputs_dict(built_object: Union[Chain, VectorStore, Runnable], inputs: dict): +async def process_inputs_dict( + built_object: Union[Chain, VectorStore, Runnable], inputs: dict +): if isinstance(built_object, Chain): if inputs is None: raise ValueError("Inputs must be provided for a Chain") @@ -190,7 +195,9 @@ async def process_inputs_list(built_object: Runnable, inputs: List[dict]): return await process_runnable(built_object, inputs) -async def generate_result(built_object: Union[Chain, VectorStore, Runnable], inputs: Union[dict, List[dict]]): +async def generate_result( + built_object: Union[Chain, VectorStore, Runnable], inputs: Union[dict, List[dict]] +): if isinstance(inputs, dict): result = await process_inputs_dict(built_object, inputs) elif isinstance(inputs, List) and isinstance(built_object, Runnable): @@ -222,7 +229,9 @@ async def process_graph_cached( if clear_cache: session_service.clear_session(session_id) if session_id is None: - session_id = session_service.generate_key(session_id=session_id, data_graph=data_graph) + session_id = session_service.generate_key( + session_id=session_id, data_graph=data_graph + ) # Load the graph using SessionService session = await session_service.load_session(session_id, data_graph) graph, artifacts = session if session else (None, None) @@ -258,14 +267,44 @@ async def build_graph_and_generate_result( return Result(result=result, session_id=session_id) -def validate_input(graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]]) -> List[Dict[str, Any]]: +async def run_graph( + graph: Union["Graph", dict], + flow_id: str, + session_id: str, + stream: bool, + inputs: Optional[Union[dict, List[dict]]] = None, + artifacts: Optional[Dict[str, Any]] = None, + session_service: Optional[SessionService] = None, +): + """Run the graph and generate the result""" + if isinstance(graph, dict): + graph_data = graph + graph = Graph.from_payload(graph, flow_id=flow_id) + else: + graph_data = graph._graph_data + if not session_id: + session_id = session_service.generate_key( + session_id=flow_id, data_graph=graph_data + ) + + outputs = await graph.run(inputs, stream=stream) + if session_id and session_service: + session_service.update_session(session_id, (graph, artifacts)) + return outputs, session_id + + +def validate_input( + graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]] +) -> List[Dict[str, Any]]: if not isinstance(graph_data, dict) or not isinstance(tweaks, dict): raise ValueError("graph_data and tweaks should be dictionaries") nodes = graph_data.get("data", {}).get("nodes") or graph_data.get("nodes") if not isinstance(nodes, list): - raise ValueError("graph_data should contain a list of nodes under 'data' key or directly under 'nodes' key") + raise ValueError( + "graph_data should contain a list of nodes under 'data' key or directly under 'nodes' key" + ) return nodes @@ -274,7 +313,9 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None: template_data = node.get("data", {}).get("node", {}).get("template") if not isinstance(template_data, dict): - logger.warning(f"Template data for node {node.get('id')} should be a dictionary") + logger.warning( + f"Template data for node {node.get('id')} should be a dictionary" + ) return for tweak_name, tweak_value in node_tweaks.items(): @@ -289,7 +330,9 @@ 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: Dict[str, Dict[str, Any]]) -> Dict[str, Any]: +def process_tweaks( + graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]] +) -> Dict[str, Any]: """ This function is used to tweak the graph data using the node id and the tweaks dict. @@ -310,7 +353,9 @@ def process_tweaks(graph_data: Dict[str, Any], tweaks: Dict[str, Dict[str, Any]] if node_tweaks := tweaks.get(node_id): apply_tweaks(node, node_tweaks) else: - logger.warning("Each node should be a dictionary with an 'id' key of type str") + logger.warning( + "Each node should be a dictionary with an 'id' key of type str" + ) return graph_data @@ -322,6 +367,8 @@ def process_tweaks_on_graph(graph: Graph, tweaks: Dict[str, Dict[str, Any]]): if node_tweaks := tweaks.get(node_id): apply_tweaks_on_vertex(vertex, node_tweaks) else: - logger.warning("Each node should be a Vertex with an 'id' attribute of type str") + logger.warning( + "Each node should be a Vertex with an 'id' attribute of type str" + ) return graph diff --git a/src/backend/langflow/services/monitor/utils.py b/src/backend/langflow/services/monitor/utils.py index 87f58aa9f..d308e7653 100644 --- a/src/backend/langflow/services/monitor/utils.py +++ b/src/backend/langflow/services/monitor/utils.py @@ -1,12 +1,13 @@ from typing import TYPE_CHECKING, Any, Dict, Optional, Type, Union import duckdb -from langflow.services.deps import get_monitor_service from loguru import logger from pydantic import BaseModel +from langflow.services.deps import get_monitor_service + if TYPE_CHECKING: - from langflow.api.v1.schemas import ResultData + from langflow.api.v1.schemas import ResultDataResponse INDEX_KEY = "index" @@ -45,7 +46,9 @@ def model_to_sql_column_definitions(model: Type[BaseModel]) -> dict: return columns -def drop_and_create_table_if_schema_mismatch(db_path: str, table_name: str, model: Type[BaseModel]): +def drop_and_create_table_if_schema_mismatch( + db_path: str, table_name: str, model: Type[BaseModel] +): with duckdb.connect(db_path) as conn: # Get the current schema from the database try: @@ -66,8 +69,12 @@ def drop_and_create_table_if_schema_mismatch(db_path: str, table_name: str, mode conn.execute(f"CREATE SEQUENCE seq_{table_name} START 1;") except duckdb.CatalogException: pass - desired_schema[INDEX_KEY] = f"INTEGER PRIMARY KEY DEFAULT NEXTVAL('seq_{table_name}')" - columns_sql = ", ".join(f"{name} {data_type}" for name, data_type in desired_schema.items()) + desired_schema[INDEX_KEY] = ( + f"INTEGER PRIMARY KEY DEFAULT NEXTVAL('seq_{table_name}')" + ) + columns_sql = ", ".join( + f"{name} {data_type}" for name, data_type in desired_schema.items() + ) create_table_sql = f"CREATE TABLE {table_name} ({columns_sql})" conn.execute(create_table_sql) @@ -138,7 +145,7 @@ async def log_vertex_build( vertex_id: str, valid: bool, params: Any, - data: "ResultData", + data: "ResultDataResponse", artifacts: Optional[dict] = None, ): try: diff --git a/src/backend/langflow/services/socket/utils.py b/src/backend/langflow/services/socket/utils.py index 64ffdc15c..48208403a 100644 --- a/src/backend/langflow/services/socket/utils.py +++ b/src/backend/langflow/services/socket/utils.py @@ -2,14 +2,15 @@ import time from typing import Callable import socketio +from sqlmodel import select + from langflow.api.utils import format_elapsed_time -from langflow.api.v1.schemas import ResultData, VertexBuildResponse +from langflow.api.v1.schemas import ResultDataResponse, VertexBuildResponse from langflow.graph.graph.base import Graph from langflow.graph.vertex.base import StatelessVertex from langflow.services.database.models.flow.model import Flow from langflow.services.deps import get_session from langflow.services.monitor.utils import log_vertex_build -from sqlmodel import select def set_socketio_server(socketio_server): @@ -73,7 +74,7 @@ async def build_vertex( artifacts = vertex.artifacts timedelta = time.perf_counter() - start_time duration = format_elapsed_time(timedelta) - result_dict = ResultData( + result_dict = ResultDataResponse( results=result_dict, artifacts=artifacts, duration=duration, @@ -82,7 +83,7 @@ async def build_vertex( except Exception as exc: params = str(exc) valid = False - result_dict = ResultData(results={}) + result_dict = ResultDataResponse(results={}) artifacts = {} set_cache(flow_id, graph) await log_vertex_build( @@ -95,7 +96,9 @@ async def build_vertex( ) # Emit the vertex build response - response = VertexBuildResponse(valid=valid, params=params, id=vertex.id, data=result_dict) + response = VertexBuildResponse( + valid=valid, params=params, id=vertex.id, data=result_dict + ) await sio.emit("vertex_build", data=response.model_dump(), to=sid) except Exception as exc: diff --git a/src/backend/langflow/template/frontend_node/base.py b/src/backend/langflow/template/frontend_node/base.py index 8b400b849..cc8356104 100644 --- a/src/backend/langflow/template/frontend_node/base.py +++ b/src/backend/langflow/template/frontend_node/base.py @@ -47,6 +47,12 @@ class FrontendNode(BaseModel): """Description of the frontend node.""" icon: Optional[str] = None """Icon of the frontend node.""" + is_input: Optional[bool] = None + """Whether the frontend node is used as an input when processing the Graph. + If True, there should be a field named 'input_value'.""" + is_output: Optional[bool] = None + """Whether the frontend node is used as an output when processing the Graph. + If True, there should be a field named 'input_value'.""" is_composition: Optional[bool] = None """Whether the frontend node is used for composition.""" base_classes: List[str] @@ -165,7 +171,9 @@ class FrontendNode(BaseModel): return _type @staticmethod - def handle_special_field(field, key: str, _type: str, SPECIAL_FIELD_HANDLERS) -> str: + def handle_special_field( + field, key: str, _type: str, SPECIAL_FIELD_HANDLERS + ) -> str: """Handles special field by using the respective handler if present.""" handler = SPECIAL_FIELD_HANDLERS.get(key) return handler(field) if handler else _type @@ -176,7 +184,11 @@ class FrontendNode(BaseModel): if "dict" in _type.lower() and field.name == "dict_": field.field_type = "file" field.file_types = [".json", ".yaml", ".yml"] - elif _type.startswith("Dict") or _type.startswith("Mapping") or _type.startswith("dict"): + elif ( + _type.startswith("Dict") + or _type.startswith("Mapping") + or _type.startswith("dict") + ): field.field_type = "dict" return _type @@ -187,7 +199,9 @@ class FrontendNode(BaseModel): field.value = value["default"] @staticmethod - def handle_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def handle_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: """Handles specific field values for certain fields.""" if key == "headers": field.value = """{"Authorization": "Bearer "}""" @@ -195,7 +209,9 @@ class FrontendNode(BaseModel): FrontendNode._handle_api_key_specific_field_values(field, key, name) @staticmethod - def _handle_model_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def _handle_model_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: """Handles specific field values related to models.""" model_dict = { "OpenAI": constants.OPENAI_MODELS, @@ -208,7 +224,9 @@ class FrontendNode(BaseModel): field.is_list = True @staticmethod - def _handle_api_key_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None: + def _handle_api_key_specific_field_values( + field: TemplateField, key: str, name: Optional[str] = None + ) -> None: """Handles specific field values related to API keys.""" if "api_key" in key and "OpenAI" in str(name): field.display_name = "OpenAI API Key" @@ -248,7 +266,10 @@ class FrontendNode(BaseModel): @staticmethod def should_be_password(key: str, show: bool) -> bool: """Determines whether the field should be a password field.""" - return any(text in key.lower() for text in {"password", "token", "api", "key"}) and show + return ( + any(text in key.lower() for text in {"password", "token", "api", "key"}) + and show + ) @staticmethod def should_be_multiline(key: str) -> bool: diff --git a/src/backend/langflow/utils/chat.py b/src/backend/langflow/utils/chat.py deleted file mode 100644 index e1621bdab..000000000 --- a/src/backend/langflow/utils/chat.py +++ /dev/null @@ -1,34 +0,0 @@ -from typing import Any, Callable, Optional, Union - -from langchain_core.prompts import PromptTemplate as LCPromptTemplate -from langflow.utils.prompt import GenericPromptTemplate -from llama_index.prompts import PromptTemplate as LIPromptTemplate - -PromptTemplate = Union[LCPromptTemplate, LIPromptTemplate] - - -class ChatDefinition: - def __init__( - self, - func: Callable, - inputs: list[str], - output_key: Optional[str] = None, - prompt_template: Optional[PromptTemplate] = None, - ): - self.func = func - self.input_keys = inputs - self.output_key = output_key - self.prompt_template = prompt_template - - @classmethod - def from_prompt_template(cls, prompt_template: PromptTemplate, func: Callable, output_key: Optional[str] = None): - prompt = GenericPromptTemplate(prompt_template) - return cls( - func=func, - inputs=prompt.input_keys, - output_key=output_key, - prompt_template=prompt_template, - ) - - def __call__(self, inputs: dict, callbacks: Optional[Any] = None) -> dict: - return self.func(inputs, callbacks) diff --git a/src/backend/langflow/utils/logger.py b/src/backend/langflow/utils/logger.py index 060ad9731..6755e53b1 100644 --- a/src/backend/langflow/utils/logger.py +++ b/src/backend/langflow/utils/logger.py @@ -25,7 +25,10 @@ def patching(record): def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None): - if os.getenv("LANGFLOW_LOG_LEVEL") in VALID_LOG_LEVELS and log_level is None: + if ( + os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS + and log_level is None + ): log_level = os.getenv("LANGFLOW_LOG_LEVEL") if log_level is None: log_level = "INFO" diff --git a/src/backend/langflow/utils/prompt.py b/src/backend/langflow/utils/prompt.py deleted file mode 100644 index 871193f45..000000000 --- a/src/backend/langflow/utils/prompt.py +++ /dev/null @@ -1,58 +0,0 @@ -from typing import Any, Union - -from langchain_core.prompts import PromptTemplate as LCPromptTemplate -from llama_index.prompts import PromptTemplate as LIPromptTemplate - -PromptTemplateTypes = Union[LCPromptTemplate, LIPromptTemplate] - - -class GenericPromptTemplate: - def __init__(self, prompt_template: PromptTemplateTypes): - object.__setattr__(self, "prompt_template", prompt_template) - - @property - def input_keys(self): - prompt_template = object.__getattribute__(self, "prompt_template") - if isinstance(prompt_template, LCPromptTemplate): - return prompt_template.input_variables - elif isinstance(prompt_template, LIPromptTemplate): - return prompt_template.template_vars - else: - raise TypeError(f"Unknown prompt template type {type(prompt_template)}") - - def to_lc_prompt(self): - prompt_template = object.__getattribute__(self, "prompt_template") - if isinstance(prompt_template, LCPromptTemplate): - return prompt_template - elif isinstance(prompt_template, LIPromptTemplate): - return LCPromptTemplate.from_template(prompt_template.get_template()) - else: - raise TypeError(f"Unknown prompt template type {type(prompt_template)}") - - def to_li_prompt(self): - prompt_template = object.__getattribute__(self, "prompt_template") - if isinstance(prompt_template, LIPromptTemplate): - return prompt_template - elif isinstance(prompt_template, LCPromptTemplate): - return LIPromptTemplate(template=prompt_template.template) - else: - raise TypeError(f"Unknown prompt template type {type(prompt_template)}") - - def __or__(self, other): - prompt_template = object.__getattribute__(self, "prompt_template") - if isinstance(prompt_template, LIPromptTemplate): - return self.to_lc_prompt() | other - else: - raise TypeError(f"Unknown prompt template type {type(other)}") - - def __getattribute__(self, name: str) -> Any: - if name in { - "input_keys", - "to_lc_prompt", - "to_li_prompt", - "__or__", - "prompt_template", - }: - return object.__getattribute__(self, name) - prompt_template = object.__getattribute__(self, "prompt_template") - return getattr(prompt_template, name) diff --git a/src/backend/langflow/utils/validate.py b/src/backend/langflow/utils/validate.py index 21821538c..6b789c08c 100644 --- a/src/backend/langflow/utils/validate.py +++ b/src/backend/langflow/utils/validate.py @@ -45,7 +45,9 @@ def validate_code(code): # Evaluate the function definition for node in tree.body: if isinstance(node, ast.FunctionDef): - code_obj = compile(ast.Module(body=[node], type_ignores=[]), "", "exec") + code_obj = compile( + ast.Module(body=[node], type_ignores=[]), "", "exec" + ) try: exec(code_obj) except Exception as e: @@ -89,15 +91,23 @@ def execute_function(code, function_name, *args, **kwargs): exec_globals, locals(), ) - exec_globals[alias.asname or alias.name] = importlib.import_module(alias.name) + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) except ModuleNotFoundError as e: - raise ModuleNotFoundError(f"Module {alias.name} not found. Please install it and try again.") from e + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e function_code = next( - node for node in module.body if isinstance(node, ast.FunctionDef) and node.name == function_name + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name ) function_code.parent = None - code_obj = compile(ast.Module(body=[function_code], type_ignores=[]), "", "exec") + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) try: exec(code_obj, exec_globals, locals()) except Exception as exc: @@ -124,15 +134,23 @@ def create_function(code, function_name): if isinstance(node, ast.Import): for alias in node.names: try: - exec_globals[alias.asname or alias.name] = importlib.import_module(alias.name) + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) except ModuleNotFoundError as e: - raise ModuleNotFoundError(f"Module {alias.name} not found. Please install it and try again.") from e + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e function_code = next( - node for node in module.body if isinstance(node, ast.FunctionDef) and node.name == function_name + node + for node in module.body + if isinstance(node, ast.FunctionDef) and node.name == function_name ) function_code.parent = None - code_obj = compile(ast.Module(body=[function_code], type_ignores=[]), "", "exec") + code_obj = compile( + ast.Module(body=[function_code], type_ignores=[]), "", "exec" + ) with contextlib.suppress(Exception): exec(code_obj, exec_globals, locals()) exec_globals[function_name] = locals()[function_name] @@ -194,9 +212,13 @@ def prepare_global_scope(code, module): if isinstance(node, ast.Import): for alias in node.names: try: - exec_globals[alias.asname or alias.name] = importlib.import_module(alias.name) + exec_globals[alias.asname or alias.name] = importlib.import_module( + alias.name + ) except ModuleNotFoundError as e: - raise ModuleNotFoundError(f"Module {alias.name} not found. Please install it and try again.") from e + raise ModuleNotFoundError( + f"Module {alias.name} not found. Please install it and try again." + ) from e elif isinstance(node, ast.ImportFrom) and node.module is not None: try: imported_module = importlib.import_module(node.module) @@ -217,7 +239,11 @@ def extract_class_code(module, class_name): :param class_name: Name of the class to extract :return: AST node of the specified class """ - class_code = next(node for node in module.body if isinstance(node, ast.ClassDef) and node.name == class_name) + class_code = next( + node + for node in module.body + if isinstance(node, ast.ClassDef) and node.name == class_name + ) class_code.parent = None return class_code @@ -230,7 +256,9 @@ def compile_class_code(class_code): :param class_code: AST node of the class :return: Compiled code object of the class """ - code_obj = compile(ast.Module(body=[class_code], type_ignores=[]), "", "exec") + code_obj = compile( + ast.Module(body=[class_code], type_ignores=[]), "", "exec" + ) return code_obj @@ -274,7 +302,9 @@ def get_default_imports(code_string): langflow_imports = list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys()) necessary_imports = find_names_in_code(code_string, langflow_imports) langflow_module = importlib.import_module("langflow.field_typing") - default_imports.update({name: getattr(langflow_module, name) for name in necessary_imports}) + default_imports.update( + {name: getattr(langflow_module, name) for name in necessary_imports} + ) return default_imports diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index e6786cc36..f366eecde 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -19,6 +19,8 @@ import { Button } from "../../../../components/ui/button"; import { LANGFLOW_SUPPORTED_TYPES, TOOLTIP_EMPTY, + inputHandleHover, + outputHandleHover, } from "../../../../constants/constants"; import { postCustomComponentUpdate } from "../../../../controllers/API"; import useAlertStore from "../../../../stores/alertStore"; @@ -182,8 +184,8 @@ export default function ParameterComponent({ {index === 0 && ( {left - ? "Avaliable input components:" - : "Avaliable output components:"} + ? inputHandleHover + : outputHandleHover} )} diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx index 733484845..5632f3c7a 100644 --- a/src/frontend/src/CustomNodes/GenericNode/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx @@ -8,7 +8,11 @@ import Checkmark from "../../components/ui/checkmark"; import Loading from "../../components/ui/loading"; import { Textarea } from "../../components/ui/textarea"; import Xmark from "../../components/ui/xmark"; -import { priorityFields } from "../../constants/constants"; +import { + priorityFields, + statusBuild, + statusBuilding, +} from "../../constants/constants"; import { BuildStatus } from "../../constants/enums"; import NodeToolbarComponent from "../../pages/FlowPage/components/nodeToolbarComponent"; import { useDarkStore } from "../../stores/darkStore"; @@ -135,7 +139,7 @@ export default function GenericNode({ const iconName = iconElement || (data.node?.flow ? "group_components" : name); const iconClassName = `generic-node-icon ${ - !showNode ? "absolute inset-x-6 h-12 w-12" : "" + !showNode ? " absolute inset-x-6 h-12 w-12 " : "" }`; if (iconElement && isEmoji) { return nodeIconFragment(iconElement); @@ -211,9 +215,7 @@ export default function GenericNode({ return "inactive-status"; } if (buildStatus === BuildStatus.BUILT && isInvalid) { - return isDark - ? "border-none ring ring-[#751C1C]" - : "built-invalid-status"; + return isDark ? "built-invalid-status-dark" : "built-invalid-status"; } else if (buildStatus === BuildStatus.BUILDING) { return "building-status"; } else { @@ -295,7 +297,7 @@ export default function GenericNode({
{iconNodeRender()} @@ -331,22 +333,21 @@ export default function GenericNode({ ) : (
- -
{ - if (nameEditable) { - setInputName(true); - } - takeSnapshot(); - event.stopPropagation(); - event.preventDefault(); - }} - data-testid={"title-" + data.node?.display_name} - className="generic-node-tooltip-div text-primary" - > - {data.node?.display_name} -
- +
{ + if (nameEditable) { + setInputName(true); + } + takeSnapshot(); + event.stopPropagation(); + event.preventDefault(); + }} + data-testid={"title-" + data.node?.display_name} + className="generic-node-tooltip-div text-primary" + > + {data.node?.display_name} +
+ {nameEditable && (
{ @@ -471,17 +472,17 @@ export default function GenericNode({ Building... + {statusBuilding} ) : !validationStatus ? ( - Build to validate status. + {statusBuild} ) : (
{typeof validationStatus.params === "string" - ? (`${durationString}\n${validationStatus.params}` + ? `${durationString}\n${validationStatus.params}` .split("\n") .map((line, index) => (
{line}
- ))) + )) : durationString}
) diff --git a/src/frontend/src/alerts/alertDropDown/index.tsx b/src/frontend/src/alerts/alertDropDown/index.tsx index 967d82174..f1eab4c60 100644 --- a/src/frontend/src/alerts/alertDropDown/index.tsx +++ b/src/frontend/src/alerts/alertDropDown/index.tsx @@ -1,3 +1,4 @@ +import { Cross2Icon } from "@radix-ui/react-icons"; import { useState } from "react"; import IconComponent from "../../components/genericIconComponent"; import { @@ -8,6 +9,7 @@ import { import useAlertStore from "../../stores/alertStore"; import { AlertDropdownType } from "../../types/alerts"; import SingleAlert from "./components/singleAlertComponent"; +import { zeroNotifications } from "../../constants/constants"; export default function AlertDropdown({ children, @@ -45,15 +47,15 @@ export default function AlertDropdown({ setTimeout(clearNotificationList, 100); }} > - +
@@ -68,7 +70,7 @@ export default function AlertDropdown({ )) ) : (
- No new notifications + {zeroNotifications}
)}
diff --git a/src/frontend/src/components/IOview/index.tsx b/src/frontend/src/components/IOview/index.tsx index 4ac7bb00d..0e3f9e7e9 100644 --- a/src/frontend/src/components/IOview/index.tsx +++ b/src/frontend/src/components/IOview/index.tsx @@ -1,6 +1,6 @@ import { cloneDeep } from "lodash"; import { useEffect, useState } from "react"; -import { CHAT_FORM_DIALOG_SUBTITLE } from "../../constants/constants"; +import { CHAT_FORM_DIALOG_SUBTITLE, outputsModalTitle, textInputModalTitle } from "../../constants/constants"; import BaseModal from "../../modals/baseModal"; import useAlertStore from "../../stores/alertStore"; import useFlowStore from "../../stores/flowStore"; @@ -56,7 +56,7 @@ export default function IOView({ children, open, setOpen }): JSX.Element { const chatInputNode = nodes.find((node) => node.id === chatInput?.id); if (chatInputNode) { let newNode = cloneDeep(chatInputNode); - newNode.data.node!.template["message"].value = chatValue; + newNode.data.node!.template["input_value"].value = chatValue; setNode(chatInput!.id, newNode); } for (let i = 0; i < count; i++) { @@ -97,14 +97,14 @@ export default function IOView({ children, open, setOpen }): JSX.Element { {selectedTab !== 0 && (
{ setSelectedTab(Number(value)); @@ -127,7 +127,7 @@ export default function IOView({ children, open, setOpen }): JSX.Element { >
- Text Inputs + {textInputModalTitle}
{nodes .filter((node) => @@ -188,7 +188,7 @@ export default function IOView({ children, open, setOpen }): JSX.Element { >
- Prompt Outputs + {outputsModalTitle}
{nodes .filter((node) => diff --git a/src/frontend/src/components/chatComponent/buildTrigger/index.tsx b/src/frontend/src/components/chatComponent/buildTrigger/index.tsx index db5a81758..2db2e0c3b 100644 --- a/src/frontend/src/components/chatComponent/buildTrigger/index.tsx +++ b/src/frontend/src/components/chatComponent/buildTrigger/index.tsx @@ -8,7 +8,7 @@ import useFlowStore from "../../../stores/flowStore"; import { validateNodes } from "../../../utils/reactflowUtils"; import RadialProgressComponent from "../../RadialProgress"; import IconComponent from "../../genericIconComponent"; -import { MISSED_ERROR_ALERT } from "../../../alerts_constants"; +import { MISSED_ERROR_ALERT } from "../../../constants/alerts_constants"; export default function BuildTrigger({ open, diff --git a/src/frontend/src/components/codeTabsComponent/index.tsx b/src/frontend/src/components/codeTabsComponent/index.tsx index 2bea4265f..d6fabb0cf 100644 --- a/src/frontend/src/components/codeTabsComponent/index.tsx +++ b/src/frontend/src/components/codeTabsComponent/index.tsx @@ -181,7 +181,7 @@ export default function CodeTabsComponent({ {tabs.map((tab, idx) => ( {idx < 4 ? ( diff --git a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx index db6fa5b2c..7dcc62ee2 100644 --- a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx +++ b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx @@ -17,6 +17,7 @@ import { cn } from "../../../../utils/utils"; import ShadTooltip from "../../../ShadTooltipComponent"; import IconComponent from "../../../genericIconComponent"; import { Button } from "../../../ui/button"; +import { savedHover } from "../../../../constants/constants"; export const MenuBar = ({ removeFunction, @@ -127,7 +128,7 @@ export const MenuBar = ({
diff --git a/src/frontend/src/components/newChatView/chatMessage/index.tsx b/src/frontend/src/components/newChatView/chatMessage/index.tsx index 31f4f0345..7c606679f 100644 --- a/src/frontend/src/components/newChatView/chatMessage/index.tsx +++ b/src/frontend/src/components/newChatView/chatMessage/index.tsx @@ -1,5 +1,5 @@ import Convert from "ansi-to-html"; -import { useEffect, useMemo, useState } from "react"; +import { useEffect, useMemo, useRef, useState } from "react"; import Markdown from "react-markdown"; import rehypeMathjax from "rehype-mathjax"; import remarkGfm from "remark-gfm"; @@ -9,6 +9,7 @@ import Robot from "../../../assets/robot.png"; import SanitizedHTMLWrapper from "../../../components/SanitizedHTMLWrapper"; import CodeTabsComponent from "../../../components/codeTabsComponent"; import IconComponent from "../../../components/genericIconComponent"; +import useFlowStore from "../../../stores/flowStore"; import { chatMessagePropsType } from "../../../types/components"; import { classNames } from "../../../utils/utils"; import FileCard from "../fileComponent"; @@ -18,14 +19,26 @@ export default function ChatMessage({ lockChat, lastMessage, updateChat, + setLockChat, }: chatMessagePropsType): JSX.Element { const convert = new Convert({ newline: true }); const [hidden, setHidden] = useState(true); const template = chat.template; const [promptOpen, setPromptOpen] = useState(false); const [streamUrl, setStreamUrl] = useState(chat.stream_url); - const [chatMessage, setChatMessage] = useState(chat.message.toString()); + // We need to check if message is not undefined because + // we need to run .toString() on it + const chatMessageString = chat.message ? chat.message.toString() : ""; + const [chatMessage, setChatMessage] = useState(chatMessageString); const [isStreaming, setIsStreaming] = useState(false); + const eventSource = useRef(undefined); + const updateFlowPool = useFlowStore((state) => state.updateFlowPool); + const chatMessageRef = useRef(chatMessage); + + // Sync ref with state + useEffect(() => { + chatMessageRef.current = chatMessage; + }, [chatMessage]); // The idea now is that chat.stream_url MAY be a URL if we should stream the output of the chat // probably the message is empty when we have a stream_url @@ -33,49 +46,50 @@ export default function ChatMessage({ const streamChunks = (url: string) => { setIsStreaming(true); // Streaming starts return new Promise((resolve, reject) => { - const eventSource = new EventSource(url); - eventSource.onmessage = (event) => { + eventSource.current = new EventSource(url); + eventSource.current.onmessage = (event) => { let parsedData = JSON.parse(event.data); if (parsedData.chunk) { setChatMessage((prev) => prev + parsedData.chunk); } }; - eventSource.onerror = (event) => { + eventSource.current.onerror = (event) => { + setIsStreaming(false); + eventSource.current?.close(); + setStreamUrl(undefined); reject(new Error("Streaming failed")); - setIsStreaming(false); - eventSource.close(); }; - eventSource.addEventListener("close", (event) => { - setStreamUrl(null); // Update state to reflect the stream is closed - resolve(true); + eventSource.current.addEventListener("close", (event) => { + setStreamUrl(undefined); // Update state to reflect the stream is closed + eventSource.current?.close(); setIsStreaming(false); - eventSource.close(); + resolve(true); }); }); }; useEffect(() => { - if (streamUrl && chat.message === "") { + if (streamUrl && !isStreaming) { + setLockChat(true); streamChunks(streamUrl) .then(() => { + setLockChat(false); if (updateChat) { - updateChat(chat, chatMessage, streamUrl); + updateChat(chat, chatMessageRef.current); } }) .catch((error) => { console.error(error); + setLockChat(false); }); } - }, [streamUrl]); + }, [streamUrl, chatMessage]); useEffect(() => { - // This effect is specifically for calling updateChat after streaming ends - if (!isStreaming && streamUrl) { - if (updateChat) { - updateChat(chat, chatMessage, streamUrl); - } - } - }, [isStreaming]); + return () => { + eventSource.current?.close(); + }; + }, []); useEffect(() => { const element = document.getElementById("last-chat-message"); @@ -146,7 +160,7 @@ export default function ChatMessage({
{useMemo( () => - chat.message.toString() === "" && lockChat ? ( + chatMessage === "" && lockChat ? ( {parts}

; }) - : chat.message.toString()} + : chatMessage} ) : ( - {chat.message.toString()} + {chatMessage} )}
)} diff --git a/src/frontend/src/components/newChatView/index.tsx b/src/frontend/src/components/newChatView/index.tsx index 94231d4b2..c4be0bcc7 100644 --- a/src/frontend/src/components/newChatView/index.tsx +++ b/src/frontend/src/components/newChatView/index.tsx @@ -1,6 +1,10 @@ -import _ from "lodash"; import { useEffect, useRef, useState } from "react"; import IconComponent from "../../components/genericIconComponent"; +import { NOCHATOUTPUT_NOTICE_ALERT } from "../../constants/alerts_constants"; +import { + chatFirstInitialText, + chatSecondInitialText, +} from "../../constants/constants"; import { deleteFlowPool } from "../../controllers/API"; import useAlertStore from "../../stores/alertStore"; import useFlowStore from "../../stores/flowStore"; @@ -14,7 +18,6 @@ import { import { classNames } from "../../utils/utils"; import ChatInput from "./chatInput"; import ChatMessage from "./chatMessage"; -import { INFO_MISSING_ALERT, NOCHATOUTPUT_NOTICE_ALERT } from "../../alerts_constants"; export default function NewChatView({ sendMessage, @@ -33,6 +36,7 @@ export default function NewChatView({ const inputIds = inputs.map((obj) => obj.id); const outputIds = outputs.map((obj) => obj.id); const outputTypes = outputs.map((obj) => obj.type); + const updateFlowPool = useFlowStore((state) => state.updateFlowPool); useEffect(() => { if (!outputTypes.includes("ChatOutput")) { @@ -66,14 +70,12 @@ export default function NewChatView({ const { sender, message, sender_name, stream_url } = output.data .artifacts as ChatOutputType; - const componentId = output.id + index; - const is_ai = sender === "Machine" || sender === null; return { isSend: !is_ai, message: message, sender_name, - id: componentId, + componentId: output.id, stream_url: stream_url, }; } catch (e) { @@ -82,7 +84,7 @@ export default function NewChatView({ isSend: false, message: "Error parsing message", sender_name: "Error", - id: output.id + index, + componentId: output.id, }; } }); @@ -119,27 +121,28 @@ export default function NewChatView({ function updateChat( chat: ChatMessageType, message: string, - stream_url: string | null + stream_url?: string ) { if (message === "") return; - console.log(`updateChat: ${message}`); - console.log("chatHistory:", chatHistory); chat.message = message; - chat.stream_url = stream_url; // chat is one of the chatHistory - setChatHistory((oldChatHistory) => { - const index = oldChatHistory.findIndex((ch) => ch.id === chat.id); - - if (index === -1) return oldChatHistory; - let newChatHistory = _.cloneDeep(oldChatHistory); - newChatHistory = [ - ...newChatHistory.slice(0, index), - chat, - ...newChatHistory.slice(index + 1), - ]; - console.log("newChatHistory:", newChatHistory); - return newChatHistory; + updateFlowPool(chat.componentId, { + message, + sender_name: chat.sender_name ?? "Bot", + sender: chat.isSend ? "User" : "Machine", }); + // setChatHistory((oldChatHistory) => { + // const index = oldChatHistory.findIndex((ch) => ch.id === chat.id); + // if (index === -1) return oldChatHistory; + // let newChatHistory = _.cloneDeep(oldChatHistory); + // newChatHistory = [ + // ...newChatHistory.slice(0, index), + // chat, + // ...newChatHistory.slice(index + 1), + // ]; + // console.log("newChatHistory:", newChatHistory); + // return newChatHistory; + // }); } return ( @@ -163,10 +166,11 @@ export default function NewChatView({ {chatHistory?.length > 0 ? ( chatHistory.map((chat, index) => ( )) @@ -178,14 +182,14 @@ export default function NewChatView({
- Start a conversation and click the agent's thoughts{" "} + {chatFirstInitialText}{" "} {" "} - to inspect the chaining process. + {chatSecondInitialText}
diff --git a/src/frontend/src/components/textAreaComponent/index.tsx b/src/frontend/src/components/textAreaComponent/index.tsx index 7b41f3757..14ba7c67e 100644 --- a/src/frontend/src/components/textAreaComponent/index.tsx +++ b/src/frontend/src/components/textAreaComponent/index.tsx @@ -4,6 +4,7 @@ import GenericModal from "../../modals/genericModal"; import { TextAreaComponentType } from "../../types/components"; import IconComponent from "../genericIconComponent"; import { Input } from "../ui/input"; +import { editTextModalTitle } from "../../constants/constants"; export default function TextAreaComponent({ value, @@ -37,7 +38,7 @@ export default function TextAreaComponent({ { onChange(value); diff --git a/src/frontend/src/alerts_constants.tsx b/src/frontend/src/constants/alerts_constants.tsx similarity index 100% rename from src/frontend/src/alerts_constants.tsx rename to src/frontend/src/constants/alerts_constants.tsx diff --git a/src/frontend/src/constants/constants.ts b/src/frontend/src/constants/constants.ts index d6eb6eb56..2c0df8fad 100644 --- a/src/frontend/src/constants/constants.ts +++ b/src/frontend/src/constants/constants.ts @@ -682,3 +682,35 @@ 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 chatFirstInitialText = "Start a conversation and click the agent's thoughts"; + +export const chatSecondInitialText = "to inspect the chaining process."; + +export const zeroNotifications = "No new notifications"; + +export const successBuild = "Built sucessfully ✨"; + +export const alertSaveWApi = "Caution: Uncheck this box only removes API keys from fields specifically designated for API keys." + +export const saveWApiCheckbox = "Save with my API keys"; +export const editTextModalTitle = "Edit Text" +export const editTextPlaceholder = "Type message here." +export const inputHandleHover = "Avaliable input components:"; +export const outputHandleHover = "Avaliable output components:"; +export const textInputModalTitle = "Text Inputs"; +export const outputsModalTitle = "Prompt Outputs" +export const langflowChatTitle = "Langflow Chat" +export const chatInputPlaceholder = "No chat input variables found. Click to run your flow." +export const chatInputPlaceholderSend = "Send a message..." +export const editCodeTitle = "Edit Code" +export const myCollectionDesc = "Manage your personal projects. Download or upload your collection." +export const storeDesc = "Search flows and components from the community."; +export const storeTitle = "Langflow Store" +export const noApi = "You don't have an API key. " +export const insertApi = "Insert your Langflow API key." +export const invalidApi = "Your API key is not valid. " +export const createApi = `Don’t have an API key? Sign up at` +export const statusBuild = "Build to validate status." +export const statusBuilding = "Building..." +export const savedHover = "Last saved at " \ No newline at end of file diff --git a/src/frontend/src/icons/VectaraIcon/Vectara.jsx b/src/frontend/src/icons/VectaraIcon/Vectara.jsx index 0566f82eb..378dc6964 100644 --- a/src/frontend/src/icons/VectaraIcon/Vectara.jsx +++ b/src/frontend/src/icons/VectaraIcon/Vectara.jsx @@ -1,1074 +1,83 @@ -const SvgVectara = (props) => ( - <> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +export const SvgVectara = (props) => ( + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ); export default SvgVectara; diff --git a/src/frontend/src/icons/VectaraIcon/vectara.svg b/src/frontend/src/icons/VectaraIcon/vectara.svg index 1faafd320..c111a3aeb 100644 --- a/src/frontend/src/icons/VectaraIcon/vectara.svg +++ b/src/frontend/src/icons/VectaraIcon/vectara.svg @@ -1,252 +1,80 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/src/frontend/src/modals/SecretKeyModal/index.tsx b/src/frontend/src/modals/SecretKeyModal/index.tsx index a09e58c36..a03ce8262 100644 --- a/src/frontend/src/modals/SecretKeyModal/index.tsx +++ b/src/frontend/src/modals/SecretKeyModal/index.tsx @@ -8,7 +8,7 @@ import useAlertStore from "../../stores/alertStore"; import { ApiKeyType } from "../../types/components"; import { nodeIconsLucide } from "../../utils/styleUtils"; import BaseModal from "../baseModal"; -import { COPIED_NOTICE_ALERT } from "../../alerts_constants"; +import { COPIED_NOTICE_ALERT } from "../../constants/alerts_constants"; export default function SecretKeyModal({ title, diff --git a/src/frontend/src/modals/StoreApiKeyModal/index.tsx b/src/frontend/src/modals/StoreApiKeyModal/index.tsx index 038e33402..168fc79fc 100644 --- a/src/frontend/src/modals/StoreApiKeyModal/index.tsx +++ b/src/frontend/src/modals/StoreApiKeyModal/index.tsx @@ -9,7 +9,8 @@ import useAlertStore from "../../stores/alertStore"; import { useStoreStore } from "../../stores/storeStore"; import { StoreApiKeyType } from "../../types/components"; import BaseModal from "../baseModal"; -import { API_ERROR_ALERT, API_SUCCESS_ALERT } from "../../alerts_constants"; +import { API_ERROR_ALERT, API_SUCCESS_ALERT } from "../../constants/alerts_constants"; +import { createApi, insertApi, invalidApi, noApi } from "../../constants/constants"; export default function StoreApiKeyModal({ children, @@ -60,10 +61,10 @@ export default function StoreApiKeyModal({ API Key @@ -98,7 +99,7 @@ export default function StoreApiKeyModal({ - Caution: Uncheck this box only removes API keys from fields - specifically designated for API keys. + {alertSaveWApi} diff --git a/src/frontend/src/modals/formModal/chatInput/index.tsx b/src/frontend/src/modals/formModal/chatInput/index.tsx index 242ce6532..bf39740d4 100644 --- a/src/frontend/src/modals/formModal/chatInput/index.tsx +++ b/src/frontend/src/modals/formModal/chatInput/index.tsx @@ -3,6 +3,7 @@ import IconComponent from "../../../components/genericIconComponent"; import { Textarea } from "../../../components/ui/textarea"; import { chatInputType } from "../../../types/components"; import { classNames } from "../../../utils/utils"; +import { chatInputPlaceholder, chatInputPlaceholderSend } from "../../../constants/constants"; export default function ChatInput({ lockChat, @@ -51,7 +52,7 @@ export default function ChatInput({ ? "Thinking..." : typeof chatValue === "object" && Object.keys(chatValue)?.length === 0 - ? "No chat input variables found. Click to run your flow." + ? chatInputPlaceholder : chatValue } onChange={(event): void => { @@ -68,8 +69,8 @@ export default function ChatInput({ )} placeholder={ noInput - ? "No chat input variables found. Click to run your flow." - : "Send a message..." + ? chatInputPlaceholder + : chatInputPlaceholderSend } />
diff --git a/src/frontend/src/modals/formModal/index.tsx b/src/frontend/src/modals/formModal/index.tsx index f18eff977..1d000ee31 100644 --- a/src/frontend/src/modals/formModal/index.tsx +++ b/src/frontend/src/modals/formModal/index.tsx @@ -20,14 +20,14 @@ import { DialogTrigger, } from "../../components/ui/dialog"; import { Textarea } from "../../components/ui/textarea"; -import { CHAT_FORM_DIALOG_SUBTITLE } from "../../constants/constants"; +import { CHAT_FORM_DIALOG_SUBTITLE, chatFirstInitialText, chatSecondInitialText, langflowChatTitle } from "../../constants/constants"; import { AuthContext } from "../../contexts/authContext"; import { getBuildStatus } from "../../controllers/API"; import useAlertStore from "../../stores/alertStore"; import useFlowStore from "../../stores/flowStore"; import { FlowState } from "../../types/tabs"; import { validateNodes } from "../../utils/reactflowUtils"; -import { CHAT_ERROR_ALERT, INFO_MISSING_ALERT, MSG_ERROR_ALERT } from "../../alerts_constants"; +import { CHAT_ERROR_ALERT, INFO_MISSING_ALERT, MSG_ERROR_ALERT } from "../../constants/alerts_constants"; export default function FormModal({ flow, @@ -585,20 +585,20 @@ export default function FormModal({ 👋{" "} - Langflow Chat + {langflowChatTitle}
- Start a conversation and click the agent's thoughts{" "} + {chatFirstInitialText} {" "} {" "} - to inspect the chaining process. + {chatSecondInitialText}
diff --git a/src/frontend/src/modals/genericModal/index.tsx b/src/frontend/src/modals/genericModal/index.tsx index 54ea08edb..b79431ac9 100644 --- a/src/frontend/src/modals/genericModal/index.tsx +++ b/src/frontend/src/modals/genericModal/index.tsx @@ -10,6 +10,8 @@ import { MAX_WORDS_HIGHLIGHT, PROMPT_DIALOG_SUBTITLE, TEXT_DIALOG_SUBTITLE, + editTextModalTitle, + editTextPlaceholder, regexHighlight, } from "../../constants/constants"; import { TypeModal } from "../../constants/enums"; @@ -19,7 +21,7 @@ import { genericModalPropsType } from "../../types/components"; import { handleKeyDown } from "../../utils/reactflowUtils"; import { classNames, varHighlightHTML } from "../../utils/utils"; import BaseModal from "../baseModal"; -import { BUG_ALERT, PROMPT_ERROR_ALERT, PROMPT_SUCCESS_ALERT, TEMP_NOTICE_ALERT } from "../../alerts_constants"; +import { BUG_ALERT, PROMPT_ERROR_ALERT, PROMPT_SUCCESS_ALERT, TEMP_NOTICE_ALERT } from "../../constants/alerts_constants"; export default function GenericModal({ field_name = "", @@ -211,7 +213,7 @@ export default function GenericModal({ setInputValue(event.target.value); checkVariables(event.target.value); }} - placeholder="Type message here." + placeholder={editTextPlaceholder} onKeyDown={(e) => { handleKeyDown(e, inputValue, ""); }} @@ -233,7 +235,7 @@ export default function GenericModal({ onChange={(event) => { setInputValue(event.target.value); }} - placeholder="Type message here." + placeholder={editTextPlaceholder} onKeyDown={(e) => { handleKeyDown(e, value, ""); }} diff --git a/src/frontend/src/pages/AdminPage/LoginPage/index.tsx b/src/frontend/src/pages/AdminPage/LoginPage/index.tsx index c94474ab8..335cf2a6a 100644 --- a/src/frontend/src/pages/AdminPage/LoginPage/index.tsx +++ b/src/frontend/src/pages/AdminPage/LoginPage/index.tsx @@ -11,7 +11,7 @@ import { inputHandlerEventType, loginInputStateType, } from "../../../types/components"; -import { SIGNIN_ERROR_ALERT } from "../../../alerts_constants"; +import { SIGNIN_ERROR_ALERT } from "../../../constants/alerts_constants"; export default function LoginAdminPage() { const navigate = useNavigate(); diff --git a/src/frontend/src/pages/AdminPage/index.tsx b/src/frontend/src/pages/AdminPage/index.tsx index 037fce720..16cce09da 100644 --- a/src/frontend/src/pages/AdminPage/index.tsx +++ b/src/frontend/src/pages/AdminPage/index.tsx @@ -33,7 +33,7 @@ import useAlertStore from "../../stores/alertStore"; import useFlowsManagerStore from "../../stores/flowsManagerStore"; import { Users } from "../../types/api"; import { UserInputType } from "../../types/components"; -import { USER_ADD_ERROR_ALERT, USER_ADD_SUCCESS_ALERT, USER_DEL_ERROR_ALERT, USER_DEL_SUCCESS_ALERT, USER_EDIT_ERROR_ALERT, USER_EDIT_SUCCESS_ALERT } from "../../alerts_constants"; +import { USER_ADD_ERROR_ALERT, USER_ADD_SUCCESS_ALERT, USER_DEL_ERROR_ALERT, USER_DEL_SUCCESS_ALERT, USER_EDIT_ERROR_ALERT, USER_EDIT_SUCCESS_ALERT } from "../../constants/alerts_constants"; export default function AdminPage() { const [inputValue, setInputValue] = useState(""); diff --git a/src/frontend/src/pages/ApiKeysPage/index.tsx b/src/frontend/src/pages/ApiKeysPage/index.tsx index 3143c52a6..bf9c62e7f 100644 --- a/src/frontend/src/pages/ApiKeysPage/index.tsx +++ b/src/frontend/src/pages/ApiKeysPage/index.tsx @@ -26,7 +26,7 @@ import { } from "../../constants/constants"; import useAlertStore from "../../stores/alertStore"; import { ApiKey } from "../../types/components"; -import { DEL_KEY_ERROR_ALERT, DEL_KEY_SUCCESS_ALERT } from "../../alerts_constants"; +import { DEL_KEY_ERROR_ALERT, DEL_KEY_SUCCESS_ALERT } from "../../constants/alerts_constants"; export default function ApiKeysPage() { const [loadingKeys, setLoadingKeys] = useState(true); diff --git a/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx index d7358b27a..8c3f99c46 100644 --- a/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx @@ -31,7 +31,7 @@ import { getRandomName, isWrappedWithClass } from "../../../../utils/utils"; import ConnectionLineComponent from "../ConnectionLineComponent"; import SelectionMenu from "../SelectionMenuComponent"; import ExtraSidebar from "../extraSidebarComponent"; -import { INVALID_SELECTION_ERROR_ALERT, UPLOAD_ALERT_LIST, UPLOAD_ERROR_ALERT, WRONG_FILE_ERROR_ALERT } from "../../../../alerts_constants"; +import { INVALID_SELECTION_ERROR_ALERT, UPLOAD_ALERT_LIST, UPLOAD_ERROR_ALERT, WRONG_FILE_ERROR_ALERT } from "../../../../constants/alerts_constants"; const nodeTypes = { genericNode: GenericNode, @@ -87,8 +87,30 @@ export default function Page({ const [lastSelection, setLastSelection] = useState(null); + const setNode = useFlowStore((state) => state.setNode); useEffect(() => { const onKeyDown = (event: KeyboardEvent) => { + const selectedNode = nodes.filter((obj) => obj.selected); + if ((event.ctrlKey || event.metaKey) && event.key === "p" && selectedNode.length > 0) { + event.preventDefault(); + setNode(selectedNode[0].id, (old) => ({ + ...old, + data: { + ...old.data, + node: { + ...old.data.node, + pinned: old.data?.node?.pinned ? false : true, + }, + }, + })); + } + if ((event.ctrlKey || event.metaKey) && event.key === "d" && selectedNode.length > 0) { + event.preventDefault(); + paste({nodes: selectedNode, edges: []}, { + x: position.current.x, + y: position.current.y, + }); + } if (!isWrappedWithClass(event, "noundo")) { if ( (event.key === "y" || (event.key === "z" && event.shiftKey)) && diff --git a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx index 84446eaad..a756ae8c3 100644 --- a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx @@ -25,7 +25,7 @@ import { } from "../../../../utils/utils"; import DisclosureComponent from "../DisclosureComponent"; import SidebarDraggableComponent from "./sideBarDraggableComponent"; -import { UPLOAD_ERROR_ALERT } from "../../../../alerts_constants"; +import { UPLOAD_ERROR_ALERT } from "../../../../constants/alerts_constants"; export default function ExtraSidebar(): JSX.Element { const data = useTypesStore((state) => state.data); diff --git a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx index 863be4f45..8e9e2358b 100644 --- a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx +++ b/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx @@ -1,4 +1,4 @@ -import { cloneDeep } from "lodash"; +import _, { cloneDeep } from "lodash"; import { useEffect, useState } from "react"; import { useUpdateNodeInternals } from "reactflow"; import ShadTooltip from "../../../../components/ShadTooltipComponent"; @@ -89,6 +89,9 @@ export default function NodeToolbarComponent({ }, [showModalAdvanced]); const updateNodeInternals = useUpdateNodeInternals(); + const setLastCopiedSelection = useFlowStore( + (state) => state.setLastCopiedSelection + ); useEffect(() => { setFlowComponent(createFlowComponent(cloneDeep(data), version)); }, [ @@ -142,6 +145,9 @@ export default function NodeToolbarComponent({ case "delete": deleteNode(data.id); break; + case "copy": + const node = nodes.filter((node) => node.id === data.id); + setLastCopiedSelection({ nodes: _.cloneDeep(node), edges: [] }); } }; @@ -229,7 +235,7 @@ export default function NodeToolbarComponent({ id={"code-input-node-toolbar-" + name} /> - + ) : ( @@ -360,6 +366,20 @@ export default function NodeToolbarComponent({ ) )} + +
+ {" "} + Copy{" "} + + C +
+ {hasStore && ( diff --git a/src/frontend/src/pages/MainPage/components/components/index.tsx b/src/frontend/src/pages/MainPage/components/components/index.tsx index 43e351e65..d93811d5b 100644 --- a/src/frontend/src/pages/MainPage/components/components/index.tsx +++ b/src/frontend/src/pages/MainPage/components/components/index.tsx @@ -9,7 +9,7 @@ import { Button } from "../../../../components/ui/button"; import useAlertStore from "../../../../stores/alertStore"; import useFlowsManagerStore from "../../../../stores/flowsManagerStore"; import { FlowType } from "../../../../types/flow"; -import { CONSOLE_ERROR_MSG, UPLOAD_ALERT_LIST, WRONG_FILE_ERROR_ALERT } from "../../../../alerts_constants"; +import { CONSOLE_ERROR_MSG, UPLOAD_ALERT_LIST, WRONG_FILE_ERROR_ALERT } from "../../../../constants/alerts_constants"; export default function ComponentsComponent({ is_component = true, diff --git a/src/frontend/src/pages/MainPage/index.tsx b/src/frontend/src/pages/MainPage/index.tsx index fb636a4d5..e8f0f4136 100644 --- a/src/frontend/src/pages/MainPage/index.tsx +++ b/src/frontend/src/pages/MainPage/index.tsx @@ -6,11 +6,11 @@ import IconComponent from "../../components/genericIconComponent"; import PageLayout from "../../components/pageLayout"; import SidebarNav from "../../components/sidebarComponent"; import { Button } from "../../components/ui/button"; -import { USER_PROJECTS_HEADER } from "../../constants/constants"; +import { USER_PROJECTS_HEADER, myCollectionDesc } from "../../constants/constants"; import useAlertStore from "../../stores/alertStore"; import useFlowsManagerStore from "../../stores/flowsManagerStore"; import { downloadFlows } from "../../utils/reactflowUtils"; -import { CONSOLE_ERROR_MSG } from "../../alerts_constants"; +import { CONSOLE_ERROR_MSG } from "../../constants/alerts_constants"; export default function HomePage(): JSX.Element { const addFlow = useFlowsManagerStore((state) => state.addFlow); const uploadFlow = useFlowsManagerStore((state) => state.uploadFlow); @@ -72,7 +72,7 @@ export default function HomePage(): JSX.Element { return (