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Merge branch 'dev' into refactor/utils

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This commit is contained in:
cristhianzl 2024-05-23 11:02:04 -03:00
commit 1dccd33f48
26 changed files with 586 additions and 332 deletions
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6
src/backend/base/langflow/api/utils.py
View file

@ -1,3 +1,4 @@
import os
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Optional
@ -140,7 +141,10 @@ 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 str(path).startswith(user_cache_dir("langflow", "langflow")):
return ""
if not path.exists():
return ""
return file_path

13
src/backend/base/langflow/api/v1/chat.py
View file

@ -53,7 +53,7 @@ async def try_running_celery_task(vertex, user_id):
@router.post("/build/{flow_id}/vertices", response_model=VerticesOrderResponse)
async def retrieve_vertices_order(
flow_id: str,
flow_id: uuid.UUID,
data: Optional[Annotated[Optional[FlowDataRequest], Body(embed=True)]] = None,
stop_component_id: Optional[str] = None,
start_component_id: Optional[str] = None,
@ -78,6 +78,7 @@ async def retrieve_vertices_order(
HTTPException: If there is an error checking the build status.
"""
try:
flow_id = str(flow_id)
# First, we need to check if the flow_id is in the cache
if not data:
graph = await build_and_cache_graph_from_db(flow_id=flow_id, session=session, chat_service=chat_service)
@ -119,7 +120,7 @@ async def retrieve_vertices_order(
@router.post("/build/{flow_id}/vertices/{vertex_id}")
async def build_vertex(
flow_id: str,
flow_id: uuid.UUID,
vertex_id: str,
background_tasks: BackgroundTasks,
inputs: Annotated[Optional[InputValueRequest], Body(embed=True)] = None,
@ -143,8 +144,8 @@ async def build_vertex(
HTTPException: If there is an error building the vertex.
"""
flow_id = str(flow_id)
start_time = time.perf_counter()
next_runnable_vertices = []
top_level_vertices = []
try:
@ -158,8 +159,7 @@ async def build_vertex(
)
else:
graph = cache.get("result")
result_data_response = ResultDataResponse(results={})
duration = ""
ResultDataResponse(results={})
vertex = graph.get_vertex(vertex_id)
try:
lock = chat_service._cache_locks[flow_id]
@ -240,7 +240,7 @@ async def build_vertex(
@router.get("/build/{flow_id}/{vertex_id}/stream", response_class=StreamingResponse)
async def build_vertex_stream(
flow_id: str,
flow_id: uuid.UUID,
vertex_id: str,
session_id: Optional[str] = None,
chat_service: "ChatService" = Depends(get_chat_service),
@ -272,6 +272,7 @@ async def build_vertex_stream(
HTTPException: If an error occurs while building the vertex.
"""
try:
flow_id = str(flow_id)
async def stream_vertex():
try:

27
src/backend/base/langflow/api/v1/endpoints.py
View file

@ -1,5 +1,6 @@
from http import HTTPStatus
from typing import Annotated, List, Optional, Union
from uuid import UUID
import sqlalchemy as sa
from fastapi import APIRouter, Body, Depends, HTTPException, UploadFile, status
@ -54,7 +55,7 @@ def get_all(
@router.post("/run/{flow_id}", response_model=RunResponse, response_model_exclude_none=True)
async def simplified_run_flow(
db: Annotated[Session, Depends(get_session)],
flow_id: str,
flow_id: UUID,
input_request: SimplifiedAPIRequest = SimplifiedAPIRequest(),
stream: bool = False,
api_key_user: User = Depends(api_key_security),
@ -111,6 +112,7 @@ async def simplified_run_flow(
session_id = input_request.session_id
try:
flow_id = str(flow_id)
task_result: List[RunOutputs] = []
artifacts = {}
if input_request.session_id:
@ -187,7 +189,7 @@ async def simplified_run_flow(
@router.post("/run/advanced/{flow_id}", response_model=RunResponse, response_model_exclude_none=True)
async def experimental_run_flow(
session: Annotated[Session, Depends(get_session)],
flow_id: str,
flow_id: UUID,
inputs: Optional[List[InputValueRequest]] = [InputValueRequest(components=[], input_value="")],
outputs: Optional[List[str]] = [],
tweaks: Annotated[Optional[Tweaks], Body(embed=True)] = None, # noqa: F821
@ -235,6 +237,7 @@ async def experimental_run_flow(
This endpoint facilitates complex flow executions with customized inputs, outputs, and configurations, catering to diverse application requirements.
"""
try:
flow_id = str(flow_id)
if outputs is None:
outputs = []
@ -357,9 +360,10 @@ async def get_task_status(task_id: str):
)
async def create_upload_file(
file: UploadFile,
flow_id: str,
flow_id: UUID,
):
try:
flow_id = str(flow_id)
file_path = save_uploaded_file(file, folder_name=flow_id)
return UploadFileResponse(
@ -400,23 +404,6 @@ async def custom_component(
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)):
from langflow.interface.custom.utils import build_custom_component_template
try:
reader = DirectoryReader("")
valid, content = reader.process_file(path)
if not valid:
raise ValueError(content)
extractor = CustomComponent(code=content)
frontend_node, _ = build_custom_component_template(extractor, user_id=user.id)
return frontend_node
except Exception as exc:
raise HTTPException(status_code=400, detail=str(exc))
@router.post("/custom_component/update", status_code=HTTPStatus.OK)
async def custom_component_update(
code_request: UpdateCustomComponentRequest,

19
src/backend/base/langflow/api/v1/files.py
View file

@ -1,6 +1,7 @@
import hashlib
from http import HTTPStatus
from io import BytesIO
from uuid import UUID
from fastapi import APIRouter, Depends, HTTPException, UploadFile
from fastapi.responses import StreamingResponse
@ -20,10 +21,11 @@ router = APIRouter(tags=["Files"], prefix="/files")
# then finds it in the database and returns it while
# using the current user as the owner
def get_flow_id(
flow_id: str,
flow_id: UUID,
current_user=Depends(get_current_active_user),
session=Depends(get_session),
):
flow_id = str(flow_id)
# AttributeError: 'SelectOfScalar' object has no attribute 'first'
flow = session.get(Flow, flow_id)
if not flow:
@ -36,10 +38,11 @@ def get_flow_id(
@router.post("/upload/{flow_id}", status_code=HTTPStatus.CREATED)
async def upload_file(
file: UploadFile,
flow_id: str = Depends(get_flow_id),
flow_id: UUID = Depends(get_flow_id),
storage_service: StorageService = Depends(get_storage_service),
):
try:
flow_id = str(flow_id)
file_content = await file.read()
file_name = file.filename or hashlib.sha256(file_content).hexdigest()
folder = flow_id
@ -50,8 +53,9 @@ async def upload_file(
@router.get("/download/{flow_id}/{file_name}")
async def download_file(file_name: str, flow_id: str, storage_service: StorageService = Depends(get_storage_service)):
async def download_file(file_name: str, flow_id: UUID, storage_service: StorageService = Depends(get_storage_service)):
try:
flow_id = str(flow_id)
extension = file_name.split(".")[-1]
if not extension:
@ -74,9 +78,10 @@ async def download_file(file_name: str, flow_id: str, storage_service: StorageSe
@router.get("/images/{flow_id}/{file_name}")
async def download_image(file_name: str, flow_id: str, storage_service: StorageService = Depends(get_storage_service)):
async def download_image(file_name: str, flow_id: UUID, storage_service: StorageService = Depends(get_storage_service)):
try:
extension = file_name.split(".")[-1]
flow_id = str(flow_id)
if not extension:
raise HTTPException(status_code=500, detail=f"Extension not found for file {file_name}")
@ -96,9 +101,10 @@ async def download_image(file_name: str, flow_id: str, storage_service: StorageS
@router.get("/list/{flow_id}")
async def list_files(
flow_id: str = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service)
flow_id: UUID = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service)
):
try:
flow_id = str(flow_id)
files = await storage_service.list_files(flow_id=flow_id)
return {"files": files}
except Exception as e:
@ -107,9 +113,10 @@ async def list_files(
@router.delete("/delete/{flow_id}/{file_name}")
async def delete_file(
file_name: str, flow_id: str = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service)
file_name: str, flow_id: UUID = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service)
):
try:
flow_id = str(flow_id)
await storage_service.delete_file(flow_id=flow_id, file_name=file_name)
return {"message": f"File {file_name} deleted successfully"}
except Exception as e:

95
src/backend/base/langflow/components/memories/AstraDBMessageReader.py Normal file
View file

@ -0,0 +1,95 @@
from typing import Optional, cast
from langchain_astradb.chat_message_histories import AstraDBChatMessageHistory
from langflow.base.memory.memory import BaseMemoryComponent
from langflow.field_typing import Text
from langflow.schema.schema import Record
class AstraDBMessageReaderComponent(BaseMemoryComponent):
display_name = "Astra DB Message Reader"
description = "Retrieves stored chat messages from Astra DB."
def build_config(self):
return {
"session_id": {
"display_name": "Session ID",
"info": "Session ID of the chat history.",
"input_types": ["Text"],
},
"collection_name": {
"display_name": "Collection Name",
"info": "Collection name for Astra DB.",
"input_types": ["Text"],
},
"token": {
"display_name": "Astra DB Application Token",
"info": "Token for the Astra DB instance.",
"password": True,
},
"api_endpoint": {
"display_name": "Astra DB API Endpoint",
"info": "API Endpoint for the Astra DB instance.",
"password": True,
},
"namespace": {
"display_name": "Namespace",
"info": "Namespace for the Astra DB instance.",
"input_types": ["Text"],
"advanced": True,
},
}
def get_messages(self, **kwargs) -> list[Record]:
"""
Retrieves messages from the AstraDBChatMessageHistory memory.
Args:
memory (AstraDBChatMessageHistory): The AstraDBChatMessageHistory instance to retrieve messages from.
Returns:
list[Record]: A list of Record objects representing the search results.
"""
memory: AstraDBChatMessageHistory = cast(
AstraDBChatMessageHistory, kwargs.get("memory")
)
if not memory:
raise ValueError("AstraDBChatMessageHistory instance is required.")
# Get messages from the memory
messages = memory.messages
results = [Record.from_lc_message(message) for message in messages]
return list(results)
def build(
self,
session_id: Text,
collection_name: str,
token: str,
api_endpoint: str,
namespace: Optional[str] = None,
) -> list[Record]:
try:
from langchain_community.chat_message_histories.astradb import (
AstraDBChatMessageHistory,
)
except ImportError:
raise ImportError(
"Could not import langchain Astra DB integration package. "
"Please install it with `pip install langchain-astradb`."
)
memory = AstraDBChatMessageHistory(
session_id=session_id,
collection_name=collection_name,
token=token,
api_endpoint=api_endpoint,
namespace=namespace,
)
records = self.get_messages(memory=memory)
self.status = records
return records

117
src/backend/base/langflow/components/memories/AstraDBMessageWriter.py Normal file
View file

@ -0,0 +1,117 @@
from typing import Optional
from langflow.base.memory.memory import BaseMemoryComponent
from langflow.field_typing import Text
from langflow.schema.schema import Record
from langchain_core.messages import BaseMessage
from langchain_community.chat_message_histories.astradb import AstraDBChatMessageHistory
class AstraDBMessageWriterComponent(BaseMemoryComponent):
display_name = "Astra DB Message Writer"
description = "Writes a message to Astra DB."
def build_config(self):
return {
"input_value": {
"display_name": "Input Record",
"info": "Record to write to Astra DB.",
},
"session_id": {
"display_name": "Session ID",
"info": "Session ID of the chat history.",
"input_types": ["Text"],
},
"collection_name": {
"display_name": "Collection Name",
"info": "Collection name for Astra DB.",
"input_types": ["Text"],
},
"token": {
"display_name": "Astra DB Application Token",
"info": "Token for the Astra DB instance.",
"password": True,
},
"api_endpoint": {
"display_name": "Astra DB API Endpoint",
"info": "API Endpoint for the Astra DB instance.",
"password": True,
},
"namespace": {
"display_name": "Namespace",
"info": "Namespace for the Astra DB instance.",
"input_types": ["Text"],
"advanced": True,
},
}
def add_message(
self,
sender: str,
sender_name: str,
text: Text,
session_id: str,
metadata: Optional[dict] = None,
**kwargs,
):
"""
Adds a message to the AstraDBChatMessageHistory memory.
Args:
sender (Text): The type of the message sender. Valid values are "Machine" or "User".
sender_name (Text): The name of the message sender.
text (Text): The content of the message.
session_id (Text): The session ID associated with the message.
metadata (dict | None, optional): Additional metadata for the message. Defaults to None.
**kwargs: Additional keyword arguments.
Raises:
ValueError: If the AstraDBChatMessageHistory instance is not provided.
"""
memory: AstraDBChatMessageHistory | None = kwargs.pop("memory", None)
if memory is None:
raise ValueError("AstraDBChatMessageHistory instance is required.")
text_list = [BaseMessage(
content=text,
sender=sender,
sender_name=sender_name,
metadata=metadata,
session_id=session_id,
)]
memory.add_messages(text_list)
def build(
self,
input_value: Record,
session_id: Text,
collection_name: str,
token: str,
api_endpoint: str,
namespace: Optional[str] = None,
) -> Record:
try:
from langchain_community.chat_message_histories.astradb import (
AstraDBChatMessageHistory,
)
except ImportError:
raise ImportError(
"Could not import langchain Astra DB integration package. "
"Please install it with `pip install langchain-astradb`."
)
memory = AstraDBChatMessageHistory(
session_id=session_id,
collection_name=collection_name,
token=token,
api_endpoint=api_endpoint,
namespace=namespace,
)
self.add_message(**input_value.data, memory=memory)
self.status = f"Added message to Astra DB memory for session {session_id}"
return input_value

73
src/backend/base/langflow/components/vectorsearch/CouchbaseSearch.py Normal file
View file

@ -0,0 +1,73 @@
from typing import List, Optional
from langflow.components.vectorstores.base.model import LCVectorStoreComponent
from langflow.components.vectorstores.Couchbase import CouchbaseComponent
from langflow.field_typing import Embeddings, NestedDict, Text
from langflow.schema import Record
class CouchbaseSearchComponent(LCVectorStoreComponent):
display_name = "Couchbase Search"
description = "Search a Couchbase Vector Store for similar documents."
documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
icon = "Couchbase"
field_order = [
"couchbase_connection_string",
"couchbase_username",
"couchbase_password",
"bucket_name",
"scope_name",
"collection_name",
"index_name",
]
def build_config(self):
return {
"input_value": {"display_name": "Input"},
"embedding": {"display_name": "Embedding"},
"couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
"couchbase_username": {"display_name": "Couchbase username","required": True},
"couchbase_password": {
"display_name": "Couchbase password",
"password": True,
"required": True
},
"bucket_name": {"display_name": "Bucket Name","required": True},
"scope_name": {"display_name": "Scope Name","required": True},
"collection_name": {"display_name": "Collection Name","required": True},
"index_name": {"display_name": "Index Name","required": True},
"number_of_results": {
"display_name": "Number of Results",
"info": "Number of results to return.",
"advanced": True,
},
}
def build( # type: ignore[override]
self,
input_value: Text,
embedding: Embeddings,
number_of_results: int = 4,
bucket_name: str = "",
scope_name: str = "",
collection_name: str = "",
index_name: str = "",
couchbase_connection_string: str = "",
couchbase_username: str = "",
couchbase_password: str = "",
) -> List[Record]:
vector_store = CouchbaseComponent().build(
couchbase_connection_string=couchbase_connection_string,
couchbase_username=couchbase_username,
couchbase_password=couchbase_password,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
embedding=embedding,
index_name=index_name,
)
if not vector_store:
raise ValueError("Failed to create Couchbase Vector Store")
return self.search_with_vector_store(
vector_store=vector_store, input_value=input_value, search_type="similarity", k=number_of_results
)

2
src/backend/base/langflow/components/vectorsearch/__init__.py
View file

@ -9,10 +9,12 @@ from .SupabaseVectorStoreSearch import SupabaseSearchComponent
from .VectaraSearch import VectaraSearchComponent
from .WeaviateSearch import WeaviateSearchVectorStore
from .pgvectorSearch import PGVectorSearchComponent
from .Couchbase import CouchbaseSearchComponent # type: ignore
__all__ = [
"AstraDBSearchComponent",
"ChromaSearchComponent",
"CouchbaseSearchComponent",
"FAISSSearchComponent",
"MongoDBAtlasSearchComponent",
"PineconeSearchComponent",

95
src/backend/base/langflow/components/vectorstores/Couchbase.py Normal file
View file

@ -0,0 +1,95 @@
from typing import List, Optional, Union
from langchain.schema import BaseRetriever
from langchain_community.vectorstores import CouchbaseVectorStore
from langflow.custom import CustomComponent
from langflow.field_typing import Embeddings, VectorStore
from langflow.schema import Record
from datetime import timedelta
from couchbase.auth import PasswordAuthenticator # type: ignore
from couchbase.cluster import Cluster # type: ignore
from couchbase.options import ClusterOptions # type: ignore
class CouchbaseComponent(CustomComponent):
display_name = "Couchbase"
description = "Construct a `Couchbase Vector Search` vector store from raw documents."
documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
icon = "Couchbase"
field_order = [
"couchbase_connection_string",
"couchbase_username",
"couchbase_password",
"bucket_name",
"scope_name",
"collection_name",
"index_name",
]
def build_config(self):
return {
"inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
"embedding": {"display_name": "Embedding"},
"couchbase_connection_string": {"display_name": "Couchbase Cluster connection string","required": True},
"couchbase_username": {"display_name": "Couchbase username","required": True},
"couchbase_password": {
"display_name": "Couchbase password",
"password": True,
"required": True
},
"bucket_name": {"display_name": "Bucket Name","required": True},
"scope_name": {"display_name": "Scope Name","required": True},
"collection_name": {"display_name": "Collection Name","required": True},
"index_name": {"display_name": "Index Name","required": True},
}
def build(
self,
embedding: Embeddings,
inputs: Optional[List[Record]] = None,
bucket_name: str = "",
scope_name: str = "",
collection_name: str = "",
index_name: str = "",
couchbase_connection_string: str = "",
couchbase_username: str = "",
couchbase_password: str = "",
) -> Union[VectorStore, BaseRetriever]:
try:
auth = PasswordAuthenticator(couchbase_username, couchbase_password)
options = ClusterOptions(auth)
cluster = Cluster(couchbase_connection_string, options)
cluster.wait_until_ready(timedelta(seconds=5))
except Exception as e:
raise ValueError(f"Failed to connect to Couchbase: {e}")
documents = []
for _input in inputs or []:
if isinstance(_input, Record):
documents.append(_input.to_lc_document())
else:
documents.append(_input)
if documents:
vector_store = CouchbaseVectorStore.from_documents(
documents=documents,
cluster=cluster,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
embedding=embedding,
index_name=index_name,
)
else:
vector_store = CouchbaseVectorStore(
cluster=cluster,
bucket_name=bucket_name,
scope_name=scope_name,
collection_name=collection_name,
embedding=embedding,
index_name=index_name,
)
return vector_store

2
src/backend/base/langflow/components/vectorstores/__init__.py
View file

@ -9,10 +9,12 @@ from .SupabaseVectorStore import SupabaseComponent
from .Vectara import VectaraComponent
from .Weaviate import WeaviateVectorStoreComponent
from .pgvector import PGVectorComponent
from .Couchbase import CouchbaseComponent
__all__ = [
"AstraDBVectorStoreComponent",
"ChromaComponent",
"CouchbaseComponent",
"FAISSComponent",
"MongoDBAtlasComponent",
"PineconeComponent",

2
src/frontend/src/components/inputGlobalComponent/index.tsx
View file

@ -34,6 +34,7 @@ export default function InputGlobalComponent({
useEffect(() => {
if (data.node?.template[name])
if (
globalVariablesEntries &&
!globalVariablesEntries.includes(data.node?.template[name].value) &&
data.node?.template[name].load_from_db
) {
@ -138,6 +139,7 @@ export default function InputGlobalComponent({
)}
selectedOption={
data?.node?.template[name].load_from_db &&
globalVariablesEntries &&
globalVariablesEntries.includes(data?.node?.template[name].value ?? "")
? data?.node?.template[name].value
: ""

17
src/frontend/src/icons/Couchbase/Couchbase.jsx Normal file
View file

@ -0,0 +1,17 @@
const SvgCouchbaseIcon = (props) => (
<svg
xmlns="http://www.w3.org/2000/svg"
width="1em"
height="1em"
preserveAspectRatio="xMidYMid"
viewBox="0 0 256 256"
{...props}
>
<path
fill="#ED2226"
d="M128 0C57.426 0 0 57.233 0 128c0 70.574 57.233 128 128 128 70.574 0 128-57.233 128-128S198.574 0 128 0zm86.429 150.429c0 7.734-4.447 14.502-13.148 16.048-15.082 2.707-46.792 4.254-73.281 4.254-26.49 0-58.2-1.547-73.281-4.254-8.7-1.546-13.148-8.314-13.148-16.048v-49.885c0-7.734 5.994-14.888 13.148-16.049 4.447-.773 14.888-1.546 23.01-1.546 3.093 0 5.606 2.32 5.606 5.994v34.997l44.858-.967 44.858.967V88.943c0-3.674 2.514-5.994 5.608-5.994 8.12 0 18.562.773 23.009 1.546 7.347 1.16 13.148 8.315 13.148 16.049-.387 16.435-.387 33.257-.387 49.885z"
/>
</svg>
);
export default SvgCouchbaseIcon;

1
src/frontend/src/icons/Couchbase/couchbase.svg Normal file
View file

@ -0,0 +1 @@
<svg xmlns="http://www.w3.org/2000/svg" width="2500" height="2500" preserveAspectRatio="xMidYMid" viewBox="0 0 256 256" id="couchbase"><path fill="#ED2226" d="M128 0C57.426 0 0 57.233 0 128c0 70.574 57.233 128 128 128 70.574 0 128-57.233 128-128S198.574 0 128 0zm86.429 150.429c0 7.734-4.447 14.502-13.148 16.048-15.082 2.707-46.792 4.254-73.281 4.254-26.49 0-58.2-1.547-73.281-4.254-8.7-1.546-13.148-8.314-13.148-16.048v-49.885c0-7.734 5.994-14.888 13.148-16.049 4.447-.773 14.888-1.546 23.01-1.546 3.093 0 5.606 2.32 5.606 5.994v34.997l44.858-.967 44.858.967V88.943c0-3.674 2.514-5.994 5.608-5.994 8.12 0 18.562.773 23.009 1.546 7.347 1.16 13.148 8.315 13.148 16.049-.387 16.435-.387 33.257-.387 49.885z"></path></svg>
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9
src/frontend/src/icons/Couchbase/index.tsx Normal file
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@ -0,0 +1,9 @@
import React, { forwardRef } from "react";
import SvgCouchbaseIcon from "./Couchbase";
export const CouchbaseIcon = forwardRef<
SVGSVGElement,
React.PropsWithChildren<{}>
>((props, ref) => {
return <SvgCouchbaseIcon ref={ref} {...props} />;
});

20
src/frontend/src/pages/MainPage/components/myCollectionComponent/components/headerTabsSearchComponent/index.tsx
View file

@ -19,6 +19,11 @@ const HeaderTabsSearchComponent = ({}: HeaderTabsSearchComponentProps) => {
const [tabActive, setTabActive] = useState("Flows");
const setErrorData = useAlertStore((state) => state.setErrorData);
const allFlows = useFlowsManagerStore((state) => state.allFlows);
const [inputValue, setInputValue] = useState("");
const setSearchFlowsComponents = useFlowsManagerStore(
(state) => state.setSearchFlowsComponents,
);
const handleDownloadFolder = () => {
if (allFlows.length === 0) {
@ -34,8 +39,19 @@ const HeaderTabsSearchComponent = ({}: HeaderTabsSearchComponentProps) => {
return (
<>
<div className="relative flex items-end gap-4">
<InputSearchComponent loading={isLoading} />
<InputSearchComponent
loading={isLoading}
value={inputValue}
onChange={(e) => {
setSearchFlowsComponents(e.target.value);
setInputValue(e.target.value);
}}
onKeyDown={(e) => {
if (e.key === "Enter") {
setSearchFlowsComponents(inputValue);
}
}}
/>
<TabsSearchComponent
tabsOptions={["All", "Flows", "Components"]}
setActiveTab={setTabActive}

39
src/frontend/src/pages/MainPage/components/myCollectionComponent/components/inputSearchComponent/index.tsx
View file

@ -1,22 +1,27 @@
import { useState } from "react";
import { ChangeEvent, KeyboardEvent } from "react";
import { Input } from "../../../../../../components/ui/input";
import useFlowsManagerStore from "../../../../../../stores/flowsManagerStore";
import ForwardedIconComponent from "../../../../../../components/genericIconComponent";
type InputSearchComponentProps = {
loading: boolean;
divClasses?: string;
onChange: (e: ChangeEvent<HTMLInputElement>) => void;
onClick?: () => void;
value: string;
onKeyDown: (e: KeyboardEvent<HTMLInputElement>) => void;
};
const InputSearchComponent = ({ loading }: InputSearchComponentProps) => {
const InputSearchComponent = ({
loading,
divClasses,
onChange,
onClick,
value,
onKeyDown,
}: InputSearchComponentProps) => {
const pagePath = window.location.pathname;
const [inputValue, setInputValue] = useState("");
const allFlows = useFlowsManagerStore((state) => state.allFlows);
const setSearchFlowsComponents = useFlowsManagerStore(
(state) => state.setSearchFlowsComponents,
);
const searchFlowsComponents = useFlowsManagerStore(
(state) => state.searchFlowsComponents,
);
@ -38,24 +43,18 @@ const InputSearchComponent = ({ loading }: InputSearchComponentProps) => {
return (
<>
<div className="relative h-12 w-[60%]">
<div className={`${divClasses ? divClasses : "relative h-12 w-[60%]"}`}>
<Input
data-testid="search-store-input"
disabled={disableInputSearch}
placeholder={getSearchPlaceholder()}
className="absolute h-12 pl-5 pr-12"
onChange={(e) => {
setSearchFlowsComponents(e.target.value);
setInputValue(e.target.value);
}}
onKeyDown={(e) => {
if (e.key === "Enter") {
setSearchFlowsComponents(inputValue);
}
}}
value={inputValue}
onChange={onChange}
onKeyDown={onKeyDown}
value={value}
/>
<button
onClick={onClick}
disabled={loading}
className="absolute bottom-0 right-4 top-0 my-auto h-6 cursor-pointer stroke-1 text-muted-foreground"
data-testid="search-store-button"

1
src/frontend/src/pages/SettingsPage/pages/GlobalVariablesPage/index.tsx
View file

@ -55,6 +55,7 @@ export default function GlobalVariablesPage() {
name: string;
default_fields: string | undefined;
}> = [];
if (globalVariablesEntries === undefined) return;
globalVariablesEntries.forEach((entrie) => {
const globalVariableObj = globalVariables[entrie];
rows.push({

49
src/frontend/src/pages/StorePage/index.tsx
View file

@ -35,6 +35,7 @@ import useFlowsManagerStore from "../../stores/flowsManagerStore";
import { useStoreStore } from "../../stores/storeStore";
import { storeComponent } from "../../types/store";
import { cn } from "../../utils/utils";
import InputSearchComponent from "../MainPage/components/myCollectionComponent/components/inputSearchComponent";
export default function StorePage(): JSX.Element {
const hasApiKey = useStoreStore((state) => state.hasApiKey);
@ -47,7 +48,7 @@ export default function StorePage(): JSX.Element {
const setErrorData = useAlertStore((state) => state.setErrorData);
const setCurrentFlowId = useFlowsManagerStore(
(state) => state.setCurrentFlowId
(state) => state.setCurrentFlowId,
);
const currentFlowId = useFlowsManagerStore((state) => state.currentFlowId);
const [loading, setLoading] = useState(true);
@ -144,7 +145,7 @@ export default function StorePage(): JSX.Element {
setTotalRowsCount(
filteredCategories?.length === 0
? Number(res?.count ?? 0)
: res?.results?.length ?? 0
: res?.results?.length ?? 0,
);
}
})
@ -187,7 +188,7 @@ export default function StorePage(): JSX.Element {
disabled={loading}
className={cn(
`${!validApiKey ? "animate-pulse border-error" : ""}`,
loading ? "cursor-not-allowed" : ""
loading ? "cursor-not-allowed" : "",
)}
variant="primary"
>
@ -202,36 +203,20 @@ export default function StorePage(): JSX.Element {
<div className="flex h-full w-full flex-col justify-between">
<div className="flex w-full flex-col gap-4 p-0">
<div className="flex items-end gap-4">
<div className="relative h-12 w-[40%]">
<Input
data-testid="search-store-input"
disabled={loading}
placeholder="Search Flows and Components"
className="absolute h-12 pl-5 pr-12"
onChange={(e) => {
setInputText(e.target.value);
}}
onKeyDown={(e) => {
if (e.key === "Enter") {
setSearchNow(uniqueId());
}
}}
value={inputText}
/>
<button
disabled={loading}
className="absolute bottom-0 right-4 top-0 my-auto h-6 cursor-pointer stroke-1 text-muted-foreground"
onClick={() => {
<InputSearchComponent
loading={loading}
divClasses="relative h-12 w-[40%]"
value={inputText}
onChange={(e) => setInputText(e.target.value)}
onKeyDown={(e) => {
if (e.key === "Enter") {
setSearchNow(uniqueId());
}}
data-testid="search-store-button"
>
<IconComponent
name={loading ? "Loader2" : "Search"}
className={loading ? " animate-spin cursor-not-allowed" : ""}
/>
</button>
</div>
}
}}
onClick={() => {
setSearchNow(uniqueId());
}}
/>
<div className="ml-4 flex w-full gap-2 border-b border-border">
<button
data-testid="all-button-store"

2
src/frontend/src/stores/globalVariablesStore/globalVariables.ts
View file

@ -13,7 +13,7 @@ export const useGlobalVariablesStore = create<GlobalVariablesStore>(
delete newFields[field];
set({ unavaliableFields: newFields });
},
globalVariablesEntries: [],
globalVariablesEntries: undefined,
globalVariables: {},
setGlobalVariables: (variables) => {
set({

2
src/frontend/src/types/zustand/globalVariables/index.ts
View file

@ -1,5 +1,5 @@
export type GlobalVariablesStore = {
globalVariablesEntries: Array<string>;
globalVariablesEntries: Array<string> | undefined;
globalVariables: {
[name: string]: {
id: string;

2
src/frontend/src/utils/styleUtils.ts
View file

@ -153,6 +153,7 @@ import { AzureIcon } from "../icons/Azure";
import { BingIcon } from "../icons/Bing";
import { BotMessageSquareIcon } from "../icons/BotMessageSquare";
import { ChromaIcon } from "../icons/ChromaIcon";
import { CouchbaseIcon } from "../icons/Couchbase";
import { CohereIcon } from "../icons/Cohere";
import { ElasticsearchIcon } from "../icons/ElasticsearchStore";
import { EvernoteIcon } from "../icons/Evernote";
@ -324,6 +325,7 @@ export const nodeIconsLucide: iconsType = {
Vectara: VectaraIcon,
ArrowUpToLine: ArrowUpToLine,
Chroma: ChromaIcon,
Couchbase: CouchbaseIcon,
AirbyteJSONLoader: AirbyteIcon,
AmazonBedrockEmbeddings: AWSIcon,
Amazon: AWSIcon,