Merge branch 'zustand/io/migration' of personal:logspace-ai/langflow into zustand/io/migration

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# Set update schedule for GitHub Actions
+
+version: 2
+updates:
+
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      # Check for updates to GitHub Actions every week
+      interval: "monthly"
+    

.github/workflows/ci.yml

@@ -16,10 +16,10 @@ jobs:
       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
     steps:
       - name: Checkout code
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
 
       - name: Cache Docker layers
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: /tmp/.buildx-cache
           key: ${{ runner.os }}-buildx-${{ github.sha }}

.github/workflows/codeql.yml

@@ -30,11 +30,11 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL
-      uses: github/codeql-action/init@v2
+      uses: github/codeql-action/init@v3
       with:
         languages: ${{ matrix.language }}
         # If you wish to specify custom queries, you can do so here or in a config file.
@@ -48,7 +48,7 @@ jobs:
     # Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift).
     # If this step fails, then you should remove it and run the build manually (see below)
     - name: Autobuild
-      uses: github/codeql-action/autobuild@v2
+      uses: github/codeql-action/autobuild@v3
 
     # ℹ️ Command-line programs to run using the OS shell.
     # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
@@ -61,6 +61,6 @@ jobs:
     #     ./location_of_script_within_repo/buildscript.sh
 
     - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v2
+      uses: github/codeql-action/analyze@v3
       with:
         category: "/language:${{matrix.language}}"

.github/workflows/deploy_gh-pages.yml

@@ -12,8 +12,8 @@ jobs:
     name: Deploy to GitHub Pages
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
-      - uses: actions/setup-node@v3
+      - uses: actions/setup-node@v4
         with:
           node-version: 18
           cache: npm

.github/workflows/lint.yml

@@ -16,13 +16,14 @@ jobs:
         python-version:
           - "3.9"
           - "3.10"
+          - "3.11"
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Install poetry
         run: |
           pipx install poetry==$POETRY_VERSION
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           cache: poetry

.github/workflows/pre-release.yml

@@ -18,11 +18,11 @@ jobs:
     if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }}
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Install poetry
         run: pipx install poetry==$POETRY_VERSION
       - name: Set up Python 3.10
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: "poetry"

.github/workflows/release.yml

@@ -17,11 +17,11 @@ jobs:
     if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'Release') }}
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Install poetry
         run: pipx install poetry==$POETRY_VERSION
       - name: Set up Python 3.10
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: "poetry"

.github/workflows/test.yml

@@ -16,14 +16,15 @@ jobs:
       matrix:
         python-version:
           - "3.10"
+          - "3.11"
     env:
       OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Install poetry
         run: pipx install poetry==$POETRY_VERSION
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
           cache: "poetry"

docker-compose.yml

@@ -22,6 +22,8 @@ services:
       dockerfile: ./cdk.Dockerfile
       args:
         - BACKEND_URL=http://backend:7860
+    depends_on:
+        - backend
     environment:
       - VITE_PROXY_TARGET=http://backend:7860
     ports:

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.6.7a1"
+version = "0.6.7a2"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [
@@ -25,18 +25,20 @@ documentation = "https://docs.langflow.org"
 langflow = "langflow.__main__:main"
 
 [tool.poetry.dependencies]
-python = ">=3.9,<3.11"
+
+
+python = ">=3.9,<3.12"
+duckdb = "^0.9.2"
 fastapi = "^0.109.0"
 uvicorn = "^0.27.0"
 beautifulsoup4 = "^4.12.2"
 google-search-results = "^2.4.1"
-google-api-python-client = "^2.79.0"
+google-api-python-client = "^2.118.0"
 typer = "^0.9.0"
 gunicorn = "^21.2.0"
 langchain = "~0.1.0"
-duckdb = "^0.9.2"
+openai = "^1.12.0"
-openai = "^1.11.0"
+pandas = "2.2.0"
-pandas = "2.0.3"
 chromadb = "^0.4.0"
 huggingface-hub = { version = "^0.20.0", extras = ["inference"] }
 rich = "^13.7.0"
@@ -50,15 +52,14 @@ fake-useragent = "^1.4.0"
 docstring-parser = "^0.15"
 psycopg2-binary = "^2.9.6"
 pyarrow = "^14.0.0"
-tiktoken = "~0.5.0"
+tiktoken = "~0.6.0"
 wikipedia = "^1.4.0"
 qdrant-client = "^1.7.0"
 weaviate-client = "*"
-jina = "*"
 sentence-transformers = { version = "^2.3.1", optional = true }
 ctransformers = { version = "^0.2.10", optional = true }
-cohere = "^4.45.0"
+cohere = "^4.47.0"
-python-multipart = "^0.0.6"
+python-multipart = "^0.0.7"
 sqlmodel = "^0.0.14"
 faiss-cpu = "^1.7.4"
 anthropic = "^0.15.0"
@@ -67,17 +68,17 @@ multiprocess = "^0.70.14"
 cachetools = "^5.3.1"
 types-cachetools = "^5.3.0.5"
 platformdirs = "^4.2.0"
-pinecone-client = "^2.2.2"
+pinecone-client = "^3.0.3"
 pymongo = "^4.6.0"
 supabase = "^2.3.0"
 certifi = "^2023.11.17"
-google-cloud-aiplatform = "^1.36.0"
+google-cloud-aiplatform = "^1.42.0"
 psycopg = "^3.1.9"
 psycopg-binary = "^3.1.9"
 fastavro = "^1.8.0"
 langchain-experimental = "*"
 celery = { extras = ["redis"], version = "^5.3.6", optional = true }
-redis = { version = "^4.6.0", optional = true }
+redis = { version = "^5.0.1", optional = true }
 flower = { version = "^2.0.0", optional = true }
 alembic = "^1.13.0"
 passlib = "^1.7.4"
@@ -90,46 +91,46 @@ zep-python = "*"
 pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
 loguru = "^0.7.1"
 langfuse = "^2.9.0"
-pillow = "^10.0.0"
+pillow = "^10.2.0"
-metal-sdk = "^2.4.0"
+metal-sdk = "^2.5.0"
 markupsafe = "^2.1.3"
-extract-msg = "^0.45.0"
+extract-msg = "^0.47.0"
 # jq is not available for windows
 jq = { version = "^1.6.0", markers = "sys_platform != 'win32'" }
 boto3 = "^1.34.0"
 numexpr = "^2.8.6"
-qianfan = "0.2.0"
+qianfan = "0.3.0"
 pgvector = "^0.2.3"
 pyautogen = "^0.2.0"
 langchain-google-genai = "^0.0.6"
-elasticsearch = "^8.11.1"
+elasticsearch = "^8.12.0"
 pytube = "^15.0.0"
 python-socketio = "^5.11.0"
-llama-index = "^0.9.44"
+llama-index = "0.9.48"
-langchain-openai = "^0.0.5"
+langchain-openai = "^0.0.6"
 
 [tool.poetry.group.dev.dependencies]
 pytest-asyncio = "^0.23.1"
 types-redis = "^4.6.0.5"
-ipykernel = "^6.27.0"
+ipykernel = "^6.29.0"
 mypy = "^1.8.0"
-ruff = "^0.1.5"
+ruff = "^0.2.1"
 httpx = "*"
-pytest = "^7.4.2"
+pytest = "^8.0.0"
 types-requests = "^2.31.0"
 requests = "^2.31.0"
 pytest-cov = "^4.1.0"
-pandas-stubs = "^2.0.0.230412"
+pandas-stubs = "^2.1.4.231227"
-types-pillow = "^9.5.0.2"
+types-pillow = "^10.2.0.20240213"
 types-pyyaml = "^6.0.12.8"
 types-python-jose = "^3.3.4.8"
 types-passlib = "^1.7.7.13"
-locust = "^2.19.1"
+locust = "^2.23.1"
 pytest-mock = "^3.12.0"
 pytest-xdist = "^3.5.0"
 types-pywin32 = "^306.0.0.4"
 types-google-cloud-ndb = "^2.2.0.0"
-pytest-sugar = "^0.9.7"
+pytest-sugar = "^1.0.0"
 pytest-instafail = "^0.5.0"
 
 

src/backend/langflow/api/v1/callback.py

@@ -2,8 +2,9 @@ import asyncio
 from typing import TYPE_CHECKING, Any, Dict, List, Optional
 from uuid import UUID
 
-from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
 from langchain.schema import AgentAction, AgentFinish
+from langchain_core.callbacks.base import (AsyncCallbackHandler,
+                                           BaseCallbackHandler)
 from langflow.api.v1.schemas import ChatResponse, PromptResponse
 from langflow.services.deps import get_chat_service
 from langflow.utils.util import remove_ansi_escape_codes

src/backend/langflow/components/chains/LLMCheckerChain.py

@@ -1,10 +1,8 @@
-from langflow import CustomComponent
+from typing import Callable, Union
+
 from langchain.chains import LLMCheckerChain
-from typing import Union, Callable
+from langflow import CustomComponent
-from langflow.field_typing import (
+from langflow.field_typing import BaseLanguageModel, Chain
-    BaseLanguageModel,
-    Chain,
-)
 
 
 class LLMCheckerChainComponent(CustomComponent):
@@ -21,4 +19,4 @@ class LLMCheckerChainComponent(CustomComponent):
         self,
         llm: BaseLanguageModel,
     ) -> Union[Chain, Callable]:
-        return LLMCheckerChain(llm=llm)
+        return LLMCheckerChain.from_llm(llm=llm)

src/backend/langflow/components/chains/RetrievalQA.py

@@ -45,4 +45,6 @@ class RetrievalQAComponent(CustomComponent):
         self.status = runnable
         result = runnable.invoke({input_key: inputs})
         result = result.content if hasattr(result, "content") else result
+        # Result is a dict with keys "query",  "result" and "source_documents"
+        # for now we just return the result
         return result.get("result")

src/backend/langflow/components/documentloaders/DirectoryLoader.py

@@ -1,6 +1,8 @@
-from langflow import CustomComponent
+from typing import Any, Dict, List
+
 from langchain.docstore.document import Document
-from typing import Optional, Dict, Any
+from langchain.document_loaders.directory import DirectoryLoader
+from langflow import CustomComponent
 
 
 class DirectoryLoaderComponent(CustomComponent):
@@ -23,20 +25,18 @@ class DirectoryLoaderComponent(CustomComponent):
         self,
         glob: str,
         path: str,
-        load_hidden: Optional[bool] = False,
+        max_concurrency: int = 2,
-        max_concurrency: Optional[int] = 10,
+        load_hidden: bool = False,
-        metadata: Optional[dict] = {},
+        recursive: bool = True,
-        recursive: Optional[bool] = True,
+        silent_errors: bool = False,
-        silent_errors: Optional[bool] = False,
+        use_multithreading: bool = True,
-        use_multithreading: Optional[bool] = True,
+    ) -> List[Document]:
-    ) -> Document:
+        return DirectoryLoader(
-        return Document(
             glob=glob,
             path=path,
             load_hidden=load_hidden,
             max_concurrency=max_concurrency,
-            metadata=metadata,
             recursive=recursive,
             silent_errors=silent_errors,
             use_multithreading=use_multithreading,
-        )
+        ).load()

src/backend/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py

@@ -0,0 +1,42 @@
+from typing import Dict, Optional
+
+from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
+from langflow import CustomComponent
+from pydantic.v1.types import SecretStr
+
+
+class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
+    display_name = "HuggingFaceInferenceAPIEmbeddings"
+    description = "HuggingFace sentence_transformers embedding models, API version."
+    documentation = "https://github.com/huggingface/text-embeddings-inference"
+
+    def build_config(self):
+        return {
+            "api_key": {"display_name": "API Key", "password": True, "advanced": True},
+            "api_url": {"display_name": "API URL", "advanced": True},
+            "model_name": {"display_name": "Model Name"},
+            "cache_folder": {"display_name": "Cache Folder", "advanced": True},
+            "encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
+            "model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
+            "multi_process": {"display_name": "Multi Process", "advanced": True},
+        }
+
+    def build(
+        self,
+        api_key: Optional[str] = "",
+        api_url: str = "http://localhost:8080",
+        model_name: str = "BAAI/bge-large-en-v1.5",
+        cache_folder: Optional[str] = None,
+        encode_kwargs: Optional[Dict] = {},
+        model_kwargs: Optional[Dict] = {},
+        multi_process: bool = False,
+    ) -> HuggingFaceInferenceAPIEmbeddings:
+        if api_key:
+            secret_api_key = SecretStr(api_key)
+        else:
+            raise ValueError("API Key is required")
+        return HuggingFaceInferenceAPIEmbeddings(
+            api_key=secret_api_key,
+            api_url=api_url,
+            model_name=model_name,
+        )

src/backend/langflow/components/embeddings/OpenAIEmbeddings.py

@@ -1,9 +1,9 @@
 from typing import Any, Callable, Dict, List, Optional, Union
 
 from langchain_openai.embeddings.base import OpenAIEmbeddings
-
 from langflow import CustomComponent
 from langflow.field_typing import NestedDict
+from pydantic.v1.types import SecretStr
 
 
 class OpenAIEmbeddingsComponent(CustomComponent):
@@ -67,7 +67,7 @@ class OpenAIEmbeddingsComponent(CustomComponent):
             },
             "skip_empty": {"display_name": "Skip Empty", "advanced": True},
             "tiktoken_model_name": {"display_name": "TikToken Model Name"},
-            "tikToken_enable": {"display_name": "TikToken Enable"},
+            "tikToken_enable": {"display_name": "TikToken Enable", "advanced": True},
         }
 
     def build(
@@ -92,14 +92,17 @@ class OpenAIEmbeddingsComponent(CustomComponent):
         request_timeout: Optional[float] = None,
         show_progress_bar: bool = False,
         skip_empty: bool = False,
-        tikToken_enable: bool = True,
+        tiktoken_enable: bool = True,
         tiktoken_model_name: Optional[str] = None,
     ) -> Union[OpenAIEmbeddings, Callable]:
         # This is to avoid errors with Vector Stores (e.g Chroma)
         if disallowed_special == ["all"]:
-            disallowed_special = "all"
+            disallowed_special = "all"  # type: ignore
+
+        api_key = SecretStr(openai_api_key) if openai_api_key else None
+
         return OpenAIEmbeddings(
-            tiktoken_enabled=tikToken_enable,
+            tiktoken_enabled=tiktoken_enable,
             default_headers=default_headers,
             default_query=default_query,
             allowed_special=set(allowed_special),
@@ -112,7 +115,7 @@ class OpenAIEmbeddingsComponent(CustomComponent):
             model=model,
             model_kwargs=model_kwargs,
             base_url=openai_api_base,
-            api_key=openai_api_key,
+            api_key=api_key,
             openai_api_type=openai_api_type,
             api_version=openai_api_version,
             organization=openai_organization,

src/backend/langflow/components/llms/ChatAnthropic.py

@@ -1,4 +1,4 @@
-from pydantic import SecretStr
+from pydantic.v1.types import SecretStr
 from langflow import CustomComponent
 from typing import Optional, Union, Callable
 from langflow.field_typing import BaseLanguageModel

src/backend/langflow/components/llms/GoogleGenerativeAI.py

@@ -1,9 +1,9 @@
 from typing import Optional
 
 from langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore
-
 from langflow import CustomComponent
 from langflow.field_typing import BaseLanguageModel, RangeSpec, TemplateField
+from pydantic.v1.types import SecretStr
 
 
 class GoogleGenerativeAIComponent(CustomComponent):
@@ -63,10 +63,10 @@ class GoogleGenerativeAIComponent(CustomComponent):
     ) -> BaseLanguageModel:
         return ChatGoogleGenerativeAI(
             model=model,
-            max_output_tokens=max_output_tokens or None,
+            max_output_tokens=max_output_tokens or None,  # type: ignore
             temperature=temperature,
             top_k=top_k or None,
-            top_p=top_p or None,
+            top_p=top_p or None,  # type: ignore
             n=n or 1,
-            google_api_key=google_api_key,
+            google_api_key=SecretStr(google_api_key),
         )

src/backend/langflow/components/textsplitters/CharacterTextSplitter.py

@@ -24,6 +24,8 @@ class CharacterTextSplitterComponent(CustomComponent):
         chunk_size: int = 1000,
         separator: str = "\n",
     ) -> List[Document]:
+        # separator may come escaped from the frontend
+        separator = separator.encode().decode("unicode_escape")
         docs = CharacterTextSplitter(
             chunk_overlap=chunk_overlap,
             chunk_size=chunk_size,

src/backend/langflow/components/toolkits/OpenAPIToolkit.py

@@ -1,8 +1,7 @@
+from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit
+from langchain_community.utilities.requests import TextRequestsWrapper
 from langflow import CustomComponent
 from langflow.field_typing import AgentExecutor
-from typing import Callable
-from langchain_community.utilities.requests import TextRequestsWrapper
-from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
 
 
 class OpenAPIToolkitComponent(CustomComponent):
@@ -19,5 +18,5 @@ class OpenAPIToolkitComponent(CustomComponent):
         self,
         json_agent: AgentExecutor,
         requests_wrapper: TextRequestsWrapper,
-    ) -> Callable:
+    ) -> BaseToolkit:
         return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)

src/backend/langflow/components/utilities/GoogleSearchAPIWrapper.py

@@ -1,6 +1,7 @@
-from langflow import CustomComponent
+from typing import Callable, Union
-from typing import Union, Callable
+
 from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
+from langflow import CustomComponent
 
 
 class GoogleSearchAPIWrapperComponent(CustomComponent):
@@ -18,4 +19,4 @@ class GoogleSearchAPIWrapperComponent(CustomComponent):
         google_api_key: str,
         google_cse_id: str,
     ) -> Union[GoogleSearchAPIWrapper, Callable]:
-        return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)
+        return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)  # type: ignore

src/backend/langflow/components/utilities/GoogleSerperAPIWrapper.py

@@ -1,9 +1,9 @@
-from langflow import CustomComponent
+from typing import Dict
-from typing import Dict, Optional
 
 # Assuming the existence of GoogleSerperAPIWrapper class in the serper module
 # If this class does not exist, you would need to create it or import the appropriate class from another module
 from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
+from langflow import CustomComponent
 
 
 class GoogleSerperAPIWrapperComponent(CustomComponent):
@@ -42,6 +42,5 @@ class GoogleSerperAPIWrapperComponent(CustomComponent):
     def build(
         self,
         serper_api_key: str,
-        result_key_for_type: Optional[Dict[str, str]] = None,
     ) -> GoogleSerperAPIWrapper:
-        return GoogleSerperAPIWrapper(result_key_for_type=result_key_for_type, serper_api_key=serper_api_key)
+        return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)

src/backend/langflow/components/vectorstores/Chroma.py

@@ -17,6 +17,7 @@ class ChromaComponent(CustomComponent):
     description: str = "Implementation of Vector Store using Chroma"
     documentation = "https://python.langchain.com/docs/integrations/vectorstores/chroma"
     beta: bool = True
+    icon = "Chroma"
 
     def build_config(self):
         """
@@ -28,7 +29,7 @@ class ChromaComponent(CustomComponent):
         return {
             "collection_name": {"display_name": "Collection Name", "value": "langflow"},
             "persist": {"display_name": "Persist"},
-            "persist_directory": {"display_name": "Persist Directory"},
+            "index_directory": {"display_name": "Persist Directory"},
             "code": {"advanced": True, "display_name": "Code"},
             "documents": {"display_name": "Documents", "is_list": True},
             "embedding": {"display_name": "Embedding"},
@@ -54,7 +55,7 @@ class ChromaComponent(CustomComponent):
         persist: bool,
         embedding: Embeddings,
         chroma_server_ssl_enabled: bool,
-        persist_directory: Optional[str] = None,
+        index_directory: Optional[str] = None,
         documents: Optional[List[Document]] = None,
         chroma_server_cors_allow_origins: Optional[str] = None,
         chroma_server_host: Optional[str] = None,
@@ -66,7 +67,7 @@ class ChromaComponent(CustomComponent):
 
         Args:
         - collection_name (str): The name of the collection.
-        - persist_directory (Optional[str]): The directory to persist the Vector Store to.
+        - index_directory (Optional[str]): The directory to persist the Vector Store to.
         - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.
         - persist (bool): Whether to persist the Vector Store or not.
         - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.
@@ -85,7 +86,8 @@ class ChromaComponent(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,
@@ -93,15 +95,25 @@ class ChromaComponent(CustomComponent):
             )
 
         # If documents, then we need to create a Chroma instance using .from_documents
+
+        # Check index_directory and expand it if it is a relative path
+
+        index_directory = self.resolve_path(index_directory)
+
         if documents is not None and embedding is not None:
             if len(documents) == 0:
-                raise ValueError("If documents are provided, there must be at least one document.")
+                raise ValueError(
-            return Chroma.from_documents(
+                    "If documents are provided, there must be at least one document."
+                )
+            chroma = Chroma.from_documents(
                 documents=documents,  # type: ignore
-                persist_directory=persist_directory if persist else None,
+                persist_directory=index_directory if persist else None,
                 collection_name=collection_name,
                 embedding=embedding,
                 client_settings=chroma_settings,
             )
-
+        else:
-        return Chroma(persist_directory=persist_directory, client_settings=chroma_settings)
+            chroma = Chroma(
+                persist_directory=index_directory, client_settings=chroma_settings
+            )
+        return chroma

src/backend/langflow/components/vectorstores/ChromaSearch.py

@@ -3,7 +3,7 @@ from typing import List, Optional
 import chromadb  # type: ignore
 from langchain_community.vectorstores.chroma import Chroma
 from langflow import CustomComponent
-from langflow.field_typing import Document, Embeddings, Text
+from langflow.field_typing import Embeddings, Text
 from langflow.schema import Record, docs_to_records
 
 
@@ -15,6 +15,7 @@ class ChromaSearchComponent(CustomComponent):
     display_name: str = "Chroma Search"
     description: str = "Search a Chroma collection for similar documents."
     beta: bool = True
+    icon = "Chroma"
 
     def build_config(self):
         """
@@ -25,13 +26,19 @@ class ChromaSearchComponent(CustomComponent):
         """
         return {
             "inputs": {"display_name": "Input"},
-            "search_type": {"display_name": "Search Type", "options": ["Similarity", "MMR"]},
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
             "collection_name": {"display_name": "Collection Name", "value": "langflow"},
-            "persist": {"display_name": "Persist"},
+            # "persist": {"display_name": "Persist"},
-            "persist_directory": {"display_name": "Persist Directory"},
+            "index_directory": {"display_name": "Index Directory"},
             "code": {"show": False, "display_name": "Code"},
             "documents": {"display_name": "Documents", "is_list": True},
-            "embedding": {"display_name": "Embedding"},
+            "embedding": {
+                "display_name": "Embedding",
+                "info": "Embedding model to vectorize inputs (make sure to use same as index)",
+            },
             "chroma_server_cors_allow_origins": {
                 "display_name": "Server CORS Allow Origins",
                 "advanced": True,
@@ -53,11 +60,9 @@ class ChromaSearchComponent(CustomComponent):
         inputs: Text,
         search_type: str,
         collection_name: str,
-        persist: bool,
         embedding: Embeddings,
         chroma_server_ssl_enabled: bool,
-        persist_directory: Optional[str] = None,
+        index_directory: Optional[str] = None,
-        documents: Optional[List[Document]] = None,
         chroma_server_cors_allow_origins: Optional[str] = None,
         chroma_server_host: Optional[str] = None,
         chroma_server_port: Optional[int] = None,
@@ -87,26 +92,20 @@ 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,
             )
 
-        # If documents, then we need to create a Chroma instance using .from_documents
+        chroma = Chroma(
-        if documents is not None and embedding is not None:
+            embedding_function=embedding,
-            if len(documents) == 0:
+            collection_name=collection_name,
-                raise ValueError("If documents are provided, there must be at least one document.")
+            persist_directory=index_directory,
-            chroma = Chroma.from_documents(
+            client_settings=chroma_settings,
-                documents=documents,  # type: ignore
+        )
-                persist_directory=persist_directory if persist else None,
-                collection_name=collection_name,
-                embedding=embedding,
-                client_settings=chroma_settings,
-            )
-        else:
-            chroma = Chroma(persist_directory=persist_directory, client_settings=chroma_settings)
 
         # Validate the inputs
         docs = []

src/backend/langflow/components/vectorstores/Pinecone.py

@@ -5,7 +5,6 @@ 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
 
@@ -31,11 +30,11 @@ class PineconeComponent(CustomComponent):
         embedding: Embeddings,
         pinecone_env: str,
         documents: List[Document],
+        text_key: str = "text",
+        pool_threads: int = 4,
         index_name: Optional[str] = None,
         pinecone_api_key: Optional[str] = None,
-        text_key: Optional[str] = "text",
         namespace: Optional[str] = "default",
-        pool_threads: Optional[int] = None,
     ) -> Union[VectorStore, Pinecone, BaseRetriever]:
         if pinecone_api_key is None or pinecone_env is None:
             raise ValueError("Pinecone API Key and Environment are required.")
@@ -43,6 +42,8 @@ class PineconeComponent(CustomComponent):
             raise ValueError("Pinecone API Key is required.")
 
         pinecone.init(api_key=pinecone_api_key, environment=pinecone_env)  # type: ignore
+        if not index_name:
+            raise ValueError("Index Name is required.")
         if documents:
             return Pinecone.from_documents(
                 documents=documents,

src/backend/langflow/components/vectorstores/Qdrant.py

@@ -1,4 +1,4 @@
-from typing import List, Optional, Union
+from typing import Optional, Union
 
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore
@@ -15,7 +15,7 @@ class QdrantComponent(CustomComponent):
         return {
             "documents": {"display_name": "Documents"},
             "embedding": {"display_name": "Embedding"},
-            "api_key": {"display_name": "API Key", "password": True},
+            "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},
@@ -36,41 +36,68 @@ class QdrantComponent(CustomComponent):
     def build(
         self,
         embedding: Embeddings,
-        documents: List[Document],
+        collection_name: str,
+        documents: Optional[Document] = None,
         api_key: Optional[str] = None,
-        collection_name: Optional[str] = None,
         content_payload_key: str = "page_content",
         distance_func: str = "Cosine",
-        grpc_port: Optional[int] = 6334,
+        grpc_port: int = 6334,
-        host: Optional[str] = None,
         https: bool = False,
-        location: str = ":memory:",
+        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[float] = None,
+        timeout: Optional[int] = None,
         url: Optional[str] = None,
     ) -> Union[VectorStore, Qdrant, BaseRetriever]:
-        return Qdrant.from_documents(
+        if documents is None:
-            documents=documents,
+            from qdrant_client import QdrantClient
-            embedding=embedding,
+
-            api_key=api_key,
+            client = QdrantClient(
-            collection_name=collection_name,
+                location=location,
-            content_payload_key=content_payload_key,
+                url=host,
-            distance_func=distance_func,
+                port=port,
-            grpc_port=grpc_port,
+                grpc_port=grpc_port,
-            host=host,
+                https=https,
-            https=https,
+                prefix=prefix,
-            location=location,
+                timeout=timeout,
-            metadata_payload_key=metadata_payload_key,
+                prefer_grpc=prefer_grpc,
-            path=path,
+                metadata_payload_key=metadata_payload_key,
-            port=port,
+                content_payload_key=content_payload_key,
-            prefer_grpc=prefer_grpc,
+                api_key=api_key,
-            prefix=prefix,
+                collection_name=collection_name,
-            search_kwargs=search_kwargs,
+                host=host,
-            timeout=timeout,
+                path=path,
-            url=url,
+            )
-        )
+            vs = Qdrant(
+                client=client,
+                collection_name=collection_name,
+                embeddings=embedding,
+            )
+            return vs
+        else:
+            vs = Qdrant.from_documents(
+                documents=documents,  # type: ignore
+                embedding=embedding,
+                api_key=api_key,
+                collection_name=collection_name,
+                content_payload_key=content_payload_key,
+                distance_func=distance_func,
+                grpc_port=grpc_port,
+                host=host,
+                https=https,
+                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,
+            )
+        return vs

src/backend/langflow/components/vectorstores/Redis.py

@@ -5,7 +5,6 @@ from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.redis import Redis
 from langchain_core.documents import Document
 from langchain_core.retrievers import BaseRetriever
-
 from langflow import CustomComponent
 
 
@@ -31,6 +30,7 @@ class RedisComponent(CustomComponent):
             "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,
@@ -43,6 +43,7 @@ class RedisComponent(CustomComponent):
         embedding: Embeddings,
         redis_server_url: str,
         redis_index_name: str,
+        schema: Optional[str] = None,
         documents: Optional[Document] = None,
     ) -> Union[VectorStore, BaseRetriever]:
         """
@@ -58,10 +59,12 @@ class RedisComponent(CustomComponent):
         - VectorStore: The Vector Store object.
         """
         if documents is None:
+            if schema is None:
+                raise ValueError("If no documents are provided, a schema must be provided.")
             redis_vs = Redis.from_existing_index(
                 embedding=embedding,
                 index_name=redis_index_name,
-                schema=None,
+                schema=schema,
                 key_prefix=None,
                 redis_url=redis_server_url,
             )

src/backend/langflow/components/vectorstores/Vectara.py

@@ -6,7 +6,6 @@ from typing import List, Optional, Union
 from langchain_community.embeddings import FakeEmbeddings
 from langchain_community.vectorstores.vectara import Vectara
 from langchain_core.vectorstores import VectorStore
-
 from langflow import CustomComponent
 from langflow.field_typing import BaseRetriever, Document
 
@@ -46,7 +45,7 @@ class VectaraComponent(CustomComponent):
 
         if documents is not None:
             return Vectara.from_documents(
-                documents=documents,
+                documents=documents,  # type: ignore
                 embedding=FakeEmbeddings(size=768),
                 vectara_customer_id=vectara_customer_id,
                 vectara_corpus_id=vectara_corpus_id,

src/backend/langflow/components/vectorstores/pgvector.py

@@ -5,7 +5,6 @@ from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.pgvector import PGVector
 from langchain_core.documents import Document
 from langchain_core.retrievers import BaseRetriever
-
 from langflow import CustomComponent
 
 
@@ -63,13 +62,13 @@ class PGVectorComponent(CustomComponent):
                     collection_name=collection_name,
                     connection_string=pg_server_url,
                 )
-
+            else:
-            vector_store = PGVector.from_documents(
+                vector_store = PGVector.from_documents(
-                embedding=embedding,
+                    embedding=embedding,
-                documents=documents,
+                    documents=documents,  # type: ignore
-                collection_name=collection_name,
+                    collection_name=collection_name,
-                connection_string=pg_server_url,
+                    connection_string=pg_server_url,
-            )
+                )
         except Exception as e:
             raise RuntimeError(f"Failed to build PGVector: {e}")
         return vector_store

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

@@ -6,7 +6,12 @@ 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.vertex.base import Vertex
-from langflow.graph.vertex.types import ChatVertex, FileToolVertex, LLMVertex, ToolkitVertex
+from langflow.graph.vertex.types import (
+    ChatVertex,
+    FileToolVertex,
+    LLMVertex,
+    ToolkitVertex,
+)
 from langflow.interface.tools.constants import FILE_TOOLS
 from langflow.utils import payload
 from loguru import logger
@@ -127,7 +132,9 @@ class Graph:
             return
         for vertex in self.vertices:
             if not self._validate_vertex(vertex):
-                raise ValueError(f"{vertex.vertex_type} is not connected to any other components")
+                raise ValueError(
+                    f"{vertex.vertex_type} is not connected to any other components"
+                )
 
     def _validate_vertex(self, vertex: Vertex) -> bool:
         """Validates a vertex."""
@@ -140,7 +147,11 @@ class Graph:
 
     def get_vertex_edges(self, vertex_id: str) -> List[ContractEdge]:
         """Returns a list of edges for a given vertex."""
-        return [edge for edge in self.edges if edge.source_id == vertex_id or edge.target_id == vertex_id]
+        return [
+            edge
+            for edge in self.edges
+            if edge.source_id == vertex_id or edge.target_id == vertex_id
+        ]
 
     def get_vertices_with_target(self, vertex_id: str) -> List[Vertex]:
         """Returns the vertices connected to a vertex."""
@@ -178,7 +189,9 @@ class Graph:
         def dfs(vertex):
             if state[vertex] == 1:
                 # We have a cycle
-                raise ValueError("Graph contains a cycle, cannot perform topological sort")
+                raise ValueError(
+                    "Graph contains a cycle, cannot perform topological sort"
+                )
             if state[vertex] == 0:
                 state[vertex] = 1
                 for edge in vertex.edges:
@@ -237,7 +250,9 @@ class Graph:
             edges.append(ContractEdge(source, target, edge))
         return edges
 
-    def _get_vertex_class(self, node_type: str, node_base_type: str, node_id: str) -> Type[Vertex]:
+    def _get_vertex_class(
+        self, node_type: str, node_base_type: str, node_id: str
+    ) -> Type[Vertex]:
         """Returns the node class based on the node type."""
         # First we check for the node_base_type
         node_name = node_id.split("-")[0]
@@ -267,14 +282,18 @@ class Graph:
             vertex_type: str = vertex_data["type"]  # type: ignore
             vertex_base_type: str = vertex_data["node"]["template"]["_type"]  # type: ignore
 
-            VertexClass = self._get_vertex_class(vertex_type, vertex_base_type, vertex_data["id"])
+            VertexClass = self._get_vertex_class(
+                vertex_type, vertex_base_type, vertex_data["id"]
+            )
             vertex_instance = VertexClass(vertex, graph=self)
             vertex_instance.set_top_level(self.top_level_vertices)
             vertices.append(vertex_instance)
 
         return vertices
 
-    def get_children_by_vertex_type(self, vertex: Vertex, vertex_type: str) -> List[Vertex]:
+    def get_children_by_vertex_type(
+        self, vertex: Vertex, vertex_type: str
+    ) -> List[Vertex]:
         """Returns the children of a vertex based on the vertex type."""
         children = []
         vertex_types = [vertex.data["type"]]
@@ -286,7 +305,9 @@ class Graph:
 
     def __repr__(self):
         vertex_ids = [vertex.id for vertex in self.vertices]
-        edges_repr = "\n".join([f"{edge.source_id} --> {edge.target_id}" for edge in self.edges])
+        edges_repr = "\n".join(
+            [f"{edge.source_id} --> {edge.target_id}" for edge in self.edges]
+        )
         return f"Graph:\nNodes: {vertex_ids}\nConnections:\n{edges_repr}"
 
     def layered_topological_sort(self):
@@ -299,7 +320,9 @@ class Graph:
             in_degree[edge.target_id] += 1
 
         # Queue for vertices with no incoming edges
-        queue = deque(vertex.id for vertex in self.vertices if in_degree[vertex.id] == 0)
+        queue = deque(
+            vertex.id for vertex in self.vertices if in_degree[vertex.id] == 0
+        )
         layers = []
 
         current_layer = 0
@@ -314,9 +337,40 @@ class Graph:
                     if in_degree[neighbor] == 0:
                         queue.append(neighbor)
             current_layer += 1  # Next layer
+        new_layers = self.refine_layers(graph, layers)
+        return new_layers
 
-        return layers
+    def refine_layers(self, graph, initial_layers):
-        return layers
+        # Map each vertex to its current layer
-        return layers
+        vertex_to_layer = {}
-        return layers
+        for layer_index, layer in enumerate(initial_layers):
-        return layers
+            for vertex in layer:
+                vertex_to_layer[vertex] = layer_index
+
+        # Build the adjacency list for reverse lookup (dependencies)
+
+        refined_layers = [[] for _ in initial_layers]  # Start with empty layers
+        new_layer_index_map = defaultdict(
+            int
+        )  # Map each vertex to its highest dependency layer
+
+        for vertex_id, deps in graph.items():
+            for dep in deps:
+                new_layer_index_map[vertex_id] = (
+                    max(new_layer_index_map[vertex_id], vertex_to_layer[dep]) - 1
+                )
+
+        for layer_index, layer in enumerate(initial_layers):
+            for vertex_id in layer:
+                # Place the vertex in the highest possible layer where its dependencies are met
+                new_layer_index = new_layer_index_map[vertex_id]
+                if new_layer_index > layer_index:
+                    refined_layers[new_layer_index].append(vertex_id)
+                    vertex_to_layer[vertex_id] = new_layer_index
+                else:
+                    refined_layers[layer_index].append(vertex_id)
+
+        # Remove empty layers if any
+        refined_layers = [layer for layer in refined_layers if layer]
+
+        return refined_layers

src/backend/langflow/interface/custom/custom_component/component.py

@@ -5,7 +5,6 @@ from typing import Any, ClassVar, Optional
 import emoji
 from cachetools import TTLCache, cachedmethod
 from fastapi import HTTPException
-
 from langflow.interface.custom.code_parser import CodeParser
 from langflow.interface.custom.eval import eval_custom_component_code
 from langflow.utils import validate
@@ -21,7 +20,9 @@ class ComponentFunctionEntrypointNameNullError(HTTPException):
 
 class Component:
     ERROR_CODE_NULL: ClassVar[str] = "Python code must be provided."
-    ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = "The name of the entrypoint function must be provided."
+    ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = (
+        "The name of the entrypoint function must be provided."
+    )
 
     code: Optional[str] = None
     _function_entrypoint_name: str = "build"
@@ -36,10 +37,6 @@ class Component:
             else:
                 setattr(self, key, value)
 
-        # Validate the emoji at the icon field
-        if self.icon:
-            self.icon = self.validate_icon(self.icon)
-
     def __setattr__(self, key, value):
         if key == "_user_id" and hasattr(self, "_user_id"):
             warnings.warn("user_id is immutable and cannot be changed.")
@@ -68,8 +65,8 @@ class Component:
 
         return validate.create_function(self.code, self._function_entrypoint_name)
 
-    def getattr_return_str(self, component, value):
+    def getattr_return_str(self, value):
-        value = getattr(component, value)
+
         return str(value) if value else ""
 
     def build_template_config(self) -> dict:
@@ -89,19 +86,22 @@ class Component:
 
         for attribute, func in attributes_func_mapping.items():
             if hasattr(component_instance, attribute):
-                template_config[attribute] = func(component=component_instance, value=attribute)
+                value = getattr(component_instance, attribute)
+                if value is not None:
+                    template_config[attribute] = func(value=value)
 
-            return template_config
+        return template_config
 
     def validate_icon(self, value: str, *args, **kwargs):
         # we are going to use the emoji library to validate the emoji
         # emojis can be defined using the :emoji_name: syntax
         if not value.startswith(":") or not value.endswith(":"):
-            raise ValueError("Invalid emoji. Please use the :emoji_name: syntax.")
+            warnings.warn("Invalid emoji. Please use the :emoji_name: syntax.")
-
+            return value
         emoji_value = emoji.emojize(value, variant="emoji_type")
         if value == emoji_value:
-            raise ValueError(f"Invalid emoji. {value} is not a valid emoji.")
+            warnings.warn(f"Invalid emoji. {value} is not a valid emoji.")
+            return value
         return emoji_value
 
     def build(self, *args: Any, **kwargs: Any) -> Any:

src/backend/langflow/interface/custom/custom_component/custom_component.py

@@ -1,11 +1,11 @@
 import operator
+from pathlib import Path
 from typing import Any, Callable, ClassVar, List, Optional, Union
 from uuid import UUID
 
 import yaml
 from cachetools import TTLCache, cachedmethod
 from fastapi import HTTPException
-
 from langflow.interface.custom.code_parser.utils import (
     extract_inner_type_from_generic_alias,
     extract_union_types_from_generic_alias,
@@ -13,7 +13,11 @@ from langflow.interface.custom.code_parser.utils import (
 from langflow.interface.custom.custom_component.component import Component
 from langflow.services.database.models.flow import Flow
 from langflow.services.database.utils import session_getter
-from langflow.services.deps import get_credential_service, get_db_service, get_storage_service
+from langflow.services.deps import (
+    get_credential_service,
+    get_db_service,
+    get_storage_service,
+)
 from langflow.services.storage.service import StorageService
 from langflow.utils import validate
 
@@ -42,6 +46,16 @@ class CustomComponent(Component):
         self.cache = TTLCache(maxsize=1024, ttl=60)
         super().__init__(**data)
 
+    @staticmethod
+    def resolve_path(path: str) -> str:
+        """Resolves the path to an absolute path."""
+        path_object = Path(path)
+        if path_object.parts[0] == "~":
+            path_object = path_object.expanduser()
+        elif path_object.is_relative_to("."):
+            path_object = path_object.resolve()
+        return str(path_object)
+
     def get_full_path(self, path: str) -> str:
         storage_svc: "StorageService" = get_storage_service()
 
@@ -78,7 +92,8 @@ 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."
                         ),
                     },
                 )
@@ -92,14 +107,20 @@ 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 {}
@@ -112,7 +133,10 @@ class CustomComponent(Component):
         return_type = build_method["return_type"]
 
         # If list or List is in the return type, then we remove it and return the inner type
-        if hasattr(return_type, "__origin__") and return_type.__origin__ in [list, List]:
+        if hasattr(return_type, "__origin__") and return_type.__origin__ in [
+            list,
+            List,
+        ]:
             return_type = extract_inner_type_from_generic_alias(return_type)
 
         # If the return type is not a Union, then we just return it as a list
@@ -153,7 +177,9 @@ 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
 
@@ -163,7 +189,9 @@ 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."""
@@ -214,7 +242,11 @@ 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")
 

src/backend/langflow/interface/custom/utils.py

@@ -7,8 +7,6 @@ from typing import Any, Dict, List, Optional, Union
 from uuid import UUID
 
 from fastapi import HTTPException
-from loguru import logger
-
 from langflow.field_typing.range_spec import RangeSpec
 from langflow.interface.custom.code_parser.utils import extract_inner_type
 from langflow.interface.custom.custom_component import CustomComponent
@@ -22,6 +20,7 @@ from langflow.template.field.base import TemplateField
 from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode
 from langflow.utils import validate
 from langflow.utils.util import get_base_classes
+from loguru import logger
 
 
 def add_output_types(frontend_node: CustomComponentFrontendNode, return_types: List[str]):
@@ -151,9 +150,6 @@ def add_extra_fields(frontend_node, field_config, function_args):
     if not function_args:
         return
 
-    # sort function_args which is a list of dicts
-    function_args.sort(key=lambda x: x["name"])
-
     for extra_field in function_args:
         if "name" not in extra_field or extra_field["name"] == "self":
             continue

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

@@ -132,12 +132,12 @@ async def instantiate_custom_component(node_type, class_object, params, user_id)
 
     if is_async:
         # Await the build method directly if it's async
-        built_object = await custom_component.build(**params_copy)
+        build_result = await custom_component.build(**params_copy)
     else:
         # Call the build method directly if it's sync
-        built_object = custom_component.build(**params_copy)
+        build_result = custom_component.build(**params_copy)
 
-    return built_object, {"repr": custom_component.custom_repr()}
+    return build_result, {"repr": custom_component.custom_repr()}
 
 
 def instantiate_wrapper(node_type, class_object, params):

src/backend/langflow/processing/base.py

@@ -2,10 +2,11 @@ from typing import TYPE_CHECKING, List, Union
 
 from langchain.agents.agent import AgentExecutor
 from langchain.callbacks.base import BaseCallbackHandler
+from loguru import logger
+
 from langflow.api.v1.callback import AsyncStreamingLLMCallbackHandler, StreamingLLMCallbackHandler
 from langflow.processing.process import fix_memory_inputs, format_actions
 from langflow.services.deps import get_plugins_service
-from loguru import logger
 
 if TYPE_CHECKING:
     from langfuse.callback import CallbackHandler  # type: ignore
@@ -28,13 +29,12 @@ def setup_callbacks(sync, trace_id, **kwargs):
 
 def get_langfuse_callback(trace_id):
     from langflow.services.deps import get_plugins_service
-    from langfuse.callback import CreateTrace
 
     logger.debug("Initializing langfuse callback")
     if langfuse := get_plugins_service().get("langfuse"):
         logger.debug("Langfuse credentials found")
         try:
-            trace = langfuse.trace(CreateTrace(name="langflow-" + trace_id, id=trace_id))
+            trace = langfuse.trace(name="langflow-" + trace_id, id=trace_id)
             return trace.getNewHandler()
         except Exception as exc:
             logger.error(f"Error initializing langfuse callback: {exc}")

src/backend/langflow/services/plugins/langfuse_plugin.py

@@ -64,14 +64,13 @@ class LangfusePlugin(CallbackPlugin):
     def get_callback(self, _id: Optional[str] = None):
         if _id is None:
             _id = "default"
-        from langfuse.callback import CreateTrace  # type: ignore
 
         logger.debug("Initializing langfuse callback")
 
         try:
             langfuse_instance = self.get()
             if langfuse_instance is not None and hasattr(langfuse_instance, "trace"):
-                trace = langfuse_instance.trace(CreateTrace(name="langflow-" + _id, id=_id))
+                trace = langfuse_instance.trace(name="langflow-" + _id, id=_id)
                 if trace:
                     return trace.getNewHandler()
 

src/frontend/package-lock.json

@@ -45,7 +45,7 @@
         "dompurify": "^3.0.5",
         "esbuild": "^0.17.19",
         "lodash": "^4.17.21",
-        "lucide-react": "^0.233.0",
+        "lucide-react": "^0.331.0",
         "moment": "^2.29.4",
         "react": "^18.2.0",
         "react-ace": "^10.1.0",
@@ -99,7 +99,7 @@
         "pretty-quick": "^3.1.3",
         "tailwindcss": "^3.3.3",
         "typescript": "^5.2.2",
-        "vite": "^4.5.1"
+        "vite": "^4.5.2"
       }
     },
     "node_modules/@adobe/css-tools": {
@@ -7220,9 +7220,9 @@
       }
     },
     "node_modules/lucide-react": {
-      "version": "0.233.0",
+      "version": "0.331.0",
-      "resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-0.233.0.tgz",
+      "resolved": "https://registry.npmjs.org/lucide-react/-/lucide-react-0.331.0.tgz",
-      "integrity": "sha512-r0jMHF0vPDq2wBbZ0B3rtIcBjDyWDKpHu+vAjD2OHn2WLUr3HN5IHovtO0EMgQXuSI7YrMZbjsEZWC2uBHr8nQ==",
+      "integrity": "sha512-CHFJ0ve9vaZ7bB2VRAl27SlX1ELh6pfNC0jS96qGpPEEzLkLDGq4pDBFU8RhOoRMqsjXqTzLm9U6bZ1OcIHq7Q==",
       "peerDependencies": {
         "react": "^16.5.1 || ^17.0.0 || ^18.0.0"
       }

src/frontend/package.json

@@ -40,7 +40,7 @@
     "dompurify": "^3.0.5",
     "esbuild": "^0.17.19",
     "lodash": "^4.17.21",
-    "lucide-react": "^0.233.0",
+    "lucide-react": "^0.331.0",
     "moment": "^2.29.4",
     "react": "^18.2.0",
     "react-ace": "^10.1.0",
@@ -121,6 +121,6 @@
     "pretty-quick": "^3.1.3",
     "tailwindcss": "^3.3.3",
     "typescript": "^5.2.2",
-    "vite": "^4.5.1"
+    "vite": "^4.5.2"
   }
 }

src/frontend/src/App.tsx

@@ -30,6 +30,7 @@ export default function App() {
   );
   const loading = useAlertStore((state) => state.loading);
   const [fetchError, setFetchError] = useState(false);
+  const isLoading = useFlowsManagerStore((state) => state.isLoading);
 
   const removeAlert = (id: string) => {
     removeFromTempNotificationList(id);
@@ -86,7 +87,7 @@ export default function App() {
             description={FETCH_ERROR_DESCRIPION}
             message={FETCH_ERROR_MESSAGE}
           ></FetchErrorComponent>
-        ) : loading ? (
+        ) : isLoading ? (
           <div className="loading-page-panel">
             <LoadingComponent remSize={50} />
           </div>

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

@@ -1,4 +1,4 @@
-import { useEffect, useState } from "react";
+import { useCallback, useEffect, useState } from "react";
 import { NodeToolbar } from "reactflow";
 import ShadTooltip from "../../components/ShadTooltipComponent";
 import Tooltip from "../../components/TooltipComponent";
@@ -112,6 +112,39 @@ export default function GenericNode({
 
   const nameEditable = data.node?.flow || data.type === "CustomComponent";
 
+  const emojiRegex = /\p{Emoji}/u;
+  const isEmoji = emojiRegex.test(data?.node?.icon!);
+
+  const iconNodeRender = useCallback(() => {
+    const iconElement = data?.node?.icon;
+    const iconColor = nodeColors[types[data.type]];
+    const iconName =
+      iconElement || (data.node?.flow ? "group_components" : name);
+    const iconClassName = `generic-node-icon ${
+      !showNode ? "absolute inset-x-6 h-12 w-12" : ""
+    }`;
+
+    if (iconElement && isEmoji) {
+      return nodeIconFragment(iconElement);
+    } else {
+      return checkNodeIconFragment(iconColor, iconName, iconClassName);
+    }
+  }, [data, isEmoji, name, showNode]);
+
+  const nodeIconFragment = (icon) => {
+    return <span className="text-lg">{icon}</span>;
+  };
+
+  const checkNodeIconFragment = (iconColor, iconName, iconClassName) => {
+    return (
+      <IconComponent
+        name={iconName}
+        className={iconClassName}
+        iconColor={iconColor}
+      />
+    );
+  };
+
   return (
     <>
       <NodeToolbar>
@@ -164,19 +197,7 @@ export default function GenericNode({
                 (!showNode && "justify-center")
               }
             >
-              {data?.node?.icon ? (
+              {iconNodeRender()}
-                <span className="text-lg">{data?.node?.icon}</span>
-              ) : (
-                <IconComponent
-                  name={data.node?.flow ? "group_components" : name}
-                  className={
-                    "generic-node-icon " +
-                    (!showNode ? "absolute inset-x-6 h-12 w-12" : "")
-                  }
-                  iconColor={`${nodeColors[types[data.type]]}`}
-                />
-              )}
-
               {showNode && (
                 <div className="generic-node-tooltip-div">
                   {nameEditable && inputName ? (
@@ -370,7 +391,7 @@ export default function GenericNode({
                         <span className="flex">
                           Build{" "}
                           <IconComponent
-                            name="Zap"
+                            name="Play"
                             className=" h-5 fill-build-trigger stroke-build-trigger stroke-1"
                           />{" "}
                           flow to validate status.
@@ -390,7 +411,7 @@ export default function GenericNode({
                   >
                     <div className="generic-node-status-position flex items-center justify-center">
                       <IconComponent
-                        name="Zap"
+                        name="Play"
                         className={classNames(
                           validationStatus && validationStatus.valid
                             ? "green-status"
@@ -399,7 +420,7 @@ export default function GenericNode({
                         )}
                       />
                       <IconComponent
-                        name="Zap"
+                        name="Play"
                         className={classNames(
                           validationStatus && !validationStatus.valid
                             ? "red-status"
@@ -408,7 +429,7 @@ export default function GenericNode({
                         )}
                       />
                       <IconComponent
-                        name="Zap"
+                        name="Play"
                         className={classNames(
                           !validationStatus || isBuilding
                             ? "yellow-status"

src/frontend/src/components/ui/button.tsx

@@ -12,7 +12,7 @@ const buttonVariants = cva(
         destructive:
           "bg-destructive text-destructive-foreground hover:bg-destructive/90",
         outline:
-          "border border-input hover:bg-accent hover:text-accent-foreground",
+          "border border-input hover:bg-input hover:text-accent-foreground",
         primary:
           "border bg-background text-secondary-foreground hover:bg-secondary-foreground/5 dark:hover:bg-background/10 hover:shadow-sm",
         secondary:

src/frontend/src/pages/MainPage/components/components/index.tsx

@@ -94,12 +94,6 @@ export default function ComponentsComponent({
     setPageSize(10);
   }
 
-  useEffect(() => {
-    setTimeout(() => {
-      setLoadingScreen(false);
-    }, 600);
-  }, []);
-
   return (
     <CardsWrapComponent
       onFileDrop={onFileDrop}
@@ -107,7 +101,7 @@ export default function ComponentsComponent({
     >
       <div className="flex h-full w-full flex-col justify-between">
         <div className="flex w-full flex-col gap-4">
-          {!loadingScreen && data.length === 0 ? (
+          {!isLoading && data.length === 0 ? (
             <div className="mt-6 flex w-full items-center justify-center text-center">
               <div className="flex-max-width h-full flex-col">
                 <div className="flex w-full flex-col gap-4">
@@ -136,7 +130,7 @@ export default function ComponentsComponent({
             </div>
           ) : (
             <div className="grid w-full gap-4 md:grid-cols-2 lg:grid-cols-2">
-              {loadingScreen === false && data?.length > 0 ? (
+              {isLoading === false && data?.length > 0 ? (
                 data?.map((item, idx) => (
                   <CollectionCardComponent
                     onDelete={() => {
@@ -185,7 +179,7 @@ export default function ComponentsComponent({
             </div>
           )}
         </div>
-        {!loadingScreen && data.length > 0 && (
+        {!isLoading && data.length > 0 && (
           <div className="relative py-6">
             <PaginatorComponent
               storeComponent={true}

src/frontend/src/stores/flowsManagerStore.ts

@@ -62,10 +62,10 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
           if (dbData) {
             const { data, flows } = processFlows(dbData, false);
             get().setFlows(flows);
-            set({ isLoading: false });
             useTypesStore.setState((state) => ({
               data: { ...state.data, ["saved_components"]: data },
             }));
+            set({ isLoading: false });
             resolve();
           }
         })

src/frontend/src/stores/typesStore.ts

@@ -4,6 +4,7 @@ import { APIDataType } from "../types/api";
 import { TypesStoreType } from "../types/zustand/types";
 import { templatesGenerator, typesGenerator } from "../utils/reactflowUtils";
 import useAlertStore from "./alertStore";
+import useFlowsManagerStore from "./flowsManagerStore";
 
 export const useTypesStore = create<TypesStoreType>((set, get) => ({
   types: {},
@@ -11,6 +12,8 @@ export const useTypesStore = create<TypesStoreType>((set, get) => ({
   data: {},
   getTypes: () => {
     return new Promise<void>(async (resolve, reject) => {
+      const setLoading = useFlowsManagerStore.getState().setIsLoading;
+      setLoading(true);
       getAll()
         .then((response) => {
           const data = response.data;
@@ -20,6 +23,7 @@ export const useTypesStore = create<TypesStoreType>((set, get) => ({
             data: { ...old.data, ...data },
             templates: templatesGenerator(data),
           }));
+          setLoading(false)
           resolve();
         })
         .catch((error) => {

src/frontend/src/utils/buildUtils.ts

@@ -26,10 +26,11 @@ export async function buildVertices({
     for (let i = 0; i < verticesOrder.length; i += 1) {
       const innerArray = verticesOrder[i];
       const idIndex = innerArray.indexOf(nodeId);
-
       if (idIndex !== -1) {
-        // If the targetId is found in the inner array, cut the array before the id
+        // If there's a nodeId, we want to run just that component and not the entire layer
-        vertices.push(innerArray.slice(0, idIndex + 1));
+        // because a layer contains dependencies for the next layer
+        // and we are stopping at the layer that contains the nodeId
+        vertices.push([innerArray[idIndex]]);
         break; // Stop searching after finding the first occurrence
       }
       // If the targetId is not found, include the entire inner array
@@ -38,7 +39,7 @@ export async function buildVertices({
   } else {
     vertices = verticesOrder;
   }
-
+  console.log("Vertices: ", vertices);
   const buildResults: Array<boolean> = [];
   for (let i = 0; i < vertices.length; i += 1) {
     await Promise.all(

src/frontend/src/utils/styleUtils.ts

@@ -34,6 +34,7 @@ import {
   FileSearch,
   FileSearch2,
   FileText,
+  Cable,
   FileUp,
   Fingerprint,
   FolderPlus,
@@ -70,6 +71,7 @@ import {
   Paperclip,
   Pencil,
   Pin,
+  Play,
   Plus,
   Redo,
   RefreshCcw,
@@ -237,6 +239,7 @@ export const nodeNames: { [char: string]: string } = {
 };
 
 export const nodeIconsLucide: iconsType = {
+  Play,
   Vectara: VectaraIcon,
   ArrowUpToLine: ArrowUpToLine,
   Chroma: ChromaIcon,
@@ -394,7 +397,7 @@ export const nodeIconsLucide: iconsType = {
   Combine,
   TerminalIcon,
   Repeat,
-  io: ArrowDownUp,
+  io: Cable,
   ScreenShare,
   Code,
 };

src/frontend/src/utils/utils.ts

@@ -139,12 +139,16 @@ export function groupByFamily(
           })))
     );
   };
+  console.log(flow);
 
   if (flow) {
+    // se existir o flow
     for (const node of flow) {
+      // para cada node do flow
+      if (node!.data!.node!.flow) break; // não faz nada se o node for um group
       const nodeData = node.data;
 
-      const foundNode = checkedNodes.get(nodeData.type);
+      const foundNode = checkedNodes.get(nodeData.type); // verifica se o tipo do node já foi checado
       checkedNodes.set(nodeData.type, {
         hasBaseClassInTemplate:
           foundNode?.hasBaseClassInTemplate ||
@@ -153,7 +157,7 @@ export function groupByFamily(
           foundNode?.hasBaseClassInBaseClasses ||
           nodeData.node!.base_classes.some((baseClass) =>
             baseClassesSet.has(baseClass)
-          ),
+          ), //seta como anterior ou verifica se o node tem base class
         displayName: nodeData.node?.display_name,
       });
     }

tests/test_endpoints.py

@@ -611,35 +611,36 @@ def test_async_task_processing(distributed_client, flow, created_api_key):
     assert "Gabriel" in task_status_json["result"]["text"], task_status_json["result"]
 
 
+# ! Deactivating this until updating the test
 # Test function without loop
-@pytest.mark.async_test
+# @pytest.mark.async_test
-def test_async_task_processing_vector_store(client, added_vector_store, created_api_key):
+# def test_async_task_processing_vector_store(client, added_vector_store, created_api_key):
-    headers = {"x-api-key": created_api_key.api_key}
+#     headers = {"x-api-key": created_api_key.api_key}
-    post_data = {"inputs": {"input": "How do I upload examples?"}}
+#     post_data = {"inputs": {"input": "How do I upload examples?"}}
 
-    # Run the /api/v1/process/{flow_id} endpoint with sync=False
+#     # Run the /api/v1/process/{flow_id} endpoint with sync=False
-    response = client.post(
+#     response = client.post(
-        f"api/v1/process/{added_vector_store.get('id')}",
+#         f"api/v1/process/{added_vector_store.get('id')}",
-        headers=headers,
+#         headers=headers,
-        json={**post_data, "sync": False},
+#         json={**post_data, "sync": False},
-    )
+#     )
-    assert response.status_code == 200, response.json()
+#     assert response.status_code == 200, response.json()
-    assert "result" in response.json()
+#     assert "result" in response.json()
-    assert "FAILURE" not in response.json()["result"]
+#     assert "FAILURE" not in response.json()["result"]
 
-    # Extract the task ID from the response
+#     # Extract the task ID from the response
-    task = response.json().get("task")
+#     task = response.json().get("task")
-    task_id = task.get("id")
+#     task_id = task.get("id")
-    task_href = task.get("href")
+#     task_href = task.get("href")
-    assert task_id is not None
+#     assert task_id is not None
-    assert task_href is not None
+#     assert task_href is not None
-    assert task_href == f"api/v1/task/{task_id}"
+#     assert task_href == f"api/v1/task/{task_id}"
 
-    # Polling the task status using the helper function
+#     # Polling the task status using the helper function
-    task_status_json = poll_task_status(client, headers, task_href)
+#     task_status_json = poll_task_status(client, headers, task_href)
-    assert task_status_json is not None, "Task did not complete in time"
+#     assert task_status_json is not None, "Task did not complete in time"
 
-    # Validate that the task completed successfully and the result is as expected
+#     # Validate that the task completed successfully and the result is as expected
-    assert "result" in task_status_json, task_status_json
+#     assert "result" in task_status_json, task_status_json
-    assert "output" in task_status_json["result"], task_status_json["result"]
+#     assert "output" in task_status_json["result"], task_status_json["result"]
-    assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]
+#     assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]