Remove console.log

.github/workflows/lint.yml

@@ -3,7 +3,15 @@ name: lint
 on:
   push:
     branches: [main]
+    paths:
+      - "poetry.lock"
+      - "pyproject.toml"
+      - "src/backend/**"
   pull_request:
+    paths:
+      - "poetry.lock"
+      - "pyproject.toml"
+      - "src/backend/**"
 
 env:
   POETRY_VERSION: "1.7.0"

.github/workflows/test.yml

@@ -3,8 +3,16 @@ name: test
 on:
   push:
     branches: [main]
+    paths:
+      - "poetry.lock"
+      - "pyproject.toml"
+      - "src/backend/**"
   pull_request:
     branches: [dev]
+    paths:
+      - "poetry.lock"
+      - "pyproject.toml"
+      - "src/backend/**"
 
 env:
   POETRY_VERSION: "1.5.0"

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

@@ -1,10 +1,11 @@
 import time
 import uuid
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING, Annotated, Optional
 
 from fastapi import (
     APIRouter,
     BackgroundTasks,
+    Body,
     Depends,
     HTTPException,
     WebSocket,
@@ -21,6 +22,7 @@ from langflow.api.utils import (
     format_exception_message,
 )
 from langflow.api.v1.schemas import (
+    InputValueRequest,
     ResultDataResponse,
     StreamData,
     VertexBuildResponse,
@@ -32,8 +34,9 @@ from langflow.services.auth.utils import (
     get_current_user_for_websocket,
 )
 from langflow.services.chat.service import ChatService
-from langflow.services.deps import get_chat_service, get_session
+from langflow.services.deps import get_chat_service, get_session, get_session_service
 from langflow.services.monitor.utils import log_vertex_build
+from langflow.services.session.service import SessionService
 
 if TYPE_CHECKING:
     from langflow.graph.vertex.types import ChatVertex
@@ -138,10 +141,12 @@ async def build_vertex(
     flow_id: str,
     vertex_id: str,
     background_tasks: BackgroundTasks,
+    inputs: Annotated[InputValueRequest, Body(embed=True)] = None,
     chat_service: "ChatService" = Depends(get_chat_service),
     current_user=Depends(get_current_active_user),
 ):
     """Build a vertex instead of the entire graph."""
+    {"inputs": {"input_value": "some value"}}
     start_time = time.perf_counter()
     try:
         start_time = time.perf_counter()
@@ -162,7 +167,8 @@ async def build_vertex(
         vertex = graph.get_vertex(vertex_id)
         try:
             if not vertex.pinned or not vertex._built:
-                await vertex.build(user_id=current_user.id)
+                inputs_dict = inputs.model_dump() if inputs else {}
+                await vertex.build(user_id=current_user.id, inputs=inputs_dict)
 
             if vertex.result is not None:
                 params = vertex._built_object_repr()
@@ -175,7 +181,7 @@ async def build_vertex(
             result_data_response = ResultDataResponse(**result_dict.model_dump())
 
         except Exception as exc:
-            logger.error(f"Error building vertex: {exc}")
+            logger.exception(f"Error building vertex: {exc}")
             params = format_exception_message(exc)
             valid = False
             result_data_response = ResultDataResponse(results={})
@@ -185,15 +191,16 @@ async def build_vertex(
             chat_service.clear_cache(flow_id)
 
         # Log the vertex build
-        background_tasks.add_task(
+        if not vertex.will_stream:
-            log_vertex_build,
+            background_tasks.add_task(
-            flow_id=flow_id,
+                log_vertex_build,
-            vertex_id=vertex_id,
+                flow_id=flow_id,
-            valid=valid,
+                vertex_id=vertex_id,
-            params=params,
+                valid=valid,
-            data=result_data_response,
+                params=params,
-            artifacts=artifacts,
+                data=result_data_response,
-        )
+                artifacts=artifacts,
+            )
 
         timedelta = time.perf_counter() - start_time
         duration = format_elapsed_time(timedelta)
@@ -226,39 +233,56 @@ async def build_vertex(
 async def build_vertex_stream(
     flow_id: str,
     vertex_id: str,
+    session_id: Optional[str] = None,
     chat_service: "ChatService" = Depends(get_chat_service),
+    session_service: "SessionService" = Depends(get_session_service),
 ):
     """Build a vertex instead of the entire graph."""
     try:
 
         async def stream_vertex():
             try:
-                cache = chat_service.get_cache(flow_id)
+                if not session_id:
-                if not cache:
+                    cache = chat_service.get_cache(flow_id)
-                    # If there's no cache
+                    if not cache:
-                    raise ValueError(f"No cache found for {flow_id}.")
+                        # If there's no cache
+                        raise ValueError(f"No cache found for {flow_id}.")
+                    else:
+                        graph = cache.get("result")
                 else:
-                    graph = cache.get("result")
+                    session_data = await session_service.load_session(session_id)
+                    graph, artifacts = session_data if session_data else (None, None)
+                    if not graph:
+                        raise ValueError(f"No graph found for {flow_id}.")
 
                 vertex: "ChatVertex" = graph.get_vertex(vertex_id)
                 if not hasattr(vertex, "stream"):
                     raise ValueError(f"Vertex {vertex_id} does not support streaming")
-                if not vertex.pinned or not vertex._built:
+                if isinstance(vertex._built_result, str) and vertex._built_result:
+                    stream_data = StreamData(
+                        event="message",
+                        data={"message": f"Streaming vertex {vertex_id}"},
+                    )
+                    yield str(stream_data)
+                    stream_data = StreamData(
+                        event="message",
+                        data={"chunk": vertex._built_result},
+                    )
+                    yield str(stream_data)
+
+                elif not vertex.pinned or not vertex._built:
                     logger.debug(f"Streaming vertex {vertex_id}")
                     stream_data = StreamData(
                         event="message",
                         data={"message": f"Streaming vertex {vertex_id}"},
                     )
                     yield str(stream_data)
-                    number_of_chunks = 0
                     async for chunk in vertex.stream():
                         stream_data = StreamData(
                             event="message",
                             data={"chunk": chunk},
                         )
-                        number_of_chunks += 1
                         yield str(stream_data)
-                    logger.debug(f"Number of chunks: {number_of_chunks}")
                 elif vertex.result is not None:
                     stream_data = StreamData(
                         event="message",

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

@@ -228,6 +228,7 @@ async def run_flow_with_caching(
     flow_id: str,
     inputs: Optional[Union[List[dict], dict]] = None,
     tweaks: Optional[dict] = None,
+    stream: Annotated[bool, Body(embed=True)] = False,  # noqa: F821
     session_id: Annotated[Union[None, str], Body(embed=True)] = None,  # noqa: F821
     api_key_user: User = Depends(api_key_security),
     session_service: SessionService = Depends(get_session_service),
@@ -239,13 +240,14 @@ async def run_flow_with_caching(
             task_result: Any = None
             if not graph:
                 raise ValueError("Graph not found in the session")
-            task_result = await run_graph(
+            task_result, session_id = await run_graph(
                 graph=graph,
                 flow_id=flow_id,
                 session_id=session_id,
                 inputs=inputs,
                 artifacts=artifacts,
                 session_service=session_service,
+                stream=stream,
             )
 
         else:
@@ -263,13 +265,14 @@ async def run_flow_with_caching(
                 raise ValueError(f"Flow {flow_id} has no data")
             graph_data = flow.data
             graph_data = process_tweaks(graph_data, tweaks)
-            task_result = await run_graph(
+            task_result, session_id = await run_graph(
                 graph=graph_data,
                 flow_id=flow_id,
                 session_id=session_id,
                 inputs=inputs,
                 artifacts={},
                 session_service=session_service,
+                stream=stream,
             )
 
         return RunResponse(outputs=task_result, session_id=session_id)

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

@@ -261,3 +261,7 @@ class VertexBuildResponse(BaseModel):
 
 class VerticesBuiltResponse(BaseModel):
     vertices: List[VertexBuildResponse]
+
+
+class InputValueRequest(BaseModel):
+    input_value: str

src/backend/langflow/components/agents/OpenAIConversationalAgent.py

@@ -16,7 +16,8 @@ from langflow.field_typing.range_spec import RangeSpec
 class ConversationalAgent(CustomComponent):
     display_name: str = "OpenAI Conversational Agent"
     description: str = "Conversational Agent that can use OpenAI's function calling API"
-
+    icon = "OpenAI"
+    
     def build_config(self):
         openai_function_models = [
             "gpt-4-turbo-preview",

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

@@ -20,7 +20,10 @@ class RetrievalQAComponent(CustomComponent):
             "input_key": {"display_name": "Input Key", "advanced": True},
             "output_key": {"display_name": "Output Key", "advanced": True},
             "return_source_documents": {"display_name": "Return Source Documents"},
-            "inputs": {"display_name": "Input", "input_types": ["Text", "Document"]},
+            "input_value": {
+                "display_name": "Input",
+                "input_types": ["Text", "Document"],
+            },
         }
 
     def build(

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

@@ -1,6 +1,6 @@
 from concurrent import futures
 from pathlib import Path
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
 from langflow import CustomComponent
 from langflow.schema import Record
@@ -12,21 +12,30 @@ class GatherRecordsComponent(CustomComponent):
 
     def build_config(self) -> Dict[str, Any]:
         return {
+            "path": {"display_name": "Path"},
+            "types": {
+                "display_name": "Types",
+                "info": "File types to load. Leave empty to load all types.",
+            },
+            "depth": {"display_name": "Depth", "info": "Depth to search for files."},
+            "max_concurrency": {"display_name": "Max Concurrency", "advanced": True},
             "load_hidden": {
-                "display_name": "Load Hidden Files",
+                "display_name": "Load Hidden",
-                "value": False,
                 "advanced": True,
+                "info": "If true, hidden files will be loaded.",
             },
-            "max_concurrency": {
+            "recursive": {
-                "display_name": "Max Concurrency",
+                "display_name": "Recursive",
-                "value": 10,
                 "advanced": True,
+                "info": "If true, the search will be recursive.",
+            },
+            "silent_errors": {
+                "display_name": "Silent Errors",
+                "advanced": True,
+                "info": "If true, errors will not raise an exception.",
             },
-            "path": {"display_name": "Local Directory"},
-            "recursive": {"display_name": "Recursive", "value": True, "advanced": True},
             "use_multithreading": {
                 "display_name": "Use Multithreading",
-                "value": True,
                 "advanced": True,
             },
         }
@@ -61,7 +70,9 @@ class GatherRecordsComponent(CustomComponent):
 
         glob = "**/*" if recursive else "*"
         paths = walk_level(path_obj, depth) if depth else path_obj.glob(glob)
-        file_paths = [str(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)]
+        file_paths = [
+            str(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)
+        ]
 
         return file_paths
 
@@ -91,13 +102,20 @@ class GatherRecordsComponent(CustomComponent):
         use_multithreading: bool,
     ) -> List[Record]:
         if use_multithreading:
-            records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)
+            records = self.parallel_load_records(
+                file_paths, silent_errors, max_concurrency
+            )
         else:
-            records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]
+            records = [
+                self.parse_file_to_record(file_path, silent_errors)
+                for file_path in file_paths
+            ]
         records = list(filter(None, records))
         return records
 
-    def parallel_load_records(self, file_paths: List[str], silent_errors: bool, max_concurrency: int) -> List[Record]:
+    def parallel_load_records(
+        self, file_paths: List[str], silent_errors: bool, max_concurrency: int
+    ) -> List[Record]:
         with futures.ThreadPoolExecutor(max_workers=max_concurrency) as executor:
             loaded_files = executor.map(
                 lambda file_path: self.parse_file_to_record(file_path, silent_errors),
@@ -108,7 +126,7 @@ class GatherRecordsComponent(CustomComponent):
     def build(
         self,
         path: str,
-        types: List[str] = None,
+        types: Optional[List[str]] = None,
         depth: int = 0,
         max_concurrency: int = 2,
         load_hidden: bool = False,
@@ -116,14 +134,23 @@ class GatherRecordsComponent(CustomComponent):
         silent_errors: bool = False,
         use_multithreading: bool = True,
     ) -> List[Record]:
+        if types is None:
+            types = []
         resolved_path = self.resolve_path(path)
-        file_paths = self.retrieve_file_paths(resolved_path, types, load_hidden, recursive, depth)
+        file_paths = self.retrieve_file_paths(
+            resolved_path, types, load_hidden, recursive, depth
+        )
         loaded_records = []
 
         if use_multithreading:
-            loaded_records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)
+            loaded_records = self.parallel_load_records(
+                file_paths, silent_errors, max_concurrency
+            )
         else:
-            loaded_records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]
+            loaded_records = [
+                self.parse_file_to_record(file_path, silent_errors)
+                for file_path in file_paths
+            ]
         loaded_records = list(filter(None, loaded_records))
         self.status = loaded_records
         return loaded_records

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

@@ -9,6 +9,7 @@ class HuggingFaceEmbeddingsComponent(CustomComponent):
     documentation = (
         "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
     )
+    icon="HuggingFace"
 
     def build_config(self):
         return {

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

@@ -9,6 +9,8 @@ class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
     display_name = "HuggingFaceInferenceAPIEmbeddings"
     description = "HuggingFace sentence_transformers embedding models, API version."
     documentation = "https://github.com/huggingface/text-embeddings-inference"
+    icon="HuggingFace"
+
 
     def build_config(self):
         return {

src/backend/langflow/components/io/ChatInput.py

@@ -1,7 +1,7 @@
 from typing import Optional, Union
 
+from langflow.components.io.base.chat import ChatComponent
 from langflow.field_typing import Text
-from langflow.io.schema import ChatComponent
 from langflow.schema import Record
 
 

src/backend/langflow/components/io/ChatOutput.py

@@ -1,7 +1,7 @@
 from typing import Optional, Union
 
+from langflow.components.io.base.chat import ChatComponent
 from langflow.field_typing import Text
-from langflow.io.schema import ChatComponent
 from langflow.schema import Record
 
 

src/backend/langflow/components/io/MessageHistory.py

@@ -12,7 +12,7 @@ class MessageHistoryComponent(CustomComponent):
     def build_config(self):
         return {
             "sender": {
-                "options": ["Machine", "User"],
+                "options": ["Machine", "User", "Machine and User"],
                 "display_name": "Sender Type",
             },
             "sender_name": {"display_name": "Sender Name"},
@@ -38,6 +38,8 @@ class MessageHistoryComponent(CustomComponent):
         session_id: Optional[str] = None,
         n_messages: int = 5,
     ) -> List[Record]:
+        if sender == "Machine and User":
+            sender = None
         messages = get_messages(
             sender=sender,
             sender_name=sender_name,

src/backend/langflow/components/io/TextInput.py

@@ -9,11 +9,11 @@ class TextInput(CustomComponent):
     description = "Used to pass text input to the next component."
 
     field_config = {
-        "value": {"display_name": "Value", "multiline": True},
+        "input_value": {"display_name": "Value", "multiline": True},
     }
 
-    def build(self, value: Optional[str] = "") -> Text:
+    def build(self, input_value: Optional[str] = "") -> Text:
-        self.status = value
+        self.status = input_value
-        if not value:
+        if not input_value:
-            value = ""
+            input_value = ""
-        return value
+        return input_value

src/backend/langflow/components/model_specs/AmazonBedrockSpecs.py

@@ -10,6 +10,8 @@ from langflow import CustomComponent
 class AmazonBedrockComponent(CustomComponent):
     display_name: str = "Amazon Bedrock"
     description: str = "LLM model from Amazon Bedrock."
+    icon = "Amazon"
+
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/AnthropicLLMSpecs.py

@@ -10,6 +10,7 @@ from langflow import CustomComponent
 class AnthropicLLM(CustomComponent):
     display_name: str = "AnthropicLLM"
     description: str = "Anthropic Chat&Completion large language models."
+    icon ="Anthropic"
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/AnthropicSpecs.py

@@ -10,6 +10,8 @@ from langflow.field_typing import BaseLanguageModel, NestedDict
 class AnthropicComponent(CustomComponent):
     display_name = "Anthropic"
     description = "Anthropic large language models."
+    icon ="Anthropic"
+
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/ChatAnthropicSpecs.py

@@ -9,6 +9,7 @@ class ChatAnthropicComponent(CustomComponent):
     display_name = "ChatAnthropic"
     description = "`Anthropic` chat large language models."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
+    icon ="Anthropic"
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/ChatOpenAISpecs.py

@@ -9,6 +9,7 @@ from langflow.field_typing import BaseLanguageModel, NestedDict
 class ChatOpenAIComponent(CustomComponent):
     display_name = "ChatOpenAI"
     description = "`OpenAI` Chat large language models API."
+    icon = "OpenAI"
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/ChatVertexAISpecs.py

@@ -10,6 +10,8 @@ from langflow.field_typing import BaseLanguageModel
 class ChatVertexAIComponent(CustomComponent):
     display_name = "ChatVertexAI"
     description = "`Vertex AI` Chat large language models API."
+    icon="VertexAI"
+
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/CohereSpecs.py

@@ -7,6 +7,7 @@ class CohereComponent(CustomComponent):
     display_name = "Cohere"
     description = "Cohere large language models."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
+    icon = "Cohere"
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/GoogleGenerativeAISpecs.py

@@ -10,6 +10,7 @@ class GoogleGenerativeAIComponent(CustomComponent):
     display_name: str = "Google Generative AI"
     description: str = "A component that uses Google Generative AI to generate text."
     documentation: str = "http://docs.langflow.org/components/custom"
+    icon = "Google"
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py

@@ -8,6 +8,8 @@ from langflow import CustomComponent
 class HuggingFaceEndpointsComponent(CustomComponent):
     display_name: str = "Hugging Face Inference API"
     description: str = "LLM model from Hugging Face Inference API."
+    icon="HuggingFace"
+
 
     def build_config(self):
         return {

src/backend/langflow/components/model_specs/VertexAISpecs.py

@@ -7,6 +7,7 @@ from langchain_community.llms.vertexai import VertexAI
 class VertexAIComponent(CustomComponent):
     display_name = "VertexAI"
     description = "Google Vertex AI large language models"
+    icon="VertexAI"
 
     def build_config(self):
         return {

src/backend/langflow/components/models/AmazonBedrockModel.py

@@ -2,13 +2,14 @@ from typing import Optional
 
 from langchain_community.chat_models.bedrock import BedrockChat
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class AmazonBedrockComponent(CustomComponent):
+class AmazonBedrockComponent(LCModelComponent):
     display_name: str = "Amazon Bedrock Model"
     description: str = "Generate text using LLM model from Amazon Bedrock."
+    icon = "Amazon"
 
     def build_config(self):
         return {
@@ -34,7 +35,11 @@ class AmazonBedrockComponent(CustomComponent):
             "model_kwargs": {"display_name": "Model Kwargs"},
             "cache": {"display_name": "Cache"},
             "code": {"advanced": True},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -47,6 +52,7 @@ class AmazonBedrockComponent(CustomComponent):
         endpoint_url: Optional[str] = None,
         streaming: bool = False,
         cache: Optional[bool] = None,
+        stream: bool = False,
     ) -> Text:
         try:
             output = BedrockChat(
@@ -60,7 +66,5 @@ class AmazonBedrockComponent(CustomComponent):
             )  # type: ignore
         except Exception as e:
             raise ValueError("Could not connect to AmazonBedrock API.") from e
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result

src/backend/langflow/components/models/AnthropicModel.py

@@ -3,15 +3,16 @@ from typing import Optional
 from langchain_community.chat_models.anthropic import ChatAnthropic
 from pydantic.v1 import SecretStr
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class AnthropicLLM(CustomComponent):
+class AnthropicLLM(LCModelComponent):
     display_name: str = "AnthropicModel"
     description: str = (
         "Generate text using Anthropic Chat&Completion large language models."
     )
+    icon = "Anthropic"
 
     def build_config(self):
         return {
@@ -49,7 +50,11 @@ class AnthropicLLM(CustomComponent):
                 "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
             },
             "code": {"show": False},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -60,6 +65,7 @@ class AnthropicLLM(CustomComponent):
         max_tokens: Optional[int] = None,
         temperature: Optional[float] = None,
         api_endpoint: Optional[str] = None,
+        stream: bool = False,
     ) -> Text:
         # Set default API endpoint if not provided
         if not api_endpoint:
@@ -77,7 +83,5 @@ class AnthropicLLM(CustomComponent):
             )
         except Exception as e:
             raise ValueError("Could not connect to Anthropic API.") from e
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result

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

@@ -3,16 +3,17 @@ from typing import Optional
 from langchain.llms.base import BaseLanguageModel
 from langchain_openai import AzureChatOpenAI
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 
 
-class AzureChatOpenAIComponent(CustomComponent):
+class AzureChatOpenAIComponent(LCModelComponent):
     display_name: str = "AzureOpenAI Model"
     description: str = "Generate text using LLM model from Azure OpenAI."
     documentation: str = (
         "https://python.langchain.com/docs/integrations/llms/azure_openai"
     )
     beta = False
+    icon = "Azure"
 
     AZURE_OPENAI_MODELS = [
         "gpt-35-turbo",
@@ -73,7 +74,11 @@ class AzureChatOpenAIComponent(CustomComponent):
                 "info": "Maximum number of tokens to generate.",
             },
             "code": {"show": False},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -86,6 +91,7 @@ class AzureChatOpenAIComponent(CustomComponent):
         api_version: str,
         temperature: float = 0.7,
         max_tokens: Optional[int] = 1000,
+        stream: bool = False,
     ) -> BaseLanguageModel:
         try:
             output = AzureChatOpenAI(
@@ -99,7 +105,5 @@ class AzureChatOpenAIComponent(CustomComponent):
             )
         except Exception as e:
             raise ValueError("Could not connect to AzureOpenAI API.") from e
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result

src/backend/langflow/components/models/BaiduQianfanChatModel.py

@@ -3,16 +3,17 @@ from typing import Optional
 from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
 from pydantic.v1 import SecretStr
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class QianfanChatEndpointComponent(CustomComponent):
+class QianfanChatEndpointComponent(LCModelComponent):
     display_name: str = "QianfanChat Model"
     description: str = (
         "Generate text using Baidu Qianfan chat models. Get more detail from "
         "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
     )
+    icon = "BaiduQianfan"
 
     def build_config(self):
         return {
@@ -68,7 +69,11 @@ class QianfanChatEndpointComponent(CustomComponent):
                 "info": "Endpoint of the Qianfan LLM, required if custom model used.",
             },
             "code": {"show": False},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -81,6 +86,7 @@ class QianfanChatEndpointComponent(CustomComponent):
         temperature: Optional[float] = None,
         penalty_score: Optional[float] = None,
         endpoint: Optional[str] = None,
+        stream: bool = False,
     ) -> Text:
         try:
             output = QianfanChatEndpoint(  # type: ignore
@@ -94,7 +100,5 @@ class QianfanChatEndpointComponent(CustomComponent):
             )
         except Exception as e:
             raise ValueError("Could not connect to Baidu Qianfan API.") from e
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result

src/backend/langflow/components/models/CTransformersModel.py

@@ -2,11 +2,11 @@ from typing import Dict, Optional
 
 from langchain_community.llms.ctransformers import CTransformers
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class CTransformersComponent(CustomComponent):
+class CTransformersComponent(LCModelComponent):
     display_name = "CTransformersModel"
     description = "Generate text using CTransformers LLM models"
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
@@ -28,7 +28,11 @@ class CTransformersComponent(CustomComponent):
                 "field_type": "dict",
                 "value": '{"top_k":40,"top_p":0.95,"temperature":0.8,"repetition_penalty":1.1,"last_n_tokens":64,"seed":-1,"max_new_tokens":256,"stop":"","stream":"False","reset":"True","batch_size":8,"threads":-1,"context_length":-1,"gpu_layers":0}',
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -38,11 +42,10 @@ class CTransformersComponent(CustomComponent):
         input_value: str,
         model_type: str,
         config: Optional[Dict] = None,
+        stream: Optional[bool] = False,
     ) -> Text:
         output = CTransformers(
             model=model, model_file=model_file, model_type=model_type, config=config
         )
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result

src/backend/langflow/components/models/CohereModel.py

@@ -1,14 +1,16 @@
 from langchain_community.chat_models.cohere import ChatCohere
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class CohereComponent(CustomComponent):
+class CohereComponent(LCModelComponent):
     display_name = "CohereModel"
     description = "Generate text using Cohere large language models."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
 
+    icon = "Cohere"
+
     def build_config(self):
         return {
             "cohere_api_key": {
@@ -28,7 +30,11 @@ class CohereComponent(CustomComponent):
                 "type": "float",
                 "show": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -37,14 +43,11 @@ class CohereComponent(CustomComponent):
         input_value: str,
         max_tokens: int = 256,
         temperature: float = 0.75,
+        stream: bool = False,
     ) -> Text:
         output = ChatCohere(
             cohere_api_key=cohere_api_key,
             max_tokens=max_tokens,
             temperature=temperature,
         )
-        message = output.invoke(input_value)
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result
-        return result

src/backend/langflow/components/models/GoogleGenerativeAIModel.py

@@ -1,16 +1,17 @@
 from typing import Optional
 
-from langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore
+from langchain_google_genai import ChatGoogleGenerativeAI
-from pydantic.v1.types import SecretStr
+from pydantic.v1 import SecretStr
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import RangeSpec, Text
 
 
-class GoogleGenerativeAIComponent(CustomComponent):
+class GoogleGenerativeAIComponent(LCModelComponent):
     display_name: str = "Google Generative AIModel"
     description: str = "Generate text using Google Generative AI to generate text."
-    documentation: str = "http://docs.langflow.org/components/custom"
+    icon = "GoogleGenerativeAI"
+    icon = "Google"
 
     def build_config(self):
         return {
@@ -50,7 +51,11 @@ class GoogleGenerativeAIComponent(CustomComponent):
             "code": {
                 "advanced": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "info": "The input to the model."},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -63,6 +68,7 @@ class GoogleGenerativeAIComponent(CustomComponent):
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
         n: Optional[int] = 1,
+        stream: bool = False,
     ) -> Text:
         output = ChatGoogleGenerativeAI(
             model=model,
@@ -73,7 +79,4 @@ class GoogleGenerativeAIComponent(CustomComponent):
             n=n or 1,
             google_api_key=SecretStr(google_api_key),
         )
-        message = output.invoke(input_value)
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result

src/backend/langflow/components/models/HuggingFaceModel.py

@@ -3,13 +3,14 @@ from typing import Optional
 from langchain_community.chat_models.huggingface import ChatHuggingFace
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class HuggingFaceEndpointsComponent(CustomComponent):
+class HuggingFaceEndpointsComponent(LCModelComponent):
     display_name: str = "Hugging Face Inference API models"
     description: str = "Generate text using LLM model from Hugging Face Inference API."
+    icon = "HuggingFace"
 
     def build_config(self):
         return {
@@ -24,7 +25,11 @@ class HuggingFaceEndpointsComponent(CustomComponent):
                 "field_type": "code",
             },
             "code": {"show": False},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -34,6 +39,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
         task: str = "text2text-generation",
         huggingfacehub_api_token: Optional[str] = None,
         model_kwargs: Optional[dict] = None,
+        stream: bool = False,
     ) -> Text:
         try:
             llm = HuggingFaceEndpoint(
@@ -45,7 +51,4 @@ class HuggingFaceEndpointsComponent(CustomComponent):
         except Exception as e:
             raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
         output = ChatHuggingFace(llm=llm)
-        message = output.invoke(input_value)alue)
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result

src/backend/langflow/components/models/LlamaCppModel.py

@@ -2,11 +2,11 @@ from typing import Any, Dict, List, Optional
 
 from langchain_community.llms.llamacpp import LlamaCpp
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class LlamaCppComponent(CustomComponent):
+class LlamaCppComponent(LCModelComponent):
     display_name = "LlamaCppModel"
     description = "Generate text using llama.cpp model."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp"
@@ -56,7 +56,11 @@ class LlamaCppComponent(CustomComponent):
             "use_mmap": {"display_name": "Use Mmap", "advanced": True},
             "verbose": {"display_name": "Verbose", "advanced": True},
             "vocab_only": {"display_name": "Vocab Only", "advanced": True},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -97,6 +101,7 @@ class LlamaCppComponent(CustomComponent):
         use_mmap: Optional[bool] = True,
         verbose: bool = True,
         vocab_only: bool = False,
+        stream: bool = False,
     ) -> Text:
         output = LlamaCpp(
             model_path=model_path,
@@ -135,9 +140,5 @@ class LlamaCppComponent(CustomComponent):
             verbose=verbose,
             vocab_only=vocab_only,
         )
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result
-        self.status = result
-        return result

src/backend/langflow/components/models/OllamaModel.py

@@ -3,17 +3,19 @@ from typing import Any, Dict, List, Optional
 # from langchain_community.chat_models import ChatOllama
 from langchain_community.chat_models import ChatOllama
 
+from langflow.components.models.base.model import LCModelComponent
+
 # from langchain.chat_models import ChatOllama
-from langflow import CustomComponent
 from langflow.field_typing import Text
 
 # whe When a callback component is added to Langflow, the comment must be uncommented.
 # from langchain.callbacks.manager import CallbackManager
 
 
-class ChatOllamaComponent(CustomComponent):
+class ChatOllamaComponent(LCModelComponent):
     display_name = "ChatOllamaModel"
     description = "Generate text using Local LLM for chat with Ollama."
+    icon = "Ollama"
 
     def build_config(self) -> dict:
         return {
@@ -164,7 +166,11 @@ class ChatOllamaComponent(CustomComponent):
                 "info": "Template to use for generating text.",
                 "advanced": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -197,6 +203,7 @@ class ChatOllamaComponent(CustomComponent):
         timeout: Optional[int] = None,
         top_k: Optional[int] = None,
         top_p: Optional[int] = None,
+        stream: Optional[bool] = False,
     ) -> Text:
         if not base_url:
             base_url = "http://localhost:11434"
@@ -250,7 +257,5 @@ class ChatOllamaComponent(CustomComponent):
             output = ChatOllama(**llm_params)  # type: ignore
         except Exception as e:
             raise ValueError("Could not initialize Ollama LLM.") from e
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result

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

@@ -2,17 +2,18 @@ from typing import Optional
 
 from langchain_openai import ChatOpenAI
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import NestedDict, Text
 
 
-class OpenAIModelComponent(CustomComponent):
+class OpenAIModelComponent(LCModelComponent):
     display_name = "OpenAI Model"
     description = "Generates text using OpenAI's models."
+    icon = "OpenAI"
 
     def build_config(self):
         return {
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
             "max_tokens": {
                 "display_name": "Max Tokens",
                 "advanced": False,
@@ -57,6 +58,10 @@ class OpenAIModelComponent(CustomComponent):
                 "required": False,
                 "value": 0.7,
             },
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -68,10 +73,11 @@ class OpenAIModelComponent(CustomComponent):
         openai_api_base: Optional[str] = None,
         openai_api_key: Optional[str] = None,
         temperature: float = 0.7,
+        stream: Optional[bool] = False,
     ) -> Text:
         if not openai_api_base:
             openai_api_base = "https://api.openai.com/v1"
-        model = ChatOpenAI(
+        output = ChatOpenAI(
             max_tokens=max_tokens,
             model_kwargs=model_kwargs,
             model=model_name,
@@ -80,7 +86,4 @@ class OpenAIModelComponent(CustomComponent):
             temperature=temperature,
         )
 
-        message = model.invoke(input_value)
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result

src/backend/langflow/components/models/VertexAiModel.py

@@ -2,13 +2,15 @@ from typing import List, Optional
 
 from langchain_core.messages.base import BaseMessage
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class ChatVertexAIComponent(CustomComponent):
+class ChatVertexAIComponent(LCModelComponent):
     display_name = "ChatVertexAIModel"
     description = "Generate text using Vertex AI Chat large language models API."
+    icon="VertexAI"
+
 
     def build_config(self):
         return {
@@ -57,7 +59,11 @@ class ChatVertexAIComponent(CustomComponent):
                 "value": False,
                 "advanced": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -73,6 +79,7 @@ class ChatVertexAIComponent(CustomComponent):
         top_k: int = 40,
         top_p: float = 0.95,
         verbose: bool = False,
+        stream: bool = False,
     ) -> Text:
         try:
             from langchain_google_vertexai import ChatVertexAI
@@ -92,7 +99,5 @@ class ChatVertexAIComponent(CustomComponent):
             top_p=top_p,
             verbose=verbose,
         )
-        message = output.invoke(input_value)
+
-        result = message.content if hasattr(message, "content") else message
+        return self.get_result(output=output, stream=stream, input_value=input_value)
-        self.status = result
-        return result

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

@@ -0,0 +1,28 @@
+from langchain_core.runnables import Runnable
+
+from langflow import CustomComponent
+
+
+class LCModelComponent(CustomComponent):
+    display_name: str = "Model Name"
+    description: str = "Model Description"
+
+    def get_result(self, output: Runnable, stream: bool, input_value: str):
+        """
+        Retrieves the result from the output of a Runnable object.
+
+        Args:
+            output (Runnable): The output object to retrieve the result from.
+            stream (bool): Indicates whether to use streaming or invocation mode.
+            input_value (str): The input value to pass to the output object.
+
+        Returns:
+            The result obtained from the output object.
+        """
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
+        return result

src/backend/langflow/components/retrievers/AmazonKendra.py

@@ -9,6 +9,7 @@ from langflow import CustomComponent
 class AmazonKendraRetrieverComponent(CustomComponent):
     display_name: str = "Amazon Kendra Retriever"
     description: str = "Retriever that uses the Amazon Kendra API."
+    icon = "Amazon"
 
     def build_config(self):
         return {

src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py

@@ -17,6 +17,8 @@ class VectaraSelfQueryRetriverComponent(CustomComponent):
     description: str = "Implementation of Vectara Self Query Retriever"
     documentation = "https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query"
     beta = True
+    icon="Vectara"
+
 
     field_config = {
         "code": {"show": True},

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

@@ -15,7 +15,7 @@ class RunnableExecComponent(CustomComponent):
                 "display_name": "Input Key",
                 "info": "The key to use for the input.",
             },
-            "inputs": {
+            "input_value": {
                 "display_name": "Inputs",
                 "info": "The inputs to pass to the runnable.",
             },

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

@@ -3,12 +3,12 @@ from typing import List, Optional
 import chromadb  # type: ignore
 from langchain_community.vectorstores.chroma import Chroma
 
-from langflow import CustomComponent
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.field_typing import Embeddings, Text
-from langflow.schema import Record, docs_to_records
+from langflow.schema import Record
 
 
-class ChromaSearchComponent(CustomComponent):
+class ChromaSearchComponent(LCVectorStoreComponent):
     """
     A custom component for implementing a Vector Store using Chroma.
     """
@@ -26,7 +26,7 @@ class ChromaSearchComponent(CustomComponent):
         - dict: A dictionary containing the configuration options for the component.
         """
         return {
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
             "search_type": {
                 "display_name": "Search Type",
                 "options": ["Similarity", "MMR"],
@@ -101,17 +101,11 @@ class ChromaSearchComponent(CustomComponent):
                 chroma_server_ssl_enabled=chroma_server_ssl_enabled,
             )
         index_directory = self.resolve_path(index_directory)
-        chroma = Chroma(
+        vector_store = Chroma(
             embedding_function=embedding,
             collection_name=collection_name,
             persist_directory=index_directory,
             client_settings=chroma_settings,
         )
 
-        # Validate the inputs
+        return self.search_with_vector_store(input_value, search_type, vector_store)
-        docs = []
-        if inputs and isinstance(inputs, str):
-            docs = chroma.search(query=inputs, search_type=search_type.lower())
-        else:
-            raise ValueError("Invalid inputs provided.")
-        return docs_to_records(docs)

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

@@ -3,24 +3,36 @@ from typing import List, Union
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.faiss import FAISS
+
 from langflow import CustomComponent
 from langflow.field_typing import Document, Embeddings
 
 
 class FAISSComponent(CustomComponent):
     display_name = "FAISS"
-    description = "Construct FAISS wrapper from raw documents."
+    description = "Ingest documents into FAISS Vector Store."
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
 
     def build_config(self):
         return {
             "documents": {"display_name": "Documents"},
             "embedding": {"display_name": "Embedding"},
+            "folder_path": {
+                "display_name": "Folder Path",
+                "info": "Path to save the FAISS index. It will be relative to where Langflow is running.",
+            },
+            "index_name": {"display_name": "Index Name"},
         }
 
     def build(
         self,
         embedding: Embeddings,
         documents: List[Document],
+        folder_path: str,
+        index_name: str = "langflow_index",
     ) -> Union[VectorStore, FAISS, BaseRetriever]:
-        return FAISS.from_documents(documents=documents, embedding=embedding)
+        vector_store = FAISS.from_documents(documents=documents, embedding=embedding)
+        if not folder_path:
+            raise ValueError("Folder path is required to save the FAISS index.")
+        path = self.resolve_path(folder_path)
+        vector_store.save_local(str(path), index_name)

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

@@ -0,0 +1,45 @@
+from typing import List
+
+from langchain_community.vectorstores.faiss import FAISS
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.field_typing import Embeddings
+from langflow.schema import Record
+
+
+class FAISSSearchComponent(LCVectorStoreComponent):
+    display_name = "FAISS Search"
+    description = "Search a FAISS Vector Store for similar documents."
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "folder_path": {
+                "display_name": "Folder Path",
+                "info": "Path to save the FAISS index. It will be relative to where Langflow is running.",
+            },
+            "input_value": {"display_name": "Input"},
+            "index_name": {"display_name": "Index Name"},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        folder_path: str,
+        index_name: str = "langflow_index",
+    ) -> List[Record]:
+        if not folder_path:
+            raise ValueError("Folder path is required to save the FAISS index.")
+        path = self.resolve_path(folder_path)
+        vector_store = FAISS.load_local(
+            folder_path=str(path), embeddings=embedding, index_name=index_name
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the FAISS index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type="similarity"
+        )

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

@@ -0,0 +1,57 @@
+from typing import List, Optional
+
+from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSearch
+
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings, NestedDict
+
+
+class MongoDBAtlasComponent(CustomComponent):
+    display_name = "MongoDB Atlas"
+    description = (
+        "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
+    )
+    icon="MongoDB"
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "collection_name": {"display_name": "Collection Name"},
+            "db_name": {"display_name": "Database Name"},
+            "index_name": {"display_name": "Index Name"},
+            "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        documents: List[Document] = None,
+        collection_name: str = "",
+        db_name: str = "",
+        index_name: str = "",
+        mongodb_atlas_cluster_uri: str = "",
+        search_kwargs: Optional[NestedDict] = None,
+    ) -> MongoDBAtlasVectorSearch:
+        search_kwargs = search_kwargs or {}
+        if documents:
+            vector_store = MongoDBAtlasVectorSearch.from_documents(
+                documents=documents,
+                embedding=embedding,
+                collection_name=collection_name,
+                db_name=db_name,
+                index_name=index_name,
+                mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
+                search_kwargs=search_kwargs,
+            )
+        else:
+            vector_store = MongoDBAtlasVectorSearch(
+                embedding=embedding,
+                collection_name=collection_name,
+                db_name=db_name,
+                index_name=index_name,
+                mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
+                search_kwargs=search_kwargs,
+            )
+        return vector_store

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

@@ -1,22 +1,22 @@
 from typing import List, Optional
 
-from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-
+from langflow.components.vectorstores.MongoDBAtlasVector import MongoDBAtlasComponent
-from langflow import CustomComponent
+from langflow.field_typing import Embeddings, NestedDict
-from langflow.field_typing import (
+from langflow.schema import Record
-    Document,
-    Embeddings,
-    NestedDict,
-)
 
 
-class MongoDBAtlasComponent(CustomComponent):
+class MongoDBAtlasSearchComponent(MongoDBAtlasComponent, LCVectorStoreComponent):
-    display_name = "MongoDB Atlas"
+    display_name = "MongoDB Atlas Search"
-    description = "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
+    description = "Search a MongoDB Atlas Vector Store for similar documents."
 
     def build_config(self):
         return {
-            "documents": {"display_name": "Documents"},
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
             "embedding": {"display_name": "Embedding"},
             "collection_name": {"display_name": "Collection Name"},
             "db_name": {"display_name": "Database Name"},
@@ -27,17 +27,16 @@ class MongoDBAtlasComponent(CustomComponent):
 
     def build(
         self,
-        documents: List[Document],
+        input_value: str,
+        search_type: str,
         embedding: Embeddings,
         collection_name: str = "",
         db_name: str = "",
         index_name: str = "",
         mongodb_atlas_cluster_uri: str = "",
         search_kwargs: Optional[NestedDict] = None,
-    ) -> MongoDBAtlasVectorSearch:
+    ) -> List[Record]:
-        search_kwargs = search_kwargs or {}
+        vector_store = super().build(
-        return MongoDBAtlasVectorSearch(
-            documents=documents,
             embedding=embedding,
             collection_name=collection_name,
             db_name=db_name,
@@ -45,3 +44,8 @@ class MongoDBAtlasComponent(CustomComponent):
             mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
             search_kwargs=search_kwargs,
         )
+        if not vector_store:
+            raise ValueError("Failed to create MongoDB Atlas Vector Store")
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )

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

@@ -5,6 +5,7 @@ import pinecone  # type: ignore
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.pinecone import Pinecone
+
 from langflow import CustomComponent
 from langflow.field_typing import Document, Embeddings
 
@@ -12,6 +13,7 @@ from langflow.field_typing import Document, Embeddings
 class PineconeComponent(CustomComponent):
     display_name = "Pinecone"
     description = "Construct Pinecone wrapper from raw documents."
+    icon = "Pinecone"
 
     def build_config(self):
         return {
@@ -19,10 +21,23 @@ class PineconeComponent(CustomComponent):
             "embedding": {"display_name": "Embedding"},
             "index_name": {"display_name": "Index Name"},
             "namespace": {"display_name": "Namespace"},
-            "pinecone_api_key": {"display_name": "Pinecone API Key", "default": "", "password": True, "required": True},
+            "pinecone_api_key": {
-            "pinecone_env": {"display_name": "Pinecone Environment", "default": "", "required": True},
+                "display_name": "Pinecone API Key",
+                "default": "",
+                "password": True,
+                "required": True,
+            },
+            "pinecone_env": {
+                "display_name": "Pinecone Environment",
+                "default": "",
+                "required": True,
+            },
             "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"},
-            "pool_threads": {"display_name": "Pool Threads", "default": 1, "advanced": True},
+            "pool_threads": {
+                "display_name": "Pool Threads",
+                "default": 1,
+                "advanced": True,
+            },
         }
 
     def build(

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

@@ -0,0 +1,70 @@
+from typing import List, Optional
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Pinecone import PineconeComponent
+from langflow.field_typing import Embeddings
+from langflow.schema import Record
+
+
+class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
+    display_name = "Pinecone Search"
+    description = "Search a Pinecone Vector Store for similar documents."
+    icon = "Pinecone"
+
+    def build_config(self):
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "embedding": {"display_name": "Embedding"},
+            "index_name": {"display_name": "Index Name"},
+            "namespace": {"display_name": "Namespace"},
+            "pinecone_api_key": {
+                "display_name": "Pinecone API Key",
+                "default": "",
+                "password": True,
+                "required": True,
+            },
+            "pinecone_env": {
+                "display_name": "Pinecone Environment",
+                "default": "",
+                "required": True,
+            },
+            "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"},
+            "pool_threads": {
+                "display_name": "Pool Threads",
+                "default": 1,
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        pinecone_env: str,
+        text_key: str = "text",
+        pool_threads: int = 4,
+        index_name: Optional[str] = None,
+        pinecone_api_key: Optional[str] = None,
+        namespace: Optional[str] = "default",
+        search_type: str = "similarity",
+    ) -> List[Record]:
+        vector_store = super().build(
+            embedding=embedding,
+            pinecone_env=pinecone_env,
+            documents=[],
+            text_key=text_key,
+            pool_threads=pool_threads,
+            index_name=index_name,
+            pinecone_api_key=pinecone_api_key,
+            namespace=namespace,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Pinecone index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )

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

@@ -10,6 +10,7 @@ from langflow.field_typing import Document, Embeddings, NestedDict
 class QdrantComponent(CustomComponent):
     display_name = "Qdrant"
     description = "Construct Qdrant wrapper from a list of texts."
+    icon="Qdrant"
 
     def build_config(self):
         return {

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

@@ -0,0 +1,93 @@
+from typing import List, Optional
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Qdrant import QdrantComponent
+from langflow.field_typing import Embeddings, NestedDict
+from langflow.schema import Record
+
+
+class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
+    display_name = "Qdrant"
+    description = "Construct Qdrant wrapper from a list of texts."
+    icon="Qdrant"
+
+
+    def build_config(self):
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "embedding": {"display_name": "Embedding"},
+            "api_key": {"display_name": "API Key", "password": True, "advanced": True},
+            "collection_name": {"display_name": "Collection Name"},
+            "content_payload_key": {
+                "display_name": "Content Payload Key",
+                "advanced": True,
+            },
+            "distance_func": {"display_name": "Distance Function", "advanced": True},
+            "grpc_port": {"display_name": "gRPC Port", "advanced": True},
+            "host": {"display_name": "Host", "advanced": True},
+            "https": {"display_name": "HTTPS", "advanced": True},
+            "location": {"display_name": "Location", "advanced": True},
+            "metadata_payload_key": {
+                "display_name": "Metadata Payload Key",
+                "advanced": True,
+            },
+            "path": {"display_name": "Path", "advanced": True},
+            "port": {"display_name": "Port", "advanced": True},
+            "prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True},
+            "prefix": {"display_name": "Prefix", "advanced": True},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+            "timeout": {"display_name": "Timeout", "advanced": True},
+            "url": {"display_name": "URL", "advanced": True},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        collection_name: str,
+        search_type: str = "similarity",
+        api_key: Optional[str] = None,
+        content_payload_key: str = "page_content",
+        distance_func: str = "Cosine",
+        grpc_port: int = 6334,
+        https: bool = False,
+        host: Optional[str] = None,
+        location: Optional[str] = None,
+        metadata_payload_key: str = "metadata",
+        path: Optional[str] = None,
+        port: Optional[int] = 6333,
+        prefer_grpc: bool = False,
+        prefix: Optional[str] = None,
+        search_kwargs: Optional[NestedDict] = None,
+        timeout: Optional[int] = None,
+        url: Optional[str] = None,
+    ) -> List[Record]:
+        vector_store = super().build(
+            embedding=embedding,
+            collection_name=collection_name,
+            api_key=api_key,
+            content_payload_key=content_payload_key,
+            distance_func=distance_func,
+            grpc_port=grpc_port,
+            https=https,
+            host=host,
+            location=location,
+            metadata_payload_key=metadata_payload_key,
+            path=path,
+            port=port,
+            prefer_grpc=prefer_grpc,
+            prefix=prefix,
+            search_kwargs=search_kwargs,
+            timeout=timeout,
+            url=url,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Qdrant index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )

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

@@ -0,0 +1,77 @@
+from typing import List, Optional
+
+from langchain.embeddings.base import Embeddings
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Redis import RedisComponent
+from langflow.schema import Record
+
+
+class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
+    """
+    A custom component for implementing a Vector Store using Redis.
+    """
+
+    display_name: str = "Redis Search"
+    description: str = "Search a Redis Vector Store for similar documents."
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis"
+    beta = True
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "index_name": {"display_name": "Index Name", "value": "your_index"},
+            "code": {"show": False, "display_name": "Code"},
+            "documents": {"display_name": "Documents", "is_list": True},
+            "embedding": {"display_name": "Embedding"},
+            "schema": {"display_name": "Schema", "file_types": [".yaml"]},
+            "redis_server_url": {
+                "display_name": "Redis Server Connection String",
+                "advanced": False,
+            },
+            "redis_index_name": {"display_name": "Redis Index", "advanced": False},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        embedding: Embeddings,
+        redis_server_url: str,
+        redis_index_name: str,
+        schema: Optional[str] = None,
+    ) -> List[Record]:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - embedding (Embeddings): The embeddings to use for the Vector Store.
+        - documents (Optional[Document]): The documents to use for the Vector Store.
+        - redis_index_name (str): The name of the Redis index.
+        - redis_server_url (str): The URL for the Redis server.
+
+        Returns:
+        - VectorStore: The Vector Store object.
+        """
+        vector_store = super().build(
+            embedding=embedding,
+            redis_server_url=redis_server_url,
+            redis_index_name=redis_index_name,
+            schema=schema,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Redis index.")
+
+        return self.search_with_vector_store(
+            input_value=input_value, search_type=search_type, vector_store=vector_store
+        )

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

@@ -0,0 +1,50 @@
+from typing import List
+
+from langchain_community.vectorstores.supabase import SupabaseVectorStore
+from supabase.client import Client, create_client
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.field_typing import Embeddings
+from langflow.schema import Record
+
+
+class SupabaseSearchComponent(LCVectorStoreComponent):
+    display_name = "Supabase Search"
+    description = "Search a Supabase Vector Store for similar documents."
+    icon="Supabase"
+
+    def build_config(self):
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "embedding": {"display_name": "Embedding"},
+            "query_name": {"display_name": "Query Name"},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+            "supabase_service_key": {"display_name": "Supabase Service Key"},
+            "supabase_url": {"display_name": "Supabase URL"},
+            "table_name": {"display_name": "Table Name", "advanced": True},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        embedding: Embeddings,
+        query_name: str = "",
+        supabase_service_key: str = "",
+        supabase_url: str = "",
+        table_name: str = "",
+    ) -> List[Record]:
+        supabase: Client = create_client(
+            supabase_url, supabase_key=supabase_service_key
+        )
+        vector_store = SupabaseVectorStore(
+            client=supabase,
+            embedding=embedding,
+            table_name=table_name,
+            query_name=query_name,
+        )
+        return self.search_with_vector_store(input_value, search_type, vector_store)

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

@@ -8,13 +8,17 @@ from langchain_community.vectorstores.vectara import Vectara
 from langchain_core.vectorstores import VectorStore
 from langflow import CustomComponent
 from langflow.field_typing import BaseRetriever, Document
+from langchain_community.vectorstores.vectara import Vectara
 
 
 class VectaraComponent(CustomComponent):
     display_name: str = "Vectara"
     description: str = "Implementation of Vector Store using Vectara"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    )
     beta = True
+    icon="Vectara"
     field_config = {
         "vectara_customer_id": {
             "display_name": "Vectara Customer ID",
@@ -26,7 +30,10 @@ class VectaraComponent(CustomComponent):
             "display_name": "Vectara API Key",
             "password": True,
         },
-        "documents": {"display_name": "Documents", "info": "If provided, will be upserted to corpus (optional)"},
+        "documents": {
+            "display_name": "Documents",
+            "info": "If provided, will be upserted to corpus (optional)",
+        },
         "files_url": {
             "display_name": "Files Url",
             "info": "Make vectara object using url of files (optional)",

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

@@ -0,0 +1,66 @@
+from typing import List
+
+from langchain_community.vectorstores.vectara import Vectara
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Vectara import VectaraComponent
+from langflow.schema import Record
+
+
+class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
+    display_name: str = "Vectara Search"
+    description: str = "Search a Vectara Vector Store for similar documents."
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    )
+    beta = True
+    icon="Vectara"
+
+    field_config = {
+        "search_type": {
+            "display_name": "Search Type",
+            "options": ["Similarity", "MMR"],
+        },
+        "input_value": {"display_name": "Input"},
+        "vectara_customer_id": {
+            "display_name": "Vectara Customer ID",
+        },
+        "vectara_corpus_id": {
+            "display_name": "Vectara Corpus ID",
+        },
+        "vectara_api_key": {
+            "display_name": "Vectara API Key",
+            "password": True,
+        },
+        "documents": {
+            "display_name": "Documents",
+            "info": "If provided, will be upserted to corpus (optional)",
+        },
+        "files_url": {
+            "display_name": "Files Url",
+            "info": "Make vectara object using url of files (optional)",
+        },
+    }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        vectara_customer_id: str,
+        vectara_corpus_id: str,
+        vectara_api_key: str,
+    ) -> List[Record]:
+        source = "Langflow"
+        vector_store = Vectara(
+            vectara_customer_id=vectara_customer_id,
+            vectara_corpus_id=vectara_corpus_id,
+            vectara_api_key=vectara_api_key,
+            source=source,
+        )
+
+        if not vector_store:
+            raise ValueError("Failed to create Vectara Vector Store")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )

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

@@ -8,10 +8,12 @@ from langchain_community.vectorstores import VectorStore, Weaviate
 from langflow import CustomComponent
 
 
-class WeaviateVectorStore(CustomComponent):
+class WeaviateVectorStoreComponent(CustomComponent):
     display_name: str = "Weaviate"
     description: str = "Implementation of Vector Store using Weaviate"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
+    )
     beta = True
     field_config = {
         "url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"},
@@ -24,7 +26,12 @@ class WeaviateVectorStore(CustomComponent):
             "display_name": "Index name",
             "required": False,
         },
-        "text_key": {"display_name": "Text Key", "required": False, "advanced": True, "value": "text"},
+        "text_key": {
+            "display_name": "Text Key",
+            "required": False,
+            "advanced": True,
+            "value": "text",
+        },
         "documents": {"display_name": "Documents", "is_list": True},
         "embedding": {"display_name": "Embedding"},
         "attributes": {
@@ -34,7 +41,11 @@ class WeaviateVectorStore(CustomComponent):
             "field_type": "str",
             "advanced": True,
         },
-        "search_by_text": {"display_name": "Search By Text", "field_type": "bool", "advanced": True},
+        "search_by_text": {
+            "display_name": "Search By Text",
+            "field_type": "bool",
+            "advanced": True,
+        },
         "code": {"show": False},
     }
 

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

@@ -0,0 +1,84 @@
+from typing import List, Optional
+
+from langchain.embeddings.base import Embeddings
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
+from langflow.schema import Record
+
+
+class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent):
+    display_name: str = "Weaviate Search"
+    description: str = "Search a Weaviate Vector Store for similar documents."
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
+    )
+    beta = True
+    icon="Weaviate"
+
+    field_config = {
+        "search_type": {
+            "display_name": "Search Type",
+            "options": ["Similarity", "MMR"],
+        },
+        "input_value": {"display_name": "Input"},
+        "url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"},
+        "api_key": {
+            "display_name": "API Key",
+            "password": True,
+            "required": False,
+        },
+        "index_name": {
+            "display_name": "Index name",
+            "required": False,
+        },
+        "text_key": {
+            "display_name": "Text Key",
+            "required": False,
+            "advanced": True,
+            "value": "text",
+        },
+        "documents": {"display_name": "Documents", "is_list": True},
+        "embedding": {"display_name": "Embedding"},
+        "attributes": {
+            "display_name": "Attributes",
+            "required": False,
+            "is_list": True,
+            "field_type": "str",
+            "advanced": True,
+        },
+        "search_by_text": {
+            "display_name": "Search By Text",
+            "field_type": "bool",
+            "advanced": True,
+        },
+        "code": {"show": False},
+    }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        url: str,
+        search_by_text: bool = False,
+        api_key: Optional[str] = None,
+        index_name: Optional[str] = None,
+        text_key: str = "text",
+        embedding: Optional[Embeddings] = None,
+        attributes: Optional[list] = None,
+    ) -> List[Record]:
+        vector_store = super().build(
+            url=url,
+            api_key=api_key,
+            index_name=index_name,
+            text_key=text_key,
+            embedding=embedding,
+            attributes=attributes,
+            search_by_text=search_by_text,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Weaviate index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )

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

@@ -0,0 +1,41 @@
+from typing import List
+
+from langchain_core.vectorstores import VectorStore
+
+from langflow import CustomComponent
+from langflow.field_typing import Text
+from langflow.schema import Record, docs_to_records
+
+
+class LCVectorStoreComponent(CustomComponent):
+
+    display_name: str = "LC Vector Store"
+    description: str = "Search a LC Vector Store for similar documents."
+    beta: bool = True
+
+    def search_with_vector_store(
+        self, input_value: Text, search_type: str, vector_store: VectorStore
+    ) -> List[Record]:
+        """
+        Search for records in the vector store based on the input value and search type.
+
+        Args:
+            input_value (Text): The input value to search for.
+            search_type (str): The type of search to perform.
+            vector_store (VectorStore): The vector store to search in.
+
+        Returns:
+            List[Record]: A list of records matching the search criteria.
+
+        Raises:
+            ValueError: If invalid inputs are provided.
+        """
+
+        docs = []
+        if input_value and isinstance(input_value, str):
+            docs = vector_store.search(
+                query=input_value, search_type=search_type.lower()
+            )
+        else:
+            raise ValueError("Invalid inputs provided.")
+        return docs_to_records(docs)

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

@@ -0,0 +1,73 @@
+from typing import List, Optional
+
+from langchain.embeddings.base import Embeddings
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.pgvector import PGVectorComponent
+from langflow.schema import Record
+
+
+class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
+    """
+    A custom component for implementing a Vector Store using PostgreSQL.
+    """
+
+    display_name: str = "PGVector Search"
+    description: str = "Search a PGVector Store for similar documents."
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
+    )
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "code": {"show": False},
+            "embedding": {"display_name": "Embedding"},
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "pg_server_url": {
+                "display_name": "PostgreSQL Server Connection String",
+                "advanced": False,
+            },
+            "collection_name": {"display_name": "Table", "advanced": False},
+            "input_value": {"display_name": "Input"},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        pg_server_url: str,
+        collection_name: str,
+        search_type: Optional[str] = None,
+    ) -> List[Record]:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - input_value (str): The input value to search for.
+        - embedding (Embeddings): The embeddings to use for the Vector Store.
+        - collection_name (str): The name of the PG table.
+        - pg_server_url (str): The URL for the PG server.
+
+        Returns:
+        - VectorStore: The Vector Store object.
+        """
+        try:
+            vector_store = super().build(
+                embedding=embedding,
+                pg_server_url=pg_server_url,
+                collection_name=collection_name,
+            )
+        except Exception as e:
+            raise RuntimeError(f"Failed to build PGVector: {e}")
+        return self.search_with_vector_store(
+            input_value=input_value, search_type=search_type, vector_store=vector_store
+        )

src/backend/langflow/config.yaml

@@ -218,24 +218,7 @@ retrievers:
   # https://github.com/supabase-community/supabase-py/issues/482
   # ZepRetriever:
   #   documentation: "https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/zep_memorystore"
-vectorstores:
+
-  # Chroma:
-  #   documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma"
-  Qdrant:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant"
-  FAISS:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
-  Pinecone:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone"
-  ElasticsearchStore:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/elasticsearch"
-  SupabaseVectorStore:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
-  MongoDBAtlasVectorSearch:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/mongodb_atlas"
-  # Requires docarray >=0.32.0 but langchain-serve requires jina 3.15.2 which doesn't support docarray >=0.32.0
-  # DocArrayInMemorySearch:
-  #   documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/docarray_in_memory"
 wrappers:
   RequestsWrapper:
     documentation: ""

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

@@ -73,7 +73,7 @@ class Graph:
                 if getattr(vertex, attribute):
                     getattr(self, f"_{attribute}_vertices").append(vertex.id)
 
-    async def _run(self, inputs: Dict[str, str]) -> List["ResultData"]:
+    async def _run(self, inputs: Dict[str, str], stream: bool) -> List["ResultData"]:
         """Runs the graph with the given inputs."""
         for vertex_id in self._is_input_vertices:
             vertex = self.get_vertex(vertex_id)
@@ -91,10 +91,14 @@ class Graph:
             vertex = self.get_vertex(vertex_id)
             if vertex is None:
                 raise ValueError(f"Vertex {vertex_id} not found")
+            if not stream and hasattr(vertex, "consume_async_generator"):
+                await vertex.consume_async_generator()
             outputs.append(vertex.result)
         return outputs
 
-    async def run(self, inputs: Dict[str, Union[str, list[str]]]) -> List["ResultData"]:
+    async def run(
+        self, inputs: Dict[str, Union[str, list[str]]], stream: bool
+    ) -> List["ResultData"]:
         """Runs the graph with the given inputs."""
 
         # inputs is {"message": "Hello, world!"}
@@ -106,7 +110,9 @@ class Graph:
         if not isinstance(inputs_values, list):
             inputs_values = [inputs_values]
         for input_value in inputs_values:
-            run_outputs = await self._run({INPUT_FIELD_NAME: input_value})
+            run_outputs = await self._run(
+                {INPUT_FIELD_NAME: input_value}, stream=stream
+            )
             logger.debug(f"Run outputs: {run_outputs}")
             outputs.extend(run_outputs)
         return outputs

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

@@ -44,6 +44,7 @@ class Vertex:
     ) -> None:
         # is_external means that the Vertex send or receives data from
         # an external source (e.g the chat)
+        self.will_stream = False
         self.updated_raw_params = False
         self.id: str = data["id"]
         self.is_input = any(
@@ -391,6 +392,8 @@ class Vertex:
             ValueError: If any key in new_params is not found in self._raw_params.
         """
         # First check if the input_value in _raw_params is not a vertex
+        if not new_params:
+            return
         if any(isinstance(self._raw_params.get(key), Vertex) for key in new_params):
             return
         self._raw_params.update(new_params)
@@ -456,7 +459,7 @@ class Vertex:
                 await self._build_node_and_update_params(key, value, user_id)
             elif isinstance(value, list) and self._is_list_of_nodes(value):
                 await self._build_list_of_nodes_and_update_params(key, value, user_id)
-            elif key not in self.params:
+            elif key not in self.params or self.updated_raw_params:
                 self.params[key] = value
 
     def _is_node(self, value):
@@ -610,6 +613,7 @@ class Vertex:
     async def build(
         self,
         user_id=None,
+        inputs: Optional[Dict[str, Any]] = None,
         requester: Optional["Vertex"] = None,
         **kwargs,
     ) -> Any:
@@ -622,6 +626,9 @@ class Vertex:
             return self.get_requester_result(requester)
         self._reset()
 
+        if self.is_input:
+            self.update_raw_params(inputs)
+
         # Run steps
         for step in self.steps:
             if step not in self.steps_ran:

src/backend/langflow/graph/vertex/types.py

@@ -11,7 +11,7 @@ from langflow.graph.utils import UnbuiltObject, flatten_list
 from langflow.graph.vertex.base import StatefulVertex, StatelessVertex
 from langflow.interface.utils import extract_input_variables_from_prompt
 from langflow.schema import Record
-from langflow.services.monitor.utils import log_message
+from langflow.services.monitor.utils import log_vertex_build
 from langflow.utils.schemas import ChatOutputResponse
 
 
@@ -394,6 +394,8 @@ class ChatVertex(StatelessVertex):
                         sender_name=sender_name,
                         stream_url=stream_url,
                     )
+
+                    self.will_stream = stream_url is not None
                 if artifacts:
                     self.artifacts = artifacts.model_dump()
             if isinstance(self._built_object, (AsyncIterator, Iterator)):
@@ -434,19 +436,25 @@ class ChatVertex(StatelessVertex):
         self._built_result = complete_message
         # Update artifacts with the message
         # and remove the stream_url
+        self._finalize_build()
         logger.debug(f"Streamed message: {complete_message}")
 
-        await log_message(
+        await log_vertex_build(
-            sender=self.params.get("sender", ""),
+            flow_id=self.graph.flow_id,
-            sender_name=self.params.get("sender_name", ""),
+            vertex_id=self.id,
-            message=complete_message,
+            valid=True,
-            session_id=self.params.get("session_id", ""),
+            params=self._built_object_repr(),
+            data=self.result,
             artifacts=self.artifacts,
         )
 
         self._validate_built_object()
         self._built = True
 
+    async def consume_async_generator(self):
+        async for _ in self.stream():
+            pass
+
 
 class RoutingVertex(StatelessVertex):
     def __init__(self, data: Dict, graph):

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

@@ -1,6 +1,7 @@
 import ast
 import os
 import zlib
+from pathlib import Path
 
 from loguru import logger
 
@@ -79,9 +80,13 @@ class DirectoryReader:
                 except Exception as e:
                     logger.error(f"Error while loading component: {e}")
                     continue
-            items.append({"name": menu["name"], "path": menu["path"], "components": components})
+            items.append(
+                {"name": menu["name"], "path": menu["path"], "components": components}
+            )
         filtered = [menu for menu in items if menu["components"]]
-        logger.debug(f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}')
+        logger.debug(
+            f'Filtered components {"with errors" if with_errors else ""}: {len(filtered)}'
+        )
         return {"menu": filtered}
 
     def validate_code(self, file_content):
@@ -114,15 +119,24 @@ class DirectoryReader:
         Walk through the directory path and return a list of all .py files.
         """
         if not (safe_path := self.get_safe_path()):
-            raise CustomComponentPathValueError(f"The path needs to start with '{self.base_path}'.")
+            raise CustomComponentPathValueError(
+                f"The path needs to start with '{self.base_path}'."
+            )
 
         file_list = []
-        for root, _, files in os.walk(safe_path):
+        safe_path_obj = Path(safe_path)
-            file_list.extend(
+        for file_path in safe_path_obj.rglob("*.py"):
-                os.path.join(root, filename)
+            # The other condtion is that it should be
-                for filename in files
+            # in the safe_path/[folder]/[file].py format
-                if filename.endswith(".py") and not filename.startswith("__")
+            # any folders below [folder] will be ignored
-            )
+            # basically the parent folder of the file should be a
+            # folder in the safe_path
+            if (
+                file_path.is_file()
+                and file_path.parent.parent == safe_path_obj
+                and not file_path.name.startswith("__")
+            ):
+                file_list.append(str(file_path))
         return file_list
 
     def find_menu(self, response, menu_name):
@@ -159,7 +173,9 @@ class DirectoryReader:
             for node in ast.walk(module):
                 if isinstance(node, ast.FunctionDef):
                     for arg in node.args.args:
-                        if self._is_type_hint_in_arg_annotation(arg.annotation, type_hint_name):
+                        if self._is_type_hint_in_arg_annotation(
+                            arg.annotation, type_hint_name
+                        ):
                             return True
         except SyntaxError:
             # Returns False if the code is not valid Python
@@ -177,14 +193,16 @@ class DirectoryReader:
             and annotation.value.id == type_hint_name
         )
 
-    def is_type_hint_used_but_not_imported(self, type_hint_name: str, code: str) -> bool:
+    def is_type_hint_used_but_not_imported(
+        self, type_hint_name: str, code: str
+    ) -> bool:
         """
         Check if a type hint is used but not imported in the given code.
         """
         try:
-            return self._is_type_hint_used_in_args(type_hint_name, code) and not self._is_type_hint_imported(
+            return self._is_type_hint_used_in_args(
                 type_hint_name, code
-            )
+            ) and not self._is_type_hint_imported(type_hint_name, code)
         except SyntaxError:
             # Returns True if there's something wrong with the code
             # TODO : Find a better way to handle this
@@ -205,9 +223,9 @@ class DirectoryReader:
             return False, "Syntax error"
         elif not self.validate_build(file_content):
             return False, "Missing build function"
-        elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported(
+        elif self._is_type_hint_used_in_args(
             "Optional", file_content
-        ):
+        ) and not self._is_type_hint_imported("Optional", file_content):
             return (
                 False,
                 "Type hint 'Optional' is used but not imported in the code.",
@@ -223,7 +241,9 @@ class DirectoryReader:
         from the .py files in the directory.
         """
         response = {"menu": []}
-        logger.debug("-------------------- Building component menu list --------------------")
+        logger.debug(
+            "-------------------- Building component menu list --------------------"
+        )
 
         for file_path in file_paths:
             menu_name = os.path.basename(os.path.dirname(file_path))
@@ -243,7 +263,9 @@ class DirectoryReader:
 
             # first check if it's already CamelCase
             if "_" in component_name:
-                component_name_camelcase = " ".join(word.title() for word in component_name.split("_"))
+                component_name_camelcase = " ".join(
+                    word.title() for word in component_name.split("_")
+                )
             else:
                 component_name_camelcase = component_name
 
@@ -251,7 +273,9 @@ class DirectoryReader:
                 try:
                     output_types = self.get_output_types_from_code(result_content)
                 except Exception as exc:
-                    logger.exception(f"Error while getting output types from code: {str(exc)}")
+                    logger.exception(
+                        f"Error while getting output types from code: {str(exc)}"
+                    )
                     output_types = [component_name_camelcase]
             else:
                 output_types = [component_name_camelcase]
@@ -267,7 +291,9 @@ class DirectoryReader:
 
             if menu_result not in response["menu"]:
                 response["menu"].append(menu_result)
-        logger.debug("-------------------- Component menu list built --------------------")
+        logger.debug(
+            "-------------------- Component menu list built --------------------"
+        )
         return response
 
     @staticmethod

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

@@ -1,11 +1,18 @@
-from langflow.interface.custom.directory_reader import DirectoryReader
-from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode
 from loguru import logger
 
+from langflow.interface.custom.directory_reader import DirectoryReader
+from langflow.template.frontend_node.custom_components import (
+    CustomComponentFrontendNode,
+)
+
 
 def merge_nested_dicts_with_renaming(dict1, dict2):
     for key, value in dict2.items():
-        if key in dict1 and isinstance(value, dict) and isinstance(dict1.get(key), dict):
+        if (
+            key in dict1
+            and isinstance(value, dict)
+            and isinstance(dict1.get(key), dict)
+        ):
             for sub_key, sub_value in value.items():
                 # if sub_key in dict1[key]:
                 #     new_key = get_new_key(dict1[key], sub_key)
@@ -62,7 +69,9 @@ def build_custom_component_list_from_path(path: str):
     file_list = load_files_from_path(path)
     reader = DirectoryReader(path, False)
 
-    valid_components, invalid_components = build_and_validate_all_files(reader, file_list)
+    valid_components, invalid_components = build_and_validate_all_files(
+        reader, file_list
+    )
 
     valid_menu = build_valid_menu(valid_components)
     invalid_menu = build_invalid_menu(invalid_components)
@@ -109,7 +118,9 @@ def build_invalid_menu_items(menu_item):
             menu_items[component_name] = component_template
             logger.debug(f"Added {component_name} to invalid menu.")
         except Exception as exc:
-            logger.exception(f"Error while creating custom component [{component_name}]: {str(exc)}")
+            logger.exception(
+                f"Error while creating custom component [{component_name}]: {str(exc)}"
+            )
     return menu_items
 
 
@@ -136,12 +147,14 @@ def determine_component_name(component):
 def build_menu_items(menu_item):
     """Build menu items for a given menu."""
     menu_items = {}
+    logger.debug(f"Building menu items for {menu_item['name']}")
+    logger.debug(f"Loading {len(menu_item['components'])} components")
     for component_name, component_template, component in menu_item["components"]:
         try:
             menu_items[component_name] = component_template
-            logger.debug(f"Added {component_name} to valid menu.")
         except Exception as exc:
             logger.error(f"Error loading Component: {component['output_types']}")
-            logger.exception(f"Error while building custom component {component['output_types']}: {exc}")
+            logger.exception(
-    return menu_items
+                f"Error while building custom component {component['output_types']}: {exc}"
+            )
     return menu_items

src/backend/langflow/processing/process.py

@@ -271,18 +271,26 @@ async def run_graph(
     graph: Union["Graph", dict],
     flow_id: str,
     session_id: str,
+    stream: bool,
     inputs: Optional[Union[dict, List[dict]]] = None,
     artifacts: Optional[Dict[str, Any]] = None,
     session_service: Optional[SessionService] = None,
 ):
     """Run the graph and generate the result"""
     if isinstance(graph, dict):
+        graph_data = graph
         graph = Graph.from_payload(graph, flow_id=flow_id)
+    else:
+        graph_data = graph._graph_data
+    if not session_id:
+        session_id = session_service.generate_key(
+            session_id=flow_id, data_graph=graph_data
+        )
 
-    outputs = await graph.run(inputs)
+    outputs = await graph.run(inputs, stream=stream)
     if session_id and session_service:
         session_service.update_session(session_id, (graph, artifacts))
-    return outputs
+    return outputs, session_id
 
 
 def validate_input(

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

@@ -5,16 +5,17 @@ from typing import TYPE_CHECKING
 import sqlalchemy as sa
 from alembic import command, util
 from alembic.config import Config
+from loguru import logger
+from sqlalchemy import inspect
+from sqlalchemy.exc import OperationalError
+from sqlmodel import Session, SQLModel, create_engine, select, text
+
 from langflow.services.base import Service
 from langflow.services.database import models  # noqa
 from langflow.services.database.models.user.crud import get_user_by_username
 from langflow.services.database.utils import Result, TableResults
 from langflow.services.deps import get_settings_service
 from langflow.services.utils import teardown_superuser
-from loguru import logger
-from sqlalchemy import inspect
-from sqlalchemy.exc import OperationalError
-from sqlmodel import Session, SQLModel, create_engine, select, text
 
 if TYPE_CHECKING:
     from sqlalchemy.engine import Engine
@@ -39,7 +40,7 @@ class DatabaseService(Service):
             connect_args = {"check_same_thread": False}
         else:
             connect_args = {}
-        return create_engine(self.database_url, connect_args=connect_args, max_overflow=-1)
+        return create_engine(self.database_url, connect_args=connect_args)
 
     def __enter__(self):
         self._session = Session(self.engine)

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

@@ -8,7 +8,11 @@ import Checkmark from "../../components/ui/checkmark";
 import Loading from "../../components/ui/loading";
 import { Textarea } from "../../components/ui/textarea";
 import Xmark from "../../components/ui/xmark";
-import { priorityFields, statusBuild, statusBuilding } from "../../constants/constants";
+import {
+  priorityFields,
+  statusBuild,
+  statusBuilding,
+} from "../../constants/constants";
 import { BuildStatus } from "../../constants/enums";
 import NodeToolbarComponent from "../../pages/FlowPage/components/nodeToolbarComponent";
 import { useDarkStore } from "../../stores/darkStore";
@@ -211,9 +215,7 @@ export default function GenericNode({
       return "inactive-status";
     }
     if (buildStatus === BuildStatus.BUILT && isInvalid) {
-      return isDark
+      return isDark ? "built-invalid-status-dark" : "built-invalid-status";
-        ? "built-invalid-status-dark"
-        : "built-invalid-status";
     } else if (buildStatus === BuildStatus.BUILDING) {
       return "building-status";
     } else {
@@ -296,7 +298,7 @@ export default function GenericNode({
             <div
               className={
                 "generic-node-title-arrangement rounded-full" +
-                (!showNode && " justify-center")
+                (!showNode && " justify-center ")
               }
             >
               {iconNodeRender()}
@@ -332,21 +334,20 @@ export default function GenericNode({
                   ) : (
                     <ShadTooltip content={data.node?.display_name}>
                       <div className="group flex items-center gap-2.5">
-
+                        <div
-                          <div
+                          onDoubleClick={(event) => {
-                            onDoubleClick={(event) => {
+                            if (nameEditable) {
-                              if (nameEditable) {
+                              setInputName(true);
-                                setInputName(true);
+                            }
-                              }
+                            takeSnapshot();
-                              takeSnapshot();
+                            event.stopPropagation();
-                              event.stopPropagation();
+                            event.preventDefault();
-                              event.preventDefault();
+                          }}
-                            }}
+                          data-testid={"title-" + data.node?.display_name}
-                            data-testid={"title-" + data.node?.display_name}
+                          className="generic-node-tooltip-div text-primary"
-                            className="generic-node-tooltip-div text-primary"
+                        >
-                          >
+                          {data.node?.display_name}
-                            {data.node?.display_name}
+                        </div>
-                          </div>
 
                         {nameEditable && (
                           <div
@@ -465,7 +466,7 @@ export default function GenericNode({
                   if (buildStatus === BuildStatus.BUILDING || isBuilding)
                     return;
                   setValidationStatus(null);
-                  buildFlow(data.id);
+                  buildFlow({nodeId: data.id});
                 }}
               >
                 <div>
@@ -478,11 +479,11 @@ export default function GenericNode({
                       ) : (
                         <div className="max-h-96 overflow-auto">
                           {typeof validationStatus.params === "string"
-                            ? (`${durationString}\n${validationStatus.params}`
+                            ? `${durationString}\n${validationStatus.params}`
                                 .split("\n")
                                 .map((line, index) => (
                                   <div key={index}>{line}</div>
-                                )))
+                                ))
                             : durationString}
                         </div>
                       )

src/frontend/src/alerts/alertDropDown/index.tsx

@@ -1,3 +1,4 @@
+import { Cross2Icon } from "@radix-ui/react-icons";
 import { useState } from "react";
 import IconComponent from "../../components/genericIconComponent";
 import {
@@ -46,15 +47,15 @@ export default function AlertDropdown({
                 setTimeout(clearNotificationList, 100);
               }}
             >
-              <IconComponent name="Trash2" className="h-[1.1rem] w-[1.1rem]" />
+              <IconComponent name="Trash2" className="h-4 w-4" />
             </button>
             <button
-              className="text-foreground hover:text-status-red"
+              className="text-foreground opacity-70 hover:opacity-100"
               onClick={() => {
                 setOpen(false);
               }}
             >
-              <IconComponent name="X" className="h-5 w-5" />
+              <Cross2Icon className="h-4 w-4" />
             </button>
           </div>
         </div>

src/frontend/src/components/IOInputField/index.tsx

@@ -19,12 +19,12 @@ export default function IOInputField({
           <Textarea
             className="w-full"
             placeholder={"Enter text..."}
-            value={node.data.node!.template["value"].value}
+            value={node.data.node!.template["input_value"].value}
             onChange={(e) => {
               e.target.value;
               if (node) {
                 let newNode = cloneDeep(node);
-                newNode.data.node!.template["value"].value = e.target.value;
+                newNode.data.node!.template["input_value"].value = e.target.value;
                 setNode(node.id, newNode);
               }
             }}
@@ -49,12 +49,12 @@ export default function IOInputField({
           <Textarea
             className="w-full custom-scroll"
             placeholder={"Enter text..."}
-            value={node.data.node!.template["value"]}
+            value={node.data.node!.template["input_value"]}
             onChange={(e) => {
               e.target.value;
               if (node) {
                 let newNode = cloneDeep(node);
-                newNode.data.node!.template["value"].value = e.target.value;
+                newNode.data.node!.template["input_value"].value = e.target.value;
                 setNode(node.id, newNode);
               }
             }}

src/frontend/src/components/IOOutputView/index.tsx

@@ -30,12 +30,12 @@ export default function IOOutputView({
           <Textarea
             className="w-full custom-scroll"
             placeholder={"Enter text..."}
-            value={node.data.node!.template["value"]}
+            value={node.data.node!.template["input_value"]}
             onChange={(e) => {
               e.target.value;
               if (node) {
                 let newNode = cloneDeep(node);
-                newNode.data.node!.template["value"].value = e.target.value;
+                newNode.data.node!.template["input_value"].value = e.target.value;
                 setNode(node.id, newNode);
               }
             }}

src/frontend/src/components/IOview/index.tsx

@@ -1,9 +1,13 @@
-import { cloneDeep } from "lodash";
 import { useEffect, useState } from "react";
-import { CHAT_FORM_DIALOG_SUBTITLE, outputsModalTitle, textInputModalTitle } from "../../constants/constants";
+import {
+  CHAT_FORM_DIALOG_SUBTITLE,
+  outputsModalTitle,
+  textInputModalTitle,
+} from "../../constants/constants";
 import BaseModal from "../../modals/baseModal";
-import useAlertStore from "../../stores/alertStore";
 import useFlowStore from "../../stores/flowStore";
+import useFlowsManagerStore from "../../stores/flowsManagerStore";
+import { updateVerticesOrder } from "../../utils/buildUtils";
 import { cn } from "../../utils/utils";
 import AccordionComponent from "../AccordionComponent";
 import IOInputField from "../IOInputField";
@@ -40,27 +44,30 @@ export default function IOView({ children, open, setOpen }): JSX.Element {
     { type: string; id: string } | undefined
   >(undefined);
 
-  const { getNode, setNode, buildFlow, getFlow } = useFlowStore();
+  const buildFlow = useFlowStore((state) => state.buildFlow);
-  const { setErrorData } = useAlertStore();
   const setIsBuilding = useFlowStore((state) => state.setIsBuilding);
   const [lockChat, setLockChat] = useState(false);
   const [chatValue, setChatValue] = useState("");
   const isBuilding = useFlowStore((state) => state.isBuilding);
+  const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
+
+  async function updateVertices() {
+    return updateVerticesOrder(currentFlow!.id, null);
+  }
+
+  useEffect(() => {
+    if (open) {
+      updateVertices();
+    }
+  }, [open, currentFlow]);
 
   async function sendMessage(count = 1): Promise<void> {
     if (isBuilding) return;
-    const { nodes, edges } = getFlow();
     setIsBuilding(true);
     setLockChat(true);
     setChatValue("");
-    const chatInputNode = nodes.find((node) => node.id === chatInput?.id);
-    if (chatInputNode) {
-      let newNode = cloneDeep(chatInputNode);
-      newNode.data.node!.template["message"].value = chatValue;
-      setNode(chatInput!.id, newNode);
-    }
     for (let i = 0; i < count; i++) {
-      await buildFlow().catch((err) => {
+      await buildFlow({ input_value: chatValue }).catch((err) => {
         console.error(err);
         setLockChat(false);
       });
@@ -104,7 +111,7 @@ export default function IOView({ children, open, setOpen }): JSX.Element {
                 <Tabs
                   value={selectedTab.toString()}
                   className={
-                    "flex h-full flex-col overflow-y-auto custom-scroll rounded-md border bg-muted text-center"
+                    "flex h-full flex-col overflow-y-auto rounded-md border bg-muted text-center custom-scroll"
                   }
                   onValueChange={(value) => {
                     setSelectedTab(Number(value));
@@ -266,24 +273,27 @@ export default function IOView({ children, open, setOpen }): JSX.Element {
                       {selectedViewField.type}
                     </div>
                     <div className="h-full">
-                    {inputs.some(
+                      {inputs.some(
-                      (input) => input.id === selectedViewField.id
+                        (input) => input.id === selectedViewField.id
-                    ) ? (
+                      ) ? (
-                      <IOInputField
+                        <IOInputField
-                        inputType={selectedViewField.type!}
+                          inputType={selectedViewField.type!}
-                        inputId={selectedViewField.id!}
+                          inputId={selectedViewField.id!}
-                      />
+                        />
-                    ) : (
+                      ) : (
-                      <IOOutputView
+                        <IOOutputView
-                        outputType={selectedViewField.type!}
+                          outputType={selectedViewField.type!}
-                        outputId={selectedViewField.id!}
+                          outputId={selectedViewField.id!}
-                      />
+                        />
-                    )}
+                      )}
                     </div>
                   </div>
                 )}
                 <div
-                  className={cn("flex w-full h-full",selectedViewField ? "hidden" : "")}
+                  className={cn(
+                    "flex h-full w-full",
+                    selectedViewField ? "hidden" : ""
+                  )}
                 >
                   <NewChatView
                     sendMessage={sendMessage}

src/frontend/src/components/chatComponent/buildTrigger/index.tsx

@@ -23,8 +23,6 @@ export default function BuildTrigger({
   const nodes = useFlowStore((state) => state.nodes);
   const edges = useFlowStore((state) => state.edges);
   const setErrorData = useAlertStore((state) => state.setErrorData);
-  const setSuccessData = useAlertStore((state) => state.setSuccessData);
-  const setFlowState = useFlowStore((state) => state.setFlowState);
 
   const eventClick = isBuilding ? "pointer-events-none" : "";
   const [progress, setProgress] = useState(0);
@@ -47,7 +45,7 @@ export default function BuildTrigger({
       setIsBuilding(true);
 
       await enforceMinimumLoadingTime(startTime, minimumLoadingTime);
-      await buildFlow();
+      await buildFlow({});
     } catch (error) {
       console.error("Error:", error);
     } finally {

src/frontend/src/components/newChatView/chatInput/index.tsx

@@ -4,6 +4,7 @@ import { Textarea } from "../../../components/ui/textarea";
 import { chatInputType } from "../../../types/components";
 import { classNames } from "../../../utils/utils";
 import { chatInputPlaceholder, chatInputPlaceholderSend } from "../../../constants/constants";
+import useFlowsManagerStore from "../../../stores/flowsManagerStore";
 
 export default function ChatInput({
   lockChat,
@@ -14,20 +15,21 @@ export default function ChatInput({
   noInput,
 }: chatInputType): JSX.Element {
   const [repeat, setRepeat] = useState(1);
+  const saveLoading = useFlowsManagerStore((state) => state.saveLoading);
   useEffect(() => {
     if (!lockChat && inputRef.current) {
       inputRef.current.focus();
     }
   }, [lockChat, inputRef]);
 
-  function handleChange(value: number) {
+/*   function handleChange(value: number) {
     console.log(value);
     if (value > 0) {
       setRepeat(value);
     } else {
       setRepeat(1);
     }
-  }
+  } */
 
   useEffect(() => {
     if (inputRef.current) {
@@ -41,13 +43,13 @@ export default function ChatInput({
       <div className="relative w-full">
         <Textarea
           onKeyDown={(event) => {
-            if (event.key === "Enter" && !lockChat && !event.shiftKey) {
+            if (event.key === "Enter" && !lockChat && !saveLoading && !event.shiftKey) {
               sendMessage(repeat);
             }
           }}
           rows={1}
           ref={inputRef}
-          disabled={lockChat || noInput}
+          disabled={lockChat || noInput || saveLoading}
           style={{
             resize: "none",
             bottom: `${inputRef?.current?.scrollHeight}px`,
@@ -58,12 +60,12 @@ export default function ChatInput({
                 : "hidden"
             }`,
           }}
-          value={lockChat ? "Thinking..." : chatValue}
+          value={lockChat ? "Thinking..." : (saveLoading ? "Saving..." : chatValue)}
           onChange={(event): void => {
             setChatValue(event.target.value);
           }}
           className={classNames(
-            lockChat
+            (lockChat || saveLoading)
               ? " form-modal-lock-true bg-input"
               : noInput
               ? "form-modal-no-input bg-input"
@@ -87,10 +89,10 @@ export default function ChatInput({
                 ? "text-primary"
                 : "bg-chat-send text-background"
             )}
-            disabled={lockChat}
+            disabled={lockChat || saveLoading}
             onClick={(): void => sendMessage(repeat)}
           >
-            {lockChat ? (
+            {lockChat || saveLoading ? (
               <IconComponent
                 name="Lock"
                 className="form-modal-lock-icon"

src/frontend/src/components/newChatView/chatMessage/index.tsx

@@ -1,5 +1,5 @@
 import Convert from "ansi-to-html";
-import { useEffect, useMemo, useState, useRef } from "react";
+import { useEffect, useMemo, useRef, useState } from "react";
 import Markdown from "react-markdown";
 import rehypeMathjax from "rehype-mathjax";
 import remarkGfm from "remark-gfm";
@@ -9,17 +9,17 @@ import Robot from "../../../assets/robot.png";
 import SanitizedHTMLWrapper from "../../../components/SanitizedHTMLWrapper";
 import CodeTabsComponent from "../../../components/codeTabsComponent";
 import IconComponent from "../../../components/genericIconComponent";
+import useFlowStore from "../../../stores/flowStore";
 import { chatMessagePropsType } from "../../../types/components";
 import { classNames } from "../../../utils/utils";
 import FileCard from "../fileComponent";
-import useFlowStore from "../../../stores/flowStore";
 
 export default function ChatMessage({
   chat,
   lockChat,
   lastMessage,
   updateChat,
-  setLockChat
+  setLockChat,
 }: chatMessagePropsType): JSX.Element {
   const convert = new Convert({ newline: true });
   const [hidden, setHidden] = useState(true);
@@ -40,8 +40,6 @@ export default function ChatMessage({
     chatMessageRef.current = chatMessage;
   }, [chatMessage]);
 
-
-
   // The idea now is that chat.stream_url MAY be a URL if we should stream the output of the chat
   // probably the message is empty when we have a stream_url
   // what we need is to update the chat_message with the SSE data
@@ -70,9 +68,7 @@ export default function ChatMessage({
     });
   };
 
-
   useEffect(() => {
-    console.log("chatMessage", chatMessage);
     if (streamUrl && !isStreaming) {
       setLockChat(true);
       streamChunks(streamUrl)
@@ -92,8 +88,8 @@ export default function ChatMessage({
   useEffect(() => {
     return () => {
       eventSource.current?.close();
-    }
+    };
-  }, [])
+  }, []);
 
   useEffect(() => {
     const element = document.getElementById("last-chat-message");
@@ -222,7 +218,7 @@ dark:prose-invert"
                                       },
                                     ]}
                                     activeTab={"0"}
-                                    setActiveTab={() => { }}
+                                    setActiveTab={() => {}}
                                   />
                                 ) : (
                                   <code className={className} {...props}>
@@ -279,33 +275,33 @@ dark:prose-invert"
                 <span className="prose text-primary word-break-break-word dark:prose-invert">
                   {promptOpen
                     ? template?.split("\n")?.map((line, index) => {
-                      const regex = /{([^}]+)}/g;
+                        const regex = /{([^}]+)}/g;
-                      let match;
+                        let match;
-                      let parts: Array<JSX.Element | string> = [];
+                        let parts: Array<JSX.Element | string> = [];
-                      let lastIndex = 0;
+                        let lastIndex = 0;
-                      while ((match = regex.exec(line)) !== null) {
+                        while ((match = regex.exec(line)) !== null) {
-                        // Push text up to the match
+                          // Push text up to the match
-                        if (match.index !== lastIndex) {
+                          if (match.index !== lastIndex) {
-                          parts.push(line.substring(lastIndex, match.index));
+                            parts.push(line.substring(lastIndex, match.index));
-                        }
+                          }
-                        // Push div with matched text
+                          // Push div with matched text
-                        if (chat.message[match[1]]) {
+                          if (chat.message[match[1]]) {
-                          parts.push(
+                            parts.push(
-                            <span className="chat-message-highlight">
+                              <span className="chat-message-highlight">
-                              {chat.message[match[1]]}
+                                {chat.message[match[1]]}
-                            </span>
+                              </span>
-                          );
+                            );
-                        }
+                          }
 
-                        // Update last index
+                          // Update last index
-                        lastIndex = regex.lastIndex;
+                          lastIndex = regex.lastIndex;
-                      }
+                        }
-                      // Push text after the last match
+                        // Push text after the last match
-                      if (lastIndex !== line.length) {
+                        if (lastIndex !== line.length) {
-                        parts.push(line.substring(lastIndex));
+                          parts.push(line.substring(lastIndex));
-                      }
+                        }
-                      return <p>{parts}</p>;
+                        return <p>{parts}</p>;
-                    })
+                      })
                     : chatMessage}
                 </span>
               </>

src/frontend/src/components/newChatView/index.tsx

@@ -1,6 +1,10 @@
-import _ from "lodash";
 import { useEffect, useRef, useState } from "react";
 import IconComponent from "../../components/genericIconComponent";
+import { NOCHATOUTPUT_NOTICE_ALERT } from "../../constants/alerts_constants";
+import {
+  chatFirstInitialText,
+  chatSecondInitialText,
+} from "../../constants/constants";
 import { deleteFlowPool } from "../../controllers/API";
 import useAlertStore from "../../stores/alertStore";
 import useFlowStore from "../../stores/flowStore";
@@ -14,8 +18,6 @@ import {
 import { classNames } from "../../utils/utils";
 import ChatInput from "./chatInput";
 import ChatMessage from "./chatMessage";
-import { INFO_MISSING_ALERT, NOCHATOUTPUT_NOTICE_ALERT } from "../../constants/alerts_constants";
-import { chatFirstInitialText, chatSecondInitialText } from "../../constants/constants";
 
 export default function NewChatView({
   sendMessage,
@@ -34,7 +36,7 @@ export default function NewChatView({
   const inputIds = inputs.map((obj) => obj.id);
   const outputIds = outputs.map((obj) => obj.id);
   const outputTypes = outputs.map((obj) => obj.type);
-  const updateFlowPool = useFlowStore((state)=>state.updateFlowPool)
+  const updateFlowPool = useFlowStore((state) => state.updateFlowPool);
 
   useEffect(() => {
     if (!outputTypes.includes("ChatOutput")) {
@@ -73,7 +75,7 @@ export default function NewChatView({
             isSend: !is_ai,
             message: message,
             sender_name,
-            componentId:  output.id,
+            componentId: output.id,
             stream_url: stream_url,
           };
         } catch (e) {
@@ -120,22 +122,26 @@ export default function NewChatView({
     chat: ChatMessageType,
     message: string,
     stream_url?: string
-    ) {
+  ) {
-      if (message === "") return;
+    if (message === "") return;
-      chat.message = message;
+    chat.message = message;
     // chat is one of the chatHistory
-    updateFlowPool(chat.componentId,{message,sender_name:chat.sender_name??"Bot",sender:"Machine"})
+    updateFlowPool(chat.componentId, {
+      message,
+      sender_name: chat.sender_name ?? "Bot",
+      sender: chat.isSend ? "User" : "Machine",
+    });
     // setChatHistory((oldChatHistory) => {
-      // const index = oldChatHistory.findIndex((ch) => ch.id === chat.id);
+    // const index = oldChatHistory.findIndex((ch) => ch.id === chat.id);
-      // if (index === -1) return oldChatHistory;
+    // if (index === -1) return oldChatHistory;
-      // let newChatHistory = _.cloneDeep(oldChatHistory);
+    // let newChatHistory = _.cloneDeep(oldChatHistory);
-      // newChatHistory = [
+    // newChatHistory = [
-      //   ...newChatHistory.slice(0, index),
+    //   ...newChatHistory.slice(0, index),
-      //   chat,
+    //   chat,
-      //   ...newChatHistory.slice(index + 1),
+    //   ...newChatHistory.slice(index + 1),
-      // ];
+    // ];
-      // console.log("newChatHistory:", newChatHistory);
+    // console.log("newChatHistory:", newChatHistory);
-      // return newChatHistory;
+    // return newChatHistory;
     // });
   }
 
@@ -160,7 +166,7 @@ export default function NewChatView({
           {chatHistory?.length > 0 ? (
             chatHistory.map((chat, index) => (
               <ChatMessage
-              setLockChat={setLockChat}
+                setLockChat={setLockChat}
                 lockChat={lockChat}
                 chat={chat}
                 lastMessage={chatHistory.length - 1 === index ? true : false}

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

@@ -123,7 +123,7 @@ function ApiInterceptor() {
   async function clearBuildVerticesState(error) {
     if (error?.response?.status === 500) {
       const vertices = useFlowStore.getState().verticesBuild;
-      useFlowStore.getState().updateBuildStatus(vertices, BuildStatus.BUILT);
+      useFlowStore.getState().updateBuildStatus(vertices?.verticesIds ?? [], BuildStatus.BUILT);
       useFlowStore.getState().setIsBuilding(false);
     }
   }

src/frontend/src/controllers/API/index.ts

@@ -869,9 +869,10 @@ export async function getVerticesOrder(
 
 export async function postBuildVertex(
   flowId: string,
-  vertexId: string
+  vertexId: string,
+  input_value: string,
 ): Promise<AxiosResponse<VertexBuildTypeAPI>> {
-  return await api.post(`${BASE_URL_API}build/${flowId}/vertices/${vertexId}`);
+  return await api.post(`${BASE_URL_API}build/${flowId}/vertices/${vertexId}`, input_value ? {inputs: {input_value: input_value}} : undefined);
 }
 
 export async function downloadImage({ flowId, fileName }): Promise<any> {

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

@@ -1,5 +1,6 @@
 import _, { cloneDeep } from "lodash";
 import { useEffect, useState } from "react";
+import { useUpdateNodeInternals } from "reactflow";
 import ShadTooltip from "../../../../components/ShadTooltipComponent";
 import CodeAreaComponent from "../../../../components/codeAreaComponent";
 import IconComponent from "../../../../components/genericIconComponent";
@@ -26,7 +27,6 @@ import {
   updateFlowPosition,
 } from "../../../../utils/reactflowUtils";
 import { classNames, cn } from "../../../../utils/utils";
-import { useUpdateNodeInternals } from "reactflow";
 
 export default function NodeToolbarComponent({
   data,
@@ -94,11 +94,12 @@ export default function NodeToolbarComponent({
   const handleModalWShortcut = useFlowStore(state => state.handleModalWShortcut);
 
   useEffect(() => {
-    console.log(openCodeModalWShortcut)
     setOpenModal(openCodeModalWShortcut)
   }, [openCodeModalWShortcut, handleModalWShortcut])
 
-  const setLastCopiedSelection = useFlowStore(state => state.setLastCopiedSelection);
+  const setLastCopiedSelection = useFlowStore(
+    (state) => state.setLastCopiedSelection
+  );
   useEffect(() => {
     setFlowComponent(createFlowComponent(cloneDeep(data), version));
   }, [
@@ -153,8 +154,8 @@ export default function NodeToolbarComponent({
         deleteNode(data.id);
         break;
       case "copy":
-        const node = nodes.filter(node => node.id === data.id)
+        const node = nodes.filter((node) => node.id === data.id);
-        setLastCopiedSelection({ nodes: _.cloneDeep(node), edges: [] })
+        setLastCopiedSelection({ nodes: _.cloneDeep(node), edges: [] });
     }
   };
 
@@ -242,7 +243,7 @@ export default function NodeToolbarComponent({
                     id={"code-input-node-toolbar-" + name}
                   />
                 </div>
-                <IconComponent name="Code" className="h-4 w-4" />
+                <IconComponent name="TerminalSquare" className="h-4 w-4" />
               </button>
             </ShadTooltip>
           ) : (
@@ -380,13 +381,11 @@ export default function NodeToolbarComponent({
                     className="relative top-0.5 mr-2 h-4 w-4 "
                   />{" "}
                   <span className="">Copy</span>{" "}
-                  
+                  <IconComponent
-                    <IconComponent
+                    name="Command"
-                      name="Command"
+                    className="absolute right-[1.15rem] top-[0.65em] h-3.5 w-3.5 stroke-2"
-                      className="absolute right-[1.15rem] top-[0.65em] h-3.5 w-3.5 stroke-2"
+                  ></IconComponent>
-                    ></IconComponent>
+                  <span className="absolute right-2 top-[0.5em]">C</span>
-                    <span className="absolute right-2 top-[0.5em]">C</span>
-  
                 </div>
               </SelectItem>
               {hasStore && (
@@ -459,7 +458,7 @@ export default function NodeToolbarComponent({
                   <span>
                     <IconComponent
                       name="Delete"
-                      className="absolute right-2 top-2 h-4 w-4 text-red-400 stroke-2"
+                      className="absolute right-2 top-2 h-4 w-4 stroke-2 text-red-400"
                     ></IconComponent>
                   </span>
                 </div>

src/frontend/src/stores/flowStore.ts

@@ -9,9 +9,12 @@ import {
   applyNodeChanges,
 } from "reactflow";
 import { create } from "zustand";
-import { FLOW_BUILD_SUCCESS_ALERT, MISSED_ERROR_ALERT } from "../constants/alerts_constants";
+import {
+  FLOW_BUILD_SUCCESS_ALERT,
+  MISSED_ERROR_ALERT,
+} from "../constants/alerts_constants";
 import { BuildStatus } from "../constants/enums";
-import { getFlowPool, updateFlowInDatabase } from "../controllers/API";
+import { getFlowPool } from "../controllers/API";
 import { VertexBuildTypeAPI } from "../types/api";
 import {
   NodeDataType,
@@ -19,7 +22,12 @@ import {
   sourceHandleType,
   targetHandleType,
 } from "../types/flow";
-import { ChatOutputType, FlowPoolObjectType, FlowStoreType, chatInputType } from "../types/zustand/flow";
+import {
+  ChatOutputType,
+  FlowPoolObjectType,
+  FlowStoreType,
+  chatInputType,
+} from "../types/zustand/flow";
 import { buildVertices } from "../utils/buildUtils";
 import {
   cleanEdges,
@@ -69,23 +77,25 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
     }
     get().setFlowPool(newFlowPool);
   },
-  updateFlowPool:(nodeId:string,data:FlowPoolObjectType| ChatOutputType | chatInputType,buildId?:string)=>{
+  updateFlowPool: (
+    nodeId: string,
+    data: FlowPoolObjectType | ChatOutputType | chatInputType,
+    buildId?: string
+  ) => {
     let newFlowPool = cloneDeep({ ...get().flowPool });
-    if (!newFlowPool[nodeId]){
+    if (!newFlowPool[nodeId]) {
       return;
-    }
+    } else {
-    else {
+      let index = newFlowPool[nodeId].length - 1;
-      let index = newFlowPool[nodeId].length-1;
+      if (buildId) {
-      if(buildId){
+        index = newFlowPool[nodeId].findIndex((flow) => flow.id === buildId);
-        index = newFlowPool[nodeId].findIndex((flow)=>flow.id===buildId);
       }
       //check if the data is a flowpool object
-      if((data as FlowPoolObjectType).data?.artifacts!==undefined){
+      if ((data as FlowPoolObjectType).data?.artifacts !== undefined) {
-        newFlowPool[nodeId][index] = (data as FlowPoolObjectType);
+        newFlowPool[nodeId][index] = data as FlowPoolObjectType;
       }
       //update data artifact
-      else
+      else {
-      {
         newFlowPool[nodeId][index].data.artifacts = data;
       }
     }
@@ -404,7 +414,13 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       });
     });
   },
-  buildFlow: async (nodeId?: string) => {
+  buildFlow: async ({
+    nodeId,
+    input_value,
+  }: {
+    nodeId?: string;
+    input_value?: string;
+  }) => {
     get().setIsBuilding(true);
     const currentFlow = useFlowsManagerStore.getState().currentFlow;
     const setSuccessData = useAlertStore.getState().setSuccessData;
@@ -427,25 +443,19 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
     function handleBuildUpdate(
       vertexBuildData: VertexBuildTypeAPI,
       status: BuildStatus,
-      buildId:string
+      buildId: string
     ) {
       if (vertexBuildData && vertexBuildData.inactive_vertices) {
         get().removeFromVerticesBuild(vertexBuildData.inactive_vertices);
       }
-      get().addDataToFlowPool({...vertexBuildData,buildId}, vertexBuildData.id);
+      get().addDataToFlowPool(
+        { ...vertexBuildData, buildId },
+        vertexBuildData.id
+      );
       useFlowStore.getState().updateBuildStatus([vertexBuildData.id], status);
     }
-    await updateFlowInDatabase({
-      data: {
-        nodes: get().nodes,
-        edges: get().edges,
-        viewport: get().reactFlowInstance?.getViewport()!,
-      },
-      id: currentFlow!.id,
-      name: currentFlow!.name,
-      description: currentFlow!.description,
-    });
     await buildVertices({
+      input_value,
       flowId: currentFlow!.id,
       nodeId,
       onGetOrderSuccess: () => {
@@ -483,16 +493,22 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       viewport: get().reactFlowInstance?.getViewport()!,
     };
   },
-  updateVerticesBuild: (vertices: string[]) => {
+  updateVerticesBuild: (
+    vertices: { verticesIds: string[], verticesOrder: string[][], verticesLayers: string[][], runId: string } | null
+  ) => {
     set({ verticesBuild: vertices });
   },
-  verticesBuild: [],
+  verticesBuild: null,
-
   removeFromVerticesBuild: (vertices: string[]) => {
+    const verticesBuild = get().verticesBuild;
+    if (!verticesBuild) return;
     set({
-      verticesBuild: get().verticesBuild.filter(
+      verticesBuild: {
-        (vertex) => !vertices.includes(vertex)
+        ...verticesBuild,
-      ),
+        verticesIds: get().verticesBuild!.verticesIds.filter(
+          (vertex) => !vertices.includes(vertex)
+        ),
+      },
     });
   },
   updateBuildStatus: (nodeIdList: string[], status: BuildStatus) => {

src/frontend/src/stores/flowsManagerStore.ts

@@ -83,6 +83,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
     if (saveTimeoutId) {
       clearTimeout(saveTimeoutId);
     }
+    set({ saveLoading: true });
     // Set up a new timeout.
     saveTimeoutId = setTimeout(() => {
       if (get().currentFlow) {
@@ -92,7 +93,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
         );
       }
       set({ saveLoading: true });
-    }, 1000); // Delay of 1000ms.
+    }, 500); // Delay of 500ms because chat message depends on it.
   },
   saveFlow: (flow: FlowType, silent?: boolean) => {
     set({ saveLoading: true });

src/frontend/src/types/zustand/flow/index.ts

@@ -88,11 +88,11 @@ export type FlowStoreType = {
   getFilterEdge: any[];
   onConnect: (connection: Connection) => void;
   unselectAll: () => void;
-  buildFlow: (nodeId?: string) => Promise<void>;
+  buildFlow: ({nodeId, input_value}: {nodeId?: string, input_value?: string}) => Promise<void>;
   getFlow: () => { nodes: Node[]; edges: Edge[]; viewport: Viewport };
-  updateVerticesBuild: (vertices: string[]) => void;
+  updateVerticesBuild: (vertices: {verticesIds: string[], verticesLayers: string[][], verticesOrder: string[][], runId: string} | null) => void;
-  removeFromVerticesBuild: (vertices: string[]) => void;
+  removeFromVerticesBuild: (vertices: string[]) => void; 
-  verticesBuild: string[];
+  verticesBuild:  {verticesIds: string[], verticesLayers: string[][], verticesOrder: string[][], runId: string} | null;
   updateBuildStatus: (nodeId: string[], status: BuildStatus) => void;
   revertBuiltStatusFromBuilding: () => void;
   flowBuildStatus: { [key: string]: BuildStatus };

src/frontend/src/utils/buildUtils.ts

@@ -7,9 +7,14 @@ import { VertexBuildTypeAPI } from "../types/api";
 
 type BuildVerticesParams = {
   flowId: string; // Assuming FlowType is the type for your flow
+  input_value?: any; // Replace any with the actual type if it's not any
   nodeId?: string | null; // Assuming nodeId is of type string, and it's optional
   onGetOrderSuccess?: () => void;
-  onBuildUpdate?: (data: VertexBuildTypeAPI, status: BuildStatus,buildId:string) => void; // Replace any with the actual type if it's not any
+  onBuildUpdate?: (
+    data: VertexBuildTypeAPI,
+    status: BuildStatus,
+    buildId: string
+  ) => void; // Replace any with the actual type if it's not any
   onBuildComplete?: (allNodesValid: boolean) => void;
   onBuildError?: (title, list, idList: string[]) => void;
   onBuildStart?: (idList: string[]) => void;
@@ -34,8 +39,54 @@ function getInactiveVertexData(vertexId: string): VertexBuildTypeAPI {
   return inactiveVertexData;
 }
 
+export async function updateVerticesOrder(flowId: string, nodeId: string | null): Promise<{ verticesLayers: string[][], verticesIds: string[], verticesOrder: string[][], runId: string }> {
+  return new Promise(async (resolve, reject) => {
+    const setErrorData = useAlertStore.getState().setErrorData;
+    let orderResponse;
+    try {
+      orderResponse = await getVerticesOrder(flowId, nodeId);
+    } catch (error: any) {
+      console.log(error);
+      setErrorData({
+        title: "Oops! Looks like you missed something",
+        list: [error.response?.data?.detail ?? "Unknown Error"],
+      });
+      useFlowStore.getState().setIsBuilding(false);
+      throw new Error("Invalid nodes");
+    }
+    let verticesOrder: Array<Array<string>> = orderResponse.data.ids;
+    const runId = orderResponse.data.run_id;
+    let verticesLayers: Array<Array<string>> = [];
+
+    if (nodeId) {
+      for (let i = 0; i < verticesOrder.length; i += 1) {
+        const innerArray = verticesOrder[i];
+        const idIndex = innerArray.indexOf(nodeId);
+        if (idIndex !== -1) {
+          // If there's a nodeId, we want to run just that component and not the entire layer
+          // because a layer contains dependencies for the next layer
+          // and we are stopping at the layer that contains the nodeId
+          verticesLayers.push([innerArray[idIndex]]);
+          break; // Stop searching after finding the first occurrence
+        }
+        // If the targetId is not found, include the entire inner array
+        verticesLayers.push(innerArray);
+      }
+    } else {
+      verticesLayers = verticesOrder;
+    }
+
+    const verticesIds = verticesLayers.flat();
+    useFlowStore
+      .getState()
+      .updateVerticesBuild({ verticesLayers, verticesIds, verticesOrder, runId });
+    resolve({ verticesLayers, verticesIds, verticesOrder, runId });
+  });
+}
+
 export async function buildVertices({
   flowId,
+  input_value,
   nodeId = null,
   onGetOrderSuccess,
   onBuildUpdate,
@@ -44,24 +95,18 @@ export async function buildVertices({
   onBuildStart,
   validateNodes,
 }: BuildVerticesParams) {
-  const setErrorData = useAlertStore.getState().setErrorData;
+  let verticesBuild = useFlowStore.getState().verticesBuild;
-  let orderResponse;
+  if (!verticesBuild || nodeId) {
-  try {
+    verticesBuild = await updateVerticesOrder(flowId, nodeId);
-    orderResponse = await getVerticesOrder(flowId, nodeId);
-  } catch (error:any) {
-    console.log(error);
-    setErrorData({
-      title: "Oops! Looks like you missed something",
-      list: [error.response?.data?.detail ?? "Unknown Error"],
-    });
-    useFlowStore.getState().setIsBuilding(false);
-    throw new Error("Invalid nodes");
   }
-  if (onGetOrderSuccess) onGetOrderSuccess();
+  const verticesIds = verticesBuild?.verticesIds!;
-  let verticesOrder: Array<Array<string>> = orderResponse.data.ids;
+  const verticesLayers = verticesBuild?.verticesLayers!;
-  const runId = orderResponse.data.run_id;
+  const verticesOrder = verticesBuild?.verticesOrder!;
-  let vertices_layers: Array<Array<string>> = [];
+  const runId = verticesBuild?.runId!;
   let stop = false;
+
+  if (onGetOrderSuccess) onGetOrderSuccess();
+    
   if (validateNodes) {
     try {
       validateNodes(verticesOrder.flatMap((id) => id));
@@ -69,48 +114,29 @@ export async function buildVertices({
       return;
     }
   }
-  if (nodeId) {
-    for (let i = 0; i < verticesOrder.length; i += 1) {
-      const innerArray = verticesOrder[i];
-      const idIndex = innerArray.indexOf(nodeId);
-      if (idIndex !== -1) {
-        // If there's a nodeId, we want to run just that component and not the entire layer
-        // because a layer contains dependencies for the next layer
-        // and we are stopping at the layer that contains the nodeId
-        vertices_layers.push([innerArray[idIndex]]);
-        break; // Stop searching after finding the first occurrence
-      }
-      // If the targetId is not found, include the entire inner array
-      vertices_layers.push(innerArray);
-    }
-  } else {
-    vertices_layers = verticesOrder;
-  }
 
-  const verticesIds = vertices_layers.flat();
   useFlowStore.getState().updateBuildStatus(verticesIds, BuildStatus.TO_BUILD);
-  useFlowStore.getState().updateVerticesBuild(verticesIds);
   useFlowStore.getState().setIsBuilding(true);
 
   // Set each vertex state to building
   const buildResults: Array<boolean> = [];
-  for (const layer of vertices_layers) {
+  for (const layer of verticesLayers) {
     if (onBuildStart) onBuildStart(layer);
     for (const id of layer) {
       // Check if id is in the list of inactive nodes
-      if (
+      if (!verticesIds.includes(id) && onBuildUpdate) {
-        !useFlowStore.getState().verticesBuild.includes(id) &&
-        onBuildUpdate
-      ) {
         // If it is, skip building and set the state to inactive
-        onBuildUpdate(getInactiveVertexData(id), BuildStatus.INACTIVE,runId);
+        onBuildUpdate(getInactiveVertexData(id), BuildStatus.INACTIVE, runId);
         buildResults.push(false);
         continue;
       }
       await buildVertex({
         flowId,
         id,
-        onBuildUpdate:(data: VertexBuildTypeAPI, status: BuildStatus) => {if(onBuildUpdate) onBuildUpdate(data, status,runId)},
+        input_value,
+        onBuildUpdate: (data: VertexBuildTypeAPI, status: BuildStatus) => {
+          if (onBuildUpdate) onBuildUpdate(data, status, runId);
+        },
         onBuildError,
         verticesIds,
         buildResults,
@@ -137,6 +163,7 @@ export async function buildVertices({
 async function buildVertex({
   flowId,
   id,
+  input_value,
   onBuildUpdate,
   onBuildError,
   verticesIds,
@@ -145,6 +172,7 @@ async function buildVertex({
 }: {
   flowId: string;
   id: string;
+  input_value: string;
   onBuildUpdate?: (data: any, status: BuildStatus) => void;
   onBuildError?: (title, list, idList: string[]) => void;
   verticesIds: string[];
@@ -152,7 +180,7 @@ async function buildVertex({
   stopBuild: () => void;
 }) {
   try {
-    const buildRes = await postBuildVertex(flowId, id);
+    const buildRes = await postBuildVertex(flowId, id, input_value);
     const buildData: VertexBuildTypeAPI = buildRes.data;
     if (onBuildUpdate) {
       if (!buildData.valid) {

src/frontend/src/utils/styleUtils.ts

@@ -219,7 +219,7 @@ export const nodeColors: { [char: string]: string } = {
   wrappers: "#E6277A",
   utilities: "#31A3CC",
   output_parsers: "#E6A627",
-  str: "#049524",
+  str: "#31a3cc",
   retrievers: "#e6b25a",
   unknown: "#9CA3AF",
   custom_components: "#ab11ab",
@@ -258,6 +258,7 @@ export const nodeIconsLucide: iconsType = {
   Chroma: ChromaIcon,
   AirbyteJSONLoader: AirbyteIcon,
   AmazonBedrockEmbeddings: AWSIcon,
+  Amazon: AWSIcon,
   Anthropic: AnthropicIcon,
   ChatAnthropic: AnthropicIcon,
   BingSearchAPIWrapper: BingIcon,
@@ -270,13 +271,17 @@ export const nodeIconsLucide: iconsType = {
   GoogleSearchAPIWrapper: GoogleIcon,
   GoogleSearchResults: GoogleIcon,
   GoogleSearchRun: GoogleIcon,
+  Google: GoogleIcon,
   HNLoader: HackerNewsIcon,
   HuggingFaceHub: HuggingFaceIcon,
+  HuggingFace: HuggingFaceIcon,
   HuggingFaceEmbeddings: HuggingFaceIcon,
   IFixitLoader: IFixIcon,
   Meta: MetaIcon,
   Midjorney: MidjourneyIcon,
   MongoDBAtlasVectorSearch: MongoDBIcon,
+  MongoDB:MongoDBIcon,
+  MongoDBChatMessageHistory: MongoDBIcon,
   NotionDirectoryLoader: NotionIcon,
   ChatOpenAI: OpenAiIcon,
   AzureChatOpenAI: OpenAiIcon,
@@ -289,6 +294,7 @@ export const nodeIconsLucide: iconsType = {
   Searx: SearxIcon,
   SlackDirectoryLoader: SvgSlackIcon,
   SupabaseVectorStore: SupabaseIcon,
+  Supabase: SupabaseIcon,
   VertexAI: VertexAIIcon,
   ChatVertexAI: VertexAIIcon,
   VertexAIEmbeddings: VertexAIIcon,