From 116518202c306b7f82138fda4e5728f82add8070 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 21:25:27 -0300
Subject: [PATCH 01/50] Refactor vertex streaming logic in build_vertex_stream
 function

---
 src/backend/langflow/api/v1/chat.py | 36 ++++++++++++++++++-----------
 1 file changed, 23 insertions(+), 13 deletions(-)

diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py
index e1b52aeb5..beb9b19b2 100644
--- a/src/backend/langflow/api/v1/chat.py
+++ b/src/backend/langflow/api/v1/chat.py
@@ -185,15 +185,16 @@ async def build_vertex(
             chat_service.clear_cache(flow_id)
 
         # Log the vertex build
-        background_tasks.add_task(
-            log_vertex_build,
-            flow_id=flow_id,
-            vertex_id=vertex_id,
-            valid=valid,
-            params=params,
-            data=result_data_response,
-            artifacts=artifacts,
-        )
+        if not vertex.will_stream:
+            background_tasks.add_task(
+                log_vertex_build,
+                flow_id=flow_id,
+                vertex_id=vertex_id,
+                valid=valid,
+                params=params,
+                data=result_data_response,
+                artifacts=artifacts,
+            )
 
         timedelta = time.perf_counter() - start_time
         duration = format_elapsed_time(timedelta)
@@ -243,22 +244,31 @@ async def build_vertex_stream(
                 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",

From a9d183bab0e17c9d433634b46cd06e66f775e9d4 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 21:25:37 -0300
Subject: [PATCH 02/50] Refactor typing imports and add 'will_stream' attribute
 to Vertex class

---
 src/backend/langflow/graph/vertex/base.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py
index dd308f9f1..b917889a4 100644
--- a/src/backend/langflow/graph/vertex/base.py
+++ b/src/backend/langflow/graph/vertex/base.py
@@ -2,16 +2,13 @@ import ast
 import inspect
 import types
 from enum import Enum
-from typing import TYPE_CHECKING, Any, Callable, Coroutine, Dict, List, Optional
+from typing import (TYPE_CHECKING, Any, Callable, Coroutine, Dict, List,
+                    Optional)
 
 from loguru import logger
 
-from langflow.graph.schema import (
-    INPUT_COMPONENTS,
-    OUTPUT_COMPONENTS,
-    InterfaceComponentTypes,
-    ResultData,
-)
+from langflow.graph.schema import (INPUT_COMPONENTS, OUTPUT_COMPONENTS,
+                                   InterfaceComponentTypes, ResultData)
 from langflow.graph.utils import UnbuiltObject, UnbuiltResult
 from langflow.graph.vertex.utils import generate_result
 from langflow.interface.initialize import loading
@@ -44,6 +41,7 @@ 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(

From b55be2aba02b40faac57d3a1ee7768ce1d7b3f2f Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 21:26:18 -0300
Subject: [PATCH 03/50] Refactor ChatVertex to include streaming capability

---
 src/backend/langflow/graph/vertex/types.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/src/backend/langflow/graph/vertex/types.py b/src/backend/langflow/graph/vertex/types.py
index 100390d11..0b2be3ab0 100644
--- a/src/backend/langflow/graph/vertex/types.py
+++ b/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)):
@@ -434,13 +436,15 @@ 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(
-            sender=self.params.get("sender", ""),
-            sender_name=self.params.get("sender_name", ""),
-            message=complete_message,
-            session_id=self.params.get("session_id", ""),
+        await log_vertex_build(
+            flow_id=self.graph.flow_id,
+            vertex_id=self.id,
+            valid=True,
+            params=self._built_object_repr(),
+            data=self.result,
             artifacts=self.artifacts,
         )
 

From a2eaed1af3412fd4c2c3ea2f51c1847528900fa2 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Tue, 27 Feb 2024 21:46:06 -0300
Subject: [PATCH 04/50] Update sender value in updateFlowPool function

---
 src/frontend/src/components/newChatView/index.tsx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/frontend/src/components/newChatView/index.tsx b/src/frontend/src/components/newChatView/index.tsx
index b1f888f1c..7a9c869c5 100644
--- a/src/frontend/src/components/newChatView/index.tsx
+++ b/src/frontend/src/components/newChatView/index.tsx
@@ -124,7 +124,7 @@ export default function NewChatView({
       if (message === "") return;
       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);
       // if (index === -1) return oldChatHistory;

From cf35b42b8e3477bc45998422b49f8522b03964d8 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 21:50:27 -0300
Subject: [PATCH 05/50] Refactor newChatView component

---
 .../src/components/newChatView/index.tsx      | 46 +++++++++++--------
 1 file changed, 26 insertions(+), 20 deletions(-)

diff --git a/src/frontend/src/components/newChatView/index.tsx b/src/frontend/src/components/newChatView/index.tsx
index 7a9c869c5..c4be0bcc7 100644
--- a/src/frontend/src/components/newChatView/index.tsx
+++ b/src/frontend/src/components/newChatView/index.tsx
@@ -1,6 +1,10 @@
-import _ from "lodash";
 import { useEffect, useRef, useState } from "react";
 import IconComponent from "../../components/genericIconComponent";
+import { NOCHATOUTPUT_NOTICE_ALERT } from "../../constants/alerts_constants";
+import {
+  chatFirstInitialText,
+  chatSecondInitialText,
+} from "../../constants/constants";
 import { deleteFlowPool } from "../../controllers/API";
 import useAlertStore from "../../stores/alertStore";
 import useFlowStore from "../../stores/flowStore";
@@ -14,8 +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;
-      chat.message = message;
+  ) {
+    if (message === "") return;
+    chat.message = message;
     // chat is one of the chatHistory
-    updateFlowPool(chat.componentId,{message,sender_name:chat.sender_name??"Bot",sender:chat.isSend?"User":"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);
-      // if (index === -1) return oldChatHistory;
-      // let newChatHistory = _.cloneDeep(oldChatHistory);
-      // newChatHistory = [
-      //   ...newChatHistory.slice(0, index),
-      //   chat,
-      //   ...newChatHistory.slice(index + 1),
-      // ];
-      // console.log("newChatHistory:", newChatHistory);
-      // return newChatHistory;
+    // const index = oldChatHistory.findIndex((ch) => ch.id === chat.id);
+    // if (index === -1) return oldChatHistory;
+    // let newChatHistory = _.cloneDeep(oldChatHistory);
+    // newChatHistory = [
+    //   ...newChatHistory.slice(0, index),
+    //   chat,
+    //   ...newChatHistory.slice(index + 1),
+    // ];
+    // console.log("newChatHistory:", newChatHistory);
+    // return newChatHistory;
     // });
   }
 
@@ -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}

From 557282cea5948af261481ff9644515fedf434b97 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 21:50:34 -0300
Subject: [PATCH 06/50] Refactor imports and remove console.log statements

---
 .../newChatView/chatMessage/index.tsx         | 68 +++++++++----------
 1 file changed, 32 insertions(+), 36 deletions(-)

diff --git a/src/frontend/src/components/newChatView/chatMessage/index.tsx b/src/frontend/src/components/newChatView/chatMessage/index.tsx
index 4f6d8e534..7c606679f 100644
--- a/src/frontend/src/components/newChatView/chatMessage/index.tsx
+++ b/src/frontend/src/components/newChatView/chatMessage/index.tsx
@@ -1,5 +1,5 @@
 import Convert from "ansi-to-html";
-import { useEffect, useMemo, useState, 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;
-                      let match;
-                      let parts: Array<JSX.Element | string> = [];
-                      let lastIndex = 0;
-                      while ((match = regex.exec(line)) !== null) {
-                        // Push text up to the match
-                        if (match.index !== lastIndex) {
-                          parts.push(line.substring(lastIndex, match.index));
-                        }
-                        // Push div with matched text
-                        if (chat.message[match[1]]) {
-                          parts.push(
-                            <span className="chat-message-highlight">
-                              {chat.message[match[1]]}
-                            </span>
-                          );
-                        }
+                        const regex = /{([^}]+)}/g;
+                        let match;
+                        let parts: Array<JSX.Element | string> = [];
+                        let lastIndex = 0;
+                        while ((match = regex.exec(line)) !== null) {
+                          // Push text up to the match
+                          if (match.index !== lastIndex) {
+                            parts.push(line.substring(lastIndex, match.index));
+                          }
+                          // Push div with matched text
+                          if (chat.message[match[1]]) {
+                            parts.push(
+                              <span className="chat-message-highlight">
+                                {chat.message[match[1]]}
+                              </span>
+                            );
+                          }
 
-                        // Update last index
-                        lastIndex = regex.lastIndex;
-                      }
-                      // Push text after the last match
-                      if (lastIndex !== line.length) {
-                        parts.push(line.substring(lastIndex));
-                      }
-                      return <p>{parts}</p>;
-                    })
+                          // Update last index
+                          lastIndex = regex.lastIndex;
+                        }
+                        // Push text after the last match
+                        if (lastIndex !== line.length) {
+                          parts.push(line.substring(lastIndex));
+                        }
+                        return <p>{parts}</p>;
+                      })
                     : chatMessage}
                 </span>
               </>

From 0a77490f8c5c4064bca96d11a7e7862551649ba1 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 22:03:40 -0300
Subject: [PATCH 07/50] Update input field names in model components

---
 .../langflow/components/chains/RetrievalQA.py        |  5 ++++-
 src/backend/langflow/components/io/TextInput.py      | 12 ++++++------
 .../langflow/components/models/AmazonBedrockModel.py |  2 +-
 .../langflow/components/models/AnthropicModel.py     |  2 +-
 .../langflow/components/models/AzureOpenAIModel.py   |  2 +-
 .../components/models/BaiduQianfanChatModel.py       |  2 +-
 .../langflow/components/models/CTransformersModel.py |  2 +-
 .../langflow/components/models/CohereModel.py        |  2 +-
 .../components/models/GoogleGenerativeAIModel.py     |  7 ++-----
 .../langflow/components/models/HuggingFaceModel.py   |  2 +-
 .../langflow/components/models/LlamaCppModel.py      |  2 +-
 .../langflow/components/models/OllamaModel.py        |  2 +-
 .../langflow/components/models/OpenAIModel.py        |  2 +-
 .../langflow/components/models/VertexAiModel.py      |  2 +-
 .../components/utilities/RunnableExecutor.py         |  2 +-
 .../langflow/components/vectorstores/ChromaSearch.py |  2 +-
 16 files changed, 25 insertions(+), 25 deletions(-)

diff --git a/src/backend/langflow/components/chains/RetrievalQA.py b/src/backend/langflow/components/chains/RetrievalQA.py
index 4968afe87..53fa24f15 100644
--- a/src/backend/langflow/components/chains/RetrievalQA.py
+++ b/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(
diff --git a/src/backend/langflow/components/io/TextInput.py b/src/backend/langflow/components/io/TextInput.py
index 3fba54fda..f8c1ad606 100644
--- a/src/backend/langflow/components/io/TextInput.py
+++ b/src/backend/langflow/components/io/TextInput.py
@@ -9,11 +9,11 @@ class TextInput(CustomComponent):
     description = "Used to pass text input to the next component."
 
     field_config = {
-        "value": {"display_name": "Value", "multiline": True},
+        "input_value": {"display_name": "Value", "multiline": True},
     }
 
-    def build(self, value: Optional[str] = "") -> Text:
-        self.status = value
-        if not value:
-            value = ""
-        return value
+    def build(self, input_value: Optional[str] = "") -> Text:
+        self.status = input_value
+        if not input_value:
+            input_value = ""
+        return input_value
diff --git a/src/backend/langflow/components/models/AmazonBedrockModel.py b/src/backend/langflow/components/models/AmazonBedrockModel.py
index 478bf8e9a..70daa2b4c 100644
--- a/src/backend/langflow/components/models/AmazonBedrockModel.py
+++ b/src/backend/langflow/components/models/AmazonBedrockModel.py
@@ -34,7 +34,7 @@ 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"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/AnthropicModel.py b/src/backend/langflow/components/models/AnthropicModel.py
index cb8e55194..fe3d63d34 100644
--- a/src/backend/langflow/components/models/AnthropicModel.py
+++ b/src/backend/langflow/components/models/AnthropicModel.py
@@ -49,7 +49,7 @@ 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"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/AzureOpenAIModel.py b/src/backend/langflow/components/models/AzureOpenAIModel.py
index df8413870..4abd7fcac 100644
--- a/src/backend/langflow/components/models/AzureOpenAIModel.py
+++ b/src/backend/langflow/components/models/AzureOpenAIModel.py
@@ -73,7 +73,7 @@ class AzureChatOpenAIComponent(CustomComponent):
                 "info": "Maximum number of tokens to generate.",
             },
             "code": {"show": False},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/BaiduQianfanChatModel.py b/src/backend/langflow/components/models/BaiduQianfanChatModel.py
index 0075316a2..af76262f8 100644
--- a/src/backend/langflow/components/models/BaiduQianfanChatModel.py
+++ b/src/backend/langflow/components/models/BaiduQianfanChatModel.py
@@ -68,7 +68,7 @@ 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"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/CTransformersModel.py b/src/backend/langflow/components/models/CTransformersModel.py
index fb292d9cf..8a556a954 100644
--- a/src/backend/langflow/components/models/CTransformersModel.py
+++ b/src/backend/langflow/components/models/CTransformersModel.py
@@ -28,7 +28,7 @@ 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"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/CohereModel.py b/src/backend/langflow/components/models/CohereModel.py
index c2a004c38..2a6bf613b 100644
--- a/src/backend/langflow/components/models/CohereModel.py
+++ b/src/backend/langflow/components/models/CohereModel.py
@@ -28,7 +28,7 @@ class CohereComponent(CustomComponent):
                 "type": "float",
                 "show": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
index 3b0c758e8..192fef8da 100644
--- a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
+++ b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
@@ -1,10 +1,7 @@
-from typing import Optional
 
-from langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore
-from pydantic.v1.types import SecretStr
 
 from langflow import CustomComponent
-from langflow.field_typing import RangeSpec, Text
+from langflow.field_typing import RangeSpec
 
 
 class GoogleGenerativeAIComponent(CustomComponent):
@@ -50,7 +47,7 @@ class GoogleGenerativeAIComponent(CustomComponent):
             "code": {
                 "advanced": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {e": {"display_name": "Input"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/HuggingFaceModel.py b/src/backend/langflow/components/models/HuggingFaceModel.py
index 99fcc6ab0..3fe97aca1 100644
--- a/src/backend/langflow/components/models/HuggingFaceModel.py
+++ b/src/backend/langflow/components/models/HuggingFaceModel.py
@@ -24,7 +24,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
                 "field_type": "code",
             },
             "code": {"show": False},
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/LlamaCppModel.py b/src/backend/langflow/components/models/LlamaCppModel.py
index 5941f213b..d3080b8b5 100644
--- a/src/backend/langflow/components/models/LlamaCppModel.py
+++ b/src/backend/langflow/components/models/LlamaCppModel.py
@@ -56,7 +56,7 @@ 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"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/OllamaModel.py b/src/backend/langflow/components/models/OllamaModel.py
index 7ae896532..72e3617df 100644
--- a/src/backend/langflow/components/models/OllamaModel.py
+++ b/src/backend/langflow/components/models/OllamaModel.py
@@ -164,7 +164,7 @@ class ChatOllamaComponent(CustomComponent):
                 "info": "Template to use for generating text.",
                 "advanced": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/models/OpenAIModel.py b/src/backend/langflow/components/models/OpenAIModel.py
index ee6809e0b..6a8d5c19e 100644
--- a/src/backend/langflow/components/models/OpenAIModel.py
+++ b/src/backend/langflow/components/models/OpenAIModel.py
@@ -12,7 +12,7 @@ class OpenAIModelComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
             "max_tokens": {
                 "display_name": "Max Tokens",
                 "advanced": False,
diff --git a/src/backend/langflow/components/models/VertexAiModel.py b/src/backend/langflow/components/models/VertexAiModel.py
index c05dc5e94..a8fd0eb1f 100644
--- a/src/backend/langflow/components/models/VertexAiModel.py
+++ b/src/backend/langflow/components/models/VertexAiModel.py
@@ -57,7 +57,7 @@ class ChatVertexAIComponent(CustomComponent):
                 "value": False,
                 "advanced": True,
             },
-            "inputs": {"display_name": "Input"},
+            "input_value": {"display_name": "Input"},
         }
 
     def build(
diff --git a/src/backend/langflow/components/utilities/RunnableExecutor.py b/src/backend/langflow/components/utilities/RunnableExecutor.py
index 5533e6d1d..502e1eec6 100644
--- a/src/backend/langflow/components/utilities/RunnableExecutor.py
+++ b/src/backend/langflow/components/utilities/RunnableExecutor.py
@@ -15,7 +15,7 @@ class RunnableExecComponent(CustomComponent):
                 "display_name": "Input Key",
                 "info": "The key to use for the input.",
             },
-            "inputs": {
+            "input_value": {
                 "display_name": "Inputs",
                 "info": "The inputs to pass to the runnable.",
             },
diff --git a/src/backend/langflow/components/vectorstores/ChromaSearch.py b/src/backend/langflow/components/vectorstores/ChromaSearch.py
index 5dd33abf2..6cb3e89df 100644
--- a/src/backend/langflow/components/vectorstores/ChromaSearch.py
+++ b/src/backend/langflow/components/vectorstores/ChromaSearch.py
@@ -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"],

From 8dd3cccfbe1282a0b520bbdc1f7ab3ad8527c305 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 22:37:38 -0300
Subject: [PATCH 08/50] Refactor run_graph function to generate session_id if
 not provided

---
 src/backend/langflow/processing/process.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py
index aa419b962..0ab6fcadd 100644
--- a/src/backend/langflow/processing/process.py
+++ b/src/backend/langflow/processing/process.py
@@ -277,12 +277,19 @@ async def run_graph(
 ):
     """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)
     if session_id and session_service:
         session_service.update_session(session_id, (graph, artifacts))
-    return outputs
+    return outputs, session_id
 
 
 def validate_input(

From fb797a7eea390612455f10e55c70c50dd275969d Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 22:37:48 -0300
Subject: [PATCH 09/50] Fix run_graph function call in endpoints.py

---
 src/backend/langflow/api/v1/endpoints.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py
index fce4f9649..8da1f0e53 100644
--- a/src/backend/langflow/api/v1/endpoints.py
+++ b/src/backend/langflow/api/v1/endpoints.py
@@ -239,7 +239,7 @@ 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,
@@ -263,7 +263,7 @@ 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,

From 13317d61e2f961648899639315131bfc1443b4e9 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 22:37:55 -0300
Subject: [PATCH 10/50] Refactor build_vertex_stream function to handle
 session_id parameter

---
 src/backend/langflow/api/v1/chat.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py
index beb9b19b2..91b4bcd65 100644
--- a/src/backend/langflow/api/v1/chat.py
+++ b/src/backend/langflow/api/v1/chat.py
@@ -32,8 +32,9 @@ 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
@@ -227,19 +228,27 @@ async def build_vertex(
 async def build_vertex_stream(
     flow_id: str,
     vertex_id: str,
+    session_id: Optional[str] = None,
     chat_service: "ChatService" = Depends(get_chat_service),
+    session_service: "SessionService" = Depends(get_session_service),
 ):
     """Build a vertex instead of the entire graph."""
     try:
 
         async def stream_vertex():
             try:
-                cache = chat_service.get_cache(flow_id)
-                if not cache:
-                    # If there's no cache
-                    raise ValueError(f"No cache found for {flow_id}.")
+                if not session_id:
+                    cache = chat_service.get_cache(flow_id)
+                    if not cache:
+                        # If there's no cache
+                        raise ValueError(f"No cache found for {flow_id}.")
+                    else:
+                        graph = cache.get("result")
                 else:
-                    graph = cache.get("result")
+                    session_data = await session_service.load_session(session_id)
+                    graph, artifacts = session_data if session_data else (None, None)
+                    if not graph:
+                        raise ValueError(f"No graph found for {flow_id}.")
 
                 vertex: "ChatVertex" = graph.get_vertex(vertex_id)
                 if not hasattr(vertex, "stream"):

From 6c2a35afb1940a36aad8ccb8fef00fcbc094fb16 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 22:44:50 -0300
Subject: [PATCH 11/50] Refactor model components to support streaming

---
 .../components/models/AmazonBedrockModel.py     | 14 +++++++++++---
 .../components/models/AnthropicModel.py         | 14 +++++++++++---
 .../components/models/AzureOpenAIModel.py       | 14 +++++++++++---
 .../components/models/BaiduQianfanChatModel.py  | 14 +++++++++++---
 .../components/models/CTransformersModel.py     | 14 +++++++++++---
 .../langflow/components/models/CohereModel.py   | 12 +++++++-----
 .../models/GoogleGenerativeAIModel.py           | 11 +++++++----
 .../langflow/components/models/LlamaCppModel.py | 16 +++++++++++-----
 .../langflow/components/models/OllamaModel.py   | 14 +++++++++++---
 .../langflow/components/models/OpenAIModel.py   | 17 ++++++++++++-----
 .../langflow/components/models/VertexAiModel.py | 14 +++++++++++---
 11 files changed, 114 insertions(+), 40 deletions(-)

diff --git a/src/backend/langflow/components/models/AmazonBedrockModel.py b/src/backend/langflow/components/models/AmazonBedrockModel.py
index 70daa2b4c..761daae65 100644
--- a/src/backend/langflow/components/models/AmazonBedrockModel.py
+++ b/src/backend/langflow/components/models/AmazonBedrockModel.py
@@ -35,6 +35,10 @@ class AmazonBedrockComponent(CustomComponent):
             "cache": {"display_name": "Cache"},
             "code": {"advanced": True},
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -47,6 +51,7 @@ class AmazonBedrockComponent(CustomComponent):
         endpoint_url: Optional[str] = None,
         streaming: bool = False,
         cache: Optional[bool] = None,
+        stream: bool = False,
     ) -> Text:
         try:
             output = BedrockChat(
@@ -60,7 +65,10 @@ 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
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/AnthropicModel.py b/src/backend/langflow/components/models/AnthropicModel.py
index fe3d63d34..230a5ab2a 100644
--- a/src/backend/langflow/components/models/AnthropicModel.py
+++ b/src/backend/langflow/components/models/AnthropicModel.py
@@ -50,6 +50,10 @@ class AnthropicLLM(CustomComponent):
             },
             "code": {"show": False},
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -60,6 +64,7 @@ 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 +82,10 @@ 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
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/AzureOpenAIModel.py b/src/backend/langflow/components/models/AzureOpenAIModel.py
index 4abd7fcac..8931bd6e4 100644
--- a/src/backend/langflow/components/models/AzureOpenAIModel.py
+++ b/src/backend/langflow/components/models/AzureOpenAIModel.py
@@ -74,6 +74,10 @@ class AzureChatOpenAIComponent(CustomComponent):
             },
             "code": {"show": False},
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -86,6 +90,7 @@ class AzureChatOpenAIComponent(CustomComponent):
         api_version: str,
         temperature: float = 0.7,
         max_tokens: Optional[int] = 1000,
+        stream: bool = False,
     ) -> BaseLanguageModel:
         try:
             output = AzureChatOpenAI(
@@ -99,7 +104,10 @@ 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
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/BaiduQianfanChatModel.py b/src/backend/langflow/components/models/BaiduQianfanChatModel.py
index af76262f8..121dd9be6 100644
--- a/src/backend/langflow/components/models/BaiduQianfanChatModel.py
+++ b/src/backend/langflow/components/models/BaiduQianfanChatModel.py
@@ -69,6 +69,10 @@ class QianfanChatEndpointComponent(CustomComponent):
             },
             "code": {"show": False},
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -81,6 +85,7 @@ 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 +99,10 @@ 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
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/CTransformersModel.py b/src/backend/langflow/components/models/CTransformersModel.py
index 8a556a954..9784f86b1 100644
--- a/src/backend/langflow/components/models/CTransformersModel.py
+++ b/src/backend/langflow/components/models/CTransformersModel.py
@@ -29,6 +29,10 @@ class CTransformersComponent(CustomComponent):
                 "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}',
             },
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -38,11 +42,15 @@ 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
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/CohereModel.py b/src/backend/langflow/components/models/CohereModel.py
index 2a6bf613b..6af3971da 100644
--- a/src/backend/langflow/components/models/CohereModel.py
+++ b/src/backend/langflow/components/models/CohereModel.py
@@ -43,8 +43,10 @@ class CohereComponent(CustomComponent):
             max_tokens=max_tokens,
             temperature=temperature,
         )
-        message = output.invoke(input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result
-        return result
+        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        return result
diff --git a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
index 192fef8da..40f1d385f 100644
--- a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
+++ b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
@@ -70,7 +70,10 @@ class GoogleGenerativeAIComponent(CustomComponent):
             n=n or 1,
             google_api_key=SecretStr(google_api_key),
         )
-        message = output.invoke(input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result
+        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
\ No newline at end of file
diff --git a/src/backend/langflow/components/models/LlamaCppModel.py b/src/backend/langflow/components/models/LlamaCppModel.py
index d3080b8b5..00c0ee0f4 100644
--- a/src/backend/langflow/components/models/LlamaCppModel.py
+++ b/src/backend/langflow/components/models/LlamaCppModel.py
@@ -57,6 +57,10 @@ class LlamaCppComponent(CustomComponent):
             "verbose": {"display_name": "Verbose", "advanced": True},
             "vocab_only": {"display_name": "Vocab Only", "advanced": True},
             "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 +140,10 @@ class LlamaCppComponent(CustomComponent):
             verbose=verbose,
             vocab_only=vocab_only,
         )
-        message = output.invoke(input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/OllamaModel.py b/src/backend/langflow/components/models/OllamaModel.py
index 72e3617df..5f0a86289 100644
--- a/src/backend/langflow/components/models/OllamaModel.py
+++ b/src/backend/langflow/components/models/OllamaModel.py
@@ -165,6 +165,10 @@ class ChatOllamaComponent(CustomComponent):
                 "advanced": True,
             },
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -197,6 +201,7 @@ 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 +255,10 @@ 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
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/OpenAIModel.py b/src/backend/langflow/components/models/OpenAIModel.py
index 6a8d5c19e..34a0252be 100644
--- a/src/backend/langflow/components/models/OpenAIModel.py
+++ b/src/backend/langflow/components/models/OpenAIModel.py
@@ -57,6 +57,10 @@ class OpenAIModelComponent(CustomComponent):
                 "required": False,
                 "value": 0.7,
             },
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -68,10 +72,11 @@ 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,
@@ -79,8 +84,10 @@ class OpenAIModelComponent(CustomComponent):
             api_key=openai_api_key,
             temperature=temperature,
         )
-
-        message = model.invoke(input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
         return result
diff --git a/src/backend/langflow/components/models/VertexAiModel.py b/src/backend/langflow/components/models/VertexAiModel.py
index a8fd0eb1f..dcaa95860 100644
--- a/src/backend/langflow/components/models/VertexAiModel.py
+++ b/src/backend/langflow/components/models/VertexAiModel.py
@@ -58,6 +58,10 @@ class ChatVertexAIComponent(CustomComponent):
                 "advanced": True,
             },
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -73,6 +77,7 @@ class ChatVertexAIComponent(CustomComponent):
         top_k: int = 40,
         top_p: float = 0.95,
         verbose: bool = False,
+        stream: bool = False,
     ) -> Text:
         try:
             from langchain_google_vertexai import ChatVertexAI
@@ -92,7 +97,10 @@ class ChatVertexAIComponent(CustomComponent):
             top_p=top_p,
             verbose=verbose,
         )
-        message = output.invoke(input_value)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
+        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

From 639c54e3eefa1aae137ee3e89eb9466dccd557ec Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 23:04:21 -0300
Subject: [PATCH 12/50] Add stream parameter to run_flow_with_caching and
 Graph.run methods

---
 src/backend/langflow/api/v1/endpoints.py   |  3 +++
 src/backend/langflow/graph/graph/base.py   | 12 +++++++++---
 src/backend/langflow/graph/vertex/types.py |  4 ++++
 src/backend/langflow/processing/process.py |  3 ++-
 4 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py
index 8da1f0e53..116c63b2c 100644
--- a/src/backend/langflow/api/v1/endpoints.py
+++ b/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),
@@ -246,6 +247,7 @@ async def run_flow_with_caching(
                 inputs=inputs,
                 artifacts=artifacts,
                 session_service=session_service,
+                stream=stream,
             )
 
         else:
@@ -270,6 +272,7 @@ async def run_flow_with_caching(
                 inputs=inputs,
                 artifacts={},
                 session_service=session_service,
+                stream=stream,
             )
 
         return RunResponse(outputs=task_result, session_id=session_id)
diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py
index 3f5e376a5..051fc6b3b 100644
--- a/src/backend/langflow/graph/graph/base.py
+++ b/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
diff --git a/src/backend/langflow/graph/vertex/types.py b/src/backend/langflow/graph/vertex/types.py
index 0b2be3ab0..721a7ccc8 100644
--- a/src/backend/langflow/graph/vertex/types.py
+++ b/src/backend/langflow/graph/vertex/types.py
@@ -451,6 +451,10 @@ class ChatVertex(StatelessVertex):
         self._validate_built_object()
         self._built = True
 
+    async def consume_async_generator(self):
+        async for _ in self.stream():
+            pass
+
 
 class RoutingVertex(StatelessVertex):
     def __init__(self, data: Dict, graph):
diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py
index 0ab6fcadd..d7cf09a6f 100644
--- a/src/backend/langflow/processing/process.py
+++ b/src/backend/langflow/processing/process.py
@@ -271,6 +271,7 @@ 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,
@@ -286,7 +287,7 @@ async def run_graph(
             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, session_id

From 2b556d5fb3ff67f8bf38a86f1ca2658567a759cd Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 23:48:04 -0300
Subject: [PATCH 13/50] Add base chat component for chat input and output

---
 src/backend/langflow/components/io/ChatInput.py                 | 2 +-
 src/backend/langflow/components/io/ChatOutput.py                | 2 +-
 src/backend/langflow/{io => components/io/base}/__init__.py     | 0
 .../langflow/{io/schema.py => components/io/base/chat.py}       | 0
 4 files changed, 2 insertions(+), 2 deletions(-)
 rename src/backend/langflow/{io => components/io/base}/__init__.py (100%)
 rename src/backend/langflow/{io/schema.py => components/io/base/chat.py} (100%)

diff --git a/src/backend/langflow/components/io/ChatInput.py b/src/backend/langflow/components/io/ChatInput.py
index 653054e0a..de8ce14cb 100644
--- a/src/backend/langflow/components/io/ChatInput.py
+++ b/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
 
 
diff --git a/src/backend/langflow/components/io/ChatOutput.py b/src/backend/langflow/components/io/ChatOutput.py
index 842a083fb..0cd51f663 100644
--- a/src/backend/langflow/components/io/ChatOutput.py
+++ b/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
 
 
diff --git a/src/backend/langflow/io/__init__.py b/src/backend/langflow/components/io/base/__init__.py
similarity index 100%
rename from src/backend/langflow/io/__init__.py
rename to src/backend/langflow/components/io/base/__init__.py
diff --git a/src/backend/langflow/io/schema.py b/src/backend/langflow/components/io/base/chat.py
similarity index 100%
rename from src/backend/langflow/io/schema.py
rename to src/backend/langflow/components/io/base/chat.py

From 6dcad54a385b568ca6facdeab5db6790e47431d4 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 23:48:36 -0300
Subject: [PATCH 14/50] Add LCModelComponent class to base/model.py

---
 .../components/models/base/__init__.py        |  0
 .../langflow/components/models/base/model.py  | 28 +++++++++++++++++++
 2 files changed, 28 insertions(+)
 create mode 100644 src/backend/langflow/components/models/base/__init__.py
 create mode 100644 src/backend/langflow/components/models/base/model.py

diff --git a/src/backend/langflow/components/models/base/__init__.py b/src/backend/langflow/components/models/base/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/backend/langflow/components/models/base/model.py b/src/backend/langflow/components/models/base/model.py
new file mode 100644
index 000000000..9f9ca7b36
--- /dev/null
+++ b/src/backend/langflow/components/models/base/model.py
@@ -0,0 +1,28 @@
+from langchain_core.runnables import Runnable
+
+from langflow import CustomComponent
+
+
+class LCModelComponent(CustomComponent):
+    display_name: str = "Model Name"
+    description: str = "Model Description"
+
+    def get_result(self, output: Runnable, stream: bool, input_value: str):
+        """
+        Retrieves the result from the output of a Runnable object.
+
+        Args:
+            output (Runnable): The output object to retrieve the result from.
+            stream (bool): Indicates whether to use streaming or invocation mode.
+            input_value (str): The input value to pass to the output object.
+
+        Returns:
+            The result obtained from the output object.
+        """
+        if stream:
+            result = output.stream(input_value)
+        else:
+            message = output.invoke(input_value)
+            result = message.content if hasattr(message, "content") else message
+            self.status = result
+        return result

From c6b837380b54ce5efa03006e00cfc9d7b796fb30 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Tue, 27 Feb 2024 23:48:53 -0300
Subject: [PATCH 15/50] Update model components

---
 .../components/models/AmazonBedrockModel.py   | 14 ++++-----
 .../components/models/AnthropicModel.py       | 14 ++++-----
 .../components/models/AzureOpenAIModel.py     | 14 ++++-----
 .../models/BaiduQianfanChatModel.py           | 14 ++++-----
 .../components/models/CTransformersModel.py   | 13 +++-----
 .../langflow/components/models/CohereModel.py | 19 ++++++------
 .../models/GoogleGenerativeAIModel.py         | 30 ++++++++++---------
 .../components/models/HuggingFaceModel.py     | 15 ++++++----
 .../components/models/LlamaCppModel.py        | 13 +++-----
 .../langflow/components/models/OllamaModel.py | 15 ++++------
 .../langflow/components/models/OpenAIModel.py | 14 ++++-----
 .../components/models/VertexAiModel.py        | 13 +++-----
 12 files changed, 78 insertions(+), 110 deletions(-)

diff --git a/src/backend/langflow/components/models/AmazonBedrockModel.py b/src/backend/langflow/components/models/AmazonBedrockModel.py
index 761daae65..4ae28e70c 100644
--- a/src/backend/langflow/components/models/AmazonBedrockModel.py
+++ b/src/backend/langflow/components/models/AmazonBedrockModel.py
@@ -2,13 +2,14 @@ from typing import Optional
 
 from langchain_community.chat_models.bedrock import BedrockChat
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class AmazonBedrockComponent(CustomComponent):
+class AmazonBedrockComponent(LCModelComponent):
     display_name: str = "Amazon Bedrock Model"
     description: str = "Generate text using LLM model from Amazon Bedrock."
+    icon = "AmazonBedrock"
 
     def build_config(self):
         return {
@@ -65,10 +66,5 @@ class AmazonBedrockComponent(CustomComponent):
             )  # type: ignore
         except Exception as e:
             raise ValueError("Could not connect to AmazonBedrock API.") from e
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/AnthropicModel.py b/src/backend/langflow/components/models/AnthropicModel.py
index 230a5ab2a..a3ba510a4 100644
--- a/src/backend/langflow/components/models/AnthropicModel.py
+++ b/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 {
@@ -82,10 +83,5 @@ class AnthropicLLM(CustomComponent):
             )
         except Exception as e:
             raise ValueError("Could not connect to Anthropic API.") from e
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/AzureOpenAIModel.py b/src/backend/langflow/components/models/AzureOpenAIModel.py
index 8931bd6e4..392f390c4 100644
--- a/src/backend/langflow/components/models/AzureOpenAIModel.py
+++ b/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",
@@ -104,10 +105,5 @@ class AzureChatOpenAIComponent(CustomComponent):
             )
         except Exception as e:
             raise ValueError("Could not connect to AzureOpenAI API.") from e
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/BaiduQianfanChatModel.py b/src/backend/langflow/components/models/BaiduQianfanChatModel.py
index 121dd9be6..f0815603f 100644
--- a/src/backend/langflow/components/models/BaiduQianfanChatModel.py
+++ b/src/backend/langflow/components/models/BaiduQianfanChatModel.py
@@ -3,16 +3,17 @@ from typing import Optional
 from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
 from pydantic.v1 import SecretStr
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class QianfanChatEndpointComponent(CustomComponent):
+class QianfanChatEndpointComponent(LCModelComponent):
     display_name: str = "QianfanChat Model"
     description: str = (
         "Generate text using Baidu Qianfan chat models. Get more detail from "
         "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
     )
+    icon = "BaiduQianfan"
 
     def build_config(self):
         return {
@@ -99,10 +100,5 @@ class QianfanChatEndpointComponent(CustomComponent):
             )
         except Exception as e:
             raise ValueError("Could not connect to Baidu Qianfan API.") from e
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/CTransformersModel.py b/src/backend/langflow/components/models/CTransformersModel.py
index 9784f86b1..31123ad7e 100644
--- a/src/backend/langflow/components/models/CTransformersModel.py
+++ b/src/backend/langflow/components/models/CTransformersModel.py
@@ -2,11 +2,11 @@ from typing import Dict, Optional
 
 from langchain_community.llms.ctransformers import CTransformers
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class CTransformersComponent(CustomComponent):
+class CTransformersComponent(LCModelComponent):
     display_name = "CTransformersModel"
     description = "Generate text using CTransformers LLM models"
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers"
@@ -47,10 +47,5 @@ class CTransformersComponent(CustomComponent):
         output = CTransformers(
             model=model, model_file=model_file, model_type=model_type, config=config
         )
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/CohereModel.py b/src/backend/langflow/components/models/CohereModel.py
index 6af3971da..a32fb9b4b 100644
--- a/src/backend/langflow/components/models/CohereModel.py
+++ b/src/backend/langflow/components/models/CohereModel.py
@@ -1,14 +1,16 @@
 from langchain_community.chat_models.cohere import ChatCohere
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class CohereComponent(CustomComponent):
+class CohereComponent(LCModelComponent):
     display_name = "CohereModel"
     description = "Generate text using Cohere large language models."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
 
+    icon = "Cohere"
+
     def build_config(self):
         return {
             "cohere_api_key": {
@@ -29,6 +31,10 @@ class CohereComponent(CustomComponent):
                 "show": True,
             },
             "input_value": {"display_name": "Input"},
+            "stream": {
+                "display_name": "Stream",
+                "info": "Stream the response from the model.",
+            },
         }
 
     def build(
@@ -37,16 +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,
         )
-        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        return result
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
index 40f1d385f..423a66df6 100644
--- a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
+++ b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
@@ -1,13 +1,16 @@
+from typing import Optional
+
+from langchain_google_genai import ChatGoogleGenerativeAI
+from pydantic.v1 import SecretStr
+
+from langflow.components.models.base.model import LCModelComponent
+from langflow.field_typing import RangeSpec, Text
 
 
-from langflow import CustomComponent
-from langflow.field_typing import RangeSpec
-
-
-class GoogleGenerativeAIComponent(CustomComponent):
+class GoogleGenerativeAIComponent(LCModelComponent):
     display_name: str = "Google Generative AIModel"
     description: str = "Generate text using Google Generative AI to generate text."
-    documentation: str = "http://docs.langflow.org/components/custom"
+    icon = "GoogleGenerativeAI"
 
     def build_config(self):
         return {
@@ -47,7 +50,11 @@ class GoogleGenerativeAIComponent(CustomComponent):
             "code": {
                 "advanced": True,
             },
-            "input_value": {e": {"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(
@@ -60,6 +67,7 @@ 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,
@@ -70,10 +78,4 @@ class GoogleGenerativeAIComponent(CustomComponent):
             n=n or 1,
             google_api_key=SecretStr(google_api_key),
         )
-        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
\ No newline at end of file
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/HuggingFaceModel.py b/src/backend/langflow/components/models/HuggingFaceModel.py
index 3fe97aca1..3d92272e6 100644
--- a/src/backend/langflow/components/models/HuggingFaceModel.py
+++ b/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 {
@@ -25,6 +26,10 @@ class HuggingFaceEndpointsComponent(CustomComponent):
             },
             "code": {"show": False},
             "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)
-        result = message.content if hasattr(message, "content") else message
-        self.status = result
-        return result
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/LlamaCppModel.py b/src/backend/langflow/components/models/LlamaCppModel.py
index 00c0ee0f4..1ca2cd3c1 100644
--- a/src/backend/langflow/components/models/LlamaCppModel.py
+++ b/src/backend/langflow/components/models/LlamaCppModel.py
@@ -2,11 +2,11 @@ from typing import Any, Dict, List, Optional
 
 from langchain_community.llms.llamacpp import LlamaCpp
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class LlamaCppComponent(CustomComponent):
+class LlamaCppComponent(LCModelComponent):
     display_name = "LlamaCppModel"
     description = "Generate text using llama.cpp model."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp"
@@ -140,10 +140,5 @@ class LlamaCppComponent(CustomComponent):
             verbose=verbose,
             vocab_only=vocab_only,
         )
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/OllamaModel.py b/src/backend/langflow/components/models/OllamaModel.py
index 5f0a86289..7929c2b43 100644
--- a/src/backend/langflow/components/models/OllamaModel.py
+++ b/src/backend/langflow/components/models/OllamaModel.py
@@ -3,17 +3,19 @@ from typing import Any, Dict, List, Optional
 # from langchain_community.chat_models import ChatOllama
 from langchain_community.chat_models import ChatOllama
 
+from langflow.components.models.base.model import LCModelComponent
+
 # from langchain.chat_models import ChatOllama
-from langflow import CustomComponent
 from langflow.field_typing import Text
 
 # whe When a callback component is added to Langflow, the comment must be uncommented.
 # from langchain.callbacks.manager import CallbackManager
 
 
-class ChatOllamaComponent(CustomComponent):
+class ChatOllamaComponent(LCModelComponent):
     display_name = "ChatOllamaModel"
     description = "Generate text using Local LLM for chat with Ollama."
+    icon = "Ollama"
 
     def build_config(self) -> dict:
         return {
@@ -255,10 +257,5 @@ class ChatOllamaComponent(CustomComponent):
             output = ChatOllama(**llm_params)  # type: ignore
         except Exception as e:
             raise ValueError("Could not initialize Ollama LLM.") from e
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/OpenAIModel.py b/src/backend/langflow/components/models/OpenAIModel.py
index 34a0252be..7a28acee6 100644
--- a/src/backend/langflow/components/models/OpenAIModel.py
+++ b/src/backend/langflow/components/models/OpenAIModel.py
@@ -2,13 +2,14 @@ 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 {
@@ -84,10 +85,5 @@ class OpenAIModelComponent(CustomComponent):
             api_key=openai_api_key,
             temperature=temperature,
         )
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)
diff --git a/src/backend/langflow/components/models/VertexAiModel.py b/src/backend/langflow/components/models/VertexAiModel.py
index dcaa95860..d7eab71ed 100644
--- a/src/backend/langflow/components/models/VertexAiModel.py
+++ b/src/backend/langflow/components/models/VertexAiModel.py
@@ -2,11 +2,11 @@ from typing import List, Optional
 
 from langchain_core.messages.base import BaseMessage
 
-from langflow import CustomComponent
+from langflow.components.models.base.model import LCModelComponent
 from langflow.field_typing import Text
 
 
-class ChatVertexAIComponent(CustomComponent):
+class ChatVertexAIComponent(LCModelComponent):
     display_name = "ChatVertexAIModel"
     description = "Generate text using Vertex AI Chat large language models API."
 
@@ -97,10 +97,5 @@ class ChatVertexAIComponent(CustomComponent):
             top_p=top_p,
             verbose=verbose,
         )
-        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
+
+        return self.get_result(output=output, stream=stream, input_value=input_value)

From 257721f86423d021afc219e13c55a70001b127d1 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 00:12:04 -0300
Subject: [PATCH 16/50] Add LCVectorStoreComponent to langflow components

---
 .../components/vectorstores/base/__init__.py  |  0
 .../components/vectorstores/base/model.py     | 27 +++++++++++++++++++
 2 files changed, 27 insertions(+)
 create mode 100644 src/backend/langflow/components/vectorstores/base/__init__.py
 create mode 100644 src/backend/langflow/components/vectorstores/base/model.py

diff --git a/src/backend/langflow/components/vectorstores/base/__init__.py b/src/backend/langflow/components/vectorstores/base/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/backend/langflow/components/vectorstores/base/model.py b/src/backend/langflow/components/vectorstores/base/model.py
new file mode 100644
index 000000000..c6af714d6
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/base/model.py
@@ -0,0 +1,27 @@
+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]:
+
+        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)

From ca6baaa0c581591d0f011d1bc7a6bcea1d3f24d4 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 00:24:18 -0300
Subject: [PATCH 17/50] Add docstring to search_with_vector_store method

---
 .../langflow/components/vectorstores/base/model.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/backend/langflow/components/vectorstores/base/model.py b/src/backend/langflow/components/vectorstores/base/model.py
index c6af714d6..1cc8b9d88 100644
--- a/src/backend/langflow/components/vectorstores/base/model.py
+++ b/src/backend/langflow/components/vectorstores/base/model.py
@@ -16,6 +16,20 @@ class LCVectorStoreComponent(CustomComponent):
     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):

From 796c8428155fea651cd366356df611b5b4636c87 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 00:24:24 -0300
Subject: [PATCH 18/50] Refactor ChromaSearchComponent to inherit from
 LCVectorStoreComponent

---
 .../components/vectorstores/ChromaSearch.py      | 16 +++++-----------
 1 file changed, 5 insertions(+), 11 deletions(-)

diff --git a/src/backend/langflow/components/vectorstores/ChromaSearch.py b/src/backend/langflow/components/vectorstores/ChromaSearch.py
index 6cb3e89df..e3f37108c 100644
--- a/src/backend/langflow/components/vectorstores/ChromaSearch.py
+++ b/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.
     """
@@ -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
-        docs = []
-        if inputs and isinstance(inputs, str):
-            docs = chroma.search(query=inputs, search_type=search_type.lower())
-        else:
-            raise ValueError("Invalid inputs provided.")
-        return docs_to_records(docs)
+        return self.search_with_vector_store(input_value, search_type, vector_store)

From da3382bbd396615676f5c8e3610694a91ba55579 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 00:24:29 -0300
Subject: [PATCH 19/50] Refactor FAISSComponent to save FAISS index locally

---
 .../langflow/components/vectorstores/FAISS.py     | 15 +++++++++++++--
 1 file changed, 13 insertions(+), 2 deletions(-)

diff --git a/src/backend/langflow/components/vectorstores/FAISS.py b/src/backend/langflow/components/vectorstores/FAISS.py
index dec14f6db..13e5bba35 100644
--- a/src/backend/langflow/components/vectorstores/FAISS.py
+++ b/src/backend/langflow/components/vectorstores/FAISS.py
@@ -3,24 +3,35 @@ 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.",
+            },
         }
 
     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)

From 436966c5d61cb432e465e50c86e2dcace3256a59 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 00:24:55 -0300
Subject: [PATCH 20/50] Add FAISSSearchComponent and PGVectorSearchComponent

---
 .../langflow/components/vectorstores/FAISS.py |  1 +
 .../components/vectorstores/FAISSSearch.py    | 45 +++++++++++
 .../components/vectorstores/pgvectorSearch.py | 75 +++++++++++++++++++
 3 files changed, 121 insertions(+)
 create mode 100644 src/backend/langflow/components/vectorstores/FAISSSearch.py
 create mode 100644 src/backend/langflow/components/vectorstores/pgvectorSearch.py

diff --git a/src/backend/langflow/components/vectorstores/FAISS.py b/src/backend/langflow/components/vectorstores/FAISS.py
index 13e5bba35..0cecab8e7 100644
--- a/src/backend/langflow/components/vectorstores/FAISS.py
+++ b/src/backend/langflow/components/vectorstores/FAISS.py
@@ -21,6 +21,7 @@ class FAISSComponent(CustomComponent):
                 "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(
diff --git a/src/backend/langflow/components/vectorstores/FAISSSearch.py b/src/backend/langflow/components/vectorstores/FAISSSearch.py
new file mode 100644
index 000000000..dbc63faac
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/FAISSSearch.py
@@ -0,0 +1,45 @@
+from typing import List
+
+from langchain_community.vectorstores.faiss import FAISS
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.field_typing import Embeddings
+from langflow.schema import Record
+
+
+class FAISSSearchComponent(LCVectorStoreComponent):
+    display_name = "FAISS Search"
+    description = "Search a FAISS Vector Store for similar documents."
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "folder_path": {
+                "display_name": "Folder Path",
+                "info": "Path to save the FAISS index. It will be relative to where Langflow is running.",
+            },
+            "input_value": {"display_name": "Input"},
+            "index_name": {"display_name": "Index Name"},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        folder_path: str,
+        index_name: str = "langflow_index",
+    ) -> List[Record]:
+        if not folder_path:
+            raise ValueError("Folder path is required to save the FAISS index.")
+        path = self.resolve_path(folder_path)
+        vector_store = FAISS.load_local(
+            folder_path=str(path), embeddings=embedding, index_name=index_name
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the FAISS index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type="similarity"
+        )
diff --git a/src/backend/langflow/components/vectorstores/pgvectorSearch.py b/src/backend/langflow/components/vectorstores/pgvectorSearch.py
new file mode 100644
index 000000000..f525611bd
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/pgvectorSearch.py
@@ -0,0 +1,75 @@
+from typing import List, Optional
+
+from langchain.embeddings.base import Embeddings
+from langchain_community.vectorstores.pgvector import PGVector
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.schema import Record
+
+
+class PGVectorSearchComponent(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 = PGVector.from_existing_index(
+                embedding=embedding,
+                collection_name=collection_name,
+                connection_string=pg_server_url,
+            )
+
+        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
+        )

From acc43aebb91193d4052431dc9dff1adbb6e319fa Mon Sep 17 00:00:00 2001
From: Lucas Oliveira <lucas.edu.oli@hotmail.com>
Date: Wed, 28 Feb 2024 13:08:57 +0100
Subject: [PATCH 21/50] Fix name of template field on message

---
 src/frontend/src/components/IOview/index.tsx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/frontend/src/components/IOview/index.tsx b/src/frontend/src/components/IOview/index.tsx
index a8edfa9b6..0e3f9e7e9 100644
--- a/src/frontend/src/components/IOview/index.tsx
+++ b/src/frontend/src/components/IOview/index.tsx
@@ -56,7 +56,7 @@ export default function IOView({ children, open, setOpen }): JSX.Element {
     const chatInputNode = nodes.find((node) => node.id === chatInput?.id);
     if (chatInputNode) {
       let newNode = cloneDeep(chatInputNode);
-      newNode.data.node!.template["message"].value = chatValue;
+      newNode.data.node!.template["input_value"].value = chatValue;
       setNode(chatInput!.id, newNode);
     }
     for (let i = 0; i < count; i++) {

From 9d7f3dda5eb3cff4ac838a0c61033bd320996a96 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 09:20:54 -0300
Subject: [PATCH 22/50] Refactor vector store components

---
 .../vectorstores/MongoDBAtlasVector.py        | 56 ++++++++++++
 .../vectorstores/MongoDBAtlasVectorSearch.py  | 38 ++++----
 .../components/vectorstores/Pinecone.py       | 21 ++++-
 .../components/vectorstores/PineconeSearch.py | 70 ++++++++++++++
 .../components/vectorstores/QdrantSearch.py   | 91 +++++++++++++++++++
 .../components/vectorstores/RedisSearch.py    | 77 ++++++++++++++++
 .../vectorstores/SupabaseVectorStoreSearch.py | 49 ++++++++++
 .../components/vectorstores/Vectara.py        | 10 +-
 .../components/vectorstores/VectaraSearch.py  | 64 +++++++++++++
 .../components/vectorstores/Weaviate.py       | 19 +++-
 .../components/vectorstores/WeaviateSearch.py | 82 +++++++++++++++++
 .../components/vectorstores/pgvectorSearch.py | 10 +-
 src/backend/langflow/config.yaml              | 19 +---
 13 files changed, 556 insertions(+), 50 deletions(-)
 create mode 100644 src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py
 create mode 100644 src/backend/langflow/components/vectorstores/PineconeSearch.py
 create mode 100644 src/backend/langflow/components/vectorstores/QdrantSearch.py
 create mode 100644 src/backend/langflow/components/vectorstores/RedisSearch.py
 create mode 100644 src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py
 create mode 100644 src/backend/langflow/components/vectorstores/VectaraSearch.py
 create mode 100644 src/backend/langflow/components/vectorstores/WeaviateSearch.py

diff --git a/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py b/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py
new file mode 100644
index 000000000..de5533b0a
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py
@@ -0,0 +1,56 @@
+from typing import List, Optional
+
+from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSearch
+
+from langflow import CustomComponent
+from langflow.field_typing import Document, Embeddings, NestedDict
+
+
+class MongoDBAtlasComponent(CustomComponent):
+    display_name = "MongoDB Atlas"
+    description = (
+        "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
+    )
+
+    def build_config(self):
+        return {
+            "documents": {"display_name": "Documents"},
+            "embedding": {"display_name": "Embedding"},
+            "collection_name": {"display_name": "Collection Name"},
+            "db_name": {"display_name": "Database Name"},
+            "index_name": {"display_name": "Index Name"},
+            "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        documents: List[Document] = None,
+        collection_name: str = "",
+        db_name: str = "",
+        index_name: str = "",
+        mongodb_atlas_cluster_uri: str = "",
+        search_kwargs: Optional[NestedDict] = None,
+    ) -> MongoDBAtlasVectorSearch:
+        search_kwargs = search_kwargs or {}
+        if documents:
+            vector_store = MongoDBAtlasVectorSearch.from_documents(
+                documents=documents,
+                embedding=embedding,
+                collection_name=collection_name,
+                db_name=db_name,
+                index_name=index_name,
+                mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
+                search_kwargs=search_kwargs,
+            )
+        else:
+            vector_store = MongoDBAtlasVectorSearch(
+                embedding=embedding,
+                collection_name=collection_name,
+                db_name=db_name,
+                index_name=index_name,
+                mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
+                search_kwargs=search_kwargs,
+            )
+        return vector_store
diff --git a/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py b/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
index d2d215f2b..6393c2a7b 100644
--- a/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
+++ b/src/backend/langflow/components/vectorstores/MongoDBAtlasVectorSearch.py
@@ -1,22 +1,22 @@
 from typing import List, Optional
 
-from langchain_community.vectorstores import MongoDBAtlasVectorSearch
-
-from langflow import CustomComponent
-from langflow.field_typing import (
-    Document,
-    Embeddings,
-    NestedDict,
-)
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.MongoDBAtlasVector import MongoDBAtlasComponent
+from langflow.field_typing import Embeddings, NestedDict
+from langflow.schema import Record
 
 
-class MongoDBAtlasComponent(CustomComponent):
-    display_name = "MongoDB Atlas"
-    description = "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
+class MongoDBAtlasSearchComponent(MongoDBAtlasComponent, LCVectorStoreComponent):
+    display_name = "MongoDB Atlas Search"
+    description = "Search a MongoDB Atlas Vector Store for similar documents."
 
     def build_config(self):
         return {
-            "documents": {"display_name": "Documents"},
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
             "embedding": {"display_name": "Embedding"},
             "collection_name": {"display_name": "Collection Name"},
             "db_name": {"display_name": "Database Name"},
@@ -27,17 +27,16 @@ class MongoDBAtlasComponent(CustomComponent):
 
     def build(
         self,
-        documents: List[Document],
+        input_value: str,
+        search_type: str,
         embedding: Embeddings,
         collection_name: str = "",
         db_name: str = "",
         index_name: str = "",
         mongodb_atlas_cluster_uri: str = "",
         search_kwargs: Optional[NestedDict] = None,
-    ) -> MongoDBAtlasVectorSearch:
-        search_kwargs = search_kwargs or {}
-        return MongoDBAtlasVectorSearch(
-            documents=documents,
+    ) -> List[Record]:
+        vector_store = super().build(
             embedding=embedding,
             collection_name=collection_name,
             db_name=db_name,
@@ -45,3 +44,8 @@ class MongoDBAtlasComponent(CustomComponent):
             mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
             search_kwargs=search_kwargs,
         )
+        if not vector_store:
+            raise ValueError("Failed to create MongoDB Atlas Vector Store")
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )
diff --git a/src/backend/langflow/components/vectorstores/Pinecone.py b/src/backend/langflow/components/vectorstores/Pinecone.py
index 147af1df8..54222b133 100644
--- a/src/backend/langflow/components/vectorstores/Pinecone.py
+++ b/src/backend/langflow/components/vectorstores/Pinecone.py
@@ -5,6 +5,7 @@ import pinecone  # type: ignore
 from langchain.schema import BaseRetriever
 from langchain_community.vectorstores import VectorStore
 from langchain_community.vectorstores.pinecone import Pinecone
+
 from langflow import CustomComponent
 from langflow.field_typing import Document, Embeddings
 
@@ -12,6 +13,7 @@ from langflow.field_typing import Document, Embeddings
 class PineconeComponent(CustomComponent):
     display_name = "Pinecone"
     description = "Construct Pinecone wrapper from raw documents."
+    icon = "Pinecone"
 
     def build_config(self):
         return {
@@ -19,10 +21,23 @@ class PineconeComponent(CustomComponent):
             "embedding": {"display_name": "Embedding"},
             "index_name": {"display_name": "Index Name"},
             "namespace": {"display_name": "Namespace"},
-            "pinecone_api_key": {"display_name": "Pinecone API Key", "default": "", "password": True, "required": True},
-            "pinecone_env": {"display_name": "Pinecone Environment", "default": "", "required": True},
+            "pinecone_api_key": {
+                "display_name": "Pinecone API Key",
+                "default": "",
+                "password": True,
+                "required": True,
+            },
+            "pinecone_env": {
+                "display_name": "Pinecone Environment",
+                "default": "",
+                "required": True,
+            },
             "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"},
-            "pool_threads": {"display_name": "Pool Threads", "default": 1, "advanced": True},
+            "pool_threads": {
+                "display_name": "Pool Threads",
+                "default": 1,
+                "advanced": True,
+            },
         }
 
     def build(
diff --git a/src/backend/langflow/components/vectorstores/PineconeSearch.py b/src/backend/langflow/components/vectorstores/PineconeSearch.py
new file mode 100644
index 000000000..7af7f627f
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/PineconeSearch.py
@@ -0,0 +1,70 @@
+from typing import List, Optional
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Pinecone import PineconeComponent
+from langflow.field_typing import Embeddings
+from langflow.schema import Record
+
+
+class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
+    display_name = "Pinecone Search"
+    description = "Search a Pinecone Vector Store for similar documents."
+    icon = "Pinecone"
+
+    def build_config(self):
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "embedding": {"display_name": "Embedding"},
+            "index_name": {"display_name": "Index Name"},
+            "namespace": {"display_name": "Namespace"},
+            "pinecone_api_key": {
+                "display_name": "Pinecone API Key",
+                "default": "",
+                "password": True,
+                "required": True,
+            },
+            "pinecone_env": {
+                "display_name": "Pinecone Environment",
+                "default": "",
+                "required": True,
+            },
+            "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"},
+            "pool_threads": {
+                "display_name": "Pool Threads",
+                "default": 1,
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        pinecone_env: str,
+        text_key: str = "text",
+        pool_threads: int = 4,
+        index_name: Optional[str] = None,
+        pinecone_api_key: Optional[str] = None,
+        namespace: Optional[str] = "default",
+        search_type: str = "similarity",
+    ) -> List[Record]:
+        vector_store = super().build(
+            embedding=embedding,
+            pinecone_env=pinecone_env,
+            documents=[],
+            text_key=text_key,
+            pool_threads=pool_threads,
+            index_name=index_name,
+            pinecone_api_key=pinecone_api_key,
+            namespace=namespace,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Pinecone index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )
diff --git a/src/backend/langflow/components/vectorstores/QdrantSearch.py b/src/backend/langflow/components/vectorstores/QdrantSearch.py
new file mode 100644
index 000000000..46e2766fd
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/QdrantSearch.py
@@ -0,0 +1,91 @@
+from typing import List, Optional
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Qdrant import QdrantComponent
+from langflow.field_typing import Embeddings, NestedDict
+from langflow.schema import Record
+
+
+class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
+    display_name = "Qdrant"
+    description = "Construct Qdrant wrapper from a list of texts."
+
+    def build_config(self):
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "embedding": {"display_name": "Embedding"},
+            "api_key": {"display_name": "API Key", "password": True, "advanced": True},
+            "collection_name": {"display_name": "Collection Name"},
+            "content_payload_key": {
+                "display_name": "Content Payload Key",
+                "advanced": True,
+            },
+            "distance_func": {"display_name": "Distance Function", "advanced": True},
+            "grpc_port": {"display_name": "gRPC Port", "advanced": True},
+            "host": {"display_name": "Host", "advanced": True},
+            "https": {"display_name": "HTTPS", "advanced": True},
+            "location": {"display_name": "Location", "advanced": True},
+            "metadata_payload_key": {
+                "display_name": "Metadata Payload Key",
+                "advanced": True,
+            },
+            "path": {"display_name": "Path", "advanced": True},
+            "port": {"display_name": "Port", "advanced": True},
+            "prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True},
+            "prefix": {"display_name": "Prefix", "advanced": True},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+            "timeout": {"display_name": "Timeout", "advanced": True},
+            "url": {"display_name": "URL", "advanced": True},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        embedding: Embeddings,
+        collection_name: str,
+        search_type: str = "similarity",
+        api_key: Optional[str] = None,
+        content_payload_key: str = "page_content",
+        distance_func: str = "Cosine",
+        grpc_port: int = 6334,
+        https: bool = False,
+        host: Optional[str] = None,
+        location: Optional[str] = None,
+        metadata_payload_key: str = "metadata",
+        path: Optional[str] = None,
+        port: Optional[int] = 6333,
+        prefer_grpc: bool = False,
+        prefix: Optional[str] = None,
+        search_kwargs: Optional[NestedDict] = None,
+        timeout: Optional[int] = None,
+        url: Optional[str] = None,
+    ) -> List[Record]:
+        vector_store = super().build(
+            embedding=embedding,
+            collection_name=collection_name,
+            api_key=api_key,
+            content_payload_key=content_payload_key,
+            distance_func=distance_func,
+            grpc_port=grpc_port,
+            https=https,
+            host=host,
+            location=location,
+            metadata_payload_key=metadata_payload_key,
+            path=path,
+            port=port,
+            prefer_grpc=prefer_grpc,
+            prefix=prefix,
+            search_kwargs=search_kwargs,
+            timeout=timeout,
+            url=url,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Qdrant index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )
diff --git a/src/backend/langflow/components/vectorstores/RedisSearch.py b/src/backend/langflow/components/vectorstores/RedisSearch.py
new file mode 100644
index 000000000..71022de1d
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/RedisSearch.py
@@ -0,0 +1,77 @@
+from typing import List, Optional
+
+from langchain.embeddings.base import Embeddings
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Redis import RedisComponent
+from langflow.schema import Record
+
+
+class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
+    """
+    A custom component for implementing a Vector Store using Redis.
+    """
+
+    display_name: str = "Redis Search"
+    description: str = "Search a Redis Vector Store for similar documents."
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/redis"
+    beta = True
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "index_name": {"display_name": "Index Name", "value": "your_index"},
+            "code": {"show": False, "display_name": "Code"},
+            "documents": {"display_name": "Documents", "is_list": True},
+            "embedding": {"display_name": "Embedding"},
+            "schema": {"display_name": "Schema", "file_types": [".yaml"]},
+            "redis_server_url": {
+                "display_name": "Redis Server Connection String",
+                "advanced": False,
+            },
+            "redis_index_name": {"display_name": "Redis Index", "advanced": False},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        embedding: Embeddings,
+        redis_server_url: str,
+        redis_index_name: str,
+        schema: Optional[str] = None,
+    ) -> List[Record]:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - embedding (Embeddings): The embeddings to use for the Vector Store.
+        - documents (Optional[Document]): The documents to use for the Vector Store.
+        - redis_index_name (str): The name of the Redis index.
+        - redis_server_url (str): The URL for the Redis server.
+
+        Returns:
+        - VectorStore: The Vector Store object.
+        """
+        vector_store = super().build(
+            embedding=embedding,
+            redis_server_url=redis_server_url,
+            redis_index_name=redis_index_name,
+            schema=schema,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Redis index.")
+
+        return self.search_with_vector_store(
+            input_value=input_value, search_type=search_type, vector_store=vector_store
+        )
diff --git a/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py b/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py
new file mode 100644
index 000000000..493632fe9
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py
@@ -0,0 +1,49 @@
+from typing import List
+
+from langchain_community.vectorstores.supabase import SupabaseVectorStore
+from supabase.client import Client, create_client
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.field_typing import Embeddings
+from langflow.schema import Record
+
+
+class SupabaseSearchComponent(LCVectorStoreComponent):
+    display_name = "Supabase Search"
+    description = "Search a Supabase Vector Store for similar documents."
+
+    def build_config(self):
+        return {
+            "search_type": {
+                "display_name": "Search Type",
+                "options": ["Similarity", "MMR"],
+            },
+            "input_value": {"display_name": "Input"},
+            "embedding": {"display_name": "Embedding"},
+            "query_name": {"display_name": "Query Name"},
+            "search_kwargs": {"display_name": "Search Kwargs", "advanced": True},
+            "supabase_service_key": {"display_name": "Supabase Service Key"},
+            "supabase_url": {"display_name": "Supabase URL"},
+            "table_name": {"display_name": "Table Name", "advanced": True},
+        }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        embedding: Embeddings,
+        query_name: str = "",
+        supabase_service_key: str = "",
+        supabase_url: str = "",
+        table_name: str = "",
+    ) -> List[Record]:
+        supabase: Client = create_client(
+            supabase_url, supabase_key=supabase_service_key
+        )
+        vector_store = SupabaseVectorStore(
+            client=supabase,
+            embedding=embedding,
+            table_name=table_name,
+            query_name=query_name,
+        )
+        return self.search_with_vector_store(input_value, search_type, vector_store)
diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py
index 31615fe7f..b5360ffd2 100644
--- a/src/backend/langflow/components/vectorstores/Vectara.py
+++ b/src/backend/langflow/components/vectorstores/Vectara.py
@@ -8,12 +8,15 @@ from langchain_community.vectorstores.vectara import Vectara
 from langchain_core.vectorstores import VectorStore
 from langflow import CustomComponent
 from langflow.field_typing import BaseRetriever, Document
+from langchain_community.vectorstores.vectara import Vectara
 
 
 class VectaraComponent(CustomComponent):
     display_name: str = "Vectara"
     description: str = "Implementation of Vector Store using Vectara"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    )
     beta = True
     field_config = {
         "vectara_customer_id": {
@@ -26,7 +29,10 @@ class VectaraComponent(CustomComponent):
             "display_name": "Vectara API Key",
             "password": True,
         },
-        "documents": {"display_name": "Documents", "info": "If provided, will be upserted to corpus (optional)"},
+        "documents": {
+            "display_name": "Documents",
+            "info": "If provided, will be upserted to corpus (optional)",
+        },
         "files_url": {
             "display_name": "Files Url",
             "info": "Make vectara object using url of files (optional)",
diff --git a/src/backend/langflow/components/vectorstores/VectaraSearch.py b/src/backend/langflow/components/vectorstores/VectaraSearch.py
new file mode 100644
index 000000000..c90148d1e
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/VectaraSearch.py
@@ -0,0 +1,64 @@
+from typing import List
+
+from langchain_community.vectorstores.vectara import Vectara
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Vectara import VectaraComponent
+from langflow.schema import Record
+
+
+class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
+    display_name: str = "Vectara Search"
+    description: str = "Search a Vectara Vector Store for similar documents."
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    )
+    beta = True
+    field_config = {
+        "search_type": {
+            "display_name": "Search Type",
+            "options": ["Similarity", "MMR"],
+        },
+        "input_value": {"display_name": "Input"},
+        "vectara_customer_id": {
+            "display_name": "Vectara Customer ID",
+        },
+        "vectara_corpus_id": {
+            "display_name": "Vectara Corpus ID",
+        },
+        "vectara_api_key": {
+            "display_name": "Vectara API Key",
+            "password": True,
+        },
+        "documents": {
+            "display_name": "Documents",
+            "info": "If provided, will be upserted to corpus (optional)",
+        },
+        "files_url": {
+            "display_name": "Files Url",
+            "info": "Make vectara object using url of files (optional)",
+        },
+    }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        vectara_customer_id: str,
+        vectara_corpus_id: str,
+        vectara_api_key: str,
+    ) -> List[Record]:
+        source = "Langflow"
+        vector_store = Vectara(
+            vectara_customer_id=vectara_customer_id,
+            vectara_corpus_id=vectara_corpus_id,
+            vectara_api_key=vectara_api_key,
+            source=source,
+        )
+
+        if not vector_store:
+            raise ValueError("Failed to create Vectara Vector Store")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )
diff --git a/src/backend/langflow/components/vectorstores/Weaviate.py b/src/backend/langflow/components/vectorstores/Weaviate.py
index 9b4967c36..59bbf4fef 100644
--- a/src/backend/langflow/components/vectorstores/Weaviate.py
+++ b/src/backend/langflow/components/vectorstores/Weaviate.py
@@ -8,10 +8,12 @@ from langchain_community.vectorstores import VectorStore, Weaviate
 from langflow import CustomComponent
 
 
-class WeaviateVectorStore(CustomComponent):
+class WeaviateVectorStoreComponent(CustomComponent):
     display_name: str = "Weaviate"
     description: str = "Implementation of Vector Store using Weaviate"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
+    )
     beta = True
     field_config = {
         "url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"},
@@ -24,7 +26,12 @@ class WeaviateVectorStore(CustomComponent):
             "display_name": "Index name",
             "required": False,
         },
-        "text_key": {"display_name": "Text Key", "required": False, "advanced": True, "value": "text"},
+        "text_key": {
+            "display_name": "Text Key",
+            "required": False,
+            "advanced": True,
+            "value": "text",
+        },
         "documents": {"display_name": "Documents", "is_list": True},
         "embedding": {"display_name": "Embedding"},
         "attributes": {
@@ -34,7 +41,11 @@ class WeaviateVectorStore(CustomComponent):
             "field_type": "str",
             "advanced": True,
         },
-        "search_by_text": {"display_name": "Search By Text", "field_type": "bool", "advanced": True},
+        "search_by_text": {
+            "display_name": "Search By Text",
+            "field_type": "bool",
+            "advanced": True,
+        },
         "code": {"show": False},
     }
 
diff --git a/src/backend/langflow/components/vectorstores/WeaviateSearch.py b/src/backend/langflow/components/vectorstores/WeaviateSearch.py
new file mode 100644
index 000000000..2d7001074
--- /dev/null
+++ b/src/backend/langflow/components/vectorstores/WeaviateSearch.py
@@ -0,0 +1,82 @@
+from typing import List, Optional
+
+from langchain.embeddings.base import Embeddings
+
+from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
+from langflow.schema import Record
+
+
+class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent):
+    display_name: str = "Weaviate Search"
+    description: str = "Search a Weaviate Vector Store for similar documents."
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
+    )
+    beta = True
+    field_config = {
+        "search_type": {
+            "display_name": "Search Type",
+            "options": ["Similarity", "MMR"],
+        },
+        "input_value": {"display_name": "Input"},
+        "url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"},
+        "api_key": {
+            "display_name": "API Key",
+            "password": True,
+            "required": False,
+        },
+        "index_name": {
+            "display_name": "Index name",
+            "required": False,
+        },
+        "text_key": {
+            "display_name": "Text Key",
+            "required": False,
+            "advanced": True,
+            "value": "text",
+        },
+        "documents": {"display_name": "Documents", "is_list": True},
+        "embedding": {"display_name": "Embedding"},
+        "attributes": {
+            "display_name": "Attributes",
+            "required": False,
+            "is_list": True,
+            "field_type": "str",
+            "advanced": True,
+        },
+        "search_by_text": {
+            "display_name": "Search By Text",
+            "field_type": "bool",
+            "advanced": True,
+        },
+        "code": {"show": False},
+    }
+
+    def build(
+        self,
+        input_value: str,
+        search_type: str,
+        url: str,
+        search_by_text: bool = False,
+        api_key: Optional[str] = None,
+        index_name: Optional[str] = None,
+        text_key: str = "text",
+        embedding: Optional[Embeddings] = None,
+        attributes: Optional[list] = None,
+    ) -> List[Record]:
+        vector_store = super().build(
+            url=url,
+            api_key=api_key,
+            index_name=index_name,
+            text_key=text_key,
+            embedding=embedding,
+            attributes=attributes,
+            search_by_text=search_by_text,
+        )
+        if not vector_store:
+            raise ValueError("Failed to load the Weaviate index.")
+
+        return self.search_with_vector_store(
+            vector_store=vector_store, input_value=input_value, search_type=search_type
+        )
diff --git a/src/backend/langflow/components/vectorstores/pgvectorSearch.py b/src/backend/langflow/components/vectorstores/pgvectorSearch.py
index f525611bd..00e291e76 100644
--- a/src/backend/langflow/components/vectorstores/pgvectorSearch.py
+++ b/src/backend/langflow/components/vectorstores/pgvectorSearch.py
@@ -1,13 +1,13 @@
 from typing import List, Optional
 
 from langchain.embeddings.base import Embeddings
-from langchain_community.vectorstores.pgvector import PGVector
 
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.components.vectorstores.pgvector import PGVectorComponent
 from langflow.schema import Record
 
 
-class PGVectorSearchComponent(LCVectorStoreComponent):
+class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
     """
     A custom component for implementing a Vector Store using PostgreSQL.
     """
@@ -60,14 +60,12 @@ class PGVectorSearchComponent(LCVectorStoreComponent):
         Returns:
         - VectorStore: The Vector Store object.
         """
-
         try:
-            vector_store = PGVector.from_existing_index(
+            vector_store = super().build(
                 embedding=embedding,
+                pg_server_url=pg_server_url,
                 collection_name=collection_name,
-                connection_string=pg_server_url,
             )
-
         except Exception as e:
             raise RuntimeError(f"Failed to build PGVector: {e}")
         return self.search_with_vector_store(
diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml
index df3b83434..102cb9016 100644
--- a/src/backend/langflow/config.yaml
+++ b/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: ""

From 609744332d16e64c05f640dbc60436aed528f8c2 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 09:57:41 -0300
Subject: [PATCH 23/50] Add linting and testing workflows for backend code
 (#1478)

* Add paths to trigger workflow on specific file changes

* Add linting workflow for backend code
---
 .github/workflows/lint.yml | 8 ++++++++
 .github/workflows/test.yml | 8 ++++++++
 2 files changed, 16 insertions(+)

diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
index 64d06a313..d4eaafb95 100644
--- a/.github/workflows/lint.yml
+++ b/.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"
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 93ec5bf7e..10ab9b324 100644
--- a/.github/workflows/test.yml
+++ b/.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"

From 43b3b082a5b9c58ae19647f4348e6bdc6938839e Mon Sep 17 00:00:00 2001
From: Cristhian Zanforlin Lousa
 <72977554+Cristhianzl@users.noreply.github.com>
Date: Wed, 28 Feb 2024 10:01:09 -0300
Subject: [PATCH 24/50] Fix vectara resolution error (#1475)

* add new vectara icon

* remove console.log
---
 .../src/CustomNodes/GenericNode/index.tsx     |    2 +-
 .../src/icons/VectaraIcon/Vectara.jsx         | 1153 ++---------------
 .../src/icons/VectaraIcon/vectara.svg         |  332 ++---
 3 files changed, 162 insertions(+), 1325 deletions(-)

diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx
index 2ddd49b8f..0d344bd65 100644
--- a/src/frontend/src/CustomNodes/GenericNode/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx
@@ -186,7 +186,7 @@ export default function GenericNode({
             <div
               className={
                 "generic-node-title-arrangement rounded-full" +
-                (!showNode && "justify-center")
+                (!showNode && " justify-center ")
               }
             >
               {iconNodeRender()}
diff --git a/src/frontend/src/icons/VectaraIcon/Vectara.jsx b/src/frontend/src/icons/VectaraIcon/Vectara.jsx
index 0566f82eb..378dc6964 100644
--- a/src/frontend/src/icons/VectaraIcon/Vectara.jsx
+++ b/src/frontend/src/icons/VectaraIcon/Vectara.jsx
@@ -1,1074 +1,83 @@
-const SvgVectara = (props) => (
-  <>
-    <svg
-      xmlns="http://www.w3.org/2000/svg"
-      width="32"
-      height="40"
-      fillRule="evenodd"
-      clipRule="evenodd"
-      imageRendering="optimizeQuality"
-      shapeRendering="geometricPrecision"
-      textRendering="geometricPrecision"
-      viewBox="0 0 720 627"
-    >
-      <path
-        fill="#16fed9"
-        d="M632.5-.5h32a43.316 43.316 0 007 3 10.136 10.136 0 01-1.5 4c-5.334 6.68-12.167 11.18-20.5 13.5-6.341 5.303-13.841 7.47-22.5 6.5a138.904 138.904 0 01-21 8 42.846 42.846 0 00-10.5-.5 1260.926 1260.926 0 01-13-9.5c14.766-12.05 31.433-20.383 50-25z"
-        opacity="0.985"
-      ></path>
-      <path
-        fill="#1ffecd"
-        d="M671.5 2.5c8.797 3.391 16.131 8.725 22 16a151.531 151.531 0 00-9.5 6 3.646 3.646 0 00-1.5-1c-14.918 6.969-29.584 12.969-44 18a3.646 3.646 0 01-1.5-1c-1.396 2.027-3.062 2.36-5 1-6.265 1.993-12.598 2.66-19 2a129.85 129.85 0 00-17 4c-2.235-.74-4.235-.74-6 0A75.125 75.125 0 01573.5 35a4.932 4.932 0 00-3-.5 86.798 86.798 0 0112-10c4.231 3.135 8.565 6.301 13 9.5a42.846 42.846 0 0110.5.5 138.904 138.904 0 0021-8c8.659.97 16.159-1.197 22.5-6.5 8.333-2.32 15.166-6.82 20.5-13.5a10.136 10.136 0 001.5-4z"
-        opacity="0.988"
-      ></path>
-      <path
-        fill="#2afec0"
-        d="M693.5 18.5c4.894 3.906 8.228 8.906 10 15a492.776 492.776 0 00-48.5 16 3.646 3.646 0 00-1.5-1 506.572 506.572 0 00-31 6 464.472 464.472 0 01-33.5 0c-8.89-3.525-17.057-8.191-24.5-14 1.062-2.878 3.062-4.878 6-6a4.932 4.932 0 013 .5A75.125 75.125 0 00590 47.5c1.765-.74 3.765-.74 6 0a129.85 129.85 0 0117-4c6.402.66 12.735-.007 19-2 1.938 1.36 3.604 1.027 5-1 .414.457.914.79 1.5 1 14.416-5.031 29.082-11.031 44-18 .586.21 1.086.543 1.5 1a151.531 151.531 0 019.5-6z"
-        opacity="0.992"
-      ></path>
-      <path
-        fill="#33feb4"
-        d="M703.5 33.5c2.938 3.447 4.938 7.447 6 12-.767.672-1.434 1.505-2 2.5-9.999 1.466-19.832 4.3-29.5 8.5a3.646 3.646 0 00-1.5-1c-5.813.763-11.48 1.93-17 3.5l-20 1c-2.916 1.75-6.082 2.917-9.5 3.5a957.926 957.926 0 01-33.5-.5c-8.301-.111-16.301-2.111-24-6l-12-9a4.932 4.932 0 00-3-.5 2.429 2.429 0 01.5-2 69.058 69.058 0 006.5-5c7.443 5.809 15.61 10.475 24.5 14 10.979.403 22.146.403 33.5 0a506.572 506.572 0 0131-6c.586.21 1.086.543 1.5 1a492.776 492.776 0 0148.5-16z"
-        opacity="0.993"
-      ></path>
-      <path
-        fill="#3efea7"
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-        opacity="0.992"
-      ></path>
-      <path
-        fill="#49fe9b"
-        d="M552.5 52.5c5.12 3.28 10.12 6.78 15 10.5a306.309 306.309 0 0118.5 6.5 25.173 25.173 0 015.5-1 340.482 340.482 0 0022 3c8.061-.866 16.228-.866 24.5 0l2-2a269.36 269.36 0 0041-5 3.942 3.942 0 002 1c6.961-3.166 14.294-5.166 22-6a3.942 3.942 0 012 1 87.122 87.122 0 015.5-3c1.934 4.389 3.267 9.055 4 14-1.56-.02-3.06-.352-4.5-1-9.881 3.02-19.715 4.686-29.5 5a1181.368 1181.368 0 01-59.5 5c-2.832-1.29-5.832-1.957-9-2-5.08.925-10.08 1.259-15 1-17.997-3.04-34.83-9.206-50.5-18.5-.752-.67-1.086-1.504-1-2.5.919-2.59 2.586-4.59 5-6z"
-        opacity="0.988"
-      ></path>
-      <path
-        fill="#52fe8e"
-        d="M547.5 58.5c-.086.996.248 1.83 1 2.5 15.67 9.294 32.503 15.46 50.5 18.5 4.92.259 9.92-.075 15-1 3.168.043 6.168.71 9 2a1181.368 1181.368 0 0059.5-5c9.785-.314 19.619-1.98 29.5-5 1.44.648 2.94.98 4.5 1 .794 3.302 1.46 6.635 2 10a852.054 852.054 0 01-57 5 40.09 40.09 0 00-7.5-2c-3.48.771-6.98 1.438-10.5 2-4.029-1.018-8.029-.685-12 1a53.594 53.594 0 00-8-2c-24.737 1.486-48.403-3.014-71-13.5a52.115 52.115 0 00-8-5.5c-.929-1.189-1.262-2.522-1-4 1-1.667 2.333-3 4-4z"
-        opacity="0.994"
-      ></path>
-      <path
-        fill="#75fe5e"
-        d="M527.5 84.5a63.445 63.445 0 0113 7.5 115.49 115.49 0 0120 7.5 3.646 3.646 0 001.5-1c7.344 3.165 14.844 4.832 22.5 5a494.147 494.147 0 0043.5 5 25.161 25.161 0 015.5-1c8.03 2.858 16.196 3.858 24.5 3a36.354 36.354 0 014.5 2 184.69 184.69 0 01-21 1 29.374 29.374 0 01-12-2 1175.5 1175.5 0 01-24 1 587.094 587.094 0 01-34-3.5 163.768 163.768 0 00-27-7A91.843 91.843 0 01523 91.5c-.383.556-.883.89-1.5 1a27.683 27.683 0 016-8z"
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-      <path
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-      <path
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-        opacity="0.994"
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-        d="M365.5 502.5a38.497 38.497 0 006 13c-3.458 3.12-6.125 6.786-8 11a864.794 864.794 0 00-8 9 36.354 36.354 0 01-2-4.5c.589-1.873-.078-3.54-2-5l1-1a86.407 86.407 0 00-4-10l1-1a59.803 59.803 0 00-4-6.5c3.639-2.306 6.639-5.306 9-9a63.077 63.077 0 015.5-8 97.787 97.787 0 015.5 12z"
-      ></path>
-      <path
-        fill="#9900f1"
-        d="M225.5 501.5c-1.617 8.402-2.284 16.736-2 25a131.02 131.02 0 00-3 14c-2.708 13.222-7.708 25.889-15 38a179.568 179.568 0 01-24-21 940.756 940.756 0 0126.5-25 72.66 72.66 0 004.5-21 95.728 95.728 0 0013-10z"
-        opacity="0.992"
-      ></path>
-      <path
-        fill="#bf0fa2"
-        d="M344.5 479.5c3.37 6.355 6.704 12.688 10 19-2.361 3.694-5.361 6.694-9 9a66.515 66.515 0 00-6 7.5l1.5 1.5a98.01 98.01 0 002.5 12.5 94.334 94.334 0 00-8 8.5c-1.672-13.042-5.339-25.375-11-37v-1a102.1 102.1 0 0012-12c1.763-3.552 4.43-6.219 8-8z"
-      ></path>
-      <path
-        fill="#e70c73"
-        d="M427.5 490.5h1c1.859 4.367 4.693 8.033 8.5 11 2.764 6.669 5.931 12.669 9.5 18-2.322 3.084-1.322 4.751 3 5a220.23 220.23 0 008 16.5 203.94 203.94 0 01-15 12c-1.641 1.129-2.641 2.629-3 4.5a3535.79 3535.79 0 01-6-18.5 3.647 3.647 0 001-1.5c-2.577-8.09-5.911-15.756-10-23a1214.284 1214.284 0 01-7-12 86.58 86.58 0 0110-12z"
-      ></path>
-      <path
-        fill="#ee096d"
-        d="M470.5 502.5c4.717 2.537 9.051 5.704 13 9.5 2.574 1.914 4.407 4.414 5.5 7.5a28.827 28.827 0 018.5 7 125.836 125.836 0 0118 12c-19.604 19.282-40.937 36.282-64 51 .203-1.544.87-2.878 2-4a137.415 137.415 0 01-14-28c.359-1.871 1.359-3.371 3-4.5a203.94 203.94 0 0015-12 220.23 220.23 0 01-8-16.5c.203-1.89-.463-3.39-2-4.5 7.052-6.835 14.718-12.668 23-17.5z"
-        opacity="0.987"
-      ></path>
-      <path
-        fill="#d30b90"
-        d="M372.5 494.5c2.048 4.202 3.715 8.202 5 12 2.342 3.837 4.009 8.004 5 12.5a9.62 9.62 0 01-1.5 4.5c-2.405 1.903-4.239 4.236-5.5 7a121.4 121.4 0 015 15c-.363.683-.696 1.35-1 2a3334.674 3334.674 0 01-7 12 468.102 468.102 0 00-4.5-22c-1.057-3.949-2.557-7.616-4.5-11 1.875-4.214 4.542-7.88 8-11a38.497 38.497 0 01-6-13c1.646-3.31 3.98-5.977 7-8z"
-      ></path>
-      <path
-        fill="#ab01de"
-        d="M264.5 500.5l2 8a4.933 4.933 0 01-1 2.5 56.834 56.834 0 011.5 16.5c-2.207 3.064-5.041 5.064-8.5 6 .371 8.752-.462 17.086-2.5 25a342.059 342.059 0 01-3 23 2323.519 2323.519 0 00-6.5 17c-.63 2.207-1.964 3.874-4 5a8231.591 8231.591 0 01-24-15c.817-2.979 1.651-5.979 2.5-9a782.756 782.756 0 0124-29 45.818 45.818 0 003.5-11c-.29-3.506-.29-7.173 0-11a188.56 188.56 0 009.5-8c-.056-4.5-.556-9-1.5-13.5 2.19-2.684 4.857-4.851 8-6.5z"
-      ></path>
-      <path
-        fill="#ba06bc"
-        d="M303.5 491.5a194.638 194.638 0 017 23 197.708 197.708 0 014 12.5c-7.165 6.956-8.831 15.122-5 24.5-.44 2.714-1.774 4.88-4 6.5l1 1a2201.348 2201.348 0 00-7 7.5c-.079-3.615.254-7.115 1-10.5a466.754 466.754 0 01-3-25.5c2.343-3.34 4.676-6.673 7-10a578.265 578.265 0 00-7-23 25.658 25.658 0 016-6z"
-      ></path>
-      <path
-        fill="#b404c8"
-        d="M297.5 497.5a578.265 578.265 0 017 23c-2.324 3.327-4.657 6.66-7 10a28.65 28.65 0 00-7.5 8c.066 5.653.233 11.32.5 17a114.347 114.347 0 01-13 22c.987-14.831.987-29.831 0-45l1-2c2.79-3.292 5.79-6.459 9-9.5-1.115-1.235-1.115-2.402 0-3.5a57.178 57.178 0 01-3-6.5 52.265 52.265 0 016-8.5 40.943 40.943 0 007-5z"
-      ></path>
-      <path
-        fill="#e40484"
-        d="M407.5 512.5c2.181 4.385 3.847 9.052 5 14a360.66 360.66 0 017 21 79.126 79.126 0 00-10 19c-.014 2.093.652 3.926 2 5.5-.65 1.609-.15 3.109 1.5 4.5l7 22c.814 2.677 2.314 4.677 4.5 6a113.427 113.427 0 00-13 6 583.481 583.481 0 00-13-35.5 146.402 146.402 0 00-6-21.5c-1.278-5.743-2.945-11.743-5-18 1.797-1.631 2.964-3.631 3.5-6l1 1c2.102-1.595 3.602-3.595 4.5-6 3.676-4.041 7.343-8.041 11-12z"
-      ></path>
-      <path
-        fill="#ae02d6"
-        d="M270.5 493.5h1c3.191 12.38 5.524 24.714 7 37l-1 2a106.308 106.308 0 01-8.5 9 153.159 153.159 0 01-3 29 741.869 741.869 0 00-19.5 28c2.238-5.92 4.405-11.587 6.5-17a342.059 342.059 0 003-23c2.038-7.914 2.871-16.248 2.5-25 3.459-.936 6.293-2.936 8.5-6a56.834 56.834 0 00-1.5-16.5 4.933 4.933 0 001-2.5l-2-8c-.111-.617-.444-1.117-1-1.5a59.425 59.425 0 007-5.5z"
-      ></path>
-      <path
-        fill="#d9098a"
-        d="M385.5 500.5c4.418 7.555 8.084 15.555 11 24-.898 2.405-2.398 4.405-4.5 6l-1-1c-.536 2.369-1.703 4.369-3.5 6 2.055 6.257 3.722 12.257 5 18-3.615 3.559-5.949 7.893-7 13a136.489 136.489 0 00-6-19c.304-.65.637-1.317 1-2a121.4 121.4 0 00-5-15c1.261-2.764 3.095-5.097 5.5-7a9.62 9.62 0 001.5-4.5c-.991-4.496-2.658-8.663-5-12.5 2.341-2.329 5.008-4.329 8-6z"
-      ></path>
-      <path
-        fill="#a300e8"
-        d="M245.5 499.5c3.063 9.497 4.063 19.163 3 29-.29 3.827-.29 7.494 0 11a45.818 45.818 0 01-3.5 11 782.756 782.756 0 00-24 29c-.849 3.021-1.683 6.021-2.5 9a141.893 141.893 0 01-13-10c7.292-12.111 12.292-24.778 15-38a307.474 307.474 0 0114.5-15 336.067 336.067 0 002-19 87.068 87.068 0 008.5-7z"
-        opacity="0.983"
-      ></path>
-      <path
-        fill="#ea0080"
-        d="M419.5 547.5a157.75 157.75 0 008.5 25 21.15 21.15 0 01-.5 10.5c.631 3.85 1.298 7.683 2 11.5-.774 2.395-.441 4.728 1 7a409.027 409.027 0 01-6 3c-2.186-1.323-3.686-3.323-4.5-6l-7-22c-1.65-1.391-2.15-2.891-1.5-4.5-1.348-1.574-2.014-3.407-2-5.5a79.126 79.126 0 0110-19z"
-        opacity="0.995"
-      ></path>
-      <path
-        fill="#b800d2"
-        d="M277.5 532.5c.987 15.169.987 30.169 0 45-1.869 13.151-5.202 25.485-10 37a416.84 416.84 0 01-25-11c2.036-1.126 3.37-2.793 4-5a741.869 741.869 0 0119.5-28 153.159 153.159 0 003-29 106.308 106.308 0 008.5-9z"
-        opacity="0.999"
-      ></path>
-      <path
-        fill="#e5008c"
-        d="M392.5 553.5a146.402 146.402 0 016 21.5 583.481 583.481 0 0113 35.5l-12 5c-6.432-16.097-11.099-32.43-14-49 1.051-5.107 3.385-9.441 7-13z"
-        opacity="0.998"
-      ></path>
-      <path
-        fill="#d7039d"
-        d="M363.5 526.5c1.943 3.384 3.443 7.051 4.5 11a468.102 468.102 0 014.5 22v2c-3.77 4.46-6.77 9.46-9 15a248.59 248.59 0 009 40.5v4c-1.442.377-2.442 1.21-3 2.5a112.641 112.641 0 01-12 2c1.135-.974 2.135-2.141 3-3.5a843.983 843.983 0 01-9-54.5 99.919 99.919 0 00-4-19.5 80.35 80.35 0 008-12.5 864.794 864.794 0 018-9z"
-      ></path>
-      <path
-        fill="#ec0179"
-        d="M424.5 514.5c4.089 7.244 7.423 14.91 10 23a3.647 3.647 0 01-1 1.5 3535.79 3535.79 0 006 18.5 137.415 137.415 0 0014 28c-1.13 1.122-1.797 2.456-2 4a324.533 324.533 0 01-21 12c-1.441-2.272-1.774-4.605-1-7a600.729 600.729 0 01-2-11.5 21.15 21.15 0 00.5-10.5 157.75 157.75 0 01-8.5-25 360.66 360.66 0 00-7-21c2.994-4.994 6.994-8.994 12-12z"
-        opacity="0.987"
-      ></path>
-      <path
-        fill="#d003a9"
-        d="M345.5 507.5a59.803 59.803 0 014 6.5l-1 1a86.407 86.407 0 014 10l-1 1c1.922 1.46 2.589 3.127 2 5a36.354 36.354 0 002 4.5 80.35 80.35 0 01-8 12.5 99.919 99.919 0 014 19.5c-3.639 5.687-6.306 11.854-8 18.5a1110.428 1110.428 0 013 20c.254 6.986.254 13.819 0 20.5h-17c1.068-.934 2.401-1.268 4-1a112.64 112.64 0 012-16 1078.595 1078.595 0 01-4-36.5 4.933 4.933 0 001-2.5 64.48 64.48 0 00-3-15c.606-2.14.273-4.307-1-6.5a113.61 113.61 0 007-11.5 94.334 94.334 0 018-8.5 98.01 98.01 0 01-2.5-12.5l-1.5-1.5a66.515 66.515 0 016-7.5z"
-      ></path>
-      <path
-        fill="#bf01c9"
-        d="M297.5 530.5a466.754 466.754 0 003 25.5c-.746 3.385-1.079 6.885-1 10.5 1.125 1.152 1.125 2.152 0 3-3.979 4.852-7.479 10.185-10.5 16-.13 11.788-2.296 23.121-6.5 34-5.311-1.05-10.311-2.716-15-5 4.798-11.515 8.131-23.849 10-37a114.347 114.347 0 0013-22 481.832 481.832 0 01-.5-17c2-3.166 4.5-5.832 7.5-8z"
-        opacity="0.995"
-      ></path>
-      <path
-        fill="#df0099"
-        d="M372.5 561.5c2.189 10.605 4.856 20.771 8 30.5.673 8.664 2.673 17.497 6 26.5-5.345 2.424-11.011 4.09-17 5 .558-1.29 1.558-2.123 3-2.5v-4a248.59 248.59 0 01-9-40.5c2.23-5.54 5.23-10.54 9-15z"
-      ></path>
-      <path
-        fill="#e20092"
-        d="M379.5 547.5a136.489 136.489 0 016 19c2.901 16.57 7.568 32.903 14 49a45.545 45.545 0 01-13 3c-3.327-9.003-5.327-17.836-6-26.5-3.144-9.729-5.811-19.895-8-30.5v-2c2.305-3.932 4.638-7.932 7-12z"
-        opacity="0.987"
-      ></path>
-      <path
-        fill="#d800a5"
-        d="M351.5 567.5a843.983 843.983 0 009 54.5c-.865 1.359-1.865 2.526-3 3.5-.992-.172-1.658.162-2 1h-9c.254-6.681.254-13.514 0-20.5a1110.428 1110.428 0 00-3-20c1.694-6.646 4.361-12.813 8-18.5z"
-        opacity="0.995"
-      ></path>
-      <path
-        fill="#c500c4"
-        d="M299.5 569.5c-.244 4.83.089 9.83 1 15a1145.614 1145.614 0 01-4 39c-4.683-1.337-9.35-2.67-14-4 4.204-10.879 6.37-22.212 6.5-34 3.021-5.815 6.521-11.148 10.5-16z"
-        opacity="0.991"
-      ></path>
-      <path
-        fill="#cd02b1"
-        d="M324.5 500.5c5.661 11.625 9.328 23.958 11 37a113.61 113.61 0 01-7 11.5c1.273 2.193 1.606 4.36 1 6.5a64.48 64.48 0 013 15 4.933 4.933 0 01-1 2.5 1078.595 1078.595 0 004 36.5 112.64 112.64 0 00-2 16c-1.599-.268-2.932.066-4 1h-9a1161.214 1161.214 0 001-52.5 6128.394 6128.394 0 00-4-28.5c1.833-4.021 4.333-7.688 7.5-11-.447-9.399-2.614-18.399-6.5-27a44.424 44.424 0 016-7z"
-        opacity="0.99"
-      ></path>
-      <path
-        fill="#c701bb"
-        d="M318.5 507.5c3.886 8.601 6.053 17.601 6.5 27-3.167 3.312-5.667 6.979-7.5 11a6128.394 6128.394 0 014 28.5c.064 17.414-.269 34.914-1 52.5h-9a298.149 298.149 0 00-15-3 1145.614 1145.614 0 004-39c-.911-5.17-1.244-10.17-1-15 1.125-.848 1.125-1.848 0-3 2.345-2.524 4.678-5.024 7-7.5l-1-1c2.226-1.62 3.56-3.786 4-6.5-3.831-9.378-2.165-17.544 5-24.5a197.708 197.708 0 00-4-12.5c2.641-2.304 5.308-4.638 8-7z"
-        opacity="0.977"
-      ></path>
-    </svg>
-  </>
+export const SvgVectara = (props) => (
+  <svg
+    xmlns="http://www.w3.org/2000/svg"
+    xmlnsXlink="http://www.w3.org/1999/xlink"
+    width={1000}
+    zoomAndPan="magnify"
+    viewBox="0 0 750 749.999995"
+    height={1000}
+    preserveAspectRatio="xMidYMid meet"
+    {...props}
+  >
+    <defs>
+      <filter x="0%" y="0%" width="100%" height="100%" id="143604a479">
+        <feColorMatrix
+          values="0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0"
+          colorInterpolationFilters="sRGB"
+        />
+      </filter>
+      <filter x="0%" y="0%" width="100%" height="100%" id="92b242bc1b">
+        <feColorMatrix
+          values="0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0.2126 0.7152 0.0722 0 0"
+          colorInterpolationFilters="sRGB"
+        />
+      </filter>
+      <clipPath id="a4e1f56fbd">
+        <path
+          d="M 0 48.4375 L 750 48.4375 L 750 701.6875 L 0 701.6875 Z M 0 48.4375 "
+          clipRule="nonzero"
+        />
+      </clipPath>
+      <image
+        x={0}
+        y={0}
+        width={720}
+        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dg2HmfDYGpitk1FQmJl7VqUmt+5cnv2iSfX6PMTMOQQEet/cnXYKKrifX/ctT3QZ2eRYXCJPsRqb2zCtMqfuX70LtTSEAwNB5uPiMRD5TbyBdgpoK6i6hgQ+jOtAw9z52urrkNZqRiVf10jPr/qX9r5Lh2QlUXxRRK3gGPatTqKDkQv0btAcaIlZQOXqKenFsNdDFifVv8ALROlwwfFlb0iVoUPvpEaRLUFXR+fo3+HDa2z/u7icpHW5CMcOou0mXoK6c9Po3+M60Bxq8FjD1/EkWHeZSOLNOQdWXTfVv0f0cuk7Eqm6kS9AYj2ETSZegrvJTDW9poe3jh7zE0CAbObR7jOIhOkooPd7wlhYCDV5TH6b+wogmHmPGMPbzKrvU8JYmAg0h88eSLkFLgp9nrIE2nqpteFMbgYa+i2hb34lihntjGWugq480vqmRQMOkJ2keFkiXkIdZGy5QdaixO0UrgfaePYOxVkcy/pEY1n5U+Y2X0JoJNEQuYGl0rzv6PMTaMyHTIUvj25oJNIx+CkeSumRuV9IVqM10yNz4Nl9C/WCOetzs231I16AFg25h7QoaTAesWugUrQQa2izC3cCd83qQqWGjAADCtdymEPNVBCsRqfcSnAXu1NhJzP0dsxw0Nb3Da+mGePJ8xjoMxPOZSdOmZuqwHDI2vaOdm0IA8F00QgNjqYgaezPpCtRnPFrb9A5vsv+F9AleS+3KZXQImx1LugT17S+xeoe/bLH7hfTh+y9haO0UCUbdQroCAo5UWr3DlxGrQwrvu3AFRwc63c7KjldWLMesH2xo6qYQAIKfxRUc7eLHMLOhipWMK9YXGfx1zTyHrtNrBYONkIsiH2JnOegm+4ut3+OTNRZow7i5uJ+QbV5jmBw1ftz6nlBjTzkAAIIeZmy+nMu6PsXif/XSs9XW7/LpGmuhAWIfZWhdehF8pg8iXQIJFzKbvctnay7QMOVh1sZHuiT8UU31ksnldEGzd3lTBaFCpPOedyvpEijk+1QM6RKIONIy0EmECnFDxN/6kS6BOlyPOSxeQcOVq83f5y3aa6EBbn4wiHQJtPFd3J50CUSca34JDXx1gvYuogEWTPAgXQJlRk9msoGGU+nN39dmCw3+r/TXWBenwgIeZbOBLrpsbv4B3pRCpBB3xS7Fwf7W7riJzT9ZFy63+ABvTCVSiLv4mQ+w+SfWtrCHGP3/ndjinhB4S5rm+goBAMDrqZvwoqPRA4NJV0BG9fnyFh/hoSbT5pfSjuvyfGfSNVCj1/R2pEsg49Lplh/hoSqFQCEyMAxfiBcddQxz4kmXQEhyQsuP8FCTTaISGfjNmUO6BEoMm8Lown+mhIKWH+KhquVltWZEzGdyNE4r7e5l9AoaMo61+hAP1ek2vlIb4p5i9NKxueG3ka6AlFRbgTZdI1CJPDxmPUy6BAqE3RVJugRCzImtH2jwACXafBINABA4l8k5Gs2xtoNbk8JTrT/GA1RmqV+KXAY8zvyzu+gnWJxICAAA2Xtbf4wHKNHsXSEA3Mn6YH/viWNIl0CK5dKl1h/UeqA9H72ZyXkajXosYnMQBwCUHrTxQR6gUrvX0ADQdTFzy8da83+AvcUZGxTttPFBHgByS2x8QjPGLWV4sD8/8H5m/0BZrrQcaQdQF+i8K2rXIqu7Z7G7ub3PYnaX3anaXWvjozwAFKaoXIq8/JfFs9pK8XeMZ3d94Yo/bM210kGguW6rQknXQEjQk21Jl0CM5ZLNCwseAMo1fVcIYBi1jLltGAAAwPAEwxPRjD9X2/owDwCQptUBd/X87ruHyYuOuAeY7VMBqN5htvVhHgAgR7vjk+p0/BuL4+685keTLoEcIcn2InY8AEBWmrrFyG/g0k6kS1DfbeMYnuFg3mrziqMu0JnJqtaiAI9JjzO33WT4w91Il0BQtaNAC6ka2q3QtsAHZjB2Gc3NvpF0CSRdsLOweV0KkjLUrEURUYviSJegriGzmJ7dsKXU9sfrAp2szZnfzQxbxNRf4MB7WJ0YCwAA5XsqbX+iLtCXU9QrRSn8zPtYeog19j52+wgB4KS9a4q6QGt1tZlm/BZMZGckZdc5EaRLIGprvp1P1N9JJeaoVopyOi/vT7oEtXhNmkG6BKJKj9q54mgI9HmN9xXWGbwkjHQJKun5OLsjDAEAjtrtCqwP9CUNzytsYpj5MBs7JwfNZ3Uljnq77D6Wqw+0KVHzT6IBAHyWT2LhVomPe4DdQUkAANfP2u5VgcZAw+lClWpRVpvX4xn4VYcvDSRdAlmHbM1VqdMQ6FP6CDQX87r+n0Z7PTCagf+1juyxP6+7IdDXdNC1AgBgGLEkmHQNCuMGLGD7jhCunbP/uYZAW85ocq+V1nzvnafz0f4BK8JJl0DY8TP2P9c4oudYmRqlqCB4wXTSJShr9lh2+o9sMp930GvSFGg7gz20J/KZUaRLUFLckwwv2wAAAFdbrznapDHQ2edtzmjRong9rz0T8FRf0iWQdtbWikkNGgNtOaSLJ9EAAPxtC/Q7DXzWrQxPUwEAgOJDjq6OGwMt/KmTu0IA8Jx3vx/pGhTS637W7wgh7ZCjzzZN87iYp3gpqmmzbIo+b5x8599MugTiEmysCt2kKdCmQzoYQtqg47oRuux7mHk3YxPNWsv/s8bRp5t+PuadRsWLUU/U6j46THTvuWyPggYAyLSxyrmVpkBbTmh6EdKWblzZWXeJbvvQRNIlEGc8dtHh55sCLeSfsihdjYoMdy3W2yRSr9twjyQo2ub481aXZDXbdfMkGgDAa95jOhtKGvt3Rneot5b6l+PPWwXa6PhqW3MCn35YV3dQ7Z8ZQroE8qp2FDv+AutfeZ7D5yHaE77sdtIlyMh71izd3RSIV7HF9voyjaz/KgsdxilajOqCeiQnka5BLvyIt/GCA8x7P3JyGWHdQlcd0tNdIQBwg/+umy11Oq7S/8wF54xbnQ3QaH7fNFxv21jGhJ++TroGWfi+fDvjo/oBACBnqZNL6GYtNFzfo1wpZPB3PRVCugY58Pfey9zqqjZYdjrLc/NAl550csWtQU8+o4fhw4MX4gU0ABi/tbe+TKNmgRYuJypXDCFez92v/SlZnZcxsyaUQwkJTu/yml9Dm7oMV6wYUgxDii7Y2tBOQ9o8cx9ecAAArNvv9DfZvOvh+nGN/+pt6fDKHG3fT3F3z2VpXVX78vc6n4TSoi/tiv6uOQA6vDBZ053gY/7O/KD+OttdWGujxW/a1EuHi6Zxgf2yLpEuQrrY10eSLoEOxjePOX9o0aKFztPViLsGfN9XtdsJ3vHv40mXQIlzzm8JW7XQ0Ga4Lp7btsB16JVqf/UoqgU8tlD7T2nk8emvLsypajke7ayjNQ+0i7th6Q2ka5DE89ZlAaRroET2IVemcbcMdMEZPU3EasKNWaPFRPMD39b7Un0uO+DSA4tWt/9+w/S5pgXXo8uFXNJFiMX1+XdfHDNap/bDna58Wash8MfPy18LHaa+NkBr4Yh4frDWSlbMcdcuhlu10DVRI7TdDWFfbKcL+ZoarBL82my9/i7E2/ClS7+71j0OFTfqdqp8bISmEu23cD4bW8a4IuuLsy59XetZdxeuyV0LPWa82l870wy95ixkfOcJa6ecTI5t0LqFtoQP1evCcAA9u1zQyopnXve91Il0DfQo+9bJ8gUNbAxyKJus37G3XHR0eirpIlwzaXU06RIocu5Ne1vHtmAj0EWD+2p6LI9DfFTfAsdL71Bi3Or++ICjkem3z138ShvRFfzG6vhehI/om62BkUo3vxWHeW6S/YGrrZCte6Q9+phXagff/1X698mOXYKLyli78rurX2nr4qIyepA+V1euw3UYVX2W7lXPerw5WTtPY1RQ8pnjJUet2LxaFqbr9zkHAHBt44oTKV4Mm4t5a5qeWxTxkle4vDKuzUDn3B6u7wu4gDj+otP5w6Rw3THPzVV/v9Hl/jDbzzPa3qTzn2jATbXXyujsNOR6rMM8N1f4cprLX2v7kqN4jq6vOQDAMLzjeTrvfXuvncr6PlctmHf/0/W527Zb6KqhPfR9zQFg6NcxN4PCNrrv6qk4IKm56hcvuv6Lsh1oi2WGfvtW6vG9B+Rdpi7R/V6d6k26BsoIF9aWu/7VdgJdNUmfw/yt8RFDqq9Qtsb7mNUTMc8tWN48IOIhq52G2NxVf0sotcIFx/uepWr/3LHrRuP9YEvXXhMz08hOoGuq72HgzoQLGBxxsZB0FY0Mt6yJJ10Dhb7+VcyfUXuXysIwJtbX9urTJ52WW0OfO9/rpfdbcQkqnr8s5svtBbrad7IMxdDPI3JkfhIVvYaBc1d3wTy3tvlbEbeE9gNt9pjGxnoQfPtx/FVRPzJFcB3nv6zHJX7cZll3WNRfULtP52o663CVO1s431GRlwpJr4AWvfYJHY/ZdcOuTwpEfb3dQFe0mcHKgC9D75uzs4g+v/OIf3caPq6zRfjnDnFtjf3+E35gF3er0QoudJyQ5nT3DuUETnt7uO47sqQ592/Xh3EAgKNAl3YZ7WYxGuI3PKokjdRlR8RjL+h+pIFU32wQuTSd/UDX+ozS2+7vDnj2GcallRE59YAV8zoSObEGpH10TuQrHPylK+vXz61iNKb90Mi8bPUbaZ+pr0/T+9BG6X75VOywdQeBLg8bxdSNim/fkd4Xq1U+accFK25g5eZbvMLP9ot9iaN7EW6YbhcFs4kPHdIvNUfNfkPDoDfuZ+tnLM6+d0RfBToKdNbgODeK0SK/2AkBl6vUijTn9+i6EWx0X0lT8sV20a9x+LTIf1hbycVoEx8cPyI3S5297Tz7vv1YJ3xa58DZt8R1qgA4CXTm6BjJxWiVd7fJXa+UK7/KAR84+/0b/fBpnQPVG78R/yKHga6OjGPqthAAADi/IRON+RUKP+/wHbp2YRjeDTp0bU2G+Bc5/pNXNlH/E1da40ImDygprFbwUtoz5qE1IxgYcO4W09YPJLzKcaBzRvRhshXhoid1NhYrNZmF7zztpXlBCh1cP/LWSNmJz8lNiWkCo3fhPoPGduELlViMhutwy+LnYvDi2Rnzn29LuTl38oMN2DqK2R+9kPnHb/KvW+k9dfpEna9LJY+ShzdLuehz1kIH3sTsKhFc4A0je/HZsvYdBoxZtOimQMyzc5ZD6yRd8jn72XbeEcvyj9+ccXrbJhfXjncu8JaZcZHMNhDiVM35RdKzU2dp9Vr3JOO/gYKLWzddleO5dNCdMwaFYUeKa4STU6RthuMs0PzwbcxvxVSau2fTXqPZncd4vGfU7VN7BzH5yEgS06L/Stuj2+n1RLsvp+KvoaY0cduvyTUSu8Q5b++Rd00M8mf52k2s47NSpL3Q6Q/ZY+ZX7PUWtmYxFh/YdKywwii2neZ9fbvMnNnVF7tRxBBefK9C2iudtxpdtjI1zt8+syXv5NajeaXlrveKGwLbdhs3emCAARtnca7NFDtTpYHzn7TfojUSj61DQuWRoyeTC6+7MHrJo01wSPf4Mb19VChLd158T+pSKS40HXHbcAUUa5ac0+evpuWXFBXba6oN7doGd4iM6X9DCLbMkiTPPiL1pS4sdpmycb7Uo+sSHxExBYqSstNTi3Oq80xFNWAqswAA5+8r+LbzDPYP7dA5IjK6Dek6NWzLFckvdaUJmfGdr+Tj65olryrLWFDF1eSaOBA8Q/wE/xDP0MAO2C67J/XxPyS/1pXliC/snyj5+LrGh0EU6Rr0aMtR6a915Rlz0mbpx0dIrLSdbqxi5UqgLafOSz8BQiIdPODGi13qBTwrfvItQhJlbxU/NbaJS4Gu2CtyxTyEJDv8mzuvdm2cxslj7pwDIddlbXZrUoVrgc74i/wa94gNp3526+UujqTbdcqtsyDkotz1JW693sVAJ5wgvWcDYsOVbe693tWxzltE7a2FkDQlG92c8OZqoA/io2ikPOHiejeP4Gqgq3+XNsULIREqvspx8wguT6/66ZqbZ0LIGUvCJnfv1VwO9PVfyexAghhi/NTtJSNcnwDr7tU6t3PVuQAAFUNJREFUQk4Ip39ze2lu1wOdtEvavHKEXGQSuWusLa4HuvbrUrfPhpADR3e632a6HmjhxHnsXEEKMv031/2DiFhEpuZztfc8Q0zZ/5cMF7UiAl27M0nNLc8QY4zfpMtwFDHLfF3/Fq85kGL+2meS4ShiAl292d1uHITsqdooS9edqIUYc6XPLkfIsb1/ybKXnqhAF32PvYVIGeW/XpTlOKICLSTuk+WkCLV0SKa//uLWfs5yd3AfQjaVbJVpwL24QJtPSF5EDyEH9krYBdkmkavzX9si03kRspIt29A3kYGu2i99XUiE7Dkt22pzYvdPSdgj15kRapD7rXtTva2IDXT+ThwWjWQmnHVvLQ5rone4OnxQtnMjBAAAhZ/J178hOtBpe5XY0R0xrHbvVvkOJn4Pwh2n5Ts7QgBFkncIskF8oM8fwKlYSEa1207IOCxZfKCF33BBAySjko/lnDgiYdvj/bjOHZKP5SdZp/ZJCLT5WzlmFiAEAAAZn8o6hFPKxvT7LuJULCSXzxNkTZOUQJd/XShnCYhlST/JO8ZeSqBhR6qsNSB2Cf9LkveAkgKd/1/coQLJ4thmmSdBSQq0ZWMGXkUjGZi+TZT5iJICDUXfyzHjHDHv4G65gyQt0KYf3Np6CyEAAChanyD3IaUFWshwe2FqhGDfL7IsXWBNWqCh/DMcFo3cVbA1Q/ZjSgw0JMs3JBuxavcP8h9TaqCLf8Q1Z5B70jYUy39QqYEWzv0kax2IOZYjMo7rbyQ10JCzGZ/cIXck/1OJuU+SAw1nfpexDMQc425F1pWTHuikTVUy1oFYk/SBIoeVHmjYu0O+MhBrqr6/oMhx3Qh00i68ikYSCZe/VqZrzo1Aw76jspWBGGP8IE2Z8W3uBPr073gVjSQR9m1R6M+7O4EWfsElOpAkle+5v2esbe4EGs7+USFXHYgp64/IPiqpnluBhi1XZSoDMSX3M6UaaDcDfeIwPuhA4n12XrFDuxdo+DJFlioQU07/JNty0K24GeiDe3ChOySS6euzyh3czUDDf7JkKQMxZM82BVtBdwN9cjs20UiUwg2yTyS04m6gLf/Ok6UOxIzdSgyDbuRuoIVEHBeNxEhZn6nk4d0NNBg/wemyyHXGbdsUPb7bgRaSvsMVDZDLrn2jbO+y24GGii+zZagDsaFsw2FlT2Bw/xBVgaPcPwhiw/ElCq8W4H4LDaUbcEV/5Jrr/8tV+AwyBBqufSXDQRADzPu+VvoUMlxyQE3l+CAZDoN0L/epNKVPIUcLDRe+l+MoSO9qvzur+LricrTQYKwd2V6O4yB9O/uc8n0WsrTQcAyXbkROVb4n/2KjrcgT6MrflRxvgvRh+w4VBrLJE2jY/4c8x0H6lf1xjgpnkSnQxi3KrIOD9OPrI2qMkZAp0PDXZpwAjhw5tl6B1aBbkyvQ5u/l3p8L6Yrp61OqnEeuQMP57dhEI/u2bFFqJY7mZAs0fH1NtkMh3Un9OlmdE8kX6IubcL9kZIf5N7XWXpYv0PBZIu6XjGw7+z+1WjtZur7rlJvHe8l3NKQjxR9tUOtUMrbQll/O42QsZIOw/3PVziVjoKHoX0psa4Q0L/Nfiq3N2Iqcga7d/gdeRaNWqn/cpd7JZLyGBqgpmolX0agFIeHvReqdTdZAC2Xd+8l5PKQHZSsOq/iHW85LDoC8D5SeA4m0xvzHNnX6COvI2kIDVASNkPeASOsynlB8HqE1eVtouL7+krwHRBpX/eFVVZ8UyBxoOKP4PHWkKbu+UvdZrtyBrvodd+NETXI+UmOaihW5Aw2nNuBunKjR5/tV7j2WPdDmLfvkPiTSrH0/KLc9kG0yP+UAgELfoQGyHxRpUsFatUaNNpK9hQb4Ga+iUZ3Nv6k+FkL+FhoqjMPayX9UpD3HXr+i+jkVaKFhyyEcRooASjceUf+kSgTa+D5OL0Qg7PyCwFmVCDQc2VatxGGRplz+mMTAHkUCbXnnqhKHRVpS9g2R5eEUuCkEgHLhZhwYzTbh0BIiPWyKtNBg/uEY3heyLfOtQiLnVSbQUPwOLtLBtJovVe9SqaPMJQcIOV0HcsocGmmA5cBKtfu86ykUaDBmTg1U6NCIfjmLSS1poVSgocQwXqlDI9oJb61XYbF+mxQLtCm3f6RSx0aU275G+d2B7FAs0FAMU5Q7OKJZ1tKTxM6tXOaEwk79FTs4otlbG0zEzq1gI1paeSOOumPRr/9QedqVNYWeQwMAwKEtCh4c0Sr148sEz67kZW510Q2dFDw8opLx4//VEjy9ovdtGW1HeCt5fESh7WuJrp6l5CUHwPq9ih4e0SflU/VnqVhT9slaiXk49hcypezTz9Rcya41hR8Vp8T09VD2DIgm5p0vqbK9pn0KB9qUMq6DsmdANElecY5wBUp35uW1GYb3hcwo/+A70uPglb0pBIDPcKg/M2p//4TkEzsAUL6Fhur0aX5KnwNRQUidn0q6BuUDLeQGxeMgJSaUrfiL7BMOABUuOaDyE7IPJpFKLF9sJjUI2ooKjWdZwSScAs6Ao6vSSZcAqgTakheF40j1L2/JIRp2qVTj8rYiZzyOI9W7mnd/oGK5LFXu1wr5cTgFXOe2vEVwELQVVQJtyo+NUeM8iJgLq0+QLqGOOk/UCitwyWhdK3z3R/JP7ABAjcd2AACwY7M650FEWLZ8Q8ETOwBQq4UGY07fbuqcCRFweDn5LsJ6anXi5XoO81fpVEht6Wt3kS6hkWq90kk9+6p0eYNUVvHf9+kZgKZaoKsLhnZU61xITebdi9Xd/dgh9cYNpfkO81HtZEg1wuVnadpSR8XLgM8OUfJkB8mp6M2jNHR5N1BxZGdNxtgg9c6G1FH7yT9peWIHAKoGGjK9b/RU8XRIDVteJLP1hD1qBtpyZUAMjunQl3MrEmm64FA30FCVNBF7wHWl+IU/KLsxUjXQQoF5IjbROmJ573OKntgBgMqBhtrsXj1UPSFS1KY3s0mX0JLK81fLrk8IUPeMSDmnVp8iXUIrKgdayOFHYw+4ThS+uZF0Ca2pvcKAMb1TX5VPiZRR/dn/kdt5wi7Vl8woLhreXu1zIgWYt72aR7oGG9RfAybTfwR2r+jA6ZXnSZdgi/qBNqd276P6SZHcCl7eSroEmwis0lVSMhQvOrSu8h8f0tVD2IDEsnOpQYNxiV1tq/3xFdp6VOqRCLTlYq+e+OxOy4QDy2lY9ssWIguDViSPDiVxXiSTlKXH6Zl01RyZlW5zPUbg7BXtKlv2K/GFze0hE2ghMboPLhqtVZY1X1B6AQ2kAg3Gs7eG4Lg7bRI2/IPo1pqOkWony3PH4k4V2nR41QXSJThAKtCWVL+huIOhFl1btZt0CY4Qu5I1JvfqSercSLrct9dTe0MIQDDQUJI7CieBa075J+/Te0MIQDLQkGMag6OUNMb40xsFpGtwjGCga1M7DiJ3diSBcOyFy6RrcILk0+DyrAGdCZ4eiZa68DDpEpwh2r2RUx3fluT5kTjZS+gcMmqNaKCFi8FxeBmtGZXrvqRwzlULZDugzUm9cC0lrTB+9XYp6RqcIzyiojgrDleN1gbLtmV0bNzmGOkhQqkQhwt1aMLhJbQ/4AAA8oGGxLAbsAtcA64uOkK6BJcQD7QpoW80Tl+hXsaK7bQO6W+OeKChLOVGHElKu/z3vqsiXYNryAdayKkYhSNJ6Vbxn/eLSdfgIvKBBss1/+F40UEz08Y3KB7S3xwFgYaapG69SdeA7DPveJGmfa4coyHQ+DSabucWnyFdguuoCDTkVIzEjZMpJVxedJB0DSLQEWjzVc+RdFSCWspb9rs2HtjVoSRGxqTO/UjXgGwpWrGBqn0InaEk0FCS2xfHRlOo9MOP6Z5y1RItgYa0sjjc8o061Z++oYERdtaoCTQkeMT7kq4BNVe78aV80jWIRE+g4XynvjhMiS6/PJ9KugSxKAp0TcKASOwxpMm+5QmkSxCNokBDcdJN7XGYEj0OrzxG5yr9jtAUaMjKuhVX2aXGueV7tJdnugItJJVNoKoglqW8tFVLHSoN6MqP+YpvPF5GUyF95U/0T/G2ga5AQ9WVyFi8jKZA/tovNdVB2IiyQEPRtbhw0jUgKPr3ezWka5CGtkBDdhruYkhc0UdrtNXh3YS6QENGdTwOJSWr+qPVFaRrkIq+QNde9BiC+3KSZPzx9ULSNUhGX6DBmBjWj8KymGH8YWUG6RqkozE5FUk9o/DhHSmm71elka7BDTQGGvKTB0TgwztCNq3U3IAka1QGGtLTRuPgaDL+fP4S6RLcQmegITXzxjaka2DSX0tOky7BPZQG2pJkGelFugj2CAcWn9TggCRrlAYaahN53JhTbZbDC09rPM/UBhqqE7r0prY4fbIcXXBW63mmN9BQfrJHT3zUoSLLkQVntThgtDl6Aw3Fl/t0wUSrRjj0Nx3kmeZAQ/alQRGka2DHvqfP6CDPVAcasvNuCCFdAyv2PHdKD3mmO9CWy0VDsINFFX8uO6H5+0EAoDzQIFytGYSbZKngz+XH9ZFnygMNpsTKITg6WnF7lmtwwQLbKA801FwI74+7Jyts7xK9tM/0BxqMJ9r3xkQrSTi4WCfXzwAaCDRUHAnth53gyrEcXXhKP3nWQKCh8lhcVxzvrxTLsQVndJRnLQQaKo706YaJVobxoC76B5toIdBQeDwW1yVVhPGn+Vf11D5rJNBQcKpXNOka9Mi4cUkm6Rpkpo1AQ37SkDDSNeiPacOKdNI1yE0jgYbM5H6YaJkVfvGalud326aVQAtJKf0x0bK6/tGb2aRrkJ9WAg1C0rWBuH+yjIo+fE+76yPZp5lAg5ByZUgo6SL0I23V53rMs4YCDUJq4lDcg0UmGc9/V0a6BkVoKNAgpJ8fgYmWg+Xi0s0aXf/ZGS0FGiwZ5zDRMrBcmP+nNtfnd05TgQZLxvmheB3trtoDTx+tJV2EUrQVaLCkXRzSgXQRGlfzhx6WK7BHY4EGIfUKPr1zS9mGFddI16AgrQUahJQk7GFxQ/6nr+uve9CK5gINQnJK93AceydR+rv/yiVdg6K0F2gQks90xRX+pbm46rsi0jUoS4OBBiHzXA8cHy2B+egLP1eTLkJhWgw0QO6pXjiHRTTz/kUHzaSLUJo2Aw15xzp2x5mz4lR9/0yifh/XNdBooKFgfyiubiBK6TtvaHi3NpdpNdBQfjg0BvfndJmQseo/10kXoQbNBhoq9hsH+pIuQivM55du1Oxux6JoN9BQfbp8IK7k6JKa3Yt363b0RnMaDjQYz+f0aU+6CC0oWr/qLOka1KLlQIPxXFavUBxO6kzOu29penNYUTQdaLBcSezeFRPtkJD4wn9LSRehHm0HGizJl9pH4gNpB4xbX9yi+94UKxoPNED68aDemGi7yv6z9jjpGlSl+UBD0cnKodjFYkfSq5/oerBoa9oPNJSeyRjqhxfSNliOLvm5hHQRKtNBoKE68WpvfNjRmvG7pUdMpItQmx4CDabLp3qH6eI7kZEl+421afpaKtcV+oiBJX1/h2gv0lVQxXTihS/LSRdBgD4CDVB4kOuDIzualP+8fI/+x4raoJdAQ/nB632CSBdBC0vqv15OIV0EGboJNNQeTwqP0M+3447aPes+YWNsXWt6SsDlc/498Yk0QPH/Vv/J5OUGgL4CDdknS2LbMP/87uwbH6ay93Sjga4CDSWnkmI7sp3omg2rf9PnQrmu0VegwXjpVLs+pIsgKeutty8wMpTfNp0FGoSswyU3MPv8rnb3sz9eZ/dyA0B/gQah5HhSrxAmLzuEvPdXXtDrus+u0l2gAUwXjrfvwmC3YfWplz4pZrt5Bl0GGoTMvZZugYw10kLO+sWHmBuK1JoeAw1QfjClcwRTa4VVHF37TgHzzTPoNdBgTjzq28mfnUb62v9e3Mv0w41GOg00QN6O4ogwRhJduuv1/+aRLoISug001J65IET5kK5CDRf/tVa/mwCJpd9AgyX1UG4n/e9eUfLDm+t1uSesNDoONEDluRO8zld0NB97+dMz2Dw30XWgoTbjYEa0jntZhOL3V+9haBUZF+g70ACVCXsDI3XaSAu1vzz3YwazA0Vt02/r1YBrN2NpjB6HSZsSPvg1Fx89t6D3FhoAqi9sDeqku4U7ajM/WbqH5XGidujt92wT5zth6YA2pKuQkyX/r38frCFdBY2YCDQAdHhiVk/dDFgSSk58/gu2zjaxEmgwDJ03JZx0EfIwHt7ydQ7pImjFTKAB2kx76CY/0kW4r/b0H19cxXtBexgKNEDslHuGaH3p3as//3gCR4nax1SgAYbOuDdKy99yyk/bDrG4wJfrtPzblSIg/va5gaSLkCrzh19PFZMugnKsBRq4oGEPTdXibnBC3rcbE4pIV0E95gINYAga/XS8t8a+8drrP3xzGePsnMZ+r/LwDLnlif5a6js0Ff306eUyfLThAg39VuXkETz9sVitzKOtzt/x8YVKjLNLNPI7lZ8heNYDPYNJV+GcUJHx23+uWjDOLmI20ABc6L139KS889BScPX3zedIV6ElDAcaANpPunMExZO0hIyzP+9MJl2FtrAdaICQSRNGRtM5hrYm8dCenUV4rSEO64EGCBw5ZuIN9HWIFx3av/dENekqtAcDDeA5eOgtN1PV12JO+nPfoRSWtuiWDQYaAICPGjBmUizpKhpUHvzlxOUC0lVoFAa6Dt82cvg9w8nPphW407t/TUtnfVFc6TDQjbxD+0+bEuFBcI1Hs6Vg128n80rwRlA6DLQVg3/HMZNu9vcikmmzqeTApqP55bhqjFsw0M15ene647buAb4q/1zMFaVn/9idVINpdhcGuiXO4NNn2vhObQNVa6dNxUVXtu5IrzXjpYb7MNA2BfS5aUKXju2V/+kIlQWZift34/pHcsFA2+PVe+Twbp06KbmjVm1edvqZ40fyFTwFczDQDhgiBgzu3b1biBJd45aClNSrCRcus7ont1Iw0E4Exvbo0bV7j3A5f1C1malJqZevXMHnc/LDQLugXddu3SK79+riL8OxilJTki+mZ2YU41WzIjDQruECQjoGR0dHxkRLTnVtQdKV9Cs51wuvl8hZGWoGAy2Cl3+bwLbte0SF9wwLEcC1H54AAHAtJzkzNaGqtKS8EhtmZWGgxeJ8fL38vAK6Bca2iQoMiAXgOIAWz6wFAUAQhPKkmszc7Py03GpjVU0NLhaqBgy0RB68N+/FG7yE0G5eQkBssxXVucJLHFeYWmMxCqba2lozjgNVz/8DOrsvkiwQe+kAAAAASUVORK5CYII="
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+      </mask>
+      <image
+        x={0}
+        y={0}
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"
+            height={627}
+            preserveAspectRatio="xMidYMid meet"
+          />
+        </g>
+      </g>
+    </g>
+  </svg>
 );
 export default SvgVectara;
diff --git a/src/frontend/src/icons/VectaraIcon/vectara.svg b/src/frontend/src/icons/VectaraIcon/vectara.svg
index 1faafd320..c111a3aeb 100644
--- a/src/frontend/src/icons/VectaraIcon/vectara.svg
+++ b/src/frontend/src/icons/VectaraIcon/vectara.svg
@@ -1,252 +1,80 @@
-<svg xmlns="http://www.w3.org/2000/svg" version="1.1" width="720px" height="627px" style="transform: scale(0.05);; shape-rendering:geometricPrecision; text-rendering:geometricPrecision; image-rendering:optimizeQuality; fill-rule:evenodd; clip-rule:evenodd" xmlns:xlink="http://www.w3.org/1999/xlink">
-<path style="opacity:0.985" fill="#16fed9" d="M 632.5,-0.5 C 643.167,-0.5 653.833,-0.5 664.5,-0.5C 666.679,0.67722 669.013,1.67722 671.5,2.5C 671.291,3.92167 670.791,5.25501 670,6.5C 664.666,13.1805 657.833,17.6805 649.5,20C 643.159,25.3027 635.659,27.4693 627,26.5C 620.18,29.7476 613.18,32.4142 606,34.5C 602.575,33.905 599.075,33.7383 595.5,34C 591.065,30.8012 586.731,27.6346 582.5,24.5C 597.266,12.4502 613.933,4.11689 632.5,-0.5 Z"/>
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"
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+      </mask>
+      <image
+        x={0}
+        y={0}
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"
+            height={627}
+            preserveAspectRatio="xMidYMid meet"
+          />
+        </g>
+      </g>
+    </g>
+  </svg>
\ No newline at end of file

From 109b5c8690b36ebb5c92e44d7ca913bbec1e01aa Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 10:02:00 -0300
Subject: [PATCH 25/50] Remove unused chat and prompt classes (#1474)

---
 src/backend/langflow/utils/chat.py   | 34 ----------------
 src/backend/langflow/utils/prompt.py | 58 ----------------------------
 2 files changed, 92 deletions(-)
 delete mode 100644 src/backend/langflow/utils/chat.py
 delete mode 100644 src/backend/langflow/utils/prompt.py

diff --git a/src/backend/langflow/utils/chat.py b/src/backend/langflow/utils/chat.py
deleted file mode 100644
index e1621bdab..000000000
--- a/src/backend/langflow/utils/chat.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from typing import Any, Callable, Optional, Union
-
-from langchain_core.prompts import PromptTemplate as LCPromptTemplate
-from langflow.utils.prompt import GenericPromptTemplate
-from llama_index.prompts import PromptTemplate as LIPromptTemplate
-
-PromptTemplate = Union[LCPromptTemplate, LIPromptTemplate]
-
-
-class ChatDefinition:
-    def __init__(
-        self,
-        func: Callable,
-        inputs: list[str],
-        output_key: Optional[str] = None,
-        prompt_template: Optional[PromptTemplate] = None,
-    ):
-        self.func = func
-        self.input_keys = inputs
-        self.output_key = output_key
-        self.prompt_template = prompt_template
-
-    @classmethod
-    def from_prompt_template(cls, prompt_template: PromptTemplate, func: Callable, output_key: Optional[str] = None):
-        prompt = GenericPromptTemplate(prompt_template)
-        return cls(
-            func=func,
-            inputs=prompt.input_keys,
-            output_key=output_key,
-            prompt_template=prompt_template,
-        )
-
-    def __call__(self, inputs: dict, callbacks: Optional[Any] = None) -> dict:
-        return self.func(inputs, callbacks)
diff --git a/src/backend/langflow/utils/prompt.py b/src/backend/langflow/utils/prompt.py
deleted file mode 100644
index 871193f45..000000000
--- a/src/backend/langflow/utils/prompt.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from typing import Any, Union
-
-from langchain_core.prompts import PromptTemplate as LCPromptTemplate
-from llama_index.prompts import PromptTemplate as LIPromptTemplate
-
-PromptTemplateTypes = Union[LCPromptTemplate, LIPromptTemplate]
-
-
-class GenericPromptTemplate:
-    def __init__(self, prompt_template: PromptTemplateTypes):
-        object.__setattr__(self, "prompt_template", prompt_template)
-
-    @property
-    def input_keys(self):
-        prompt_template = object.__getattribute__(self, "prompt_template")
-        if isinstance(prompt_template, LCPromptTemplate):
-            return prompt_template.input_variables
-        elif isinstance(prompt_template, LIPromptTemplate):
-            return prompt_template.template_vars
-        else:
-            raise TypeError(f"Unknown prompt template type {type(prompt_template)}")
-
-    def to_lc_prompt(self):
-        prompt_template = object.__getattribute__(self, "prompt_template")
-        if isinstance(prompt_template, LCPromptTemplate):
-            return prompt_template
-        elif isinstance(prompt_template, LIPromptTemplate):
-            return LCPromptTemplate.from_template(prompt_template.get_template())
-        else:
-            raise TypeError(f"Unknown prompt template type {type(prompt_template)}")
-
-    def to_li_prompt(self):
-        prompt_template = object.__getattribute__(self, "prompt_template")
-        if isinstance(prompt_template, LIPromptTemplate):
-            return prompt_template
-        elif isinstance(prompt_template, LCPromptTemplate):
-            return LIPromptTemplate(template=prompt_template.template)
-        else:
-            raise TypeError(f"Unknown prompt template type {type(prompt_template)}")
-
-    def __or__(self, other):
-        prompt_template = object.__getattribute__(self, "prompt_template")
-        if isinstance(prompt_template, LIPromptTemplate):
-            return self.to_lc_prompt() | other
-        else:
-            raise TypeError(f"Unknown prompt template type {type(other)}")
-
-    def __getattribute__(self, name: str) -> Any:
-        if name in {
-            "input_keys",
-            "to_lc_prompt",
-            "to_li_prompt",
-            "__or__",
-            "prompt_template",
-        }:
-            return object.__getattribute__(self, name)
-        prompt_template = object.__getattribute__(self, "prompt_template")
-        return getattr(prompt_template, name)

From c348b4204afebaac814d2430d8222cfde544f00a Mon Sep 17 00:00:00 2001
From: Cristhian Zanforlin Lousa
 <72977554+Cristhianzl@users.noreply.github.com>
Date: Wed, 28 Feb 2024 10:29:52 -0300
Subject: [PATCH 26/50] Change the color on str output, change X icon on
 Notification tab, change the icon on nodeToolbar (Code) (#1473)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* 🐛 fix(GenericNode): simplify return statement for build status classes to improve code readability
🔥 chore(GenericNode): remove console.log statement used for debugging
🔥 chore(alertDropDown): remove unused import
🔥 chore(nodeToolbarComponent): remove unused import
🔥 chore(styleUtils): remove unused color definition

* 🎨 style(styleUtils.ts): update color for 'str' key in nodeColors object to match the color used in other keys

* 🔥 refactor(GenericNode/index.tsx): remove unnecessary console.log statement for data.node?.template
---
 .../src/alerts/alertDropDown/index.tsx        |  7 ++---
 .../components/nodeToolbarComponent/index.tsx | 26 +++++++++----------
 src/frontend/src/utils/styleUtils.ts          |  2 +-
 3 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/src/frontend/src/alerts/alertDropDown/index.tsx b/src/frontend/src/alerts/alertDropDown/index.tsx
index cf3fa13dc..f1eab4c60 100644
--- a/src/frontend/src/alerts/alertDropDown/index.tsx
+++ b/src/frontend/src/alerts/alertDropDown/index.tsx
@@ -1,3 +1,4 @@
+import { Cross2Icon } from "@radix-ui/react-icons";
 import { useState } from "react";
 import IconComponent from "../../components/genericIconComponent";
 import {
@@ -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>
diff --git a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx
index a0136ba85..f7ee2eba4 100644
--- a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx
+++ b/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,
@@ -89,7 +89,9 @@ export default function NodeToolbarComponent({
   }, [showModalAdvanced]);
   const updateNodeInternals = useUpdateNodeInternals();
 
-  const setLastCopiedSelection = useFlowStore(state => state.setLastCopiedSelection);
+  const setLastCopiedSelection = useFlowStore(
+    (state) => state.setLastCopiedSelection
+  );
   useEffect(() => {
     setFlowComponent(createFlowComponent(cloneDeep(data), version));
   }, [
@@ -144,8 +146,8 @@ export default function NodeToolbarComponent({
         deleteNode(data.id);
         break;
       case "copy":
-        const node = nodes.filter(node => node.id === data.id)
-        setLastCopiedSelection({ nodes: _.cloneDeep(node), edges: [] })
+        const node = nodes.filter((node) => node.id === data.id);
+        setLastCopiedSelection({ nodes: _.cloneDeep(node), edges: [] });
     }
   };
 
@@ -233,7 +235,7 @@ 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>
           ) : (
@@ -371,13 +373,11 @@ export default function NodeToolbarComponent({
                     className="relative top-0.5 mr-2 h-4 w-4 "
                   />{" "}
                   <span className="">Copy</span>{" "}
-                  
-                    <IconComponent
-                      name="Command"
-                      className="absolute right-[1.15rem] top-[0.65em] h-3.5 w-3.5 stroke-2"
-                    ></IconComponent>
-                    <span className="absolute right-2 top-[0.5em]">C</span>
-  
+                  <IconComponent
+                    name="Command"
+                    className="absolute right-[1.15rem] top-[0.65em] h-3.5 w-3.5 stroke-2"
+                  ></IconComponent>
+                  <span className="absolute right-2 top-[0.5em]">C</span>
                 </div>
               </SelectItem>
               {hasStore && (
@@ -450,7 +450,7 @@ 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>
diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts
index 62e1c6ec7..4abcecdaf 100644
--- a/src/frontend/src/utils/styleUtils.ts
+++ b/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",

From 5c210f609e249cd8017cd24aa04bed8d8272f580 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 10:37:01 -0300
Subject: [PATCH 27/50] Refactor get_files to exclude folders inside the
 component path

---
 .../directory_reader/directory_reader.py      | 64 +++++++++++++------
 1 file changed, 45 insertions(+), 19 deletions(-)

diff --git a/src/backend/langflow/interface/custom/directory_reader/directory_reader.py b/src/backend/langflow/interface/custom/directory_reader/directory_reader.py
index 57bacc9bc..278d014c3 100644
--- a/src/backend/langflow/interface/custom/directory_reader/directory_reader.py
+++ b/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):
-            file_list.extend(
-                os.path.join(root, filename)
-                for filename in files
-                if filename.endswith(".py") and not filename.startswith("__")
-            )
+        safe_path_obj = Path(safe_path)
+        for file_path in safe_path_obj.rglob("*.py"):
+            # The other condtion is that it should be
+            # in the safe_path/[folder]/[file].py format
+            # 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

From 8aaa0ee91a10c427d22c5ed43798b003681b25de Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 10:37:13 -0300
Subject: [PATCH 28/50] Refactor directory_reader utils.py

---
 .../custom/directory_reader/utils.py          | 29 ++++++++++++++-----
 1 file changed, 21 insertions(+), 8 deletions(-)

diff --git a/src/backend/langflow/interface/custom/directory_reader/utils.py b/src/backend/langflow/interface/custom/directory_reader/utils.py
index f7378b8d0..34defe5c3 100644
--- a/src/backend/langflow/interface/custom/directory_reader/utils.py
+++ b/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}")
-    return menu_items
+            logger.exception(
+                f"Error while building custom component {component['output_types']}: {exc}"
+            )
     return menu_items

From f09ebeba340428bff12a97cb1d82dcfbdff85a64 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 11:03:55 -0300
Subject: [PATCH 29/50] Add support for input values in build_vertex API

---
 src/backend/langflow/api/v1/chat.py       |  8 ++++++--
 src/backend/langflow/api/v1/schemas.py    |  4 ++++
 src/backend/langflow/graph/vertex/base.py | 15 +++++++++++----
 3 files changed, 21 insertions(+), 6 deletions(-)

diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py
index 91b4bcd65..85a3a7abe 100644
--- a/src/backend/langflow/api/v1/chat.py
+++ b/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,
@@ -139,10 +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()
@@ -163,7 +167,7 @@ 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)
+                await vertex.build(user_id=current_user.id, inputs=inputs.model_dump())
 
             if vertex.result is not None:
                 params = vertex._built_object_repr()
diff --git a/src/backend/langflow/api/v1/schemas.py b/src/backend/langflow/api/v1/schemas.py
index 0092efa4e..c85f946da 100644
--- a/src/backend/langflow/api/v1/schemas.py
+++ b/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
diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py
index b917889a4..c5b993f27 100644
--- a/src/backend/langflow/graph/vertex/base.py
+++ b/src/backend/langflow/graph/vertex/base.py
@@ -2,13 +2,16 @@ import ast
 import inspect
 import types
 from enum import Enum
-from typing import (TYPE_CHECKING, Any, Callable, Coroutine, Dict, List,
-                    Optional)
+from typing import TYPE_CHECKING, Any, Callable, Coroutine, Dict, List, Optional
 
 from loguru import logger
 
-from langflow.graph.schema import (INPUT_COMPONENTS, OUTPUT_COMPONENTS,
-                                   InterfaceComponentTypes, ResultData)
+from langflow.graph.schema import (
+    INPUT_COMPONENTS,
+    OUTPUT_COMPONENTS,
+    InterfaceComponentTypes,
+    ResultData,
+)
 from langflow.graph.utils import UnbuiltObject, UnbuiltResult
 from langflow.graph.vertex.utils import generate_result
 from langflow.interface.initialize import loading
@@ -608,6 +611,7 @@ class Vertex:
     async def build(
         self,
         user_id=None,
+        inputs: Optional[Dict[str, Any]] = None,
         requester: Optional["Vertex"] = None,
         **kwargs,
     ) -> Any:
@@ -620,6 +624,9 @@ class Vertex:
             return self.get_requester_result(requester)
         self._reset()
 
+        if inputs and self.is_input:
+            self.update_raw_params(inputs)
+
         # Run steps
         for step in self.steps:
             if step not in self.steps_ran:

From 11bf5ee4604f3c42566b83ca7aa71d1388c23d32 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 11:07:16 -0300
Subject: [PATCH 30/50] Refactor build_vertex function to handle inputs
 dictionary correctly

---
 src/backend/langflow/api/v1/chat.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py
index 85a3a7abe..a4937789e 100644
--- a/src/backend/langflow/api/v1/chat.py
+++ b/src/backend/langflow/api/v1/chat.py
@@ -167,7 +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=inputs.model_dump())
+                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()

From 04bbf4eaf0b54ae05df578b2a256148890c33b68 Mon Sep 17 00:00:00 2001
From: Lucas Oliveira <lucas.edu.oli@hotmail.com>
Date: Wed, 28 Feb 2024 17:20:57 +0100
Subject: [PATCH 31/50] Made vertices be retrieved when opening chat or when
 changing anything. Added chat input in request.

---
 .../src/CustomNodes/GenericNode/index.tsx     |   2 +-
 .../src/components/IOInputField/index.tsx     |   8 +-
 .../src/components/IOOutputView/index.tsx     |   4 +-
 src/frontend/src/components/IOview/index.tsx  |  83 ++++++++-----
 .../chatComponent/buildTrigger/index.tsx      |   4 +-
 .../newChatView/chatInput/index.tsx           |  18 +--
 src/frontend/src/controllers/API/api.tsx      |   2 +-
 src/frontend/src/controllers/API/index.ts     |   5 +-
 src/frontend/src/stores/flowStore.ts          |  82 ++++++++-----
 src/frontend/src/stores/flowsManagerStore.ts  |   3 +-
 src/frontend/src/types/zustand/flow/index.ts  |   8 +-
 src/frontend/src/utils/buildUtils.ts          | 115 +++++++++++-------
 12 files changed, 203 insertions(+), 131 deletions(-)

diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx
index 5632f3c7a..a536ec6d6 100644
--- a/src/frontend/src/CustomNodes/GenericNode/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx
@@ -465,7 +465,7 @@ export default function GenericNode({
                   if (buildStatus === BuildStatus.BUILDING || isBuilding)
                     return;
                   setValidationStatus(null);
-                  buildFlow(data.id);
+                  buildFlow({nodeId: data.id});
                 }}
               >
                 <div>
diff --git a/src/frontend/src/components/IOInputField/index.tsx b/src/frontend/src/components/IOInputField/index.tsx
index e7aac5928..20c6dceb3 100644
--- a/src/frontend/src/components/IOInputField/index.tsx
+++ b/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);
               }
             }}
diff --git a/src/frontend/src/components/IOOutputView/index.tsx b/src/frontend/src/components/IOOutputView/index.tsx
index acf041356..00ef69a14 100644
--- a/src/frontend/src/components/IOOutputView/index.tsx
+++ b/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);
               }
             }}
diff --git a/src/frontend/src/components/IOview/index.tsx b/src/frontend/src/components/IOview/index.tsx
index 0e3f9e7e9..6f836fe41 100644
--- a/src/frontend/src/components/IOview/index.tsx
+++ b/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 +44,47 @@ export default function IOView({ children, open, setOpen }): JSX.Element {
     { type: string; id: string } | undefined
   >(undefined);
 
-  const { getNode, setNode, buildFlow, getFlow } = useFlowStore();
-  const { setErrorData } = useAlertStore();
+  const buildFlow = useFlowStore((state) => state.buildFlow);
   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);
+  const verticesBuild = useFlowStore((state) => state.verticesBuild);
+  const updateVerticesBuild = useFlowStore(
+    (state) => state.updateVerticesBuild
+  );
+
+  async function updateVertices() {
+    return new Promise<void>((resolve, reject) => {
+      if (!verticesBuild) {
+        updateVerticesOrder(currentFlow!.id, null).then((orderResponse) => {
+          resolve();
+        });
+      }
+      resolve();
+    });
+  }
+
+  useEffect(() => {
+    updateVertices();
+  }, [currentFlow]);
+
+  useEffect(() => {
+    if (open) {
+      updateVerticesBuild(null);
+      updateVertices();
+    }
+  }, [open]);
 
   async function sendMessage(count = 1): Promise<void> {
     if (isBuilding) return;
-    const { nodes, edges } = getFlow();
+    await updateVertices();
     setIsBuilding(true);
     setLockChat(true);
     setChatValue("");
-    const chatInputNode = nodes.find((node) => node.id === chatInput?.id);
-    if (chatInputNode) {
-      let newNode = cloneDeep(chatInputNode);
-      newNode.data.node!.template["input_value"].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 +128,7 @@ 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 +290,27 @@ export default function IOView({ children, open, setOpen }): JSX.Element {
                       {selectedViewField.type}
                     </div>
                     <div className="h-full">
-                    {inputs.some(
-                      (input) => input.id === selectedViewField.id
-                    ) ? (
-                      <IOInputField
-                        inputType={selectedViewField.type!}
-                        inputId={selectedViewField.id!}
-                      />
-                    ) : (
-                      <IOOutputView
-                        outputType={selectedViewField.type!}
-                        outputId={selectedViewField.id!}
-                      />
-                    )}
+                      {inputs.some(
+                        (input) => input.id === selectedViewField.id
+                      ) ? (
+                        <IOInputField
+                          inputType={selectedViewField.type!}
+                          inputId={selectedViewField.id!}
+                        />
+                      ) : (
+                        <IOOutputView
+                          outputType={selectedViewField.type!}
+                          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}
diff --git a/src/frontend/src/components/chatComponent/buildTrigger/index.tsx b/src/frontend/src/components/chatComponent/buildTrigger/index.tsx
index 2db2e0c3b..2014b2b47 100644
--- a/src/frontend/src/components/chatComponent/buildTrigger/index.tsx
+++ b/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 {
diff --git a/src/frontend/src/components/newChatView/chatInput/index.tsx b/src/frontend/src/components/newChatView/chatInput/index.tsx
index 8d60c2e8e..7c112bfd2 100644
--- a/src/frontend/src/components/newChatView/chatInput/index.tsx
+++ b/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"
diff --git a/src/frontend/src/controllers/API/api.tsx b/src/frontend/src/controllers/API/api.tsx
index 0942374e5..a7224dbc4 100644
--- a/src/frontend/src/controllers/API/api.tsx
+++ b/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);
     }
   }
diff --git a/src/frontend/src/controllers/API/index.ts b/src/frontend/src/controllers/API/index.ts
index 5df2ebfa5..6e7b456f8 100644
--- a/src/frontend/src/controllers/API/index.ts
+++ b/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}`, {inputs: {input_value: input_value}});
 }
 
 export async function downloadImage({ flowId, fileName }): Promise<any> {
diff --git a/src/frontend/src/stores/flowStore.ts b/src/frontend/src/stores/flowStore.ts
index f9a211e86..ba3bebf46 100644
--- a/src/frontend/src/stores/flowStore.ts
+++ b/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,
@@ -59,23 +67,25 @@ 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 {
-      let index = newFlowPool[nodeId].length-1;
-      if(buildId){
-        index = newFlowPool[nodeId].findIndex((flow)=>flow.id===buildId);
+    } else {
+      let index = newFlowPool[nodeId].length - 1;
+      if (buildId) {
+        index = newFlowPool[nodeId].findIndex((flow) => flow.id === buildId);
       }
       //check if the data is a flowpool object
-      if((data as FlowPoolObjectType).data?.artifacts!==undefined){
-        newFlowPool[nodeId][index] = (data as FlowPoolObjectType);
+      if ((data as FlowPoolObjectType).data?.artifacts !== undefined) {
+        newFlowPool[nodeId][index] = data as FlowPoolObjectType;
       }
       //update data artifact
-      else
-      {
+      else {
         newFlowPool[nodeId][index].data.artifacts = data;
       }
     }
@@ -394,7 +404,13 @@ 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;
@@ -417,25 +433,19 @@ 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: () => {
@@ -473,16 +483,22 @@ 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(
-        (vertex) => !vertices.includes(vertex)
-      ),
+      verticesBuild: {
+        ...verticesBuild,
+        verticesIds: get().verticesBuild!.verticesIds.filter(
+          (vertex) => !vertices.includes(vertex)
+        ),
+      },
     });
   },
   updateBuildStatus: (nodeIdList: string[], status: BuildStatus) => {
diff --git a/src/frontend/src/stores/flowsManagerStore.ts b/src/frontend/src/stores/flowsManagerStore.ts
index a65c686b4..49849ba54 100644
--- a/src/frontend/src/stores/flowsManagerStore.ts
+++ b/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 });
diff --git a/src/frontend/src/types/zustand/flow/index.ts b/src/frontend/src/types/zustand/flow/index.ts
index c506b034f..b4cd0cdfa 100644
--- a/src/frontend/src/types/zustand/flow/index.ts
+++ b/src/frontend/src/types/zustand/flow/index.ts
@@ -86,11 +86,11 @@ 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;
-  removeFromVerticesBuild: (vertices: string[]) => void;
-  verticesBuild: string[];
+  updateVerticesBuild: (vertices: {verticesIds: string[], verticesLayers: string[][], verticesOrder: string[][], runId: string} | null) => void;
+  removeFromVerticesBuild: (vertices: string[]) => void; 
+  verticesBuild:  {verticesIds: string[], verticesLayers: string[][], verticesOrder: string[][], runId: string} | null;
   updateBuildStatus: (nodeId: string[], status: BuildStatus) => void;
   revertBuiltStatusFromBuilding: () => void;
   flowBuildStatus: { [key: string]: BuildStatus };
diff --git a/src/frontend/src/utils/buildUtils.ts b/src/frontend/src/utils/buildUtils.ts
index 3acc3d972..8243a8e0d 100644
--- a/src/frontend/src/utils/buildUtils.ts
+++ b/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 +39,53 @@ function getInactiveVertexData(vertexId: string): VertexBuildTypeAPI {
   return inactiveVertexData;
 }
 
+export async function updateVerticesOrder(flowId: string, nodeId: string | null) {
+  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 });
+  });
+}
+
 export async function buildVertices({
   flowId,
+  input_value,
   nodeId = null,
   onGetOrderSuccess,
   onBuildUpdate,
@@ -44,24 +94,18 @@ export async function buildVertices({
   onBuildStart,
   validateNodes,
 }: BuildVerticesParams) {
-  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");
+  const verticesBuild = useFlowStore.getState().verticesBuild;
+  if (!verticesBuild) {
+    await updateVerticesOrder(flowId, nodeId);
   }
-  if (onGetOrderSuccess) onGetOrderSuccess();
-  let verticesOrder: Array<Array<string>> = orderResponse.data.ids;
-  const runId = orderResponse.data.run_id;
-  let vertices_layers: Array<Array<string>> = [];
+  const verticesIds = verticesBuild?.verticesIds!;
+  const verticesLayers = verticesBuild?.verticesLayers!;
+  const verticesOrder = verticesBuild?.verticesOrder!;
+  const runId = verticesBuild?.runId!;
   let stop = false;
+
+  if (onGetOrderSuccess) onGetOrderSuccess();
+    
   if (validateNodes) {
     try {
       validateNodes(verticesOrder.flatMap((id) => id));
@@ -69,48 +113,29 @@ 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 (
-        !useFlowStore.getState().verticesBuild.includes(id) &&
-        onBuildUpdate
-      ) {
+      if (!verticesIds.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 +162,7 @@ export async function buildVertices({
 async function buildVertex({
   flowId,
   id,
+  input_value,
   onBuildUpdate,
   onBuildError,
   verticesIds,
@@ -145,6 +171,7 @@ 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 +179,7 @@ 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) {

From 0c6d40d20790b5c09ec18678445fe07d24a825dd Mon Sep 17 00:00:00 2001
From: Lucas Oliveira <lucas.edu.oli@hotmail.com>
Date: Wed, 28 Feb 2024 17:30:19 +0100
Subject: [PATCH 32/50] Fix vertices being executed when sending message

---
 src/frontend/src/components/IOview/index.tsx | 21 ++------------------
 1 file changed, 2 insertions(+), 19 deletions(-)

diff --git a/src/frontend/src/components/IOview/index.tsx b/src/frontend/src/components/IOview/index.tsx
index 6f836fe41..8f914c716 100644
--- a/src/frontend/src/components/IOview/index.tsx
+++ b/src/frontend/src/components/IOview/index.tsx
@@ -50,36 +50,19 @@ export default function IOView({ children, open, setOpen }): JSX.Element {
   const [chatValue, setChatValue] = useState("");
   const isBuilding = useFlowStore((state) => state.isBuilding);
   const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
-  const verticesBuild = useFlowStore((state) => state.verticesBuild);
-  const updateVerticesBuild = useFlowStore(
-    (state) => state.updateVerticesBuild
-  );
 
   async function updateVertices() {
-    return new Promise<void>((resolve, reject) => {
-      if (!verticesBuild) {
-        updateVerticesOrder(currentFlow!.id, null).then((orderResponse) => {
-          resolve();
-        });
-      }
-      resolve();
-    });
+    return updateVerticesOrder(currentFlow!.id, null);
   }
 
-  useEffect(() => {
-    updateVertices();
-  }, [currentFlow]);
-
   useEffect(() => {
     if (open) {
-      updateVerticesBuild(null);
       updateVertices();
     }
-  }, [open]);
+  }, [open, currentFlow]);
 
   async function sendMessage(count = 1): Promise<void> {
     if (isBuilding) return;
-    await updateVertices();
     setIsBuilding(true);
     setLockChat(true);
     setChatValue("");

From 63a79b9106f157813afbcdd5539852b95b0bdfb4 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 13:40:08 -0300
Subject: [PATCH 33/50] Fix condition to update params in Vertex class

---
 src/backend/langflow/graph/vertex/base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py
index c5b993f27..2519002b9 100644
--- a/src/backend/langflow/graph/vertex/base.py
+++ b/src/backend/langflow/graph/vertex/base.py
@@ -457,7 +457,7 @@ 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):

From 533a1ef925f405de07e02c226399acae2b10fbdf Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 13:40:17 -0300
Subject: [PATCH 34/50] Fix error logging in build_vertex function

---
 src/backend/langflow/api/v1/chat.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py
index a4937789e..10b9d9e38 100644
--- a/src/backend/langflow/api/v1/chat.py
+++ b/src/backend/langflow/api/v1/chat.py
@@ -181,7 +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={})

From fdba9ebd9956b991e644fce45daba8951b7e966c Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 13:40:28 -0300
Subject: [PATCH 35/50] Update sender options in MessageHistoryComponent

---
 src/backend/langflow/components/io/MessageHistory.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/langflow/components/io/MessageHistory.py b/src/backend/langflow/components/io/MessageHistory.py
index 345de4ba0..e9c3f1a87 100644
--- a/src/backend/langflow/components/io/MessageHistory.py
+++ b/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,

From 9cada189432b1fce11ca19cc59e022709a1cb00c Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 13:41:04 -0300
Subject: [PATCH 36/50] Add optional typing for List in GatherRecordsComponent

---
 .../documentloaders/GatherRecords.py          | 61 +++++++++++++------
 1 file changed, 44 insertions(+), 17 deletions(-)

diff --git a/src/backend/langflow/components/documentloaders/GatherRecords.py b/src/backend/langflow/components/documentloaders/GatherRecords.py
index e25a2d189..745d0655b 100644
--- a/src/backend/langflow/components/documentloaders/GatherRecords.py
+++ b/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",
-                "value": False,
+                "display_name": "Load Hidden",
                 "advanced": True,
+                "info": "If true, hidden files will be loaded.",
             },
-            "max_concurrency": {
-                "display_name": "Max Concurrency",
-                "value": 10,
+            "recursive": {
+                "display_name": "Recursive",
                 "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

From 63ed8c324bcb7bfe55e4c79128edb77efa81de01 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 13:42:19 -0300
Subject: [PATCH 37/50] Fix handling of empty new_params in update_raw_params()
 method

---
 src/backend/langflow/graph/vertex/base.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py
index 2519002b9..fe2b29a02 100644
--- a/src/backend/langflow/graph/vertex/base.py
+++ b/src/backend/langflow/graph/vertex/base.py
@@ -392,6 +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)
@@ -624,7 +626,7 @@ class Vertex:
             return self.get_requester_result(requester)
         self._reset()
 
-        if inputs and self.is_input:
+        if self.is_input:
             self.update_raw_params(inputs)
 
         # Run steps

From 2670da5ba8a693e45d59b316b12883795dff3f84 Mon Sep 17 00:00:00 2001
From: Lucas Oliveira <lucas.edu.oli@hotmail.com>
Date: Wed, 28 Feb 2024 18:11:01 +0100
Subject: [PATCH 38/50] Fix build not working when building at node

---
 src/frontend/src/controllers/API/index.ts | 2 +-
 src/frontend/src/utils/buildUtils.ts      | 9 +++++----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/frontend/src/controllers/API/index.ts b/src/frontend/src/controllers/API/index.ts
index 6e7b456f8..bc7a0fa72 100644
--- a/src/frontend/src/controllers/API/index.ts
+++ b/src/frontend/src/controllers/API/index.ts
@@ -872,7 +872,7 @@ export async function postBuildVertex(
   vertexId: string,
   input_value: string,
 ): Promise<AxiosResponse<VertexBuildTypeAPI>> {
-  return await api.post(`${BASE_URL_API}build/${flowId}/vertices/${vertexId}`, {inputs: {input_value: input_value}});
+  return await api.post(`${BASE_URL_API}build/${flowId}/vertices/${vertexId}`, input_value ? {inputs: {input_value: input_value}} : undefined);
 }
 
 export async function downloadImage({ flowId, fileName }): Promise<any> {
diff --git a/src/frontend/src/utils/buildUtils.ts b/src/frontend/src/utils/buildUtils.ts
index 8243a8e0d..b50c90b23 100644
--- a/src/frontend/src/utils/buildUtils.ts
+++ b/src/frontend/src/utils/buildUtils.ts
@@ -39,7 +39,7 @@ function getInactiveVertexData(vertexId: string): VertexBuildTypeAPI {
   return inactiveVertexData;
 }
 
-export async function updateVerticesOrder(flowId: string, nodeId: string | null) {
+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;
@@ -80,6 +80,7 @@ export async function updateVerticesOrder(flowId: string, nodeId: string | null)
     useFlowStore
       .getState()
       .updateVerticesBuild({ verticesLayers, verticesIds, verticesOrder, runId });
+    resolve({ verticesLayers, verticesIds, verticesOrder, runId });
   });
 }
 
@@ -94,9 +95,9 @@ export async function buildVertices({
   onBuildStart,
   validateNodes,
 }: BuildVerticesParams) {
-  const verticesBuild = useFlowStore.getState().verticesBuild;
-  if (!verticesBuild) {
-    await updateVerticesOrder(flowId, nodeId);
+  let verticesBuild = useFlowStore.getState().verticesBuild;
+  if (!verticesBuild || nodeId) {
+    verticesBuild = await updateVerticesOrder(flowId, nodeId);
   }
   const verticesIds = verticesBuild?.verticesIds!;
   const verticesLayers = verticesBuild?.verticesLayers!;

From a7de13ab85f4485afccc07f50d98c742c79dcf58 Mon Sep 17 00:00:00 2001
From: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai>
Date: Wed, 28 Feb 2024 16:16:29 -0300
Subject: [PATCH 39/50] Remove max_overflow from create_engine

---
 src/backend/langflow/services/database/service.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/backend/langflow/services/database/service.py b/src/backend/langflow/services/database/service.py
index 693da8143..9765d858f 100644
--- a/src/backend/langflow/services/database/service.py
+++ b/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)

From 795068b4a4543fe6f73d7c7d4f0bc4fc06812974 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:32:52 -0300
Subject: [PATCH 40/50] Add OpenAI icon to ConversationalAgent and
 ChatOpenAIComponent

---
 .../langflow/components/agents/OpenAIConversationalAgent.py    | 3 ++-
 src/backend/langflow/components/model_specs/ChatOpenAISpecs.py | 1 +
 2 files changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
index 5dfb53387..5a411ecec 100644
--- a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
+++ b/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",
diff --git a/src/backend/langflow/components/model_specs/ChatOpenAISpecs.py b/src/backend/langflow/components/model_specs/ChatOpenAISpecs.py
index 2ad4f0309..0f20f0852 100644
--- a/src/backend/langflow/components/model_specs/ChatOpenAISpecs.py
+++ b/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 {

From b25d5b4a5b2b9c56a15d53f778b60a13994acaa7 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:38:08 -0300
Subject: [PATCH 41/50] Add Google icon to components

---
 .../langflow/components/model_specs/GoogleGenerativeAISpecs.py   | 1 +
 .../langflow/components/models/GoogleGenerativeAIModel.py        | 1 +
 src/frontend/src/utils/styleUtils.ts                             | 1 +
 3 files changed, 3 insertions(+)

diff --git a/src/backend/langflow/components/model_specs/GoogleGenerativeAISpecs.py b/src/backend/langflow/components/model_specs/GoogleGenerativeAISpecs.py
index eb1134583..9ff17e389 100644
--- a/src/backend/langflow/components/model_specs/GoogleGenerativeAISpecs.py
+++ b/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 {
diff --git a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
index 423a66df6..50a3f0f7a 100644
--- a/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
+++ b/src/backend/langflow/components/models/GoogleGenerativeAIModel.py
@@ -11,6 +11,7 @@ class GoogleGenerativeAIComponent(LCModelComponent):
     display_name: str = "Google Generative AIModel"
     description: str = "Generate text using Google Generative AI to generate text."
     icon = "GoogleGenerativeAI"
+    icon = "Google"
 
     def build_config(self):
         return {
diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts
index 4abcecdaf..0c0d54014 100644
--- a/src/frontend/src/utils/styleUtils.ts
+++ b/src/frontend/src/utils/styleUtils.ts
@@ -270,6 +270,7 @@ export const nodeIconsLucide: iconsType = {
   GoogleSearchAPIWrapper: GoogleIcon,
   GoogleSearchResults: GoogleIcon,
   GoogleSearchRun: GoogleIcon,
+  Google: GoogleIcon,
   HNLoader: HackerNewsIcon,
   HuggingFaceHub: HuggingFaceIcon,
   HuggingFaceEmbeddings: HuggingFaceIcon,

From 907300881bf14f327ee3abcca114da96394bdf74 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:41:17 -0300
Subject: [PATCH 42/50] Update Amazon icons in code

---
 .../langflow/components/model_specs/AmazonBedrockSpecs.py       | 2 ++
 src/backend/langflow/components/models/AmazonBedrockModel.py    | 2 +-
 src/backend/langflow/components/retrievers/AmazonKendra.py      | 1 +
 src/frontend/src/utils/styleUtils.ts                            | 1 +
 4 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/backend/langflow/components/model_specs/AmazonBedrockSpecs.py b/src/backend/langflow/components/model_specs/AmazonBedrockSpecs.py
index 0c1e3b159..3b79ff76c 100644
--- a/src/backend/langflow/components/model_specs/AmazonBedrockSpecs.py
+++ b/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 {
diff --git a/src/backend/langflow/components/models/AmazonBedrockModel.py b/src/backend/langflow/components/models/AmazonBedrockModel.py
index 4ae28e70c..fdddc07d1 100644
--- a/src/backend/langflow/components/models/AmazonBedrockModel.py
+++ b/src/backend/langflow/components/models/AmazonBedrockModel.py
@@ -9,7 +9,7 @@ from langflow.field_typing import Text
 class AmazonBedrockComponent(LCModelComponent):
     display_name: str = "Amazon Bedrock Model"
     description: str = "Generate text using LLM model from Amazon Bedrock."
-    icon = "AmazonBedrock"
+    icon = "Amazon"
 
     def build_config(self):
         return {
diff --git a/src/backend/langflow/components/retrievers/AmazonKendra.py b/src/backend/langflow/components/retrievers/AmazonKendra.py
index c0aedb1bc..886afeff5 100644
--- a/src/backend/langflow/components/retrievers/AmazonKendra.py
+++ b/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 {
diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts
index 0c0d54014..8cc5ce054 100644
--- a/src/frontend/src/utils/styleUtils.ts
+++ b/src/frontend/src/utils/styleUtils.ts
@@ -258,6 +258,7 @@ export const nodeIconsLucide: iconsType = {
   Chroma: ChromaIcon,
   AirbyteJSONLoader: AirbyteIcon,
   AmazonBedrockEmbeddings: AWSIcon,
+  Amazon: AWSIcon,
   Anthropic: AnthropicIcon,
   ChatAnthropic: AnthropicIcon,
   BingSearchAPIWrapper: BingIcon,

From 4fea0d4b13ba19cf82166f2940a1ee71eeab440d Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:44:43 -0300
Subject: [PATCH 43/50] Add icon to CohereComponent

---
 src/backend/langflow/components/model_specs/CohereSpecs.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/backend/langflow/components/model_specs/CohereSpecs.py b/src/backend/langflow/components/model_specs/CohereSpecs.py
index 3b74fc9b4..8cb0f3624 100644
--- a/src/backend/langflow/components/model_specs/CohereSpecs.py
+++ b/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 {

From 72a0eec57298ec1dd57c537419ca907f838a6a30 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:47:55 -0300
Subject: [PATCH 44/50] Add MongoDB icon to MongoDBAtlasComponent and
 styleUtils

---
 .../langflow/components/vectorstores/MongoDBAtlasVector.py      | 1 +
 src/frontend/src/utils/styleUtils.ts                            | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py b/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py
index de5533b0a..5d4537408 100644
--- a/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py
+++ b/src/backend/langflow/components/vectorstores/MongoDBAtlasVector.py
@@ -11,6 +11,7 @@ class MongoDBAtlasComponent(CustomComponent):
     description = (
         "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
     )
+    icon="MongoDB"
 
     def build_config(self):
         return {
diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts
index 8cc5ce054..c57530104 100644
--- a/src/frontend/src/utils/styleUtils.ts
+++ b/src/frontend/src/utils/styleUtils.ts
@@ -279,6 +279,8 @@ export const nodeIconsLucide: iconsType = {
   Meta: MetaIcon,
   Midjorney: MidjourneyIcon,
   MongoDBAtlasVectorSearch: MongoDBIcon,
+  MongoDB:MongoDBIcon,
+  MongoDBChatMessageHistory: MongoDBIcon,
   NotionDirectoryLoader: NotionIcon,
   ChatOpenAI: OpenAiIcon,
   AzureChatOpenAI: OpenAiIcon,

From ddaa9a2c8c773269100df79dbe7f58097ba99c65 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:50:54 -0300
Subject: [PATCH 45/50] Add icon to model specs classes

---
 .../langflow/components/model_specs/AnthropicLLMSpecs.py        | 1 +
 src/backend/langflow/components/model_specs/AnthropicSpecs.py   | 2 ++
 .../langflow/components/model_specs/ChatAnthropicSpecs.py       | 1 +
 3 files changed, 4 insertions(+)

diff --git a/src/backend/langflow/components/model_specs/AnthropicLLMSpecs.py b/src/backend/langflow/components/model_specs/AnthropicLLMSpecs.py
index 94c4ed8f5..121c5b2c2 100644
--- a/src/backend/langflow/components/model_specs/AnthropicLLMSpecs.py
+++ b/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 {
diff --git a/src/backend/langflow/components/model_specs/AnthropicSpecs.py b/src/backend/langflow/components/model_specs/AnthropicSpecs.py
index c1b776617..7a0f3e4db 100644
--- a/src/backend/langflow/components/model_specs/AnthropicSpecs.py
+++ b/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 {
diff --git a/src/backend/langflow/components/model_specs/ChatAnthropicSpecs.py b/src/backend/langflow/components/model_specs/ChatAnthropicSpecs.py
index c0e376d16..9534c7cf1 100644
--- a/src/backend/langflow/components/model_specs/ChatAnthropicSpecs.py
+++ b/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 {

From 0dc45c3c5080655c24ae4fa158e9a4f6f1ad7e3a Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:54:08 -0300
Subject: [PATCH 46/50] Add HuggingFace icon to components

---
 .../langflow/components/embeddings/HuggingFaceEmbeddings.py     | 1 +
 .../components/embeddings/HuggingFaceInferenceAPIEmbeddings.py  | 2 ++
 .../components/model_specs/HuggingFaceEndpointsSpecs.py         | 2 ++
 src/frontend/src/utils/styleUtils.ts                            | 1 +
 4 files changed, 6 insertions(+)

diff --git a/src/backend/langflow/components/embeddings/HuggingFaceEmbeddings.py b/src/backend/langflow/components/embeddings/HuggingFaceEmbeddings.py
index 6f3540358..4b25dffe7 100644
--- a/src/backend/langflow/components/embeddings/HuggingFaceEmbeddings.py
+++ b/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 {
diff --git a/src/backend/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py b/src/backend/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
index acc828d7f..edb83ed72 100644
--- a/src/backend/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
+++ b/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 {
diff --git a/src/backend/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py b/src/backend/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py
index 579e3c0b3..704de9029 100644
--- a/src/backend/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py
+++ b/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 {
diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts
index c57530104..c43081381 100644
--- a/src/frontend/src/utils/styleUtils.ts
+++ b/src/frontend/src/utils/styleUtils.ts
@@ -274,6 +274,7 @@ export const nodeIconsLucide: iconsType = {
   Google: GoogleIcon,
   HNLoader: HackerNewsIcon,
   HuggingFaceHub: HuggingFaceIcon,
+  HuggingFace: HuggingFaceIcon,
   HuggingFaceEmbeddings: HuggingFaceIcon,
   IFixitLoader: IFixIcon,
   Meta: MetaIcon,

From fa6ff4178836e60420e4efbc3f8962b4e9d98bf9 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:55:20 -0300
Subject: [PATCH 47/50] Add icon to QdrantComponent and QdrantSearchComponent

---
 src/backend/langflow/components/vectorstores/Qdrant.py       | 1 +
 src/backend/langflow/components/vectorstores/QdrantSearch.py | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/src/backend/langflow/components/vectorstores/Qdrant.py b/src/backend/langflow/components/vectorstores/Qdrant.py
index 6d68cdd55..3541f09e2 100644
--- a/src/backend/langflow/components/vectorstores/Qdrant.py
+++ b/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 {
diff --git a/src/backend/langflow/components/vectorstores/QdrantSearch.py b/src/backend/langflow/components/vectorstores/QdrantSearch.py
index 46e2766fd..742690a5d 100644
--- a/src/backend/langflow/components/vectorstores/QdrantSearch.py
+++ b/src/backend/langflow/components/vectorstores/QdrantSearch.py
@@ -9,6 +9,8 @@ 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 {

From 59db705a195fee11c44b2254a4608b4dbe4e401d Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 17:59:43 -0300
Subject: [PATCH 48/50] Add VertexAI icon to ChatVertexAI and VertexAI
 components

---
 .../langflow/components/model_specs/ChatVertexAISpecs.py        | 2 ++
 src/backend/langflow/components/model_specs/VertexAISpecs.py    | 1 +
 src/backend/langflow/components/models/VertexAiModel.py         | 2 ++
 3 files changed, 5 insertions(+)

diff --git a/src/backend/langflow/components/model_specs/ChatVertexAISpecs.py b/src/backend/langflow/components/model_specs/ChatVertexAISpecs.py
index 66235a1d8..8cc7b4256 100644
--- a/src/backend/langflow/components/model_specs/ChatVertexAISpecs.py
+++ b/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 {
diff --git a/src/backend/langflow/components/model_specs/VertexAISpecs.py b/src/backend/langflow/components/model_specs/VertexAISpecs.py
index 008451bc8..a6013e17d 100644
--- a/src/backend/langflow/components/model_specs/VertexAISpecs.py
+++ b/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 {
diff --git a/src/backend/langflow/components/models/VertexAiModel.py b/src/backend/langflow/components/models/VertexAiModel.py
index d7eab71ed..5a1950f39 100644
--- a/src/backend/langflow/components/models/VertexAiModel.py
+++ b/src/backend/langflow/components/models/VertexAiModel.py
@@ -9,6 +9,8 @@ from langflow.field_typing import Text
 class ChatVertexAIComponent(LCModelComponent):
     display_name = "ChatVertexAIModel"
     description = "Generate text using Vertex AI Chat large language models API."
+    icon="VertexAI"
+
 
     def build_config(self):
         return {

From 706e80aeedf602b3f91f3b63f9e846a3632f8b54 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 18:01:36 -0300
Subject: [PATCH 49/50] Add icons to Vectara and Weaviate components

---
 .../langflow/components/retrievers/VectaraSelfQueryRetriver.py  | 2 ++
 src/backend/langflow/components/vectorstores/Vectara.py         | 1 +
 src/backend/langflow/components/vectorstores/VectaraSearch.py   | 2 ++
 src/backend/langflow/components/vectorstores/WeaviateSearch.py  | 2 ++
 4 files changed, 7 insertions(+)

diff --git a/src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py b/src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py
index 26afd765c..aa4f412a8 100644
--- a/src/backend/langflow/components/retrievers/VectaraSelfQueryRetriver.py
+++ b/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},
diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py
index b5360ffd2..5ac105543 100644
--- a/src/backend/langflow/components/vectorstores/Vectara.py
+++ b/src/backend/langflow/components/vectorstores/Vectara.py
@@ -18,6 +18,7 @@ class VectaraComponent(CustomComponent):
         "https://python.langchain.com/docs/integrations/vectorstores/vectara"
     )
     beta = True
+    icon="Vectara"
     field_config = {
         "vectara_customer_id": {
             "display_name": "Vectara Customer ID",
diff --git a/src/backend/langflow/components/vectorstores/VectaraSearch.py b/src/backend/langflow/components/vectorstores/VectaraSearch.py
index c90148d1e..da2c083d1 100644
--- a/src/backend/langflow/components/vectorstores/VectaraSearch.py
+++ b/src/backend/langflow/components/vectorstores/VectaraSearch.py
@@ -14,6 +14,8 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
         "https://python.langchain.com/docs/integrations/vectorstores/vectara"
     )
     beta = True
+    icon="Vectara"
+
     field_config = {
         "search_type": {
             "display_name": "Search Type",
diff --git a/src/backend/langflow/components/vectorstores/WeaviateSearch.py b/src/backend/langflow/components/vectorstores/WeaviateSearch.py
index 2d7001074..3eda5c583 100644
--- a/src/backend/langflow/components/vectorstores/WeaviateSearch.py
+++ b/src/backend/langflow/components/vectorstores/WeaviateSearch.py
@@ -14,6 +14,8 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
         "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
     )
     beta = True
+    icon="Weaviate"
+
     field_config = {
         "search_type": {
             "display_name": "Search Type",

From f7fbf1465973208e11fa7fd4b59b42f11271d3c1 Mon Sep 17 00:00:00 2001
From: anovazzi1 <otavio2204@gmail.com>
Date: Wed, 28 Feb 2024 18:04:28 -0300
Subject: [PATCH 50/50] Add Supabase icon to search component

---
 .../components/vectorstores/SupabaseVectorStoreSearch.py         | 1 +
 src/frontend/src/utils/styleUtils.ts                             | 1 +
 2 files changed, 2 insertions(+)

diff --git a/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py b/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py
index 493632fe9..3a57f7951 100644
--- a/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py
+++ b/src/backend/langflow/components/vectorstores/SupabaseVectorStoreSearch.py
@@ -11,6 +11,7 @@ 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 {
diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts
index c43081381..2cd74d6d1 100644
--- a/src/frontend/src/utils/styleUtils.ts
+++ b/src/frontend/src/utils/styleUtils.ts
@@ -294,6 +294,7 @@ export const nodeIconsLucide: iconsType = {
   Searx: SearxIcon,
   SlackDirectoryLoader: SvgSlackIcon,
   SupabaseVectorStore: SupabaseIcon,
+  Supabase: SupabaseIcon,
   VertexAI: VertexAIIcon,
   ChatVertexAI: VertexAIIcon,
   VertexAIEmbeddings: VertexAIIcon,