Does the langflow can use the workflow components to search chat message into vectorstore

[jack197906]

In my current application case I want store all the chat message into the vectorestore, and then search with a question word to retrieval all related chat message from the vectorestore, could you please help for this case?

[dosubot[bot]]

Hey @jack197906! I'm a bot here to help you with any questions or issues you have. Feel free to ask me anything while you wait for a human maintainer to assist you.

Yes, Langflow can utilize workflow components to store all chat messages into a vector store and then search for related chat messages using a query word. Here is how you can set this up in your application:

1. Chat Input Component: This component will capture the chat messages.
2. Store Message Component: This component will store the chat messages.
3. OpenAI Embeddings Component: This component will generate embeddings for the chat messages.
4. Vector Store Component: This component will store the embeddings.
5. Search Component: This component will search the vector store for related chat messages using a query word.

Here is a JSON configuration that demonstrates this setup:

{
  "data": {
    "edges": [
      {
        "data": {
          "sourceHandle": {
            "dataType": "ChatInput",
            "id": "ChatInput-c4xn9",
            "name": "message",
            "output_types": [
              "Message"
            ]
          },
          "targetHandle": {
            "fieldName": "message",
            "id": "StoreMessageComponent-1",
            "inputTypes": [
              "Message"
            ],
            "type": "str"
          }
        },
        "id": "edge-ChatInput-StoreMessage",
        "source": "ChatInput-c4xn9",
        "sourceHandle": "message",
        "target": "StoreMessageComponent-1",
        "targetHandle": "message"
      },
      {
        "data": {
          "sourceHandle": {
            "dataType": "StoreMessageComponent",
            "id": "StoreMessageComponent-1",
            "name": "message",
            "output_types": [
              "Message"
            ]
          },
          "targetHandle": {
            "fieldName": "input_value",
            "id": "OpenAIEmbeddings-BKXc6",
            "inputTypes": [
              "Message"
            ],
            "type": "str"
          }
        },
        "id": "edge-StoreMessage-OpenAIEmbeddings",
        "source": "StoreMessageComponent-1",
        "sourceHandle": "message",
        "target": "OpenAIEmbeddings-BKXc6",
        "targetHandle": "input_value"
      },
      {
        "data": {
          "sourceHandle": {
            "dataType": "OpenAIEmbeddings",
            "id": "OpenAIEmbeddings-BKXc6",
            "name": "embeddings",
            "output_types": [
              "Embeddings"
            ]
          },
          "targetHandle": {
            "fieldName": "embedding",
            "id": "VectorStore-1",
            "inputTypes": [
              "Embeddings"
            ],
            "type": "other"
          }
        },
        "id": "edge-OpenAIEmbeddings-VectorStore",
        "source": "OpenAIEmbeddings-BKXc6",
        "sourceHandle": "embeddings",
        "target": "VectorStore-1",
        "targetHandle": "embedding"
      },
      {
        "data": {
          "sourceHandle": {
            "dataType": "VectorStore",
            "id": "VectorStore-1",
            "name": "search_results",
            "output_types": [
              "Data"
            ]
          },
          "targetHandle": {
            "fieldName": "data",
            "id": "SearchComponent-1",
            "inputTypes": [
              "Data"
            ],
            "type": "other"
          }
        },
        "id": "edge-VectorStore-SearchComponent",
        "source": "VectorStore-1",
        "sourceHandle": "search_results",
        "target": "SearchComponent-1",
        "targetHandle": "data"
      }
    ],
    "nodes": [
      {
        "data": {
          "description": "Get chat inputs from the Playground.",
          "display_name": "Chat Input",
          "id": "ChatInput-c4xn9",
          "node": {
            "base_classes": [
              "Message"
            ],
            "description": "Get chat inputs from the Playground.",
            "display_name": "Chat Input",
            "icon": "ChatInput",
            "outputs": [
              {
                "display_name": "Message",
                "method": "message_response",
                "name": "message",
                "types": [
                  "Message"
                ]
              }
            ],
            "template": {
              "_type": "Component",
              "code": {
                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, MessageTextInput, Output\nfrom langflow.schema.message import Message\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MessageTextInput(name=\"input_value\", display_name=\"Text\", info=\"Message to be passed as input.\"),\n        DropdownInput(name=\"sender\", display_name=\"Sender Type\", options=[\"Machine\", \"User\"], value=\"User\", info=\"Type of sender.\"),\n        MessageTextInput(name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"User\"),\n        MessageTextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\"),\n        MessageTextInput(name=\"files\", display_name=\"Files\", info=\"Files to be sent with the message.\", is_list=True)\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\")\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        self.store_message(message)\n        self.message.value = message\n        return message\n"
              }
            }
          },
          "type": "ChatInput"
        },
        "position": {
          "x": 100,
          "y": 100
        }
      },
      {
        "data": {
          "id": "StoreMessageComponent-1",
          "node": {
            "base_classes": [
              "Message"
            ],
            "description": "Stores a chat message.",
            "display_name": "Store Message",
            "icon": "StoreMessage",
            "outputs": [
              {
                "display_name": "Message",
                "method": "build",
                "name": "message",
                "types": [
                  "Message"
                ]
              }
            ],
            "template": {
              "_type": "Component",
              "code": {
                "value": "from langflow.custom import CustomComponent\nfrom langflow.memory import get_messages, store_message\nfrom langflow.schema.message import Message\n\nclass StoreMessageComponent(CustomComponent):\n    display_name = \"Store Message\"\n    description = \"Stores a chat message.\"\n\n    def build_config(self):\n        return {\n            \"message\": {\"display_name\": \"Message\"},\n        }\n\n    def build(\n        self,\n        message: Message,\n    ) -> Message:\n        store_message(message, flow_id=self.graph.flow_id)\n        self.status = get_messages()\n\n        return message\n"
              }
            }
          }
        },
        "position": {
          "x": 300,
          "y": 100
        }
      },
      {
        "data": {
          "id": "OpenAIEmbeddings-BKXc6",
          "node": {
            "base_classes": [
              "Embeddings"
            ],
            "description": "Generate embeddings using OpenAI models.",
            "display_name": "OpenAI Embeddings",
            "icon": "OpenAI",
            "outputs": [
              {
                "display_name": "Embeddings",
                "method": "build_embeddings",
                "name": "embeddings",
                "types": [
                  "Embeddings"
                ]
              }
            ],
            "template": {
              "_type": "Component",
              "code": {
                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nclass OpenAIEmbeddingsComponent:\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n"
              }
            }
          }
        },
        "position": {
          "x": 500,
          "y": 100
        }
      },
      {
        "data": {
          "id": "VectorStore-1",
          "node": {
            "base_classes": [
              "VectorStore"
            ],
            "description": "Stores embeddings for search.",
            "display_name": "Vector Store",
            "icon": "VectorStore",
            "outputs": [
              {
                "display_name": "Search Results",
                "method": "search",
                "name": "search_results",
                "types": [
                  "Data"
                ]
              }
            ],
            "template": {
              "_type": "Component",
              "code": {
                "value": "from langflow.vector_store import VectorStore\n\nclass VectorStoreComponent(VectorStore):\n    display_name = \"Vector Store\"\n    description = \"Stores embeddings for search.\"\n    icon = \"VectorStore\"\n\n    def search(self, query: str) -> Data:\n        return self.vector_store.search(query)\n"
              }
            }
          }
        },
        "position": {
          "x": 700,
          "y": 100
        }
      },
      {
        "data": {
          "id": "SearchComponent-1",
          "node": {
            "base_classes": [
              "Search"
            ],
            "description": "Searches for related chat messages.",
            "display_name": "Search",
            "icon": "Search",
            "outputs": [
              {
                "display_name": "Search Results",
                "method": "search_results",
                "name": "search_results",
                "types": [
                  "Data"
                ]
              }
            ],
            "template": {
              "_type": "Component",
              "code": {
                "value": "from langflow.search import SearchComponent\n\nclass SearchComponent(SearchComponent):\n    display_name = \"Search\"\n    description = \"Searches for related chat messages.\"\n    icon = \"Search\"\n\n    def search_results(self, query: str) -> Data:\n        return self.search(query)\n"
              }
            }
          }
        },
        "position": {
          "x": 900,
          "y": 100
        }
      }
    ]
  }
}

This configuration captures chat messages, stores them, generates embeddings, stores the embeddings in a vector store, and allows for searching related messages using a query word ^{[[1]](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json)[[2]](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/langflow/initial_setup/starter_projects/Vector Store RAG.json)[3][4][5]}.

To continue talking to Dosu, mention @dosu.

[jack197906]

I can not access url, and when I import the json to localhost(v1.09), the workflow can not display normally, which version is the json?

[dosubot[bot]]

The VectorStore-RAG-Flows.json file is not found in the dev branch of the langflow repository, and the workflow is not displaying normally in Langflow v1.09. The JSON configuration appears to be compatible with Langflow v1.09, but exact compatibility cannot be fully confirmed without additional information about specific changes and updates in Langflow versions ^[1].

To resolve the issue, ensure that the VectorStore-RAG-Flows.json file is correctly placed in the expected directory and that there are no discrepancies in the file path or branch. Additionally, there are dependencies and additional configurations required for the VectorStore-RAG-Flows.json file to work correctly in Langflow v1.0.9, such as creating an Astra DB instance, obtaining a token and API endpoint, and defining a collection name ^[1].

If the JSON configuration fails to import or display correctly, specific error messages like UPLOAD_ERROR_ALERT, WRONG_FILE_ERROR_ALERT, UPLOAD_ALERT_LIST, and CONSOLE_ERROR_MSG may be generated ^[2].

To continue talking to Dosu, mention @dosu.