How to load session to print out messenges via python api

[LeeMoofon0222]

Hi,
I am currently using Langflow and would like to know how to load session messages via the Python API.
I have searched for similar discussions and tried the following code to load a session:

def load_session(session_id):
    url = f"http://127.0.0.1:7860/monitor/messages?session_id={session_id}&sender=User&sender_name=User&order_by=timestamp"
    
    response = requests.get(url)
    
    if response.status_code == 200:
        messages = response.json()
        for message in messages:
            print(f"Sender: {message['sender']}, Text: {message['message']}, Session ID: {message['session_id']}")
    else:
        print(f"Failed to retrieve messages: {response.status_code} - {response.text}")

However, when I print out the variable response: <Response [200]>. So obviously, The response is wrong and an error occurs

JSONDecodeError: Expecting value: line 1 column 1 (char 0)

[dosubot[bot]]

To correctly load and print out the session messages using the Python API for Langflow, you can use the retrieve_messages and retrieve_messages_as_text methods from the MemoryComponent class. Here is an example of how you can achieve this:

from langflow.components.helpers.Memory import MemoryComponent

# Create an instance of the MemoryComponent
memory_component = MemoryComponent()

# Set the necessary inputs
memory_component.sender = "Machine and User"
memory_component.sender_name = "User"
memory_component.session_id = "your_session_id"
memory_component.n_messages = 100
memory_component.order = "Ascending"
memory_component.template = "{sender_name}: {text}"

# Retrieve messages
messages = memory_component.retrieve_messages()

# Print out the messages
for message in messages:
    print(message)

# Alternatively, retrieve messages as text
messages_text = memory_component.retrieve_messages_as_text()

# Print out the messages as text
print(messages_text.text)

This code snippet demonstrates how to use the retrieve_messages method to get the session messages and print them out. Additionally, it shows how to use the retrieve_messages_as_text method to get the messages in text format and print them. Make sure to set the necessary inputs such as sender, sender_name, session_id, n_messages, order, and template before calling the methods ^{[[1]](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json)[2]}.

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[LeeMoofon0222]

@dosu
How to use the same way to write some string to session memory?

[dosubot[bot]]

To write a string to the session memory using the Langflow Python API, you can use a similar approach as shown in the ZepMessageWriterComponent class. Here is an example of how you can adapt this approach to write a string to the session memory:

from typing import Optional, TYPE_CHECKING
from langflow.base.memory.memory import BaseMemoryComponent
from langflow.field_typing import Text
from langflow.schema import Data

if TYPE_CHECKING:
    from zep_python.langchain import ZepChatMessageHistory

class SessionMemoryWriterComponent(BaseMemoryComponent):
    display_name = "Session Memory Writer"
    description = "Writes a string to the session memory."

    def build_config(self):
        return {
            "session_id": {
                "display_name": "Session ID",
                "info": "Session ID of the chat history.",
                "input_types": ["Text"],
            },
            "input_value": {
                "display_name": "Input Data",
                "info": "Data to write to session memory.",
            },
        }

    def add_message(
        self, sender: Text, sender_name: Text, text: Text, session_id: Text, metadata: dict | None = None, **kwargs
    ):
        """
        Adds a message to the session memory.

        Args:
            sender (Text): The type of the message sender. Valid values are "Machine" or "User".
            sender_name (Text): The name of the message sender.
            text (Text): The content of the message.
            session_id (Text): The session ID associated with the message.
            metadata (dict | None, optional): Additional metadata for the message. Defaults to None.
            **kwargs: Additional keyword arguments.

        Raises:
            ValueError: If the SessionMemory instance is not provided.
        """
        memory: ZepChatMessageHistory | None = kwargs.pop("memory", None)
        if memory is None:
            raise ValueError("SessionMemory instance is required.")
        if metadata is None:
            metadata = {}
        metadata["sender_name"] = sender_name
        metadata.update(kwargs)
        if sender == "Machine":
            memory.add_ai_message(text, metadata=metadata)
        elif sender == "User":
            memory.add_user_message(text, metadata=metadata)
        else:
            raise ValueError(f"Invalid sender type: {sender}")

    def build(
        self,
        input_value: Data,
        session_id: Text,
    ) -> Data:
        try:
            from zep_python.langchain import ZepChatMessageHistory
            from zep_python import ZepClient

            zep_client = ZepClient(api_url=None, api_key=None)
            memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
        except ImportError:
            raise ImportError(
                "Could not import zep-python package. Please install it with `pip install zep-python`."
            )

        self.add_message(**input_value.data, memory=memory)
        self.status = f"Added message to session memory for session {session_id}"
        return input_value

# Example usage
input_data = Data(data={
    "sender": "User",
    "sender_name": "User",
    "text": "Hello, this is a test message.",
    "session_id": "12345",
})

session_memory_writer = SessionMemoryWriterComponent()
session_memory_writer.build(input_value=input_data, session_id="12345")

In this example, SessionMemory is a placeholder for the actual session memory implementation you would use. The add_message method is adapted to add a message to the session memory, and the build method constructs the memory and adds the message ^[1].

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[LeeMoofon0222]

@dosu
Is there any other way? The code is too long and complex.

[dosubot[bot]]

To write a string to the session memory in a simpler way, you can use the add_message method of the ZepMessageWriterComponent class. Here is a more concise example:

from langflow.base.memory.memory import BaseMemoryComponent
from langflow.field_typing import Text

class ZepMessageWriterComponent(BaseMemoryComponent):
    display_name = "Zep Message Writer"
    description = "Writes a message to Zep."

    def add_message(
        self, sender: Text, sender_name: Text, text: Text, session_id: Text, metadata: dict | None = None, **kwargs
    ):
        from zep_python.langchain import ZepChatMessageHistory
        memory: ZepChatMessageHistory | None = kwargs.pop("memory", None)
        if memory is None:
            raise ValueError("ZepChatMessageHistory instance is required.")
        if metadata is None:
            metadata = {}
        metadata["sender_name"] = sender_name
        metadata.update(kwargs)
        if sender == "Machine":
            memory.add_ai_message(text, metadata=metadata)
        elif sender == "User":
            memory.add_user_message(text, metadata=metadata)
        else:
            raise ValueError(f"Invalid sender type: {sender}")

# Example usage
from zep_python import ZepClient
from zep_python.langchain import ZepChatMessageHistory

# Create a ZepClient instance
zep_client = ZepClient(api_url="your_zep_url", api_key="your_api_key")

# Create a ZepChatMessageHistory instance
memory = ZepChatMessageHistory(session_id="your_session_id", zep_client=zep_client)

# Define the message details
message = "Hello, this is a test message."
sender = "User"
sender_name = "John Doe"
session_id = "your_session_id"

# Create an instance of ZepMessageWriterComponent and add the message
zep_writer = ZepMessageWriterComponent()
zep_writer.add_message(
    sender=sender,
    sender_name=sender_name,
    text=message,
    session_id=session_id,
    memory=memory
)

This code snippet demonstrates how to use the add_message method to write a message to the session memory, including the sender, sender name, text, and session ID. Make sure to replace the placeholders with your actual data and provide a valid ZepChatMessageHistory instance ^[1].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

[dosubot[bot]]

To determine or configure the api_url and api_key for your ZepClient, you need to provide these values when calling the build method of the ZepMessageWriterComponent class. Here is how you can do it:

from langflow.schema import Data
from langflow.base.memory.memory import Text
from langflow.components.memories.ZepMessageWriter import ZepMessageWriterComponent

# Create an instance of ZepMessageWriterComponent
zep_writer = ZepMessageWriterComponent()

# Define the input data
input_value = Data(data={
    "sender": "User",
    "sender_name": "John Doe",
    "text": "Hello, this is a test message.",
    "session_id": "12345"
})

# Define the session ID, URL, and API key
session_id = "12345"
url = "https://your-zep-instance-url.com"
api_key = "your-zep-api-key"

# Call the build method with the required parameters
zep_writer.build(
    input_value=input_value,
    session_id=session_id,
    url=url,
    api_key=api_key
)

Replace "https://your-zep-instance-url.com" with the actual URL of your Zep instance and "your-zep-api-key" with your actual API key. The build method will configure the ZepClient with the provided api_url and api_key and then use it to add a message to the ZepChatMessageHistory memory ^[1][2].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu
ImportError: cannot import name 'ZepMessageWriterComponent' from 'langflow.base.memory.memory'

[dosubot[bot]]

To resolve the ImportError and correctly write a string to session messages using the Langflow Python API, you need to ensure that the zep-python package is installed. You can do this by running:

pip install zep-python

Once the package is installed, you can use the ZepMessageWriterComponent class to add a message to the session. Here is an example of how you can do this:

from zep_python import ZepClient
from zep_python.langchain import ZepChatMessageHistory

class ZepMessageWriterComponent:
    def add_message(
        self, sender, sender_name, text, session_id, memory, metadata=None
    ):
        if metadata is None:
            metadata = {}
        metadata["sender_name"] = sender_name
        if sender == "Machine":
            memory.add_ai_message(text, metadata=metadata)
        elif sender == "User":
            memory.add_user_message(text, metadata=metadata)
        else:
            raise ValueError(f"Invalid sender type: {sender}")

# Create a ZepClient instance
zep_client = ZepClient(api_url="your_zep_url", api_key="your_api_key")

# Create a ZepChatMessageHistory instance
memory = ZepChatMessageHistory(session_id="your_session_id", zep_client=zep_client)

# Define the message details
message = "Hello, this is a test message."
sender = "User"
sender_name = "John Doe"
session_id = "your_session_id"

# Create an instance of ZepMessageWriterComponent and add the message
zep_writer = ZepMessageWriterComponent()
zep_writer.add_message(
    sender=sender,
    sender_name=sender_name,
    text=message,
    session_id=session_id,
    memory=memory
)

This code snippet demonstrates how to use the add_message method to write a message to the session memory, including the sender, sender name, text, and session ID. Make sure to replace the placeholders with your actual data and provide a valid ZepChatMessageHistory instance ^[1][2].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu
APIError: Failed to connect to Zep server. Please check that: - the status of the service - your internet connection is working - the API URL is correct (not required for Zep Cloud) - No other process is using the same port (not required for Zep Cloud) : None

def post_explaintion(message, sessionId):

    # Create a ZepClient instance
    zep_client = ZepClient(api_url="http://localhost:8000", api_key="api_key")

    # Create a ZepChatMessageHistory instance
    memory = ZepChatMessageHistory(session_id=sessionId, zep_client=zep_client)

    # Create an instance of ZepMessageWriterComponent and add the message
    zep_writer = ZepMessageWriterComponent()
    zep_writer.add_message(
        sender="User",
        sender_name="Explaintion",
        text=message,
        session_id=sessionId,
        memory=memory
    )

[dosubot[bot]]

To resolve the APIError: Failed to connect to Zep server issue, follow these steps:

1. Verify URL and API Key: Ensure that the url and api_key parameters are correctly set. The url should point to the correct Zep server instance, and the api_key should be valid.

2. Install Required Package: Make sure the zep-python package is installed. You can install it using:

   pip install zep-python

3. Set API Base Path: Ensure the api_base_path is correctly set to avoid 404 errors. The code snippet already includes a monkey patch for this:

   import zep_python.zep_client
   from zep_python import ZepClient
   from zep_python.langchain import ZepChatMessageHistory
   
   zep_python.zep_client.API_BASE_PATH = "api/v1"

4. Check Network Connectivity: Ensure that your network allows connections to the Zep server URL.

Here is a sample code snippet to connect to the Zep server:

from zep_python import ZepClient
from zep_python.langchain import ZepChatMessageHistory

def connect_to_zep(session_id, url, api_key, api_base_path="api/v1"):
    try:
        import zep_python.zep_client
        zep_python.zep_client.API_BASE_PATH = api_base_path
    except ImportError:
        raise ImportError("Could not import zep-python package. Please install it with `pip install zep-python`.")
    
    if url == "":
        url = None

    zep_client = ZepClient(api_url=url, api_key=api_key)
    memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
    
    return memory

# Example usage
session_id = "your_session_id"
url = "http://localhost:8000"
api_key = "Mf39255846"
memory = connect_to_zep(session_id, url, api_key)

# Create an instance of ZepMessageWriterComponent and add the message
zep_writer = ZepMessageWriterComponent()
zep_writer.add_message(
    sender="User",
    sender_name="Explanation",
    text="Your message here",
    session_id=session_id,
    memory=memory
)

Ensure that the session_id, url, and api_key are correctly provided. This should help in resolving the connection issue to the Zep server ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu
I am currently using Langflow and would like to know how to load session messages via the Python API.
I have searched for similar discussions and tried the following code to load a session:

def load_session(session_id):
        from langflow.components.helpers.Memory import MemoryComponent
        import streamlit as st
        # Create an instance of the MemoryComponent
        memory_component = MemoryComponent()

        # Set the necessary inputs
        memory_component.sender = "Machine and User"
        memory_component.session_id = session_id
        memory_component.n_messages = 100
        memory_component.order = "Ascending"
        memory_component.template = "{sender_name}: {text}"


        st.session_state.messages = []

        # Retrieve messages
        messages = memory_component.retrieve_messages()

        for message in messages:
            # Determine the role based on the sender
            role = "user" if message.sender == "User" else "assistant"
            
            # Append the message to session state
            st.session_state.messages.append({
                "role": role,
                "content": message.text,
            })

However, when I print out the variable response:
ImportError: cannot import name 'Component' from partially initialized module 'langflow.custom' (most likely due to a circular import) (c:\Users\Moofon\桌面\Vizuro_Intern\causalAi_chatBot\causal_chatbot\Lib\site-packages\langflow\custom_init_.py)
And this error:
OperationalError: (sqlite3.OperationalError) no such table: message [SQL: SELECT message.timestamp, message.sender, message.sender_name, message.session_id, message.text, message.id, message.flow_id, message.files FROM message WHERE message.session_id = ? ORDER BY message.timestamp ASC LIMIT ? OFFSET ?] [parameters: ('sessionID', 100, 0)] (Background on this error at: https://sqlalche.me/e/20/e3q8).

So obviously, The response is wrong and an error occurs

[dosubot[bot]]

To resolve the issue of being unable to write messages to your SQLite database when running your application in a Docker container, you should ensure that the directory where the SQLite database is stored has the correct permissions. Here is a step-by-step approach to troubleshoot and resolve the issue:

1. Check Directory Permissions: Ensure that the directory /app/db has the appropriate write permissions for the user running the Docker container.

2. Modify Dockerfile: You may need to modify your Dockerfile to set the correct permissions. Add the following lines to your Dockerfile to ensure the directory has the correct permissions:

   FROM python:3.11.9-slim
   
   WORKDIR /app
   
   RUN apt-get update && apt-get install -y \
       build-essential \
       curl \
       software-properties-common \
       git \
       && rm -rf /var/lib/apt/lists/*
   
   COPY . .
   
   RUN pip install --upgrade pip
   RUN pip install -r requirements.txt
   
   # Ensure the directory has the correct permissions
   RUN mkdir -p /app/db && \
       chown -R root:root /app/db
   
   EXPOSE 8502
   
   HEALTHCHECK CMD curl --fail http://localhost:8502/_stcore/health
   
   ENTRYPOINT ["streamlit", "run", "Chatbot.py", "--server.port=8502", "--server.address=0.0.0.0"]

3. Verify Volume Mounting: Ensure that the volume is correctly mounted at the specified path /app/db in your docker-compose.yml file.

4. Database Initialization and Migration: Ensure that the database is properly initialized and migrations are applied. Here is a snippet to help with database initialization:

   import sqlite3
   
   def sql_create():
       conn = sqlite3.connect('/app/db/messages.db')
       cursor = conn.cursor()
       cursor.execute('''
       CREATE TABLE IF NOT EXISTS message (
           id INTEGER PRIMARY KEY AUTOINCREMENT,
           flow_id TEXT,
           sender TEXT,
           sender_name TEXT,
           session_id TEXT,
           text TEXT,
           files TEXT,
           timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
       )
       ''')
       conn.commit()
       conn.close()
   
   def main():
       # Initialize the database
       sql_create()
   
       # Your application logic here
       print("Application started")
   
   if __name__ == "__main__":
       main()

5. Ensure Correct Environment Variables: Ensure that the LANGFLOW_DATABASE_URL environment variable is correctly set in your Docker container. It should point to the correct path with the appropriate number of slashes for SQLite.

By ensuring the directory has the correct permissions, the environment variable is set correctly, the disk is properly mounted, and the database is initialized and migrated correctly, you should be able to write messages to your SQLite database when running your application in a Docker container ^[1][2].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu
Same error:

OperationalError: (sqlite3.OperationalError) no such table: message [SQL: SELECT message.timestamp, message.sender, message.sender_name, message.session_id, message.text, message.id, message.flow_id, message.files FROM message WHERE message.sender = ? AND message.sender_name = ? AND message.session_id = ? ORDER BY message.timestamp ASC LIMIT ? OFFSET ?] [parameters: ('User', 'User', 'moo-fon.lee@vizuro.com-242', 100, 0)] (Background on this error at: https://sqlalche.me/e/20/e3q8)

[dosubot[bot]]

To resolve the OperationalError indicating that the message table does not exist, you need to ensure that the database schema is properly initialized and that the necessary migrations are run. Here is how you can do it:

1. Initialize the Database and Run Migrations:
   Ensure that the database and tables are created and that the necessary migrations are applied. You can use the create_db_and_tables method from the DatabaseService class.

2. Check Database Schema Health:
   Use the check_schema_health method to verify that all required tables and columns exist.

Here is a step-by-step guide:

Step 1: Create Database and Tables

Call the create_db_and_tables method to create the necessary tables if they do not exist.

from langflow.services.database.service import DatabaseService
from langflow.services.settings.service import SettingsService

settings_service = SettingsService()
database_service = DatabaseService(settings_service=settings_service)
database_service.create_db_and_tables()

Step 2: Ensure Database is Writable

Make sure that the database file or directory has the correct permissions to be writable by the Docker container. This typically involves setting the correct file system permissions.

Example Code

Here is a complete example:

from langflow.services.database.service import DatabaseService
from langflow.services.settings.service import SettingsService

# Initialize the settings service
settings_service = SettingsService()

# Initialize the database service
database_service = DatabaseService(settings_service=settings_service)

# Ensure the database and tables exist
database_service.create_db_and_tables()

# Example of adding a message to the 'messages' table
message_data = {
    "flow_id": "example_flow_id",
    "sender_name": "example_sender",
    "sender": "example_sender",
    "session_id": "example_session_id",
    "text": "example_text",
    "files": None,
    "timestamp": database_service.get_timestamp()
}
database_service.add_message(message_data)

Make sure to replace the example data with your actual data.

Docker Configuration

Ensure your Dockerfile and docker-compose.yml are correctly set up to initialize the database and handle volume mounting.

Dockerfile

Ensure your Dockerfile includes the necessary steps to run the initialization code.

FROM python:3.11.9-slim

WORKDIR /app

RUN apt-get update && apt-get install -y \
    build-essential \
    curl \
    software-properties-common \
    git \
    && rm -rf /var/lib/apt/lists/*

COPY . .

RUN pip install --upgrade pip
RUN pip install -r requirements.txt

# Ensure the directory has the correct permissions
RUN mkdir -p /app/db && \
    chown -R root:root /app/db

EXPOSE 8502

HEALTHCHECK CMD curl --fail http://localhost:8502/_stcore/health

ENTRYPOINT ["streamlit", "run", "Chatbot.py", "--server.port=8502", "--server.address=0.0.0.0"]

docker-compose.yml

Ensure your docker-compose.yml file is correctly set up to initialize the database and handle volume mounting.

version: '3'

services:
  streamlit:
    build: .
    ports:
      - "8502:8502"
    depends_on:
      - langflow
      - ollama
    environment:
      - BASE_API_URL=http://langflow:7860
    volumes:
      - shared-data:/app/json_file_storage
      - shared-data:/app/db

  langflow:
    image: langflowai/langflow:latest
    user: root
    ports:
      - "7860:7860"
    environment:
      - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
      - LANGFLOW_CONFIG_DIR=/var/lib/langflow
    volumes:
      - langflow-data:/var/lib/langflow
      - shared-data:/app/json_file_storage
      - shared-data:/app/db
    depends_on:
      - postgres

  postgres:
    image: postgres:16
    environment:
      - POSTGRES_USER=langflow
      - POSTGRES_PASSWORD=langflow
      - POSTGRES_DB=langflow
    ports:
      - "5432:5432"
    volumes:
      - langflow-postgres:/var/lib/postgresql/data
      - shared-data:/app/db

  ollama:
    image: ollama/ollama
    ports:
      - "11434:11434"

volumes:
  langflow-data:
  langflow-postgres:
  shared-data:

networks:
  default:
    driver: bridge

By following these steps, you can ensure that the SQLite database is properly initialized and accessible within the Docker container, preventing the OperationalError related to the missing message table ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu
Is there any api_url like below code will helps me load my session messages:

def load_session(session_id):
    url = f"http://127.0.0.1:7860/monitor/messages?session_id={session_id}&sender=User&sender_name=User&order_by=timestamp"
    
    response = requests.get(url)
    
    if response.status_code == 200:
        messages = response.json()
        for message in messages:
            print(f"Sender: {message['sender']}, Text: {message['message']}, Session ID: {message['session_id']}")
    else:
        print(f"Failed to retrieve messages: {response.status_code} - {response.text}")

[dosubot[bot]]

To load session messages via the Langflow Python API using an api_url without encountering a JSONDecodeError, you can use the following approach. This method ensures that the API client is correctly configured and handles the responses properly:

import json
import requests

BASE_API_URL = "http://your-langflow-instance/api/v1/run"
FLOW_ID = "your_flow_id"
ENDPOINT = "your_endpoint_name"

def run_flow(message: str, endpoint: str, tweaks: dict = None, api_key: str = None) -> dict:
    """
    Run a flow with a given message and optional tweaks.
    :param message: The message to send to the flow
    :param endpoint: The ID or the endpoint name of the flow
    :param tweaks: Optional tweaks to customize the flow
    :param api_key: Optional API key for authentication
    :return: The JSON response from the flow
    """
    api_url = f"{BASE_API_URL}/{endpoint}"

    payload = {
        "input_value": message,
        "output_type": "chat",
        "input_type": "chat",
    }
    headers = None
    if tweaks:
        payload["tweaks"] = tweaks
    if api_key:
        headers = {"x-api-key": api_key}
    
    response = requests.post(api_url, json=payload, headers=headers)
    response.raise_for_status()  # Ensure we raise an error for bad responses
    return response.json()

def main():
    message = "your_message_here"
    tweaks = {"key": "value"}  # Customize as needed
    api_key = "your_api_key"  # Optional

    try:
        response = run_flow(message, ENDPOINT, tweaks, api_key)
        print(json.dumps(response, indent=2))
    except requests.exceptions.RequestException as e:
        print(f"Request failed: {e}")
    except json.JSONDecodeError as e:
        print(f"Failed to decode JSON response: {e}")

if __name__ == "__main__":
    main()

This code sets up a function run_flow to send a message to the Langflow instance and handle the response. It includes error handling to catch and report JSONDecodeError and other request-related exceptions. Make sure to replace "your_langflow_instance", "your_flow_id", "your_endpoint_name", "your_message_here", and other parameters with your actual values. This should help you avoid the JSONDecodeError by correctly configuring the API client and handling the responses ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu
I can't still load my session message. I running the code in docker and have use postgres service.

def load_session(session_id):
    

    # Create an instance of the MemoryComponent
    memory_component = MemoryComponent()

    # Set the necessary inputs
    memory_component.sender = "Machine and User"
    memory_component.session_id = session_id
    memory_component.n_messages = 100
    memory_component.order = "Ascending"
    memory_component.template = "{sender_name}: {text}"


    st.session_state.messages = []

    # Retrieve messages
    messages = memory_component.retrieve_messages()

    for message in messages:
        # Determine the role based on the sender
        role = "user" if message.sender == "User" else "assistant"
        
        # Append the message to session state
        st.session_state.messages.append({
            "role": role,
            "content": message.text,
        })
        
        
initialize_database()

streamlit-1 | 2024-08-13 01:38:09.075 | WARNING | langflow.services.database.utils:migrate_transactions_from_monitor_service_to_database:166 - Error retrieving transactions from monitor service: IO Error: Cannot open database "/root/.cache/langflow/monitor.duckdb" in read-only mode: database does not exist
streamlit-1 | 2024-08-13 01:38:09.076 | DEBUG | langflow.services.database.utils:initialize_database:129 - Database initialized

[dosubot[bot]]

To resolve the issue of not being able to load session messages in your Docker container, ensure that the database is properly initialized and accessible. Here are the steps you should follow:

1. Ensure Database Initialization: Make sure the database and required tables are created if they do not already exist. This can be done using the initialize_database function.

2. Check Database URL and Environment Variables: Ensure that the LANGFLOW_DATABASE_URL environment variable is correctly set in your Docker Compose file.

3. Run Migrations: Ensure that Alembic migrations are properly applied to keep the database schema up to date.

Step-by-Step Guide

Step 1: Ensure Database Initialization

Ensure that your initialize_database function is called during the application startup to create the database and tables if they do not exist.

from langflow.services.database.service import DatabaseService
from langflow.services.settings.service import SettingsService

def initialize_database():
    settings_service = SettingsService()
    database_service = DatabaseService(settings_service=settings_service)
    database_service.create_db_and_tables()
    database_service.run_migrations()

# Call the function to initialize the database
initialize_database()

Step 2: Check Database URL and Environment Variables

Ensure that the LANGFLOW_DATABASE_URL environment variable is correctly set in your Docker Compose file. Here is an example:

version: '3.8'

services:
  streamlit:
    build: .
    ports:
      - "8502:8502"
    depends_on:
      - langflow
      - postgres
    environment:
      - BASE_API_URL=http://langflow:7860
      - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
    volumes:
      - shared-data:/app/json_file_storage
      - shared-data:/app/db

  langflow:
    image: langflowai/langflow:latest
    user: root
    ports:
      - "7860:7860"
    environment:
      - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
      - LANGFLOW_CONFIG_DIR=/var/lib/langflow
    volumes:
      - langflow-data:/var/lib/langflow
      - shared-data:/app/json_file_storage
      - shared-data:/app/db
    depends_on:
      - postgres

  postgres:
    image: postgres:16
    environment:
      - POSTGRES_USER=langflow
      - POSTGRES_PASSWORD=langflow
      - POSTGRES_DB=langflow
    ports:
      - "5432:5432"
    volumes:
      - langflow-postgres:/var/lib/postgresql/data
      - shared-data:/app/db

volumes:
  langflow-data:
  langflow-postgres:
  shared-data:

Step 3: Run Migrations

Ensure that Alembic migrations are properly applied to keep the database schema up to date.

Example Code to Load Session Messages

Here is an example of how to load session messages using the MemoryComponent class:

from langflow.custom import Component
from langflow.helpers.data import data_to_text
from langflow.io import DropdownInput, IntInput, MultilineInput, Output, MessageTextInput
from langflow.memory import get_messages
from langflow.schema import Data
from langflow.schema.message import Message

class MemoryComponent(Component):
    display_name = "Chat Memory"
    description = "Retrieves stored chat messages."
    icon = "message-square-more"

    inputs = [
        DropdownInput(
            name="sender",
            display_name="Sender Type",
            options=["Machine", "User", "Machine and User"],
            value="Machine and User",
            info="Type of sender.",
            advanced=True,
        ),
        MessageTextInput(
            name="sender_name",
            display_name="Sender Name",
            info="Name of the sender.",
            advanced=True,
        ),
        IntInput(
            name="n_messages",
            display_name="Number of Messages",
            value=100,
            info="Number of messages to retrieve.",
            advanced=True,
        ),
        MessageTextInput(
            name="session_id",
            display_name="Session ID",
            info="Session ID of the chat history.",
            advanced=True,
        ),
        DropdownInput(
            name="order",
            display_name="Order",
            options=["Ascending", "Descending"],
            value="Ascending",
            info="Order of the messages.",
            advanced=True,
        ),
        MultilineInput(
            name="template",
            display_name="Template",
            info="The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
            value="{sender_name}: {text}",
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Chat History", name="messages", method="retrieve_messages"),
        Output(display_name="Messages (Text)", name="messages_text", method="retrieve_messages_as_text"),
    ]

    def retrieve_messages(self) -> Data:
        sender = self.sender
        sender_name = self.sender_name
        session_id = self.session_id
        n_messages = self.n_messages
        order = "DESC" if self.order == "Descending" else "ASC"

        if sender == "Machine and User":
            sender = None

        messages = get_messages(
            sender=sender,
            sender_name=sender_name,
            session_id=session_id,
            limit=n_messages,
            order=order,
        )
        self.status = messages
        return messages

    def retrieve_messages_as_text(self) -> Message:
        messages_text = data_to_text(self.template, self.retrieve_messages())
        self.status = messages_text
        return Message(text=messages_text)

# Example usage
memory = MemoryComponent()
messages = memory.retrieve_messages()
messages_text = memory.retrieve_messages_as_text()
print(messages_text.text)

By following these steps and ensuring your Docker Compose configuration is correct, you should be able to resolve the issue of the database not being properly initialized or accessed in your Docker container ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu

TypeError: SettingsService.__init__() missing 2 required positional arguments: 'settings' and 'auth_settings'
Traceback:
File "/usr/local/lib/python3.11/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 589, in _run_script
    exec(code, module.__dict__)
File "/app/Chatbot.py", line 406, in <module>
    initialize_database()
File "/app/Chatbot.py", line 393, in initialize_database
    settings_service = SettingsService()

[dosubot[bot]]

To resolve the TypeError indicating that the SettingsService.__init__() is missing required positional arguments (settings and auth_settings), you need to provide these arguments when initializing the SettingsService class. Here is how you can do it:

1. Instantiate the Settings and AuthSettings classes: These classes need to be instantiated and passed to the SettingsService constructor.

2. Update the initialize_database function: Ensure that the initialize_database function correctly initializes the SettingsService with the required arguments.

Example Code

Here is an updated version of your initialize_database function:

from langflow.services.database.service import DatabaseService
from langflow.services.settings.base import Settings
from langflow.services.settings.auth import AuthSettings
from langflow.services.settings.service import SettingsService

def initialize_database():
    # Instantiate the Settings and AuthSettings classes
    settings = Settings()
    auth_settings = AuthSettings(CONFIG_DIR=settings.CONFIG_DIR)

    # Initialize the SettingsService with the required arguments
    settings_service = SettingsService(settings=settings, auth_settings=auth_settings)

    # Initialize the DatabaseService
    database_service = DatabaseService(settings_service=settings_service)

    # Create the database and tables
    database_service.create_db_and_tables()

    # Run migrations
    database_service.run_migrations()

# Call the function to initialize the database
initialize_database()

Ensure Correct Environment Variables

Make sure that the environment variables required by the Settings and AuthSettings classes are correctly set in your Docker Compose file:

version: '3.8'

services:
  streamlit:
    build: .
    ports:
      - "8502:8502"
    depends_on:
      - langflow
      - postgres
    environment:
      - BASE_API_URL=http://langflow:7860
      - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
      - LANGFLOW_CONFIG_DIR=/var/lib/langflow
    volumes:
      - shared-data:/app/json_file_storage
      - shared-data:/app/db

  langflow:
    image: langflowai/langflow:latest
    user: root
    ports:
      - "7860:7860"
    environment:
      - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
      - LANGFLOW_CONFIG_DIR=/var/lib/langflow
    volumes:
      - langflow-data:/var/lib/langflow
      - shared-data:/app/json_file_storage
      - shared-data:/app/db
    depends_on:
      - postgres

  postgres:
    image: postgres:16
    environment:
      - POSTGRES_USER=langflow
      - POSTGRES_PASSWORD=langflow
      - POSTGRES_DB=langflow
    ports:
      - "5432:5432"
    volumes:
      - langflow-postgres:/var/lib/postgresql/data
      - shared-data:/app/db

volumes:
  langflow-data:
  langflow-postgres:
  shared-data:

By following these steps, you should be able to resolve the TypeError and ensure that the SettingsService is correctly initialized with the required arguments ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[LeeMoofon0222]

@dosu

AttributeError: 'Settings' object has no attribute 'CONFIG_DIR'

[dosubot[bot]]

To resolve the AttributeError indicating that the Settings object has no attribute CONFIG_DIR, you need to ensure that the Settings class has the config_dir attribute properly defined and initialized. Additionally, you need to ensure that the database_url is set correctly in the Settings object. Here is an example of how you can modify the Settings class and initialize both services:

Step 1: Modify the Settings Class

Ensure that the Settings class includes the config_dir and database_url attributes.

# src/backend/base/langflow/services/settings/base.py

class Settings:
    def __init__(self, config_dir=None, database_url=None):
        self.config_dir = config_dir or "/default/config/dir"  # Set a default value or fetch from environment
        self.database_url = database_url or "sqlite:///default.db"  # Set a default value or fetch from environment

Step 2: Initialize the SettingsService and DatabaseService

Ensure that the SettingsService and DatabaseService are correctly initialized with the required settings.

# src/backend/base/langflow/services/settings/service.py

from langflow.services.base import Service
from langflow.services.settings.auth import AuthSettings
from langflow.services.settings.base import Settings

class SettingsService(Service):
    name = "settings_service"

    def __init__(self, settings: Settings, auth_settings: AuthSettings):
        super().__init__()
        self.settings: Settings = settings
        self.auth_settings: AuthSettings = auth_settings

    @classmethod
    def initialize(cls) -> "SettingsService":
        settings = Settings()
        if not settings.config_dir:
            raise ValueError("CONFIG_DIR must be set in settings")

        auth_settings = AuthSettings(
            CONFIG_DIR=settings.config_dir,
        )
        return cls(settings, auth_settings)

    def set(self, key, value):
        setattr(self.settings, key, value)
        return self.settings

Step 3: Initialize the DatabaseService

Ensure that the DatabaseService is correctly initialized with the SettingsService.

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

from langflow.services.base import Service
from langflow.services.database.models.user.crud import get_user_by_username
from langflow.services.deps import get_settings_service
from loguru import logger
from sqlalchemy import create_engine, inspect
from sqlalchemy.exc import OperationalError
from sqlmodel import Session, SQLModel, select, text

class DatabaseService(Service):
    name = "database_service"

    def __init__(self, settings_service: "SettingsService"):
        self.settings_service = settings_service
        if settings_service.settings.database_url is None:
            raise ValueError("No database URL provided")
        self.database_url: str = settings_service.settings.database_url
        self.engine = self._create_engine()

    def _create_engine(self):
        settings_service = get_settings_service()
        if settings_service.settings.database_url and settings_service.settings.database_url.startswith("sqlite"):
            connect_args = {"check_same_thread": False}
        else:
            connect_args = {}
        try:
            return create_engine(
                self.database_url,
                connect_args=connect_args,
                max_overflow=-1
            )
        except sa.exc.NoSuchModuleError as exc:
            if "postgres" in str(exc) and not self.database_url.startswith("postgresql"):
                self.database_url = self.database_url.replace("postgres://", "postgresql://")
                logger.warning(
                    "Fixed postgres dialect in database URL. Replacing postgres:// with postgresql://. To avoid this warning, update the database URL."
                )
                return self._create_engine()
            raise RuntimeError("Error creating database engine") from exc

    def get_session(self):
        with Session(self.engine) as session:
            yield session

Step 4: Initialize the Services

Ensure that the services are initialized correctly.

# Initialize the services
settings_service = SettingsService.initialize()
database_service = DatabaseService(settings_service)

Step 5: Use the ServiceManager to Manage Services

Optionally, you can use the ServiceManager to manage the creation and initialization of these services.

# src/backend/base/langflow/services/manager.py

from langflow.services.manager import service_manager, initialize_settings_service, initialize_session_service

# Initialize the settings service
initialize_settings_service()

# Initialize the session service
initialize_session_service()

# Retrieve the settings service
settings_service = service_manager.get("settings_service")

# Retrieve the database service
database_service = service_manager.get("database_service")

By following these steps, you should be able to resolve the AttributeError and ensure that the SettingsService and DatabaseService are correctly initialized within the Docker container ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.