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Hi, 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) |
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To correctly load and print out the session messages using the Python API for Langflow, you can use the 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 |
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@dosu |
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To write a string to the session memory using the Langflow Python API, you can use a similar approach as shown in the 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, |
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@dosu |
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To write a string to the session memory in a simpler way, you can use the 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 |
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To determine or configure the 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 |
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@dosu |
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To resolve the pip install zep-pythonOnce the package is installed, you can use the 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 |
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@dosu |
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To resolve the
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 |
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@dosu However, when I print out the variable response: So obviously, The response is wrong and an error occurs |
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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:
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]. |
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@dosu |
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To resolve the
Here is a step-by-step guide: Step 1: Create Database and TablesCall the 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 WritableMake 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 CodeHere 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 ConfigurationEnsure your Dockerfile and docker-compose.yml are correctly set up to initialize the database and handle volume mounting. DockerfileEnsure 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.ymlEnsure your 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: bridgeBy following these steps, you can ensure that the SQLite database is properly initialized and accessible within the Docker container, preventing the |
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@dosu |
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To load session messages via the Langflow Python API using an 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 |
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@dosu 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 |
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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:
Step-by-Step GuideStep 1: Ensure Database InitializationEnsure that your 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 VariablesEnsure that the 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 MigrationsEnsure that Alembic migrations are properly applied to keep the database schema up to date. Example Code to Load Session MessagesHere is an example of how to load session messages using the 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]. |
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To resolve the
Example CodeHere is an updated version of your 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 VariablesMake sure that the environment variables required by the 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 |
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To resolve the Step 1: Modify the
|
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To correctly load and print out the session messages using the Python API for Langflow, you can use the
retrieve_messagesandretrieve_messages_as_textmethods from theMemoryComponentclass. Here is an example of how you can achieve this: