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I'm currently exploring the capabilities of LangFlow and am impressed by what it can do. I have a question regarding its potential deployment for production use. Specifically, I'm curious whether it's feasible to deploy a fully functional LangFlow application using a Docker image, perhaps hosted on Google Cloud Run, for production purposes. I've already conducted a preliminary test and found that it functions as follows:
FROM python:3.9-slim
WORKDIR /app
# Install dependencies
RUN apt-get update && apt-get install -y \
build-essential \
cmake \
git \
&& rm -rf /var/lib/apt/lists/*
# Install LangFlow
RUN pip install --no-cache-dir langflow
# Copy local code to the container
COPY . .
# Set the port the app runs on
EXPOSE 8080
# Set environment variables
ENV LANGFLOW_HOST=0.0.0.0
ENV LANGFLOW_PORT=8080
ENV LANGFLOW_LOG_LEVEL=info
# Run the application
CMD ["langflow", "run"]
I've noticed a potential issue with this configuration: it temporarily stores flows in memory, which may not be ideal for production. I'm considering the use of cloud storage so that the flows can be shared more effectively. Additionally, I have concerns regarding the integration of LangFlow's API querying within a serverless environment for production. Could you provide some insights on whether LangFlow can be efficiently utilized in such an environment?
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Hello,
I'm currently exploring the capabilities of LangFlow and am impressed by what it can do. I have a question regarding its potential deployment for production use. Specifically, I'm curious whether it's feasible to deploy a fully functional LangFlow application using a Docker image, perhaps hosted on Google Cloud Run, for production purposes. I've already conducted a preliminary test and found that it functions as follows:
I've noticed a potential issue with this configuration: it temporarily stores flows in memory, which may not be ideal for production. I'm considering the use of cloud storage so that the flows can be shared more effectively. Additionally, I have concerns regarding the integration of LangFlow's API querying within a serverless environment for production. Could you provide some insights on whether LangFlow can be efficiently utilized in such an environment?
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