Merge branch 'main' into bump-langflow-nightly
This commit is contained in:
Edwin Jose
GitHub
commit
d85db21adf
10 changed files with 310 additions and 122 deletions
25
docs/docs/_partial-wsl-install.mdx
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25
docs/docs/_partial-wsl-install.mdx
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@ -0,0 +1,25 @@
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1. [Install WSL](https://learn.microsoft.com/en-us/windows/wsl/install) with the Ubuntu distribution using WSL 2:
|
||||
|
||||
```powershell
|
||||
wsl --install -d Ubuntu
|
||||
```
|
||||
|
||||
For new installations, the `wsl --install` command uses WSL 2 and Ubuntu by default.
|
||||
|
||||
For existing WSL installations, you can [change the distribution](https://learn.microsoft.com/en-us/windows/wsl/install#change-the-default-linux-distribution-installed) and [check the WSL version](https://learn.microsoft.com/en-us/windows/wsl/install#upgrade-version-from-wsl-1-to-wsl-2).
|
||||
|
||||
:::warning Known limitation
|
||||
OpenRAG isn't compatible with nested virtualization, which can cause networking issues.
|
||||
Don't install OpenRAG on a WSL distribution that is installed inside a Windows VM.
|
||||
Instead, install OpenRAG on your base OS or a non-nested Linux VM.
|
||||
:::
|
||||
|
||||
2. [Start your WSL Ubuntu distribution](https://learn.microsoft.com/en-us/windows/wsl/install#ways-to-run-multiple-linux-distributions-with-wsl) if it doesn't start automatically.
|
||||
|
||||
3. [Set up a username and password for your WSL distribution](https://learn.microsoft.com/en-us/windows/wsl/setup/environment#set-up-your-linux-username-and-password).
|
||||
|
||||
4. [Install Docker Desktop for Windows with WSL 2](https://learn.microsoft.com/en-us/windows/wsl/tutorials/wsl-containers). When you reach the Docker Desktop **WSL integration** settings, make sure your Ubuntu distribution is enabled, and then click **Apply & Restart** to enable Docker support in WSL.
|
||||
|
||||
5. Install and run OpenRAG from within your WSL Ubuntu distribution.
|
||||
<br/>
|
||||
If you encounter issues with port forwarding or the Windows Firewall, you might need to adjust the [Hyper-V firewall settings](https://learn.microsoft.com/en-us/windows/security/operating-system-security/network-security/windows-firewall/hyper-v-firewall) to allow communication between your WSL distribution and the Windows host. For more troubleshooting advice for networking issues, see [Troubleshooting WSL common issues](https://learn.microsoft.com/en-us/windows/wsl/troubleshooting#common-issues).
|
||||
|
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@ -40,7 +40,7 @@ This flow contains eight components connected together to chat with your data:
|
|||
* The [**Agent** component](https://docs.langflow.org/agents) orchestrates the entire flow by deciding when to search the knowledge base, how to formulate search queries, and how to combine retrieved information with the user's question to generate a comprehensive response.
|
||||
The **Agent** behaves according to the prompt in the **Agent Instructions** field.
|
||||
* The [**Chat Input** component](https://docs.langflow.org/components-io) is connected to the Agent component's Input port. This allows to flow to be triggered by an incoming prompt from a user or application.
|
||||
* The [**OpenSearch** component](https://docs.langflow.org/bundles-elastic#opensearch) is connected to the Agent component's Tools port. The agent may not use this database for every request; the agent only uses this connection if it decides the knowledge can help respond to the prompt.
|
||||
* The [**OpenSearch** component](https://docs.langflow.org/bundles-elastic#opensearch) is connected to the Agent component's Tools port. The agent might not use this database for every request; the agent only uses this connection if it decides the knowledge can help respond to the prompt.
|
||||
* The [**Language Model** component](https://docs.langflow.org/components-models) is connected to the Agent component's Language Model port. The agent uses the connected LLM to reason through the request sent through Chat Input.
|
||||
* The [**Embedding Model** component](https://docs.langflow.org/components-embedding-models) is connected to the OpenSearch component's Embedding port. This component converts text queries into vector representations that are compared with document embeddings stored in OpenSearch for semantic similarity matching. This gives your Agent's queries context.
|
||||
* The [**Text Input** component](https://docs.langflow.org/components-io) is populated with the global variable `OPENRAG-QUERY-FILTER`.
|
||||
|
|
|
|||
|
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@ -27,7 +27,7 @@ To start or stop `docling serve` or any other native services, in the TUI main m
|
|||
**Embedding model** determines which AI model is used to create vector embeddings. The default is the OpenAI `text-embedding-3-small` model.
|
||||
|
||||
**Chunk size** determines how large each text chunk is in number of characters.
|
||||
Larger chunks yield more context per chunk, but may include irrelevant information. Smaller chunks yield more precise semantic search, but may lack context.
|
||||
Larger chunks yield more context per chunk, but can include irrelevant information. Smaller chunks yield more precise semantic search, but can lack context.
|
||||
The default value of `1000` characters provides a good starting point that balances these considerations.
|
||||
|
||||
**Chunk overlap** controls the number of characters that overlap over chunk boundaries.
|
||||
|
|
|
|||
|
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@ -83,7 +83,7 @@ The **Add Cloud Knowledge** page opens.
|
|||
Select the files or folders you want and click **Select**.
|
||||
You can select multiple files.
|
||||
3. When your files are selected, click **Ingest Files**.
|
||||
The ingestion process may take some time, depending on the size of your documents.
|
||||
The ingestion process can take some time depending on the size of your documents.
|
||||
4. When ingestion is complete, your documents are available in the Knowledge screen.
|
||||
|
||||
If ingestion fails, click **Status** to view the logged error.
|
||||
|
|
@ -151,7 +151,7 @@ The complete list of supported models is available at [`models_service.py` in th
|
|||
|
||||
You can use custom embedding models by specifying them in your configuration.
|
||||
|
||||
If you use an unknown embedding model, OpenRAG will automatically fall back to `1536` dimensions and log a warning. The system will continue to work, but search quality may be affected if the actual model dimensions differ from `1536`.
|
||||
If you use an unknown embedding model, OpenRAG automatically falls back to `1536` dimensions and logs a warning. The system continues to work, but search quality can be affected if the actual model dimensions differ from `1536`.
|
||||
|
||||
The default embedding dimension is `1536` and the default model is `text-embedding-3-small`.
|
||||
|
||||
|
|
|
|||
|
|
@ -6,8 +6,9 @@ slug: /docker
|
|||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
|
||||
import PartialWsl from '@site/docs/_partial-wsl-install.mdx';
|
||||
|
||||
OpenRAG has two Docker Compose files. Both files deploy the same applications and containers locally, but they are for different environments.
|
||||
OpenRAG has two Docker Compose files. Both files deploy the same applications and containers locally, but they are for different environments:
|
||||
|
||||
- [`docker-compose.yml`](https://github.com/langflow-ai/openrag/blob/main/docker-compose.yml) is an OpenRAG deployment with GPU support for accelerated AI processing. This Docker Compose file requires an NVIDIA GPU with [CUDA](https://docs.nvidia.com/cuda/) support.
|
||||
|
||||
|
|
@ -15,12 +16,36 @@ OpenRAG has two Docker Compose files. Both files deploy the same applications an
|
|||
|
||||
## Prerequisites
|
||||
|
||||
- Install [Python Version 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/)
|
||||
- Install [uv](https://docs.astral.sh/uv/getting-started/installation/)
|
||||
- Install [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/)
|
||||
- Install [Docker Compose](https://docs.docker.com/compose/install/). If using Podman, use [podman-compose](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or alias Docker compose commands to Podman commands.
|
||||
- Optional: Create an [OpenAI API key](https://platform.openai.com/api-keys). You can provide this key during [Application Onboarding](#application-onboarding) or choose a different model provider.
|
||||
- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment.
|
||||
- Install the following:
|
||||
|
||||
- [Python](https://www.python.org/downloads/release/python-3100/) version 3.10 to 3.13.
|
||||
- [uv](https://docs.astral.sh/uv/getting-started/installation/).
|
||||
- [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/).
|
||||
- [`podman-compose`](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or [Docker Compose](https://docs.docker.com/compose/install/). To use Docker Compose with Podman, you must alias Docker Compose commands to Podman commands.
|
||||
|
||||
- Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL).
|
||||
|
||||
<details>
|
||||
<summary>Install WSL for OpenRAG</summary>
|
||||
|
||||
<PartialWsl />
|
||||
|
||||
</details>
|
||||
|
||||
- Prepare model providers and credentials.
|
||||
|
||||
During [Application Onboarding](#application-onboarding), you must select language model and embedding model providers.
|
||||
If your chosen provider offers both types, you can use the same provider for both selections.
|
||||
If your provider offers only one type, such as Anthropic, you must select two providers.
|
||||
|
||||
Gather the credentials and connection details for your chosen model providers before starting onboarding:
|
||||
|
||||
- OpenAI: Create an [OpenAI API key](https://platform.openai.com/api-keys).
|
||||
- Anthropic language models: Create an [Anthropic API key](https://www.anthropic.com/docs/api/reference).
|
||||
- IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
|
||||
- Ollama: Use the [Ollama documentation](https://docs.ollama.com/) to set up your Ollama instance locally, in the cloud, or on a remote server, and then get your Ollama server's base URL.
|
||||
|
||||
- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. This is required to use the GPU-accelerated Docker Compose file. If you choose not to use GPU support, you must use the CPU-only Docker Compose file instead.
|
||||
|
||||
## Install OpenRAG with Docker Compose
|
||||
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@ slug: /install
|
|||
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
|
||||
import PartialOnboarding from '@site/docs/_partial-onboarding.mdx';
|
||||
import PartialWsl from '@site/docs/_partial-wsl-install.mdx';
|
||||
|
||||
[Install OpenRAG](#install) and then run the [OpenRAG Terminal User Interface(TUI)](#setup) to start your OpenRAG deployment with a guided setup process.
|
||||
|
||||
|
|
@ -21,19 +22,40 @@ If you prefer running Podman or Docker containers and manually editing `.env` fi
|
|||
|
||||
## Prerequisites
|
||||
|
||||
- Install [Python Version 3.10 to 3.13](https://www.python.org/downloads/release/python-3100/)
|
||||
- Install [uv](https://docs.astral.sh/uv/getting-started/installation/)
|
||||
- Install [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/)
|
||||
- Install [Docker Compose](https://docs.docker.com/compose/install/). If using Podman, use [podman-compose](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or alias Docker compose commands to Podman commands.
|
||||
- Optional: Create an [OpenAI API key](https://platform.openai.com/api-keys). During [Application Onboarding](#application-onboarding), you can provide this key or choose a different model provider.
|
||||
- All OpenRAG installations require [Python](https://www.python.org/downloads/release/python-3100/) version 3.10 to 3.13.
|
||||
|
||||
- If you aren't using the automatic installer script, install the following:
|
||||
|
||||
- [uv](https://docs.astral.sh/uv/getting-started/installation/).
|
||||
- [Podman](https://podman.io/docs/installation) (recommended) or [Docker](https://docs.docker.com/get-docker/).
|
||||
- [`podman-compose`](https://docs.podman.io/en/latest/markdown/podman-compose.1.html) or [Docker Compose](https://docs.docker.com/compose/install/). To use Docker Compose with Podman, you must alias Docker Compose commands to Podman commands.
|
||||
|
||||
- Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL).
|
||||
|
||||
<details>
|
||||
<summary>Install WSL for OpenRAG</summary>
|
||||
|
||||
<PartialWsl />
|
||||
|
||||
</details>
|
||||
|
||||
- Prepare model providers and credentials.
|
||||
|
||||
During [Application Onboarding](#application-onboarding), you must select language model and embedding model providers.
|
||||
If your chosen provider offers both types, you can use the same provider for both selections.
|
||||
If your provider offers only one type, such as Anthropic, you must select two providers.
|
||||
|
||||
Gather the credentials and connection details for your chosen model providers before starting onboarding:
|
||||
|
||||
- OpenAI: Create an [OpenAI API key](https://platform.openai.com/api-keys).
|
||||
- Anthropic language models: Create an [Anthropic API key](https://www.anthropic.com/docs/api/reference).
|
||||
- IBM watsonx.ai: Get your watsonx.ai API endpoint, IBM project ID, and IBM API key from your watsonx deployment.
|
||||
- Ollama: Use the [Ollama documentation](https://docs.ollama.com/) to set up your Ollama instance locally, in the cloud, or on a remote server, and then get your Ollama server's base URL.
|
||||
|
||||
- Optional: Install GPU support with an NVIDIA GPU, [CUDA](https://docs.nvidia.com/cuda/) support, and compatible NVIDIA drivers on the OpenRAG host machine. If you don't have GPU capabilities, OpenRAG provides an alternate CPU-only deployment.
|
||||
|
||||
## Install OpenRAG {#install}
|
||||
|
||||
:::note Windows users
|
||||
To use OpenRAG on Windows, use [WSL (Windows Subsystem for Linux)](https://learn.microsoft.com/en-us/windows/wsl/install).
|
||||
:::
|
||||
|
||||
Choose an installation method based on your needs:
|
||||
|
||||
* For new users, the automatic installer script detects and installs prerequisites and then runs OpenRAG.
|
||||
|
|
@ -154,6 +176,8 @@ Choose an installation method based on your needs:
|
|||
|
||||
Continue with [Set up OpenRAG with the TUI](#setup).
|
||||
|
||||
If you encounter errors during installation, see [Troubleshoot OpenRAG](/support/troubleshoot).
|
||||
|
||||
## Set up OpenRAG with the TUI {#setup}
|
||||
|
||||
The TUI creates a `.env` file in your OpenRAG directory root and starts OpenRAG.
|
||||
|
|
|
|||
|
|
@ -6,12 +6,29 @@ slug: /quickstart
|
|||
import Icon from "@site/src/components/icon/icon";
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import PartialWsl from '@site/docs/_partial-wsl-install.mdx';
|
||||
|
||||
Use this quickstart to install OpenRAG, and then try some of OpenRAG's core features.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
This quickstart requires an [OpenAI API key](https://platform.openai.com/api-keys) and [Python](https://www.python.org/downloads/release/python-3100/) version 3.10 to 3.13.
|
||||
This quickstart requires the following:
|
||||
|
||||
- An [OpenAI API key](https://platform.openai.com/api-keys).
|
||||
This quickstart uses OpenAI for simplicity.
|
||||
For other providers, see the complete [installation guide](/install).
|
||||
|
||||
- [Python](https://www.python.org/downloads/release/python-3100/) version 3.10 to 3.13.
|
||||
|
||||
- Microsoft Windows only: To run OpenRAG on Windows, you must use the Windows Subsystem for Linux (WSL).
|
||||
|
||||
<details>
|
||||
<summary>Install WSL for OpenRAG</summary>
|
||||
|
||||
<PartialWsl />
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## Install OpenRAG
|
||||
|
||||
|
|
|
|||
|
|
@ -9,9 +9,9 @@ import TabItem from '@theme/TabItem';
|
|||
|
||||
OpenRAG recognizes environment variables from the following sources:
|
||||
|
||||
* [Environment variables](#configure-environment-variables) - Values set in the `.env` file.
|
||||
* [Langflow runtime overrides](#langflow-runtime-overrides) - Langflow components may tweak environment variables at runtime.
|
||||
* [Default or fallback values](#default-values-and-fallbacks) - These values are default or fallback values if OpenRAG doesn't find a value.
|
||||
* [Environment variables](#configure-environment-variables): Values set in the `.env` file.
|
||||
* [Langflow runtime overrides](#langflow-runtime-overrides): Langflow components can set environment variables at runtime.
|
||||
* [Default or fallback values](#default-values-and-fallbacks): These values are default or fallback values if OpenRAG doesn't find a value.
|
||||
|
||||
## Configure environment variables
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
---
|
||||
title: Troubleshooting
|
||||
title: Troubleshoot OpenRAG
|
||||
slug: /support/troubleshoot
|
||||
---
|
||||
|
||||
|
|
@ -13,7 +13,7 @@ This page provides troubleshooting advice for issues you might encounter when us
|
|||
Check that `OPENSEARCH_PASSWORD` set in [Environment variables](/reference/configuration) meets requirements.
|
||||
The password must contain at least 8 characters, and must contain at least one uppercase letter, one lowercase letter, one digit, and one special character that is strong.
|
||||
|
||||
## OpenRAG fails to start from the TUI with "Operation not supported" error
|
||||
## OpenRAG fails to start from the TUI with operation not supported
|
||||
|
||||
This error occurs when starting OpenRAG with the TUI in [WSL (Windows Subsystem for Linux)](https://learn.microsoft.com/en-us/windows/wsl/install).
|
||||
|
||||
|
|
@ -21,27 +21,36 @@ The error occurs because OpenRAG is running within a WSL environment, so `webbro
|
|||
|
||||
To access the OpenRAG application, open a web browser and enter `http://localhost:3000` in the address bar.
|
||||
|
||||
## OpenRAG installation fails with unable to get local issuer certificate
|
||||
|
||||
If you are installing OpenRAG on macOS, and the installation fails with `unable to get local issuer certificate`, run the following command, and then retry the installation:
|
||||
|
||||
```bash
|
||||
open "/Applications/Python VERSION/Install Certificates.command"
|
||||
```
|
||||
|
||||
Replace `VERSION` with your installed Python version, such as `3.13`.
|
||||
|
||||
## Langflow connection issues
|
||||
|
||||
Verify the `LANGFLOW_SUPERUSER` credentials set in [Environment variables](/reference/configuration) are correct.
|
||||
|
||||
## Memory errors
|
||||
|
||||
### Container out of memory errors
|
||||
## Container out of memory errors
|
||||
|
||||
Increase Docker memory allocation or use [docker-compose-cpu.yml](https://github.com/langflow-ai/openrag/blob/main/docker-compose-cpu.yml) to deploy OpenRAG.
|
||||
|
||||
### Podman on macOS memory issues
|
||||
## Memory issue with Podman on macOS
|
||||
|
||||
If you're using Podman on macOS, you may need to increase VM memory on your Podman machine.
|
||||
If you're using Podman on macOS, you might need to increase VM memory on your Podman machine.
|
||||
This example increases the machine size to 8 GB of RAM, which should be sufficient to run OpenRAG.
|
||||
```bash
|
||||
podman machine stop
|
||||
podman machine rm
|
||||
podman machine init --memory 8192 # 8 GB example
|
||||
podman machine start
|
||||
```
|
||||
|
||||
|
||||
```bash
|
||||
podman machine stop
|
||||
podman machine rm
|
||||
podman machine init --memory 8192 # 8 GB example
|
||||
podman machine start
|
||||
```
|
||||
|
||||
## Port conflicts
|
||||
|
||||
Ensure ports 3000, 7860, 8000, 9200, 5601 are available.
|
||||
|
|
@ -52,101 +61,153 @@ If Docling ingestion fails with an OCR-related error and mentions `easyocr` is m
|
|||
|
||||
`easyocr` is already included as a dependency in OpenRAG's `pyproject.toml`. Project-managed installations using `uv sync` and `uv run` always sync dependencies directly from your `pyproject.toml`, so they should have `easyocr` installed.
|
||||
|
||||
If you're running OpenRAG with `uvx openrag`, `uvx` creates a cached, ephemeral environment that doesn't modify your project. This cache may become stale.
|
||||
If you're running OpenRAG with `uvx openrag`, `uvx` creates a cached, ephemeral environment that doesn't modify your project. This cache can become stale.
|
||||
|
||||
On macOS, this cache directory is typically a user cache directory such as `/Users/USER_NAME/.cache/uv`.
|
||||
|
||||
1. To clear the uv cache, run:
|
||||
```bash
|
||||
uv cache clean
|
||||
```
|
||||
|
||||
```bash
|
||||
uv cache clean
|
||||
```
|
||||
|
||||
2. Start OpenRAG:
|
||||
```bash
|
||||
uvx openrag
|
||||
```
|
||||
|
||||
```bash
|
||||
uvx openrag
|
||||
```
|
||||
|
||||
If you do not need OCR, you can disable OCR-based processing in your ingestion settings to avoid requiring `easyocr`.
|
||||
|
||||
## Langflow container already exists {#langflow-container-already-exists-during-upgrade}
|
||||
## Upgrade fails due to Langflow container already exists {#langflow-container-already-exists-during-upgrade}
|
||||
|
||||
If you encounter a `langflow container already exists` error when upgrading OpenRAG, this typically means you upgraded OpenRAG with `uv`, but didn't remove or upgrade containers from a previous installation.
|
||||
If you encounter a `langflow container already exists` error when upgrading OpenRAG, this typically means you upgraded OpenRAG with `uv`, but you didn't remove or upgrade containers from a previous installation.
|
||||
|
||||
1. Remove only the problematic Langflow container:
|
||||
To resolve this issue, do the following:
|
||||
|
||||
<Tabs groupId="Container software">
|
||||
<TabItem value="Podman" label="Podman">
|
||||
```bash
|
||||
# Stop the langflow container
|
||||
podman stop langflow
|
||||
|
||||
# Remove the langflow container
|
||||
podman rm langflow --force
|
||||
```
|
||||
</TabItem>
|
||||
<TabItem value="Docker" label="Docker" default>
|
||||
```bash
|
||||
# Stop the langflow container
|
||||
docker stop langflow
|
||||
|
||||
# Remove the langflow container
|
||||
docker rm langflow --force
|
||||
```
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
First, try removing only the Langflow container, and then retry the upgrade in the OpenRAG TUI by clicking **Status** and then **Upgrade**.
|
||||
|
||||
2. After removing the container, retry the upgrade in the OpenRAG TUI by clicking **Status** > **Upgrade**.
|
||||
<Tabs groupId="Container software">
|
||||
<TabItem value="Podman" label="Podman">
|
||||
|
||||
### Reinstall all containers
|
||||
1. Stop the Langflow container:
|
||||
|
||||
If reinstalling the Langflow container doesn't resolve the issue, or if you want a completely fresh installation, remove all OpenRAG containers and data, and then retry the upgrade.
|
||||
:::warning Data loss
|
||||
The complete reset removes all your data, including OpenSearch data, uploaded documents, and authentication. Your `.env` file is preserved, so your configuration settings remain intact.
|
||||
```bash
|
||||
podman stop langflow
|
||||
```
|
||||
|
||||
2. Remove the Langflow container:
|
||||
|
||||
```bash
|
||||
podman rm langflow --force
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="Docker" label="Docker" default>
|
||||
|
||||
1. Stop the Langflow container:
|
||||
|
||||
```bash
|
||||
docker stop langflow
|
||||
```
|
||||
|
||||
2. Remove the Langflow container:
|
||||
|
||||
```bash
|
||||
docker rm langflow --force
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
If reinstalling the Langflow container doesn't resolve the issue, you must reset to a fresh installation by removing all OpenRAG containers and data.
|
||||
Then, you can retry the upgrade.
|
||||
|
||||
:::warning
|
||||
This is a destructive operation that completely resets your OpenRAG containers and removes all OpenRAG data, including OpenSearch data, uploaded documents, and authentication details.
|
||||
Your `.env` file is preserved, so your configuration settings remain intact, but all other data is lost.
|
||||
:::
|
||||
|
||||
1. Stop your containers and completely remove them.
|
||||
To reset your installation, stop your containers, and then completely remove them.
|
||||
After removing the containers, retry the upgrade in the OpenRAG TUI by clicking **Status** and then **Upgrade**.
|
||||
|
||||
<Tabs groupId="Container software">
|
||||
<TabItem value="Podman" label="Podman">
|
||||
```bash
|
||||
# Stop all running containers
|
||||
podman stop --all
|
||||
|
||||
# Remove all containers (including stopped ones)
|
||||
podman rm --all --force
|
||||
|
||||
# Remove all images
|
||||
podman rmi --all --force
|
||||
|
||||
# Remove all volumes
|
||||
podman volume prune --force
|
||||
|
||||
# Remove all networks (except default)
|
||||
podman network prune --force
|
||||
|
||||
# Clean up any leftover data
|
||||
podman system prune --all --force --volumes
|
||||
```
|
||||
</TabItem>
|
||||
<TabItem value="Docker" label="Docker" default>
|
||||
```bash
|
||||
# Stop all running containers
|
||||
docker stop $(docker ps -q)
|
||||
|
||||
# Remove all containers (including stopped ones)
|
||||
docker rm --force $(docker ps -aq)
|
||||
|
||||
# Remove all images
|
||||
docker rmi --force $(docker images -q)
|
||||
|
||||
# Remove all volumes
|
||||
docker volume prune --force
|
||||
|
||||
# Remove all networks (except default)
|
||||
docker network prune --force
|
||||
|
||||
# Clean up any leftover data
|
||||
docker system prune --all --force --volumes
|
||||
```
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
<Tabs groupId="Container software">
|
||||
<TabItem value="Podman" label="Podman">
|
||||
|
||||
2. After removing the containers, retry the upgrade in the OpenRAG TUI by clicking **Status** > **Upgrade**.
|
||||
1. Stop all running containers:
|
||||
|
||||
```bash
|
||||
podman stop --all
|
||||
```
|
||||
|
||||
2. Remove all containers, including stopped containers:
|
||||
|
||||
```bash
|
||||
podman rm --all --force
|
||||
```
|
||||
|
||||
3. Remove all images:
|
||||
|
||||
```bash
|
||||
podman rmi --all --force
|
||||
```
|
||||
|
||||
4. Remove all volumes:
|
||||
|
||||
```bash
|
||||
podman volume prune --force
|
||||
```
|
||||
|
||||
5. Remove all networks except the default network:
|
||||
|
||||
```bash
|
||||
podman network prune --force
|
||||
```
|
||||
|
||||
6. Clean up any leftover data:
|
||||
|
||||
```bash
|
||||
podman system prune --all --force --volumes
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="Docker" label="Docker" default>
|
||||
|
||||
1. Stop all running containers:
|
||||
|
||||
```bash
|
||||
docker stop $(docker ps -q)
|
||||
```
|
||||
|
||||
2. Remove all containers, including stopped containers:
|
||||
|
||||
```bash
|
||||
docker rm --force $(docker ps -aq)
|
||||
```
|
||||
|
||||
3. Remove all images:
|
||||
|
||||
```bash
|
||||
docker rmi --force $(docker images -q)
|
||||
```
|
||||
|
||||
4. Remove all volumes:
|
||||
|
||||
```bash
|
||||
docker volume prune --force
|
||||
```
|
||||
|
||||
5. Remove all networks except the default network:
|
||||
|
||||
```bash
|
||||
docker network prune --force
|
||||
```
|
||||
|
||||
6. Clean up any leftover data:
|
||||
|
||||
```bash
|
||||
docker system prune --all --force --volumes
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
import { useQueryClient } from "@tanstack/react-query";
|
||||
import { AnimatePresence, motion } from "framer-motion";
|
||||
import { Info, X } from "lucide-react";
|
||||
import { X } from "lucide-react";
|
||||
import { useEffect, useState } from "react";
|
||||
import { toast } from "sonner";
|
||||
import {
|
||||
|
|
@ -74,6 +74,42 @@ const OnboardingCard = ({
|
|||
// Fetch current settings to check if providers are already configured
|
||||
const { data: currentSettings } = useGetSettingsQuery();
|
||||
|
||||
// Auto-select the first provider that has an API key set in env vars
|
||||
useEffect(() => {
|
||||
if (!currentSettings?.providers) return;
|
||||
|
||||
// Define provider order based on whether it's embedding or not
|
||||
const providerOrder = isEmbedding
|
||||
? ["openai", "watsonx", "ollama"]
|
||||
: ["anthropic", "openai", "watsonx", "ollama"];
|
||||
|
||||
// Find the first provider with an API key
|
||||
for (const provider of providerOrder) {
|
||||
if (
|
||||
provider === "anthropic" &&
|
||||
currentSettings.providers.anthropic?.has_api_key
|
||||
) {
|
||||
setModelProvider("anthropic");
|
||||
return;
|
||||
} else if (provider === "openai" && currentSettings.providers.openai?.has_api_key) {
|
||||
setModelProvider("openai");
|
||||
return;
|
||||
} else if (
|
||||
provider === "watsonx" &&
|
||||
currentSettings.providers.watsonx?.has_api_key
|
||||
) {
|
||||
setModelProvider("watsonx");
|
||||
return;
|
||||
} else if (
|
||||
provider === "ollama" &&
|
||||
currentSettings.providers.ollama?.endpoint
|
||||
) {
|
||||
setModelProvider("ollama");
|
||||
return;
|
||||
}
|
||||
}
|
||||
}, [currentSettings, isEmbedding]);
|
||||
|
||||
const handleSetModelProvider = (provider: string) => {
|
||||
setIsLoadingModels(false);
|
||||
setModelProvider(provider);
|
||||
|
|
@ -305,7 +341,7 @@ const OnboardingCard = ({
|
|||
</AnimatePresence>
|
||||
<div className={`w-full flex flex-col gap-6`}>
|
||||
<Tabs
|
||||
defaultValue={modelProvider}
|
||||
value={modelProvider}
|
||||
onValueChange={handleSetModelProvider}
|
||||
>
|
||||
<TabsList className="mb-4">
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue