First try app packaging : librechat - issue with postgresql extention (pgvector)

[Valexico]

Hi @canadaduane
I think I did have a working app at the end but not 100% features. And then I didn't had time (and interest) to finish the setup
If somebody is interested I can share my past work but I guess litellm have change quite a bit since

[canadaduane]

I have a working setup, based on @Valexico 's work here:

github.com/canadaduane/librechat-cloudron
https://github.com/halecraft/librechat-cloudron

The package has been updated to the latest version of LibreChat 0.7.8, and I fixed a few minor wrinkles such as OIDC login, write access to the public dir, and postgres support. I haven't yet tested it with all of the providers (this requires access keys in the .env file).

[marcusquinn]

@canadaduane Amazing work, well done, and thanks @Valexico, too!

[girish]

@canadaduane great stuff! Is there any packaging issues to be aware of? Can you also attach some LICENSE to your work and we can then look into publishing it in the App Store.

[canadaduane]

Thanks @girish! I've added an MIT license. Also, I tested with a basic OPENROUTER_KEY and it seems to work well. I'm not aware of any issues at this time.

[canadaduane]

@Valexico is the MIT license ok with you also?

[Valexico]

@canadaduane
Yes no problem

[girish]

Thanks, cc @vladimir.d . We will take a look .

[firmansi]

It seems the package is not shipped with RAG API ?

[nebulon]

The package does have pgvector extension, that should work, however I am not sure I understand how the app needs to be configured for RAG API and the local vectordb. Does anyone have upstream docs for that? I can only find https://www.librechat.ai/docs/configuration/rag_api

[luckow]

more or less the same question here -> https://forum.cloudron.io/topic/14037/libcrechat-postgresql-pgvector

[firmansi]

@nebulon the documentation for RAG API can be found in https://github.com/danny-avila/rag_api

As seen that the pgvector can't be used if the RAG API not installed and running. This RAG API is somehow critical part, if it's not running then the librechat can't have capability that openwebui has for files upload, processing files to embedding model and so on

[nebulon]

I see, so this is actually another project which needs to be bundled together with librechat or as a separate app.

This one is using pgvector, not librechat itself!

(will also move the thread here to the librechat category)

[LoudLemur]

@Valexico hey there! Trying this is awesome and thank you so much for putting your ai skills towards packaging.

I came here to offer congratulations on having LibreChat packaged, but to hear you have stated an ai cloudron packaging initiative is just fantastic. I am very glad you are here and thanks for your efforts.

[Valexico]

This post is deleted!

[Valexico]

Hi @LoudLemur
Thanks

For those who want to dig "AI assisted packaging", here is how I proceeded

• I use Zed Editor which has a nice prompt library tool allowing prompt nesting (prompts in prompts. Sins Zed last "agentic" update, you need to use what they call "text thread" to have this feature/behaviour
• I have a main big prompt with all main specificities of packaging (Cloudron documentation mainly)
• I have smaller prompts about addon to be loaded only when needed
• And then I made specific prompts loading target app source files, and the same for example of packaged apps to guide the AI

I did this in 2024. This could be largely improved in 2025 with more agentic behaviour to let the AI fetch the context it needs

[jdaviescoates]

See also the prompt put together by @canadaduane here https://git.knownelement.com/KNEL/KNELProductionContainers/src/commit/9f74e0fc3977d368f1ca4846843607c75cd05b1c/Techops/CloudronPackagePrompt.md

[robi]

With an MCP server if you install context7.com it has the ability to ingest all the relevant docs for the agent task. https://github.com/upstash/context7

There are other nice combos of MCPs, perhaps for another thread. Start and share.

[LoudLemur]

@robi @girish there is an option to add cloudron's docs here, if they are on github

https://context7.com/add-library?tab=github

[Valexico]

I think building a RAG-like knowledge base is not the best approach for this use case.
It would be much mode efficient an agent with

• General knowledge about cloudron packaging
• Toolset (sub agent) to retrieve relevant files from repositories (dockerfile, docker-compose...)
• Toolset to retrieve additional context for each addon needed
• Toolset to retrieve relevant example (existing cloudron packaged apps)

The process is complex but quite straightforward and I see no need for a RAG knowledge base