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Hi @LoudLemur
ThanksFor 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
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See also the prompt put together by @canadaduane here https://git.knownelement.com/KNEL/KNELProductionContainers/src/commit/9f74e0fc3977d368f1ca4846843607c75cd05b1c/Techops/CloudronPackagePrompt.md
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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.
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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.
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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
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We should split in 2 different thread
Yes Librechat needs RAG API to have all its featureNo a cloudron packaging agent does not need a RAG knowledge bases to be efficient (I think)
Those are 2 totally different subjects
@Valexico Sure, to make it more efficient,but somehow RAG knowledgebase deem necessary for business usage as RAG can enhance model to understand the business context of the company using tool like librechat, without RAG knowledgebase, the model will only have vast general knowledge
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I agree with you @firmansi
the problem is there are 2 topics discussed in this thread :- Topic #1: Packaging of librechat
- Topic #2: How to build an AI agent to automate the process of packaging Cloudron app
I think that lead to our misunderstanding
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@LoudLemur said in First try app packaging : librechat - issue with postgresql extention (pgvector):
@robi @girish there is an option to add cloudron's docs here, if they are on github
Great suggestion! Might need it's own post to make sure it doesn't get lost in this thread.
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@LoudLemur said in First try app packaging : librechat - issue with postgresql extention (pgvector):
@robi @girish there is an option to add cloudron's docs here, if they are on github
Great suggestion! Might need it's own post to make sure it doesn't get lost in this thread.
@marcusquinn Thanks! I have checked and context7 can also ingest from websites, which I shall try instead. That website is very busy ingesting and it keeps saying, "Try again in a few minutes..." Eventually it said "failed to find project name" from cloudron's:
https://docs.cloudron.io/so it is still not working and now it is rate limiting me. perhaps somebody else could try?
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We have Cloudron docs in context7, now.
Thanks to whomever sorted that!