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The assistant gets a bit to funny in it’s answers 😄  https://bitcrowd.atlassian.net/browse/INTERNAL-499
LLM frequently use Markdown to structure their output. sth like \ # lib/chatbot_web/live/chat_live.ex # (..) markdown_html = String.trim(assigns.content) |> Earmark.as_html!() |> Phoenix.HTML.raw() assigns = assigns |> assign(:class, "u-max-width-75 u-bg-white " <> justify_self) |> assign(:markdown, markdown_html) ~H""" <.ui_card id={@id} class={@Class}> <%= @markdown %> https://bitcrowd.atlassian.net/browse/INTERNAL-500
[#STEP-1-Generator](https://bitcrowd.dev/a-rag-library-for-elixir)
[#STEP-1-Generator](https://bitcrowd.dev/a-rag-library-for-elixir)
[#STEP-2-Custoisations](https://bitcrowd.dev/a-rag-library-for-elixir)
xhr15
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Mar 13, 2025
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BLOG POST EXAMPLE
see [A RAG Library for Elixir](https://bitcrowd.dev/a-rag-library-for-elixir#build-your-rag-system)
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See A RAG Library for Elixir
We want to turn
chatbot_ex
into a RAG system that answers question aboutecto
. Out of the box,chatbot_ex
knows nothing aboutecto
.In Step one, we ran the generator.
In Step two, we removed the LLM serving as we will use Ollama to generate responses.
In Step three, we set everything up to ingest
ecto
into our RAG system.In Step four, we integrated
rag
withlangchain
.In Step five, we hooked up
telemetry
to display the current activity of the RAG system to the user.This PR contains all the five steps and some additional fixes, so you're ready to play with it.