Python, Local AI and Rag concept

Its_Alive · January 13

1, Make sure PyRx is installed and pip install lmstudio

2, Download LM Studio

3, find a load a local model your computer can run, I used qwen3-coder-30b-a3b-instruct but it’s quite large, openai/gpt-oss-20b should run on a modern workstation.

4, Upload your documents, maybe some sort of job specification

5, load in a routine that can connect to LM studio. Check lmstudio docs for the details

For one computer, probably just use LM studio, but some small company could have a dedicated machine for inference. Best of all its all private

from pyrx import Ap, Db, Ed, Ge, Gi
import lmstudio as lms

@Ap.Command()
def yeehaw():
    try:
        ps , question  = Ed.Editor.getString(1,"Wuut? ")
        if ps != Ed.PromptStatus.eNormal:
            print("Oof: ")
            return
        model = lms.llm("qwen3-coder-30b-a3b-instruct")
        print(model.respond(question))
    except Exception as err:
        print(err)

Its_Alive · January 15

Another test, instead of using RAG, I fed the data directly into the context window. Not the most optimal way of creating a table. I guess the hard part is building up context from a bunch of lines

from pyrx import Ap, Db, Ed, Ge, Gi
import lmstudio as lms
from collections import defaultdict
import traceback

#scan for room blocks

@Ap.Command()
def yeehaw():
    try:
        blockinfo = defaultdict(list)
        db = Db.curDb()
        for bname, bid in db.getBlocks().items():

            if bname.casefold() != "elev".casefold():
                continue

            btr = Db.BlockTableRecord(bid)
            if not btr.hasAttributeDefinitions():
                continue

            refs = [Db.BlockReference(id) for id in btr.getBlockReferenceIds()]
            atts = [Db.AttributeReference(id) for ref in refs for id in ref.attributeIds()]
            for att in atts:
                blockinfo[bname].append(att.textString())

        question = "please create a table of rooms.{}".format(str(blockinfo))
        model = lms.llm("qwen3-coder-30b-a3b-instruct")
        resonse = model.respond(question)
        print(resonse.content)
    except Exception:
        print(traceback.format_exc())

Its_Alive · January 15

Okay, last test, would you live here? lol

from pyrx import Ap, Db, Ed, Ge, Gi
import lmstudio as lms
from collections import defaultdict
import json
import traceback

@Ap.Command()
def yeehaw():
    try:
        question = """Please generate a floor plan of a typical single-story house,
        please only give me the room parameters in the format of a list of 2d points, 
        I will then use these points to generate polylines. 
        the end format should be JSON, {roomname, list_of_points}"""
        
        model = lms.llm("qwen3-coder-30b-a3b-instruct")
        resonse = model.respond(question)
        js_anwser = resonse.content
        js_anwser = js_anwser.strip('```')
        js_anwser = js_anwser.strip('json')
        data_dict = json.loads(js_anwser)
        
        plines = []
        db = Db.curDb()
        for room, points in data_dict.items():
            pl = Db.Polyline(points)
            pl.setClosed(True)
            print("Creating room {}: ".format(room))
            plines.append(pl)
        
        db.addToModelspace(plines)
    except Exception:
        print(traceback.format_exc())

Owen Wengerd · January 15

Neat experiment. Is that a bay bathroom? Hallway to garage? You should have added room labels!

Its_Alive · January 15

Exactly, DOH! Should have added labels. I had already lost the context and the next generation would likely be different.

Like with coding, I can imagine drafting in collaboration with AI. Thoughts:

There needs to be some mechanism to manage the state & IO:
Conversation “why did you make that abnormal sized room? Can you make it bigger” or "I made the room bigger, what do you think?"
Data, models seem to do well with JSON, maybe something like { conversation : data }
Action, apply/collect data from/to the drawing.
RAG, upload design documentation to the model.
Context Window, there needs to be a way to edit/prune the context window for when the model starts to go wonky, or to explore chains of thought

Edit: AI said it would be happy with something like

{

  "conversation": [

     {"role": "user", "message": "Make the living room bigger"},

     {"role": "ai", "message": "I've increased it to 20x15 feet"}

],

"design_state": {

   ..data..

  }

}

Python, Local AI and Rag concept

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