Doubts in my project

John6666
Wed, 27 Aug 2025 13:52:17 +0000

can we use LLM for recommendations or not.

We can use them for that purpose. However, LLMs of the Instruct model, trained as a chatbot, is unpredictable and not well-suited for highly structured output. Therefore, when incorporating it into RAG workflows, some coding will likely be necessary to compensate for this.

If we can format the input and output of LLMs and/or embedding models to some extent in Python, they can interact effectively with other components. While system prompt exerts some coercive power, you shouldn’t expect strictness with LLMs…

# pip install -U transformers json_repair
import json, torch
from json_repair import repair_json
from transformers import AutoTokenizer, AutoModelForCausalLM

device = "cuda" if torch.cuda.is_available() else "cpu"
MODEL = "Qwen/Qwen2.5-1.5B-Instruct"  # any chat model
tok = AutoTokenizer.from_pretrained(MODEL)
llm = AutoModelForCausalLM.from_pretrained(MODEL).to(device)

def _parse_json(s: str):
    a, b = s.find("{"), s.rfind("}")
    if a < 0 or b <= a:
        return None
    blob = s[a:b+1].strip()
    try:
        return json.loads(blob)
    except json.JSONDecodeError:
        try:
            return json.loads(repair_json(blob))
        except Exception:
            return None

def llm_pick(request, items, top_n=5):
    catalog = "\n".join(
        f"{i+1}. {it['id']} | {it['title']} | {it.get('color','-')} | {it.get('fabric','-')} | {it.get('price','-')}"
        for i, it in enumerate(items)
    )
    system = (
        'You are a fashion recommender. Choose ONLY items from CATALOG.\n'
        'Output strictly this JSON:\n'
        '{"chosen_ids":[string], "reasons":{"<id>":"one short reason using color/fabric/price or match to request"}}\n'
        'Use double quotes. No prose. No code fences.'
    )
    user = f"REQUEST: {request}\nN_TARGET: {top_n}\nCATALOG:\n{catalog}"

    msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
    inputs = tok.apply_chat_template(msgs, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(device)

    out = llm.generate(**inputs, max_new_tokens=512)
    text = tok.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0]

    obj = _parse_json(text)
    if obj is None: # to avoid crashing
        mentioned = [it["id"] for it in items if it["id"] in text][:top_n]
        obj = {"chosen_ids": mentioned, "reasons": {}}
    return obj

items = [
    {"id":"dress01","title":"Red floral midi dress","color":"red","fabric":"cotton","price":1499},
    {"id":"shirt01","title":"Beige linen short-sleeve shirt","color":"beige","fabric":"linen","price":1299},
    {"id":"sneaker01","title":"White low-top sneakers","color":"white","fabric":"leather","price":2499},
    {"id":"jeans01","title":"Blue straight-fit jeans","color":"blue","fabric":"denim","price":1999},
]
print(llm_pick("beach wedding outfit under 2000 rupees, breathable", items, top_n=2))
#{'chosen_ids': ['dress01'], 'reasons': {'dress01': 'Red floral midi dress is both affordable and breathable for a beach wedding.'}}
print(llm_pick("light summer outfit under 3000 rupees", items, top_n=2))
#{'chosen_ids': ['dress01', 'jeans01'], 'reasons': {'dress01': 'Light and airy, perfect for summer', 'jeans01': 'Comfortable and affordable, suitable for a casual look'}}