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gemma-3-270m-it-vismem-rag-parrot-2-vismem-rag-0314-1302-vismem-rag-0315-03201import requests
2from vismem_core import VisMem
3from sentence_transformers import SentenceTransformer
4from transformers import pipeline
5# 1. Load VisMem
6data = requests.get(
7 "https://huggingface.co/datasets/broadfield-dev/gemma-3-270m-it-vismem-rag-parrot-2-vismem-rag-0314-1302-vismem-kb-0315-0320/resolve/main/vismem.png",
8 headers={"Authorization":"Bearer <HF_TOKEN>"}).content
9mem = VisMem.from_png_bytes(data)
10emb = SentenceTransformer('all-MiniLM-L6-v2')
11# 2. RAG query
12q_vec = emb.encode([your_question])[0]
13results = mem.search(q_vec, k=3)
14context = "\n---\n".join(results)
15# 3. Prompt
16system = (
17 "You are a helpful AI Assistant with visual memory.\n"
18 "### RAG MEMORY (Vector Database):\n"
19 "[Uploaded Doc]: None\n"
20 f"[Knowledge Base]: {context}\n"
21 "### EPISODIC MEMORY (Past Chat):\n"
22 "[History]: None"
23)
24pipe = pipeline("text-generation", model="broadfield-dev/gemma-3-270m-it-vismem-rag-parrot-2-vismem-rag-0314-1302-vismem-rag-0315-0320")
25print(pipe([
26 {"role":"system","content":system},
27 {"role":"user","content":your_question}
28], max_new_tokens=200)){ "dataset_name": "openai/gsm8k", "rag_columns": [ "question", "answer" ], "question_col": "question", "answer_col": "answer", "split": "train", "data_config": "main", "total_kb_docs": 7473, "vismem_dim": 384, "vismem_width": 4800, "vismem_height": 4800, "kb_repo": "broadfield-dev/gemma-3-270m-it-vismem-rag-parrot-2-vismem-rag-0314-1302-vismem-kb-0315-0320" }