{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfe61441",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install chromadb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bae84f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: mpmath, tqdm, threadpoolctl, sympy, shellingham, setuptools, safetensors, regex, pyyaml, numpy, networkx, mdurl, MarkupSafe, joblib, hf-xet, fsspec, filelock, click, annotated-doc, scipy, markdown-it-py, jinja2, torch, scikit-learn, rich, typer, huggingface-hub, tokenizers, transformers, sentence-transformers\n",
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      "\n",
      "Successfully installed MarkupSafe-3.0.3 annotated-doc-0.0.4 click-8.3.1 filelock-3.25.1 fsspec-2026.2.0 hf-xet-1.4.0 huggingface-hub-1.6.0 jinja2-3.1.6 joblib-1.5.3 markdown-it-py-4.0.0 mdurl-0.1.2 mpmath-1.3.0 networkx-3.6.1 numpy-2.4.3 pyyaml-6.0.3 regex-2026.2.28 rich-14.3.3 safetensors-0.7.0 scikit-learn-1.8.0 scipy-1.17.1 sentence-transformers-5.2.3 setuptools-82.0.1 shellingham-1.5.4 sympy-1.14.0 threadpoolctl-3.6.0 tokenizers-0.22.2 torch-2.10.0 tqdm-4.67.3 transformers-5.3.0 typer-0.24.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 25.1.1 -> 26.0.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install sentence-transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d297e272",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sndes\\venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
      "Loading weights: 100%|██████████| 103/103 [00:00<00:00, 7642.47it/s]\n",
      "\u001b[1mBertModel LOAD REPORT\u001b[0m from: sentence-transformers/all-MiniLM-L6-v2\n",
      "Key                     | Status     |  | \n",
      "------------------------+------------+--+-\n",
      "embeddings.position_ids | UNEXPECTED |  | \n",
      "\n",
      "\u001b[3mNotes:\n",
      "- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top results:\n",
      "- What is a for loop? (score: 0.590)\n",
      "- Iterating over a list in Python (score: 0.409)\n",
      "- How to use list comprehensions (score: 0.253)\n",
      "- Python file I/O tutorial (score: 0.208)\n",
      "- What is a Dockerfile? (score: 0.119)\n",
      "[[ 0.01545032  0.06672422 -0.10842565 ...  0.12807032  0.05996446\n",
      "  -0.00023202]\n",
      " [ 0.04272832  0.05857249 -0.06462938 ...  0.03630496  0.05146503\n",
      "   0.03034442]\n",
      " [-0.09018744  0.04470504 -0.04212139 ...  0.14796032  0.06810074\n",
      "  -0.05837488]\n",
      " ...\n",
      " [-0.06089142  0.07327832 -0.02317577 ...  0.09920333  0.10328437\n",
      "   0.08065025]\n",
      " [ 0.00752256  0.051953    0.04772322 ...  0.0143225   0.0937846\n",
      "   0.06545071]\n",
      " [ 0.01770419  0.08949164  0.05973819 ...  0.02115932 -0.07152769\n",
      "   0.04811272]]\n",
      "tensor([0.0810, 0.0512, 0.2078, 0.5901, 0.4086, 0.2528, 0.0207, 0.1187, 0.0763,\n",
      "        0.0594])\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "import numpy as np\n",
    "\n",
    "model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
    "sentences = [\n",
    "    \"How do I read a file in Python?\",\n",
    "    \"Open a file using the open() function\",\n",
    "    \"Python file I/O tutorial\",\n",
    "    \"What is a for loop?\",\n",
    "    \"Iterating over a list in Python\",\n",
    "    \"How to use list comprehensions\",\n",
    "    \"Docker container vs Docker image\",\n",
    "    \"What is a Dockerfile?\",\n",
    "    \"Sort a list in Python\",\n",
    "    \"The sky is blue and the sun is yellow\"\n",
    "]\n",
    "\n",
    "user_query = \"What is iteration?\"\n",
    "\n",
    "embeddings = model.encode(sentences)\n",
    "\n",
    "user_embedding = model.encode(user_query)\n",
    "user_similarities = model.similarity(user_embedding, embeddings)[0]\n",
    "\n",
    "top_k = min(5, len(user_similarities))\n",
    "top_results = np.argpartition(-user_similarities, range(top_k))[0:top_k]\n",
    "\n",
    "print(\"top results:\")\n",
    "for i in top_results:\n",
    "    print(f\"- {sentences[i]} (score: {user_similarities[i]:.3f})\")\n",
    "\n",
    "print(embeddings)\n",
    "print(user_similarities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4bc9140",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading weights: 100%|██████████| 105/105 [00:00<00:00, 4403.45it/s]\n",
      "\u001b[1mRobertaForSequenceClassification LOAD REPORT\u001b[0m from: cross-encoder/stsb-distilroberta-base\n",
      "Key                             | Status     |  | \n",
      "--------------------------------+------------+--+-\n",
      "roberta.embeddings.position_ids | UNEXPECTED |  | \n",
      "\n",
      "\u001b[3mNotes:\n",
      "- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers.cross_encoder import CrossEncoder\n",
    "\n",
    "model_cross = CrossEncoder(\"cross-encoder/stsb-distilroberta-base\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dccad4d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.423 - What is a for loop?\n",
      "0.358 - Iterating over a list in Python\n",
      "0.095 - What is a Dockerfile?\n",
      "0.049 - Sort a list in Python\n",
      "0.036 - Open a file using the open() function\n",
      "0.029 - How to use list comprehensions\n",
      "0.019 - Docker container vs Docker image\n",
      "0.012 - How do I read a file in Python?\n",
      "0.010 - Python file I/O tutorial\n",
      "0.009 - The sky is blue and the sun is yellow\n"
     ]
    }
   ],
   "source": [
    "ranks = model_cross.rank(user_query, sentences)\n",
    "for rank in ranks:\n",
    "    print(f\"{rank['score']:.3f} - {sentences[rank['corpus_id']]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bdad8616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. ['How do I read a file in Python?', 'Python file I/O tutorial', 'Open a file using the open() function']\n"
     ]
    }
   ],
   "source": [
    "import chromadb\n",
    "import hashlib\n",
    "\n",
    "client = chromadb.PersistentClient(path=\"chroma_db\")\n",
    "collection = client.get_or_create_collection(\"test_collection\", metadata={\"hnsw:space\": \"cosine\"})\n",
    "\n",
    "ids = [hashlib.sha256(sentence.encode()).hexdigest() for sentence in sentences]\n",
    "\n",
    "collection.upsert(\n",
    "    ids=ids,\n",
    "    documents=sentences,\n",
    "    # metadatas=metadatas,\n",
    "    embeddings=embeddings,\n",
    ")\n",
    "\n",
    "test_query = \"reading and writing files in Python\"\n",
    "test_embedding = model.encode(test_query)\n",
    "\n",
    "results = collection.query(\n",
    "    query_embeddings=test_embedding,\n",
    "    n_results=3,\n",
    ")\n",
    "\n",
    "for i, result in enumerate(results[\"documents\"]):\n",
    "    print(f\"{i+1}. {result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5739f61e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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