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"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "venv (3.13.5)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|