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Add notebooks for multi-vector search course #92

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ebe4dde
Add installing-dependencies.ipynb with outputs
kacperlukawski Dec 19, 2025
5a22806
Add course notebooks
kacperlukawski Jan 22, 2026
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99 changes: 99 additions & 0 deletions course-multi-vector-search/module-0/installing-dependencies.ipynb
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{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "# Module 0: Installing Dependencies",
"id": "9af5bc4b49d17ebc"
},
{
"metadata": {
"jupyter": {
"is_executing": true
}
},
"cell_type": "code",
"outputs": [],
"execution_count": 1,
"source": "!pip install -q \"qdrant-client==1.16.2\" \"fastembed==0.7.4\"",
"id": "466eb04e8e447feb"
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8e66867cd6ebf200",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All dependencies installed successfully!\n"
]
}
],
"source": [
"from qdrant_client import QdrantClient\n",
"from fastembed import TextEmbedding\n",
"\n",
"print(\"All dependencies installed successfully!\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1ea6882564a624f5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Connected to Qdrant: collections=[]\n"
]
}
],
"source": [
"# For Qdrant Cloud\n",
"client = QdrantClient(\n",
" url=\"https://your-cluster-url.cloud.qdrant.io\",\n",
" api_key=\"your-api-key\"\n",
")\n",
"\n",
"# For local Qdrant\n",
"# client = QdrantClient(url=\"http://localhost:6333\")\n",
"\n",
"print(f\"Connected to Qdrant: {client.get_collections()}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9bdeae2-ae97-4110-92f4-531168f3b31c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
267 changes: 267 additions & 0 deletions course-multi-vector-search/module-1/late-interaction-basics.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "3ebe879db87a4432",
"metadata": {},
"source": [
"# Module 1: Late Interaction Basics"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "396986ed-a976-41a0-aad3-6de8f2406c5b",
"metadata": {},
"outputs": [],
"source": [
"documents = [\n",
" \"Qdrant is an AI-native vector database and a semantic search engine\",\n",
" \"Relational databases are not well-suited for search\",\n",
"]\n",
"query = \"What is Qdrant?\""
]
},
{
"cell_type": "markdown",
"id": "11591ce2-f88c-438d-9594-beb4d7cff0bb",
"metadata": {},
"source": [
"## Single-Vector Embeddings (No Interaction)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a54183c0-4a75-4981-a06c-3f1bc14b6b9b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(384,)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fastembed import TextEmbedding\n",
"\n",
"# Load the BAAI/bge-small-en-v1.5 model\n",
"dense_model = TextEmbedding(\"BAAI/bge-small-en-v1.5\")\n",
"# Pass the documents through the model. The .passage_embed \n",
"# method returns a generator we can iterate over and is \n",
"# supposed to be used for the documents only.\n",
"dense_generator = dense_model.passage_embed(documents)\n",
"# Running next on the generator yields one vector at\n",
"# the time, representing a single document.\n",
"dense_vector = next(dense_generator)\n",
"dense_vector.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "21f340ad-8ba3-4572-84ee-6171cdf5aa38",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(384,)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Generate a dense vector for the query as well, using \n",
"# the .query_embed method this time.\n",
"dense_query_vector = next(dense_model.query_embed(query))\n",
"dense_query_vector.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d7416f7b-c2d1-4642-b3b6-a5df3549a8b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.6554835"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"# Calculate the dot product between the query\n",
"# and the first document vector\n",
"np.dot(dense_query_vector, dense_vector)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bada239c-dc12-47d5-8923-407452cb7731",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.43436375"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calculate the dot product between the same query\n",
"# and the second document vectors\n",
"np.dot(dense_query_vector, next(dense_generator))"
]
},
{
"cell_type": "markdown",
"id": "811caba5-69d2-45bc-9552-5aeac7adf37b",
"metadata": {},
"source": [
"## Cross-Encoders (Early Interaction)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e995ba5b-ebd4-49e1-b06f-69f0cdd9052f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[10.133832931518555, -11.428644180297852]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fastembed.rerank.cross_encoder import TextCrossEncoder\n",
"\n",
"# Load the Xenova/ms-marco-MiniLM-L-6-v2 cross encoder model\n",
"cross_encoder = TextCrossEncoder(\"Xenova/ms-marco-MiniLM-L-6-v2\")\n",
"# Run .rerank method on the query and all the documents.\n",
"# It does not create any vector representations, but gives\n",
"# the score indicating the relevance of the document for\n",
"# the provided query.\n",
"score_generator = cross_encoder.rerank(query, documents)\n",
"list(score_generator)"
]
},
{
"cell_type": "markdown",
"id": "9c154200-a65c-4f47-bc97-e0d092d2623f",
"metadata": {},
"source": [
"## Late Interaction: The Core Paradigm\n",
"\n",
"### The ColBERT Approach"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "27c39129-3acb-4876-ba07-5cda22f00bcc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(17, 128)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fastembed import LateInteractionTextEmbedding\n",
"\n",
"# Load the colbert-ir/colbertv2.0 model\n",
"colbert_model = LateInteractionTextEmbedding(\"colbert-ir/colbertv2.0\")\n",
"# Run .passage_embed on all the documents and create\n",
"# a generator of the multi-vector representations\n",
"colbert_generator = colbert_model.passage_embed(documents)\n",
"colbert_vector = next(colbert_generator)\n",
"colbert_vector.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6f88562d-deef-4ce5-a448-0c854742521c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(32, 128)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create multi-vector representation for the query\n",
"colbert_query_vector = next(colbert_model.query_embed(query))\n",
"colbert_query_vector.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a768b8df-75a0-4c66-a5d9-5c7be934af0a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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