File size: 5,039 Bytes
2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 4779f10 2af0eb7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of document chunks: 0\n",
"Number of githb chunks: 0\n",
"\n",
"Sample search result(n=2): \n"
]
}
],
"source": [
"from shared import getQdrantClient, getEmbeddingsModel\n",
"qClient = getQdrantClient()\n",
"\n",
"# Show everything in the Document collection\n",
"numDocumentChunks = 0\n",
"# Note with_vectors defaults to false, so the vectors are not returned\n",
"chunks = qClient.scroll(collection_name='Document', limit=100)\n",
"while True:\n",
" for chunk in chunks[0]:\n",
" if numDocumentChunks == 0:\n",
" sampleDocumentChunk = chunk\n",
" numDocumentChunks += 1\n",
" chunks = qClient.scroll(collection_name='Document', limit=100, with_payload=False, offset=chunks[1])\n",
" if chunks[1] is None:\n",
" break\n",
"print(\"Number of document chunks: \", numDocumentChunks)\n",
"if numDocumentChunks > 0:\n",
" print(\"\\nSample document chunk(metadata not the vector): \")\n",
" print(sampleDocumentChunk, '\\n')\n",
"\n",
"# Show everything in the Github collection\n",
"numGithubChunks = 0\n",
"# Note with_vectors defaults to false, so the vectors are not returned\n",
"chunks = qClient.scroll(collection_name='Github', limit=100)\n",
"while True:\n",
" for chunk in chunks[0]:\n",
" if numGithubChunks == 0:\n",
" sampleGithubChunk = chunk\n",
" numGithubChunks += 1\n",
" chunks = qClient.scroll(collection_name='Github', limit=100, with_payload=False, offset=chunks[1])\n",
" if chunks[1] is None:\n",
" break\n",
"print(\"Number of githb chunks: \", numDocumentChunks)\n",
"if numGithubChunks > 0:\n",
" print(\"\\nSample github chunk(metadata not the vector): \")\n",
" print(sampleGithubChunk, '\\n')\n",
"\n",
"# Show a sample search\n",
"embeddingsModel = getEmbeddingsModel()\n",
"results = qClient.search(\n",
" collection_name=\"Document\",\n",
" query_vector = embeddingsModel.embed_query(\"What operating system is ROS made for?\"),\n",
" limit=10\n",
")\n",
"print(\"\\nSample search result(n=2): \")\n",
"for result in results:\n",
" print(result)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cosine Similarity for related sentences: 0.7035977848391597\n",
"Cosine Similarity for unrelated sentences: 0.3566534327076298\n"
]
}
],
"source": [
"import numpy as np\n",
"# How cosine distance works\n",
"\n",
"embedding1 = embeddingsModel.embed_query(\"What is the weather like?\")\n",
"embedding2 = embeddingsModel.embed_query(\"It is raining today.\")\n",
"embedding3 = embeddingsModel.embed_query(\"ROS is an open source platform\")\n",
"def cosine_similarity(vec1, vec2):\n",
" dot_product = np.dot(vec1, vec2)\n",
" norm_vec1 = np.linalg.norm(vec1)\n",
" norm_vec2 = np.linalg.norm(vec2)\n",
" return dot_product / (norm_vec1 * norm_vec2)\n",
"similarity1 = cosine_similarity(embedding1, embedding2)\n",
"similarity2 = cosine_similarity(embedding1, embedding3)\n",
"print(\"Cosine Similarity for related sentences:\", similarity1)\n",
"print(\"Cosine Similarity for unrelated sentences:\", similarity2)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from qdrant_client.http.models import Distance, VectorParams\n",
"# Delete all collections and vectors inside them\n",
"qClient.delete_collection(collection_name = \"Document\")\n",
"qClient.delete_collection(collection_name = \"Github\")\n",
"# Recreate the empty collections\n",
"qClient.create_collection(\n",
" collection_name = \"Document\",\n",
" vectors_config=VectorParams(size=3072, distance=Distance.COSINE)\n",
")\n",
"qClient.create_collection(\n",
" collection_name = \"Github\",\n",
" vectors_config=VectorParams(size=3072, distance=Distance.COSINE)\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|