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Sam Partee
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Documentation Update (#37)
GA and user guide fixes
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‎docs/_templates/layout.html‎

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{% extends "!layout.html" %}
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{% block footer %}
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{{ super() }}
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<!-- Google tag (gtag.js) -->
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<script async src="https://www.googletagmanager.com/gtag/js?id=G-2EW85FTY8C"></script>
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<script>
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window.dataLayer = window.dataLayer || [];
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function gtag(){dataLayer.push(arguments);}
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gtag('js', new Date());
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gtag('config', 'G-2EW85FTY8C');
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</script>
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{% endblock %}

‎docs/user_guide/getting_started_01.ipynb‎

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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from pprint import pprint\n",
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"from jupyterutils import table_print\n",
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"\n",
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"data = [\n",
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" {'user': 'john', 'age': 1, 'job': 'engineer', 'credit_score': 'high'},\n",
@@ -53,7 +53,9 @@
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"source": [
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"This will make up 3 entries in Redis (hashes) each with 4 sub-keys (users, age, job, credit_score).\n",
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"\n",
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"Now, we want to add vectors to represent each user. These are just dummy vectors to illustrate the point, but more complex vectors can be created and used as well. For more information on creating embeddings, see this [article](https://mlops.community/vector-similarity-search-from-basics-to-production/).\n"
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"Now, we want to add vectors to represent each user. These are just dummy vectors to illustrate the point, but more complex vectors can be created and used as well. For more information on creating embeddings, see this [article](https://mlops.community/vector-similarity-search-from-basics-to-production/).\n",
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"\n",
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"As seen below, the sample vectors need to be turned into bytes before they can be loaded into Redis. Using ``NumPy``, this is fairly trivial."
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]
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},
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{
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[{'age': 1,\n",
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" 'credit_score': 'high',\n",
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" 'job': 'engineer',\n",
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" 'user': 'john',\n",
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" 'user_embedding': b'\\xcd\\xcc\\xcc=\\xcd\\xcc\\xcc=\\x00\\x00\\x00?'},\n",
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" {'age': 2,\n",
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" 'credit_score': 'low',\n",
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" 'job': 'doctor',\n",
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" 'user': 'mary',\n",
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" 'user_embedding': b'\\xcd\\xcc\\xcc=\\xcd\\xcc\\xcc=\\x00\\x00\\x00?'},\n",
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" {'age': 3,\n",
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" 'credit_score': 'medium',\n",
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" 'job': 'dentist',\n",
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" 'user': 'joe',\n",
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" 'user_embedding': b'fff?fff?\\xcd\\xcc\\xcc='}]\n"
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]
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"data": {
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"text/html": [
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"<table><tr><th>user</th><th>age</th><th>job</th><th>credit_score</th><th>user_embedding</th></tr><tr><td>john</td><td>1</td><td>engineer</td><td>high</td><td>b'\\xcd\\xcc\\xcc=\\xcd\\xcc\\xcc=\\x00\\x00\\x00?'</td></tr><tr><td>mary</td><td>2</td><td>doctor</td><td>low</td><td>b'\\xcd\\xcc\\xcc=\\xcd\\xcc\\xcc=\\x00\\x00\\x00?'</td></tr><tr><td>joe</td><td>3</td><td>dentist</td><td>medium</td><td>b'fff?fff?\\xcd\\xcc\\xcc='</td></tr></table>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
@@ -94,17 +87,16 @@
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"for record, vector in zip(data, vectors):\n",
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" record[\"user_embedding\"] = vector\n",
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"\n",
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"pprint(data)"
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"table_print(data)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"As seen above, the vectors themselves need to be turned into bytes before they can be loaded into Redis. Using ``NumPy``, this is fairly trivial. \n",
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"\n",
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"Our dataset is now ready to be used with ``redisvl``"
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"Our dataset is now ready to be used with ``redisvl``. The next step is to define the schema for the data."
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]
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},
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{
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" # define vector fields\n",
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" vector:\n",
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" - name: user_embedding\n",
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" algorithm: hnsw\n",
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" distance_metric: cosine\n",
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"\n",
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" algorithm: hnsw\n",
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" distance_metric: cosine\n",
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"\n",
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"```\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32m16:42:28\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[4009]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
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"\u001b[32m16:42:28\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[4009]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. user_index\n"
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"\u001b[32m13:15:04\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[13683]\u001b[0m \u001b[1;30mINFO\u001b[0m Indices:\n",
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"\u001b[32m13:15:04\u001b[0m \u001b[35msam.partee-NW9MQX5Y74\u001b[0m \u001b[34mredisvl.cli.index[13683]\u001b[0m \u001b[1;30mINFO\u001b[0m 1. user_index\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table><tr><th>vector_distance</th><th>user</th><th>age</th><th>job</th><th>credit_score</th></tr><tr><td>0</td><td>john</td><td>1</td><td>engineer</td><td>high</td></tr><tr><td>0</td><td>mary</td><td>2</td><td>doctor</td><td>low</td></tr><tr><td>0.653301358223</td><td>joe</td><td>3</td><td>dentist</td><td>medium</td></tr></table>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from redisvl.query import VectorQuery\n",
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"from jupyterutils import result_print\n",
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"\n",
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"# create a vector query returning a number of results\n",
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"# with specific fields to return.\n",
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")\n",
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"\n",
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"# use the SearchIndex instance (or Redis client) to execute the query\n",
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"results = index.query(query)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Score: 0\n",
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"Document {'id': 'v1:john', 'payload': None, 'vector_distance': '0', 'user': 'john', 'age': '1', 'job': 'engineer', 'credit_score': 'high'}\n",
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"Score: 0\n",
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"Document {'id': 'v1:mary', 'payload': None, 'vector_distance': '0', 'user': 'mary', 'age': '2', 'job': 'doctor', 'credit_score': 'low'}\n",
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"Score: 0.653301358223\n",
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"Document {'id': 'v1:joe', 'payload': None, 'vector_distance': '0.653301358223', 'user': 'joe', 'age': '3', 'job': 'dentist', 'credit_score': 'medium'}\n"
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]
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}
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],
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"source": [
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"for doc in results.docs:\n",
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" print(\"Score:\", doc.vector_distance)\n",
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" print(doc)\n"
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"results = index.query(query)\n",
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"result_print(results)"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"ename": "ResponseError",
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"evalue": "Unknown Index name",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mResponseError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[11], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m redis_client \u001b[39m=\u001b[39m Redis(\u001b[39m\"\u001b[39m\u001b[39mlocalhost\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m6379\u001b[39m)\n\u001b[1;32m 6\u001b[0m \u001b[39m# create a new SearchIndex instance from an existing index\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m existing_index \u001b[39m=\u001b[39m SearchIndex\u001b[39m.\u001b[39;49mfrom_existing(redis_client, \u001b[39m\"\u001b[39;49m\u001b[39muser_index\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[1;32m 9\u001b[0m \u001b[39m# run the same query\u001b[39;00m\n\u001b[1;32m 10\u001b[0m existing_index\u001b[39m.\u001b[39mquery(query)\n",
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"File \u001b[0;32m~/Dropbox/Redis/data-loader/redisvl/index.py:107\u001b[0m, in \u001b[0;36mfrom_existing\u001b[0;34m(cls, client, index_name)\u001b[0m\n\u001b[1;32m 103\u001b[0m @classmethod\n\u001b[1;32m 104\u001b[0m def from_existing(cls, client: redis.Redis, index_name: str):\n\u001b[1;32m 105\u001b[0m \"\"\"Create a SearchIndex from an existing index in Redis\"\"\"\n\u001b[1;32m 106\u001b[0m # TODO assert client connected\n\u001b[0;32m--> 107\u001b[0m # TODO try/except\n\u001b[1;32m 108\u001b[0m info = convert_bytes(client.ft(index_name).info()) # TODO catch response error\n\u001b[1;32m 109\u001b[0m index_definition = make_dict(info[\"index_definition\"])\n",
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"File \u001b[0;32m~/.virtualenvs/rvl/lib/python3.8/site-packages/redis/commands/search/commands.py:370\u001b[0m, in \u001b[0;36mSearchCommands.info\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minfo\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m 363\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[39m Get info an stats about the the current index, including the number of\u001b[39;00m\n\u001b[1;32m 365\u001b[0m \u001b[39m documents, memory consumption, etc\u001b[39;00m\n\u001b[1;32m 366\u001b[0m \n\u001b[1;32m 367\u001b[0m \u001b[39m For more information see `FT.INFO <https://redis.io/commands/ft.info>`_.\u001b[39;00m\n\u001b[1;32m 368\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 370\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mexecute_command(INFO_CMD, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mindex_name)\n\u001b[1;32m 371\u001b[0m it \u001b[39m=\u001b[39m \u001b[39mmap\u001b[39m(to_string, res)\n\u001b[1;32m 372\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mdict\u001b[39m(\u001b[39mzip\u001b[39m(it, it))\n",
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"File \u001b[0;32m~/.virtualenvs/rvl/lib/python3.8/site-packages/redis/client.py:1269\u001b[0m, in \u001b[0;36mRedis.execute_command\u001b[0;34m(self, *args, **options)\u001b[0m\n\u001b[1;32m 1266\u001b[0m conn \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconnection \u001b[39mor\u001b[39;00m pool\u001b[39m.\u001b[39mget_connection(command_name, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n\u001b[1;32m 1268\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1269\u001b[0m \u001b[39mreturn\u001b[39;00m conn\u001b[39m.\u001b[39;49mretry\u001b[39m.\u001b[39;49mcall_with_retry(\n\u001b[1;32m 1270\u001b[0m \u001b[39mlambda\u001b[39;49;00m: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_command_parse_response(\n\u001b[1;32m 1271\u001b[0m conn, command_name, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptions\n\u001b[1;32m 1272\u001b[0m ),\n\u001b[1;32m 1273\u001b[0m \u001b[39mlambda\u001b[39;49;00m error: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_disconnect_raise(conn, error),\n\u001b[1;32m 1274\u001b[0m )\n\u001b[1;32m 1275\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 1276\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconnection:\n",
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"File \u001b[0;32m~/.virtualenvs/rvl/lib/python3.8/site-packages/redis/retry.py:46\u001b[0m, in \u001b[0;36mRetry.call_with_retry\u001b[0;34m(self, do, fail)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 45\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 46\u001b[0m \u001b[39mreturn\u001b[39;00m do()\n\u001b[1;32m 47\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_supported_errors \u001b[39mas\u001b[39;00m error:\n\u001b[1;32m 48\u001b[0m failures \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n",
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"File \u001b[0;32m~/.virtualenvs/rvl/lib/python3.8/site-packages/redis/client.py:1270\u001b[0m, in \u001b[0;36mRedis.execute_command.<locals>.<lambda>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1266\u001b[0m conn \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconnection \u001b[39mor\u001b[39;00m pool\u001b[39m.\u001b[39mget_connection(command_name, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39moptions)\n\u001b[1;32m 1268\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1269\u001b[0m \u001b[39mreturn\u001b[39;00m conn\u001b[39m.\u001b[39mretry\u001b[39m.\u001b[39mcall_with_retry(\n\u001b[0;32m-> 1270\u001b[0m \u001b[39mlambda\u001b[39;00m: \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_send_command_parse_response(\n\u001b[1;32m 1271\u001b[0m conn, command_name, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptions\n\u001b[1;32m 1272\u001b[0m ),\n\u001b[1;32m 1273\u001b[0m \u001b[39mlambda\u001b[39;00m error: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disconnect_raise(conn, error),\n\u001b[1;32m 1274\u001b[0m )\n\u001b[1;32m 1275\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m 1276\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconnection:\n",
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"File \u001b[0;32m~/.virtualenvs/rvl/lib/python3.8/site-packages/redis/client.py:1246\u001b[0m, in \u001b[0;36mRedis._send_command_parse_response\u001b[0;34m(self, conn, command_name, *args, **options)\u001b[0m\n\u001b[1;32m 1242\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1243\u001b[0m \u001b[39mSend a command and parse the response\u001b[39;00m\n\u001b[1;32m 1244\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 1245\u001b[0m conn\u001b[39m.\u001b[39msend_command(\u001b[39m*\u001b[39margs)\n\u001b[0;32m-> 1246\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparse_response(conn, command_name, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49moptions)\n",
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"File \u001b[0;32m~/.virtualenvs/rvl/lib/python3.8/site-packages/redis/client.py:1286\u001b[0m, in \u001b[0;36mRedis.parse_response\u001b[0;34m(self, connection, command_name, **options)\u001b[0m\n\u001b[1;32m 1284\u001b[0m options\u001b[39m.\u001b[39mpop(NEVER_DECODE)\n\u001b[1;32m 1285\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1286\u001b[0m response \u001b[39m=\u001b[39m connection\u001b[39m.\u001b[39;49mread_response()\n\u001b[1;32m 1287\u001b[0m \u001b[39mexcept\u001b[39;00m ResponseError:\n\u001b[1;32m 1288\u001b[0m \u001b[39mif\u001b[39;00m EMPTY_RESPONSE \u001b[39min\u001b[39;00m options:\n",
336-
"File \u001b[0;32m~/.virtualenvs/rvl/lib/python3.8/site-packages/redis/connection.py:905\u001b[0m, in \u001b[0;36mAbstractConnection.read_response\u001b[0;34m(self, disable_decoding, disconnect_on_error)\u001b[0m\n\u001b[1;32m 902\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnext_health_check \u001b[39m=\u001b[39m time() \u001b[39m+\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhealth_check_interval\n\u001b[1;32m 904\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(response, ResponseError):\n\u001b[0;32m--> 905\u001b[0m \u001b[39mraise\u001b[39;00m response\n\u001b[1;32m 906\u001b[0m \u001b[39mreturn\u001b[39;00m response\n",
337-
"\u001b[0;31mResponseError\u001b[0m: Unknown Index name"
338-
]
304+
"data": {
305+
"text/html": [
306+
"<table><tr><th>vector_distance</th><th>user</th><th>age</th><th>job</th><th>credit_score</th></tr><tr><td>0</td><td>john</td><td>1</td><td>engineer</td><td>high</td></tr><tr><td>0</td><td>mary</td><td>2</td><td>doctor</td><td>low</td></tr><tr><td>0.653301358223</td><td>joe</td><td>3</td><td>dentist</td><td>medium</td></tr></table>"
307+
],
308+
"text/plain": [
309+
"<IPython.core.display.HTML object>"
310+
]
311+
},
312+
"metadata": {},
313+
"output_type": "display_data"
339314
}
340315
],
341316
"source": [
342-
"from redis import Redis\n",
343-
"\n",
344-
"# initialize a redis client to connect to existing index\n",
345-
"redis_client = Redis(\"localhost\", 6379)\n",
346-
"\n",
347317
"# create a new SearchIndex instance from an existing index\n",
348-
"existing_index = SearchIndex.from_existing(redis_client, \"user_index\")\n",
318+
"existing_index = SearchIndex.from_existing(\"user_index\", \"redis://localhost:6379\")\n",
349319
"\n",
350320
"# run the same query\n",
351-
"existing_index.query(query)\n",
352-
"\n",
353-
"for doc in results.docs:\n",
354-
" print(\"Score:\", doc.vector_distance)\n",
355-
" print(doc)"
321+
"results = existing_index.query(query)\n",
322+
"result_print(results)\n"
356323
]
357324
},
358325
{
@@ -366,25 +333,24 @@
366333
},
367334
{
368335
"cell_type": "code",
369-
"execution_count": 12,
336+
"execution_count": 10,
370337
"metadata": {},
371338
"outputs": [
372339
{
373-
"name": "stdout",
374-
"output_type": "stream",
375-
"text": [
376-
"Score: 0\n",
377-
"Document {'id': 'v1:john', 'payload': None, 'vector_distance': '0', 'user': 'john', 'age': '1', 'job': 'engineer', 'credit_score': 'high'}\n",
378-
"Score: 0\n",
379-
"Document {'id': 'v1:mary', 'payload': None, 'vector_distance': '0', 'user': 'mary', 'age': '2', 'job': 'doctor', 'credit_score': 'low'}\n",
380-
"Score: 0.653301358223\n",
381-
"Document {'id': 'v1:joe', 'payload': None, 'vector_distance': '0.653301358223', 'user': 'joe', 'age': '3', 'job': 'dentist', 'credit_score': 'medium'}\n"
382-
]
340+
"data": {
341+
"text/html": [
342+
"<table><tr><th>vector_distance</th><th>user</th><th>age</th><th>job</th><th>credit_score</th></tr><tr><td>0</td><td>john</td><td>1</td><td>engineer</td><td>high</td></tr><tr><td>0</td><td>mary</td><td>2</td><td>doctor</td><td>low</td></tr><tr><td>0.653301358223</td><td>joe</td><td>3</td><td>dentist</td><td>medium</td></tr></table>"
343+
],
344+
"text/plain": [
345+
"<IPython.core.display.HTML object>"
346+
]
347+
},
348+
"metadata": {},
349+
"output_type": "display_data"
383350
}
384351
],
385352
"source": [
386353
"## Asynchronous Search\n",
387-
"\n",
388354
"from redisvl.index import AsyncSearchIndex\n",
389355
"\n",
390356
"# construct a search index from the schema\n",
@@ -401,18 +367,8 @@
401367
"\n",
402368
"# run the same vector query but asynchronously\n",
403369
"results = await index.query(query)\n",
404-
"\n",
405-
"for doc in results.docs:\n",
406-
" print(\"Score:\", doc.vector_distance)\n",
407-
" print(doc)"
370+
"result_print(results)"
408371
]
409-
},
410-
{
411-
"cell_type": "code",
412-
"execution_count": null,
413-
"metadata": {},
414-
"outputs": [],
415-
"source": []
416372
}
417373
],
418374
"metadata": {

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