Redis as a vector database quick start guide
Understand how to use Redis as a vector database
This quick start guide helps you to:
- Understand what a vector database is
- Create a Redis vector database
- Create vector embeddings and store vectors
- Query data and perform a vector search
Understand vector databases
Data is often unstructured, which means that it isn't described by a well-defined schema. Examples of unstructured data include text passages, images, videos, or audio. One approach to storing and searching through unstructured data is to use vector embeddings.
What are vectors? In machine learning and AI, vectors are sequences of numbers that represent data. They are the inputs and outputs of models, encapsulating underlying information in a numerical form. Vectors transform unstructured data, such as text, images, videos, and audio, into a format that machine learning models can process.
- Why are they important? Vectors capture complex patterns and semantic meanings inherent in data, making them powerful tools for a variety of applications. They allow machine learning models to understand and manipulate unstructured data more effectively.
- Enhancing traditional search. Traditional keyword or lexical search relies on exact matches of words or phrases, which can be limiting. In contrast, vector search, or semantic search, leverages the rich information captured in vector embeddings. By mapping data into a vector space, similar items are positioned near each other based on their meaning. This approach allows for more accurate and meaningful search results, as it considers the context and semantic content of the query rather than just the exact words used.
Create a Redis vector database
You can use Redis Stack as a vector database. It allows you to:
- Store vectors and the associated metadata within hashes or JSON documents
- Create and configure secondary indices for search
- Perform vector searches
- Update vectors and metadata
- Delete and cleanup
The easiest way to get started is to use Redis Cloud:
-
Create a free account.
-
Follow the instructions to create a free database.
This free Redis Cloud database comes out of the box with all the Redis Stack features.
You can alternatively use the installation guides to install Redis Stack on your local machine.
You need to have the following features configured for your Redis server: JSON and Search and query.
Install the required Python packages
Create a Python virtual environment and install the following dependencies using pip
:
redis
: You can find further details about theredis-py
client library in the clients section of this documentation site.pandas
: Pandas is a data analysis library.sentence-transformers
: You will use the SentenceTransformers framework to generate embeddings on full text.tabulate
:pandas
usestabulate
to render Markdown.
You will also need the following imports in your Python code:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Connect
Connect to Redis. By default, Redis returns binary responses. To decode them, you pass the decode_responses
parameter set to True
:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
us-east-1
and listens on port 16379: redis-16379.c283.us-east-1-4.ec2.cloud.redislabs.com:16379
. The connection string has the format host:port
. You must also copy and paste the username and password of your Cloud database. The line of code for connecting with the default user changes then to client = redis.Redis(host="redis-16379.c283.us-east-1-4.ec2.cloud.redislabs.com", port=16379, password="your_password_here", decode_responses=True)
.Prepare the demo dataset
This quick start guide also uses the bikes dataset. Here is an example document from it:
{
"model": "Jigger",
"brand": "Velorim",
"price": 270,
"type": "Kids bikes",
"specs": {
"material": "aluminium",
"weight": "10"
},
"description": "Small and powerful, the Jigger is the best ride for the smallest of tikes! ..."
}
The description
field contains free-form text descriptions of bikes and will be used to create vector embeddings.
1. Fetch the demo data
You need to first fetch the demo dataset as a JSON array:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Inspect the structure of one of the bike JSON documents:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
2. Store the demo data in Redis
Now iterate over the bikes
array to store the data as JSON documents in Redis by using the JSON.SET command. The below code uses a pipeline to minimize the network round-trip times:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Once loaded, you can retrieve a specific attribute from one of the JSON documents in Redis using a JSONPath expression:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
3. Select a text embedding model
HuggingFace has a large catalog of text embedding models that are locally servable through the SentenceTransformers
framework. Here we use the MS MARCO model that is widely used in search engines, chatbots, and other AI applications.
from sentence_transformers import SentenceTransformer
embedder = SentenceTransformer('msmarco-distilbert-base-v4')
4. Generate text embeddings
Iterate over all the Redis keys with the prefix bikes:
:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Use the keys as input to the JSON.MGET command, along with the $.description
field, to collect the descriptions in a list. Then, pass the list of descriptions to the .encode()
method:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Insert the vectorized descriptions to the bike documents in Redis using the JSON.SET command. The following command inserts a new field into each of the documents under the JSONPath $.description_embeddings
. Once again, do this using a pipeline to avoid unnecessary network round-trips:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Inspect one of the updated bike documents using the JSON.GET command:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Create an index
1. Create an index with a vector field
You must create an index to query document metadata or to perform vector searches. Use the FT.CREATE command:
FT.CREATE idx:bikes_vss ON JSON
PREFIX 1 bikes: SCORE 1.0
SCHEMA
$.model TEXT WEIGHT 1.0 NOSTEM
$.brand TEXT WEIGHT 1.0 NOSTEM
$.price NUMERIC
$.type TAG SEPARATOR ","
$.description AS description TEXT WEIGHT 1.0
$.description_embeddings AS vector VECTOR FLAT 6 TYPE FLOAT32 DIM 768 DISTANCE_METRIC COSINE
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Here is a breakdown of the VECTOR
field definition:
$.description_embeddings AS vector
: The vector field's JSON path and its field aliasvector
.FLAT
: Specifies the indexing method, which is either a flat index or a hierarchical navigable small world graph (HNSW).TYPE FLOAT32
: Sets the float precision of a vector component, in this case a 32-bit floating point number.DIM 768
: The length or dimension of the embeddings, determined by the chosen embedding model.DISTANCE_METRIC COSINE
: The chosen distance function: cosine distance.
You can find further details about all these options in the vector reference documentation.
2. Check the state of the index
As soon as you execute the FT.CREATE command, the indexing process runs in the background. In a short time, all JSON documents should be indexed and ready to be queried. To validate that, you can use the FT.INFO command, which provides details and statistics about the index. Of particular interest are the number of documents successfully indexed and the number of failures:
FT.INFO idx:bikes_vss
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
Perform vector searches
This quick start guide focuses on vector search. However, you can learn more about how to query based on document metadata in the document database quick start guide.
1. Embed your queries
The following code snippet shows a list of text queries you will use to perform vector search in Redis:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
First, encode each input query as a vector embedding using the same SentenceTransformers model:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
2. K-nearest neighbors (KNN) search
The KNN algorithm calculates the distance between the query vector and each vector in Redis based on the chosen distance function. It then returns the top K items with the smallest distances to the query vector. These are the most semantically similar items.
Now construct a query to do just that:
query = (
Query('(*)=>[KNN 3 @vector $query_vector AS vector_score]')
.sort_by('vector_score')
.return_fields('vector_score', 'id', 'brand', 'model', 'description')
.dialect(2)
)
Let's break down the above query template:
- The filter expression
(*)
meansall
. In other words, no filtering was applied. You could replace it with an expression that filters by additional metadata. - The
KNN
part of the query searches for the top 3 nearest neighbors. - The query vector must be passed in as the param
query_vector
. - The distance to the query vector is returned as
vector_score
. - The results are sorted by this
vector_score
. - Finally, it returns the fields
vector_score
,id
,brand
,model
, anddescription
for each result.
FT.SEARCH
command, you must specify DIALECT 2 or greater.You must pass the vectorized query as a byte array with the param name query_vector
. The following code creates a Python NumPy array from the query vector and converts it into a compact, byte-level representation that can be passed as a parameter to the query:
client.ft('idx:bikes_vss').search(
query,
{
'query_vector': np.array(encoded_query, dtype=np.float32).tobytes()
}
).docs
With the template for the query in place, you can execute all queries in a loop. Notice that the script calculates the vector_score
for each result as 1 - doc.vector_score
. Because the cosine distance is used as the metric, the items with the smallest distance are closer and, therefore, more similar to the query.
Then, loop over the matched documents and create a list of results that can be converted into a Pandas table to visualize the results:
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.ft("idx:bikes_vss")
.search(
query,
{"query_vector": np.array(encoded_query, dtype=np.float32).tobytes()}
| (extra_params if extra_params else {}),
)
.docs
)
for doc in result_docs:
vector_score = round(1 - float(doc.vector_score), 2)
results_list.append(
{
"query": queries[i],
"score": vector_score,
"id": doc.id,
"brand": doc.brand,
"model": doc.model,
"description": doc.description,
}
)
# Optional: convert the table to Markdown using Pandas
queries_table = pd.DataFrame(results_list)
queries_table.sort_values(
by=["query", "score"], ascending=[True, False], inplace=True
)
queries_table["query"] = queries_table.groupby("query")["query"].transform(
lambda x: [x.iloc[0]] + [""] * (len(x) - 1)
)
queries_table["description"] = queries_table["description"].apply(
lambda x: (x[:497] + "...") if len(x) > 500 else x
)
return queries_table.to_markdown(index=False)
query = (
Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.54 | bikes:003...
hybrid_query = (
Query("(@brand:Peaknetic)=>[KNN 3 @vector $query_vector AS vector_score]")
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.dialect(2)
)
table = create_query_table(hybrid_query, queries, encoded_queries)
print(table)
# >>> | Best Mountain bikes for kids | 0.3 | bikes:008...
range_query = (
Query(
"@vector:[VECTOR_RANGE $range $query_vector]=>"
"{$YIELD_DISTANCE_AS: vector_score}"
)
.sort_by("vector_score")
.return_fields("vector_score", "id", "brand", "model", "description")
.paging(0, 4)
.dialect(2)
)
table = create_query_table(
range_query, queries[:1],
encoded_queries[:1],
{"range": 0.55}
)
print(table)
# >>> | Bike for small kids | 0.52 | bikes:001 | Velorim |...
The query results show the individual queries' top three matches (our K parameter) along with the bike's id, brand, and model for each query.
For example, for the query "Best Mountain bikes for kids", the highest similarity score (0.54
) and, therefore the closest match was the 'Nord' brand 'Chook air 5' bike model, described as:
The Chook Air 5 gives kids aged six years and older a durable and uberlight mountain bike for their first experience on tracks and easy cruising through forests and fields. The lower top tube makes it easy to mount and dismount in any situation, giving your kids greater safety on the trails. The Chook Air 5 is the perfect intro to mountain biking.
From the description, this bike is an excellent match for younger children, and the embeddings accurately captured the semantics of the description.
"""
Code samples for vector database quickstart pages:
https://redis.io/docs/latest/develop/get-started/vector-database/
"""
import json
import time
import numpy as np
import pandas as pd
import requests
import redis
from redis.commands.search.field import (
NumericField,
TagField,
TextField,
VectorField,
)
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from sentence_transformers import SentenceTransformer
URL = ("https://raw.githubusercontent.com/bsbodden/redis_vss_getting_started"
"/main/data/bikes.json"
)
response = requests.get(URL, timeout=10)
bikes = response.json()
json.dumps(bikes[0], indent=2)
client = redis.Redis(host="localhost", port=6379, decode_responses=True)
res = client.ping()
# >>> True
pipeline = client.pipeline()
for i, bike in enumerate(bikes, start=1):
redis_key = f"bikes:{i:03}"
pipeline.json().set(redis_key, "$", bike)
res = pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010", "$.model")
# >>> ['Summit']
keys = sorted(client.keys("bikes:*"))
# >>> ['bikes:001', 'bikes:002', ..., 'bikes:011']
descriptions = client.json().mget(keys, "$.description")
descriptions = [item for sublist in descriptions for item in sublist]
embedder = SentenceTransformer("msmarco-distilbert-base-v4")
embeddings = embedder.encode(descriptions).astype(np.float32).tolist()
VECTOR_DIMENSION = len(embeddings[0])
# >>> 768
pipeline = client.pipeline()
for key, embedding in zip(keys, embeddings):
pipeline.json().set(key, "$.description_embeddings", embedding)
pipeline.execute()
# >>> [True, True, True, True, True, True, True, True, True, True, True]
res = client.json().get("bikes:010")
# >>>
# {
# "model": "Summit",
# "brand": "nHill",
# "price": 1200,
# "type": "Mountain Bike",
# "specs": {
# "material": "alloy",
# "weight": "11.3"
# },
# "description": "This budget mountain bike from nHill performs well..."
# "description_embeddings": [
# -0.538114607334137,
# -0.49465855956077576,
# -0.025176964700222015,
# ...
# ]
# }
schema = (
TextField("$.model", no_stem=True, as_name="model"),
TextField("$.brand", no_stem=True, as_name="brand"),
NumericField("$.price", as_name="price"),
TagField("$.type", as_name="type"),
TextField("$.description", as_name="description"),
VectorField(
"$.description_embeddings",
"FLAT",
{
"TYPE": "FLOAT32",
"DIM": VECTOR_DIMENSION,
"DISTANCE_METRIC": "COSINE",
},
as_name="vector",
),
)
definition = IndexDefinition(prefix=["bikes:"], index_type=IndexType.JSON)
res = client.ft("idx:bikes_vss").create_index(fields=schema, definition=definition)
# >>> 'OK'
info = client.ft("idx:bikes_vss").info()
num_docs = info["num_docs"]
indexing_failures = info["hash_indexing_failures"]
# print(f"{num_docs} documents indexed with {indexing_failures} failures")
# >>> 11 documents indexed with 0 failures
query = Query("@brand:Peaknetic")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950', 'description_embeddings': ...
query = Query("@brand:Peaknetic").return_fields("id", "brand", "model", "price")
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:008',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Soothe Electric bike',
# 'price': '1950'
# },
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
query = Query("@brand:Peaknetic @price:[0 1000]").return_fields(
"id", "brand", "model", "price"
)
res = client.ft("idx:bikes_vss").search(query).docs
# print(res)
# >>> [
# Document {
# 'id': 'bikes:009',
# 'payload': None,
# 'brand': 'Peaknetic',
# 'model': 'Secto',
# 'price': '430'
# }
# ]
queries = [
"Bike for small kids",
"Best Mountain bikes for kids",
"Cheap Mountain bike for kids",
"Female specific mountain bike",
"Road bike for beginners",
"Commuter bike for people over 60",
"Comfortable commuter bike",
"Good bike for college students",
"Mountain bike for beginners",
"Vintage bike",
"Comfortable city bike",
]
encoded_queries = embedder.encode(queries)
len(encoded_queries)
# >>> 11
def create_query_table(query, queries, encoded_queries, extra_params=None):
"""
Creates a query table.
"""
results_list = []
for i, encoded_query in enumerate(encoded_queries):
result_docs = (
client.