0.5.1 Feature Overview

This notebook provides an overview of what's new with the 0.5.1 release of redisvl. It also highlights changes and potential enhancements for existing usage.

What's new?

  • Hybrid query and text query classes
  • Threshold optimizer classes
  • Schema validation
  • Timestamp filters
  • Batched queries
  • Vector normalization
  • Hybrid policy on knn with filters

Define and load index for examples

from redisvl.utils.vectorize import HFTextVectorizer
from redisvl.index import SearchIndex
import datetime as dt

import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="redis")

# Embedding model
emb_model = HFTextVectorizer()

REDIS_URL = "redis://localhost:6379/0"
NOW = dt.datetime.now()

job_data = [
  {
    "job_title": "Software Engineer",
    "job_description": "Develop and maintain web applications using JavaScript, React, and Node.js.",
    "posted": (NOW - dt.timedelta(days=1)).timestamp() # day ago
  },
  {
    "job_title": "Data Analyst",
    "job_description": "Analyze large datasets to provide business insights and create data visualizations.",
    "posted": (NOW - dt.timedelta(days=7)).timestamp() # week ago
  },
  {
    "job_title": "Marketing Manager",
    "job_description": "Develop and implement marketing strategies to drive brand awareness and customer engagement.",
    "posted": (NOW - dt.timedelta(days=30)).timestamp() # month ago
  }
]

job_data = [{**job, "job_embedding": emb_model.embed(job["job_description"], as_buffer=True)} for job in job_data]


job_schema = {
    "index": {
        "name": "jobs",
        "prefix": "jobs",
        "storage_type": "hash",
    },
    "fields": [
        {"name": "job_title", "type": "text"},
        {"name": "job_description", "type": "text"},
        {"name": "posted", "type": "numeric"},
        {
            "name": "job_embedding",
            "type": "vector",
            "attrs": {
                "dims": 768,
                "distance_metric": "cosine",
                "algorithm": "flat",
                "datatype": "float32"
            }

        }
    ],
}

index = SearchIndex.from_dict(job_schema, redis_url=REDIS_URL)
index.create(overwrite=True, drop=True)
index.load(job_data)

12:44:52 redisvl.index.index INFO   Index already exists, overwriting.





['jobs:01JR0V1SA29RVD9AAVSTBV9P5H',
 'jobs:01JR0V1SA209KMVHMD7G54P3H5',
 'jobs:01JR0V1SA23ZE7BRERXTZWC33Z']

HybridQuery class

Perform hybrid lexical (BM25) and vector search where results are ranked by: hybrid_score = (1-alpha)*lexical_Score + alpha*vector_similarity.

from redisvl.query import HybridQuery

text = "Find a job as a where you develop software"
vec = emb_model.embed(text, as_buffer=True)

query = HybridQuery(
    text=text,
    text_field_name="job_description",
    vector=vec,
    vector_field_name="job_embedding",
    alpha=0.7,
    num_results=10,
    return_fields=["job_title"],
)

results = index.query(query)
results

[{'vector_distance': '0.61871612072',
  'job_title': 'Software Engineer',
  'vector_similarity': '0.69064193964',
  'text_score': '49.6242910712',
  'hybrid_score': '15.3707366791'},
 {'vector_distance': '0.937997639179',
  'job_title': 'Marketing Manager',
  'vector_similarity': '0.53100118041',
  'text_score': '49.6242910712',
  'hybrid_score': '15.2589881476'},
 {'vector_distance': '0.859166145325',
  'job_title': 'Data Analyst',
  'vector_similarity': '0.570416927338',
  'text_score': '0',
  'hybrid_score': '0.399291849136'}]

TextQueries

TextQueries make it easy to perform pure lexical search with redisvl.

from redisvl.query import TextQuery

text = "Find where you develop software"

query = TextQuery(
    text=text,
    text_field_name="job_description",
    return_fields=["job_title"],
    num_results=10,
)

results = index.query(query)
results

[{'id': 'jobs:01JR0V1SA29RVD9AAVSTBV9P5H',
  'score': 49.62429107116745,
  'job_title': 'Software Engineer'},
 {'id': 'jobs:01JR0V1SA23ZE7BRERXTZWC33Z',
  'score': 49.62429107116745,
  'job_title': 'Marketing Manager'}]

Threshold optimization

In redis 0.5.0 we added the ability to quickly configure either your semantic cache or semantic router with test data examples.

For a step by step guide see: 09_threshold_optimization.ipynb.

For a more advanced routing example see: this example.

from redisvl.utils.optimize import CacheThresholdOptimizer
from redisvl.extensions.cache.llm import SemanticCache

sem_cache = SemanticCache(
    name="sem_cache",                    # underlying search index name
    redis_url="redis://localhost:6379",  # redis connection url string
    distance_threshold=0.5               # semantic cache distance threshold
)

paris_key = sem_cache.store(prompt="what is the capital of france?", response="paris")
rabat_key = sem_cache.store(prompt="what is the capital of morocco?", response="rabat")

test_data = [
    {
        "query": "What's the capital of Britain?",
        "query_match": ""
    },
    {
        "query": "What's the capital of France??",
        "query_match": paris_key
    },
    {
        "query": "What's the capital city of Morocco?",
        "query_match": rabat_key
    },
]

print(f"\nDistance threshold before: {sem_cache.distance_threshold} \n")
optimizer = CacheThresholdOptimizer(sem_cache, test_data)
optimizer.optimize()
print(f"\nDistance threshold after: {sem_cache.distance_threshold} \n")

Distance threshold before: 0.5 


Distance threshold after: 0.13050847457627118

Schema validation

This feature makes it easier to make sure your data is in the right format. To demo this we will create a new index with the validate_on_load flag set to True

# NBVAL_SKIP
from redisvl.index import SearchIndex

# sample schema
car_schema = {
    "index": {
        "name": "cars",
        "prefix": "cars",
        "storage_type": "json",
    },
    "fields": [
        {"name": "make", "type": "text"},
        {"name": "model", "type": "text"},
        {"name": "description", "type": "text"},
        {"name": "mpg", "type": "numeric"},
        {
            "name": "car_embedding",
            "type": "vector",
            "attrs": {
                "dims": 3,
                "distance_metric": "cosine",
                "algorithm": "flat",
                "datatype": "float32"
            }

        }
    ],
}

sample_data_bad = [
    {
        "make": "Toyota",
        "model": "Camry",
        "description": "A reliable sedan with great fuel economy.",
        "mpg": 28,
        "car_embedding": [0.1, 0.2, 0.3]
    },
    {
        "make": "Honda",
        "model": "CR-V",
        "description": "A practical SUV with advanced technology.",
        # incorrect type will throw an error
        "mpg": "twenty-two",
        "car_embedding": [0.4, 0.5, 0.6]
    }
]

# this should now throw an error
car_index = SearchIndex.from_dict(car_schema, redis_url=REDIS_URL, validate_on_load=True)
car_index.create(overwrite=True)

try:
    car_index.load(sample_data_bad)
except Exception as e:
    print(f"Error loading data: {e}")

16:20:25 redisvl.index.index ERROR   Schema validation error while loading data
Traceback (most recent call last):
  File "/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/redisvl/index/storage.py", line 204, in _preprocess_and_validate_objects
    processed_obj = self._validate(processed_obj)
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/redisvl/index/storage.py", line 160, in _validate
    return validate_object(self.index_schema, obj)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/redisvl/schema/validation.py", line 276, in validate_object
    validated = model_class.model_validate(flat_obj)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/pydantic/main.py", line 627, in model_validate
    return cls.__pydantic_validator__.validate_python(
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 2 validation errors for cars__PydanticModel
mpg.int
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='twenty-two', input_type=str]
    For further information visit https://errors.pydantic.dev/2.10/v/int_parsing
mpg.float
  Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='twenty-two', input_type=str]
    For further information visit https://errors.pydantic.dev/2.10/v/float_parsing

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/redisvl/index/index.py", line 615, in load
    return self._storage.write(
           ^^^^^^^^^^^^^^^^^^^^
  File "/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/redisvl/index/storage.py", line 265, in write
    prepared_objects = self._preprocess_and_validate_objects(
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/robert.shelton/.pyenv/versions/3.11.9/lib/python3.11/site-packages/redisvl/index/storage.py", line 211, in _preprocess_and_validate_objects
    raise SchemaValidationError(str(e), index=i) from e
redisvl.exceptions.SchemaValidationError: Validation failed for object at index 1: 2 validation errors for cars__PydanticModel
mpg.int
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='twenty-two', input_type=str]
    For further information visit https://errors.pydantic.dev/2.10/v/int_parsing
mpg.float
  Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='twenty-two', input_type=str]
    For further information visit https://errors.pydantic.dev/2.10/v/float_parsing
Error loading data: Validation failed for object at index 1: 2 validation errors for cars__PydanticModel
mpg.int
  Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='twenty-two', input_type=str]
    For further information visit https://errors.pydantic.dev/2.10/v/int_parsing
mpg.float
  Input should be a valid number, unable to parse string as a number [type=float_parsing, input_value='twenty-two', input_type=str]
    For further information visit https://errors.pydantic.dev/2.10/v/float_parsing

Timestamp filters

In Redis datetime objects are stored as numeric epoch times. Timestamp filter makes it easier to handle querying by these fields by handling conversion for you.

from redisvl.query import FilterQuery
from redisvl.query.filter import Timestamp

# find all jobs
ts = Timestamp("posted") < NOW # now datetime created above

filter_query = FilterQuery(
    return_fields=["job_title", "job_description", "posted"], 
    filter_expression=ts,
    num_results=10,
)
res = index.query(filter_query)
res

[{'id': 'jobs:01JQYMYZBA6NM6DX9YW35MCHJZ',
  'job_title': 'Software Engineer',
  'job_description': 'Develop and maintain web applications using JavaScript, React, and Node.js.',
  'posted': '1743625199.9'},
 {'id': 'jobs:01JQYMYZBABXYR96H96SQ99ZPS',
  'job_title': 'Data Analyst',
  'job_description': 'Analyze large datasets to provide business insights and create data visualizations.',
  'posted': '1743106799.9'},
 {'id': 'jobs:01JQYMYZBAGEBDS270EZADQ1TM',
  'job_title': 'Marketing Manager',
  'job_description': 'Develop and implement marketing strategies to drive brand awareness and customer engagement.',
  'posted': '1741123199.9'}]

# jobs posted in the last 3 days => 1 job
ts = Timestamp("posted") > NOW - dt.timedelta(days=3)

filter_query = FilterQuery(
    return_fields=["job_title", "job_description", "posted"], 
    filter_expression=ts,
    num_results=10,
)
res = index.query(filter_query)
res

[{'id': 'jobs:01JQYMYZBA6NM6DX9YW35MCHJZ',
  'job_title': 'Software Engineer',
  'job_description': 'Develop and maintain web applications using JavaScript, React, and Node.js.',
  'posted': '1743625199.9'}]

# more than 3 days ago but less than 14 days ago => 1 job
ts = Timestamp("posted").between(
    NOW - dt.timedelta(days=14),
    NOW - dt.timedelta(days=3),
)

filter_query = FilterQuery(
    return_fields=["job_title", "job_description", "posted"], 
    filter_expression=ts,
    num_results=10,
)

res = index.query(filter_query)
res

[{'id': 'jobs:01JQYMYZBABXYR96H96SQ99ZPS',
  'job_title': 'Data Analyst',
  'job_description': 'Analyze large datasets to provide business insights and create data visualizations.',
  'posted': '1743106799.9'}]

Batch search

This enhancement allows you to speed up the execution of queries by reducing the impact of network latency.

import time
num_queries = 200

start = time.time()
for i in range(num_queries):
    # run the same filter query 
    res = index.query(filter_query)
end = time.time()
print(f"Time taken for {num_queries} queries: {end - start:.2f} seconds")

Time taken for 200 queries: 0.11 seconds

batched_queries = [filter_query] * num_queries

start = time.time()

index.batch_search(batched_queries, batch_size=10)

end = time.time()
print(f"Time taken for {num_queries} batched queries: {end - start:.2f} seconds")

Time taken for 200 batched queries: 0.03 seconds

Vector normalization

By default, Redis returns the vector cosine distance when performing a search, which yields a value between 0 and 2, where 0 represents a perfect match. However, you may sometimes prefer a similarity score between 0 and 1, where 1 indicates a perfect match. When enabled, this flag performs the conversion for you. Additionally, if this flag is set to true for L2 distance, it normalizes the Euclidean distance to a value between 0 and 1 as well.

from redisvl.query import VectorQuery

query = VectorQuery(
    vector=emb_model.embed("Software Engineer", as_buffer=True),
    vector_field_name="job_embedding",
    return_fields=["job_title", "job_description", "posted"],
    normalize_vector_distance=True,
)

res = index.query(query)
res

[{'id': 'jobs:01JQYMYZBA6NM6DX9YW35MCHJZ',
  'vector_distance': '0.7090711295605',
  'job_title': 'Software Engineer',
  'job_description': 'Develop and maintain web applications using JavaScript, React, and Node.js.',
  'posted': '1743625199.9'},
 {'id': 'jobs:01JQYMYZBABXYR96H96SQ99ZPS',
  'vector_distance': '0.6049451231955',
  'job_title': 'Data Analyst',
  'job_description': 'Analyze large datasets to provide business insights and create data visualizations.',
  'posted': '1743106799.9'},
 {'id': 'jobs:01JQYMYZBAGEBDS270EZADQ1TM',
  'vector_distance': '0.553376108408',
  'job_title': 'Marketing Manager',
  'job_description': 'Develop and implement marketing strategies to drive brand awareness and customer engagement.',
  'posted': '1741123199.9'}]

Hybrid policy on knn with filters

Within the default redis client you can set the HYBRID_POLICY which specifies the filter mode to use during vector search with filters. It can take values BATCHES or ADHOC_BF. Previously this option was not exposed by redisvl.

from redisvl.query.filter import Text

filter = Text("job_description") % "Develop"

query = VectorQuery(
    vector=emb_model.embed("Software Engineer", as_buffer=True),
    vector_field_name="job_embedding",
    return_fields=["job_title", "job_description", "posted"],
    hybrid_policy="BATCHES"
)

query.set_filter(filter)

res = index.query(query)
res

[{'id': 'jobs:01JQYMYZBA6NM6DX9YW35MCHJZ',
  'vector_distance': '0.581857740879',
  'job_title': 'Software Engineer',
  'job_description': 'Develop and maintain web applications using JavaScript, React, and Node.js.',
  'posted': '1743625199.9'},
 {'id': 'jobs:01JQYMYZBAGEBDS270EZADQ1TM',
  'vector_distance': '0.893247783184',
  'job_title': 'Marketing Manager',
  'job_description': 'Develop and implement marketing strategies to drive brand awareness and customer engagement.',
  'posted': '1741123199.9'}]
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