Getting Started with RedisVL

redisvl is a versatile Python library with an integrated CLI, designed to enhance AI applications using Redis. This guide will walk you through the following steps:

1. Defining an IndexSchema

2. Preparing a sample dataset

3. Creating a SearchIndex object

4. Testing rvl CLI functionality

5. Loading the sample data

6. Building VectorQuery objects and executing searches

7. Updating a SearchIndex object

…and more!

Prerequisites:

• Ensure redisvl is installed in your Python environment.

• Have a running instance of Redis Stack or Redis Cloud.

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Define an IndexSchema

The IndexSchema maintains crucial index configuration and field definitions to enable search with Redis. For ease of use, the schema can be constructed from a python dictionary or yaml file.

Example Schema Creation

Consider a dataset with user information, including job, age, credit_score, and a 3-dimensional user_embedding vector.

You must also decide on a Redis index name and key prefix to use for this dataset. Below are example schema definitions in both YAML and Dict format.

YAML Definition:

version: '0.1.0'

index:
  name: user_simple
  prefix: user_simple_docs

fields:
    - name: user
      type: tag
    - name: credit_score
      type: tag
    - name: job
      type: text
    - name: age
      type: numeric
    - name: user_embedding
      type: vector
      attrs:
        algorithm: flat
        dims: 3
        distance_metric: cosine
        datatype: float32

Store this in a local file, such as schema.yaml, for RedisVL usage.

Python Dictionary:

schema = {
    "index": {
        "name": "user_simple",
        "prefix": "user_simple_docs",
    },
    "fields": [
        {"name": "user", "type": "tag"},
        {"name": "credit_score", "type": "tag"},
        {"name": "job", "type": "text"},
        {"name": "age", "type": "numeric"},
        {
            "name": "user_embedding",
            "type": "vector",
            "attrs": {
                "dims": 3,
                "distance_metric": "cosine",
                "algorithm": "flat",
                "datatype": "float32"
            }
        }
    ]
}

Sample Dataset Preparation

Below, create a mock dataset with user, job, age, credit_score, and user_embedding fields. The user_embedding vectors are synthetic examples for demonstration purposes.

For more information on creating real-world embeddings, refer to this article.

import numpy as np


data = [
    {
        'user': 'john',
        'age': 1,
        'job': 'engineer',
        'credit_score': 'high',
        'user_embedding': np.array([0.1, 0.1, 0.5], dtype=np.float32).tobytes()
    },
    {
        'user': 'mary',
        'age': 2,
        'job': 'doctor',
        'credit_score': 'low',
        'user_embedding': np.array([0.1, 0.1, 0.5], dtype=np.float32).tobytes()
    },
    {
        'user': 'joe',
        'age': 3,
        'job': 'dentist',
        'credit_score': 'medium',
        'user_embedding': np.array([0.9, 0.9, 0.1], dtype=np.float32).tobytes()
    }
]

As seen above, the sample user_embedding vectors are converted into bytes. Using the NumPy, this is fairly trivial.

Create a SearchIndex

With the schema and sample dataset ready, instantiate a SearchIndex:

from redisvl.index import SearchIndex

index = SearchIndex.from_dict(schema)
# or use .from_yaml('schema_file.yaml')

Now we also need to facilitate a Redis connection. There are a few ways to do this:

• Create & manage your own client connection (recommended)

• Provide a simple Redis URL and let RedisVL connect on your behalf

Bring your own Redis connection instance

This is ideal in scenarios where you have custom settings on the connection instance or if your application will share a connection pool:

from redis import Redis

client = Redis.from_url("redis://localhost:6379")

index.set_client(client)
# optionally provide an async Redis client object to enable async index operations

Let the index manage the connection instance

This is ideal for simple cases:

index.connect("redis://localhost:6379")
# optionally use an async client by passing use_async=True

<redisvl.index.index.SearchIndex at 0x7f8670a51190>

Create the underlying index

Now that we are connected to Redis, we need to run the create command.

index.create(overwrite=True)

Note that at this point, the index has no entries. Data loading follows.

Inspect with the rvl CLI

Use the rvl CLI to inspect the created index and its fields:

!rvl index listall

11:53:23 [RedisVL] INFO   Indices:
11:53:23 [RedisVL] INFO   1. user_simple

!rvl index info -i user_simple

Index Information:
╭──────────────┬────────────────┬──────────────────────┬─────────────────┬────────────╮
│ Index Name   │ Storage Type   │ Prefixes             │ Index Options   │   Indexing │
├──────────────┼────────────────┼──────────────────────┼─────────────────┼────────────┤
│ user_simple  │ HASH           │ ['user_simple_docs'] │ []              │          0 │
╰──────────────┴────────────────┴──────────────────────┴─────────────────┴────────────╯
Index Fields:
╭────────────────┬────────────────┬─────────┬────────────────┬────────────────╮
│ Name           │ Attribute      │ Type    │ Field Option   │ Option Value   │
├────────────────┼────────────────┼─────────┼────────────────┼────────────────┤
│ user           │ user           │ TAG     │ SEPARATOR      │ ,              │
│ credit_score   │ credit_score   │ TAG     │ SEPARATOR      │ ,              │
│ job            │ job            │ TEXT    │ WEIGHT         │ 1              │
│ age            │ age            │ NUMERIC │                │                │
│ user_embedding │ user_embedding │ VECTOR  │                │                │
╰────────────────┴────────────────┴─────────┴────────────────┴────────────────╯

Load Data to SearchIndex

Load the sample dataset to Redis:

keys = index.load(data)

print(keys)

['user_simple_docs:d424b73c516442f7919cc11ed3bb1882', 'user_simple_docs:6da16f88342048e79b3500bec5448805', 'user_simple_docs:ef5a590ef85e4d4888fd8ebe79ae1e8c']

By default, load will create a unique Redis “key” as a combination of the index key prefix and a UUID. You can also customize the key by providing direct keys or pointing to a specified id_field on load.

Upsert the index with new data

Upsert data by using the load method again:

# Add more data
new_data = [{
    'user': 'tyler',
    'age': 9,
    'job': 'engineer',
    'credit_score': 'high',
    'user_embedding': np.array([0.1, 0.3, 0.5], dtype=np.float32).tobytes()
}]
keys = index.load(new_data)

print(keys)

['user_simple_docs:9806a362604f4700b17513cc94fcf10d']

Creating VectorQuery Objects

Next we will create a vector query object for our newly populated index. This example will use a simple vector to demonstrate how vector similarity works. Vectors in production will likely be much larger than 3 floats and often require Machine Learning models (i.e. Huggingface sentence transformers) or an embeddings API (Cohere, OpenAI). redisvl provides a set of Vectorizers to assist in vector creation.

from redisvl.query import VectorQuery
from jupyterutils import result_print

query = VectorQuery(
    vector=[0.1, 0.1, 0.5],
    vector_field_name="user_embedding",
    return_fields=["user", "age", "job", "credit_score", "vector_distance"],
    num_results=3
)

Executing queries

With our VectorQuery object defined above, we can execute the query over the SearchIndex using the query method.

results = index.query(query)
result_print(results)

vector_distance	user	age	job	credit_score
0	john	1	engineer	high
0	mary	2	doctor	low
0.0566299557686	tyler	9	engineer	high

Using an Asynchronous Redis Client

The AsyncSearchIndex class along with an async Redis python client allows for queries, index creation, and data loading to be done asynchronously. This is the recommended route for working with redisvl in production-like settings.

from redisvl.index import AsyncSearchIndex
from redis.asyncio import Redis

client = Redis.from_url("redis://localhost:6379")

index = AsyncSearchIndex.from_dict(schema)
index.set_client(client)

# execute the vector query async
results = await index.query(query)
result_print(results)

vector_distance	user	age	job	credit_score
0	john	1	engineer	high
0	mary	2	doctor	low
0.0566299557686	tyler	9	engineer	high

Updating a schema

In some scenarios, it makes sense to update the index schema. With Redis and redisvl, this is easy because Redis can keep the underlying data in place while you change or make updates to the index configuration.

So for our scenario, let’s imagine we want to reindex this data in 2 ways:

• by using a Tag type for job field instead of Text

• by using an hnsw vector index for the user_embedding field instead of a flat vector index

# Modify this schema to have what we want

index.schema.remove_field("job")
index.schema.remove_field("user_embedding")
index.schema.add_fields([
    {"name": "job", "type": "tag"},
    {
        "name": "user_embedding",
        "type": "vector",
        "attrs": {
            "dims": 3,
            "distance_metric": "cosine",
            "algorithm": "hnsw",
            "datatype": "float32"
        }
    }
])

# Run the index update but keep underlying data in place
await index.create(overwrite=True, drop=False)

11:53:25 redisvl.index.index INFO   Index already exists, overwriting.

# Execute the vector query async
results = await index.query(query)
result_print(results)

vector_distance	user	age	job	credit_score
0	john	1	engineer	high
0	mary	2	doctor	low
0.0566299557686	tyler	9	engineer	high

Check Index Stats

Use the rvl CLI to check the stats for the index:

!rvl stats -i user_simple

Statistics:
╭─────────────────────────────┬─────────────╮
│ Stat Key                    │ Value       │
├─────────────────────────────┼─────────────┤
│ num_docs                    │ 4           │
│ num_terms                   │ 0           │
│ max_doc_id                  │ 4           │
│ num_records                 │ 20          │
│ percent_indexed             │ 1           │
│ hash_indexing_failures      │ 0           │
│ number_of_uses              │ 2           │
│ bytes_per_record_avg        │ 1           │
│ doc_table_size_mb           │ 0.00044632  │
│ inverted_sz_mb              │ 1.90735e-05 │
│ key_table_size_mb           │ 0.000138283 │
│ offset_bits_per_record_avg  │ nan         │
│ offset_vectors_sz_mb        │ 0           │
│ offsets_per_term_avg        │ 0           │
│ records_per_doc_avg         │ 5           │
│ sortable_values_size_mb     │ 0           │
│ total_indexing_time         │ 1.796       │
│ total_inverted_index_blocks │ 11          │
│ vector_index_sz_mb          │ 0.235603    │
╰─────────────────────────────┴─────────────╯

Cleanup

# clean up the index
await index.delete()