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:

Defining an IndexSchema
Preparing a sample dataset
Creating a SearchIndex object
Testing rvl CLI functionality
Loading the sample data
Building VectorQuery objects and executing searches
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.

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)
await 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#

Below we will clean up after our work. First, you can optionally flush all data from Redis associated with the index by using the .clear() method. This will leave the secondary index in place for future insertions or updates.

But if you want to clean up everything, including the index, just use .delete() which will by default remove the index AND the underlying data.

# (optionally) clear all data from Redis associated with the index
await index.clear()

# but the index is still in place
await index.exists()

# remove / delete the index in its entirety
await index.delete()