Redis Document Index

Install dependencies

To use RedisDocumentIndex, you need to install extra dependencies with the following command:

pip install "docarray[redis]"

This is the user guide for the RedisDocumentIndex, focusing on special features and configurations of Redis.

Basic usage

This snippet demonstrates the basic usage of RedisDocumentIndex. It defines a document schema with a title and an embedding, creates ten dummy documents with random embeddings, initializes an instance of RedisDocumentIndex to index these documents, and performs a vector similarity search to retrieve the ten most similar documents to a given query vector.

from docarray import BaseDoc, DocList
from docarray.index import RedisDocumentIndex
from docarray.typing import NdArray
import numpy as np

# Define the document schema.
class MyDoc(BaseDoc):
    title: str 
    embedding: NdArray[128]

# Create dummy documents.
docs = DocList[MyDoc](MyDoc(title=f'title #{i}', embedding=np.random.rand(128)) for i in range(10))

# Initialize a new RedisDocumentIndex instance and add the documents to the index.
doc_index = RedisDocumentIndex[MyDoc](host='localhost')
doc_index.index(docs)

# Perform a vector search.
query = np.ones(128)
retrieved_docs = doc_index.find(query, search_field='embedding', limit=10)

Initialize

Before initializing RedisDocumentIndex, make sure that you have a Redis service that you can connect to.

You can create a local Redis service with the following command:

docker run --name redis-stack-server -p 6379:6379 -d redis/redis-stack-server:7.2.0-RC2

Next, you can create RedisDocumentIndex:

from docarray import BaseDoc
from docarray.index import RedisDocumentIndex
from docarray.typing import NdArray


class MyDoc(BaseDoc):
    embedding: NdArray[128]
    text: str


doc_index = RedisDocumentIndex[MyDoc](host='localhost')

Schema definition

In this code snippet, RedisDocumentIndex takes a schema of the form of MyDoc. The Document Index then creates a column for each field in MyDoc.

The column types in the backend database are determined by the type hints of the document's fields. Optionally, you can customize the database types for every field.

Most vector databases need to know the dimensionality of the vectors that will be stored. Here, that is automatically inferred from the type hint of the embedding field: NdArray[128] means that the database will store vectors with 128 dimensions.

PyTorch and TensorFlow support

Instead of using NdArray you can use TorchTensor or TensorFlowTensor and the Document Index will handle that for you. This is supported for all Document Index backends. No need to convert your tensors to NumPy arrays manually!

Using a predefined document as schema

DocArray offers a number of predefined Documents, like ImageDoc and TextDoc. If you try to use these directly as a schema for a Document Index, you will get unexpected behavior: Depending on the backend, an exception will be raised, or no vector index for ANN lookup will be built.

The reason for this is that predefined Documents don't hold information about the dimensionality of their .embedding field. But this is crucial information for any vector database to work properly!

You can work around this problem by subclassing the predefined document and adding the dimensionality information:

Using type hintUsing Field()

from docarray.documents import TextDoc
from docarray.typing import NdArray
from docarray.index import RedisDocumentIndex


class MyDoc(TextDoc):
    embedding: NdArray[128]


doc_index = RedisDocumentIndex[MyDoc]()

from docarray.documents import TextDoc
from docarray.typing import AnyTensor
from docarray.index import RedisDocumentIndex
from pydantic import Field


class MyDoc(TextDoc):
    embedding: AnyTensor = Field(dim=128)


doc_index = RedisDocumentIndex[MyDoc]()

Once you have defined the schema of your Document Index in this way, the data that you index can be either the predefined Document type or your custom Document type.

The next section goes into more detail about data indexing, but note that if you have some TextDocs, ImageDocs etc. that you want to index, you don't need to cast them to MyDoc:

from docarray import DocList

# data of type TextDoc
data = DocList[TextDoc](
    [
        TextDoc(text='hello world', embedding=np.random.rand(128)),
        TextDoc(text='hello world', embedding=np.random.rand(128)),
        TextDoc(text='hello world', embedding=np.random.rand(128)),
    ]
)

# you can index this into Document Index of type MyDoc
doc_index.index(data)

Index

Now that you have a Document Index, you can add data to it, using the index() method:

import numpy as np
from docarray import DocList

# create some random data
docs = DocList[MyDoc](
    [MyDoc(embedding=np.random.rand(128), text=f'text {i}') for i in range(100)]
)

# index the data
doc_index.index(docs)

That call to index() stores all Documents in docs in the Document Index, ready to be retrieved in the next step.

As you can see, DocList[MyDoc] and RedisDocumentIndex[MyDoc] both have MyDoc as a parameter. This means that they share the same schema, and in general, both the Document Index and the data that you want to store need to have compatible schemas.

When are two schemas compatible?

The schemas of your Document Index and data need to be compatible with each other.

Let's say A is the schema of your Document Index and B is the schema of your data. There are a few rules that determine if schema A is compatible with schema B. If any of the following are true, then A and B are compatible:

A and B are the same class
A and B have the same field names and field types
A and B have the same field names, and, for every field, the type of B is a subclass of the type of A

In particular, this means that you can easily index predefined Documents into a Document Index.

Vector search

Now that you have indexed your data, you can perform vector similarity search using the find() method.

You can perform a similarity search and find relevant documents by passing MyDoc or a raw vector to the find() method:

Search by DocumentSearch by raw vector

# create a query document
query = MyDoc(embedding=np.random.rand(128), text='query')

# find similar documents
matches, scores = doc_index.find(query, search_field='embedding', limit=5)

print(f'{matches=}')
print(f'{matches.text=}')
print(f'{scores=}')

# create a query vector
query = np.random.rand(128)

# find similar documents
matches, scores = doc_index.find(query, search_field='embedding', limit=5)

print(f'{matches=}')
print(f'{matches.text=}')
print(f'{scores=}')

To peform a vector search, you need to specify a search_field. This is the field that serves as the basis of comparison between your query and the documents in the Document Index.

In this example you only have one field (embedding) that is a vector, so you can trivially choose that one. In general, you could have multiple fields of type NdArray or TorchTensor or TensorFlowTensor, and you can choose which one to use for the search.

The find() method returns a named tuple containing the closest matching documents and their associated similarity scores.

When searching on the subindex level, you can use the find_subindex() method, which returns a named tuple containing the subindex documents, similarity scores and their associated root documents.

How these scores are calculated depends on the backend, and can usually be configured.

Batched search

You can also search for multiple documents at once, in a batch, using the find_batched() method.

Search by documentsSearch by raw vectors

# create some query documents
queries = DocList[MyDoc](
    MyDoc(embedding=np.random.rand(128), text=f'query {i}') for i in range(3)
)

# find similar documents
matches, scores = doc_index.find_batched(queries, search_field='embedding', limit=5)

print(f'{matches=}')
print(f'{matches[0].text=}')
print(f'{scores=}')

# create some query vectors
query = np.random.rand(3, 128)

# find similar documents
matches, scores = doc_index.find_batched(query, search_field='embedding', limit=5)

print(f'{matches=}')
print(f'{matches[0].text=}')
print(f'{scores=}')

The find_batched() method returns a named tuple containing a list of DocLists, one for each query, containing the closest matching documents and their similarity scores.

Filter

You can filter your documents by using the filter() or filter_batched() method with a corresponding filter query. The query should follow the query language of the Redis.

In the following example let's filter for all the books that are cheaper than 29 dollars:

from docarray import BaseDoc, DocList


class Book(BaseDoc):
    title: str
    price: int


books = DocList[Book]([Book(title=f'title {i}', price=i * 10) for i in range(10)])
book_index = RedisDocumentIndex[Book]()
book_index.index(books)

# filter for books that are cheaper than 29 dollars
query = '@price:[-inf 29]'
cheap_books = book_index.filter(filter_query=query)

assert len(cheap_books) == 3
for doc in cheap_books:
    doc.summary()

Text search

In addition to vector similarity search, the Document Index interface offers methods for text search: text_search(), as well as the batched version text_search_batched().

You can use text search directly on the field of type str:

class NewsDoc(BaseDoc):
    text: str


doc_index = RedisDocumentIndex[NewsDoc]()
index_docs = [
    NewsDoc(id='0', text='this is a news for sport'),
    NewsDoc(id='1', text='this is a news for finance'),
    NewsDoc(id='2', text='this is another news for sport'),
]
doc_index.index(index_docs)
query = 'finance'

# search with text
docs, scores = doc_index.text_search(query, search_field='text')

Hybrid search

Document Index supports atomic operations for vector similarity search, text search and filter search.

To combine these operations into a single, hybrid search query, you can use the query builder that is accessible through build_query():

# Define the document schema.
class SimpleSchema(BaseDoc):
    price: int
    embedding: NdArray[128]

# Create dummy documents.
docs = DocList[SimpleSchema](SimpleSchema(price=i, embedding=np.random.rand(128)) for i in range(10))

doc_index = RedisDocumentIndex[SimpleSchema](host='localhost')
doc_index.index(docs)

query = (
    doc_index.build_query()  # get empty query object
    .find(query=np.random.rand(128), search_field='embedding')  # add vector similarity search
    .filter(filter_query='@price:[-inf 3]')  # add filter search
    .build()
)
# execute the combined query and return the results
results = doc_index.execute_query(query)
print(f'{results=}')

In the example above you can see how to form a hybrid query that combines vector similarity search and filtered search to obtain a combined set of results.

The kinds of atomic queries that can be combined in this way depends on the backend. Some backends can combine text search and vector search, while others can perform filters and vectors search, etc.

Access documents

To retrieve a document from a Document Index you don't necessarily need to perform a fancy search.

You can also access data by the id that was assigned to each document:

# prepare some data
data = DocList[MyDoc](
    MyDoc(embedding=np.random.rand(128), text=f'query {i}') for i in range(3)
)

# remember the Document ids and index the data
ids = data.id
db.index(data)

# access the Documents by id
doc = db[ids[0]]  # get by single id
docs = db[ids]  # get by list of ids

Delete documents

In the same way you can access Documents by id, you can also delete them:

# prepare some data
data = DocList[MyDoc](
    MyDoc(embedding=np.random.rand(128), text=f'query {i}') for i in range(3)
)

# remember the Document ids and index the data
ids = data.id
db.index(data)

# access the Documents by id
del db[ids[0]]  # del by single id
del db[ids[1:]]  # del by list of ids

Update documents

In order to update a Document inside the index, you only need to re-index it with the updated attributes.

First, let's create a schema for our Document Index:

import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from docarray.index import RedisDocumentIndex
class MyDoc(BaseDoc):
    text: str
    embedding: NdArray[128]

Now, we can instantiate our Index and add some data:

docs = DocList[MyDoc](
    [MyDoc(embedding=np.random.rand(128), text=f'I am the first version of Document {i}') for i in range(100)]
)
index = RedisDocumentIndex[MyDoc]()
index.index(docs)
assert index.num_docs() == 100

Let's retrieve our data and check its content:

res = index.find(query=docs[0], search_field='embedding', limit=100)
assert len(res.documents) == 100
for doc in res.documents:
    assert 'I am the first version' in doc.text

Then, let's update all of the text of these documents and re-index them:

for i, doc in enumerate(docs):
    doc.text = f'I am the second version of Document {i}'

index.index(docs)
assert index.num_docs() == 100

When we retrieve them again we can see that their text attribute has been updated accordingly:

res = index.find(query=docs[0], search_field='embedding', limit=100)
assert len(res.documents) == 100
for doc in res.documents:
    assert 'I am the second version' in doc.text

Configuration

This section lays out the configurations and options that are specific to RedisDocumentIndex.

DBConfig

The following configs can be set in DBConfig:

Name	Description	Default
`host`	The host address for the Redis server.	`localhost`
`port`	The port number for the Redis server	6379
`index_name`	The name of the index in the Redis database	`None`. Data will be stored in an index named after the Document type used as schema.
`username`	The username for the Redis server	`None`
`password`	The password for the Redis server	`None`
`text_scorer`	The method for scoring text during text search	`BM25`
`default_column_config`	The default configurations for every column type.	dict

You can pass any of the above as keyword arguments to the __init__() method or pass an entire configuration object.

Field-wise configuration

default_column_config is the default configurations for every column type. Since there are many column types in Redis, you can also consider changing the column config when defining the schema.

class SimpleDoc(BaseDoc):
    tensor: NdArray[128] = Field(algorithm='FLAT', distance='COSINE')


doc_index = RedisDocumentIndex[SimpleDoc]()

RuntimeConfig

The RuntimeConfig dataclass of RedisDocumentIndex consists of batch_size index/get/del operations. You can change batch_size in the following way:

doc_index = RedisDocumentIndex[SimpleDoc]()
doc_index.configure(RedisDocumentIndex.RuntimeConfig(batch_size=128))

You can pass the above as keyword arguments to the configure() method or pass an entire configuration object.

Nested data and subindex search

The examples provided primarily operate on a basic schema where each field corresponds to a straightforward type such as str or NdArray. However, it is also feasible to represent and store nested documents in a Document Index, including scenarios where a document contains a DocList of other documents.

Go to the Nested Data section to learn more.