Redis for AI and Search

https://redis.io/docs/latest/develop/ai/
Admin
Redis for AI and Search enables storing, indexing, and querying vector embeddings for AI...
Tokens:329,693
Snippets:2,849
Trust Score:10
Update:3 months ago
Show doc for...
Tokens:
Raw
### Full Setup and Query Example

Source: https://redis.io/docs/latest/develop/ai/search-and-query/query/range

A complete example demonstrating index creation, JSON object population, and range query execution.

```java
import io.lettuce.core.*;
import io.lettuce.core.api.async.RedisAsyncCommands;

import io.lettuce.core.search.arguments.*;
import io.lettuce.core.search.SearchReply;

import io.lettuce.core.json.JsonPath;
import io.lettuce.core.json.JsonParser;
import io.lettuce.core.json.JsonObject;
import io.lettuce.core.api.StatefulRedisConnection;

import java.util.*;
import java.util.concurrent.CompletableFuture;

public class QueryRangeExample {

    public void run() {
        RedisClient redisClient = RedisClient.create("redis://localhost:6379");

        try (StatefulRedisConnection<String, String> connection = redisClient.connect()) {
            RedisAsyncCommands<String, String> asyncCommands = connection.async();

            List<FieldArgs<String>> bicycleSchema = Arrays.asList(
                    TextFieldArgs.<String> builder().name("$.brand").as("brand").build(),
                    TextFieldArgs.<String> builder().name("$.model").as("model").build(),
                    TextFieldArgs.<String> builder().name("$.description").as("description").build(),
                    NumericFieldArgs.<String> builder().name("$.price").as("price").build(),
                    TagFieldArgs.<String> builder().name("$.condition").as("condition").build());

            CreateArgs<String, String> bicycleCreateArgs = CreateArgs.<String, String> builder().on(CreateArgs.TargetType.JSON)
                    .withPrefix("bicycle:").build();

            asyncCommands.ftCreate("idx:bicycle", bicycleCreateArgs, bicycleSchema).toCompletableFuture().join();

            JsonParser parser = asyncCommands.getJsonParser();

            List<JsonObject> bicycleJsons = Arrays.asList(parser.createJsonObject()
                    .put("brand", parser.createJsonValue("\"Velorim\"")).put("model", parser.createJsonValue("\"Jigger\""))
                    .put("description", parser
                            .createJsonValue("\"Small and powerful, the Jigger is the best ride for the smallest of tikes! "
                                    + "This is the tiniest kids’ pedal bike on the market available without a coaster brake, the Jigger "
                                    + "is the vehicle of choice for the rare tenacious little rider raring to go.\""))
                    .put("price", parser.createJsonValue("270")).put("condition", parser.createJsonValue("\"new\"")),
                    parser.createJsonObject().put("brand", parser.createJsonValue("\"Bicyk\""))
                            .put("model", parser.createJsonValue("\"Hillcraft\""))
                            .put("description",
                                    parser.createJsonValue("\"Kids want to ride with as little weight as possible. Especially "
                                            + "on an incline! They may be at the age when a 27.5'' wheel bike is just too clumsy coming "
                                            + "off a 24'' bike. The Hillcraft 26 is just the solution they need!\""))
                            .put("price", parser.createJsonValue("1200")).put("condition", parser.createJsonValue("\"used\"")),
                    parser.createJsonObject().put("brand", parser.createJsonValue("\"Nord\""))
                            .put("model", parser.createJsonValue("\"Chook air 5\""))
                            .put("description",
                                    parser.createJsonValue("\"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.\""))
                            .put("price", parser.createJsonValue("815")).put("condition", parser.createJsonValue("\"used\"")),
                    parser.createJsonObject().put("brand", parser.createJsonValue("\"Eva\""))
                            .put("model", parser.createJsonValue("\"Eva 291\""))
                            .put("description",
                                    parser.createJsonValue("\"The sister company to Nord, Eva launched in 2005 as the first "))
```

--------------------------------

### Setup and Index Creation for Numeric Queries

Source: https://redis.io/docs/latest/develop/ai/search-and-query/query/exact-match

Full setup example including connection, index creation with a numeric field, and data ingestion.

```csharp
using NRedisStack.RedisStackCommands;
using NRedisStack.Search;
using NRedisStack.Search.Literals.Enums;
using NRedisStack.Tests;
using StackExchange.Redis;



public class QueryEmExample
{
    public void Run()
    {
        var muxer = ConnectionMultiplexer.Connect("localhost:6379");
        var db = muxer.GetDatabase();

        FTCreateParams idxParams = new FTCreateParams()
            .AddPrefix("bicycle:")
            .On(IndexDataType.JSON);

        Schema bikeSchema = new Schema()
            .AddTextField(new FieldName("$.description", "description"))
            .AddNumericField(new FieldName("$.price", "price"))
            .AddTagField(new FieldName("$.condition", "condition"));

        db.FT().Create("idx:bicycle", idxParams, bikeSchema);


        var bicycles = new object[]
            {
                new
                {
                    brand = "Velorim",
                    model = "Jigger",
                    price = 270,
                    description = "Small and powerful, the Jigger is the best ride " +
                                    "for the smallest of tikes! This is the tiniest " +
                                    "kids’ pedal bike on the market available without" +
                                    " a coaster brake, the Jigger is the vehicle of " +
                                    "choice for the rare tenacious little rider " +
                                    "raring to go.",
                    condition = "used"
                },
                new
                {
                    brand = "Bicyk",
                    model = "Hillcraft",
                    price = 1200,
                    description = "Kids want to ride with as little weight as possible." +
                        " Especially on an incline! They may be at the age " +
                        "when a 27.5 inch wheel bike is just too clumsy coming " +
                        "off a 24 inch bike. The Hillcraft 26 is just the solution" +
                        " they need!",
                    condition = "used",
                },
                new
                {
                    brand = "Nord",
                    model = "Chook air 5",
                    price = 815,
                    description = "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.",
                    condition = "used",
                },
                new
                {
                    brand = "Eva",
                    model = "Eva 291",
                    price = 3400,
                    description = "The sister company to Nord, Eva launched in 2005 as the" +
                        " first and only women-dedicated bicycle brand. Designed" +
                        " by women for women, allEva bikes are optimized for the" +
                        " feminine physique using analytics from a body metrics" +
                        " database. If you like 29ers, try the Eva 291. It’s a " +
                        "brand new bike for 2022.. This full-suspension, " +
                        "cross-country ride has been designed for velocity. The" +
                        " 291 has 100mm of front and rear travel, a superlight " +
                        "aluminum frame and fast-rolling 29-inch wheels. Yippee!",
                    condition = "used",
                },
                new
                {
                    brand = "Noka Bikes",
                    model = "Kahuna",
                    price = 3200,
                    description = "Whether you want to try your hand at XC racing or are " +
```

--------------------------------

### Full Geospatial Indexing and Querying Example

Source: https://redis.io/docs/latest/develop/ai/search-and-query/query/geo-spatial

Complete setup including index creation, data loading, and executing radius and geometric shape queries.

```python
import json
import sys
import redis
from redis.commands.json.path import Path
from redis.commands.search.field import GeoField, GeoShapeField
from redis.commands.search.index_definition import IndexDefinition, IndexType
from redis.commands.search.query import Query

r = redis.Redis(decode_responses=True)

# create index
schema = (
    GeoField("$.store_location", as_name="store_location"),
    GeoShapeField("$.pickup_zone", coord_system=GeoShapeField.FLAT, as_name="pickup_zone")
)

index = r.ft("idx:bicycle")
index.create_index(
    schema,
    definition=IndexDefinition(prefix=["bicycle:"], index_type=IndexType.JSON),
)

# load data
with open("data/query_em.json") as f:
    bicycles = json.load(f)

pipeline = r.pipeline(transaction=False)
for bid, bicycle in enumerate(bicycles):
    pipeline.json().set(f'bicycle:{bid}', Path.root_path(), bicycle)
pipeline.execute()

params_dict = {"lon": -0.1778, "lat": 51.5524, "radius": 20, "units": "mi"}
q = Query("@store_location:[$lon $lat $radius $units]").dialect(2)
res = index.search(q, query_params=params_dict)
print(res)
# >>> Result{1 total, docs: [Document {'id': 'bicycle:5', ...

params_dict = {"bike": "POINT(-0.1278 51.5074)"}
q = Query("@pickup_zone:[CONTAINS $bike]").dialect(3)
res = index.search(q, query_params=params_dict)
print(res.total) # >>> 1

params_dict = {"europe": "POLYGON((-25 35, 40 35, 40 70, -25 70, -25 35))"}
q = Query("@pickup_zone:[WITHIN $europe]").dialect(3)
res = index.search(q, query_params=params_dict)
print(res.total) # >>> 5
```

--------------------------------

### Install and configure the CLI

Source: https://redis.io/docs/latest/develop/ai/context-engine/context-retriever/install

Installs the CLI via pip and sets up the configuration profile.

```bash
pip install redis-context-retriever

ctxctl config set default_output_format table --profile onprem
export CTX_PROFILE=onprem

ctxctl config set api_url "<admin_service_ingress_url>"
ctxctl config set mcp_url "<mcp_service_ingress_url>"
```

--------------------------------

### Install RediSearch Prerequisites Script

Source: https://redis.io/docs/latest/develop/ai/search-and-query/deprecated/development

Navigate to the 'install' directory and execute the setup scripts to install prerequisites for building and testing RediSearch. 'sudo' is used for system-wide installations but is not required in a Docker environment.

```bash
cd ./install
./install_script.sh sudo
./install_boost.sh 1.83.0

```

--------------------------------

### Install the Helm chart

Source: https://redis.io/docs/latest/develop/ai/context-engine/context-retriever/install

Installs the chart and waits for the pods to reach a ready state.

```bash
helm install cr redis-ai/redis-context-retriever \
  --version $CR_VERSION \
  --namespace $NS \
  --set redis.addr="<your-redis-host>:<port>" \
  --set secrets.existingSecret=cr-config \
  --set license.existingSecret=cr-license

kubectl wait --for=condition=ready pod \
  -l app.kubernetes.io/name=redis-context-retriever \
  -n $NS \
  --timeout=180s
```

--------------------------------

### MigrationWizard Output Example

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/index_migration

Example console output showing the interactive steps and the final generated migration patch.

```text
Loaded 600 documents into 'wizard_products'
Building a migration plan for index 'wizard_products'
Current schema:
- Index name: wizard_products
- Storage type: hash
  - name (text)
  - category (tag)
  - embedding (vector)

Choose an action:
1. Add field        (text, tag, numeric, geo)
2. Update field     (sortable, weight, separator, vector config)
3. Remove field
4. Rename field     (rename field in all documents)
5. Rename index     (change index name)
6. Change prefix    (rename all keys)
7. Preview patch    (show pending changes as YAML)
8. Finish
Enter a number: 2
Updatable fields:
1. name (text)
2. category (tag)
3. embedding (vector)
Select a field to update by number or name: embedding
Current vector config for 'embedding':
  algorithm: FLAT
  datatype: float32
  distance_metric: cosine
  dims: 8 (cannot be changed)

Leave blank to keep current value.
  Algorithm: vector search method (FLAT=brute force, HNSW=graph, SVS-VAMANA=compressed graph)
Algorithm [current: FLAT]: 
  Datatype: float16, float32, bfloat16, float64, int8, uint8
            (float16 reduces memory ~50%, int8/uint8 reduce ~75%)
Datatype [current: float32]: float16
  Distance metric: how similarity is measured (cosine, l2, ip)
Distance metric [current: cosine]: 

Choose an action:
1. Add field        (text, tag, numeric, geo)
2. Update field     (sortable, weight, separator, vector config)
3. Remove field
4. Rename field     (rename field in all documents)
5. Rename index     (change index name)
6. Change prefix    (rename all keys)
7. Preview patch    (show pending changes as YAML)
8. Finish
Enter a number: 8

Wizard patch:
version: 1
changes:
  add_fields: []
  remove_fields: []
  update_fields:
  - name: embedding
    attrs:
      datatype: float16
    options: {}
  rename_fields: []
  index: {}

Wizard plan mode: drop_recreate
Wizard warnings: ['Index downtime is required']
```

--------------------------------

### Install RedisVL with Multiple Optional Dependencies

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/installation

Install redisvl with a combination of multiple optional dependencies simultaneously. This allows for a more customized installation.

```bash
$ pip install redisvl[mcp,openai,cohere,sentence-transformers]

```

--------------------------------

### Install in Air-Gapped Environment

Source: https://redis.io/docs/latest/develop/ai/context-engine/context-retriever/install

Creates a registry secret and installs the chart using the local directory and internal image repositories.

```bash
kubectl create secret docker-registry regcred \
  --namespace $NS \
  --docker-server=registry.internal.example.com \
  --docker-username="<user>" --docker-password="<pass>"

helm install cr ./redis-context-retriever \
  --namespace $NS \
  --set redis.addr="<your-redis-host>:<port>" \
  --set secrets.existingSecret=cr-config \
  --set license.existingSecret=cr-license \
  --set admin.image.repository=registry.internal.example.com/context-retriever-admin \
  --set mcp.image.repository=registry.internal.example.com/context-retriever \
  --set admin.image.tag="${CR_VERSION}" \
  --set mcp.image.tag="${CR_VERSION}" \
  --set 'imagePullSecrets[0].name=regcred'
```

--------------------------------

### Install Python Client

Source: https://redis.io/docs/latest/develop/ai/context-engine/context-retriever

Install the Python client package which includes the ctxctl CLI tool.

```bash
pip install redis-context-retriever
```

--------------------------------

### Install RedisVL with SQL support

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/sql_to_redis_queries

Install the necessary package to enable SQL-like query translation in RedisVL.

```bash
pip install redisvl[sql-redis]
```

--------------------------------

### Install RedisVL with All Optional Dependencies

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/installation

Install redisvl with all available optional dependencies. Use this command when you need the full feature set of RedisVL.

```bash
$ pip install redisvl[all]

```

--------------------------------

### Migration Output Example

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/index_migration

Example output showing the results of a successful migration.

```text
Wizard migration result: succeeded
Wizard schema match:     True
Wizard doc count match:  True
Wizard backup dir:       /Users/nitin.kanukolanu/workspace/redis-vl-python/docs/user_guide/wizard_migration_backups
Wizard backup prefixes:  ['/Users/nitin.kanukolanu/workspace/redis-vl-python/docs/user_guide/wizard_migration_backups/migration_backup_wizard_products_def8cdf8']
Wizard embedding dtype:  float16
```

--------------------------------

### Migration Resume Output Example

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/index_migration

Example console output showing the executor skipping completed phases during a resumed migration.

```text
Checkpoint says 6 batch(es) were already quantized.
Current phase: completed

Re-running apply with the same backup_dir to exercise resume detection...
[enumerate] skipped (resume from backup)
[drop] skipped (already dropped)
[quantize] skipped (already completed)
[create] Creating index with new schema...
[create] done (0.004s)
[index] Waiting for re-indexing...
[index] 600/600 docs (100%)
[index] done (0.001s)
[validate] Validating migration...
[validate] done (0.008s)
Resume result:      succeeded
Resume backup dir:  /Users/nitin.kanukolanu/workspace/redis-vl-python/docs/user_guide/migration_backups
Resume prefixes:    ['/Users/nitin.kanukolanu/workspace/redis-vl-python/docs/user_guide/migration_backups/migration_backup_products_0a3e27b8']
```

--------------------------------

### Batch Migration Status Output Example

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/migrate-indexes

Example output format for the batch status command.

```text
Batch Migration Status
======================
Batch ID: batch_20260320_100000
Started: 2026-03-20T10:00:00Z
Updated: 2026-03-20T10:25:00Z

Completed: 2
  - products_idx: success (10:02:30)
  - users_idx: failed - Redis connection timeout (10:05:45)

In Progress: inventory_idx
Remaining: 1 (analytics_idx)
```

--------------------------------

### Install VertexAI dependencies

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/vectorizers

Install the required Google Cloud AI platform library.

```bash
pip install google-cloud-aiplatform>=1.26
```

--------------------------------

### Install RedisVL

Source: https://redis.io/docs/latest/develop/ai/redisvl/api/cli

Install the RedisVL package to gain access to the rvl command.

```bash
pip install redisvl
```

--------------------------------

### Import necessary modules

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/vectorizers

Initial setup for importing required modules.

```python
# import necessary modules
import os
```

--------------------------------

### Install RedisVL MCP with Provider

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/mcp

Install the MCP extra along with specific provider dependencies like OpenAI.

```bash
pip install redisvl[mcp,openai]
```

--------------------------------

### Group and Collect Movies Example

Source: https://redis.io/docs/latest/develop/ai/search-and-query/advanced-concepts/aggregations

A practical example demonstrating how to group movies by genre and collect the top 5 movies per group sorted by rating.

```redis
FT.AGGREGATE idx:movies "*"
    LOAD *
    GROUPBY 1 @genre
      REDUCE COLLECT 10 FIELDS 1 * SORTBY 2 @rating DESC LIMIT 0 5 AS top_movies
    SORTBY 1 @genre LIMIT 0 50
```

--------------------------------

### Install Pre-commit Hooks

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/installation

Install and run pre-commit hooks to ensure code quality before committing. This helps maintain a consistent codebase.

```bash
$ pre-commit install

Run hooks manually on all files:
$ pre-commit run --all-files

```

--------------------------------

### Install RedisVL with Escaped Brackets for ZSH

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/installation

When using ZSH, brackets in pip install commands must be escaped. This example shows how to install redisvl with OpenAI support in ZSH.

```bash
$ pip install redisvl\[openai\]

```

--------------------------------

### Setup and Indexing in C#

Source: https://redis.io/docs/latest/develop/ai/search-and-query/query/full-text

Initializes a connection, defines a schema, and creates a search index for JSON documents.

```csharp
using NRedisStack.RedisStackCommands;
using NRedisStack.Search;
using NRedisStack.Search.Literals.Enums;
using NRedisStack.Tests;
using StackExchange.Redis;



public class QueryFtExample
{
    public void Run()
    {
        var muxer = ConnectionMultiplexer.Connect("localhost:6379");
        var db = muxer.GetDatabase();

        Schema bikeSchema = new Schema()
            .AddTextField(new FieldName("$.brand", "brand"))
            .AddTextField(new FieldName("$.model", "model"))
            .AddTextField(new FieldName("$.description", "description"));

        FTCreateParams bikeParams = new FTCreateParams()
            .AddPrefix("bicycle:")
            .On(IndexDataType.JSON);

        db.FT().Create("idx:bicycle", bikeParams, bikeSchema);

        var bicycles = new object[]
        {
            new
            {
                brand = "Velorim",
                model = "Jigger",
                price = 270,
                description = "Small and powerful, the Jigger is the best ride " +
                                "for the smallest of tikes! This is the tiniest " +
                                "kids’ pedal bike on the market available without" +
                                " a coaster brake, the Jigger is the vehicle of " +
                                "choice for the rare tenacious little rider " +
                                "raring to go.",
                condition = "used"
            },
            new
            {
                brand = "Bicyk",
                model = "Hillcraft",
                price = 1200,
                description = "Kids want to ride with as little weight as possible." +
                    " Especially on an incline! They may be at the age " +
                    "when a 27.5 inch wheel bike is just too clumsy coming " +
                    "off a 24 inch bike. The Hillcraft 26 is just the solution" +
                    " they need!",
                condition = "used",
            },
            new
            {
                brand = "Nord",
                model = "Chook air 5",
                price = 815,
                description = "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.",
                condition = "used",
            },
            new
            {
                brand = "Eva",
                model = "Eva 291",
                price = 3400,
                description = "The sister company to Nord, Eva launched in 2005 as the" +
                    " first and only women-dedicated bicycle brand. Designed" +
                    " by women for women, allEva bikes are optimized for the" +
                    " feminine physique using analytics from a body metrics" +
                    " database. If you like 29ers, try the Eva 291. It’s a " +
                    "brand new bike for 2022.. This full-suspension, " +
                    "cross-country ride has been designed for velocity. The" +
                    " 291 has 100mm of front and rear travel, a superlight " +
                    "aluminum frame and fast-rolling 29-inch wheels. Yippee!",
                condition = "used",
            },
            new
            {
                brand = "Noka Bikes",
                model = "Kahuna",
                price = 3200,
                description = "Whether you want to try your hand at XC racing or are " +
                    "looking for a lively trail bike that's just as inspiring" +
                    " on the climbs as it is over rougher ground, the Wilder" +
                    " is one heck of a bike built specifically for short women." +
                    " Both the frames and components have been tweaked to " +
                    "include a women’s saddle, different bars and unique " +
                    "colourway.",
                condition = "used",
            },
            new
            {
                brand = "Breakout",
                model = "XBN 2.1 Alloy",
                price = 810,
                description = "The XBN 2.1 Alloy is our entry-level road bike – but that’s" +
                    " not to say that it’s a basic machine. With an internal " +
                    "
```

--------------------------------

### Development Help Summary

Source: https://redis.io/docs/latest/develop/ai/search-and-query/deprecated/development

Use `make help` to get a quick summary of available development features and commands.

```bash
make help
```

--------------------------------

### Apply a definitions file with plan

Source: https://redis.io/docs/latest/develop/ai/featureform/providers

Executes a Python entrypoint to preview the desired state without applying changes.

```bash
ff apply --workspace <workspace-id> --file examples/featureform/docs/resources.py --plan
```

--------------------------------

### Run RediSearch in a Docker Container

Source: https://redis.io/docs/latest/develop/ai/search-and-query/deprecated/development

Start a Debian Bullseye container, mount the current directory for build access, and enter the container's bash shell. This isolates installations within the container.

```bash
search=$(docker run -d -it -v $PWD:/build debian:bullseye bash)
docker exec -it $search bash

```

--------------------------------

### Example wizard session: quantizing vectors

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/migrate-indexes

Demonstrates the interactive flow for updating vector configuration, such as changing the datatype for quantization.

```text
Choose an action:
1. Add field        (text, tag, numeric, geo)
2. Update field     (sortable, weight, separator, vector config)
3. Remove field
4. Rename field     (rename field in all documents)
5. Rename index     (change index name)
6. Change prefix    (rename all keys)
7. Preview patch    (show pending changes as YAML)
8. Finish
Enter a number: 2

Updatable fields:
1. title (text)
2. embedding (vector)
Select a field to update by number or name: 2

Current vector config for 'embedding':
  algorithm: HNSW
  datatype: float32
  distance_metric: cosine
  dims: 384 (cannot be changed)
  m: 16
  ef_construction: 200

Leave blank to keep current value.
  Algorithm: vector search method (FLAT=brute force, HNSW=graph, SVS-VAMANA=compressed graph)
Algorithm [current: HNSW]:
  Datatype: float16, float32, bfloat16, float64, int8, uint8
            (float16 reduces memory ~50%, int8/uint8 reduce ~75%)
Datatype [current: float32]: float16
  Distance metric: how similarity is measured (cosine, l2, ip)
Distance metric [current: cosine]:
  M: number of connections per node (higher=better recall, more memory)
M [current: 16]:
  EF_CONSTRUCTION: build-time search depth (higher=better recall, slower build)
EF_CONSTRUCTION [current: 200]:

Choose an action:
...
8. Finish
Enter a number: 8

Migration plan written to /path/to/migration_plan.yaml
Mode: drop_recreate
Supported: True
```

--------------------------------

### Java-Sync Quick-Start for Geo Indexing

Source: https://redis.io/docs/latest/develop/ai/search-and-query/indexing/geoindex

Demonstrates creating a geo index, adding JSON documents with location data, and performing a radius search.

```go
	geoCreateResult, err := rdb.FTCreate(ctx,
		"productidx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []interface{}{"product:"},
		},
		&redis.FieldSchema{
			FieldName: "$.location",
			As:        "location",
			FieldType: redis.SearchFieldTypeGeo,
		},
	).Result()

	if err != nil {
		panic(err)
	}

	fmt.Println(geoCreateResult) // >>> OK

	prd46885 := map[string]interface{}{
		"description": "Navy Blue Slippers",
		"price":       45.99,
		"city":        "Denver",
		"location":    "-104.991531, 39.742043",
	}

	gjResult1, err := rdb.JSONSet(ctx, "product:46885", "$", prd46885).Result()

	if err != nil {
		panic(err)
	}

	fmt.Println(gjResult1) // >>> OK

	prd46886 := map[string]interface{}{
		"description": "Bright Green Socks",
		"price":       25.50,
		"city":        "Fort Collins",
		"location":    "-105.0618814,40.5150098",
	}

	gjResult2, err := rdb.JSONSet(ctx, "product:46886", "$", prd46886).Result()

	if err != nil {
		panic(err)
	}

	fmt.Println(gjResult2) // >>> OK

	geoQueryResult, err := rdb.FTSearch(ctx, "productidx",
		"@location:[-104.800644 38.846127 100 mi]",
	).Result()

	if err != nil {
		panic(err)
	}

	fmt.Println(geoQueryResult)
	// >>> {1 [{product:46885...
```

--------------------------------

### Install VoyageAI dependency

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/vectorizers

Install the VoyageAI Python client.

```bash
pip install voyageai
```

--------------------------------

### Install Cohere dependency

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/vectorizers

Install the Cohere Python client.

```bash
pip install cohere
```

--------------------------------

### Initialize Index and Data with Go-Redis

Source: https://redis.io/docs/latest/develop/ai/search-and-query/query/exact-match

Sets up a JSON index with various field types and prepares sample data for searching.

```go
package example_commands_test

import (
	"context"
	"fmt"
	"slices"
	"strings"

	"github.com/redis/go-redis/v9"
)

func ExampleClient_query_em() {
	ctx := context.Background()

	rdb := redis.NewClient(&redis.Options{
		Addr:     "localhost:6379",
		Password: "", // no password docs
		DB:       0,  // use default DB
		Protocol: 2,
	})


	_, err := rdb.FTCreate(ctx, "idx:bicycle",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []interface{}{"bicycle:"},
		},
		&redis.FieldSchema{
			FieldName: "$.brand",
			As:        "brand",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.model",
			As:        "model",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.description",
			As:        "description",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.price",
			As:        "price",
			FieldType: redis.SearchFieldTypeNumeric,
		},
		&redis.FieldSchema{
			FieldName: "$.condition",
			As:        "condition",
			FieldType: redis.SearchFieldTypeTag,
		},
	).Result()

	if err != nil {
		panic(err)
	}

	exampleJsons := []map[string]interface{}{
		{
			"pickup_zone": "POLYGON((-74.0610 40.7578, -73.9510 40.7578, -73.9510 40.6678, " +
				"-74.0610 40.6678, -74.0610 40.7578))",
```

--------------------------------

### Verify Installation

Source: https://redis.io/docs/latest/develop/ai/redisvl/api/cli

Check the installed version of the rvl CLI.

```bash
rvl version
```

--------------------------------

### Install RedisVL MCP

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/mcp

Install the core MCP functionality for RedisVL.

```bash
pip install redisvl[mcp]
```

--------------------------------

### Aggregation output example

Source: https://redis.io/docs/latest/develop/ai/search-and-query/query/aggregation

Example output format for the aggregation query.

```text
1) "3"
2) 1) "condition"
   1) "refurbished"
   2) "bicycles"
   3) 1) "bicycle:9"
3) 1) "condition"
   1) "used"
   2) "bicycles"
   3) 1) "bicycle:1"
      1) "bicycle:2"
      2) "bicycle:3"
      3) "bicycle:4"
4) 1) "condition"
   1) "new"
   2) "bicycles"
   3) 1) "bicycle:0"
      1) "bicycle:5"
      2) "bicycle:6"
      3) "bicycle:8"
      4) "bicycle:7"
```

--------------------------------

### Example Backup File Output

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/index_migration

Sample output showing the file paths and sizes for generated migration backup files.

```text
/Users/nitin.kanukolanu/workspace/redis-vl-python/docs/user_guide/migration_backups/migration_backup_products_0a3e27b8.data  (47,730 bytes)
/Users/nitin.kanukolanu/workspace/redis-vl-python/docs/user_guide/migration_backups/migration_backup_products_0a3e27b8.header  (209 bytes)
```

--------------------------------

### Install RedisVL with Optional Dependencies

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/installation

Install redisvl with specific optional dependencies for features like embeddings providers or MCP server support. Use these commands to tailor your installation to your needs.

```bash
# Vectorizer providers
$ pip install redisvl[openai]              # OpenAI embeddings
$ pip install redisvl[cohere]              # Cohere embeddings and reranking
$ pip install redisvl[mistralai]           # Mistral AI embeddings
$ pip install redisvl[voyageai]            # Voyage AI embeddings and reranking
$ pip install redisvl[sentence-transformers]  # HuggingFace local embeddings
$ pip install redisvl[vertexai]            # Google Vertex AI embeddings
$ pip install redisvl[bedrock]             # AWS Bedrock embeddings

# Other optional features
$ pip install redisvl[mcp]                 # RedisVL MCP server support (Python 3.10+)
$ pip install redisvl[langcache]           # LangCache managed service integration
$ pip install redisvl[sql-redis]           # SQL query support

```

--------------------------------

### Install Mistral AI dependency

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/vectorizers

Install the Mistral AI Python client.

```bash
pip install mistralai
```

--------------------------------

### Install OpenAI dependency

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/vectorizers

Install the required openai package to use the OpenAITextVectorizer.

```bash
pip install openai
```

--------------------------------

### Full Python search implementation

Source: https://redis.io/docs/latest/develop/ai/search-and-query/query/full-text

A complete example showing index creation, data loading, and various search queries including prefix, suffix, and fuzzy matching.

```Python
import json
import sys
import redis
from redis.commands.json.path import Path
from redis.commands.search.field import TextField, NumericField, TagField
from redis.commands.search.index_definition import IndexDefinition, IndexType
from redis.commands.search.query import NumericFilter, Query

r = redis.Redis(decode_responses=True)

# create index
schema = (
    TextField("$.brand", as_name="brand"),
    TextField("$.model", as_name="model"),
    TextField("$.description", as_name="description"),
)

index = r.ft("idx:bicycle")
index.create_index(
    schema,
    definition=IndexDefinition(prefix=["bicycle:"], index_type=IndexType.JSON),
)

# load data
with open("data/query_em.json") as f:
    bicycles = json.load(f)

pipeline = r.pipeline(transaction=False)
for bid, bicycle in enumerate(bicycles):
    pipeline.json().set(f'bicycle:{bid}', Path.root_path(), bicycle)
pipeline.execute()

res = index.search(Query("@description: kids"))
print(res.total)
# >>> 2

res = index.search(Query("@model: ka*"))
print(res.total)
# >>> 1

res = index.search(Query("@brand: *bikes"))
print(res.total)
# >>> 2

res = index.search(Query("%optamized%"))
print(res)
# >>> Result{1 total, docs: [Document {'id': 'bicycle:3', 'payload': None, 'json': '{"pickup_zone":"POLYGON((-80.2433 25.8067, -80.1333 25.8067, -80.1333 25.6967, -80.2433 25.6967, -80.2433 25.8067))","store_location":"-80.1918,25.7617","brand":"Eva","model":"Eva 291","price":3400,"description":"The sister company to Nord, Eva launched in 2005 as the first and only women-dedicated bicycle brand. Designed by women for women, allEva bikes are optimized for the feminine physique using analytics from a body metrics database. If you like 29ers, try the Eva 291. It’s a brand new bike for 2022.. This full-suspension, cross-country ride has been designed for velocity. The 291 has 100mm of front and rear travel, a superlight aluminum frame and fast-rolling 29-inch wheels. Yippee!","condition":"used"}'}]}

res = index.search(Query("%%optamised%%"))
print(res)
# >>> Result{1 total, docs: [Document {'id': 'bicycle:3', 'payload': None, 'json': '{"pickup_zone":"POLYGON((-80.2433 25.8067, -80.1333 25.8067, -80.1333 25.6967, -80.2433 25.6967, -80.2433 25.8067))","store_location":"-80.1918,25.7617","brand":"Eva","model":"Eva 291","price":3400,"description":"The sister company to Nord, Eva launched in 2005 as the first and only women-dedicated bicycle brand. Designed by women for women, allEva bikes are optimized for the feminine physique using analytics from a body metrics database. If you like 29ers, try the Eva 291. It’s a brand new bike for 2022.. This full-suspension, cross-country ride has been designed for velocity. The 291 has 100mm of front and rear travel, a superlight aluminum frame and fast-rolling 29-inch wheels. Yippee!","condition":"used"}'}]}
```

--------------------------------

### Run the migration wizard

Source: https://redis.io/docs/latest/develop/ai/redisvl/api/cli

Starts an interactive session to generate a schema patch and migration plan.

```bash
rvl migrate wizard -i my_index --plan-out plan.yaml
```

--------------------------------

### Set the installation version

Source: https://redis.io/docs/latest/develop/ai/context-engine/context-retriever/install

Defines the version variable for the Helm chart installation.

```bash
export CR_VERSION="<version-from-the-helm-search-output>"
```

--------------------------------

### Quick Start: Batch Migration Workflow

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/migrate-indexes

A complete workflow for creating a schema patch, generating a batch plan, applying the migration, and checking the status.

```bash
# 1. Create a shared patch (applies to any index with an 'embedding' field)
cat > quantize_patch.yaml << 'EOF'
version: 1
changes:
  update_fields:
    - name: embedding
      attrs:
        datatype: float16
EOF

# 2. Create a batch plan for all indexes matching a pattern
rvl migrate batch-plan \
  --pattern "*_idx" \
  --schema-patch quantize_patch.yaml \
  --plan-out batch_plan.yaml \
  --url redis://localhost:6379

# 3. Apply the batch plan
rvl migrate batch-apply \
  --plan batch_plan.yaml \
  --backup-dir ./migration_backups \
  --accept-data-loss \
  --url redis://localhost:6379

# 4. Check status
rvl migrate batch-status --state batch_state.yaml
```

--------------------------------

### Connect to Redis via CLI

Source: https://redis.io/docs/latest/develop/ai/redisvl/api/cli

Examples of connecting to a Redis instance using different configuration methods.

```bash
export REDIS_URL="redis://localhost:6379"
rvl index listall
```

```bash
rvl index listall --host myredis.example.com --port 6380
```

```bash
rvl index listall --user admin --password secret --ssl
```

--------------------------------

### Install Amazon Bedrock dependencies

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/vectorizers

Install the necessary RedisVL package with Bedrock support.

```bash
pip install 'redisvl[bedrock]'  # Installs boto3
```

--------------------------------

### Setup and Helper Functions for Index Migration

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/index_migration

Initializes the environment with necessary imports and provides a utility function to delete keys matching a specific pattern.

```python
import os
import glob
import numpy as np
import yaml

from redisvl.index import SearchIndex
from redisvl.redis.utils import array_to_buffer
from redisvl.query import VectorQuery

REDIS_URL = "redis://localhost:6379"
INDEX_NAME = "products"
DIMS = 8
N_DOCS = 600

np.random.seed(42)


def delete_matching(client, pattern, batch_size=500):
    deleted = 0
    batch = []
    for key in client.scan_iter(match=pattern, count=batch_size):
        batch.append(key)
        if len(batch) >= batch_size:
            deleted += client.delete(*batch)
            batch = []
    if batch:
        deleted += client.delete(*batch)
    return deleted
```

--------------------------------

### Example wizard session: adding a field

Source: https://redis.io/docs/latest/develop/ai/redisvl/user_guide/how_to_guides/migrate-indexes

Demonstrates the interactive flow for adding a new tag field to an existing index.

```text
Building a migration plan for index 'myindex'
Current schema:
- Index name: myindex
- Storage type: hash
  - title (text)
  - embedding (vector)

Choose an action:
1. Add field        (text, tag, numeric, geo)
2. Update field     (sortable, weight, separator, vector config)
3. Remove field
4. Rename field     (rename field in all documents)
5. Rename index     (change index name)
6. Change prefix    (rename all keys)
7. Preview patch    (show pending changes as YAML)
8. Finish
Enter a number: 1

Field name: category
Field type options: text, tag, numeric, geo
Field type: tag
  Sortable: enables sorting and aggregation on this field
Sortable [y/n]: n
  Separator: character that splits multiple values (default: comma)
Separator [leave blank to keep existing/default]: |

Choose an action:
1. Add field        (text, tag, numeric, geo)
2. Update field     (sortable, weight, separator, vector config)
3. Remove field
4. Rename field     (rename field in all documents)
5. Rename index     (change index name)
6. Change prefix    (rename all keys)
7. Preview patch    (show pending changes as YAML)
8. Finish
Enter a number: 8

Migration plan written to /path/to/migration_plan.yaml
Mode: drop_recreate
Supported: True
Warnings:
- Index downtime is required
```

--------------------------------

### Initialize and use VertexAIVectorizer

Source: https://redis.io/docs/latest/develop/ai/redisvl/api/vectorizer

Demonstrates basic initialization, caching configuration, batch processing, and multimodal embedding usage with VertexAI.

```python
# Basic usage
vectorizer = VertexAIVectorizer(
    model="textembedding-gecko",
    api_config={
        "project_id": "your_gcp_project_id", # OR set GCP_PROJECT_ID
        "location": "your_gcp_location",     # OR set GCP_LOCATION
    })
embedding = vectorizer.embed("Hello, world!")

# With caching enabled
from redisvl.extensions.cache.embeddings import EmbeddingsCache
cache = EmbeddingsCache(name="vertexai_embeddings_cache")

vectorizer = VertexAIVectorizer(
    model="textembedding-gecko",
    api_config={
        "project_id": "your_gcp_project_id",
        "location": "your_gcp_location",
    },
    cache=cache
)

# First call will compute and cache the embedding
embedding1 = vectorizer.embed("Hello, world!")

# Second call will retrieve from cache
embedding2 = vectorizer.embed("Hello, world!")

# Batch embedding of multiple texts
embeddings = vectorizer.embed_many(
    ["Hello, world!", "Goodbye, world!"],
    batch_size=2
)

# Multimodal usage
from vertexai.vision_models import Image, Video

vectorizer = VertexAIVectorizer(
    model="multimodalembedding@001",
    api_config={
        "project_id": "your_gcp_project_id", # OR set GCP_PROJECT_ID
        "location": "your_gcp_location",     # OR set GCP_LOCATION
    }
)
text_embedding = vectorizer.embed("Hello, world!")
image_embedding = vectorizer.embed(Image.load_from_file("path/to/your/image.jpg"))
video_embedding = vectorizer.embed(Video.load_from_file("path/to/your/video.mp4"))
```
Tokens:
Raw