Index and query vectors

Learn how to index and query vector embeddings with Redis

Redis Query Engine lets you index vector fields in hash or JSON objects (see the Vectors reference page for more information). Among other things, vector fields can store text embeddings, which are AI-generated vector representations of the semantic information in pieces of text. The vector distance between two embeddings indicates how similar they are semantically. By comparing the similarity of an embedding generated from some query text with embeddings stored in hash or JSON fields, Redis can retrieve documents that closely match the query in terms of their meaning.

In the example below, we use the Hugot library to generate vector embeddings to store and index with Redis Query Engine. The code is first demonstrated for hash documents with a separate section to explain the differences with JSON documents.

Note:
From v9.8.0 onwards, go-redis uses query dialect 2 by default. Redis query engine methods such as FTSearch() will explicitly request this dialect, overriding the default set for the server. See Query dialects for more information.

Initialize

First, install go-redis if you haven't already done so. Then, install Hugot using the following command:

go get github.com/knights-analytics/hugot

Add the following imports to your module's main program file:

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Add a helper function

The Hugot model outputs the embeddings as a []float32 array. If you are storing your documents as hash objects, then you must convert this array to a byte string before adding it as a hash field. The function shown below uses Go's binary package to produce the byte string:

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Note that if you are using JSON objects to store your documents instead of hashes, then you should store the []float32 array directly without first converting it to a byte string (see Differences with JSON documents below).

Create the index

In the main() function, connect to Redis and delete any index previously created with the name vector_idx:

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Next, create the index. The schema in the example below specifies hash objects for storage and includes three fields: the text content to index, a tag field to represent the "genre" of the text, and the embedding vector generated from the original text content. The embedding field specifies HNSW indexing, the L2 vector distance metric, Float32 values to represent the vector's components, and 384 dimensions, as required by the all-MiniLM-L6-v2 embedding model.

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Create an embedder instance

You need an instance of the FeatureExtractionPipeline class to generate the embeddings. Use the code below to create an instance that uses the sentence-transformers/all-MiniLM-L6-v2 model:

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Add data

You can now supply the data objects, which will be indexed automatically when you add them with HSet(), as long as you use the doc: prefix specified in the index definition.

Use the RunPipeline() method of FeatureExtractionPipeline as shown below to create the embeddings that represent the content fields. This method takes an array of strings and outputs a corresponding array of FeatureExtractionOutput objects. The Embeddings field of FeatureExtractionOutput contains the array of float values that you need for the index. Use the floatsToBytes() function defined above to convert this array to a byte string.

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Run a query

After you have created the index and added the data, you are ready to run a query. To do this, you must create another embedding vector from your chosen query text. Redis calculates the similarity between the query vector and each embedding vector in the index as it runs the query. It then ranks the results in order of this numeric similarity value.

The code below creates the query embedding using RunPipeline(), as with the indexing, and passes it as a parameter when the query executes (see Vector search for more information about using query parameters with embeddings).

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

The code is now ready to run, but note that it may take a while to complete when you run it for the first time (which happens because Hugot must download the all-MiniLM-L6-v2 model data before it can generate the embeddings). When you run the code, it outputs the following text:

ID: doc:0, Distance:2.96992516518, Content:'That is a very happy person'
ID: doc:1, Distance:17.3678302765, Content:'That is a happy dog'
ID: doc:2, Distance:43.7771987915, Content:'Today is a sunny day'

The results are ordered according to the value of the vector_distance field, with the lowest distance indicating the greatest similarity to the query. As you would expect, the result for doc:0 with the content text "That is a very happy person" is the result that is most similar in meaning to the query text "That is a happy person".

Differences with JSON documents

Indexing JSON documents is similar to hash indexing, but there are some important differences. JSON allows much richer data modelling with nested fields, so you must supply a path in the schema to identify each field you want to index. However, you can declare a short alias for each of these paths (using the As option) to avoid typing it in full for every query. Also, you must set OnJSON to true when you create the index.

The code below shows these differences, but the index is otherwise very similar to the one created previously for hashes:

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Use JSONSet() to add the data instead of HSet(). The maps that specify the fields have the same structure as the ones used for HSet().

An important difference with JSON indexing is that the vectors are specified using lists instead of binary strings. The loop below is similar to the one used previously to add the hash data, but it doesn't use the floatsToBytes() function to encode the float32 array.

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

The query is almost identical to the one for the hash documents. This demonstrates how the right choice of aliases for the JSON paths can save you having to write complex queries. An important thing to notice is that the vector parameter for the query is still specified as a binary string (using the floatsToBytes() method), even though the data for the embedding field of the JSON was specified as an array.

package main

import (
	"context"
	"encoding/binary"
	"fmt"
	"math"

	"github.com/knights-analytics/hugot"
	"github.com/redis/go-redis/v9"
)


func floatsToBytes(fs []float32) []byte {
	buf := make([]byte, len(fs)*4)

	for i, f := range fs {
		u := math.Float32bits(f)
		binary.NativeEndian.PutUint32(buf[i*4:], u)
	}

	return buf
}


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

	rdb.FTDropIndexWithArgs(ctx,
		"vector_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err := rdb.FTCreate(ctx,
		"vector_idx",
		&redis.FTCreateOptions{
			OnHash: true,
			Prefix: []any{"doc:"},
		},
		&redis.FieldSchema{
			FieldName: "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	// Create a Hugot session
	session, err := hugot.NewGoSession()
	if err != nil {
		panic(err)
	}
	defer func() {
		err := session.Destroy()
		if err != nil {
			panic(err)
		}
	}()

	// Download the model
	downloadOptions := hugot.NewDownloadOptions()
	downloadOptions.OnnxFilePath = "onnx/model.onnx" // Specify which ONNX file to use
	modelPath, err := hugot.DownloadModel("sentence-transformers/all-MiniLM-L6-v2", "./models/", downloadOptions)
	if err != nil {
		panic(err)
	}

	// Create feature extraction pipeline configuration
	config := hugot.FeatureExtractionConfig{
		ModelPath: modelPath,
		Name:      "embeddingPipeline",
	}

	// Create the feature extraction pipeline
	embeddingPipeline, err := hugot.NewPipeline(session, config)
	if err != nil {
		panic(err)
	}

	sentences := []string{
		"That is a very happy person",
		"That is a happy dog",
		"Today is a sunny day",
	}

	tags := []string{
		"persons", "pets", "weather",
	}

	// Generate embeddings using Hugot
	embeddingResult, err := embeddingPipeline.RunPipeline(sentences)
	if err != nil {
		panic(err)
	}

	// Extract the embeddings from the result
	embeddings := embeddingResult.Embeddings

	for i, emb := range embeddings {
		buffer := floatsToBytes(emb)

		_, err = rdb.HSet(ctx,
			fmt.Sprintf("doc:%v", i),
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": buffer,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding using Hugot
	queryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	buffer := floatsToBytes(queryResult.Embeddings[0])

	results, err := rdb.FTSearchWithArgs(ctx,
		"vector_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": buffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range results.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}

	rdb.FTDropIndexWithArgs(ctx,
		"vector_json_idx",
		&redis.FTDropIndexOptions{
			DeleteDocs: true,
		},
	)

	_, err = rdb.FTCreate(ctx,
		"vector_json_idx",
		&redis.FTCreateOptions{
			OnJSON: true,
			Prefix: []any{"jdoc:"},
		},
		&redis.FieldSchema{
			FieldName: "$.content",
			As:        "content",
			FieldType: redis.SearchFieldTypeText,
		},
		&redis.FieldSchema{
			FieldName: "$.genre",
			As:        "genre",
			FieldType: redis.SearchFieldTypeTag,
		},
		&redis.FieldSchema{
			FieldName: "$.embedding",
			As:        "embedding",
			FieldType: redis.SearchFieldTypeVector,
			VectorArgs: &redis.FTVectorArgs{
				HNSWOptions: &redis.FTHNSWOptions{
					Dim:            384,
					DistanceMetric: "L2",
					Type:           "FLOAT32",
				},
			},
		},
	).Result()
	if err != nil {
		panic(err)
	}

	for i, emb := range embeddings {
		_, err = rdb.JSONSet(ctx,
			fmt.Sprintf("jdoc:%v", i),
			"$",
			map[string]any{
				"content":   sentences[i],
				"genre":     tags[i],
				"embedding": emb,
			},
		).Result()

		if err != nil {
			panic(err)
		}
	}

	// Generate query embedding for JSON search using Hugot
	jsonQueryResult, err := embeddingPipeline.RunPipeline([]string{
		"That is a happy person",
	})

	if err != nil {
		panic(err)
	}

	jsonBuffer := floatsToBytes(jsonQueryResult.Embeddings[0])

	jsonResults, err := rdb.FTSearchWithArgs(ctx,
		"vector_json_idx",
		"*=>[KNN 3 @embedding $vec AS vector_distance]",
		&redis.FTSearchOptions{
			Return: []redis.FTSearchReturn{
				{FieldName: "vector_distance"},
				{FieldName: "content"},
			},
			DialectVersion: 2,
			Params: map[string]any{
				"vec": jsonBuffer,
			},
		},
	).Result()

	if err != nil {
		panic(err)
	}

	for _, doc := range jsonResults.Docs {
		fmt.Printf(
			"ID: %v, Distance:%v, Content:'%v'\n",
			doc.ID, doc.Fields["vector_distance"], doc.Fields["content"],
		)
	}
}

Apart from the jdoc: prefixes for the keys, the result from the JSON query is the same as for hash:

ID: jdoc:0, Distance:2.96992516518, Content:'That is a very happy person'
ID: jdoc:1, Distance:17.3678302765, Content:'That is a happy dog'
ID: jdoc:2, Distance:43.7771987915, Content:'Today is a sunny day'

Learn more

See Vector search for more information about the indexing options, distance metrics, and query format for vectors.

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