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【新增】AI 知识库：文档向量化 demo
This commit is contained in:
xiaoxin
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/*
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* Copyright 2023 - 2024 the original author or authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.springframework.ai.autoconfigure.vectorstore.redis;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.vectorstore.RedisVectorStore;
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import org.springframework.ai.vectorstore.RedisVectorStore.RedisVectorStoreConfig;
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import org.springframework.boot.autoconfigure.AutoConfiguration;
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import org.springframework.boot.autoconfigure.condition.ConditionalOnClass;
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import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean;
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import org.springframework.boot.autoconfigure.data.redis.RedisAutoConfiguration;
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import org.springframework.boot.context.properties.EnableConfigurationProperties;
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import org.springframework.context.annotation.Bean;
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import org.springframework.data.redis.connection.jedis.JedisConnectionFactory;
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import redis.clients.jedis.JedisPooled;
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/**
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* TODO @xin 先拿 spring-ai 最新代码覆盖，1.0.0-M1 跟 redis 自动配置会冲突
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*
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* @author Christian Tzolov
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* @author Eddú Meléndez
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*/
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@AutoConfiguration(after = RedisAutoConfiguration.class)
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@ConditionalOnClass({JedisPooled.class, JedisConnectionFactory.class, RedisVectorStore.class, EmbeddingModel.class})
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//@ConditionalOnBean(JedisConnectionFactory.class)
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@EnableConfigurationProperties(RedisVectorStoreProperties.class)
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public class RedisVectorStoreAutoConfiguration {
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@Bean
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@ConditionalOnMissingBean
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public RedisVectorStore vectorStore(EmbeddingModel embeddingModel, RedisVectorStoreProperties properties,
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JedisConnectionFactory jedisConnectionFactory) {
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var config = RedisVectorStoreConfig.builder()
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.withIndexName(properties.getIndex())
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.withPrefix(properties.getPrefix())
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.build();
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return new RedisVectorStore(config, embeddingModel,
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new JedisPooled(jedisConnectionFactory.getHostName(), jedisConnectionFactory.getPort()),
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properties.isInitializeSchema());
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}
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}
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@@ -0,0 +1,456 @@
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/*
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* Copyright 2023 - 2024 the original author or authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* https://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.springframework.ai.vectorstore;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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import org.springframework.ai.document.Document;
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import org.springframework.ai.embedding.EmbeddingModel;
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import org.springframework.ai.vectorstore.filter.FilterExpressionConverter;
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import org.springframework.beans.factory.InitializingBean;
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import org.springframework.util.Assert;
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import org.springframework.util.CollectionUtils;
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import redis.clients.jedis.JedisPooled;
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import redis.clients.jedis.Pipeline;
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import redis.clients.jedis.json.Path2;
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import redis.clients.jedis.search.*;
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import redis.clients.jedis.search.Schema.FieldType;
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import redis.clients.jedis.search.schemafields.*;
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import redis.clients.jedis.search.schemafields.VectorField.VectorAlgorithm;
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import java.text.MessageFormat;
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import java.util.*;
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import java.util.function.Function;
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import java.util.function.Predicate;
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import java.util.stream.Collectors;
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/**
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* The RedisVectorStore is for managing and querying vector data in a Redis database. It
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* offers functionalities like adding, deleting, and performing similarity searches on
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* documents.
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*
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* The store utilizes RedisJSON and RedisSearch to handle JSON documents and to index and
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* search vector data. It supports various vector algorithms (e.g., FLAT, HSNW) for
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* efficient similarity searches. Additionally, it allows for custom metadata fields in
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* the documents to be stored alongside the vector and content data.
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*
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* This class requires a RedisVectorStoreConfig configuration object for initialization,
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* which includes settings like Redis URI, index name, field names, and vector algorithms.
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* It also requires an EmbeddingModel to convert documents into embeddings before storing
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* them.
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*
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* @author Julien Ruaux
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* @author Christian Tzolov
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* @author Eddú Meléndez
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* @see VectorStore
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* @see RedisVectorStoreConfig
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* @see EmbeddingModel
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*/
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public class RedisVectorStore implements VectorStore, InitializingBean {
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public enum Algorithm {
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FLAT, HSNW
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}
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public record MetadataField(String name, FieldType fieldType) {
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public static MetadataField text(String name) {
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return new MetadataField(name, FieldType.TEXT);
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}
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public static MetadataField numeric(String name) {
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return new MetadataField(name, FieldType.NUMERIC);
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}
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public static MetadataField tag(String name) {
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return new MetadataField(name, FieldType.TAG);
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}
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}
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/**
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* Configuration for the Redis vector store.
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*/
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public static final class RedisVectorStoreConfig {
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private final String indexName;
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private final String prefix;
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private final String contentFieldName;
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private final String embeddingFieldName;
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private final Algorithm vectorAlgorithm;
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private final List<MetadataField> metadataFields;
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private RedisVectorStoreConfig() {
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this(builder());
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}
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private RedisVectorStoreConfig(Builder builder) {
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this.indexName = builder.indexName;
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this.prefix = builder.prefix;
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this.contentFieldName = builder.contentFieldName;
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this.embeddingFieldName = builder.embeddingFieldName;
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this.vectorAlgorithm = builder.vectorAlgorithm;
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this.metadataFields = builder.metadataFields;
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}
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/**
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* Start building a new configuration.
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* @return The entry point for creating a new configuration.
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*/
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public static Builder builder() {
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return new Builder();
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}
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/**
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* {@return the default config}
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*/
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public static RedisVectorStoreConfig defaultConfig() {
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return builder().build();
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}
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public static class Builder {
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private String indexName = DEFAULT_INDEX_NAME;
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private String prefix = DEFAULT_PREFIX;
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private String contentFieldName = DEFAULT_CONTENT_FIELD_NAME;
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private String embeddingFieldName = DEFAULT_EMBEDDING_FIELD_NAME;
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private Algorithm vectorAlgorithm = DEFAULT_VECTOR_ALGORITHM;
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private List<MetadataField> metadataFields = new ArrayList<>();
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private Builder() {
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}
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/**
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* Configures the Redis index name to use.
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* @param name the index name to use
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* @return this builder
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*/
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public Builder withIndexName(String name) {
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this.indexName = name;
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return this;
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}
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/**
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* Configures the Redis key prefix to use (default: "embedding:").
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* @param prefix the prefix to use
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* @return this builder
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*/
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public Builder withPrefix(String prefix) {
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this.prefix = prefix;
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return this;
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}
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/**
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* Configures the Redis content field name to use.
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* @param name the content field name to use
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* @return this builder
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*/
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public Builder withContentFieldName(String name) {
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this.contentFieldName = name;
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return this;
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}
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/**
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* Configures the Redis embedding field name to use.
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* @param name the embedding field name to use
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* @return this builder
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*/
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public Builder withEmbeddingFieldName(String name) {
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this.embeddingFieldName = name;
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return this;
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}
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/**
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* Configures the Redis vector algorithmto use.
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* @param algorithm the vector algorithm to use
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* @return this builder
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*/
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public Builder withVectorAlgorithm(Algorithm algorithm) {
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this.vectorAlgorithm = algorithm;
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return this;
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}
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public Builder withMetadataFields(MetadataField... fields) {
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return withMetadataFields(Arrays.asList(fields));
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}
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public Builder withMetadataFields(List<MetadataField> fields) {
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this.metadataFields = fields;
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return this;
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}
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/**
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* {@return the immutable configuration}
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*/
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public RedisVectorStoreConfig build() {
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return new RedisVectorStoreConfig(this);
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}
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}
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}
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private final boolean initializeSchema;
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public static final String DEFAULT_INDEX_NAME = "spring-ai-index";
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public static final String DEFAULT_CONTENT_FIELD_NAME = "content";
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public static final String DEFAULT_EMBEDDING_FIELD_NAME = "embedding";
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public static final String DEFAULT_PREFIX = "embedding:";
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public static final Algorithm DEFAULT_VECTOR_ALGORITHM = Algorithm.HSNW;
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private static final String QUERY_FORMAT = "%s=>[KNN %s @%s $%s AS %s]";
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private static final Path2 JSON_SET_PATH = Path2.of("$");
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private static final String JSON_PATH_PREFIX = "$.";
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private static final Logger logger = LoggerFactory.getLogger(RedisVectorStore.class);
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private static final Predicate<Object> RESPONSE_OK = Predicate.isEqual("OK");
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private static final Predicate<Object> RESPONSE_DEL_OK = Predicate.isEqual(1l);
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private static final String VECTOR_TYPE_FLOAT32 = "FLOAT32";
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private static final String EMBEDDING_PARAM_NAME = "BLOB";
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public static final String DISTANCE_FIELD_NAME = "vector_score";
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private static final String DEFAULT_DISTANCE_METRIC = "COSINE";
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private final JedisPooled jedis;
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private final EmbeddingModel embeddingModel;
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private final RedisVectorStoreConfig config;
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private FilterExpressionConverter filterExpressionConverter;
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public RedisVectorStore(RedisVectorStoreConfig config, EmbeddingModel embeddingModel, JedisPooled jedis,
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boolean initializeSchema) {
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Assert.notNull(config, "Config must not be null");
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Assert.notNull(embeddingModel, "Embedding model must not be null");
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this.initializeSchema = initializeSchema;
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this.jedis = jedis;
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this.embeddingModel = embeddingModel;
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this.config = config;
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this.filterExpressionConverter = new RedisFilterExpressionConverter(this.config.metadataFields);
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}
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public JedisPooled getJedis() {
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return this.jedis;
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}
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@Override
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public void add(List<Document> documents) {
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try (Pipeline pipeline = this.jedis.pipelined()) {
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for (Document document : documents) {
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var embedding = this.embeddingModel.embed(document);
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document.setEmbedding(embedding);
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var fields = new HashMap<String, Object>();
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fields.put(this.config.embeddingFieldName, embedding);
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fields.put(this.config.contentFieldName, document.getContent());
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fields.putAll(document.getMetadata());
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pipeline.jsonSetWithEscape(key(document.getId()), JSON_SET_PATH, fields);
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}
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List<Object> responses = pipeline.syncAndReturnAll();
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Optional<Object> errResponse = responses.stream().filter(Predicate.not(RESPONSE_OK)).findAny();
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if (errResponse.isPresent()) {
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String message = MessageFormat.format("Could not add document: {0}", errResponse.get());
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if (logger.isErrorEnabled()) {
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logger.error(message);
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}
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throw new RuntimeException(message);
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}
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}
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}
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private String key(String id) {
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return this.config.prefix + id;
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}
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@Override
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public Optional<Boolean> delete(List<String> idList) {
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try (Pipeline pipeline = this.jedis.pipelined()) {
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for (String id : idList) {
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pipeline.jsonDel(key(id));
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}
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List<Object> responses = pipeline.syncAndReturnAll();
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Optional<Object> errResponse = responses.stream().filter(Predicate.not(RESPONSE_DEL_OK)).findAny();
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if (errResponse.isPresent()) {
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if (logger.isErrorEnabled()) {
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logger.error("Could not delete document: {}", errResponse.get());
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}
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return Optional.of(false);
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}
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return Optional.of(true);
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}
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}
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@Override
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public List<Document> similaritySearch(SearchRequest request) {
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Assert.isTrue(request.getTopK() > 0, "The number of documents to returned must be greater than zero");
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Assert.isTrue(request.getSimilarityThreshold() >= 0 && request.getSimilarityThreshold() <= 1,
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"The similarity score is bounded between 0 and 1; least to most similar respectively.");
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String filter = nativeExpressionFilter(request);
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String queryString = String.format(QUERY_FORMAT, filter, request.getTopK(), this.config.embeddingFieldName,
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EMBEDDING_PARAM_NAME, DISTANCE_FIELD_NAME);
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List<String> returnFields = new ArrayList<>();
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this.config.metadataFields.stream().map(MetadataField::name).forEach(returnFields::add);
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returnFields.add(this.config.embeddingFieldName);
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returnFields.add(this.config.contentFieldName);
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returnFields.add(DISTANCE_FIELD_NAME);
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var embedding = toFloatArray(this.embeddingModel.embed(request.getQuery()));
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Query query = new Query(queryString).addParam(EMBEDDING_PARAM_NAME, RediSearchUtil.toByteArray(embedding))
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.returnFields(returnFields.toArray(new String[0]))
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.setSortBy(DISTANCE_FIELD_NAME, true)
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.dialect(2);
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SearchResult result = this.jedis.ftSearch(this.config.indexName, query);
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return result.getDocuments()
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.stream()
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.filter(d -> similarityScore(d) >= request.getSimilarityThreshold())
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.map(this::toDocument)
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.toList();
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}
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private Document toDocument(redis.clients.jedis.search.Document doc) {
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var id = doc.getId().substring(this.config.prefix.length());
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var content = doc.hasProperty(this.config.contentFieldName) ? doc.getString(this.config.contentFieldName)
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: null;
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Map<String, Object> metadata = this.config.metadataFields.stream()
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.map(MetadataField::name)
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.filter(doc::hasProperty)
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.collect(Collectors.toMap(Function.identity(), doc::getString));
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metadata.put(DISTANCE_FIELD_NAME, 1 - similarityScore(doc));
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return new Document(id, content, metadata);
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}
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private float similarityScore(redis.clients.jedis.search.Document doc) {
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return (2 - Float.parseFloat(doc.getString(DISTANCE_FIELD_NAME))) / 2;
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}
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private String nativeExpressionFilter(SearchRequest request) {
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if (request.getFilterExpression() == null) {
|
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return "*";
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||||
}
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return "(" + this.filterExpressionConverter.convertExpression(request.getFilterExpression()) + ")";
|
||||
}
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||||
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||||
@Override
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||||
public void afterPropertiesSet() {
|
||||
|
||||
if (!this.initializeSchema) {
|
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return;
|
||||
}
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// If index already exists don't do anything
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if (this.jedis.ftList().contains(this.config.indexName)) {
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return;
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}
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String response = this.jedis.ftCreate(this.config.indexName,
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FTCreateParams.createParams().on(IndexDataType.JSON).addPrefix(this.config.prefix), schemaFields());
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if (!RESPONSE_OK.test(response)) {
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String message = MessageFormat.format("Could not create index: {0}", response);
|
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throw new RuntimeException(message);
|
||||
}
|
||||
}
|
||||
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private Iterable<SchemaField> schemaFields() {
|
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Map<String, Object> vectorAttrs = new HashMap<>();
|
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vectorAttrs.put("DIM", this.embeddingModel.dimensions());
|
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vectorAttrs.put("DISTANCE_METRIC", DEFAULT_DISTANCE_METRIC);
|
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vectorAttrs.put("TYPE", VECTOR_TYPE_FLOAT32);
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List<SchemaField> fields = new ArrayList<>();
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fields.add(TextField.of(jsonPath(this.config.contentFieldName)).as(this.config.contentFieldName).weight(1.0));
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fields.add(VectorField.builder()
|
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.fieldName(jsonPath(this.config.embeddingFieldName))
|
||||
.algorithm(vectorAlgorithm())
|
||||
.attributes(vectorAttrs)
|
||||
.as(this.config.embeddingFieldName)
|
||||
.build());
|
||||
|
||||
if (!CollectionUtils.isEmpty(this.config.metadataFields)) {
|
||||
for (MetadataField field : this.config.metadataFields) {
|
||||
fields.add(schemaField(field));
|
||||
}
|
||||
}
|
||||
return fields;
|
||||
}
|
||||
|
||||
private SchemaField schemaField(MetadataField field) {
|
||||
String fieldName = jsonPath(field.name);
|
||||
switch (field.fieldType) {
|
||||
case NUMERIC:
|
||||
return NumericField.of(fieldName).as(field.name);
|
||||
case TAG:
|
||||
return TagField.of(fieldName).as(field.name);
|
||||
case TEXT:
|
||||
return TextField.of(fieldName).as(field.name);
|
||||
default:
|
||||
throw new IllegalArgumentException(
|
||||
MessageFormat.format("Field {0} has unsupported type {1}", field.name, field.fieldType));
|
||||
}
|
||||
}
|
||||
|
||||
private VectorAlgorithm vectorAlgorithm() {
|
||||
if (config.vectorAlgorithm == Algorithm.HSNW) {
|
||||
return VectorAlgorithm.HNSW;
|
||||
}
|
||||
return VectorAlgorithm.FLAT;
|
||||
}
|
||||
|
||||
private String jsonPath(String field) {
|
||||
return JSON_PATH_PREFIX + field;
|
||||
}
|
||||
|
||||
private static float[] toFloatArray(List<Double> embeddingDouble) {
|
||||
float[] embeddingFloat = new float[embeddingDouble.size()];
|
||||
int i = 0;
|
||||
for (Double d : embeddingDouble) {
|
||||
embeddingFloat[i++] = d.floatValue();
|
||||
}
|
||||
return embeddingFloat;
|
||||
}
|
||||
|
||||
}
|
||||
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Reference in New Issue
Block a user