Reputation: 23607
I am trying to do something where I have 2 advisors, one is a typical RAG vector store for my QuestionAnswerAdvisor and one is using redis for an embedding based cache of questions. The original QuestionAnswerAdvisor works great but when I add the redis store I don't see how to qualify the injected bean to make sure it is using redis instead of pinecone. I tried this
@Bean(name = "openAiBuildClient")
public ChatClient buildClient(
@Qualifier("openAiChatClientBuilder") ChatClient.Builder openAiBuilder,
MessageChatMemoryAdvisor messageChatMemoryAdvisor,
RedisVectorStore redisVectorStore,
PineconeVectorStore pineconeVectorStore
) {
spring:
application:
name: "my-spring-ai"
ai:
vectorstore:
redis:
index: spring-ai-example
uri: redis://localhost:6379
pinecone:
apiKey: ${PINECONE_API_KEY}
index-name: spring-ai-example
But it doesn't work like that...
Description:
Parameter 2 of method buildClient in org.example.config.clients.OpenAIClientConfig required a bean of type 'org.springframework.ai.vectorstore.RedisVectorStore' that could not be found.
The injection point has the following annotations:
- @org.springframework.beans.factory.annotation.Qualifier("openAiBuildClient")
So is it possible to have multiple vector stores at the same time with an @Qualifier
? If so how do I do it?
It looks like this is why it isn't working
RedisVectorStoreAutoConfiguration:
Did not match:
- @ConditionalOnBean (types: org.springframework.data.redis.connection.jedis.JedisConnectionFactory; SearchStrategy: all) did not find any beans of type org.springframework.data.redis.connection.jedis.JedisConnectionFactory (OnBeanCondition)
Matched:
- @ConditionalOnClass found required classes 'redis.clients.jedis.JedisPooled', 'org.springframework.data.redis.connection.jedis.JedisConnectionFactory', 'org.springframework.ai.vectorstore.RedisVectorStore', 'org.springframework.ai.embedding.EmbeddingModel' (OnClassCondition)
But I have
implementation "org.springframework.ai:spring-ai-redis-store-spring-boot-starter:${springAiVersion}"
implementation 'org.springframework.boot:spring-boot-starter-data-redis'
I also tried
implementation 'org.springframework.data:spring-data-redis'
I also don't see anything about needing to declare a JedisConnectionFactory
in the redis docs.
https://docs.spring.io/spring-ai/reference/api/vectordbs/redis.html#_manual_configuration
I added (even though it must be missed in the docs)
@Bean
public JedisConnectionFactory redisConnectionFactory() {
return new JedisConnectionFactory();
}
And now I get
Description:
The bean 'vectorStore', defined in class path resource [org/springframework/ai/autoconfigure/vectorstore/redis/RedisVectorStoreAutoConfiguration.class], could not be registered. A bean with that name has already been defined in class path resource [org/springframework/ai/autoconfigure/vectorstore/pinecone/PineconeVectorStoreAutoConfiguration.class] and overriding is disabled.
Upvotes: 0
Views: 170
Reputation: 23607
I am not going to accept this answer because it is a hack work around, however, manually creating the VectorStore worked...
package org.example.vectorstore;
import io.micrometer.observation.ObservationRegistry;
import org.springframework.ai.embedding.BatchingStrategy;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.vectorstore.RedisVectorStore;
import org.springframework.ai.vectorstore.observation.VectorStoreObservationConvention;
import org.springframework.beans.factory.ObjectProvider;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.data.redis.connection.Message;
import org.springframework.data.redis.connection.RedisConnectionFactory;
import org.springframework.data.redis.connection.jedis.JedisConnectionFactory;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.serializer.StringRedisSerializer;
import redis.clients.jedis.JedisPooled;
// TODO: add conditional
@Configuration
public class CustomRedisVectorStoreConfig {
@Bean
public JedisConnectionFactory redisConnectionFactory() {
return new JedisConnectionFactory();
}
@Bean
public RedisTemplate<String, Message> redisTemplate(RedisConnectionFactory connectionFactory) {
RedisTemplate<String, Message> template = new RedisTemplate<>();
template.setConnectionFactory(connectionFactory);
template.setKeySerializer(new StringRedisSerializer());
template.setValueSerializer(new StringRedisSerializer());
return template;
}
@Bean(name = "customRedisVectorStore")
public RedisVectorStore redisVectorStore(EmbeddingModel embeddingModel, JedisConnectionFactory jedisConnectionFactory,
ObjectProvider<ObservationRegistry> observationRegistry,
ObjectProvider<VectorStoreObservationConvention> customObservationConvention,
BatchingStrategy batchingStrategy) {
var config = RedisVectorStore.RedisVectorStoreConfig.builder()
.withIndexName("spring-ai-example")
.withPrefix("prefix")
.build();
return new RedisVectorStore(config, embeddingModel,
new JedisPooled(jedisConnectionFactory.getHostName(), jedisConnectionFactory.getPort()),
true, observationRegistry.getIfUnique(() -> ObservationRegistry.NOOP),
customObservationConvention.getIfAvailable(() -> null), batchingStrategy);
}
}
Then I could inject it in with a qualifier...
@Qualifier("customRedisVectorStore") VectorStore redisVectorStore,
@Qualifier("vectorStore") VectorStore pineconeVectorStore
The problem here is the documentation do not say you should need to declare public JedisConnectionFactory redisConnectionFactory()
or public RedisTemplate<String, Message> redisTemplate(RedisConnectionFactory connectionFactory)
Upvotes: 0