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Update document for SearchRequest builder changes #1991

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2 changes: 1 addition & 1 deletion spring-ai-docs/src/main/antora/modules/ROOT/pages/api/advisors.adoc
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Expand Up @@ -14,7 +14,7 @@ You can configure existing advisors using the xref:api/chatclient.adoc#_advisor_
var chatClient = ChatClient.builder(chatModel)
.defaultAdvisors(
new MessageChatMemoryAdvisor(chatMemory), // chat-memory advisor
new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults()) // RAG advisor
new QuestionAnswerAdvisor(vectorStore, SearchRequest.builder().build()) // RAG advisor
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@markpollack markpollack Dec 21, 2024

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we don't even need to show SearchRequest.builder().build() as there is an overloaded ctor that does that anyway.

)
.build();

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4 changes: 2 additions & 2 deletions spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chatclient.adoc
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Expand Up @@ -355,7 +355,7 @@ ChatClient.builder(chatModel)
.prompt()
.advisors(
new MessageChatMemoryAdvisor(chatMemory),
new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults())
new QuestionAnswerAdvisor(vectorStore, SearchRequest.builder().build())
)
.user(userText)
.call()
Expand Down Expand Up @@ -424,7 +424,7 @@ public class CustomerSupportAssistant {
""")
.defaultAdvisors(
new MessageChatMemoryAdvisor(chatMemory), // CHAT MEMORY
new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults()), // RAG
new QuestionAnswerAdvisor(vectorStore, SearchRequest.builder().build()), // RAG
new SimpleLoggerAdvisor())
.defaultFunctions("getBookingDetails", "changeBooking", "cancelBooking") // FUNCTION CALLING
.build();
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6 changes: 3 additions & 3 deletions ...docs/src/main/antora/modules/ROOT/pages/api/retrieval-augmented-generation.adoc
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Expand Up @@ -26,13 +26,13 @@ Assuming you have already loaded data into a `VectorStore`, you can perform Retr
----
ChatResponse response = ChatClient.builder(chatModel)
.build().prompt()
.advisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults()))
.advisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.builder().build()))
.user(userText)
.call()
.chatResponse();
----

In this example, the `SearchRequest.defaults()` will perform a similarity search over all documents in the Vector Database.
In this example, the `SearchRequest.builder().build()` will perform a similarity search over all documents in the Vector Database.
To restrict the types of documents that are searched, the `SearchRequest` takes an SQL like filter expression that is portable across all `VectorStores`.

==== Dynamic Filter Expressions
Expand All @@ -42,7 +42,7 @@ Update the `SearchRequest` filter expression at runtime using the `FILTER_EXPRES
[source,java]
----
ChatClient chatClient = ChatClient.builder(chatModel)
.defaultAdvisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults()))
.defaultAdvisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.builder().build()))
.build();

// Update filter expression at runtime
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2 changes: 1 addition & 1 deletion spring-ai-docs/src/main/antora/modules/ROOT/pages/api/testing.adoc
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Expand Up @@ -75,7 +75,7 @@ void testEvaluation() {

ChatResponse response = ChatClient.builder(chatModel)
.build().prompt()
.advisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.defaults()))
.advisors(new QuestionAnswerAdvisor(vectorStore, SearchRequest.builder().build()))
.user(userText)
.call()
.chatResponse();
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71 changes: 60 additions & 11 deletions spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs.adoc
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Expand Up @@ -44,20 +44,69 @@ and the related `SearchRequest` builder:
```java
public class SearchRequest {

public final String query;
private int topK = 4;
private double similarityThreshold = SIMILARITY_THRESHOLD_ALL;
private Filter.Expression filterExpression;
public static final double SIMILARITY_THRESHOLD_ACCEPT_ALL = 0.0;

public static final int DEFAULT_TOP_K = 4;

private String query = "";

public static SearchRequest query(String query) { return new SearchRequest(query); }
private int topK = DEFAULT_TOP_K;

private SearchRequest(String query) { this.query = query; }
private double similarityThreshold = SIMILARITY_THRESHOLD_ACCEPT_ALL;

@Nullable
private Filter.Expression filterExpression;

public SearchRequest topK(int topK) {...}
public SearchRequest similarityThreshold(double threshold) {...}
public SearchRequest similarityThresholdAll() {...}
public SearchRequest filterExpression(Filter.Expression expression) {...}
public SearchRequest filterExpression(String textExpression) {...}
public static Builder from(SearchRequest originalSearchRequest) {
return builder().query(originalSearchRequest.getQuery())
.topK(originalSearchRequest.getTopK())
.similarityThreshold(originalSearchRequest.getSimilarityThreshold())
.filterExpression(originalSearchRequest.getFilterExpression());
}

public static class Builder {

private final SearchRequest searchRequest = new SearchRequest();

public Builder query(String query) {
Assert.notNull(query, "Query can not be null.");
this.searchRequest.query = query;
return this;
}

public Builder topK(int topK) {
Assert.isTrue(topK >= 0, "TopK should be positive.");
this.searchRequest.topK = topK;
return this;
}

public Builder similarityThreshold(double threshold) {
Assert.isTrue(threshold >= 0 && threshold <= 1, "Similarity threshold must be in [0,1] range.");
this.searchRequest.similarityThreshold = threshold;
return this;
}

public Builder similarityThresholdAll() {
this.searchRequest.similarityThreshold = 0.0;
return this;
}

public Builder filterExpression(@Nullable Filter.Expression expression) {
this.searchRequest.filterExpression = expression;
return this;
}

public Builder filterExpression(@Nullable String textExpression) {
this.searchRequest.filterExpression = (textExpression != null)
? new FilterExpressionTextParser().parse(textExpression) : null;
return this;
}

public SearchRequest build() {
return this.searchRequest;
}

}

public String getQuery() {...}
public int getTopK() {...}
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14 changes: 7 additions & 7 deletions ...-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/apache-cassandra.adoc
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Expand Up @@ -142,17 +142,17 @@ And retrieve documents similar to a query:
[source,java]
----
List<Document> results = vectorStore.similaritySearch(
SearchRequest.query("Spring").topK(5));
SearchRequest.builder().query("Spring").topK(5).build());
----

You can also limit results based on a similarity threshold:

[source,java]
----
List<Document> results = vectorStore.similaritySearch(
SearchRequest.query("Spring")
SearchRequest.builder().query("Spring")
.topK(5)
.similarityThreshold(0.5d));
.similarityThreshold(0.5d).build());
----

=== Advanced Configuration
Expand Down Expand Up @@ -192,9 +192,9 @@ For example, you can use either the text expression language:
[source,java]
----
vectorStore.similaritySearch(
SearchRequest.query("The World")
SearchRequest.builder().query("The World")
.topK(5)
.filterExpression("country in ['UK', 'NL'] && year >= 2020"));
.filterExpression("country in ['UK', 'NL'] && year >= 2020").build());
----

or programmatically using the expression DSL:
Expand All @@ -208,9 +208,9 @@ Filter.Expression f = new FilterExpressionBuilder()
).build();

vectorStore.similaritySearch(
SearchRequest.query("The World")
SearchRequest.builder().query("The World")
.topK(5)
.filterExpression(f));
.filterExpression(f).build());
----

The portable filter expressions get automatically converted into link:https://cassandra.apache.org/doc/latest/cassandra/developing/cql/index.html[CQL queries].
Expand Down
8 changes: 4 additions & 4 deletions ...g-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/azure-cosmos-db.adoc
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Expand Up @@ -69,7 +69,7 @@ public class DemoApplication implements CommandLineRunner {
Document document1 = new Document(UUID.randomUUID().toString(), "Sample content1", Map.of("key1", "value1"));
Document document2 = new Document(UUID.randomUUID().toString(), "Sample content2", Map.of("key2", "value2"));
this.vectorStore.add(List.of(document1, document2));
List<Document> results = this.vectorStore.similaritySearch(SearchRequest.query("Sample content").topK(1));
List<Document> results = this.vectorStore.similaritySearch(SearchRequest.builder().query("Sample content").topK(1).build());

log.info("Search results: {}", results);

Expand Down Expand Up @@ -139,9 +139,9 @@ Document document2 = new Document("2", "A document about the Netherlands", this.
vectorStore.add(List.of(document1, document2));

FilterExpressionBuilder builder = new FilterExpressionBuilder();
List<Document> results = vectorStore.similaritySearch(SearchRequest.query("The World")
List<Document> results = vectorStore.similaritySearch(SearchRequest.builder().query("The World")
.topK(10)
.filterExpression((this.builder.in("country", "UK", "NL")).build()));
.filterExpression((this.builder.in("country", "UK", "NL")).build()).build());
----

== Setting up Azure Cosmos DB Vector Store without Auto Configuration
Expand Down Expand Up @@ -192,7 +192,7 @@ public class DemoApplication implements CommandLineRunner {
Document document2 = new Document(UUID.randomUUID().toString(), "Sample content2", Map.of("key2", "value2"));
this.vectorStore.add(List.of(document1, document2));

List<Document> results = this.vectorStore.similaritySearch(SearchRequest.query("Sample content").topK(1));
List<Document> results = this.vectorStore.similaritySearch(SearchRequest.builder().query("Sample content").topK(1).build());
log.info("Search results: {}", results);
}

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12 changes: 6 additions & 6 deletions spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/azure.adoc
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Expand Up @@ -169,9 +169,9 @@ And finally, retrieve documents similar to a query:
[source,java]
----
List<Document> results = vectorStore.similaritySearch(
SearchRequest
SearchRequest.builder()
.query("Spring")
.topK(5));
.topK(5).build());
----

If all goes well, you should retrieve the document containing the text "Spring AI rocks!!".
Expand All @@ -185,11 +185,11 @@ For example, you can use either the text expression language:
[source,java]
----
vectorStore.similaritySearch(
SearchRequest
SearchRequest.builder()
.query("The World")
.topK(TOP_K)
.similarityThreshold(SIMILARITY_THRESHOLD)
.filterExpression("country in ['UK', 'NL'] && year >= 2020"));
.filterExpression("country in ['UK', 'NL'] && year >= 2020").build());
----

or programmatically using the expression DSL:
Expand All @@ -199,13 +199,13 @@ or programmatically using the expression DSL:
FilterExpressionBuilder b = new FilterExpressionBuilder();

vectorStore.similaritySearch(
SearchRequest
SearchRequest.builder()
.query("The World")
.topK(TOP_K)
.similarityThreshold(SIMILARITY_THRESHOLD)
.filterExpression(b.and(
b.in("country", "UK", "NL"),
b.gte("year", 2020)).build()));
b.gte("year", 2020)).build()).build());
----

The portable filter expressions get automatically converted into the proprietary Azure Search link:https://learn.microsoft.com/en-us/azure/search/search-query-odata-filter[OData filters]. For example, the following portable filter expression:
Expand Down
14 changes: 7 additions & 7 deletions spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/chroma.adoc
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Expand Up @@ -101,7 +101,7 @@ List <Document> documents = List.of(
vectorStore.add(documents);

// Retrieve documents similar to a query
List<Document> results = this.vectorStore.similaritySearch(SearchRequest.query("Spring").topK(5));
List<Document> results = this.vectorStore.similaritySearch(SearchRequest.builder().query("Spring").topK(5).build());
----

=== Configuration properties
Expand Down Expand Up @@ -137,11 +137,11 @@ For example, you can use either the text expression language:
[source,java]
----
vectorStore.similaritySearch(
SearchRequest.defaults()
.queryString("The World")
SearchRequest.builder()
.query("The World")
.topK(TOP_K)
.similarityThreshold(SIMILARITY_THRESHOLD)
.filterExpression("author in ['john', 'jill'] && article_type == 'blog'"));
.filterExpression("author in ['john', 'jill'] && article_type == 'blog'").build());
----

or programmatically using the `Filter.Expression` DSL:
Expand All @@ -150,13 +150,13 @@ or programmatically using the `Filter.Expression` DSL:
----
FilterExpressionBuilder b = new FilterExpressionBuilder();

vectorStore.similaritySearch(SearchRequest.defaults()
.queryString("The World")
vectorStore.similaritySearch(SearchRequest.builder()
.query("The World")
.topK(TOP_K)
.similarityThreshold(SIMILARITY_THRESHOLD)
.filterExpression(b.and(
b.in("john", "jill"),
b.eq("article_type", "blog")).build()));
b.eq("article_type", "blog")).build()).build());
----

NOTE: Those (portable) filter expressions get automatically converted into the proprietary Chroma `where` link:https://docs.trychroma.com/usage-guide#using-where-filters[filter expressions].
Expand Down
14 changes: 7 additions & 7 deletions spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc
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Expand Up @@ -98,7 +98,7 @@ List <Document> documents = List.of(
vectorStore.add(documents);

// Retrieve documents similar to a query
List<Document> results = this.vectorStore.similaritySearch(SearchRequest.query("Spring").topK(5));
List<Document> results = this.vectorStore.similaritySearch(SearchRequest.builder().query("Spring").topK(5).build());
----

[[elasticsearchvector-properties]]
Expand Down Expand Up @@ -171,11 +171,11 @@ For example, you can use either the text expression language:

[source,java]
----
vectorStore.similaritySearch(SearchRequest.defaults()
.queryString("The World")
vectorStore.similaritySearch(SearchRequest.builder()
.query("The World")
.topK(TOP_K)
.similarityThreshold(SIMILARITY_THRESHOLD)
.filterExpression("author in ['john', 'jill'] && 'article_type' == 'blog'"));
.filterExpression("author in ['john', 'jill'] && 'article_type' == 'blog'").build());
----

or programmatically using the `Filter.Expression` DSL:
Expand All @@ -184,13 +184,13 @@ or programmatically using the `Filter.Expression` DSL:
----
FilterExpressionBuilder b = new FilterExpressionBuilder();

vectorStore.similaritySearch(SearchRequest.defaults()
.queryString("The World")
vectorStore.similaritySearch(SearchRequest.builder()
.query("The World")
.topK(TOP_K)
.similarityThreshold(SIMILARITY_THRESHOLD)
.filterExpression(b.and(
b.in("author", "john", "jill"),
b.eq("article_type", "blog")).build()));
b.eq("article_type", "blog")).build()).build());
----

NOTE: Those (portable) filter expressions get automatically converted into the proprietary Elasticsearch link:https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html[Query string query].
Expand Down
6 changes: 3 additions & 3 deletions spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/gemfire.adoc
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Expand Up @@ -122,7 +122,7 @@ vectorStore.add(documents);
[source,java]
----
List<Document> results = vectorStore.similaritySearch(
SearchRequest.query("Spring").topK(5));
SearchRequest.builder().query("Spring").topK(5).build());
----

You should retrieve the document containing the text "Spring AI rocks!!".
Expand All @@ -131,7 +131,7 @@ You can also limit the number of results using a similarity threshold:
[source,java]
----
List<Document> results = vectorStore.similaritySearch(
SearchRequest.query("Spring").topK(5)
.similarityThreshold(0.5d));
SearchRequest.builder().query("Spring").topK(5)
.similarityThreshold(0.5d).build());
----

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