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Refactoring of ChatClient to add fluent API and introduce Model as dependent object

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* Rename the ModelClient class hierarchy into Model:
  - Rename ModelClient into Model. Update all code and doc references.
  - Rename ChatClient to ChatModel. Update all ChatClient suffixes and chatClient fields and variables in code and doc.
  - Rename EmbeddingClient into EmbeddingModel. Update the XxxEmbeddingClient class and variable suffixes and embeddingClient variables and fields in code and docs.
  - Rename ImageClient into ImageModel.
  - Rename SpeechClient into SpeechModel.
  - Rename TranscriptionClient into TranscriptionModel.
  - Update all javadocs and antora pages. Update the related diagrams.

* Create fluent API in ChatClient interface that now includes streaming support
* Add OpenAI FunctionCallbackWrapper2IT auto-config tests.
* Add ChatClientTest mockito testing.
* Add ChatModel#getDefaultOptions(), and remove @FunctionalInterface

* ChatModel enums extend the new ModelDescription interface.
* Implement fromOptions copy method in every ChatOptions implementation.
* Extend ChatClient to use the model default options if not provided explicitly.

* Update readme to provide guidance on how to adapt to breaking changes.

Co-authored-by: Christian Tzolov <ctzolov@vmware.com>
Co-authored-by: Mark Pollack <mpollack@vmware.com>
This commit is contained in:
Josh Long
2024-05-18 21:30:51 +02:00
committed by Mark Pollack
parent bce45c2d2f
commit fbfc87e814
426 changed files with 4815 additions and 2912 deletions
Download Patch File Download Diff File Expand all files Collapse all files

24
vector-stores/spring-ai-pgvector-store/src/main/java/org/springframework/ai/vectorstore/PgVectorStore.java
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@@ -32,7 +32,7 @@ import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingClient;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.vectorstore.filter.FilterExpressionConverter;
import org.springframework.ai.vectorstore.filter.converter.PgVectorFilterExpressionConverter;
import org.springframework.beans.factory.InitializingBean;
@@ -66,7 +66,7 @@ public class PgVectorStore implements VectorStore, InitializingBean {
private final JdbcTemplate jdbcTemplate;
private final EmbeddingClient embeddingClient;
private final EmbeddingModel embeddingModel;
private int dimensions;
@@ -197,21 +197,21 @@ public class PgVectorStore implements VectorStore, InitializingBean {
}
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient) {
this(jdbcTemplate, embeddingClient, INVALID_EMBEDDING_DIMENSION, PgVectorStore.PgDistanceType.COSINE_DISTANCE,
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
this(jdbcTemplate, embeddingModel, INVALID_EMBEDDING_DIMENSION, PgVectorStore.PgDistanceType.COSINE_DISTANCE,
false, PgIndexType.NONE);
}
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient, int dimensions) {
this(jdbcTemplate, embeddingClient, dimensions, PgVectorStore.PgDistanceType.COSINE_DISTANCE, false,
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel, int dimensions) {
this(jdbcTemplate, embeddingModel, dimensions, PgVectorStore.PgDistanceType.COSINE_DISTANCE, false,
PgIndexType.NONE);
}
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient, int dimensions,
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel, int dimensions,
PgDistanceType distanceType, boolean removeExistingVectorStoreTable, PgIndexType createIndexMethod) {
this.jdbcTemplate = jdbcTemplate;
this.embeddingClient = embeddingClient;
this.embeddingModel = embeddingModel;
this.dimensions = dimensions;
this.distanceType = distanceType;
this.removeExistingVectorStoreTable = removeExistingVectorStoreTable;
@@ -237,7 +237,7 @@ public class PgVectorStore implements VectorStore, InitializingBean {
var document = documents.get(i);
var content = document.getContent();
var json = toJson(document.getMetadata());
var pGvector = new PGvector(toFloatArray(embeddingClient.embed(document)));
var pGvector = new PGvector(toFloatArray(embeddingModel.embed(document)));
StatementCreatorUtils.setParameterValue(ps, 1, SqlTypeValue.TYPE_UNKNOWN,
UUID.fromString(document.getId()));
@@ -320,7 +320,7 @@ public class PgVectorStore implements VectorStore, InitializingBean {
}
private PGvector getQueryEmbedding(String query) {
List<Double> embedding = this.embeddingClient.embed(query);
List<Double> embedding = this.embeddingModel.embed(query);
return new PGvector(toFloatArray(embedding));
}
@@ -366,13 +366,13 @@ public class PgVectorStore implements VectorStore, InitializingBean {
}
try {
int embeddingDimensions = this.embeddingClient.dimensions();
int embeddingDimensions = this.embeddingModel.dimensions();
if (embeddingDimensions > 0) {
return embeddingDimensions;
}
}
catch (Exception e) {
logger.warn("Failed to obtain the embedding dimensions from the embedding client and fall backs to default:"
logger.warn("Failed to obtain the embedding dimensions from the embedding model and fall backs to default:"
+ OPENAI_EMBEDDING_DIMENSION_SIZE, e);
}
return OPENAI_EMBEDDING_DIMENSION_SIZE;

22
vector-stores/spring-ai-pgvector-store/src/test/java/org/springframework/ai/vectorstore/PgVectorEmbeddingDimensionsTests.java
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@@ -20,7 +20,7 @@ import org.junit.jupiter.api.extension.ExtendWith;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;
import org.springframework.ai.embedding.EmbeddingClient;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.jdbc.core.JdbcTemplate;
import static org.assertj.core.api.Assertions.assertThat;
@@ -36,7 +36,7 @@ import static org.mockito.Mockito.when;
public class PgVectorEmbeddingDimensionsTests {
@Mock
private EmbeddingClient embeddingClient;
private EmbeddingModel embeddingModel;
@Mock
private JdbcTemplate jdbcTemplate;
@@ -46,32 +46,32 @@ public class PgVectorEmbeddingDimensionsTests {
final int explicitDimensions = 696;
var dim = new PgVectorStore(jdbcTemplate, embeddingClient, explicitDimensions).embeddingDimensions();
var dim = new PgVectorStore(jdbcTemplate, embeddingModel, explicitDimensions).embeddingDimensions();
assertThat(dim).isEqualTo(explicitDimensions);
verify(embeddingClient, never()).dimensions();
verify(embeddingModel, never()).dimensions();
}
@Test
public void embeddingClientDimensions() {
when(embeddingClient.dimensions()).thenReturn(969);
public void embeddingModelDimensions() {
when(embeddingModel.dimensions()).thenReturn(969);
var dim = new PgVectorStore(jdbcTemplate, embeddingClient).embeddingDimensions();
var dim = new PgVectorStore(jdbcTemplate, embeddingModel).embeddingDimensions();
assertThat(dim).isEqualTo(969);
verify(embeddingClient, only()).dimensions();
verify(embeddingModel, only()).dimensions();
}
@Test
public void fallBackToDefaultDimensions() {
when(embeddingClient.dimensions()).thenThrow(new RuntimeException());
when(embeddingModel.dimensions()).thenThrow(new RuntimeException());
var dim = new PgVectorStore(jdbcTemplate, embeddingClient).embeddingDimensions();
var dim = new PgVectorStore(jdbcTemplate, embeddingModel).embeddingDimensions();
assertThat(dim).isEqualTo(PgVectorStore.OPENAI_EMBEDDING_DIMENSION_SIZE);
verify(embeddingClient, only()).dimensions();
verify(embeddingModel, only()).dimensions();
}
}

12
vector-stores/spring-ai-pgvector-store/src/test/java/org/springframework/ai/vectorstore/PgVectorStoreIT.java
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@@ -35,9 +35,9 @@ import org.testcontainers.junit.jupiter.Container;
import org.testcontainers.junit.jupiter.Testcontainers;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.EmbeddingClient;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.openai.api.OpenAiApi;
import org.springframework.ai.openai.OpenAiEmbeddingClient;
import org.springframework.ai.openai.OpenAiEmbeddingModel;
import org.springframework.ai.vectorstore.PgVectorStore.PgIndexType;
import org.springframework.ai.vectorstore.filter.FilterExpressionTextParser.FilterExpressionParseException;
import org.springframework.beans.factory.annotation.Value;
@@ -306,8 +306,8 @@ public class PgVectorStoreIT {
PgVectorStore.PgDistanceType distanceType;
@Bean
public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient) {
return new PgVectorStore(jdbcTemplate, embeddingClient, PgVectorStore.INVALID_EMBEDDING_DIMENSION,
public VectorStore vectorStore(JdbcTemplate jdbcTemplate, EmbeddingModel embeddingModel) {
return new PgVectorStore(jdbcTemplate, embeddingModel, PgVectorStore.INVALID_EMBEDDING_DIMENSION,
distanceType, true, PgIndexType.HNSW);
}
@@ -329,8 +329,8 @@ public class PgVectorStoreIT {
}
@Bean
public EmbeddingClient embeddingClient() {
return new OpenAiEmbeddingClient(new OpenAiApi(System.getenv("OPENAI_API_KEY")));
public EmbeddingModel embeddingModel() {
return new OpenAiEmbeddingModel(new OpenAiApi(System.getenv("OPENAI_API_KEY")));
}
}