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Capitalize the enumeration names

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Resolves #208
This commit is contained in:
Christian Tzolov
2024-01-14 20:36:07 +01:00
parent 025d6af033
commit 7262afb14d
19 changed files with 65 additions and 57 deletions
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1
models/spring-ai-azure-openai/src/main/java/org/springframework/ai/azure/openai/AzureOpenAiChatClient.java
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@@ -198,7 +198,6 @@ public class AzureOpenAiChatClient implements ChatClient, StreamingChatClient {
.map(ChatCompletions::getChoices)
.flatMap(List::stream)
.map(choice -> {
System.out.println(choice.getDelta());
var content = (choice.getDelta() != null) ? choice.getDelta().getContent() : null;
var generation = new Generation(content).withChoiceMetadata(generateChoiceMetadata(choice));
return new ChatResponse(List.of(generation));

2
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/cohere/BedrockCohereEmbeddingClient.java
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@@ -40,7 +40,7 @@ public class BedrockCohereEmbeddingClient extends AbstractEmbeddingClient {
private final CohereEmbeddingBedrockApi embeddingApi;
private CohereEmbeddingRequest.InputType inputType = CohereEmbeddingRequest.InputType.search_document;
private CohereEmbeddingRequest.InputType inputType = CohereEmbeddingRequest.InputType.SEARCH_DOCUMENT;
private CohereEmbeddingRequest.Truncate truncate = CohereEmbeddingRequest.Truncate.NONE;

8
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/cohere/api/CohereEmbeddingBedrockApi.java
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@@ -90,19 +90,19 @@ public class CohereEmbeddingBedrockApi extends
* In search use-cases, use search_document when you encode documents for embeddings that you store in a
* vector database.
*/
search_document,
@JsonProperty("search_document") SEARCH_DOCUMENT,
/**
* Use search_query when querying your vector DB to find relevant documents.
*/
search_query,
@JsonProperty("search_query") SEARCH_QUERY,
/**
* Use classification when using embeddings as an input to a text classifier.
*/
classification,
@JsonProperty("classification") CLASSIFICATION,
/**
* Use clustering to cluster the embeddings.
*/
clustering
@JsonProperty("clustering") CLUSTERING
}
/**

4
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama2/api/Llama2ChatBedrockApi.java
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@@ -150,11 +150,11 @@ public class Llama2ChatBedrockApi extends
/**
* The model has finished generating text for the input prompt.
*/
stop,
@JsonProperty("stop") STOP,
/**
* The response was truncated because of the response length you set.
*/
length
@JsonProperty("length") LENGTH
}
}

2
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/cohere/api/CohereEmbeddingBedrockApiIT.java
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@@ -46,7 +46,7 @@ public class CohereEmbeddingBedrockApiIT {
CohereEmbeddingRequest request = new CohereEmbeddingRequest(
List.of("I like to eat apples", "I like to eat oranges"),
CohereEmbeddingRequest.InputType.search_document, CohereEmbeddingRequest.Truncate.NONE);
CohereEmbeddingRequest.InputType.SEARCH_DOCUMENT, CohereEmbeddingRequest.Truncate.NONE);
CohereEmbeddingResponse response = api.embedding(request);

6
models/spring-ai-ollama/src/main/java/org/springframework/ai/ollama/OllamaChatClient.java
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@@ -138,11 +138,11 @@ public class OllamaChatClient implements ChatClient, StreamingChatClient {
switch (message.getMessageType()) {
case USER:
return Role.user;
return Role.USER;
case ASSISTANT:
return Role.assistant;
return Role.ASSISTANT;
case SYSTEM:
return Role.system;
return Role.SYSTEM;
default:
throw new IllegalArgumentException("Unsupported message type: " + message.getMessageType());
}

8
models/spring-ai-ollama/src/main/java/org/springframework/ai/ollama/api/OllamaApi.java
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@@ -349,15 +349,15 @@ public class OllamaApi {
/**
* System message type used as instructions to the model.
*/
system,
@JsonProperty("system") SYSTEM,
/**
* User message type.
*/
user,
@JsonProperty("user") USER,
/**
* Assistant message type. Usually the response from the model.
*/
assistant;
@JsonProperty("assistant") ASSISTANT;
}
@@ -466,7 +466,7 @@ public class OllamaApi {
*
* @param model The model name used for completion.
* @param createdAt When the request was made.
* @param message The response {@link Message} with {@link Message.Role#assistant}.
* @param message The response {@link Message} with {@link Message.Role#ASSISTANT}.
* @param done Whether this is the final response. For streaming response only the
* last message is marked as done. If true, this response may be followed by another
* response with the following, additional fields: context, prompt_eval_count,

1
models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaEmbeddingClientIT.java
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@@ -21,7 +21,6 @@ import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.context.annotation.Bean;
import static org.assertj.core.api.Assertions.assertThat;
import static org.assertj.core.api.Assertions.assertThatThrownBy;
@SpringBootTest
@Disabled("For manual smoke testing only.")

6
models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/api/OllamaApiIT.java
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@@ -87,7 +87,7 @@ public class OllamaApiIT {
var request = ChatRequest.builder("orca-mini")
.withStream(false)
.withMessages(List.of(Message.builder(Role.user)
.withMessages(List.of(Message.builder(Role.USER)
.withContent("What is the capital of Bulgaria and what is the size? " + "What it the national anthem?")
.build()))
.withOptions(OllamaOptions.create().withTemperature(0.9f))
@@ -100,7 +100,7 @@ public class OllamaApiIT {
assertThat(response).isNotNull();
assertThat(response.model()).isEqualTo(response.model());
assertThat(response.done()).isTrue();
assertThat(response.message().role()).isEqualTo(Role.assistant);
assertThat(response.message().role()).isEqualTo(Role.ASSISTANT);
assertThat(response.message().content()).contains("Sofia");
}
@@ -109,7 +109,7 @@ public class OllamaApiIT {
var request = ChatRequest.builder("orca-mini")
.withStream(true)
.withMessages(List.of(Message.builder(Role.user)
.withMessages(List.of(Message.builder(Role.USER)
.withContent("What is the capital of Bulgaria and what is the size? " + "What it the national anthem?")
.build()))
.withOptions(OllamaOptions.create().withTemperature(0.9f).toMap())

4
models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatClient.java
View File

@@ -100,7 +100,7 @@ public class OpenAiChatClient implements ChatClient, StreamingChatClient {
List<ChatCompletionMessage> chatCompletionMessages = messages.stream()
.map(m -> new ChatCompletionMessage(m.getContent(),
ChatCompletionMessage.Role.valueOf(m.getMessageType().getValue())))
ChatCompletionMessage.Role.valueOf(m.getMessageType().name())))
.toList();
ResponseEntity<ChatCompletion> completionEntity = this.openAiApi
@@ -132,7 +132,7 @@ public class OpenAiChatClient implements ChatClient, StreamingChatClient {
List<ChatCompletionMessage> chatCompletionMessages = messages.stream()
.map(m -> new ChatCompletionMessage(m.getContent(),
ChatCompletionMessage.Role.valueOf(m.getMessageType().getValue())))
ChatCompletionMessage.Role.valueOf(m.getMessageType().name())))
.toList();
Flux<OpenAiApi.ChatCompletionChunk> completionChunks = this.openAiApi

26
models/spring-ai-openai/src/main/java/org/springframework/ai/openai/api/OpenAiApi.java
View File

@@ -163,7 +163,7 @@ public class OpenAiApi {
* @param function function definition.
*/
public FunctionTool(Function function) {
this(Type.function, function);
this(Type.FUNCTION, function);
}
/**
@@ -173,7 +173,7 @@ public class OpenAiApi {
/**
* Function tool type.
*/
function
@JsonProperty("function") FUNCTION
}
/**
@@ -347,10 +347,10 @@ public class OpenAiApi {
* @param role The role of the messages author. Could be one of the {@link Role} types.
* @param name An optional name for the participant. Provides the model information to differentiate between
* participants of the same role.
* @param toolCallId Tool call that this message is responding to. Only applicable for the {@link Role#tool} role
* @param toolCallId Tool call that this message is responding to. Only applicable for the {@link Role#TOOL} role
* and null otherwise.
* @param toolCalls The tool calls generated by the model, such as function calls. Applicable only for
* {@link Role#assistant} role and null otherwise.
* {@link Role#ASSISTANT} role and null otherwise.
* @param functionCall Deprecated and replaced by tool_calls. The name and arguments of a function that should be
* called, as generated by the model.
*/
@@ -379,19 +379,19 @@ public class OpenAiApi {
/**
* System message.
*/
system,
@JsonProperty("system") SYSTEM,
/**
* User message.
*/
user,
@JsonProperty("user") USER,
/**
* Assistant message.
*/
assistant,
@JsonProperty("assistant") ASSISTANT,
/**
* Tool message.
*/
tool
@JsonProperty("tool") TOOL
}
/**
@@ -429,23 +429,23 @@ public class OpenAiApi {
/**
* The model hit a natural stop point or a provided stop sequence.
*/
stop,
@JsonProperty("stop") STOP,
/**
* The maximum number of tokens specified in the request was reached.
*/
length,
@JsonProperty("length") LENGTH,
/**
* The content was omitted due to a flag from our content filters.
*/
content_filter,
@JsonProperty("content_filter") CONTENT_FILTER,
/**
* The model called a tool.
*/
tool_calls,
@JsonProperty("tool_calls") TOOL_CALLS,
/**
* (deprecated) The model called a function.
*/
function_call
@JsonProperty("function_call") FUNCTION_CALL
}
/**

2
spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/bedrock/cohere/BedrockCohereEmbeddingProperties.java
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@@ -48,7 +48,7 @@ public class BedrockCohereEmbeddingProperties {
* retrieval. In this case, embed your corpus with the search_document type and
* embedded queries with type search_query type.
*/
private InputType inputType = InputType.search_document;
private InputType inputType = InputType.SEARCH_DOCUMENT;
/**
* Specifies how the API handles inputs longer than the maximum token length.

2
spring-ai-spring-boot-autoconfigure/src/main/java/org/springframework/ai/autoconfigure/vectorstore/pgvector/PgVectorStoreProperties.java
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@@ -33,7 +33,7 @@ public class PgVectorStoreProperties {
private PgIndexType indexType = PgIndexType.HNSW;
private PgDistanceType distanceType = PgDistanceType.CosineDistance;
private PgDistanceType distanceType = PgDistanceType.COSINE_DISTANCE;
private boolean removeExistingVectorStoreTable = false;

6
spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/bedrock/cohere/BedrockCohereEmbeddingAutoConfigurationIT.java
View File

@@ -47,7 +47,7 @@ public class BedrockCohereEmbeddingAutoConfigurationIT {
"spring.ai.bedrock.aws.secret-key=" + System.getenv("AWS_SECRET_ACCESS_KEY"),
"spring.ai.bedrock.aws.region=" + Region.US_EAST_1.id(),
"spring.ai.bedrock.cohere.embedding.model=" + CohereEmbeddingModel.COHERE_EMBED_MULTILINGUAL_V1.id(),
"spring.ai.bedrock.cohere.embedding.inputType=search_document",
"spring.ai.bedrock.cohere.embedding.inputType=SEARCH_DOCUMENT",
"spring.ai.bedrock.cohere.embedding.truncate=NONE")
.withConfiguration(AutoConfigurations.of(BedrockCohereEmbeddingAutoConfiguration.class));
@@ -91,7 +91,7 @@ public class BedrockCohereEmbeddingAutoConfigurationIT {
"spring.ai.bedrock.aws.access-key=ACCESS_KEY", "spring.ai.bedrock.aws.secret-key=SECRET_KEY",
"spring.ai.bedrock.aws.region=" + Region.EU_CENTRAL_1.id(),
"spring.ai.bedrock.cohere.embedding.model=MODEL_XYZ",
"spring.ai.bedrock.cohere.embedding.inputType=classification",
"spring.ai.bedrock.cohere.embedding.inputType=CLASSIFICATION",
"spring.ai.bedrock.cohere.embedding.truncate=RIGHT")
.withConfiguration(AutoConfigurations.of(BedrockCohereEmbeddingAutoConfiguration.class))
.run(context -> {
@@ -102,7 +102,7 @@ public class BedrockCohereEmbeddingAutoConfigurationIT {
assertThat(awsProperties.getRegion()).isEqualTo(Region.EU_CENTRAL_1.id());
assertThat(properties.getModel()).isEqualTo("MODEL_XYZ");
assertThat(properties.getInputType()).isEqualTo(InputType.classification);
assertThat(properties.getInputType()).isEqualTo(InputType.CLASSIFICATION);
assertThat(properties.getTruncate()).isEqualTo(CohereEmbeddingRequest.Truncate.RIGHT);
assertThat(awsProperties.getAccessKey()).isEqualTo("ACCESS_KEY");

2
spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/vectorstore/pgvector/PgVectorStoreAutoConfigurationIT.java
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@@ -72,7 +72,7 @@ public class PgVectorStoreAutoConfigurationIT {
.withConfiguration(AutoConfigurations.of(PgVectorStoreAutoConfiguration.class,
JdbcTemplateAutoConfiguration.class, DataSourceAutoConfiguration.class))
.withUserConfiguration(Config.class)
.withPropertyValues("spring.ai.vectorstore.pgvector.distanceType=CosineDistance",
.withPropertyValues("spring.ai.vectorstore.pgvector.distanceType=COSINE_DISTANCE",
// JdbcTemplate configuration
String.format("spring.datasource.url=jdbc:postgresql://%s:%d/%s", postgresContainer.getHost(),
postgresContainer.getMappedPort(5432), "postgres"),

6
spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/vectorstore/pgvector/PgVectorStorePropertiesTests.java
View File

@@ -33,7 +33,7 @@ public class PgVectorStorePropertiesTests {
public void defaultValues() {
var props = new PgVectorStoreProperties();
assertThat(props.getDimensions()).isEqualTo(PgVectorStore.INVALID_EMBEDDING_DIMENSION);
assertThat(props.getDistanceType()).isEqualTo(PgDistanceType.CosineDistance);
assertThat(props.getDistanceType()).isEqualTo(PgDistanceType.COSINE_DISTANCE);
assertThat(props.getIndexType()).isEqualTo(PgIndexType.HNSW);
assertThat(props.isRemoveExistingVectorStoreTable()).isFalse();
}
@@ -43,12 +43,12 @@ public class PgVectorStorePropertiesTests {
var props = new PgVectorStoreProperties();
props.setDimensions(1536);
props.setDistanceType(PgDistanceType.EuclideanDistance);
props.setDistanceType(PgDistanceType.EUCLIDEAN_DISTANCE);
props.setIndexType(PgIndexType.IVFFLAT);
props.setRemoveExistingVectorStoreTable(true);
assertThat(props.getDimensions()).isEqualTo(1536);
assertThat(props.getDistanceType()).isEqualTo(PgDistanceType.EuclideanDistance);
assertThat(props.getDistanceType()).isEqualTo(PgDistanceType.EUCLIDEAN_DISTANCE);
assertThat(props.getIndexType()).isEqualTo(PgIndexType.IVFFLAT);
assertThat(props.isRemoveExistingVectorStoreTable()).isTrue();
}

14
vector-stores/spring-ai-chroma/src/main/java/org/springframework/ai/chroma/ChromaApi.java
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@@ -196,9 +196,19 @@ public class ChromaApi {
public enum Include {
metadatas, documents, distances, embeddings;
@JsonProperty("metadatas")
METADATAS,
public static final List<Include> all = List.of(metadatas, documents, distances, embeddings);
@JsonProperty("documents")
DOCUMENTS,
@JsonProperty("distances")
DISTANCES,
@JsonProperty("embeddings")
EMBEDDINGS;
public static final List<Include> all = List.of(METADATAS, DOCUMENTS, DISTANCES, EMBEDDINGS);
}

10
vector-stores/spring-ai-pgvector-store/src/main/java/org/springframework/ai/vectorstore/PgVectorStore.java
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@@ -116,17 +116,17 @@ public class PgVectorStore implements VectorStore, InitializingBean {
// embeddings), use inner product for best performance.
// The Sentence transformers are NOT normalized:
// https://github.com/UKPLab/sentence-transformers/issues/233
EuclideanDistance("<->", "vector_l2_ops",
EUCLIDEAN_DISTANCE("<->", "vector_l2_ops",
"SELECT *, embedding <-> ? AS distance FROM %s WHERE embedding <-> ? < ? %s ORDER BY distance LIMIT ? "),
// NOTE: works only if If vectors are normalized to length 1 (like OpenAI
// embeddings), use inner product for best performance.
// The Sentence transformers are NOT normalized:
// https://github.com/UKPLab/sentence-transformers/issues/233
NegativeInnerProduct("<#>", "vector_ip_ops",
NEGATIVE_INNER_PRODUCT("<#>", "vector_ip_ops",
"SELECT *, (1 + (embedding <#> ?)) AS distance FROM %s WHERE (1 + (embedding <#> ?)) < ? %s ORDER BY distance LIMIT ? "),
CosineDistance("<=>", "vector_cosine_ops",
COSINE_DISTANCE("<=>", "vector_cosine_ops",
"SELECT *, embedding <=> ? AS distance FROM %s WHERE embedding <=> ? < ? %s ORDER BY distance LIMIT ? ");
public final String operator;
@@ -197,12 +197,12 @@ public class PgVectorStore implements VectorStore, InitializingBean {
}
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient) {
this(jdbcTemplate, embeddingClient, INVALID_EMBEDDING_DIMENSION, PgVectorStore.PgDistanceType.CosineDistance,
this(jdbcTemplate, embeddingClient, INVALID_EMBEDDING_DIMENSION, PgVectorStore.PgDistanceType.COSINE_DISTANCE,
false, PgIndexType.NONE);
}
public PgVectorStore(JdbcTemplate jdbcTemplate, EmbeddingClient embeddingClient, int dimensions) {
this(jdbcTemplate, embeddingClient, dimensions, PgVectorStore.PgDistanceType.CosineDistance, false,
this(jdbcTemplate, embeddingClient, dimensions, PgVectorStore.PgDistanceType.COSINE_DISTANCE, false,
PgIndexType.NONE);
}

12
vector-stores/spring-ai-pgvector-store/src/test/java/org/springframework/ai/vectorstore/PgVectorStoreIT.java
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@@ -87,7 +87,7 @@ public class PgVectorStoreIT {
private final ApplicationContextRunner contextRunner = new ApplicationContextRunner()
.withUserConfiguration(TestApplication.class)
.withPropertyValues("test.spring.ai.vectorstore.pgvector.distanceType=CosineDistance",
.withPropertyValues("test.spring.ai.vectorstore.pgvector.distanceType=COSINE_DISTANCE",
// JdbcTemplate configuration
String.format("app.datasource.url=jdbc:postgresql://%s:%d/%s", postgresContainer.getHost(),
@@ -101,7 +101,7 @@ public class PgVectorStoreIT {
}
@ParameterizedTest(name = "{0} : {displayName} ")
@ValueSource(strings = { "CosineDistance", "EuclideanDistance", "NegativeInnerProduct" })
@ValueSource(strings = { "COSINE_DISTANCE", "EUCLIDEAN_DISTANCE", "NEGATIVE_INNER_PRODUCT" })
public void addAndSearch(String distanceType) {
contextRunner.withPropertyValues("test.spring.ai.vectorstore.pgvector.distanceType=" + distanceType)
.run(context -> {
@@ -130,7 +130,7 @@ public class PgVectorStoreIT {
}
@ParameterizedTest(name = "{0} : {displayName} ")
@ValueSource(strings = { "CosineDistance", "EuclideanDistance", "NegativeInnerProduct" })
@ValueSource(strings = { "COSINE_DISTANCE", "EUCLIDEAN_DISTANCE", "NEGATIVE_INNER_PRODUCT" })
public void searchWithFilters(String distanceType) {
contextRunner.withPropertyValues("test.spring.ai.vectorstore.pgvector.distanceType=" + distanceType)
@@ -204,7 +204,7 @@ public class PgVectorStoreIT {
}
@ParameterizedTest(name = "{0} : {displayName} ")
@ValueSource(strings = { "CosineDistance", "EuclideanDistance", "NegativeInnerProduct" })
@ValueSource(strings = { "COSINE_DISTANCE", "EUCLIDEAN_DISTANCE", "NEGATIVE_INNER_PRODUCT" })
public void documentUpdate(String distanceType) {
contextRunner.withPropertyValues("test.spring.ai.vectorstore.pgvector.distanceType=" + distanceType)
@@ -244,8 +244,8 @@ public class PgVectorStoreIT {
}
@ParameterizedTest(name = "{0} : {displayName} ")
@ValueSource(strings = { "CosineDistance", "EuclideanDistance", "NegativeInnerProduct" })
// @ValueSource(strings = { "CosineDistance" })
@ValueSource(strings = { "COSINE_DISTANCE", "EUCLIDEAN_DISTANCE", "NEGATIVE_INNER_PRODUCT" })
// @ValueSource(strings = { "COSINE_DISTANCE" })
public void searchWithThreshold(String distanceType) {
contextRunner.withPropertyValues("test.spring.ai.vectorstore.pgvector.distanceType=" + distanceType)