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Updated versions to 1.1.0, 1.1.0-rc1 and 1.1.0-beta7
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‎anthropic-examples/pom.xml‎

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<groupId>dev.langchain4j</groupId>
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<artifactId>anthropic-examples</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-rc1</version>
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<properties>
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<maven.compiler.source>17</maven.compiler.source>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-anthropic</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-rc1</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j</artifactId>
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<version>1.0.1</version>
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<version>1.1.0</version>
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</dependency>
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<dependency>

‎azure-open-ai-customer-support-agent-example/pom.xml‎

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<groupId>dev.langchain4j</groupId>
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<artifactId>azure-open-ai-customer-support-agent-example</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-beta7</version>
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<properties>
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<java.version>17</java.version>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-spring-boot-starter</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-beta7</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-azure-open-ai-spring-boot-starter</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-beta7</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-beta7</version>
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</dependency>
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<dependency>

‎azure-open-ai-customer-support-agent-example/src/main/java/dev/langchain4j/example/CustomerSupportAgentApplication.java‎

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@@ -90,7 +90,9 @@ EmbeddingModel embeddingModel() {
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}
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@Bean
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EmbeddingStore<TextSegment> embeddingStore(EmbeddingModel embeddingModel, ResourceLoader resourceLoader) throws IOException {
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EmbeddingStore<TextSegment> embeddingStore(EmbeddingModel embeddingModel,
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ResourceLoader resourceLoader,
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TokenCountEstimator tokenCountEstimator) throws IOException {
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// Normally, you would already have your embedding store filled with your data.
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// However, for the purpose of this demonstration, we will:
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// 4. Convert segments into embeddings
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// 5. Store embeddings into embedding store
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// All this can be done manually, but we will use EmbeddingStoreIngestor to automate this:
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DocumentSplitter documentSplitter = DocumentSplitters.recursive(100, 0, new AzureOpenAiTokenCountEstimator("gpt-4o-mini"));
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DocumentSplitter documentSplitter = DocumentSplitters.recursive(100, 0, tokenCountEstimator);
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EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
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.documentSplitter(documentSplitter)
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.embeddingModel(embeddingModel)
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return embeddingStore;
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}
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@Bean
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TokenCountEstimator tokenCountEstimator() {
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return new AzureOpenAiTokenCountEstimator("gpt-4o-mini");
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}
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public static void main(String[] args) {
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SpringApplication.run(CustomerSupportAgentApplication.class, args);
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}

‎azure-open-ai-examples/pom.xml‎

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<groupId>dev.langchain4j</groupId>
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<artifactId>azure-open-ai-examples</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-rc1</version>
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<properties>
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<maven.compiler.source>17</maven.compiler.source>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-azure-open-ai</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-rc1</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j</artifactId>
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<version>1.0.1</version>
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<version>1.1.0</version>
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</dependency>
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<dependency>
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<groupId>com.azure</groupId>
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<artifactId>azure-identity</artifactId>
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<version>1.13.0</version>
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<scope>compile</scope>
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</dependency>
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<dependency>
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<groupId>org.tinylog</groupId>
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<artifactId>tinylog-impl</artifactId>

‎bedrock-examples/pom.xml‎

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<groupId>dev.langchain4j</groupId>
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<artifactId>bedrock-examples</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-rc1</version>
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<properties>
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<maven.compiler.source>17</maven.compiler.source>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-bedrock</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-rc1</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j</artifactId>
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<version>1.0.1</version>
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<version>1.1.0</version>
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</dependency>
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<dependency>

‎bedrock-examples/src/main/java/converse/BedrockChatModelExample.java‎

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ChatRequestParameters defaultParameters = BedrockChatRequestParameters.builder()
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// enabling reasoning with a budget of 1024 tokens
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.enableReasoning(1024L)
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.enableReasoning(1024)
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.build();
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ChatModel chatModel = BedrockChatModel.builder()

‎bedrock-examples/src/main/java/converse/BedrockStreamingChatModelExample.java‎

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StreamingChatModel model = BedrockStreamingChatModel.builder()
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.modelId("anthropic.claude-3-5-sonnet-20240620-v1:0")
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.region(Region.US_EAST_1)
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.maxRetries(2)
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.timeout(Duration.ofMinutes(1))
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// Other parameters can be set as well
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.build();

‎bedrock-examples/src/main/java/invoke/BedrockChatModelExample.java‎

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‎bedrock-examples/src/main/java/invoke/BedrockStreamingChatModelExample.java‎

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‎chroma-example/pom.xml‎

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<groupId>dev.langchain4j</groupId>
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<artifactId>chroma-example</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-beta7</version>
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<properties>
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<maven.compiler.source>17</maven.compiler.source>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-chroma</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-beta7</version>
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</dependency>
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>
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<version>1.0.1-beta6</version>
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<version>1.1.0-beta7</version>
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</dependency>
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<dependency>

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