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new e65324f74fc7 CAMEL-22850: Use LangChain4j Spring Boot starters in
Camel Spring Boot (#20919)
e65324f74fc7 is described below
commit e65324f74fc755fe41c8dd3355f20658a1b354a2
Author: Guillaume Nodet <[email protected]>
AuthorDate: Fri Jan 23 10:11:23 2026 +0100
CAMEL-22850: Use LangChain4j Spring Boot starters in Camel Spring Boot
(#20919)
This commit integrates LangChain4j Spring Boot starters with Apache Camel's
langchain4j components, enabling auto-configuration and simplified setup for
Spring Boot applications.
---
.../src/main/docs/langchain4j-agent-component.adoc | 70 +++
.../src/main/docs/langchain4j-chat-component.adoc | 147 +++++-
.../docs/langchain4j-embeddings-component.adoc | 149 ++++++
.../pages/langchain4j-spring-boot-integration.adoc | 540 +++++++++++++++++++++
4 files changed, 890 insertions(+), 16 deletions(-)
diff --git
a/components/camel-ai/camel-langchain4j-agent/src/main/docs/langchain4j-agent-component.adoc
b/components/camel-ai/camel-langchain4j-agent/src/main/docs/langchain4j-agent-component.adoc
index 05eaec16a3cb..551433aee59e 100644
---
a/components/camel-ai/camel-langchain4j-agent/src/main/docs/langchain4j-agent-component.adoc
+++
b/components/camel-ai/camel-langchain4j-agent/src/main/docs/langchain4j-agent-component.adoc
@@ -993,6 +993,76 @@ public class SecureAgentRoute extends RouteBuilder {
==== Spring Boot Configuration Example
+===== Using LangChain4j Spring Boot Starters (Recommended)
+
+When using Camel with Spring Boot, leverage LangChain4j's Spring Boot starters
for automatic configuration of the ChatModel.
+
+Add the dependency for LangChain4j OpenAI Spring Boot starter:
+
+.pom.xml
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
+ <version>1.10.0</version>
+ <!-- use the same version as your LangChain4j version -->
+</dependency>
+----
+
+Configure the OpenAI Chat Model in `application.properties`:
+
+.application.properties
+[source,properties]
+----
+langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
+langchain4j.open-ai.chat-model.model-name=gpt-4o
+langchain4j.open-ai.chat-model.temperature=0.7
+----
+
+Create the Agent bean using the auto-configured ChatLanguageModel:
+
+[source,java]
+----
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import dev.langchain4j.model.chat.ChatLanguageModel;
+import org.apache.camel.component.langchain4j.agent.api.Agent;
+import org.apache.camel.component.langchain4j.agent.api.AgentConfiguration;
+import org.apache.camel.component.langchain4j.agent.api.AgentWithoutMemory;
+import org.apache.camel.component.langchain4j.agent.api.Guardrails;
+
+@Configuration
+public class AgentConfig {
+
+ @Bean("secureAgent")
+ public Agent secureAgent(ChatLanguageModel chatLanguageModel) {
+ AgentConfiguration config = new AgentConfiguration()
+ .withChatModel(chatLanguageModel)
+ .withInputGuardrailClasses(Guardrails.defaultInputGuardrails())
+ .withOutputGuardrailClasses(Guardrails.defaultOutputGuardrails());
+
+ return new AgentWithoutMemory(config);
+ }
+}
+----
+
+[NOTE]
+====
+The `ChatLanguageModel` bean is automatically configured by the LangChain4j
Spring Boot starter and injected into your configuration. You can also use
other auto-configured beans such as:
+
+* `StreamingChatLanguageModel` - For streaming responses
+* `EmbeddingModel` - For generating embeddings
+* `ImageModel` - For image generation
+* `ModerationModel` - For content moderation
+
+Refer to the
https://docs.langchain4j.dev/tutorials/spring-boot-integration[LangChain4j
Spring Boot Integration documentation] for complete configuration options.
+====
+
+===== Manual Configuration (Alternative)
+
+Alternatively, you can manually configure the ChatModel bean:
+
[source,java]
----
import org.springframework.context.annotation.Bean;
diff --git
a/components/camel-ai/camel-langchain4j-chat/src/main/docs/langchain4j-chat-component.adoc
b/components/camel-ai/camel-langchain4j-chat/src/main/docs/langchain4j-chat-component.adoc
index e68626300988..926134c68b5c 100644
---
a/components/camel-ai/camel-langchain4j-chat/src/main/docs/langchain4j-chat-component.adoc
+++
b/components/camel-ai/camel-langchain4j-chat/src/main/docs/langchain4j-chat-component.adoc
@@ -58,39 +58,154 @@ The Camel LangChain4j chat component provides an
abstraction for interacting wit
To integrate with a specific LLM, users should follow the steps described
below, which explain
how to integrate with OpenAI.
+===== Using LangChain4j Spring Boot Starters (Recommended for Spring Boot)
+
+When using Camel with Spring Boot, you can leverage LangChain4j's Spring Boot
starters for automatic configuration.
+
+Add the dependency for LangChain4j OpenAI Spring Boot starter:
+
+.pom.xml
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
+ <version>1.10.0</version>
+ <!-- use the same version as your LangChain4j version -->
+</dependency>
+----
+
+Configure the OpenAI Chat Model in `application.properties` or
`application.yml`:
+
+.application.properties
+[source,properties]
+----
+langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
+langchain4j.open-ai.chat-model.model-name=gpt-3.5-turbo
+langchain4j.open-ai.chat-model.temperature=0.3
+langchain4j.open-ai.chat-model.timeout=3000s
+----
+
+.application.yml
+[source,yaml]
+----
+langchain4j:
+ open-ai:
+ chat-model:
+ api-key: ${OPENAI_API_KEY}
+ model-name: gpt-3.5-turbo
+ temperature: 0.3
+ timeout: 3000s
+----
+
+The `ChatLanguageModel` bean will be automatically configured and available in
the Spring context. Use it in your Camel routes:
+
+[tabs]
+====
+Java::
++
+[source,java]
+----
+from("direct:chat")
+ .to("langchain4j-chat:test?chatModel=#chatLanguageModel");
+----
+
+YAML::
++
+[source,yaml]
+----
+- route:
+ from:
+ uri: "direct:chat"
+ steps:
+ - to:
+ uri: "langchain4j-chat:test"
+ parameters:
+ chatModel: "#chatLanguageModel"
+----
+
+XML::
++
+[source,xml]
+----
+<route>
+ <from uri="direct:chat"/>
+ <to uri="langchain4j-chat:test?chatModel=#chatLanguageModel"/>
+</route>
+----
+====
+
+[NOTE]
+====
+LangChain4j Spring Boot starters provide auto-configuration for various LLM
providers including:
+
+* `langchain4j-open-ai-spring-boot-starter` - OpenAI
+* `langchain4j-azure-open-ai-spring-boot-starter` - Azure OpenAI
+* `langchain4j-google-ai-gemini-spring-boot-starter` - Google Gemini
+* `langchain4j-ollama-spring-boot-starter` - Ollama
+* `langchain4j-anthropic-spring-boot-starter` - Anthropic Claude
+* `langchain4j-mistral-ai-spring-boot-starter` - Mistral AI
+* `langchain4j-hugging-face-spring-boot-starter` - Hugging Face
+
+For a complete list of available starters and their configuration options,
refer to the
https://docs.langchain4j.dev/tutorials/spring-boot-integration[LangChain4j
Spring Boot Integration documentation].
+====
+
+===== Manual Configuration (Alternative)
+
+Alternatively, you can manually initialize the Chat Model and add it to the
Camel Registry:
+
Add the dependency for LangChain4j OpenAI support:
-.Example
+.pom.xml
[source,xml]
----
<dependency>
- <groupId>dev.langchain4j</groupId>
- <artifactId>langchain4j-open-ai</artifactId>
- <version>x.x.x</version>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai</artifactId>
+ <version>1.10.0</version>
+ <!-- use the same version as your LangChain4j version -->
</dependency>
----
Initialize the OpenAI Chat Model, and add it to the Camel Registry:
-[source, java]
+[source,java]
----
-ChatModel model = OpenAiChatModel.builder()
- .apiKey(openApiKey)
- .modelName(GPT_3_5_TURBO)
- .temperature(0.3)
- .timeout(ofSeconds(3000))
- .build();
-context.getRegistry().bind("chatModel", model);
+ChatLanguageModel model = OpenAiChatModel.builder()
+ .apiKey(openApiKey)
+ .modelName(GPT_3_5_TURBO)
+ .temperature(0.3)
+ .timeout(ofSeconds(3000))
+ .build();
+context.getRegistry().bind("myChatModel", model);
----
-Use the model in the Camel LangChain4j Chat Producer
+Use the model in the Camel LangChain4j Chat Producer:
-[source, java]
+[tabs]
+====
+Java::
++
+[source,java]
+----
+from("direct:chat")
+ .to("langchain4j-chat:test?chatModel=#myChatModel");
----
- from("direct:chat")
- .to("langchain4j-chat:test?chatModel=#chatModel")
+YAML::
++
+[source,yaml]
----
+- route:
+ from:
+ uri: "direct:chat"
+ steps:
+ - to:
+ uri: "langchain4j-chat:test"
+ parameters:
+ chatModel: "#myChatModel"
+----
+====
[NOTE]
====
diff --git
a/components/camel-ai/camel-langchain4j-embeddings/src/main/docs/langchain4j-embeddings-component.adoc
b/components/camel-ai/camel-langchain4j-embeddings/src/main/docs/langchain4j-embeddings-component.adoc
index 3d837ab2f392..3003058387e4 100644
---
a/components/camel-ai/camel-langchain4j-embeddings/src/main/docs/langchain4j-embeddings-component.adoc
+++
b/components/camel-ai/camel-langchain4j-embeddings/src/main/docs/langchain4j-embeddings-component.adoc
@@ -33,3 +33,152 @@ include::partial$component-endpoint-headers.adoc[]
// component options: END
include::spring-boot:partial$starter.adoc[]
+
+== Usage
+
+=== Using Embedding Models
+
+The Camel LangChain4j embeddings component provides support for generating
embeddings using various embedding models supported by
https://docs.langchain4j.dev/[LangChain4j].
+
+==== Integrating with specific Embedding Model
+
+===== Using LangChain4j Spring Boot Starters (Recommended for Spring Boot)
+
+When using Camel with Spring Boot, you can leverage LangChain4j's Spring Boot
starters for automatic configuration of embedding models.
+
+Add the dependency for LangChain4j OpenAI Spring Boot starter:
+
+.pom.xml
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
+ <version>1.10.0</version>
+ <!-- use the same version as your LangChain4j version -->
+</dependency>
+----
+
+Configure the OpenAI Embedding Model in `application.properties` or
`application.yml`:
+
+.application.properties
+[source,properties]
+----
+langchain4j.open-ai.embedding-model.api-key=${OPENAI_API_KEY}
+langchain4j.open-ai.embedding-model.model-name=text-embedding-ada-002
+----
+
+.application.yml
+[source,yaml]
+----
+langchain4j:
+ open-ai:
+ embedding-model:
+ api-key: ${OPENAI_API_KEY}
+ model-name: text-embedding-ada-002
+----
+
+The `EmbeddingModel` bean will be automatically configured and available in
the Spring context. Use it in your Camel routes:
+
+[tabs]
+====
+Java::
++
+[source,java]
+----
+from("direct:embeddings")
+ .to("langchain4j-embeddings:test?embeddingModel=#embeddingModel");
+----
+
+YAML::
++
+[source,yaml]
+----
+- route:
+ from:
+ uri: "direct:embeddings"
+ steps:
+ - to:
+ uri: "langchain4j-embeddings:test"
+ parameters:
+ embeddingModel: "#embeddingModel"
+----
+
+XML::
++
+[source,xml]
+----
+<route>
+ <from uri="direct:embeddings"/>
+ <to uri="langchain4j-embeddings:test?embeddingModel=#embeddingModel"/>
+</route>
+----
+====
+
+[NOTE]
+====
+LangChain4j Spring Boot starters provide auto-configuration for various
embedding model providers including:
+
+* `langchain4j-open-ai-spring-boot-starter` - OpenAI embeddings
+* `langchain4j-azure-open-ai-spring-boot-starter` - Azure OpenAI embeddings
+* `langchain4j-ollama-spring-boot-starter` - Ollama embeddings
+* `langchain4j-hugging-face-spring-boot-starter` - Hugging Face embeddings
+* `langchain4j-vertex-ai-spring-boot-starter` - Google Vertex AI embeddings
+
+For a complete list of available starters and their configuration options,
refer to the
https://docs.langchain4j.dev/tutorials/spring-boot-integration[LangChain4j
Spring Boot Integration documentation].
+====
+
+===== Manual Configuration (Alternative)
+
+Alternatively, you can manually initialize the Embedding Model and add it to
the Camel Registry:
+
+Add the dependency for LangChain4j OpenAI support:
+
+.pom.xml
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai</artifactId>
+ <version>1.10.0</version>
+ <!-- use the same version as your LangChain4j version -->
+</dependency>
+----
+
+Initialize the OpenAI Embedding Model:
+
+[source,java]
+----
+EmbeddingModel embeddingModel = OpenAiEmbeddingModel.builder()
+ .apiKey(openApiKey)
+ .modelName("text-embedding-ada-002")
+ .build();
+context.getRegistry().bind("myEmbeddingModel", embeddingModel);
+----
+
+Use the model in the Camel LangChain4j Embeddings Producer:
+
+[tabs]
+====
+Java::
++
+[source,java]
+----
+from("direct:embeddings")
+ .to("langchain4j-embeddings:test?embeddingModel=#myEmbeddingModel");
+----
+
+YAML::
++
+[source,yaml]
+----
+- route:
+ from:
+ uri: "direct:embeddings"
+ steps:
+ - to:
+ uri: "langchain4j-embeddings:test"
+ parameters:
+ embeddingModel: "#myEmbeddingModel"
+----
+====
diff --git
a/docs/user-manual/modules/ROOT/pages/langchain4j-spring-boot-integration.adoc
b/docs/user-manual/modules/ROOT/pages/langchain4j-spring-boot-integration.adoc
new file mode 100644
index 000000000000..90b1ded8c295
--- /dev/null
+++
b/docs/user-manual/modules/ROOT/pages/langchain4j-spring-boot-integration.adoc
@@ -0,0 +1,540 @@
+= LangChain4j Spring Boot Integration
+:doctitle: LangChain4j Spring Boot Integration
+:shortname: langchain4j-spring-boot
+:description: Integrating LangChain4j Spring Boot Starters with Apache Camel
+:since: 4.18
+:supportlevel: Stable
+:tabs-sync-option:
+
+*Since Camel {since}*
+
+This guide explains how to integrate LangChain4j Spring Boot starters with
Apache Camel Spring Boot applications.
+
+== Overview
+
+LangChain4j provides Spring Boot starters that offer auto-configuration for
various AI/LLM providers. When using Camel's langchain4j components in a Spring
Boot application, you can leverage these starters to simplify configuration and
reduce boilerplate code.
+
+== Benefits
+
+* *Auto-configuration*: Automatic bean creation and configuration based on
properties
+* *Type-safe configuration*: Configuration properties with IDE auto-completion
support
+* *Simplified dependency management*: Single starter dependency per provider
+* *Production-ready*: Built-in health checks and metrics (when using Spring
Boot Actuator)
+* *Consistent configuration*: Unified configuration approach across different
LLM providers
+
+== Available LangChain4j Spring Boot Starters
+
+LangChain4j provides Spring Boot starters for various AI/LLM providers:
+
+=== Chat Models
+
+* `langchain4j-open-ai-spring-boot-starter` - OpenAI (GPT-3.5, GPT-4, etc.)
+* `langchain4j-azure-open-ai-spring-boot-starter` - Azure OpenAI
+* `langchain4j-google-ai-gemini-spring-boot-starter` - Google Gemini
+* `langchain4j-ollama-spring-boot-starter` - Ollama (local models)
+* `langchain4j-anthropic-spring-boot-starter` - Anthropic Claude
+* `langchain4j-mistral-ai-spring-boot-starter` - Mistral AI
+* `langchain4j-hugging-face-spring-boot-starter` - Hugging Face
+* `langchain4j-vertex-ai-spring-boot-starter` - Google Vertex AI
+
+=== Embedding Models
+
+Most chat model starters also include embedding model auto-configuration.
+
+=== Other Components
+
+* `langchain4j-spring-boot-starter` - Core LangChain4j functionality
+* Various embedding store starters (Chroma, Pinecone, Qdrant, etc.)
+
+== Getting Started
+
+=== Step 1: Add Dependencies
+
+Add the Camel Spring Boot starter and the LangChain4j component you need:
+
+[source,xml]
+----
+<dependencies>
+ <!-- Camel Spring Boot -->
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-spring-boot-starter</artifactId>
+ </dependency>
+
+ <!-- Camel LangChain4j Component -->
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-langchain4j-chat-starter</artifactId>
+ </dependency>
+
+ <!-- LangChain4j Spring Boot Starter for OpenAI -->
+ <dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
+ <version>1.10.0</version>
+ </dependency>
+</dependencies>
+----
+
+=== Step 2: Configure Properties
+
+Configure the LangChain4j provider in `application.properties` or
`application.yml`:
+
+.application.properties
+[source,properties]
+----
+# OpenAI Chat Model Configuration
+langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
+langchain4j.open-ai.chat-model.model-name=gpt-4o
+langchain4j.open-ai.chat-model.temperature=0.7
+langchain4j.open-ai.chat-model.max-tokens=1000
+
+# OpenAI Embedding Model Configuration
+langchain4j.open-ai.embedding-model.api-key=${OPENAI_API_KEY}
+langchain4j.open-ai.embedding-model.model-name=text-embedding-ada-002
+----
+
+.application.yml
+[source,yaml]
+----
+langchain4j:
+ open-ai:
+ chat-model:
+ api-key: ${OPENAI_API_KEY}
+ model-name: gpt-4o
+ temperature: 0.7
+ max-tokens: 1000
+ embedding-model:
+ api-key: ${OPENAI_API_KEY}
+ model-name: text-embedding-ada-002
+----
+
+=== Step 3: Use in Camel Routes
+
+The auto-configured beans are automatically available in your Camel routes:
+
+[source,java]
+----
+@Component
+public class MyRoutes extends RouteBuilder {
+
+ @Override
+ public void configure() {
+ // Chat endpoint using auto-configured ChatLanguageModel
+ from("direct:chat")
+ .to("langchain4j-chat:openai?chatModel=#chatLanguageModel");
+
+ // Embeddings endpoint using auto-configured EmbeddingModel
+ from("direct:embeddings")
+
.to("langchain4j-embeddings:openai?embeddingModel=#embeddingModel");
+ }
+}
+----
+
+== Complete Examples
+
+=== Example 1: OpenAI Chat Integration
+
+.pom.xml
+[source,xml]
+----
+<dependencies>
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-spring-boot-starter</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-langchain4j-chat-starter</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai-spring-boot-starter</artifactId>
+ <version>1.10.0</version>
+ </dependency>
+</dependencies>
+----
+
+.application.properties
+[source,properties]
+----
+langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
+langchain4j.open-ai.chat-model.model-name=gpt-4o
+langchain4j.open-ai.chat-model.temperature=0.7
+----
+
+.ChatRoute.java
+[source,java]
+----
+import org.apache.camel.builder.RouteBuilder;
+import org.springframework.stereotype.Component;
+
+@Component
+public class ChatRoute extends RouteBuilder {
+
+ @Override
+ public void configure() {
+ from("direct:chat")
+ .log("Sending message to OpenAI: ${body}")
+ .to("langchain4j-chat:openai?chatModel=#chatLanguageModel")
+ .log("Received response: ${body}");
+ }
+}
+----
+
+=== Example 2: Azure OpenAI with RAG
+
+.pom.xml
+[source,xml]
+----
+<dependencies>
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-spring-boot-starter</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-langchain4j-chat-starter</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-langchain4j-embeddings-starter</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-azure-open-ai-spring-boot-starter</artifactId>
+ <version>1.10.0</version>
+ </dependency>
+</dependencies>
+----
+
+.application.yml
+[source,yaml]
+----
+langchain4j:
+ azure-open-ai:
+ chat-model:
+ endpoint: ${AZURE_OPENAI_ENDPOINT}
+ api-key: ${AZURE_OPENAI_API_KEY}
+ deployment-name: gpt-4
+ embedding-model:
+ endpoint: ${AZURE_OPENAI_ENDPOINT}
+ api-key: ${AZURE_OPENAI_API_KEY}
+ deployment-name: text-embedding-ada-002
+----
+
+.RagRoute.java
+[source,java]
+----
+import org.apache.camel.builder.RouteBuilder;
+import
org.apache.camel.component.langchain4j.chat.LangChain4jRagAggregatorStrategy;
+import org.springframework.stereotype.Component;
+
+@Component
+public class RagRoute extends RouteBuilder {
+
+ @Override
+ public void configure() {
+ LangChain4jRagAggregatorStrategy ragStrategy = new
LangChain4jRagAggregatorStrategy();
+
+ from("direct:rag-chat")
+ .log("Processing RAG query: ${body}")
+ .enrich("direct:retrieve-context", ragStrategy)
+ .to("langchain4j-chat:azure?chatModel=#chatLanguageModel")
+ .log("RAG response: ${body}");
+
+ from("direct:retrieve-context")
+ .to("langchain4j-embeddings:azure?embeddingModel=#embeddingModel")
+
.to("langchain4j-embeddingstore:search?embeddingStore=#embeddingStore")
+ .log("Retrieved context: ${body}");
+ }
+}
+----
+
+=== Example 3: Ollama (Local LLM)
+
+.pom.xml
+[source,xml]
+----
+<dependencies>
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-spring-boot-starter</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>org.apache.camel.springboot</groupId>
+ <artifactId>camel-langchain4j-chat-starter</artifactId>
+ </dependency>
+ <dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-ollama-spring-boot-starter</artifactId>
+ <version>1.10.0</version>
+ </dependency>
+</dependencies>
+----
+
+.application.properties
+[source,properties]
+----
+langchain4j.ollama.chat-model.base-url=http://localhost:11434
+langchain4j.ollama.chat-model.model-name=llama2
+langchain4j.ollama.chat-model.temperature=0.8
+----
+
+== Configuration Properties Reference
+
+=== OpenAI Configuration
+
+[source,properties]
+----
+# Chat Model
+langchain4j.open-ai.chat-model.api-key=
+langchain4j.open-ai.chat-model.model-name=gpt-4o
+langchain4j.open-ai.chat-model.temperature=0.7
+langchain4j.open-ai.chat-model.max-tokens=
+langchain4j.open-ai.chat-model.timeout=60s
+langchain4j.open-ai.chat-model.max-retries=3
+
+# Embedding Model
+langchain4j.open-ai.embedding-model.api-key=
+langchain4j.open-ai.embedding-model.model-name=text-embedding-ada-002
+----
+
+=== Azure OpenAI Configuration
+
+[source,properties]
+----
+# Chat Model
+langchain4j.azure-open-ai.chat-model.endpoint=
+langchain4j.azure-open-ai.chat-model.api-key=
+langchain4j.azure-open-ai.chat-model.deployment-name=
+langchain4j.azure-open-ai.chat-model.temperature=0.7
+
+# Embedding Model
+langchain4j.azure-open-ai.embedding-model.endpoint=
+langchain4j.azure-open-ai.embedding-model.api-key=
+langchain4j.azure-open-ai.embedding-model.deployment-name=
+----
+
+=== Ollama Configuration
+
+[source,properties]
+----
+# Chat Model
+langchain4j.ollama.chat-model.base-url=http://localhost:11434
+langchain4j.ollama.chat-model.model-name=llama2
+langchain4j.ollama.chat-model.temperature=0.8
+langchain4j.ollama.chat-model.timeout=60s
+
+# Embedding Model
+langchain4j.ollama.embedding-model.base-url=http://localhost:11434
+langchain4j.ollama.embedding-model.model-name=nomic-embed-text
+----
+
+== Advanced Configuration
+
+=== Using Multiple LLM Providers
+
+You can configure multiple LLM providers in the same application by using
different bean names:
+
+.application.yml
+[source,yaml]
+----
+langchain4j:
+ open-ai:
+ chat-model:
+ api-key: ${OPENAI_API_KEY}
+ model-name: gpt-4o
+ ollama:
+ chat-model:
+ base-url: http://localhost:11434
+ model-name: llama2
+----
+
+.MultiProviderRoute.java
+[source,java]
+----
+@Component
+public class MultiProviderRoute extends RouteBuilder {
+
+ @Override
+ public void configure() {
+ // Use OpenAI for production
+ from("direct:production-chat")
+ .to("langchain4j-chat:openai?chatModel=#chatLanguageModel");
+
+ // Use Ollama for development/testing
+ from("direct:dev-chat")
+ .to("langchain4j-chat:ollama?chatModel=#ollamaChatModel");
+ }
+}
+----
+
+=== Custom Bean Configuration
+
+You can customize the auto-configured beans or create additional beans:
+
+[source,java]
+----
+import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+import dev.langchain4j.model.chat.ChatLanguageModel;
+import dev.langchain4j.model.openai.OpenAiChatModel;
+
+@Configuration
+public class CustomLangChain4jConfig {
+
+ @Bean
+ @ConditionalOnProperty(name = "custom.llm.enabled", havingValue = "true")
+ public ChatLanguageModel customChatModel() {
+ return OpenAiChatModel.builder()
+ .apiKey(System.getenv("CUSTOM_API_KEY"))
+ .modelName("gpt-4o-mini")
+ .temperature(0.5)
+ .logRequests(true)
+ .logResponses(true)
+ .build();
+ }
+}
+----
+
+=== Environment-Specific Configuration
+
+Use Spring profiles for environment-specific configuration:
+
+.application-dev.yml
+[source,yaml]
+----
+langchain4j:
+ ollama:
+ chat-model:
+ base-url: http://localhost:11434
+ model-name: llama2
+----
+
+.application-prod.yml
+[source,yaml]
+----
+langchain4j:
+ open-ai:
+ chat-model:
+ api-key: ${OPENAI_API_KEY}
+ model-name: gpt-4o
+ max-retries: 5
+ timeout: 120s
+----
+
+== Best Practices
+
+=== 1. Secure API Keys
+
+Never hardcode API keys in your code or configuration files. Use environment
variables or external configuration:
+
+[source,properties]
+----
+langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
+----
+
+Or use Spring Cloud Config, Vault, or other secret management solutions.
+
+=== 2. Configure Timeouts and Retries
+
+Set appropriate timeouts and retry policies for production:
+
+[source,properties]
+----
+langchain4j.open-ai.chat-model.timeout=60s
+langchain4j.open-ai.chat-model.max-retries=3
+langchain4j.open-ai.chat-model.log-requests=false
+langchain4j.open-ai.chat-model.log-responses=false
+----
+
+=== 3. Use Streaming for Long Responses
+
+For long-running conversations, consider using streaming chat models:
+
+[source,java]
+----
+@Component
+public class StreamingChatRoute extends RouteBuilder {
+
+ @Autowired
+ private StreamingChatLanguageModel streamingChatModel;
+
+ @Override
+ public void configure() {
+ from("direct:streaming-chat")
+ .process(exchange -> {
+ String prompt = exchange.getIn().getBody(String.class);
+ streamingChatModel.generate(prompt, new
StreamingResponseHandler<AiMessage>() {
+ @Override
+ public void onNext(String token) {
+ // Handle streaming tokens
+ }
+
+ @Override
+ public void onComplete(Response<AiMessage> response) {
+ exchange.getIn().setBody(response.content().text());
+ }
+
+ @Override
+ public void onError(Throwable error) {
+ // Handle errors
+ }
+ });
+ });
+ }
+}
+----
+
+=== 4. Monitor and Log
+
+Enable logging for debugging during development:
+
+[source,properties]
+----
+# Development
+langchain4j.open-ai.chat-model.log-requests=true
+langchain4j.open-ai.chat-model.log-responses=true
+
+# Production (disable for security and performance)
+langchain4j.open-ai.chat-model.log-requests=false
+langchain4j.open-ai.chat-model.log-responses=false
+----
+
+== Troubleshooting
+
+=== Common Issues
+
+==== Bean Not Found
+
+If you encounter "No qualifying bean" errors, ensure:
+
+1. The LangChain4j Spring Boot starter is in your dependencies
+2. The configuration properties are correctly set
+3. The bean name matches what you're referencing in your routes
+
+==== API Key Issues
+
+If you get authentication errors:
+
+1. Verify the API key is correctly set in environment variables
+2. Check that the property name matches the provider (e.g.,
`langchain4j.open-ai.chat-model.api-key`)
+3. Ensure the API key has the necessary permissions
+
+==== Timeout Errors
+
+If you experience timeout errors:
+
+1. Increase the timeout value: `langchain4j.open-ai.chat-model.timeout=120s`
+2. Check your network connectivity
+3. Verify the LLM service is available
+
+== Additional Resources
+
+* https://docs.langchain4j.dev/tutorials/spring-boot-integration[LangChain4j
Spring Boot Integration Documentation]
+*
https://camel.apache.org/components/latest/langchain4j-chat-component.html[Camel
LangChain4j Chat Component]
+*
https://camel.apache.org/components/latest/langchain4j-embeddings-component.html[Camel
LangChain4j Embeddings Component]
+*
https://camel.apache.org/components/latest/langchain4j-agent-component.html[Camel
LangChain4j Agent Component]
+
