orpiske commented on code in PR #13341:
URL: https://github.com/apache/camel/pull/13341#discussion_r1521651406
##########
components/camel-ai/camel-langchain-chat/src/main/java/docs/langchain-chat-component.adoc:
##########
@@ -0,0 +1,146 @@
+= Langchain4j Chat Component
+:doctitle: Langchain4j Chat
+:shortname: langchain-chat
+:artifactid: camel-langchain-chat
+:description: Langchain4j Chat
+:since: 4.5
+:supportlevel: Preview
+:tabs-sync-option:
+:component-header: Only producer is supported
+//Manually maintained attributes
+:camel-spring-boot-name: langchain-chat
+
+*Since Camel {since}*
+
+*{component-header}*
+
+The Langchain Chat Component allows you to integrate with any LLM supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+Maven users will need to add the following dependency to their `pom.xml`
+for this component:
+
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.camel</groupId>
+ <artifactId>camel-langchain-chat</artifactId>
+ <version>x.x.x</version>
+ <!-- use the same version as your Camel core version -->
+</dependency>
+----
+
+== URI format
+
+[source]
+----
+langchain-chat:chatIdId[?options]
+----
+
+Where *chatId* can be any string to uniquely identify the endpoint
+
+
+// component-configure options: START
+
+// component-configure options: END
+
+// component options: START
+include::partial$component-configure-options.adoc[]
+include::partial$component-endpoint-options.adoc[]
+// component options: END
+
+// endpoint options: START
+
+// endpoint options: END
+
+include::spring-boot:partial$starter.adoc[]
+
+== Using a specific Chat Model
+The Camel Langchain chat component provides an abstraction for interacting
with various types of Large Language Models supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+To integrate with a specific Large Language Model, users should follow these
steps:
+
+=== Example of Integrating with OpenAI
+Add the dependency for Langchain4j OpenAI support:
+
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai</artifactId>
+ <version>x.x.x</version>
+</dependency>
+----
+
+Init the OpenAI Chat Language Model, add add it to the Camel Registry:
+[source, java]
+----
+ChatLanguageModel model = OpenAiChatModel.builder()
+ .apiKey(openApiKey)
+ .modelName(GPT_3_5_TURBO)
+ .temperature(0.3)
+ .timeout(ofSeconds(3000))
+ .build();
+context.getRegistry().bind("chatModel", model);
+----
+
+Use the model in the Camel Langchain Chat Producer
+[source, java]
+----
+ from("direct:chat")
+ .to("langchain-chat:test?chatModel=#chatModel")
+
+----
+
+_NOTE:_ To switch to another Large Language Model and its corresponding
dependency, simply replace the `langchain4j-open-ai` dependency with the
appropriate dependency for the desired model. Update the initialization
parameters accordingly in the code snippet provided above.
Review Comment:
Use this: ```NOTE: To switch to another Large Language Model and its
corresponding dependency...``` so it's properly rendered on the website as a
note paragraph. Alternatively:
```
NOTE
=====
text goes here
=====
```
##########
components/camel-ai/camel-langchain-chat/src/main/java/docs/langchain-chat-component.adoc:
##########
@@ -0,0 +1,146 @@
+= Langchain4j Chat Component
+:doctitle: Langchain4j Chat
+:shortname: langchain-chat
+:artifactid: camel-langchain-chat
+:description: Langchain4j Chat
+:since: 4.5
+:supportlevel: Preview
+:tabs-sync-option:
+:component-header: Only producer is supported
+//Manually maintained attributes
+:camel-spring-boot-name: langchain-chat
+
+*Since Camel {since}*
+
+*{component-header}*
+
+The Langchain Chat Component allows you to integrate with any LLM supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+Maven users will need to add the following dependency to their `pom.xml`
+for this component:
+
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.camel</groupId>
+ <artifactId>camel-langchain-chat</artifactId>
+ <version>x.x.x</version>
+ <!-- use the same version as your Camel core version -->
+</dependency>
+----
+
+== URI format
+
+[source]
+----
+langchain-chat:chatIdId[?options]
+----
+
+Where *chatId* can be any string to uniquely identify the endpoint
+
+
+// component-configure options: START
+
+// component-configure options: END
+
+// component options: START
+include::partial$component-configure-options.adoc[]
+include::partial$component-endpoint-options.adoc[]
+// component options: END
+
+// endpoint options: START
+
+// endpoint options: END
+
+include::spring-boot:partial$starter.adoc[]
+
+== Using a specific Chat Model
+The Camel Langchain chat component provides an abstraction for interacting
with various types of Large Language Models supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+To integrate with a specific Large Language Model, users should follow these
steps:
+
+=== Example of Integrating with OpenAI
+Add the dependency for Langchain4j OpenAI support:
+
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai</artifactId>
+ <version>x.x.x</version>
+</dependency>
+----
+
+Init the OpenAI Chat Language Model, add add it to the Camel Registry:
+[source, java]
+----
+ChatLanguageModel model = OpenAiChatModel.builder()
+ .apiKey(openApiKey)
+ .modelName(GPT_3_5_TURBO)
+ .temperature(0.3)
+ .timeout(ofSeconds(3000))
+ .build();
+context.getRegistry().bind("chatModel", model);
+----
+
+Use the model in the Camel Langchain Chat Producer
+[source, java]
+----
+ from("direct:chat")
+ .to("langchain-chat:test?chatModel=#chatModel")
+
+----
+
+_NOTE:_ To switch to another Large Language Model and its corresponding
dependency, simply replace the `langchain4j-open-ai` dependency with the
appropriate dependency for the desired model. Update the initialization
parameters accordingly in the code snippet provided above.
+
+== Send a prompt with variables
+To send a prompt with variables, use the Operation type
`LangchainChatOperations.CHAT_SINGLE_MESSAGE_WITH_PROMPT`.
+This operation allows you to send a single prompt message with dynamic
variables, which will be replaced with values provided in the request.
+
+Example of Route :
Review Comment:
Nitpick: `Example of route:`
##########
components/camel-ai/camel-langchain-chat/src/main/java/docs/langchain-chat-component.adoc:
##########
@@ -0,0 +1,146 @@
+= Langchain4j Chat Component
+:doctitle: Langchain4j Chat
+:shortname: langchain-chat
+:artifactid: camel-langchain-chat
+:description: Langchain4j Chat
+:since: 4.5
+:supportlevel: Preview
+:tabs-sync-option:
+:component-header: Only producer is supported
+//Manually maintained attributes
+:camel-spring-boot-name: langchain-chat
+
+*Since Camel {since}*
+
+*{component-header}*
+
+The Langchain Chat Component allows you to integrate with any LLM supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+Maven users will need to add the following dependency to their `pom.xml`
+for this component:
+
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.camel</groupId>
+ <artifactId>camel-langchain-chat</artifactId>
+ <version>x.x.x</version>
+ <!-- use the same version as your Camel core version -->
+</dependency>
+----
+
+== URI format
+
+[source]
+----
+langchain-chat:chatIdId[?options]
+----
+
+Where *chatId* can be any string to uniquely identify the endpoint
+
+
+// component-configure options: START
+
+// component-configure options: END
+
+// component options: START
+include::partial$component-configure-options.adoc[]
+include::partial$component-endpoint-options.adoc[]
+// component options: END
+
+// endpoint options: START
+
+// endpoint options: END
+
+include::spring-boot:partial$starter.adoc[]
+
+== Using a specific Chat Model
+The Camel Langchain chat component provides an abstraction for interacting
with various types of Large Language Models supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
Review Comment:
Use `... Large Language Models (LLMs) ...`.
##########
components/camel-ai/camel-langchain-chat/src/main/java/docs/langchain-chat-component.adoc:
##########
@@ -0,0 +1,146 @@
+= Langchain4j Chat Component
+:doctitle: Langchain4j Chat
+:shortname: langchain-chat
+:artifactid: camel-langchain-chat
+:description: Langchain4j Chat
+:since: 4.5
+:supportlevel: Preview
+:tabs-sync-option:
+:component-header: Only producer is supported
+//Manually maintained attributes
+:camel-spring-boot-name: langchain-chat
+
+*Since Camel {since}*
+
+*{component-header}*
+
+The Langchain Chat Component allows you to integrate with any LLM supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+Maven users will need to add the following dependency to their `pom.xml`
+for this component:
+
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.camel</groupId>
+ <artifactId>camel-langchain-chat</artifactId>
+ <version>x.x.x</version>
+ <!-- use the same version as your Camel core version -->
+</dependency>
+----
+
+== URI format
+
+[source]
+----
+langchain-chat:chatIdId[?options]
+----
+
+Where *chatId* can be any string to uniquely identify the endpoint
+
+
+// component-configure options: START
+
+// component-configure options: END
+
+// component options: START
+include::partial$component-configure-options.adoc[]
+include::partial$component-endpoint-options.adoc[]
+// component options: END
+
+// endpoint options: START
+
+// endpoint options: END
+
+include::spring-boot:partial$starter.adoc[]
+
+== Using a specific Chat Model
+The Camel Langchain chat component provides an abstraction for interacting
with various types of Large Language Models supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+To integrate with a specific Large Language Model, users should follow these
steps:
+
+=== Example of Integrating with OpenAI
+Add the dependency for Langchain4j OpenAI support:
+
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai</artifactId>
+ <version>x.x.x</version>
+</dependency>
+----
+
+Init the OpenAI Chat Language Model, add add it to the Camel Registry:
+[source, java]
+----
+ChatLanguageModel model = OpenAiChatModel.builder()
+ .apiKey(openApiKey)
+ .modelName(GPT_3_5_TURBO)
+ .temperature(0.3)
+ .timeout(ofSeconds(3000))
+ .build();
+context.getRegistry().bind("chatModel", model);
+----
+
+Use the model in the Camel Langchain Chat Producer
+[source, java]
+----
+ from("direct:chat")
+ .to("langchain-chat:test?chatModel=#chatModel")
+
+----
+
+_NOTE:_ To switch to another Large Language Model and its corresponding
dependency, simply replace the `langchain4j-open-ai` dependency with the
appropriate dependency for the desired model. Update the initialization
parameters accordingly in the code snippet provided above.
+
+== Send a prompt with variables
+To send a prompt with variables, use the Operation type
`LangchainChatOperations.CHAT_SINGLE_MESSAGE_WITH_PROMPT`.
+This operation allows you to send a single prompt message with dynamic
variables, which will be replaced with values provided in the request.
+
+Example of Route :
+[source, java]
+----
+ from("direct:chat")
+
.to("langchain-chat:test?chatModel=#chatModel&chatOperation=CHAT_SINGLE_MESSAGE_WITH_PROMPT")
+
+----
+
+Example of usage:
+[source, java]
+----
+var promptTemplate = "Create a recipe for a {{dishType}} with the following
ingredients: {{ingredients}}";
+
+Map<String, Object> variables = new HashMap<>();
+variables.put("dishType", "oven dish");
+variables.put("ingredients", "potato, tomato, feta, olive oil");
+
+String response = template.requestBodyAndHeader("direct:chat", variables,
+ LangchainChat.Headers.PROMPT_TEMPLATE, promptTemplate,
String.class);
+----
+
+== Chat with history
+You can send a new prompt along with the chat message history by passing all
messages in a List of Type `dev.langchain4j.data.message.ChatMessage`.
Review Comment:
Capitalization consistency: ``` ... messages in a list of type ... ```
##########
components/camel-ai/camel-langchain-embeddings/src/main/docs/langchain-embeddings-component.adoc:
##########
@@ -0,0 +1,42 @@
+= Langchain4j Embeddings Component
+:doctitle: Langchain4j Embeddings
+:shortname: langchain-embeddings
+:artifactid: camel-langchain-embeddings
+:description: Langchain4j Embeddings
+:since: 4.5
+:supportlevel: Preview
+:tabs-sync-option:
+:component-header: Only producer is supported
+//Manually maintained attributes
+:camel-spring-boot-name: langchain-embeddings
+
+*Since Camel {since}*
+
+*{component-header}*
+
+The Langchain Embeddings Component provides support to compute Embeddings
using Langchain4j Embeddings.
Review Comment:
Capitalization consistency.
##########
components/camel-ai/camel-langchain-chat/pom.xml:
##########
@@ -0,0 +1,72 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+
+-->
+<project xmlns="http://maven.apache.org/POM/4.0.0";
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance";
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
http://maven.apache.org/xsd/maven-4.0.0.xsd";>
+ <modelVersion>4.0.0</modelVersion>
+ <parent>
+ <artifactId>camel-ai-parent</artifactId>
+ <groupId>org.apache.camel</groupId>
+ <version>4.5.0-SNAPSHOT</version>
+ </parent>
+
+ <artifactId>camel-langchain-chat</artifactId>
+ <packaging>jar</packaging>
+
+ <name>Camel Langchain</name>
+ <description>Langchain4j Chat component</description>
+
+ <properties>
+ <firstVersion>4.5.0</firstVersion>
+ <supportLevel>Preview</supportLevel>
+ <label>ai</label>
+ </properties>
+
+ <dependencies>
+ <dependency>
+ <groupId>org.apache.camel</groupId>
+ <artifactId>camel-support</artifactId>
+ </dependency>
+ <!-- for testing -->
+ <dependency>
+ <groupId>org.apache.camel</groupId>
+ <artifactId>camel-test-spring-junit5</artifactId>
+ <scope>test</scope>
+ </dependency>
+
+ <dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>
+ <version>${langchain4j.version}</version>
+ <scope>test</scope>
+ </dependency>
+ <dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-ollama</artifactId>
+ <version>${langchain4j.version}</version>
+ </dependency>
Review Comment:
Should this one be declared at test scope?
##########
components/camel-ai/camel-langchain-chat/src/main/java/docs/langchain-chat-component.adoc:
##########
@@ -0,0 +1,146 @@
+= Langchain4j Chat Component
+:doctitle: Langchain4j Chat
+:shortname: langchain-chat
+:artifactid: camel-langchain-chat
+:description: Langchain4j Chat
+:since: 4.5
+:supportlevel: Preview
+:tabs-sync-option:
+:component-header: Only producer is supported
+//Manually maintained attributes
+:camel-spring-boot-name: langchain-chat
+
+*Since Camel {since}*
+
+*{component-header}*
+
+The Langchain Chat Component allows you to integrate with any LLM supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+Maven users will need to add the following dependency to their `pom.xml`
+for this component:
+
+[source,xml]
+----
+<dependency>
+ <groupId>org.apache.camel</groupId>
+ <artifactId>camel-langchain-chat</artifactId>
+ <version>x.x.x</version>
+ <!-- use the same version as your Camel core version -->
+</dependency>
+----
+
+== URI format
+
+[source]
+----
+langchain-chat:chatIdId[?options]
+----
+
+Where *chatId* can be any string to uniquely identify the endpoint
+
+
+// component-configure options: START
+
+// component-configure options: END
+
+// component options: START
+include::partial$component-configure-options.adoc[]
+include::partial$component-endpoint-options.adoc[]
+// component options: END
+
+// endpoint options: START
+
+// endpoint options: END
+
+include::spring-boot:partial$starter.adoc[]
+
+== Using a specific Chat Model
+The Camel Langchain chat component provides an abstraction for interacting
with various types of Large Language Models supported by
https://github.com/langchain4j/langchain4j[Langchain4j].
+
+To integrate with a specific Large Language Model, users should follow these
steps:
+
+=== Example of Integrating with OpenAI
+Add the dependency for Langchain4j OpenAI support:
+
+[source,xml]
+----
+<dependency>
+ <groupId>dev.langchain4j</groupId>
+ <artifactId>langchain4j-open-ai</artifactId>
+ <version>x.x.x</version>
+</dependency>
+----
+
+Init the OpenAI Chat Language Model, add add it to the Camel Registry:
+[source, java]
+----
+ChatLanguageModel model = OpenAiChatModel.builder()
+ .apiKey(openApiKey)
+ .modelName(GPT_3_5_TURBO)
+ .temperature(0.3)
+ .timeout(ofSeconds(3000))
+ .build();
+context.getRegistry().bind("chatModel", model);
+----
+
+Use the model in the Camel Langchain Chat Producer
+[source, java]
+----
+ from("direct:chat")
+ .to("langchain-chat:test?chatModel=#chatModel")
+
+----
+
+_NOTE:_ To switch to another Large Language Model and its corresponding
dependency, simply replace the `langchain4j-open-ai` dependency with the
appropriate dependency for the desired model. Update the initialization
parameters accordingly in the code snippet provided above.
+
+== Send a prompt with variables
+To send a prompt with variables, use the Operation type
`LangchainChatOperations.CHAT_SINGLE_MESSAGE_WITH_PROMPT`.
+This operation allows you to send a single prompt message with dynamic
variables, which will be replaced with values provided in the request.
+
+Example of Route :
+[source, java]
+----
+ from("direct:chat")
+
.to("langchain-chat:test?chatModel=#chatModel&chatOperation=CHAT_SINGLE_MESSAGE_WITH_PROMPT")
+
+----
+
+Example of usage:
+[source, java]
+----
+var promptTemplate = "Create a recipe for a {{dishType}} with the following
ingredients: {{ingredients}}";
+
+Map<String, Object> variables = new HashMap<>();
+variables.put("dishType", "oven dish");
+variables.put("ingredients", "potato, tomato, feta, olive oil");
+
+String response = template.requestBodyAndHeader("direct:chat", variables,
+ LangchainChat.Headers.PROMPT_TEMPLATE, promptTemplate,
String.class);
+----
+
+== Chat with history
+You can send a new prompt along with the chat message history by passing all
messages in a List of Type `dev.langchain4j.data.message.ChatMessage`.
+Use the Operation type `LangchainChatOperations.CHAT_MULTIPLE_MESSAGES`.
+This operation allows you to continue the conversation with the context of
previous messages.
+
+Example of Route :
Review Comment:
`Example of route:`
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