Typo fixes

Author: rsvoboda

core/deployment/src/main/java/io/quarkiverse/langchain4j/deployment/AiServicesProcessor.java

@@ -477,7 +477,7 @@ public void handleDeclarativeServices(AiServicesRecorder recorder,
             List<DeclarativeAiServiceBuildItem> declarativeAiServiceItems,
             List<SelectedChatModelProviderBuildItem> selectedChatModelProvider,
             BuildProducer<SyntheticBeanBuildItem> syntheticBeanProducer,
-            BuildProducer<UnremovableBeanBuildItem> unremoveableProducer) {
+            BuildProducer<UnremovableBeanBuildItem> unremovableProducer) {
 
         boolean needsChatModelBean = false;
         boolean needsStreamingChatModelBean = false;
@@ -668,7 +668,7 @@ public void handleDeclarativeServices(AiServicesRecorder recorder,
                     // constructor to obtain an instance.
                     if (bi.isCustomRetrievalAugmentorSupplierClassIsABean()) {
                         configurator.addInjectionPoint(ClassType.create(retrievalAugmentorSupplierClassName));
-                        unremoveableProducer
+                        unremovableProducer
                                 .produce(UnremovableBeanBuildItem.beanClassNames(retrievalAugmentorSupplierClassName));
                     }
                 }
@@ -724,34 +724,34 @@ public void handleDeclarativeServices(AiServicesRecorder recorder,
         }
 
         if (needsChatModelBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.CHAT_MODEL));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.CHAT_MODEL));
         }
         if (needsStreamingChatModelBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.STREAMING_CHAT_MODEL));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.STREAMING_CHAT_MODEL));
         }
         if (needsChatMemoryProviderBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.CHAT_MEMORY_PROVIDER));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.CHAT_MEMORY_PROVIDER));
         }
         if (needsRetrieverBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.RETRIEVER));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.RETRIEVER));
         }
         if (needsRetrievalAugmentorBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.RETRIEVAL_AUGMENTOR));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.RETRIEVAL_AUGMENTOR));
         }
         if (needsAuditServiceBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.AUDIT_SERVICE));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.AUDIT_SERVICE));
         }
         if (needsModerationModelBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.MODERATION_MODEL));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.MODERATION_MODEL));
         }
         if (needsImageModelBean) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.IMAGE_MODEL));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(LangChain4jDotNames.IMAGE_MODEL));
         }
         if (!allToolProviders.isEmpty()) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(allToolProviders));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(allToolProviders));
         }
         if (!allToolNames.isEmpty()) {
-            unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(allToolNames));
+            unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(allToolNames));
         }
     }
 

core/deployment/src/main/java/io/quarkiverse/langchain4j/deployment/BeansProcessor.java

@@ -600,9 +600,9 @@ public void cleanUp(LangChain4jRecorder recorder, ShutdownContextBuildItem shutd
     }
 
     @BuildStep
-    public void unremoveableBeans(BuildProducer<UnremovableBeanBuildItem> unremoveableProducer) {
-        unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(ObjectMapper.class));
-        unremoveableProducer.produce(UnremovableBeanBuildItem.beanTypes(ModelAuthProvider.class));
+    public void unremovableBeans(BuildProducer<UnremovableBeanBuildItem> unremovableProducer) {
+        unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(ObjectMapper.class));
+        unremovableProducer.produce(UnremovableBeanBuildItem.beanTypes(ModelAuthProvider.class));
     }
 
     @BuildStep

core/runtime/src/main/java/io/quarkiverse/langchain4j/runtime/aiservice/GuardrailException.java

@@ -1,7 +1,7 @@
 package io.quarkiverse.langchain4j.runtime.aiservice;
 
 /**
- * Exception thrown when a input or output guardrail validation fails.
+ * Exception thrown when an input or output guardrail validation fails.
  * <p>
  * This exception is not intended to be used in guardrail implementation.
  */

docs/modules/ROOT/pages/easy-rag.adoc

@@ -14,7 +14,7 @@ ingest them into an in-memory embedding store.
 
 Apache Tika, a library for parsing various file formats, is used under the
 hood, so your documents can be in any of its supported formats (plain text,
-PDF, DOCX, HTML, etc), including images with text, which will be parsed
+PDF, DOCX, HTML, etc.), including images with text, which will be parsed
 using OCR (OCR requires to have the Tesseract library installed in your
 system - see https://cwiki.apache.org/confluence/display/TIKA/TikaOCR).
 

docs/modules/ROOT/pages/enable-disable-integrations.adoc

@@ -2,9 +2,9 @@
 
 include::./includes/attributes.adoc[]
 
-By default, all integrations with AI providers (OpenAI, HuggingFace, Azure OpenAI, etc) are enabled. This means that live calls are made to the configured AI provider.
+By default, all integrations with AI providers (OpenAI, HuggingFace, Azure OpenAI, etc.) are enabled. This means that live calls are made to the configured AI provider.
 
-Each provider has an `enable-integration` property (i.e. `quarkus.langchain4j.openai.enable-integration`, `quarkus.langchain4j.huggingface.enable-integration`, etc) that can be set to `false` to disable the integration. This property is read at runtime.
+Each provider has an `enable-integration` property (i.e. `quarkus.langchain4j.openai.enable-integration`, `quarkus.langchain4j.huggingface.enable-integration`, etc.) that can be set to `false` to disable the integration. This property is read at runtime.
 
 When disabled, any call made to the AI provider will end up in an `dev.langchain4j.model.ModelDisabledException` runtime exception being thrown.
 

docs/modules/ROOT/pages/guardrails.adoc

@@ -163,7 +163,7 @@ public interface Simulator {
 In this example, the `VerifyHeroFormat` is executed first to check that the passed hero is valid.
 Then, the `VerifyVillainFormat` is executed to check that the villain is valid.
 
-If the `VerifyHeroFormat` fails, the `VerifyVillainFormat` may or may not be executed depending on whether the failure is fatal or not. For instance the `VerifyHeroFormat` could be implemented as it follows.
+If the `VerifyHeroFormat` fails, the `VerifyVillainFormat` may or may not be executed depending on whether the failure is fatal or not. For instance, the `VerifyHeroFormat` could be implemented as it follows.
 
 [source,java]
 ----
@@ -511,7 +511,7 @@ public class HallucinationGuard implements OutputGuardrail {
 ----
 
 === Rewriting the LLM output
-It may happen that the output generated by the LLM is not completely satisfying, but it can be programmatically adjusted instead of attempting a retry or a remprompt, both implying a costly, time consuming and less reliable new interaction with the LLM. For instance it is quite common that an LLM produces the json of the data object that it is required to extract from the user prompt, but appends to it some unwanted explanation of why it generated that result, making the json unparsable, something like
+It may happen that the output generated by the LLM is not completely satisfying, but it can be programmatically adjusted instead of attempting a retry or a reprompt, both implying a costly, time consuming and less reliable new interaction with the LLM. For instance, it is quite common that an LLM produces the json of the data object that it is required to extract from the user prompt, but appends to it some unwanted explanation of why it generated that result, making the json unparsable, something like
 
 [source]
 ----

docs/modules/ROOT/pages/includes/quarkus-langchain4j-watsonx.adoc

@@ -420,7 +420,7 @@ a| [[quarkus-langchain4j-watsonx_quarkus-langchain4j-watsonx-chat-model-temperat
 
 [.description]
 --
-What sampling temperature to use,. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
+What sampling temperature to use. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
 
 We generally recommend altering this or `top_p` but not both.
 
@@ -1332,7 +1332,7 @@ a| [[quarkus-langchain4j-watsonx_quarkus-langchain4j-watsonx-model-name-chat-mod
 
 [.description]
 --
-What sampling temperature to use,. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
+What sampling temperature to use. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
 
 We generally recommend altering this or `top_p` but not both.
 

docs/modules/ROOT/pages/includes/quarkus-langchain4j-watsonx_quarkus.langchain4j.adoc

@@ -420,7 +420,7 @@ a| [[quarkus-langchain4j-watsonx_quarkus-langchain4j-watsonx-chat-model-temperat
 
 [.description]
 --
-What sampling temperature to use,. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
+What sampling temperature to use. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
 
 We generally recommend altering this or `top_p` but not both.
 
@@ -1332,7 +1332,7 @@ a| [[quarkus-langchain4j-watsonx_quarkus-langchain4j-watsonx-model-name-chat-mod
 
 [.description]
 --
-What sampling temperature to use,. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
+What sampling temperature to use. Higher values like `0.8` will make the output more random, while lower values like `0.2` will make it more focused and deterministic.
 
 We generally recommend altering this or `top_p` but not both.
 

docs/modules/ROOT/pages/web-search.adoc

@@ -13,7 +13,7 @@ WebSearchEngine engine;
 
 and then use it by calling its `search` method.
 
-If you want to let an chat model use web search by itself, there are
+If you want to let a chat model use web search by itself, there are
 generally two recommended ways to accomplish this: either by implementing a
 tool that uses it, or as a content retriever inside a RAG pipeline. The
 https://github.com/quarkiverse/quarkus-langchain4j/tree/main/samples/chatbot-web-search[chatbot-web-search]

model-providers/watsonx/runtime/src/main/java/io/quarkiverse/langchain4j/watsonx/runtime/config/ChatModelConfig.java

@@ -70,7 +70,7 @@ public interface ChatModelConfig {
     Double presencePenalty();
 
     /**
-     * What sampling temperature to use,. Higher values like <code>0.8</code> will make the output more random, while lower
+     * What sampling temperature to use. Higher values like <code>0.8</code> will make the output more random, while lower
      * values
      * like <code>0.2</code> will make it more focused and deterministic.
      * <p>