From 5b4d4c19d1f74fe7e780aab8ae04cc29c88ca60e Mon Sep 17 00:00:00 2001
From: Hyoseop Song <crad_on25@naver.com>
Date: Fri, 28 Jun 2024 05:37:31 +0900
Subject: [PATCH] Fix typo in documentation (#965)

* Fixed typographical errors in key documents
---
 .../main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc    | 2 +-
 .../modules/ROOT/pages/api/embeddings/ollama-embeddings.adoc    | 2 +-
 .../main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc  | 2 +-
 .../main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc
index 31b5fe461..14863eadc 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc
@@ -241,7 +241,7 @@ dependencies {
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
 TIP: The `spring-ai-ollama` dependency provides access also to the `OllamaEmbeddingModel`.
-For more information about the `OllamaEmbeddingModel` refer to the link:../embeddings/ollama-embeddings.html[Ollama Embedding MOdel] section.
+For more information about the `OllamaEmbeddingModel` refer to the link:../embeddings/ollama-embeddings.html[Ollama Embedding Model] section.
 
 Next, create an `OllamaChatModel` instance and use it to text generations requests:
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/ollama-embeddings.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/ollama-embeddings.adoc
index f7a710034..f5e7d1e08 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/ollama-embeddings.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/ollama-embeddings.adoc
@@ -24,7 +24,7 @@ To help with dependency management, Spring AI provides a BOM (bill of materials)
 
 == Auto-configuration
 
-Spring AI provides Spring Boot auto-configuration for the Azure Ollama Embedding Mpdel.
+Spring AI provides Spring Boot auto-configuration for the Azure Ollama Embedding Model.
 To enable it add the following dependency to your Maven `pom.xml` file:
 
 [source,xml]
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc
index b798b6628..6aa3a7f63 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc
@@ -112,7 +112,7 @@ You can use the following properties in your Spring Boot configuration to custom
 |`spring.ai.vectorstore.pinecone.index-name`| Pinecone index name | -
 |`spring.ai.vectorstore.pinecone.namespace`| Pinecone namespace | -
 |`spring.ai.vectorstore.pinecone.namespace`| Pinecone namespace | -
-|`spring.ai.vectorstore.pinecone.content-field-name`| Pinecone metadata field name used to store the origina text content. | `document_content`
+|`spring.ai.vectorstore.pinecone.content-field-name`| Pinecone metadata field name used to store the original text content. | `document_content`
 |`spring.ai.vectorstore.pinecone.distance-metadata-field-name`| Pinecone metadata field name used to store the computed distance. | `distance`
 |`spring.ai.vectorstore.pinecone.server-side-timeout`|  | 20 sec.
 
diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc
index 9acbc3379..375c8795a 100644
--- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc
+++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc
@@ -235,7 +235,7 @@ If all goes well, you should retrieve the document containing the text "Spring A
 
 [NOTE]
 ====
-If you are not retrieveing the documents in the expected order or the search results are not as expected, check the embedding model you are using.
+If you are not retrieving the documents in the expected order or the search results are not as expected, check the embedding model you are using.
 
 Embedding models can have a significant impact on the search results (i.e. make sure if your data is in Spanish to use a Spanish or multilingual embedding model).
 ====