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Fix typo in documentation (#965)

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* Fixed typographical errors in key documents
This commit is contained in:
Hyoseop Song
2024-06-28 05:37:31 +09:00
committed by GitHub
parent a1c4a29073
commit 5b4d4c19d1
4 changed files with 4 additions and 4 deletions
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2
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc
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@@ -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:

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spring-ai-docs/src/main/antora/modules/ROOT/pages/api/embeddings/ollama-embeddings.adoc
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@@ -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]

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spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pinecone.adoc
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@@ -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.

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spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/typesense.adoc
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@@ -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).
====