feat:Refactor LLM schemas, add OpenAI/Vertex variants and auth, mime_type

src/libs/Vectara/Generated/Vectara..JsonSerializerContext.g.cs

@@ -45,6 +45,8 @@ namespace Vectara
             typeof(global::Vectara.JsonConverters.ChatStreamedResponseDiscriminatorTypeNullableJsonConverter),
             typeof(global::Vectara.JsonConverters.RemoteAuthDiscriminatorTypeJsonConverter),
             typeof(global::Vectara.JsonConverters.RemoteAuthDiscriminatorTypeNullableJsonConverter),
+            typeof(global::Vectara.JsonConverters.VertexAiAuthDiscriminatorTypeJsonConverter),
+            typeof(global::Vectara.JsonConverters.VertexAiAuthDiscriminatorTypeNullableJsonConverter),
             typeof(global::Vectara.JsonConverters.CreateLLMRequestDiscriminatorTypeJsonConverter),
             typeof(global::Vectara.JsonConverters.CreateLLMRequestDiscriminatorTypeNullableJsonConverter),
             typeof(global::Vectara.JsonConverters.CreateChatCompletionResponseObjectJsonConverter),
@@ -77,6 +79,9 @@ namespace Vectara
             typeof(global::Vectara.JsonConverters.QueryHistorySpanJsonConverter),
             typeof(global::Vectara.JsonConverters.ChatStreamedResponseJsonConverter),
             typeof(global::Vectara.JsonConverters.RemoteAuthJsonConverter),
+            typeof(global::Vectara.JsonConverters.CreateOpenAILLMRequestJsonConverter),
+            typeof(global::Vectara.JsonConverters.CreateOpenAIResponsesLLMRequestJsonConverter),
+            typeof(global::Vectara.JsonConverters.VertexAiAuthJsonConverter),
             typeof(global::Vectara.JsonConverters.CreateLLMRequestJsonConverter),
             typeof(global::Vectara.JsonConverters.CreateEncoderRequestJsonConverter),
             typeof(global::Vectara.JsonConverters.CreateAppClientRequestJsonConverter),

src/libs/Vectara/Generated/Vectara.CorporaClient.UpdateCorpus.g.cs

@@ -287,7 +287,7 @@ partial void ProcessUpdateCorpusResponseContent(
         /// </param>
         /// <param name="name">
         /// The name for the corpus. If unset or null, then the corpus will remain in the same state.<br/>
-        /// Example: new-name
+        /// Example: new-corpus-name
         /// </param>
         /// <param name="description">
         /// Description of the corpus. If unset or null, then the corpus will remain in the same state.<br/>

src/libs/Vectara/Generated/Vectara.DocumentsClient.SummarizeCorpusDocument.g.cs

@@ -289,10 +289,12 @@ partial void ProcessSummarizeCorpusDocumentResponseContent(
         /// </param>
         /// <param name="documentId"></param>
         /// <param name="llmName">
-        /// The name of the LLM.
+        /// The name of the LLM.<br/>
+        /// Example: mockingbird-2.0
         /// </param>
         /// <param name="promptTemplate">
-        /// The prompt template to use when generating the summary. Vectara manages both system and user roles and prompts for the generative LLM out of the box by default. However, users can override the `prompt_template` via this variable. The `prompt_template` is in the form of an Apache Velocity template. For more details on how to configure the `prompt_template`, see the [long-form documentation](https://docs.vectara.com/docs/prompts/vectara-prompt-engine).
+        /// The prompt template to use when generating the summary. Vectara manages both system and user roles and prompts for the generative LLM out of the box by default. However, users can override the `prompt_template` via this variable. The `prompt_template` is in the form of an Apache Velocity template. For more details on how to configure the `prompt_template`, see the [long-form documentation](https://docs.vectara.com/docs/prompts/vectara-prompt-engine).<br/>
+        /// Example: Provide a concise summary of the document.
         /// </param>
         /// <param name="modelParameters">
         /// Optional parameters for the specified model used when generating the summary.

src/libs/Vectara/Generated/Vectara.GenerationPresetsClient.ListGenerationPresets.g.cs

@@ -35,7 +35,9 @@ partial void ProcessListGenerationPresetsResponseContent(
         /// </summary>
         /// <param name="requestTimeout"></param>
         /// <param name="requestTimeoutMillis"></param>
-        /// <param name="llmName"></param>
+        /// <param name="llmName">
+        /// Example: mockingbird-2.0
+        /// </param>
         /// <param name="limit">
         /// Default Value: 10
         /// </param>

src/libs/Vectara/Generated/Vectara.HallucinationCorrectorsClient.CorrectHallucinations.g.cs

@@ -276,7 +276,7 @@ partial void ProcessCorrectHallucinationsResponseContent(
         /// </param>
         /// <param name="modelName">
         /// The name of the LLM model to use for hallucination correction.<br/>
-        /// Example: vhc-large-10
+        /// Example: vhc-large-1.0
         /// </param>
         /// <param name="query">
         /// Optional query that provides context for the expected response format and factual information. When provided, enables query-aware hallucination correction that considers the specific response format and factual context expected for the query.

src/libs/Vectara/Generated/Vectara.ICorporaClient.UpdateCorpus.g.cs

@@ -40,7 +40,7 @@ public partial interface ICorporaClient
         /// </param>
         /// <param name="name">
         /// The name for the corpus. If unset or null, then the corpus will remain in the same state.<br/>
-        /// Example: new-name
+        /// Example: new-corpus-name
         /// </param>
         /// <param name="description">
         /// Description of the corpus. If unset or null, then the corpus will remain in the same state.<br/>

src/libs/Vectara/Generated/Vectara.IDocumentsClient.SummarizeCorpusDocument.g.cs

@@ -38,10 +38,12 @@ public partial interface IDocumentsClient
         /// </param>
         /// <param name="documentId"></param>
         /// <param name="llmName">
-        /// The name of the LLM.
+        /// The name of the LLM.<br/>
+        /// Example: mockingbird-2.0
         /// </param>
         /// <param name="promptTemplate">
-        /// The prompt template to use when generating the summary. Vectara manages both system and user roles and prompts for the generative LLM out of the box by default. However, users can override the `prompt_template` via this variable. The `prompt_template` is in the form of an Apache Velocity template. For more details on how to configure the `prompt_template`, see the [long-form documentation](https://docs.vectara.com/docs/prompts/vectara-prompt-engine).
+        /// The prompt template to use when generating the summary. Vectara manages both system and user roles and prompts for the generative LLM out of the box by default. However, users can override the `prompt_template` via this variable. The `prompt_template` is in the form of an Apache Velocity template. For more details on how to configure the `prompt_template`, see the [long-form documentation](https://docs.vectara.com/docs/prompts/vectara-prompt-engine).<br/>
+        /// Example: Provide a concise summary of the document.
         /// </param>
         /// <param name="modelParameters">
         /// Optional parameters for the specified model used when generating the summary.

src/libs/Vectara/Generated/Vectara.IGenerationPresetsClient.ListGenerationPresets.g.cs

@@ -10,7 +10,9 @@ public partial interface IGenerationPresetsClient
         /// </summary>
         /// <param name="requestTimeout"></param>
         /// <param name="requestTimeoutMillis"></param>
-        /// <param name="llmName"></param>
+        /// <param name="llmName">
+        /// Example: mockingbird-2.0
+        /// </param>
         /// <param name="limit">
         /// Default Value: 10
         /// </param>

src/libs/Vectara/Generated/Vectara.IHallucinationCorrectorsClient.CorrectHallucinations.g.cs

@@ -33,7 +33,7 @@ public partial interface IHallucinationCorrectorsClient
         /// </param>
         /// <param name="modelName">
         /// The name of the LLM model to use for hallucination correction.<br/>
-        /// Example: vhc-large-10
+        /// Example: vhc-large-1.0
         /// </param>
         /// <param name="query">
         /// Optional query that provides context for the expected response format and factual information. When provided, enables query-aware hallucination correction that considers the specific response format and factual context expected for the query.

src/libs/Vectara/Generated/Vectara.IQueriesClient.Query.g.cs

@@ -7,7 +7,7 @@ public partial interface IQueriesClient
         /// <summary>
         /// Multiple Corpora Query<br/>
         /// Perform a multipurpose query to retrieve relevant information from one or more corpora and generate a response using Retrieval Augmented Generation (RAG).<br/>
-        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
+        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus), and the corpus key is part of that process. When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
         /// * Customize your search by specifying the query text (`query`), pagination details (`offset` and `limit`), and metadata filters (`metadata_filter`) to tailor your search results. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#query-definition)<br/>
         /// * Leverage advanced search capabilities like reranking (`reranker`) and opt-in Retrieval Augmented Generation (RAG) (`generation`) for enhanced query performance. Generation is opt-in by setting the `generation` property. By excluding the property or by setting it to null, the response will not include generation. [Learn more](https://docs.vectara.com/docs/learn/grounded-generation/configure-query-summarization)<br/>
         /// * Specify Vectara's RAG-focused LLM (Mockingbird) for the `generation_preset_name`. [Learn more](https://docs.vectara.com/docs/learn/mockingbird-llm)<br/>
@@ -29,7 +29,7 @@ public partial interface IQueriesClient
         /// <summary>
         /// Multiple Corpora Query<br/>
         /// Perform a multipurpose query to retrieve relevant information from one or more corpora and generate a response using Retrieval Augmented Generation (RAG).<br/>
-        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
+        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus), and the corpus key is part of that process. When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
         /// * Customize your search by specifying the query text (`query`), pagination details (`offset` and `limit`), and metadata filters (`metadata_filter`) to tailor your search results. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#query-definition)<br/>
         /// * Leverage advanced search capabilities like reranking (`reranker`) and opt-in Retrieval Augmented Generation (RAG) (`generation`) for enhanced query performance. Generation is opt-in by setting the `generation` property. By excluding the property or by setting it to null, the response will not include generation. [Learn more](https://docs.vectara.com/docs/learn/grounded-generation/configure-query-summarization)<br/>
         /// * Specify Vectara's RAG-focused LLM (Mockingbird) for the `generation_preset_name`. [Learn more](https://docs.vectara.com/docs/learn/mockingbird-llm)<br/>

src/libs/Vectara/Generated/Vectara.IQueriesClient.QueryCorpus.g.cs

@@ -7,7 +7,7 @@ public partial interface IQueriesClient
         /// <summary>
         /// Advanced Single Corpus Query<br/>
         /// Perform an advanced query on a specific corpus to find relevant results, highlight relevant snippets, and use Retrieval Augmented Generation.<br/>
-        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
+        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus), and the corpus key is part of that process. When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
         /// * Customize your search by specifying the query text (`query`), pagination details (`offset` and `limit`), and metadata filters (`metadata_filter`) to tailor your search results. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#query-definition)<br/>
         /// * Leverage advanced search capabilities like reranking (`reranker`) and Retrieval Augmented Generation (RAG) (`generation`) for enhanced query performance. Generation is opt in by setting the `generation` property. By excluding the property or by setting it to null, the response will not include generation. [Learn more](https://docs.vectara.com/docs/learn/grounded-generation/configure-query-summarization).<br/>
         /// * Use hybrid search to achieve optimal results by setting different values for `lexical_interpolation` (e.g., `0.005`). [Learn more](https://docs.vectara.com/docs/learn/hybrid-search)<br/>
@@ -34,7 +34,7 @@ public partial interface IQueriesClient
         /// <summary>
         /// Advanced Single Corpus Query<br/>
         /// Perform an advanced query on a specific corpus to find relevant results, highlight relevant snippets, and use Retrieval Augmented Generation.<br/>
-        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
+        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus), and the corpus key is part of that process. When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
         /// * Customize your search by specifying the query text (`query`), pagination details (`offset` and `limit`), and metadata filters (`metadata_filter`) to tailor your search results. [Learn more](https://docs.vectara.com/docs/api-reference/search-apis/search#query-definition)<br/>
         /// * Leverage advanced search capabilities like reranking (`reranker`) and Retrieval Augmented Generation (RAG) (`generation`) for enhanced query performance. Generation is opt in by setting the `generation` property. By excluding the property or by setting it to null, the response will not include generation. [Learn more](https://docs.vectara.com/docs/learn/grounded-generation/configure-query-summarization).<br/>
         /// * Use hybrid search to achieve optimal results by setting different values for `lexical_interpolation` (e.g., `0.005`). [Learn more](https://docs.vectara.com/docs/learn/hybrid-search)<br/>

src/libs/Vectara/Generated/Vectara.IQueriesClient.SearchCorpus.g.cs

@@ -7,7 +7,7 @@ public partial interface IQueriesClient
         /// <summary>
         /// Simple Single Corpus Query<br/>
         /// Search a single corpus with a straightforward query request, specifying the corpus key and query parameters.<br/>
-        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus). When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
+        /// * Specify the unique `corpus_key` identifying the corpus to query. The `corpus_key` is [created in the Vectara Console UI](https://docs.vectara.com/docs/console-ui/creating-a-corpus) or the [Create Corpus API definition](https://docs.vectara.com/docs/api-reference/admin-apis/create-corpus), and the corpus key is part of that process. When creating a new corpus, you have the option to assign a custom `corpus_key` following your preferred naming convention. This key serves as a unique identifier for the corpus, allowing it to be referenced in search requests. For more information, see [Corpus Key Definition](https://docs.vectara.com/docs/api-reference/search-apis/search#corpus-key-definition).<br/>
         /// * Enter the search `query` string for the corpus, which is the question you want to ask.<br/>
         /// * Set the maximum number of results (`limit`) to return. **Default**: 10, **minimum**: 1<br/>
         /// For more detailed information, see this [Query API guide](https://docs.vectara.com/docs/api-reference/search-apis/search).