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feat(firebaseml): update the api
#### firebaseml:v2beta The following keys were added: - schemas.GoogleCloudAiplatformV1beta1RagRetrievalConfig (Total Keys: 28) - schemas.GoogleCloudAiplatformV1beta1VertexRagStore.properties.ragRetrievalConfig.$ref (Total Keys: 1)
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docs/dyn/firebaseml_v1.operations.html

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@@ -76,7 +76,7 @@ <h1><a href="firebaseml_v1.html">Firebase ML API</a> . <a href="firebaseml_v1.op
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<h2>Instance Methods</h2>
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<p class="toc_element">
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<code><a href="#cancel">cancel(name, body=None, x__xgafv=None)</a></code></p>
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<p class="firstline">Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`.</p>
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<p class="firstline">Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of `1`, corresponding to `Code.CANCELLED`.</p>
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<p class="toc_element">
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<code><a href="#close">close()</a></code></p>
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<p class="firstline">Close httplib2 connections.</p>
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<h3>Method Details</h3>
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<div class="method">
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<code class="details" id="cancel">cancel(name, body=None, x__xgafv=None)</code>
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<pre>Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn&#x27;t support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`.
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<pre>Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn&#x27;t support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of `1`, corresponding to `Code.CANCELLED`.
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Args:
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name: string, The name of the operation resource to be cancelled. (required)

docs/dyn/firebaseml_v2beta.projects.locations.publishers.models.html

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@@ -348,6 +348,25 @@ <h3>Method Details</h3>
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],
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},
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],
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&quot;ragRetrievalConfig&quot;: { # Specifies the context retrieval config. # Optional. The retrieval config for the Rag query.
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&quot;filter&quot;: { # Config for filters. # Optional. Config for filters.
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&quot;metadataFilter&quot;: &quot;A String&quot;, # Optional. String for metadata filtering.
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&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only returns contexts with vector distance smaller than the threshold.
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&quot;vectorSimilarityThreshold&quot;: 3.14, # Optional. Only returns contexts with vector similarity larger than the threshold.
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},
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&quot;hybridSearch&quot;: { # Config for Hybrid Search. # Optional. Config for Hybrid Search.
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&quot;alpha&quot;: 3.14, # Optional. Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], while 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5 which balances sparse and dense vector search equally.
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},
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&quot;ranking&quot;: { # Config for ranking and reranking. # Optional. Config for ranking and reranking.
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&quot;llmRanker&quot;: { # Config for LlmRanker. # Optional. Config for LlmRanker.
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&quot;modelName&quot;: &quot;A String&quot;, # Optional. The model name used for ranking. Format: `gemini-1.5-pro`
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},
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&quot;rankService&quot;: { # Config for Rank Service. # Optional. Config for Rank Service.
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&quot;modelName&quot;: &quot;A String&quot;, # Optional. The model name of the rank service. Format: `semantic-ranker-512@latest`
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},
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},
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&quot;topK&quot;: 42, # Optional. The number of contexts to retrieve.
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},
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&quot;similarityTopK&quot;: 42, # Optional. Number of top k results to return from the selected corpora.
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&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only return results with vector distance smaller than the threshold.
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},
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],
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},
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],
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&quot;ragRetrievalConfig&quot;: { # Specifies the context retrieval config. # Optional. The retrieval config for the Rag query.
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&quot;filter&quot;: { # Config for filters. # Optional. Config for filters.
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&quot;metadataFilter&quot;: &quot;A String&quot;, # Optional. String for metadata filtering.
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&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only returns contexts with vector distance smaller than the threshold.
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&quot;vectorSimilarityThreshold&quot;: 3.14, # Optional. Only returns contexts with vector similarity larger than the threshold.
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},
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&quot;hybridSearch&quot;: { # Config for Hybrid Search. # Optional. Config for Hybrid Search.
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&quot;alpha&quot;: 3.14, # Optional. Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], while 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5 which balances sparse and dense vector search equally.
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},
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&quot;ranking&quot;: { # Config for ranking and reranking. # Optional. Config for ranking and reranking.
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&quot;llmRanker&quot;: { # Config for LlmRanker. # Optional. Config for LlmRanker.
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&quot;modelName&quot;: &quot;A String&quot;, # Optional. The model name used for ranking. Format: `gemini-1.5-pro`
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},
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&quot;rankService&quot;: { # Config for Rank Service. # Optional. Config for Rank Service.
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&quot;modelName&quot;: &quot;A String&quot;, # Optional. The model name of the rank service. Format: `semantic-ranker-512@latest`
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},
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},
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&quot;topK&quot;: 42, # Optional. The number of contexts to retrieve.
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},
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&quot;similarityTopK&quot;: 42, # Optional. Number of top k results to return from the selected corpora.
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&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only return results with vector distance smaller than the threshold.
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},
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],
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},
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],
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&quot;ragRetrievalConfig&quot;: { # Specifies the context retrieval config. # Optional. The retrieval config for the Rag query.
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&quot;filter&quot;: { # Config for filters. # Optional. Config for filters.
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&quot;metadataFilter&quot;: &quot;A String&quot;, # Optional. String for metadata filtering.
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&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only returns contexts with vector distance smaller than the threshold.
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&quot;vectorSimilarityThreshold&quot;: 3.14, # Optional. Only returns contexts with vector similarity larger than the threshold.
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},
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&quot;hybridSearch&quot;: { # Config for Hybrid Search. # Optional. Config for Hybrid Search.
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&quot;alpha&quot;: 3.14, # Optional. Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], while 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5 which balances sparse and dense vector search equally.
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},
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&quot;ranking&quot;: { # Config for ranking and reranking. # Optional. Config for ranking and reranking.
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&quot;llmRanker&quot;: { # Config for LlmRanker. # Optional. Config for LlmRanker.
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&quot;modelName&quot;: &quot;A String&quot;, # Optional. The model name used for ranking. Format: `gemini-1.5-pro`
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},
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&quot;rankService&quot;: { # Config for Rank Service. # Optional. Config for Rank Service.
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&quot;modelName&quot;: &quot;A String&quot;, # Optional. The model name of the rank service. Format: `semantic-ranker-512@latest`
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},
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},
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&quot;topK&quot;: 42, # Optional. The number of contexts to retrieve.
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},
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&quot;similarityTopK&quot;: 42, # Optional. Number of top k results to return from the selected corpora.
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&quot;vectorDistanceThreshold&quot;: 3.14, # Optional. Only return results with vector distance smaller than the threshold.
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},

googleapiclient/discovery_cache/documents/firebaseml.v1.json

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"operations": {
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"methods": {
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"cancel": {
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"description": "Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to `Code.CANCELLED`.",
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"description": "Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of `1`, corresponding to `Code.CANCELLED`.",
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"flatPath": "v1/operations/{operationsId}:cancel",
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"httpMethod": "POST",
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"id": "firebaseml.operations.cancel",
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}
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}
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},
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"revision": "20240705",
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"revision": "20241117",
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"rootUrl": "https://firebaseml.googleapis.com/",
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"schemas": {
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"CancelOperationRequest": {

googleapiclient/discovery_cache/documents/firebaseml.v2beta.json

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}
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}
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},
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"revision": "20241101",
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"revision": "20241117",
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"rootUrl": "https://firebaseml.googleapis.com/",
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"schemas": {
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"Date": {
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1RagRetrievalConfig": {
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"description": "Specifies the context retrieval config.",
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"id": "GoogleCloudAiplatformV1beta1RagRetrievalConfig",
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"properties": {
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"filter": {
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"$ref": "GoogleCloudAiplatformV1beta1RagRetrievalConfigFilter",
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"description": "Optional. Config for filters."
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},
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"hybridSearch": {
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"$ref": "GoogleCloudAiplatformV1beta1RagRetrievalConfigHybridSearch",
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"description": "Optional. Config for Hybrid Search."
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},
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"ranking": {
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"$ref": "GoogleCloudAiplatformV1beta1RagRetrievalConfigRanking",
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"description": "Optional. Config for ranking and reranking."
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},
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"topK": {
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"description": "Optional. The number of contexts to retrieve.",
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"format": "int32",
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"type": "integer"
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1RagRetrievalConfigFilter": {
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"description": "Config for filters.",
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"id": "GoogleCloudAiplatformV1beta1RagRetrievalConfigFilter",
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"properties": {
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"metadataFilter": {
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"description": "Optional. String for metadata filtering.",
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"type": "string"
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},
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"vectorDistanceThreshold": {
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"description": "Optional. Only returns contexts with vector distance smaller than the threshold.",
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"format": "double",
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"type": "number"
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},
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"vectorSimilarityThreshold": {
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"description": "Optional. Only returns contexts with vector similarity larger than the threshold.",
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"format": "double",
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"type": "number"
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1RagRetrievalConfigHybridSearch": {
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"description": "Config for Hybrid Search.",
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"id": "GoogleCloudAiplatformV1beta1RagRetrievalConfigHybridSearch",
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"properties": {
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"alpha": {
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"description": "Optional. Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], while 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5 which balances sparse and dense vector search equally.",
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"format": "float",
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"type": "number"
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1RagRetrievalConfigRanking": {
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"description": "Config for ranking and reranking.",
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"id": "GoogleCloudAiplatformV1beta1RagRetrievalConfigRanking",
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"properties": {
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"llmRanker": {
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"$ref": "GoogleCloudAiplatformV1beta1RagRetrievalConfigRankingLlmRanker",
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"description": "Optional. Config for LlmRanker."
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},
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"rankService": {
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"$ref": "GoogleCloudAiplatformV1beta1RagRetrievalConfigRankingRankService",
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"description": "Optional. Config for Rank Service."
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1RagRetrievalConfigRankingLlmRanker": {
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"description": "Config for LlmRanker.",
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"id": "GoogleCloudAiplatformV1beta1RagRetrievalConfigRankingLlmRanker",
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"properties": {
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"modelName": {
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"description": "Optional. The model name used for ranking. Format: `gemini-1.5-pro`",
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"type": "string"
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1RagRetrievalConfigRankingRankService": {
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"description": "Config for Rank Service.",
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"id": "GoogleCloudAiplatformV1beta1RagRetrievalConfigRankingRankService",
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"properties": {
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"modelName": {
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"description": "Optional. The model name of the rank service. Format: `semantic-ranker-512@latest`",
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"type": "string"
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}
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},
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"type": "object"
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},
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"GoogleCloudAiplatformV1beta1Retrieval": {
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"description": "Defines a retrieval tool that model can call to access external knowledge.",
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"id": "GoogleCloudAiplatformV1beta1Retrieval",
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},
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"type": "array"
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},
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"ragRetrievalConfig": {
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"$ref": "GoogleCloudAiplatformV1beta1RagRetrievalConfig",
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"description": "Optional. The retrieval config for the Rag query."
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},
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"similarityTopK": {
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"deprecated": true,
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"description": "Optional. Number of top k results to return from the selected corpora.",

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