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Merge remote-tracking branch 'upstream/main' into update-api-docs

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Sai Soundararaj 2025-07-24 09:53:57 -07:00
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docs/_static/llama-stack-spec.html vendored
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@ -11494,8 +11494,44 @@
"description": "A trace representing the complete execution path of a request across multiple operations."
},
"Checkpoint": {
"description": "Checkpoint created during training runs.",
"title": "Checkpoint"
"type": "object",
"properties": {
"identifier": {
"type": "string",
"description": "Unique identifier for the checkpoint"
},
"created_at": {
"type": "string",
"format": "date-time",
"description": "Timestamp when the checkpoint was created"
},
"epoch": {
"type": "integer",
"description": "Training epoch when the checkpoint was saved"
},
"post_training_job_id": {
"type": "string",
"description": "Identifier of the training job that created this checkpoint"
},
"path": {
"type": "string",
"description": "File system path where the checkpoint is stored"
},
"training_metrics": {
"$ref": "#/components/schemas/PostTrainingMetric",
"description": "(Optional) Training metrics associated with this checkpoint"
}
},
"additionalProperties": false,
"required": [
"identifier",
"created_at",
"epoch",
"post_training_job_id",
"path"
],
"title": "Checkpoint",
"description": "Checkpoint created during training runs."
},
"PostTrainingJobArtifactsResponse": {
"type": "object",
@ -11520,6 +11556,36 @@
"title": "PostTrainingJobArtifactsResponse",
"description": "Artifacts of a finetuning job."
},
"PostTrainingMetric": {
"type": "object",
"properties": {
"epoch": {
"type": "integer",
"description": "Training epoch number"
},
"train_loss": {
"type": "number",
"description": "Loss value on the training dataset"
},
"validation_loss": {
"type": "number",
"description": "Loss value on the validation dataset"
},
"perplexity": {
"type": "number",
"description": "Perplexity metric indicating model confidence"
}
},
"additionalProperties": false,
"required": [
"epoch",
"train_loss",
"validation_loss",
"perplexity"
],
"title": "PostTrainingMetric",
"description": "Training metrics captured during post-training jobs."
},
"PostTrainingJobStatusResponse": {
"type": "object",
"properties": {
@ -11657,6 +11723,9 @@
"embedding_dimension": {
"type": "integer",
"description": "Dimension of the embedding vectors"
},
"vector_db_name": {
"type": "string"
}
},
"additionalProperties": false,
@ -14041,16 +14110,9 @@
"provider_id": {
"type": "string",
"description": "The ID of the provider to use for this vector store."
},
"provider_vector_db_id": {
"type": "string",
"description": "The provider-specific vector database ID."
}
},
"additionalProperties": false,
"required": [
"name"
],
"title": "OpenaiCreateVectorStoreRequest"
},
"VectorStoreFileCounts": {
@ -14992,6 +15054,15 @@
"gamma": {
"type": "number",
"description": "Discount factor for future rewards"
},
"beta": {
"type": "number",
"description": "Temperature parameter for the DPO loss"
},
"loss_type": {
"$ref": "#/components/schemas/DPOLossType",
"default": "sigmoid",
"description": "The type of loss function to use for DPO"
}
},
"additionalProperties": false,
@ -14999,11 +15070,23 @@
"reward_scale",
"reward_clip",
"epsilon",
"gamma"
"gamma",
"beta",
"loss_type"
],
"title": "DPOAlignmentConfig",
"description": "Configuration for Direct Preference Optimization (DPO) alignment."
},
"DPOLossType": {
"type": "string",
"enum": [
"sigmoid",
"hinge",
"ipo",
"kto_pair"
],
"title": "DPOLossType"
},
"DataConfig": {
"type": "object",
"properties": {
@ -15343,7 +15426,8 @@
"description": "Template for formatting each retrieved chunk in the context. Available placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk content string), {metadata} (chunk metadata dict). Default: \"Result {index}\\nContent: {chunk.content}\\nMetadata: {metadata}\\n\""
},
"mode": {
"type": "string",
"$ref": "#/components/schemas/RAGSearchMode",
"default": "vector",
"description": "Search mode for retrieval—either \"vector\", \"keyword\", or \"hybrid\". Default \"vector\"."
},
"ranker": {
@ -15378,6 +15462,16 @@
}
}
},
"RAGSearchMode": {
"type": "string",
"enum": [
"vector",
"keyword",
"hybrid"
],
"title": "RAGSearchMode",
"description": "Search modes for RAG query retrieval: - VECTOR: Uses vector similarity search for semantic matching - KEYWORD: Uses keyword-based search for exact matching - HYBRID: Combines both vector and keyword search for better results"
},
"RRFRanker": {
"type": "object",
"properties": {
@ -16203,6 +16297,10 @@
"type": "string",
"description": "The identifier of the provider."
},
"vector_db_name": {
"type": "string",
"description": "The name of the vector database."
},
"provider_vector_db_id": {
"type": "string",
"description": "The identifier of the vector database in the provider."