phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-07-28 06:58:53 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

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

Browse source
This commit is contained in:
Sai Soundararaj 2025-07-24 09:53:57 -07:00
commit 48c5d089c6
445 changed files with 15118 additions and 17426 deletions
Download patch file Download diff file Expand all files Collapse all files

120
docs/_static/llama-stack-spec.html vendored
View file

@ -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."

102
docs/_static/llama-stack-spec.yaml vendored
View file

@ -8505,8 +8505,41 @@ components:
A trace representing the complete execution path of a request across multiple
operations.
Checkpoint:
description: Checkpoint created during training runs.
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
properties:
@ -8525,6 +8558,31 @@ components:
- checkpoints
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:
@ -8630,6 +8688,8 @@ components:
embedding_dimension:
type: integer
description: Dimension of the embedding vectors
vector_db_name:
type: string
additionalProperties: false
required:
- identifier
@ -10367,13 +10427,7 @@ components:
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:
type: object
@ -11096,15 +11150,32 @@ components:
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
required:
- reward_scale
- reward_clip
- epsilon
- 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:
@ -11389,7 +11460,8 @@ components:
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".
@ -11416,6 +11488,17 @@ components:
mapping:
default: '#/components/schemas/DefaultRAGQueryGeneratorConfig'
llm: '#/components/schemas/LLMRAGQueryGeneratorConfig'
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:
@ -12029,6 +12112,9 @@ components:
provider_id:
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: >-