mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-28 04:21:58 +00:00
Merge branch 'main' into feat/litellm_sambanova_usage
This commit is contained in:
Jorge Piedrahita Ortiz
GitHub
commit
02a4f9ac59
69 changed files with 1128 additions and 445 deletions
263
docs/_static/llama-stack-spec.html
vendored
263
docs/_static/llama-stack-spec.html
vendored
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@ -1101,14 +1101,7 @@
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@ -1150,14 +1143,7 @@
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@ -1363,14 +1335,7 @@
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@ -1673,14 +1638,7 @@
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"content": {
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"application/json": {
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@ -1722,14 +1680,7 @@
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"content": {
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@ -1804,14 +1755,7 @@
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@ -1913,14 +1857,7 @@
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@ -2246,14 +2183,7 @@
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"$ref": "#/components/schemas/JobStatus"
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@ -4229,70 +4159,80 @@
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{
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"type": "object",
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{
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"type": "string"
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{
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{
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"type": "number"
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{
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"type": "boolean"
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},
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{
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"type": "null"
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},
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{
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"type": "object",
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"additionalProperties": {
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"oneOf": [
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{
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"type": "string"
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{
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"type": "integer"
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},
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{
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"type": "number"
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{
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"type": "boolean"
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||||
"arguments_json": {
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||||
"type": "string"
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}
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},
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||||
"additionalProperties": false,
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||||
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|||
"type": "object",
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||||
"properties": {
|
||||
"document_id": {
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||||
"type": "string"
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||||
"type": "string",
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||||
"description": "The unique identifier for the document."
|
||||
},
|
||||
"content": {
|
||||
"oneOf": [
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||||
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|||
{
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||||
"$ref": "#/components/schemas/URL"
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||||
}
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]
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||||
],
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||||
"description": "The content of the document."
|
||||
},
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||||
"mime_type": {
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||||
"type": "string"
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||||
"type": "string",
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||||
"description": "The MIME type of the document."
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||||
},
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||||
"metadata": {
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||||
"type": "object",
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||||
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|||
"type": "object"
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}
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||||
]
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||||
}
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||||
},
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||||
"description": "Additional metadata for the document."
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||||
}
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||||
},
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||||
"additionalProperties": false,
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||||
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|||
"content",
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||||
"metadata"
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||||
],
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||||
"title": "RAGDocument"
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||||
"title": "RAGDocument",
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||||
"description": "A document to be used for document ingestion in the RAG Tool."
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||||
},
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||||
"InsertRequest": {
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||||
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||||
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],
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||||
"title": "ScoreBatchResponse"
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||||
},
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||||
"AlgorithmConfig": {
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||||
"oneOf": [
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{
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||||
"$ref": "#/components/schemas/LoraFinetuningConfig"
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{
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||||
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||||
}
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||||
],
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||||
"discriminator": {
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||||
"propertyName": "type",
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||||
"mapping": {
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||||
"LoRA": "#/components/schemas/LoraFinetuningConfig",
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||||
"QAT": "#/components/schemas/QATFinetuningConfig"
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||||
}
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||||
}
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||||
},
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||||
"LoraFinetuningConfig": {
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||||
"type": "object",
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||||
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"algorithm_config": {
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||||
"$ref": "#/components/schemas/AlgorithmConfig"
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||||
"oneOf": [
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||||
{
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||||
"$ref": "#/components/schemas/LoraFinetuningConfig"
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},
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{
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"$ref": "#/components/schemas/QATFinetuningConfig"
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}
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]
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"additionalProperties": false,
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|||
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|||
content:
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application/json:
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schema:
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oneOf:
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- $ref: '#/components/schemas/Benchmark'
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||||
- type: 'null'
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||||
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||||
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schema:
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oneOf:
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- $ref: '#/components/schemas/Dataset'
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||||
- type: 'null'
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schema:
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oneOf:
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- $ref: '#/components/schemas/Model'
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||||
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||||
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|||
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||||
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schema:
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||||
oneOf:
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||||
- $ref: '#/components/schemas/ScoringFn'
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||||
- type: 'null'
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||||
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|||
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||||
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||||
oneOf:
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||||
- $ref: '#/components/schemas/PostTrainingJobArtifactsResponse'
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||||
- type: 'null'
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||||
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|
||||
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||||
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||||
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|||
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||||
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||||
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||||
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||||
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||||
- type: 'null'
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||||
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|
||||
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||||
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||||
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|||
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||||
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||||
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||||
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||||
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||||
- type: 'null'
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||||
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||||
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|
||||
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||||
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|
||||
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|||
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||||
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||||
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||||
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||||
- $ref: '#/components/schemas/VectorDB'
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||||
- type: 'null'
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||||
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||||
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|
||||
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||||
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|
||||
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|
|||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
|||
title: BuiltinTool
|
||||
- type: string
|
||||
arguments:
|
||||
type: object
|
||||
additionalProperties:
|
||||
oneOf:
|
||||
- type: string
|
||||
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|
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|
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|
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||||
items:
|
||||
oneOf:
|
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|
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|
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additionalProperties:
|
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|
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- type: string
|
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- type: integer
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oneOf:
|
||||
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||||
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additionalProperties:
|
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
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|
||||
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|
||||
arguments_json:
|
||||
type: string
|
||||
additionalProperties: false
|
||||
required:
|
||||
- call_id
|
||||
|
|
@ -5395,6 +5379,7 @@ components:
|
|||
properties:
|
||||
document_id:
|
||||
type: string
|
||||
description: The unique identifier for the document.
|
||||
content:
|
||||
oneOf:
|
||||
- type: string
|
||||
|
|
@ -5403,8 +5388,10 @@ components:
|
|||
items:
|
||||
$ref: '#/components/schemas/InterleavedContentItem'
|
||||
- $ref: '#/components/schemas/URL'
|
||||
description: The content of the document.
|
||||
mime_type:
|
||||
type: string
|
||||
description: The MIME type of the document.
|
||||
metadata:
|
||||
type: object
|
||||
additionalProperties:
|
||||
|
|
@ -5415,12 +5402,15 @@ components:
|
|||
- type: string
|
||||
- type: array
|
||||
- type: object
|
||||
description: Additional metadata for the document.
|
||||
additionalProperties: false
|
||||
required:
|
||||
- document_id
|
||||
- content
|
||||
- metadata
|
||||
title: RAGDocument
|
||||
description: >-
|
||||
A document to be used for document ingestion in the RAG Tool.
|
||||
InsertRequest:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -6698,15 +6688,6 @@ components:
|
|||
required:
|
||||
- results
|
||||
title: ScoreBatchResponse
|
||||
AlgorithmConfig:
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/LoraFinetuningConfig'
|
||||
- $ref: '#/components/schemas/QATFinetuningConfig'
|
||||
discriminator:
|
||||
propertyName: type
|
||||
mapping:
|
||||
LoRA: '#/components/schemas/LoraFinetuningConfig'
|
||||
QAT: '#/components/schemas/QATFinetuningConfig'
|
||||
LoraFinetuningConfig:
|
||||
type: object
|
||||
properties:
|
||||
|
|
@ -6790,7 +6771,9 @@ components:
|
|||
checkpoint_dir:
|
||||
type: string
|
||||
algorithm_config:
|
||||
$ref: '#/components/schemas/AlgorithmConfig'
|
||||
oneOf:
|
||||
- $ref: '#/components/schemas/LoraFinetuningConfig'
|
||||
- $ref: '#/components/schemas/QATFinetuningConfig'
|
||||
additionalProperties: false
|
||||
required:
|
||||
- job_uuid
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@
|
|||
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import sys
|
||||
import fire
|
||||
import ruamel.yaml as yaml
|
||||
|
||||
|
|
@ -21,7 +21,7 @@ from llama_stack.distribution.stack import LlamaStack # noqa: E402
|
|||
|
||||
from .pyopenapi.options import Options # noqa: E402
|
||||
from .pyopenapi.specification import Info, Server # noqa: E402
|
||||
from .pyopenapi.utility import Specification # noqa: E402
|
||||
from .pyopenapi.utility import Specification, validate_api_method_return_types # noqa: E402
|
||||
|
||||
|
||||
def str_presenter(dumper, data):
|
||||
|
|
@ -39,6 +39,14 @@ def main(output_dir: str):
|
|||
if not output_dir.exists():
|
||||
raise ValueError(f"Directory {output_dir} does not exist")
|
||||
|
||||
# Validate API protocols before generating spec
|
||||
print("Validating API method return types...")
|
||||
return_type_errors = validate_api_method_return_types()
|
||||
if return_type_errors:
|
||||
print("\nAPI Method Return Type Validation Errors:\n")
|
||||
for error in return_type_errors:
|
||||
print(error)
|
||||
sys.exit(1)
|
||||
now = str(datetime.now())
|
||||
print(
|
||||
"Converting the spec to YAML (openapi.yaml) and HTML (openapi.html) at " + now
|
||||
|
|
|
|||
|
|
@ -6,16 +6,19 @@
|
|||
|
||||
import json
|
||||
import typing
|
||||
import inspect
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import TextIO
|
||||
from typing import Any, Dict, List, Optional, Protocol, Type, Union, get_type_hints, get_origin, get_args
|
||||
|
||||
from llama_stack.strong_typing.schema import object_to_json, StrictJsonType
|
||||
from llama_stack.distribution.resolver import api_protocol_map
|
||||
|
||||
from .generator import Generator
|
||||
from .options import Options
|
||||
from .specification import Document
|
||||
|
||||
|
||||
THIS_DIR = Path(__file__).parent
|
||||
|
||||
|
||||
|
|
@ -114,3 +117,37 @@ class Specification:
|
|||
)
|
||||
|
||||
f.write(html)
|
||||
|
||||
def is_optional_type(type_: Any) -> bool:
|
||||
"""Check if a type is Optional."""
|
||||
origin = get_origin(type_)
|
||||
args = get_args(type_)
|
||||
return origin is Optional or (origin is Union and type(None) in args)
|
||||
|
||||
|
||||
def validate_api_method_return_types() -> List[str]:
|
||||
"""Validate that all API methods have proper return types."""
|
||||
errors = []
|
||||
protocols = api_protocol_map()
|
||||
|
||||
for protocol_name, protocol in protocols.items():
|
||||
methods = inspect.getmembers(protocol, predicate=inspect.isfunction)
|
||||
|
||||
for method_name, method in methods:
|
||||
if not hasattr(method, '__webmethod__'):
|
||||
continue
|
||||
|
||||
# Only check GET methods
|
||||
if method.__webmethod__.method != "GET":
|
||||
continue
|
||||
|
||||
hints = get_type_hints(method)
|
||||
|
||||
if 'return' not in hints:
|
||||
errors.append(f"Method {protocol_name}.{method_name} has no return type annotation")
|
||||
else:
|
||||
return_type = hints['return']
|
||||
if is_optional_type(return_type):
|
||||
errors.append(f"Method {protocol_name}.{method_name} returns Optional type")
|
||||
|
||||
return errors
|
||||
|
|
|
|||
|
|
@ -15,8 +15,6 @@ Llama Stack defines and standardizes the core building blocks needed to bring ge
|
|||
- **Multiple developer interfaces** like CLI and SDKs for Python, Node, iOS, and Android
|
||||
- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack
|
||||
|
||||
We focus on making it easy to build production applications with the Llama model family - from the latest Llama 3.3 to specialized models like Llama Guard for safety.
|
||||
|
||||
```{image} ../_static/llama-stack.png
|
||||
:alt: Llama Stack
|
||||
:width: 400px
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
|
|||
|
||||
**Robust Ecosystem**
|
||||
- Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies).
|
||||
- Ecosystem offers tailored infrastructure, software, and services for deploying Llama models.
|
||||
- Ecosystem offers tailored infrastructure, software, and services for deploying a variety of models.
|
||||
|
||||
|
||||
### Our Philosophy
|
||||
|
|
@ -57,7 +57,6 @@ Llama Stack addresses these challenges through a service-oriented, API-first app
|
|||
- **Composability**: Every component is independent but works together seamlessly
|
||||
- **Production Ready**: Built for real-world applications, not just demos
|
||||
- **Turnkey Solutions**: Easy to deploy built in solutions for popular deployment scenarios
|
||||
- **Llama First**: Explicit focus on Meta's Llama models and partnering ecosystem
|
||||
|
||||
|
||||
With Llama Stack, you can focus on building your application while we handle the infrastructure complexity, essential capabilities, and provider integrations.
|
||||
|
|
|
|||
|
|
@ -3,21 +3,36 @@ orphan: true
|
|||
---
|
||||
# Qdrant
|
||||
|
||||
[Qdrant](https://qdrant.tech/documentation/) is a remote vector database provider for Llama Stack. It
|
||||
[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
|
||||
allows you to store and query vectors directly in memory.
|
||||
That means you'll get fast and efficient vector retrieval.
|
||||
|
||||
> By default, Qdrant stores vectors in RAM, delivering incredibly fast access for datasets that fit comfortably in
|
||||
> memory. But when your dataset exceeds RAM capacity, Qdrant offers Memmap as an alternative.
|
||||
>
|
||||
> \[[An Introduction to Vector Databases](https://qdrant.tech/articles/what-is-a-vector-database/)\]
|
||||
|
||||
|
||||
|
||||
## Features
|
||||
|
||||
- Easy to use
|
||||
- Lightweight and easy to use
|
||||
- Fully integrated with Llama Stack
|
||||
- Apache 2.0 license terms
|
||||
- Store embeddings and their metadata
|
||||
- Supports search by
|
||||
[Keyword](https://qdrant.tech/articles/qdrant-introduces-full-text-filters-and-indexes/)
|
||||
and [Hybrid](https://qdrant.tech/articles/hybrid-search/#building-a-hybrid-search-system-in-qdrant) search
|
||||
- [Multilingual and Multimodal retrieval](https://qdrant.tech/documentation/multimodal-search/)
|
||||
- [Medatata filtering](https://qdrant.tech/articles/vector-search-filtering/)
|
||||
- [GPU support](https://qdrant.tech/documentation/guides/running-with-gpu/)
|
||||
|
||||
## Usage
|
||||
|
||||
To use Qdrant in your Llama Stack project, follow these steps:
|
||||
|
||||
1. Install the necessary dependencies.
|
||||
2. Configure your Llama Stack project to use Faiss.
|
||||
2. Configure your Llama Stack project to use Qdrant.
|
||||
3. Start storing and querying vectors.
|
||||
|
||||
## Installation
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue