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feat(api): (1/n) datasets api clean up (#1573)

Browse source 
## PR Stack
- https://github.com/meta-llama/llama-stack/pull/1573
- https://github.com/meta-llama/llama-stack/pull/1625
- https://github.com/meta-llama/llama-stack/pull/1656
- https://github.com/meta-llama/llama-stack/pull/1657
- https://github.com/meta-llama/llama-stack/pull/1658
- https://github.com/meta-llama/llama-stack/pull/1659
- https://github.com/meta-llama/llama-stack/pull/1660

**Client SDK**
- https://github.com/meta-llama/llama-stack-client-python/pull/203

**CI**
- 1391130488
<img width="1042" alt="image"
src="https://github.com/user-attachments/assets/69636067-376d-436b-9204-896e2dd490ca"
/>
-- the test_rag_agent_with_attachments is flaky and not related to this
PR

## Doc
<img width="789" alt="image"
src="https://github.com/user-attachments/assets/b88390f3-73d6-4483-b09a-a192064e32d9"
/>


## Client Usage
```python
client.datasets.register(
    source={
        "type": "uri",
        "uri": "lsfs://mydata.jsonl",
    },
    schema="jsonl_messages",
    # optional 
    dataset_id="my_first_train_data"
)

# quick prototype debugging
client.datasets.register(
    data_reference={
        "type": "rows",
        "rows": [
                "messages": [...],
        ],
    },
    schema="jsonl_messages",
)
```

## Test Plan
- CI:
1387805545

```
LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/datasets/test_datasets.py
```

```
LLAMA_STACK_CONFIG=fireworks pytest -v tests/integration/scoring/test_scoring.py
```

```
pytest -v -s --nbval-lax ./docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
```
This commit is contained in:
Xi Yan 2025-03-17 16:55:45 -07:00 • committed by GitHub
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29 changed files with 2593 additions and 2296 deletions
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715
docs/_static/llama-stack-spec.html vendored
View file

@ -40,75 +40,7 @@
}
],
"paths": {
"/v1/datasetio/rows": {
"get": {
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/PaginatedRowsResult"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"DatasetIO"
],
"description": "Get a paginated list of rows from a dataset.",
"parameters": [
{
"name": "dataset_id",
"in": "query",
"description": "The ID of the dataset to get the rows from.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "rows_in_page",
"in": "query",
"description": "The number of rows to get per page.",
"required": true,
"schema": {
"type": "integer"
}
},
{
"name": "page_token",
"in": "query",
"description": "The token to get the next page of rows.",
"required": false,
"schema": {
"type": "string"
}
},
{
"name": "filter_condition",
"in": "query",
"description": "(Optional) A condition to filter the rows by.",
"required": false,
"schema": {
"type": "string"
}
}
]
},
"/v1/datasetio/append-rows/{dataset_id}": {
"post": {
"responses": {
"200": {
@ -131,7 +63,16 @@
"DatasetIO"
],
"description": "",
"parameters": [],
"parameters": [
{
"name": "dataset_id",
"in": "path",
"required": true,
"schema": {
"type": "string"
}
}
],
"requestBody": {
"content": {
"application/json": {
@ -583,7 +524,7 @@
}
},
"tags": [
"Files (Coming Soon)"
"Files"
],
"description": "List all buckets.",
"parameters": [
@ -623,7 +564,7 @@
}
},
"tags": [
"Files (Coming Soon)"
"Files"
],
"description": "Create a new upload session for a file identified by a bucket and key.",
"parameters": [],
@ -850,7 +791,7 @@
}
},
"tags": [
"Files (Coming Soon)"
"Files"
],
"description": "Get a file info identified by a bucket and key.",
"parameters": [
@ -900,7 +841,7 @@
}
},
"tags": [
"Files (Coming Soon)"
"Files"
],
"description": "Delete a file identified by a bucket and key.",
"parameters": [
@ -1889,7 +1830,7 @@
}
},
"tags": [
"Files (Coming Soon)"
"Files"
],
"description": "Returns information about an existsing upload session",
"parameters": [
@ -1937,7 +1878,7 @@
}
},
"tags": [
"Files (Coming Soon)"
"Files"
],
"description": "Upload file content to an existing upload session. On the server, request body will have the raw bytes that are uploaded.",
"parameters": [
@ -2236,6 +2177,67 @@
}
}
},
"/v1/datasetio/iterrows/{dataset_id}": {
"get": {
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/IterrowsResponse"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"DatasetIO"
],
"description": "Get a paginated list of rows from a dataset. Uses cursor-based pagination.",
"parameters": [
{
"name": "dataset_id",
"in": "path",
"description": "The ID of the dataset to get the rows from.",
"required": true,
"schema": {
"type": "string"
}
},
{
"name": "start_index",
"in": "query",
"description": "Index into dataset for the first row to get. Get all rows if None.",
"required": false,
"schema": {
"type": "integer"
}
},
{
"name": "limit",
"in": "query",
"description": "The number of rows to get.",
"required": false,
"schema": {
"type": "integer"
}
}
]
}
},
"/v1/eval/benchmarks/{benchmark_id}/jobs/{job_id}": {
"get": {
"responses": {
@ -2535,7 +2537,14 @@
"post": {
"responses": {
"200": {
"description": "OK"
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/Dataset"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
@ -2553,7 +2562,7 @@
"tags": [
"Datasets"
],
"description": "",
"description": "Register a new dataset.",
"parameters": [],
"requestBody": {
"content": {
@ -2594,7 +2603,7 @@
}
},
"tags": [
"Files (Coming Soon)"
"Files"
],
"description": "List all files in a bucket.",
"parameters": [
@ -3824,9 +3833,6 @@
"AppendRowsRequest": {
"type": "object",
"properties": {
"dataset_id": {
"type": "string"
},
"rows": {
"type": "array",
"items": {
@ -3858,7 +3864,6 @@
},
"additionalProperties": false,
"required": [
"dataset_id",
"rows"
],
"title": "AppendRowsRequest"
@ -6824,6 +6829,224 @@
],
"title": "Benchmark"
},
"DataSource": {
"oneOf": [
{
"$ref": "#/components/schemas/URIDataSource"
},
{
"$ref": "#/components/schemas/RowsDataSource"
}
],
"discriminator": {
"propertyName": "type",
"mapping": {
"uri": "#/components/schemas/URIDataSource",
"rows": "#/components/schemas/RowsDataSource"
}
}
},
"Dataset": {
"type": "object",
"properties": {
"identifier": {
"type": "string"
},
"provider_resource_id": {
"type": "string"
},
"provider_id": {
"type": "string"
},
"type": {
"type": "string",
"const": "dataset",
"default": "dataset"
},
"purpose": {
"type": "string",
"enum": [
"post-training/messages",
"eval/question-answer",
"eval/messages-answer"
],
"title": "DatasetPurpose",
"description": "Purpose of the dataset. Each purpose has a required input data schema."
},
"source": {
"$ref": "#/components/schemas/DataSource"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
}
},
"additionalProperties": false,
"required": [
"identifier",
"provider_resource_id",
"provider_id",
"type",
"purpose",
"source",
"metadata"
],
"title": "Dataset"
},
"RowsDataSource": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "rows",
"default": "rows"
},
"rows": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
},
"description": "The dataset is stored in rows. E.g. - [ {\"messages\": [{\"role\": \"user\", \"content\": \"Hello, world!\"}, {\"role\": \"assistant\", \"content\": \"Hello, world!\"}]} ]"
}
},
"additionalProperties": false,
"required": [
"type",
"rows"
],
"title": "RowsDataSource",
"description": "A dataset stored in rows."
},
"URIDataSource": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "uri",
"default": "uri"
},
"uri": {
"type": "string",
"description": "The dataset can be obtained from a URI. E.g. - \"https://mywebsite.com/mydata.jsonl\" - \"lsfs://mydata.jsonl\" - \"data:csv;base64,{base64_content}\""
}
},
"additionalProperties": false,
"required": [
"type",
"uri"
],
"title": "URIDataSource",
"description": "A dataset that can be obtained from a URI."
},
"Model": {
"type": "object",
"properties": {
"identifier": {
"type": "string"
},
"provider_resource_id": {
"type": "string"
},
"provider_id": {
"type": "string"
},
"type": {
"type": "string",
"const": "model",
"default": "model"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
},
"model_type": {
"$ref": "#/components/schemas/ModelType",
"default": "llm"
}
},
"additionalProperties": false,
"required": [
"identifier",
"provider_resource_id",
"provider_id",
"type",
"metadata",
"model_type"
],
"title": "Model"
},
"ModelType": {
"type": "string",
"enum": [
"llm",
"embedding"
],
"title": "ModelType"
},
"AgentTurnInputType": {
"type": "object",
"properties": {
@ -6899,70 +7122,6 @@
],
"title": "CompletionInputType"
},
"Dataset": {
"type": "object",
"properties": {
"identifier": {
"type": "string"
},
"provider_resource_id": {
"type": "string"
},
"provider_id": {
"type": "string"
},
"type": {
"type": "string",
"const": "dataset",
"default": "dataset"
},
"dataset_schema": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/ParamType"
}
},
"url": {
"$ref": "#/components/schemas/URL"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
}
},
"additionalProperties": false,
"required": [
"identifier",
"provider_resource_id",
"provider_id",
"type",
"dataset_schema",
"url",
"metadata"
],
"title": "Dataset"
},
"JsonType": {
"type": "object",
"properties": {
@ -7057,151 +7216,6 @@
}
}
},
"StringType": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "string",
"default": "string"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "StringType"
},
"UnionType": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "union",
"default": "union"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "UnionType"
},
"Model": {
"type": "object",
"properties": {
"identifier": {
"type": "string"
},
"provider_resource_id": {
"type": "string"
},
"provider_id": {
"type": "string"
},
"type": {
"type": "string",
"const": "model",
"default": "model"
},
"metadata": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
},
"model_type": {
"$ref": "#/components/schemas/ModelType",
"default": "llm"
}
},
"additionalProperties": false,
"required": [
"identifier",
"provider_resource_id",
"provider_id",
"type",
"metadata",
"model_type"
],
"title": "Model"
},
"ModelType": {
"type": "string",
"enum": [
"llm",
"embedding"
],
"title": "ModelType"
},
"PaginatedRowsResult": {
"type": "object",
"properties": {
"rows": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
},
"description": "The rows in the current page."
},
"total_count": {
"type": "integer",
"description": "The total number of rows in the dataset."
},
"next_page_token": {
"type": "string",
"description": "The token to get the next page of rows."
}
},
"additionalProperties": false,
"required": [
"rows",
"total_count"
],
"title": "PaginatedRowsResult",
"description": "A paginated list of rows from a dataset."
},
"ScoringFn": {
"type": "object",
"properties": {
@ -7265,6 +7279,36 @@
],
"title": "ScoringFn"
},
"StringType": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "string",
"default": "string"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "StringType"
},
"UnionType": {
"type": "object",
"properties": {
"type": {
"type": "string",
"const": "union",
"default": "union"
}
},
"additionalProperties": false,
"required": [
"type"
],
"title": "UnionType"
},
"Shield": {
"type": "object",
"properties": {
@ -8084,6 +8128,50 @@
],
"title": "ToolInvocationResult"
},
"IterrowsResponse": {
"type": "object",
"properties": {
"data": {
"type": "array",
"items": {
"type": "object",
"additionalProperties": {
"oneOf": [
{
"type": "null"
},
{
"type": "boolean"
},
{
"type": "number"
},
{
"type": "string"
},
{
"type": "array"
},
{
"type": "object"
}
]
}
},
"description": "The rows in the current page."
},
"next_start_index": {
"type": "integer",
"description": "Index into dataset for the first row in the next page. None if there are no more rows."
}
},
"additionalProperties": false,
"required": [
"data"
],
"title": "IterrowsResponse",
"description": "A paginated list of rows from a dataset."
},
"ListAgentSessionsResponse": {
"type": "object",
"properties": {
@ -9330,23 +9418,18 @@
"RegisterDatasetRequest": {
"type": "object",
"properties": {
"dataset_id": {
"type": "string"
"purpose": {
"type": "string",
"enum": [
"post-training/messages",
"eval/question-answer",
"eval/messages-answer"
],
"description": "The purpose of the dataset. One of - \"post-training/messages\": The dataset contains a messages column with list of messages for post-training. { \"messages\": [ {\"role\": \"user\", \"content\": \"Hello, world!\"}, {\"role\": \"assistant\", \"content\": \"Hello, world!\"}, ] } - \"eval/question-answer\": The dataset contains a question column and an answer column for evaluation. { \"question\": \"What is the capital of France?\", \"answer\": \"Paris\" } - \"eval/messages-answer\": The dataset contains a messages column with list of messages and an answer column for evaluation. { \"messages\": [ {\"role\": \"user\", \"content\": \"Hello, my name is John Doe.\"}, {\"role\": \"assistant\", \"content\": \"Hello, John Doe. How can I help you today?\"}, {\"role\": \"user\", \"content\": \"What's my name?\"}, ], \"answer\": \"John Doe\" }"
},
"dataset_schema": {
"type": "object",
"additionalProperties": {
"$ref": "#/components/schemas/ParamType"
}
},
"url": {
"$ref": "#/components/schemas/URL"
},
"provider_dataset_id": {
"type": "string"
},
"provider_id": {
"type": "string"
"source": {
"$ref": "#/components/schemas/DataSource",
"description": "The data source of the dataset. Ensure that the data source schema is compatible with the purpose of the dataset. Examples: - { \"type\": \"uri\", \"uri\": \"https://mywebsite.com/mydata.jsonl\" } - { \"type\": \"uri\", \"uri\": \"lsfs://mydata.jsonl\" } - { \"type\": \"uri\", \"uri\": \"data:csv;base64,{base64_content}\" } - { \"type\": \"uri\", \"uri\": \"huggingface://llamastack/simpleqa?split=train\" } - { \"type\": \"rows\", \"rows\": [ { \"messages\": [ {\"role\": \"user\", \"content\": \"Hello, world!\"}, {\"role\": \"assistant\", \"content\": \"Hello, world!\"}, ] } ] }"
},
"metadata": {
"type": "object",
@ -9371,14 +9454,18 @@
"type": "object"
}
]
}
},
"description": "The metadata for the dataset. - E.g. {\"description\": \"My dataset\"}"
},
"dataset_id": {
"type": "string",
"description": "The ID of the dataset. If not provided, an ID will be generated."
}
},
"additionalProperties": false,
"required": [
"dataset_id",
"dataset_schema",
"url"
"purpose",
"source"
],
"title": "RegisterDatasetRequest"
},
@ -10197,7 +10284,7 @@
"x-displayName": "Llama Stack Evaluation API for running evaluations on model and agent candidates."
},
{
"name": "Files (Coming Soon)"
"name": "Files"
},
{
"name": "Inference",
@ -10258,7 +10345,7 @@
"DatasetIO",
"Datasets",
"Eval",
"Files (Coming Soon)",
"Files",
"Inference",
"Inspect",
"Models",

499
docs/_static/llama-stack-spec.yaml vendored
View file

@ -10,56 +10,7 @@ info:
servers:
- url: http://any-hosted-llama-stack.com
paths:
/v1/datasetio/rows:
get:
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/PaginatedRowsResult'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- DatasetIO
description: >-
Get a paginated list of rows from a dataset.
parameters:
- name: dataset_id
in: query
description: >-
The ID of the dataset to get the rows from.
required: true
schema:
type: string
- name: rows_in_page
in: query
description: The number of rows to get per page.
required: true
schema:
type: integer
- name: page_token
in: query
description: The token to get the next page of rows.
required: false
schema:
type: string
- name: filter_condition
in: query
description: >-
(Optional) A condition to filter the rows by.
required: false
schema:
type: string
/v1/datasetio/append-rows/{dataset_id}:
post:
responses:
'200':
@ -77,7 +28,12 @@ paths:
tags:
- DatasetIO
description: ''
parameters: []
parameters:
- name: dataset_id
in: path
required: true
schema:
type: string
requestBody:
content:
application/json:
@ -394,7 +350,7 @@ paths:
default:
$ref: '#/components/responses/DefaultError'
tags:
- Files (Coming Soon)
- Files
description: List all buckets.
parameters:
- name: bucket
@ -421,7 +377,7 @@ paths:
default:
$ref: '#/components/responses/DefaultError'
tags:
- Files (Coming Soon)
- Files
description: >-
Create a new upload session for a file identified by a bucket and key.
parameters: []
@ -580,7 +536,7 @@ paths:
default:
$ref: '#/components/responses/DefaultError'
tags:
- Files (Coming Soon)
- Files
description: >-
Get a file info identified by a bucket and key.
parameters:
@ -616,7 +572,7 @@ paths:
default:
$ref: '#/components/responses/DefaultError'
tags:
- Files (Coming Soon)
- Files
description: >-
Delete a file identified by a bucket and key.
parameters:
@ -1268,7 +1224,7 @@ paths:
default:
$ref: '#/components/responses/DefaultError'
tags:
- Files (Coming Soon)
- Files
description: >-
Returns information about an existsing upload session
parameters:
@ -1299,7 +1255,7 @@ paths:
default:
$ref: '#/components/responses/DefaultError'
tags:
- Files (Coming Soon)
- Files
description: >-
Upload file content to an existing upload session. On the server, request
body will have the raw bytes that are uploaded.
@ -1501,6 +1457,50 @@ paths:
schema:
$ref: '#/components/schemas/InvokeToolRequest'
required: true
/v1/datasetio/iterrows/{dataset_id}:
get:
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/IterrowsResponse'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- DatasetIO
description: >-
Get a paginated list of rows from a dataset. Uses cursor-based pagination.
parameters:
- name: dataset_id
in: path
description: >-
The ID of the dataset to get the rows from.
required: true
schema:
type: string
- name: start_index
in: query
description: >-
Index into dataset for the first row to get. Get all rows if None.
required: false
schema:
type: integer
- name: limit
in: query
description: The number of rows to get.
required: false
schema:
type: integer
/v1/eval/benchmarks/{benchmark_id}/jobs/{job_id}:
get:
responses:
@ -1710,6 +1710,10 @@ paths:
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/Dataset'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
@ -1722,7 +1726,7 @@ paths:
$ref: '#/components/responses/DefaultError'
tags:
- Datasets
description: ''
description: Register a new dataset.
parameters: []
requestBody:
content:
@ -1750,7 +1754,7 @@ paths:
default:
$ref: '#/components/responses/DefaultError'
tags:
- Files (Coming Soon)
- Files
description: List all files in a bucket.
parameters:
- name: bucket
@ -2607,8 +2611,6 @@ components:
AppendRowsRequest:
type: object
properties:
dataset_id:
type: string
rows:
type: array
items:
@ -2623,7 +2625,6 @@ components:
- type: object
additionalProperties: false
required:
- dataset_id
- rows
title: AppendRowsRequest
CompletionMessage:
@ -4726,6 +4727,148 @@ components:
- scoring_functions
- metadata
title: Benchmark
DataSource:
oneOf:
- $ref: '#/components/schemas/URIDataSource'
- $ref: '#/components/schemas/RowsDataSource'
discriminator:
propertyName: type
mapping:
uri: '#/components/schemas/URIDataSource'
rows: '#/components/schemas/RowsDataSource'
Dataset:
type: object
properties:
identifier:
type: string
provider_resource_id:
type: string
provider_id:
type: string
type:
type: string
const: dataset
default: dataset
purpose:
type: string
enum:
- post-training/messages
- eval/question-answer
- eval/messages-answer
title: DatasetPurpose
description: >-
Purpose of the dataset. Each purpose has a required input data schema.
source:
$ref: '#/components/schemas/DataSource'
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
additionalProperties: false
required:
- identifier
- provider_resource_id
- provider_id
- type
- purpose
- source
- metadata
title: Dataset
RowsDataSource:
type: object
properties:
type:
type: string
const: rows
default: rows
rows:
type: array
items:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: >-
The dataset is stored in rows. E.g. - [ {"messages": [{"role": "user",
"content": "Hello, world!"}, {"role": "assistant", "content": "Hello,
world!"}]} ]
additionalProperties: false
required:
- type
- rows
title: RowsDataSource
description: A dataset stored in rows.
URIDataSource:
type: object
properties:
type:
type: string
const: uri
default: uri
uri:
type: string
description: >-
The dataset can be obtained from a URI. E.g. - "https://mywebsite.com/mydata.jsonl"
- "lsfs://mydata.jsonl" - "data:csv;base64,{base64_content}"
additionalProperties: false
required:
- type
- uri
title: URIDataSource
description: >-
A dataset that can be obtained from a URI.
Model:
type: object
properties:
identifier:
type: string
provider_resource_id:
type: string
provider_id:
type: string
type:
type: string
const: model
default: model
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
model_type:
$ref: '#/components/schemas/ModelType'
default: llm
additionalProperties: false
required:
- identifier
- provider_resource_id
- provider_id
- type
- metadata
- model_type
title: Model
ModelType:
type: string
enum:
- llm
- embedding
title: ModelType
AgentTurnInputType:
type: object
properties:
@ -4781,45 +4924,6 @@ components:
required:
- type
title: CompletionInputType
Dataset:
type: object
properties:
identifier:
type: string
provider_resource_id:
type: string
provider_id:
type: string
type:
type: string
const: dataset
default: dataset
dataset_schema:
type: object
additionalProperties:
$ref: '#/components/schemas/ParamType'
url:
$ref: '#/components/schemas/URL'
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
additionalProperties: false
required:
- identifier
- provider_resource_id
- provider_id
- type
- dataset_schema
- url
- metadata
title: Dataset
JsonType:
type: object
properties:
@ -4878,97 +4982,6 @@ components:
chat_completion_input: '#/components/schemas/ChatCompletionInputType'
completion_input: '#/components/schemas/CompletionInputType'
agent_turn_input: '#/components/schemas/AgentTurnInputType'
StringType:
type: object
properties:
type:
type: string
const: string
default: string
additionalProperties: false
required:
- type
title: StringType
UnionType:
type: object
properties:
type:
type: string
const: union
default: union
additionalProperties: false
required:
- type
title: UnionType
Model:
type: object
properties:
identifier:
type: string
provider_resource_id:
type: string
provider_id:
type: string
type:
type: string
const: model
default: model
metadata:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
model_type:
$ref: '#/components/schemas/ModelType'
default: llm
additionalProperties: false
required:
- identifier
- provider_resource_id
- provider_id
- type
- metadata
- model_type
title: Model
ModelType:
type: string
enum:
- llm
- embedding
title: ModelType
PaginatedRowsResult:
type: object
properties:
rows:
type: array
items:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: The rows in the current page.
total_count:
type: integer
description: The total number of rows in the dataset.
next_page_token:
type: string
description: The token to get the next page of rows.
additionalProperties: false
required:
- rows
- total_count
title: PaginatedRowsResult
description: A paginated list of rows from a dataset.
ScoringFn:
type: object
properties:
@ -5007,6 +5020,28 @@ components:
- metadata
- return_type
title: ScoringFn
StringType:
type: object
properties:
type:
type: string
const: string
default: string
additionalProperties: false
required:
- type
title: StringType
UnionType:
type: object
properties:
type:
type: string
const: union
default: union
additionalProperties: false
required:
- type
title: UnionType
Shield:
type: object
properties:
@ -5506,6 +5541,32 @@ components:
required:
- content
title: ToolInvocationResult
IterrowsResponse:
type: object
properties:
data:
type: array
items:
type: object
additionalProperties:
oneOf:
- type: 'null'
- type: boolean
- type: number
- type: string
- type: array
- type: object
description: The rows in the current page.
next_start_index:
type: integer
description: >-
Index into dataset for the first row in the next page. None if there are
no more rows.
additionalProperties: false
required:
- data
title: IterrowsResponse
description: A paginated list of rows from a dataset.
ListAgentSessionsResponse:
type: object
properties:
@ -6313,18 +6374,35 @@ components:
RegisterDatasetRequest:
type: object
properties:
dataset_id:
type: string
dataset_schema:
type: object
additionalProperties:
$ref: '#/components/schemas/ParamType'
url:
$ref: '#/components/schemas/URL'
provider_dataset_id:
type: string
provider_id:
purpose:
type: string
enum:
- post-training/messages
- eval/question-answer
- eval/messages-answer
description: >-
The purpose of the dataset. One of - "post-training/messages": The dataset
contains a messages column with list of messages for post-training. {
"messages": [ {"role": "user", "content": "Hello, world!"}, {"role": "assistant",
"content": "Hello, world!"}, ] } - "eval/question-answer": The dataset
contains a question column and an answer column for evaluation. { "question":
"What is the capital of France?", "answer": "Paris" } - "eval/messages-answer":
The dataset contains a messages column with list of messages and an answer
column for evaluation. { "messages": [ {"role": "user", "content": "Hello,
my name is John Doe."}, {"role": "assistant", "content": "Hello, John
Doe. How can I help you today?"}, {"role": "user", "content": "What's
my name?"}, ], "answer": "John Doe" }
source:
$ref: '#/components/schemas/DataSource'
description: >-
The data source of the dataset. Ensure that the data source schema is
compatible with the purpose of the dataset. Examples: - { "type": "uri",
"uri": "https://mywebsite.com/mydata.jsonl" } - { "type": "uri", "uri":
"lsfs://mydata.jsonl" } - { "type": "uri", "uri": "data:csv;base64,{base64_content}"
} - { "type": "uri", "uri": "huggingface://llamastack/simpleqa?split=train"
} - { "type": "rows", "rows": [ { "messages": [ {"role": "user", "content":
"Hello, world!"}, {"role": "assistant", "content": "Hello, world!"}, ]
} ] }
metadata:
type: object
additionalProperties:
@ -6335,11 +6413,16 @@ components:
- type: string
- type: array
- type: object
description: >-
The metadata for the dataset. - E.g. {"description": "My dataset"}
dataset_id:
type: string
description: >-
The ID of the dataset. If not provided, an ID will be generated.
additionalProperties: false
required:
- dataset_id
- dataset_schema
- url
- purpose
- source
title: RegisterDatasetRequest
RegisterModelRequest:
type: object
@ -6855,7 +6938,7 @@ tags:
- name: Eval
x-displayName: >-
Llama Stack Evaluation API for running evaluations on model and agent candidates.
- name: Files (Coming Soon)
- name: Files
- name: Inference
description: >-
This API provides the raw interface to the underlying models. Two kinds of models
@ -6893,7 +6976,7 @@ x-tagGroups:
- DatasetIO
- Datasets
- Eval
- Files (Coming Soon)
- Files
- Inference
- Inspect
- Models

2178
docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb
View file

File diff suppressed because one or more lines are too long

352
docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb
View file

@ -84,16 +84,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Not in Google Colab environment\n",
"\u001b[33mWarning: `bwrap` is not available. Code interpreter tool will not work correctly.\u001b[0m\n"
"Not in Google Colab environment\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda3/envs/master/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
"Warning: `bwrap` is not available. Code interpreter tool will not work correctly.\n"
]
},
{
@ -117,76 +115,146 @@
"- datasetio\n",
"- eval\n",
"- inference\n",
"- memory\n",
"- safety\n",
"- scoring\n",
"- telemetry\n",
"- tool_runtime\n",
"datasets: <span style=\"font-weight: bold\">[]</span>\n",
"container_image: null\n",
"- vector_io\n",
"benchmarks: <span style=\"font-weight: bold\">[]</span>\n",
"container_image: null\n",
"datasets: <span style=\"font-weight: bold\">[]</span>\n",
"image_name: together\n",
"memory_banks: <span style=\"font-weight: bold\">[]</span>\n",
"logging: null\n",
"metadata_store:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">registry.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
"models:\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-8B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-70B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-FP8\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.1</span>-405B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-3B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-11B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.2</span>-90B-Vision-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.3</span>-70B-Instruct-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-8B\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
"- metadata: <span style=\"font-weight: bold\">{}</span>\n",
" model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-Guard-<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>-11B-Vision-Turbo\n",
"- metadata:\n",
" context_length: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8192</span>\n",
" embedding_dimension: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">768</span>\n",
" model_id: togethercomputer/m2-bert-80M-8k-retrieval\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - embedding\n",
" provider_id: together\n",
" provider_model_id: togethercomputer/m2-bert-80M-8k-retrieval\n",
"- metadata:\n",
" context_length: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">32768</span>\n",
" embedding_dimension: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">768</span>\n",
" model_id: togethercomputer/m2-bert-80M-32k-retrieval\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - embedding\n",
" provider_id: together\n",
" provider_model_id: togethercomputer/m2-bert-80M-32k-retrieval\n",
"- metadata:\n",
" embedding_dimension: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">384</span>\n",
" model_id: all-MiniLM-L6-v2\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
@ -203,14 +271,26 @@
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" datasetio:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" - config:\n",
" kvstore:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">huggingface_datasetio.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: huggingface\n",
" provider_type: remote::huggingface\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" - config:\n",
" kvstore:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">localfs_datasetio.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: localfs\n",
" provider_type: inline::localfs\n",
" eval:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" - config:\n",
" kvstore:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">meta_reference_eval.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" inference:\n",
@ -222,16 +302,9 @@
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: sentence-transformers\n",
" provider_type: inline::sentence-transformers\n",
" memory:\n",
" - config:\n",
" kvstore:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">faiss_store.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::fa</span>iss\n",
" safety:\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" - config:\n",
" excluded_categories: <span style=\"font-weight: bold\">[]</span>\n",
" provider_id: llama-guard\n",
" provider_type: inline::llama-guard\n",
" scoring:\n",
@ -269,7 +342,26 @@
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: rag-runtime\n",
" provider_type: inline::rag-runtime\n",
" - config: <span style=\"font-weight: bold\">{}</span>\n",
" provider_id: model-context-protocol\n",
" provider_type: remote::model-context-protocol\n",
" - config:\n",
" api_key: <span style=\"color: #008000; text-decoration-color: #008000\">'********'</span>\n",
" provider_id: wolfram-alpha\n",
" provider_type: remote::wolfram-alpha\n",
" vector_io:\n",
" - config:\n",
" kvstore:\n",
" db_path: <span style=\"color: #800080; text-decoration-color: #800080\">/Users/xiyan/.llama/distributions/together/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">faiss_store.db</span>\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin<span style=\"color: #00ff00; text-decoration-color: #00ff00; font-weight: bold\">e::fa</span>iss\n",
"scoring_fns: <span style=\"font-weight: bold\">[]</span>\n",
"server:\n",
" port: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8321</span>\n",
" tls_certfile: null\n",
" tls_keyfile: null\n",
"shields:\n",
"- params: null\n",
" provider_id: null\n",
@ -288,6 +380,11 @@
" mcp_endpoint: null\n",
" provider_id: code-interpreter\n",
" toolgroup_id: builtin::code_interpreter\n",
"- args: null\n",
" mcp_endpoint: null\n",
" provider_id: wolfram-alpha\n",
" toolgroup_id: builtin::wolfram_alpha\n",
"vector_dbs: <span style=\"font-weight: bold\">[]</span>\n",
"version: <span style=\"color: #008000; text-decoration-color: #008000\">'2'</span>\n",
"\n",
"</pre>\n"
@ -298,76 +395,146 @@
"- datasetio\n",
"- eval\n",
"- inference\n",
"- memory\n",
"- safety\n",
"- scoring\n",
"- telemetry\n",
"- tool_runtime\n",
"datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"container_image: null\n",
"- vector_io\n",
"benchmarks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"container_image: null\n",
"datasets: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"image_name: together\n",
"memory_banks: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"logging: null\n",
"metadata_store:\n",
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mregistry.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
"models:\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-8B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-70B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-FP8\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-\u001b[1;36m3.1\u001b[0m-405B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-3B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-11B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.2\u001b[0m-90B-Vision-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-\u001b[1;36m3.3\u001b[0m-70B-Instruct-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Meta-Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Meta-Llama-Guard-\u001b[1;36m3\u001b[0m-8B\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
"- metadata: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - llm\n",
" provider_id: together\n",
" provider_model_id: meta-llama/Llama-Guard-\u001b[1;36m3\u001b[0m-11B-Vision-Turbo\n",
"- metadata:\n",
" context_length: \u001b[1;36m8192\u001b[0m\n",
" embedding_dimension: \u001b[1;36m768\u001b[0m\n",
" model_id: togethercomputer/m2-bert-80M-8k-retrieval\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - embedding\n",
" provider_id: together\n",
" provider_model_id: togethercomputer/m2-bert-80M-8k-retrieval\n",
"- metadata:\n",
" context_length: \u001b[1;36m32768\u001b[0m\n",
" embedding_dimension: \u001b[1;36m768\u001b[0m\n",
" model_id: togethercomputer/m2-bert-80M-32k-retrieval\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
" - embedding\n",
" provider_id: together\n",
" provider_model_id: togethercomputer/m2-bert-80M-32k-retrieval\n",
"- metadata:\n",
" embedding_dimension: \u001b[1;36m384\u001b[0m\n",
" model_id: all-MiniLM-L6-v2\n",
" model_type: !!python/object/apply:llama_stack.apis.models.models.ModelType\n",
@ -384,14 +551,26 @@
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" datasetio:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" - config:\n",
" kvstore:\n",
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mhuggingface_datasetio.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: huggingface\n",
" provider_type: remote::huggingface\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" - config:\n",
" kvstore:\n",
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mlocalfs_datasetio.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: localfs\n",
" provider_type: inline::localfs\n",
" eval:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" - config:\n",
" kvstore:\n",
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mmeta_reference_eval.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: meta-reference\n",
" provider_type: inline::meta-reference\n",
" inference:\n",
@ -403,16 +582,9 @@
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: sentence-transformers\n",
" provider_type: inline::sentence-transformers\n",
" memory:\n",
" - config:\n",
" kvstore:\n",
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin\u001b[1;92me::fa\u001b[0miss\n",
" safety:\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" - config:\n",
" excluded_categories: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
" provider_id: llama-guard\n",
" provider_type: inline::llama-guard\n",
" scoring:\n",
@ -450,7 +622,26 @@
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: rag-runtime\n",
" provider_type: inline::rag-runtime\n",
" - config: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
" provider_id: model-context-protocol\n",
" provider_type: remote::model-context-protocol\n",
" - config:\n",
" api_key: \u001b[32m'********'\u001b[0m\n",
" provider_id: wolfram-alpha\n",
" provider_type: remote::wolfram-alpha\n",
" vector_io:\n",
" - config:\n",
" kvstore:\n",
" db_path: \u001b[35m/Users/xiyan/.llama/distributions/together/\u001b[0m\u001b[95mfaiss_store.db\u001b[0m\n",
" namespace: null\n",
" type: sqlite\n",
" provider_id: faiss\n",
" provider_type: inlin\u001b[1;92me::fa\u001b[0miss\n",
"scoring_fns: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"server:\n",
" port: \u001b[1;36m8321\u001b[0m\n",
" tls_certfile: null\n",
" tls_keyfile: null\n",
"shields:\n",
"- params: null\n",
" provider_id: null\n",
@ -469,6 +660,11 @@
" mcp_endpoint: null\n",
" provider_id: code-interpreter\n",
" toolgroup_id: builtin::code_interpreter\n",
"- args: null\n",
" mcp_endpoint: null\n",
" provider_id: wolfram-alpha\n",
" toolgroup_id: builtin::wolfram_alpha\n",
"vector_dbs: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
"version: \u001b[32m'2'\u001b[0m\n",
"\n"
]
@ -532,7 +728,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -643,17 +839,7 @@
"id": "DJkmoG2kq1_P",
"outputId": "8493ee59-c6ff-4bb6-d787-f295944db1cf"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating dev split: 100%|██████████| 5/5 [00:00<00:00, 139.81 examples/s]\n",
"Generating validation split: 100%|██████████| 30/30 [00:00<00:00, 258.29 examples/s]\n",
"Generating test split: 100%|██████████| 287/287 [00:01<00:00, 197.69 examples/s]\n"
]
}
],
"outputs": [],
"source": [
"import datasets\n",
"\n",
@ -676,7 +862,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -691,7 +877,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [00:42<00:00, 8.60s/it]\n"
"100%|██████████| 5/5 [00:33<00:00, 6.71s/it]\n"
]
},
{
@ -699,16 +885,18 @@
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">EvaluateResponse</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">generations</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Answer: D'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The image shows a sunflower leaf with small, dark spots and white powdery patches. The dark spots are likely caused by a fungal pathogen, such as rust or septoria leaf spot, while the white powdery patches are likely caused by a fungal pathogen, such as powdery mildew.\\n\\nSince there are two distinct types of lesions on the leaf, it is likely that there are two different pathogens infecting the leaf.\\n\\n**Answer:** B) Two pathogens'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'**Potato Pests**\\n\\nThe two insects depicted are:\\n\\n* **Colorado Potato Beetle (Leptinotarsa decemlineata)**: Characterized by black and yellow stripes, this beetle is a significant pest of potatoes. It feeds on the leaves and can cause substantial damage to the crop.\\n* **False Potato Beetle (Leptinotarsa juncta)**: Also known as the false Colorado beetle, this species has similar coloring but is not as harmful to potatoes as the Colorado potato beetle.'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The question requires the identification of the reason behind the massive gum production on the trunks of grapefruit trees in Cyprus, despite appearing healthy from a distance. The correct answer can be deduced by analyzing the symptoms and considering the possible causes.\\n\\nTo determine the correct answer, let's evaluate each option:\\n\\nA) Don't know or not sure: This option is incorrect because it does not provide a specific reason for the gum production.\\n\\nB) Physiological stress: This option is also incorrect because it is too broad and does not specifically explain the gum production.\\n\\nC) Bacterial disease: This option is incorrect because bacterial diseases typically cause different symptoms such as leaf spots, blights, or wilting.\\n\\nD) Harvesting damage when cutting with knives: This option is incorrect because harvesting damage would likely cause wounds or scars on the tree, but it would not lead to massive gum production.\\n\\nE) Fungal gummosis: This option is the most likely cause of the gum production. Fungal gummosis is a common disease in citrus trees, including grapefruit, that causes the production of gum or sap on the trunks and branches. The disease is typically caused by fungi such as Phytophthora or Diplodia, which infect the tree through wounds or natural openings. The gum production is a defense mechanism by the tree to try to seal off the infection and prevent further damage.\\n\\nTherefore, the correct answer is:\\n\\nAnswer: E\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"The image shows a sunflower leaf with a powdery mildew, which is a fungal disease caused by various species of fungi. The white powdery coating on the leaves is a characteristic symptom of this disease. The leaf also has some black spots, which could be indicative of a secondary infection or another type of disease. However, without more information or a closer examination, it's difficult to determine the exact cause of the black spots.\\n\\nBased on the image alone, we can see at least two types of symptoms: the powdery mildew and the black spots. This suggests that there may be more than one pathogen involved, but it's also possible that the black spots are a result of the same fungal infection causing the powdery mildew.\\n\\nAnswer: B) Two pathogens\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'The symptoms observed, characterized by the massive gum production on the trunks of the grapefruit trees in Cyprus, suggest a physiological or pathological response. Given the absence of visible signs of damage or pests from a higher point on a hillside, and considering the specific nature of the symptom (gum production), we can infer that the cause is more likely related to an internal process within the tree rather than external damage from harvesting. While physiological stress (B) could lead to such symptoms, the primary reason for gum production in trees, especially in citrus species, is typically linked to disease. Among the options provided, fungal gummosis (E) is a condition known to cause gumming in citrus trees, which aligns with the observed symptoms. Therefore, without direct evidence of external damage (harvesting) or confirmation of physiological stress being the primary cause, the most appropriate answer based on the information given is:\\n\\nAnswer: E'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Answer: D'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'**Causes of Splitting Petioles in Rhubarb**\\n\\nThe following factors can cause the petioles of rhubarb to split:\\n\\n* **Physiological Problems**: Issues such as water stress, nutrient deficiencies, or extreme temperatures can lead to splitting.\\n* **Phytoplasma Infection**: A bacterial infection caused by phytoplasma can lead to splitting of the petioles.\\n* **Animal Damage**: Pests like slugs, snails, or rodents can damage the plant and cause splitting.\\n* **Bacterial Infection**: Bacterial infections can also cause splitting.\\n\\nAs a result, the correct answer is:\\n\\n*Answer*: A) Physiological problems'</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"**Analysis of the Image**\\n\\nThe image provided shows a rhubarb plant with split petioles. To determine the cause of this issue, we need to consider various factors that could lead to such damage.\\n\\n**Possible Causes of Petiole Splitting**\\n\\n* **Physiological Problems**: Rhubarb plants can experience physiological stress due to environmental factors like extreme temperatures, waterlogging, or nutrient deficiencies. This stress can cause the petioles to split.\\n* **Phytoplasma Infection**: Phytoplasma is a type of bacteria that can infect plants, including rhubarb. It can cause symptoms such as yellowing leaves, stunted growth, and splitting of petioles.\\n* **Animal Damage**: Animals like rabbits, deer, or insects can damage rhubarb plants by eating the leaves or stems, which can lead to splitting of the petioles.\\n* **Bacteria**: Bacterial infections can also cause damage to rhubarb plants, including splitting of the petioles.\\n\\n**Conclusion**\\n\\nBased on the analysis, it is clear that all the options listed (A) Physiological problems, B) Phytoplasma infection, D) Animal damage, and E) Bacteria) could potentially cause the petioles of the rhubarb plant to split. Therefore, there is no single option that would not be a cause for the petioles splitting.\\n\\n**Answer**: C) I don't know and don't want to guess.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">scores</span>=<span style=\"font-weight: bold\">{</span>\n",
@ -723,16 +911,18 @@
"text/plain": [
"\u001b[1;35mEvaluateResponse\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mgenerations\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Answer: D'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The image shows a sunflower leaf with small, dark spots and white powdery patches. The dark spots are likely caused by a fungal pathogen, such as rust or septoria leaf spot, while the white powdery patches are likely caused by a fungal pathogen, such as powdery mildew.\\n\\nSince there are two distinct types of lesions on the leaf, it is likely that there are two different pathogens infecting the leaf.\\n\\n**Answer:** B\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Two pathogens'\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'**Potato Pests**\\n\\nThe two insects depicted are:\\n\\n* **Colorado Potato Beetle \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLeptinotarsa decemlineata\u001b[0m\u001b[32m)\u001b[0m\u001b[32m**: Characterized by black and yellow stripes, this beetle is a significant pest of potatoes. It feeds on the leaves and can cause substantial damage to the crop.\\n* **False Potato Beetle \u001b[0m\u001b[32m(\u001b[0m\u001b[32mLeptinotarsa juncta\u001b[0m\u001b[32m)\u001b[0m\u001b[32m**: Also known as the false Colorado beetle, this species has similar coloring but is not as harmful to potatoes as the Colorado potato beetle.'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The question requires the identification of the reason behind the massive gum production on the trunks of grapefruit trees in Cyprus, despite appearing healthy from a distance. The correct answer can be deduced by analyzing the symptoms and considering the possible causes.\\n\\nTo determine the correct answer, let's evaluate each option:\\n\\nA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Don't know or not sure: This option is incorrect because it does not provide a specific reason for the gum production.\\n\\nB\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Physiological stress: This option is also incorrect because it is too broad and does not specifically explain the gum production.\\n\\nC\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Bacterial disease: This option is incorrect because bacterial diseases typically cause different symptoms such as leaf spots, blights, or wilting.\\n\\nD\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Harvesting damage when cutting with knives: This option is incorrect because harvesting damage would likely cause wounds or scars on the tree, but it would not lead to massive gum production.\\n\\nE\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Fungal gummosis: This option is the most likely cause of the gum production. Fungal gummosis is a common disease in citrus trees, including grapefruit, that causes the production of gum or sap on the trunks and branches. The disease is typically caused by fungi such as Phytophthora or Diplodia, which infect the tree through wounds or natural openings. The gum production is a defense mechanism by the tree to try to seal off the infection and prevent further damage.\\n\\nTherefore, the correct answer is:\\n\\nAnswer: E\"\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"The image shows a sunflower leaf with a powdery mildew, which is a fungal disease caused by various species of fungi. The white powdery coating on the leaves is a characteristic symptom of this disease. The leaf also has some black spots, which could be indicative of a secondary infection or another type of disease. However, without more information or a closer examination, it's difficult to determine the exact cause of the black spots.\\n\\nBased on the image alone, we can see at least two types of symptoms: the powdery mildew and the black spots. This suggests that there may be more than one pathogen involved, but it's also possible that the black spots are a result of the same fungal infection causing the powdery mildew.\\n\\nAnswer: B\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Two pathogens\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'The symptoms observed, characterized by the massive gum production on the trunks of the grapefruit trees in Cyprus, suggest a physiological or pathological response. Given the absence of visible signs of damage or pests from a higher point on a hillside, and considering the specific nature of the symptom \u001b[0m\u001b[32m(\u001b[0m\u001b[32mgum production\u001b[0m\u001b[32m)\u001b[0m\u001b[32m, we can infer that the cause is more likely related to an internal process within the tree rather than external damage from harvesting. While physiological stress \u001b[0m\u001b[32m(\u001b[0m\u001b[32mB\u001b[0m\u001b[32m)\u001b[0m\u001b[32m could lead to such symptoms, the primary reason for gum production in trees, especially in citrus species, is typically linked to disease. Among the options provided, fungal gummosis \u001b[0m\u001b[32m(\u001b[0m\u001b[32mE\u001b[0m\u001b[32m)\u001b[0m\u001b[32m is a condition known to cause gumming in citrus trees, which aligns with the observed symptoms. Therefore, without direct evidence of external damage \u001b[0m\u001b[32m(\u001b[0m\u001b[32mharvesting\u001b[0m\u001b[32m)\u001b[0m\u001b[32m or confirmation of physiological stress being the primary cause, the most appropriate answer based on the information given is:\\n\\nAnswer: E'\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'Answer: D'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'**Causes of Splitting Petioles in Rhubarb**\\n\\nThe following factors can cause the petioles of rhubarb to split:\\n\\n* **Physiological Problems**: Issues such as water stress, nutrient deficiencies, or extreme temperatures can lead to splitting.\\n* **Phytoplasma Infection**: A bacterial infection caused by phytoplasma can lead to splitting of the petioles.\\n* **Animal Damage**: Pests like slugs, snails, or rodents can damage the plant and cause splitting.\\n* **Bacterial Infection**: Bacterial infections can also cause splitting.\\n\\nAs a result, the correct answer is:\\n\\n*Answer*: A\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Physiological problems'\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"**Analysis of the Image**\\n\\nThe image provided shows a rhubarb plant with split petioles. To determine the cause of this issue, we need to consider various factors that could lead to such damage.\\n\\n**Possible Causes of Petiole Splitting**\\n\\n* **Physiological Problems**: Rhubarb plants can experience physiological stress due to environmental factors like extreme temperatures, waterlogging, or nutrient deficiencies. This stress can cause the petioles to split.\\n* **Phytoplasma Infection**: Phytoplasma is a type of bacteria that can infect plants, including rhubarb. It can cause symptoms such as yellowing leaves, stunted growth, and splitting of petioles.\\n* **Animal Damage**: Animals like rabbits, deer, or insects can damage rhubarb plants by eating the leaves or stems, which can lead to splitting of the petioles.\\n* **Bacteria**: Bacterial infections can also cause damage to rhubarb plants, including splitting of the petioles.\\n\\n**Conclusion**\\n\\nBased on the analysis, it is clear that all the options listed \u001b[0m\u001b[32m(\u001b[0m\u001b[32mA\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Physiological problems, B\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Phytoplasma infection, D\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Animal damage, and E\u001b[0m\u001b[32m)\u001b[0m\u001b[32m Bacteria\u001b[0m\u001b[32m)\u001b[0m\u001b[32m could potentially cause the petioles of the rhubarb plant to split. Therefore, there is no single option that would not be a cause for the petioles splitting.\\n\\n**Answer**: C\u001b[0m\u001b[32m)\u001b[0m\u001b[32m I don't know and don't want to guess.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mscores\u001b[0m=\u001b[1m{\u001b[0m\n",
@ -815,7 +1005,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 5,
"metadata": {
"id": "HXmZf3Ymw-aX"
},
@ -823,39 +1013,33 @@
"source": [
"simpleqa_dataset_id = \"huggingface::simpleqa\"\n",
"\n",
"_ = client.datasets.register(\n",
"register_dataset_response = client.datasets.register(\n",
" purpose=\"eval/messages-answer\",\n",
" source={\n",
" \"type\": \"uri\",\n",
" \"uri\": \"huggingface://datasets/llamastack/simpleqa?split=train\",\n",
" },\n",
" dataset_id=simpleqa_dataset_id,\n",
" provider_id=\"huggingface\",\n",
" url={\"uri\": \"https://huggingface.co/datasets/llamastack/simpleqa\"},\n",
" metadata={\n",
" \"path\": \"llamastack/simpleqa\",\n",
" \"split\": \"train\",\n",
" },\n",
" dataset_schema={\n",
" \"input_query\": {\"type\": \"string\"},\n",
" \"expected_answer\": {\"type\": \"string\"},\n",
" \"chat_completion_input\": {\"type\": \"chat_completion_input\"},\n",
" },\n",
")\n"
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 6,
"metadata": {
"id": "Gc8azb4Rxr5J"
},
"outputs": [],
"source": [
"eval_rows = client.datasetio.get_rows_paginated(\n",
"eval_rows = client.datasets.iterrows(\n",
" dataset_id=simpleqa_dataset_id,\n",
" rows_in_page=5,\n",
")\n"
" limit=5,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -876,7 +1060,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [00:31<00:00, 6.38s/it]\n"
"100%|██████████| 5/5 [00:13<00:00, 2.71s/it]\n"
]
},
{
@ -889,14 +1073,14 @@
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"Radcliffe College was a women's liberal arts college in Cambridge, Massachusetts. However, it merged with Harvard University in 1977 and is now known as the Radcliffe Institute for Advanced Study at Harvard University.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'I do not have information on the Leipzig 1877 tournament.'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'I am unable to verify in whose honor the Leipzig 1877 tournament was organized.'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'generated_answer'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">\"I am unable to verify what Empress Elizabeth of Austria's favorite sculpture depicted at her villa Achilleion at Corfu, according to Karl Küchler.\"</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"font-weight: bold\">}</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"font-weight: bold\">]</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ </span><span style=\"color: #808000; text-decoration-color: #808000\">scores</span>=<span style=\"font-weight: bold\">{</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ </span><span style=\"color: #008000; text-decoration-color: #008000\">'llm-as-judge::405b-simpleqa'</span>: <span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">ScoringResult</span><span style=\"font-weight: bold\">(</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">aggregated_results</span>=<span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'categorical_count'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'categorical_count'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'A'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span><span style=\"font-weight: bold\">}}}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ </span><span style=\"color: #808000; text-decoration-color: #808000\">score_rows</span>=<span style=\"font-weight: bold\">[</span>\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span><span style=\"font-weight: bold\">}</span>,\n",
"<span style=\"color: #7fbf7f; text-decoration-color: #7fbf7f\">│ │ │ │ </span><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'score'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'judge_feedback'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'C'</span><span style=\"font-weight: bold\">}</span>,\n",
@ -917,14 +1101,14 @@
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"Radcliffe College was a women's liberal arts college in Cambridge, Massachusetts. However, it merged with Harvard University in 1977 and is now known as the Radcliffe Institute for Advanced Study at Harvard University.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'I do not have information on the Leipzig 1877 tournament.'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m'I am unable to verify in whose honor the Leipzig 1877 tournament was organized.'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'generated_answer'\u001b[0m: \u001b[32m\"I am unable to verify what Empress Elizabeth of Austria's favorite sculpture depicted at her villa Achilleion at Corfu, according to Karl Küchler.\"\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m\n",
"\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n",
"\u001b[2;32m│ \u001b[0m\u001b[33mscores\u001b[0m=\u001b[1m{\u001b[0m\n",
"\u001b[2;32m│ │ \u001b[0m\u001b[32m'llm-as-judge::405b-simpleqa'\u001b[0m: \u001b[1;35mScoringResult\u001b[0m\u001b[1m(\u001b[0m\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33maggregated_results\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'categorical_count'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'categorical_count'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'A'\u001b[0m: \u001b[1;36m1\u001b[0m, \u001b[32m'C'\u001b[0m: \u001b[1;36m4\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore_rows\u001b[0m=\u001b[1m[\u001b[0m\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\u001b[0m\u001b[1m}\u001b[0m,\n",
"\u001b[2;32m│ │ │ │ \u001b[0m\u001b[1m{\u001b[0m\u001b[32m'score'\u001b[0m: \u001b[32m'C'\u001b[0m, \u001b[32m'judge_feedback'\u001b[0m: \u001b[32m'C'\u001b[0m\u001b[1m}\u001b[0m,\n",
@ -957,7 +1141,7 @@
"\n",
"response = client.eval.evaluate_rows_alpha(\n",
" benchmark_id=\"meta-reference::simpleqa\",\n",
" input_rows=eval_rows.rows,\n",
" input_rows=eval_rows.data,\n",
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"],\n",
" benchmark_config={\n",
" \"type\": \"benchmark\",\n",
@ -1106,7 +1290,7 @@
"\n",
"response = client.eval.evaluate_rows_alpha(\n",
" benchmark_id=\"meta-reference::simpleqa\",\n",
" input_rows=eval_rows.rows,\n",
" input_rows=eval_rows.data,\n",
" scoring_functions=[\"llm-as-judge::405b-simpleqa\"],\n",
" benchmark_config={\n",
" \"type\": \"benchmark\",\n",

39
docs/openapi_generator/pyopenapi/generator.py
View file

@ -435,7 +435,7 @@ class Generator:
)
self.schema_builder = SchemaBuilder(schema_generator)
self.responses = {}
# Create standard error responses
self._create_standard_error_responses()
@ -446,7 +446,7 @@ class Generator:
"""
# Get the Error schema
error_schema = self.schema_builder.classdef_to_ref(Error)
# Create standard error responses
self.responses["BadRequest400"] = Response(
description="The request was invalid or malformed",
@ -457,11 +457,11 @@ class Generator:
"status": 400,
"title": "Bad Request",
"detail": "The request was invalid or malformed",
}
},
)
}
},
)
self.responses["TooManyRequests429"] = Response(
description="The client has sent too many requests in a given amount of time",
content={
@ -471,11 +471,11 @@ class Generator:
"status": 429,
"title": "Too Many Requests",
"detail": "You have exceeded the rate limit. Please try again later.",
}
},
)
}
},
)
self.responses["InternalServerError500"] = Response(
description="The server encountered an unexpected error",
content={
@ -485,11 +485,11 @@ class Generator:
"status": 500,
"title": "Internal Server Error",
"detail": "An unexpected error occurred. Our team has been notified.",
}
},
)
}
},
)
# Add a default error response for any unhandled error cases
self.responses["DefaultError"] = Response(
description="An unexpected error occurred",
@ -500,9 +500,9 @@ class Generator:
"status": 0,
"title": "Error",
"detail": "An unexpected error occurred",
}
},
)
}
},
)
def _build_type_tag(self, ref: str, schema: Schema) -> Tag:
@ -547,11 +547,14 @@ class Generator:
"SyntheticDataGeneration",
"PostTraining",
"BatchInference",
"Files",
]:
op.defining_class.__name__ = f"{op.defining_class.__name__} (Coming Soon)"
print(op.defining_class.__name__)
# TODO (xiyan): temporary fix for datasetio inner impl + datasets api
# if op.defining_class.__name__ in ["DatasetIO"]:
# op.defining_class.__name__ = "Datasets"
doc_string = parse_type(op.func_ref)
doc_params = dict(
(param.name, param.description) for param in doc_string.params.values()
@ -598,7 +601,9 @@ class Generator:
# data passed in request body as raw bytes cannot have request parameters
if raw_bytes_request_body and op.request_params:
raise ValueError("Cannot have both raw bytes request body and request parameters")
raise ValueError(
"Cannot have both raw bytes request body and request parameters"
)
# data passed in request body as raw bytes
if raw_bytes_request_body:
@ -719,7 +724,7 @@ class Generator:
responses.update(response_builder.build_response(response_options))
assert len(responses.keys()) > 0, f"No responses found for {op.name}"
# Add standard error response references
if self.options.include_standard_error_responses:
if "400" not in responses:
@ -730,7 +735,7 @@ class Generator:
responses["500"] = ResponseRef("InternalServerError500")
if "default" not in responses:
responses["default"] = ResponseRef("DefaultError")
if op.event_type is not None:
builder = ContentBuilder(self.schema_builder)
callbacks = {

24
docs/source/references/evals_reference/index.md
View file

@ -114,23 +114,17 @@ pprint(response)
simpleqa_dataset_id = "huggingface::simpleqa"
_ = client.datasets.register(
purpose="eval/messages-answer",
source={
"type": "uri",
"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
},
dataset_id=simpleqa_dataset_id,
provider_id="huggingface",
url={"uri": "https://huggingface.co/datasets/llamastack/simpleqa"},
metadata={
"path": "llamastack/simpleqa",
"split": "train",
},
dataset_schema={
"input_query": {"type": "string"},
"expected_answer": {"type": "string"},
"chat_completion_input": {"type": "chat_completion_input"},
},
)
eval_rows = client.datasetio.get_rows_paginated(
eval_rows = client.datasets.iterrows(
dataset_id=simpleqa_dataset_id,
rows_in_page=5,
limit=5,
)
```
@ -143,7 +137,7 @@ client.benchmarks.register(
response = client.eval.evaluate_rows(
benchmark_id="meta-reference::simpleqa",
input_rows=eval_rows.rows,
input_rows=eval_rows.data,
scoring_functions=["llm-as-judge::405b-simpleqa"],
benchmark_config={
"eval_candidate": {
@ -191,7 +185,7 @@ agent_config = {
response = client.eval.evaluate_rows(
benchmark_id="meta-reference::simpleqa",
input_rows=eval_rows.rows,
input_rows=eval_rows.data,
scoring_functions=["llm-as-judge::405b-simpleqa"],
benchmark_config={
"eval_candidate": {