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Merge remote-tracking branch 'origin/main' into TamiTakamiya/tool-param-definition-update

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This commit is contained in:
Ashwin Bharambe 2025-09-27 11:24:11 -07:00
commit 27c5365f99
47 changed files with 49304 additions and 1334 deletions
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169
docs/static/llama-stack-spec.html vendored
View file

@ -210,55 +210,6 @@
}
}
},
"/v1/inference/completion": {
"post": {
"responses": {
"200": {
"description": "If stream=False, returns a CompletionResponse with the full completion. If stream=True, returns an SSE event stream of CompletionResponseStreamChunk.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CompletionResponse"
}
},
"text/event-stream": {
"schema": {
"$ref": "#/components/schemas/CompletionResponseStreamChunk"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"Inference"
],
"summary": "Generate a completion for the given content using the specified model.",
"description": "Generate a completion for the given content using the specified model.",
"parameters": [],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/CompletionRequest"
}
}
},
"required": true
}
}
},
"/v1/agents": {
"get": {
"responses": {
@ -7324,126 +7275,6 @@
"title": "ToolCallDelta",
"description": "A tool call content delta for streaming responses."
},
"CompletionRequest": {
"type": "object",
"properties": {
"model_id": {
"type": "string",
"description": "The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint."
},
"content": {
"$ref": "#/components/schemas/InterleavedContent",
"description": "The content to generate a completion for."
},
"sampling_params": {
"$ref": "#/components/schemas/SamplingParams",
"description": "(Optional) Parameters to control the sampling strategy."
},
"response_format": {
"$ref": "#/components/schemas/ResponseFormat",
"description": "(Optional) Grammar specification for guided (structured) decoding."
},
"stream": {
"type": "boolean",
"description": "(Optional) If True, generate an SSE event stream of the response. Defaults to False."
},
"logprobs": {
"type": "object",
"properties": {
"top_k": {
"type": "integer",
"default": 0,
"description": "How many tokens (for each position) to return log probabilities for."
}
},
"additionalProperties": false,
"description": "(Optional) If specified, log probabilities for each token position will be returned."
}
},
"additionalProperties": false,
"required": [
"model_id",
"content"
],
"title": "CompletionRequest"
},
"CompletionResponse": {
"type": "object",
"properties": {
"metrics": {
"type": "array",
"items": {
"$ref": "#/components/schemas/MetricInResponse"
},
"description": "(Optional) List of metrics associated with the API response"
},
"content": {
"type": "string",
"description": "The generated completion text"
},
"stop_reason": {
"type": "string",
"enum": [
"end_of_turn",
"end_of_message",
"out_of_tokens"
],
"description": "Reason why generation stopped"
},
"logprobs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/TokenLogProbs"
},
"description": "Optional log probabilities for generated tokens"
}
},
"additionalProperties": false,
"required": [
"content",
"stop_reason"
],
"title": "CompletionResponse",
"description": "Response from a completion request."
},
"CompletionResponseStreamChunk": {
"type": "object",
"properties": {
"metrics": {
"type": "array",
"items": {
"$ref": "#/components/schemas/MetricInResponse"
},
"description": "(Optional) List of metrics associated with the API response"
},
"delta": {
"type": "string",
"description": "New content generated since last chunk. This can be one or more tokens."
},
"stop_reason": {
"type": "string",
"enum": [
"end_of_turn",
"end_of_message",
"out_of_tokens"
],
"description": "Optional reason why generation stopped, if complete"
},
"logprobs": {
"type": "array",
"items": {
"$ref": "#/components/schemas/TokenLogProbs"
},
"description": "Optional log probabilities for generated tokens"
}
},
"additionalProperties": false,
"required": [
"delta"
],
"title": "CompletionResponseStreamChunk",
"description": "A chunk of a streamed completion response."
},
"AgentConfig": {
"type": "object",
"properties": {

143
docs/static/llama-stack-spec.yaml vendored
View file

@ -132,43 +132,6 @@ paths:
schema:
$ref: '#/components/schemas/ChatCompletionRequest'
required: true
/v1/inference/completion:
post:
responses:
'200':
description: >-
If stream=False, returns a CompletionResponse with the full completion.
If stream=True, returns an SSE event stream of CompletionResponseStreamChunk.
content:
application/json:
schema:
$ref: '#/components/schemas/CompletionResponse'
text/event-stream:
schema:
$ref: '#/components/schemas/CompletionResponseStreamChunk'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- Inference
summary: >-
Generate a completion for the given content using the specified model.
description: >-
Generate a completion for the given content using the specified model.
parameters: []
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/CompletionRequest'
required: true
/v1/agents:
get:
responses:
@ -5302,112 +5265,6 @@ components:
title: ToolCallDelta
description: >-
A tool call content delta for streaming responses.
CompletionRequest:
type: object
properties:
model_id:
type: string
description: >-
The identifier of the model to use. The model must be registered with
Llama Stack and available via the /models endpoint.
content:
$ref: '#/components/schemas/InterleavedContent'
description: >-
The content to generate a completion for.
sampling_params:
$ref: '#/components/schemas/SamplingParams'
description: >-
(Optional) Parameters to control the sampling strategy.
response_format:
$ref: '#/components/schemas/ResponseFormat'
description: >-
(Optional) Grammar specification for guided (structured) decoding.
stream:
type: boolean
description: >-
(Optional) If True, generate an SSE event stream of the response. Defaults
to False.
logprobs:
type: object
properties:
top_k:
type: integer
default: 0
description: >-
How many tokens (for each position) to return log probabilities for.
additionalProperties: false
description: >-
(Optional) If specified, log probabilities for each token position will
be returned.
additionalProperties: false
required:
- model_id
- content
title: CompletionRequest
CompletionResponse:
type: object
properties:
metrics:
type: array
items:
$ref: '#/components/schemas/MetricInResponse'
description: >-
(Optional) List of metrics associated with the API response
content:
type: string
description: The generated completion text
stop_reason:
type: string
enum:
- end_of_turn
- end_of_message
- out_of_tokens
description: Reason why generation stopped
logprobs:
type: array
items:
$ref: '#/components/schemas/TokenLogProbs'
description: >-
Optional log probabilities for generated tokens
additionalProperties: false
required:
- content
- stop_reason
title: CompletionResponse
description: Response from a completion request.
CompletionResponseStreamChunk:
type: object
properties:
metrics:
type: array
items:
$ref: '#/components/schemas/MetricInResponse'
description: >-
(Optional) List of metrics associated with the API response
delta:
type: string
description: >-
New content generated since last chunk. This can be one or more tokens.
stop_reason:
type: string
enum:
- end_of_turn
- end_of_message
- out_of_tokens
description: >-
Optional reason why generation stopped, if complete
logprobs:
type: array
items:
$ref: '#/components/schemas/TokenLogProbs'
description: >-
Optional log probabilities for generated tokens
additionalProperties: false
required:
- delta
title: CompletionResponseStreamChunk
description: >-
A chunk of a streamed completion response.
AgentConfig:
type: object
properties:

1
llama_stack/apis/inference/inference.py
View file

@ -1008,7 +1008,6 @@ class InferenceProvider(Protocol):
model_store: ModelStore | None = None
@webmethod(route="/inference/completion", method="POST", level=LLAMA_STACK_API_V1)
async def completion(
self,
model_id: str,

4
llama_stack/providers/inline/telemetry/meta_reference/telemetry.py
View file

@ -224,10 +224,6 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry):
return _GLOBAL_STORAGE["gauges"][name]
def _log_metric(self, event: MetricEvent) -> None:
# Always log to console if console sink is enabled (debug)
if TelemetrySink.CONSOLE in self.config.sinks:
logger.debug(f"METRIC: {event.metric}={event.value} {event.unit} {event.attributes}")
# Add metric as an event to the current span
try:
with self._lock:

1
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -61,6 +61,7 @@ logger = get_logger(name=__name__, category="inference::fireworks")
class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData):
embedding_model_metadata = {
"nomic-ai/nomic-embed-text-v1.5": {"embedding_dimension": 768, "context_length": 8192},
"accounts/fireworks/models/qwen3-embedding-8b": {"embedding_dimension": 4096, "context_length": 40960},
}
def __init__(self, config: FireworksImplConfig) -> None:

8
llama_stack/providers/utils/inference/prompt_adapter.py
View file

@ -192,6 +192,14 @@ async def localize_image_content(uri: str) -> tuple[bytes, str] | None:
format = "png"
return content, format
elif uri.startswith("data"):
# data:image/{format};base64,{data}
match = re.match(r"data:image/(\w+);base64,(.+)", uri)
if not match:
raise ValueError(f"Invalid data URL format, {uri[:40]}...")
fmt, image_data = match.groups()
content = base64.b64decode(image_data)
return content, fmt
else:
return None

303
tests/integration/inference/test_embedding.py
View file

@ -1,303 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
#
# Test plan:
#
# Types of input:
# - array of a string
# - array of a image (ImageContentItem, either URL or base64 string)
# - array of a text (TextContentItem)
# Types of output:
# - list of list of floats
# Params:
# - text_truncation
# - absent w/ long text -> error
# - none w/ long text -> error
# - absent w/ short text -> ok
# - none w/ short text -> ok
# - end w/ long text -> ok
# - end w/ short text -> ok
# - start w/ long text -> ok
# - start w/ short text -> ok
# - output_dimension
# - response dimension matches
# - task_type, only for asymmetric models
# - query embedding != passage embedding
# Negative:
# - long string
# - long text
#
# Todo:
# - negative tests
# - empty
# - empty list
# - empty string
# - empty text
# - empty image
# - long
# - large image
# - appropriate combinations
# - batch size
# - many inputs
# - invalid
# - invalid URL
# - invalid base64
#
# Notes:
# - use llama_stack_client fixture
# - use pytest.mark.parametrize when possible
# - no accuracy tests: only check the type of output, not the content
#
import pytest
from llama_stack_client import BadRequestError as LlamaStackBadRequestError
from llama_stack_client.types import EmbeddingsResponse
from llama_stack_client.types.shared.interleaved_content import (
ImageContentItem,
ImageContentItemImage,
ImageContentItemImageURL,
TextContentItem,
)
from openai import BadRequestError as OpenAIBadRequestError
from llama_stack.core.library_client import LlamaStackAsLibraryClient
DUMMY_STRING = "hello"
DUMMY_STRING2 = "world"
DUMMY_LONG_STRING = "NVDA " * 10240
DUMMY_TEXT = TextContentItem(text=DUMMY_STRING, type="text")
DUMMY_TEXT2 = TextContentItem(text=DUMMY_STRING2, type="text")
DUMMY_LONG_TEXT = TextContentItem(text=DUMMY_LONG_STRING, type="text")
# TODO(mf): add a real image URL and base64 string
DUMMY_IMAGE_URL = ImageContentItem(
image=ImageContentItemImage(url=ImageContentItemImageURL(uri="https://example.com/image.jpg")), type="image"
)
DUMMY_IMAGE_BASE64 = ImageContentItem(image=ImageContentItemImage(data="base64string"), type="image")
SUPPORTED_PROVIDERS = {"remote::nvidia"}
MODELS_SUPPORTING_MEDIA = {}
MODELS_SUPPORTING_OUTPUT_DIMENSION = {"nvidia/llama-3.2-nv-embedqa-1b-v2"}
MODELS_REQUIRING_TASK_TYPE = {
"nvidia/llama-3.2-nv-embedqa-1b-v2",
"nvidia/nv-embedqa-e5-v5",
"nvidia/nv-embedqa-mistral-7b-v2",
"snowflake/arctic-embed-l",
}
MODELS_SUPPORTING_TASK_TYPE = MODELS_REQUIRING_TASK_TYPE
def default_task_type(model_id):
"""
Some models require a task type parameter. This provides a default value for
testing those models.
"""
if model_id in MODELS_REQUIRING_TASK_TYPE:
return {"task_type": "query"}
return {}
@pytest.mark.parametrize(
"contents",
[
[DUMMY_STRING, DUMMY_STRING2],
[DUMMY_TEXT, DUMMY_TEXT2],
],
ids=[
"list[string]",
"list[text]",
],
)
def test_embedding_text(llama_stack_client, embedding_model_id, contents, inference_provider_type):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
response = llama_stack_client.inference.embeddings(
model_id=embedding_model_id, contents=contents, **default_task_type(embedding_model_id)
)
assert isinstance(response, EmbeddingsResponse)
assert len(response.embeddings) == sum(len(content) if isinstance(content, list) else 1 for content in contents)
assert isinstance(response.embeddings[0], list)
assert isinstance(response.embeddings[0][0], float)
@pytest.mark.parametrize(
"contents",
[
[DUMMY_IMAGE_URL, DUMMY_IMAGE_BASE64],
[DUMMY_IMAGE_URL, DUMMY_STRING, DUMMY_IMAGE_BASE64, DUMMY_TEXT],
],
ids=[
"list[url,base64]",
"list[url,string,base64,text]",
],
)
def test_embedding_image(llama_stack_client, embedding_model_id, contents, inference_provider_type):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
if embedding_model_id not in MODELS_SUPPORTING_MEDIA:
pytest.xfail(f"{embedding_model_id} doesn't support media")
response = llama_stack_client.inference.embeddings(
model_id=embedding_model_id, contents=contents, **default_task_type(embedding_model_id)
)
assert isinstance(response, EmbeddingsResponse)
assert len(response.embeddings) == sum(len(content) if isinstance(content, list) else 1 for content in contents)
assert isinstance(response.embeddings[0], list)
assert isinstance(response.embeddings[0][0], float)
@pytest.mark.parametrize(
"text_truncation",
[
"end",
"start",
],
)
@pytest.mark.parametrize(
"contents",
[
[DUMMY_LONG_TEXT],
[DUMMY_STRING],
],
ids=[
"long",
"short",
],
)
def test_embedding_truncation(
llama_stack_client, embedding_model_id, text_truncation, contents, inference_provider_type
):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
response = llama_stack_client.inference.embeddings(
model_id=embedding_model_id,
contents=contents,
text_truncation=text_truncation,
**default_task_type(embedding_model_id),
)
assert isinstance(response, EmbeddingsResponse)
assert len(response.embeddings) == 1
assert isinstance(response.embeddings[0], list)
assert isinstance(response.embeddings[0][0], float)
@pytest.mark.parametrize(
"text_truncation",
[
None,
"none",
],
)
@pytest.mark.parametrize(
"contents",
[
[DUMMY_LONG_TEXT],
[DUMMY_LONG_STRING],
],
ids=[
"long-text",
"long-str",
],
)
def test_embedding_truncation_error(
llama_stack_client, embedding_model_id, text_truncation, contents, inference_provider_type
):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
# Using LlamaStackClient from llama_stack_client will raise llama_stack_client.BadRequestError
# While using LlamaStackAsLibraryClient from llama_stack.distribution.library_client will raise the error that the backend raises
error_type = (
OpenAIBadRequestError
if isinstance(llama_stack_client, LlamaStackAsLibraryClient)
else LlamaStackBadRequestError
)
with pytest.raises(error_type):
llama_stack_client.inference.embeddings(
model_id=embedding_model_id,
contents=[DUMMY_LONG_TEXT],
text_truncation=text_truncation,
**default_task_type(embedding_model_id),
)
def test_embedding_output_dimension(llama_stack_client, embedding_model_id, inference_provider_type):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
if embedding_model_id not in MODELS_SUPPORTING_OUTPUT_DIMENSION:
pytest.xfail(f"{embedding_model_id} doesn't support output_dimension")
base_response = llama_stack_client.inference.embeddings(
model_id=embedding_model_id, contents=[DUMMY_STRING], **default_task_type(embedding_model_id)
)
test_response = llama_stack_client.inference.embeddings(
model_id=embedding_model_id,
contents=[DUMMY_STRING],
**default_task_type(embedding_model_id),
output_dimension=32,
)
assert len(base_response.embeddings[0]) != len(test_response.embeddings[0])
assert len(test_response.embeddings[0]) == 32
def test_embedding_task_type(llama_stack_client, embedding_model_id, inference_provider_type):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
if embedding_model_id not in MODELS_SUPPORTING_TASK_TYPE:
pytest.xfail(f"{embedding_model_id} doesn't support task_type")
query_embedding = llama_stack_client.inference.embeddings(
model_id=embedding_model_id, contents=[DUMMY_STRING], task_type="query"
)
document_embedding = llama_stack_client.inference.embeddings(
model_id=embedding_model_id, contents=[DUMMY_STRING], task_type="document"
)
assert query_embedding.embeddings != document_embedding.embeddings
@pytest.mark.parametrize(
"text_truncation",
[
None,
"none",
"end",
"start",
],
)
def test_embedding_text_truncation(llama_stack_client, embedding_model_id, text_truncation, inference_provider_type):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
response = llama_stack_client.inference.embeddings(
model_id=embedding_model_id,
contents=[DUMMY_STRING],
text_truncation=text_truncation,
**default_task_type(embedding_model_id),
)
assert isinstance(response, EmbeddingsResponse)
assert len(response.embeddings) == 1
assert isinstance(response.embeddings[0], list)
assert isinstance(response.embeddings[0][0], float)
@pytest.mark.parametrize(
"text_truncation",
[
"NONE",
"END",
"START",
"left",
"right",
],
)
def test_embedding_text_truncation_error(
llama_stack_client, embedding_model_id, text_truncation, inference_provider_type
):
if inference_provider_type not in SUPPORTED_PROVIDERS:
pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
error_type = ValueError if isinstance(llama_stack_client, LlamaStackAsLibraryClient) else LlamaStackBadRequestError
with pytest.raises(error_type):
llama_stack_client.inference.embeddings(
model_id=embedding_model_id,
contents=[DUMMY_STRING],
text_truncation=text_truncation,
**default_task_type(embedding_model_id),
)

242
tests/integration/inference/test_openai_completion.py
View file

@ -9,6 +9,7 @@ import time
import unicodedata
import pytest
from pydantic import BaseModel
from ..test_cases.test_case import TestCase
@ -62,6 +63,14 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.")
def skip_if_doesnt_support_completions_logprobs(client_with_models, model_id):
provider_type = provider_from_model(client_with_models, model_id).provider_type
if provider_type in (
"remote::ollama", # logprobs is ignored
):
pytest.skip(f"Model {model_id} hosted by {provider_type} doesn't support /v1/completions logprobs.")
def skip_if_model_doesnt_support_suffix(client_with_models, model_id):
# To test `fim` ( fill in the middle ) completion, we need to use a model that supports suffix.
# Use this to specifically test this API functionality.
@ -205,28 +214,6 @@ def test_openai_completion_streaming(llama_stack_client, client_with_models, tex
assert len(content_str) > 10
@pytest.mark.parametrize(
"prompt_logprobs",
[
1,
0,
],
)
def test_openai_completion_prompt_logprobs(llama_stack_client, client_with_models, text_model_id, prompt_logprobs):
skip_if_provider_isnt_vllm(client_with_models, text_model_id)
prompt = "Hello, world!"
response = llama_stack_client.completions.create(
model=text_model_id,
prompt=prompt,
stream=False,
prompt_logprobs=prompt_logprobs,
)
assert len(response.choices) > 0
choice = response.choices[0]
assert len(choice.prompt_logprobs) > 0
def test_openai_completion_guided_choice(llama_stack_client, client_with_models, text_model_id):
skip_if_provider_isnt_vllm(client_with_models, text_model_id)
@ -518,3 +505,214 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi
message_content = response.choices[0].message.content.lower().strip()
normalized_content = _normalize_text(message_content)
assert "hello world" in normalized_content
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:stop_sequence",
],
)
def test_openai_completion_stop_sequence(client_with_models, openai_client, text_model_id, test_case):
skip_if_model_doesnt_support_openai_completion(client_with_models, text_model_id)
tc = TestCase(test_case)
response = openai_client.completions.create(
model=text_model_id,
prompt=tc["content"],
stop="1963",
stream=False,
)
assert len(response.choices) > 0
choice = response.choices[0]
assert "1963" not in choice.text
response = openai_client.completions.create(
model=text_model_id,
prompt=tc["content"],
stop=["blathering", "1963"],
stream=False,
)
assert len(response.choices) > 0
choice = response.choices[0]
assert "1963" not in choice.text
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:log_probs",
],
)
def test_openai_completion_logprobs(client_with_models, openai_client, text_model_id, test_case):
skip_if_model_doesnt_support_openai_completion(client_with_models, text_model_id)
skip_if_doesnt_support_completions_logprobs(client_with_models, text_model_id)
tc = TestCase(test_case)
response = openai_client.completions.create(
model=text_model_id,
prompt=tc["content"],
logprobs=5,
)
assert len(response.choices) > 0
choice = response.choices[0]
assert choice.text, "Response text should not be empty"
assert choice.logprobs, "Logprobs should not be empty"
logprobs = choice.logprobs
assert logprobs.token_logprobs, "Response tokens should not be empty"
assert len(logprobs.tokens) == len(logprobs.token_logprobs)
assert len(logprobs.token_logprobs) == len(logprobs.top_logprobs)
for i, (token, prob) in enumerate(zip(logprobs.tokens, logprobs.token_logprobs, strict=True)):
assert logprobs.top_logprobs[i][token] == prob
assert len(logprobs.top_logprobs[i]) == 5
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:log_probs",
],
)
def test_openai_completion_logprobs_streaming(client_with_models, openai_client, text_model_id, test_case):
skip_if_model_doesnt_support_openai_completion(client_with_models, text_model_id)
skip_if_doesnt_support_completions_logprobs(client_with_models, text_model_id)
tc = TestCase(test_case)
response = openai_client.completions.create(
model=text_model_id,
prompt=tc["content"],
logprobs=3,
stream=True,
max_tokens=5,
)
for chunk in response:
choice = chunk.choices[0]
choice = response.choices[0]
if choice.text: # if there's a token, we expect logprobs
assert choice.logprobs, "Logprobs should not be empty"
logprobs = choice.logprobs
assert logprobs.token_logprobs, "Response tokens should not be empty"
assert len(logprobs.tokens) == len(logprobs.token_logprobs)
assert len(logprobs.token_logprobs) == len(logprobs.top_logprobs)
for i, (token, prob) in enumerate(zip(logprobs.tokens, logprobs.token_logprobs, strict=True)):
assert logprobs.top_logprobs[i][token] == prob
assert len(logprobs.top_logprobs[i]) == 3
else: # no token, no logprobs
assert not choice.logprobs, "Logprobs should be empty"
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_openai_chat_completion_with_tools(openai_client, text_model_id, test_case):
tc = TestCase(test_case)
response = openai_client.chat.completions.create(
model=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_choice="auto",
stream=False,
)
assert len(response.choices) == 1
assert len(response.choices[0].message.tool_calls) == 1
tool_call = response.choices[0].message.tool_calls[0]
assert tool_call.function.name == tc["tools"][0]["function"]["name"]
assert "location" in tool_call.function.arguments
assert tc["expected"]["location"] in tool_call.function.arguments
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_openai_chat_completion_with_tools_and_streaming(openai_client, text_model_id, test_case):
tc = TestCase(test_case)
response = openai_client.chat.completions.create(
model=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_choice="auto",
stream=True,
)
# Accumulate tool calls from streaming chunks
tool_calls = []
for chunk in response:
if chunk.choices and chunk.choices[0].delta.tool_calls:
for i, tc_delta in enumerate(chunk.choices[0].delta.tool_calls):
while len(tool_calls) <= i:
tool_calls.append({"function": {"name": "", "arguments": ""}})
if tc_delta.function and tc_delta.function.name:
tool_calls[i]["function"]["name"] = tc_delta.function.name
if tc_delta.function and tc_delta.function.arguments:
tool_calls[i]["function"]["arguments"] += tc_delta.function.arguments
assert len(tool_calls) == 1
tool_call = tool_calls[0]
assert tool_call["function"]["name"] == tc["tools"][0]["function"]["name"]
assert "location" in tool_call["function"]["arguments"]
assert tc["expected"]["location"] in tool_call["function"]["arguments"]
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_openai_chat_completion_with_tool_choice_none(openai_client, text_model_id, test_case):
tc = TestCase(test_case)
response = openai_client.chat.completions.create(
model=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_choice="none",
stream=False,
)
assert len(response.choices) == 1
tool_calls = response.choices[0].message.tool_calls
assert tool_calls is None or len(tool_calls) == 0
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:structured_output",
],
)
def test_openai_chat_completion_structured_output(openai_client, text_model_id, test_case):
# Note: Skip condition may need adjustment for OpenAI client
class AnswerFormat(BaseModel):
first_name: str
last_name: str
year_of_birth: int
tc = TestCase(test_case)
response = openai_client.chat.completions.create(
model=text_model_id,
messages=tc["messages"],
response_format={
"type": "json_schema",
"json_schema": {
"name": "AnswerFormat",
"schema": AnswerFormat.model_json_schema(),
},
},
stream=False,
)
print(response.choices[0].message.content)
answer = AnswerFormat.model_validate_json(response.choices[0].message.content)
expected = tc["expected"]
assert answer.first_name == expected["first_name"]
assert answer.last_name == expected["last_name"]
assert answer.year_of_birth == expected["year_of_birth"]

545
tests/integration/inference/test_text_inference.py
View file

@ -1,545 +0,0 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from time import sleep
import pytest
from pydantic import BaseModel
from llama_stack.models.llama.sku_list import resolve_model
from ..test_cases.test_case import TestCase
PROVIDER_LOGPROBS_TOP_K = {"remote::together", "remote::fireworks", "remote::vllm"}
def skip_if_model_doesnt_support_completion(client_with_models, model_id):
models = {m.identifier: m for m in client_with_models.models.list()}
models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
provider_id = models[model_id].provider_id
providers = {p.provider_id: p for p in client_with_models.providers.list()}
provider = providers[provider_id]
if (
provider.provider_type
in (
"remote::openai",
"remote::anthropic",
"remote::gemini",
"remote::vertexai",
"remote::groq",
"remote::sambanova",
"remote::azure",
)
or "openai-compat" in provider.provider_type
):
pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support completion")
def skip_if_model_doesnt_support_json_schema_structured_output(client_with_models, model_id):
models = {m.identifier: m for m in client_with_models.models.list()}
models.update({m.provider_resource_id: m for m in client_with_models.models.list()})
provider_id = models[model_id].provider_id
providers = {p.provider_id: p for p in client_with_models.providers.list()}
provider = providers[provider_id]
if provider.provider_type in ("remote::sambanova", "remote::azure", "remote::watsonx"):
pytest.skip(
f"Model {model_id} hosted by {provider.provider_type} doesn't support json_schema structured output"
)
def get_llama_model(client_with_models, model_id):
models = {}
for m in client_with_models.models.list():
models[m.identifier] = m
models[m.provider_resource_id] = m
assert model_id in models, f"Model {model_id} not found"
model = models[model_id]
ids = (model.identifier, model.provider_resource_id)
for mid in ids:
if resolve_model(mid):
return mid
return model.metadata.get("llama_model", None)
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:sanity",
],
)
def test_text_completion_non_streaming(client_with_models, text_model_id, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=False,
model_id=text_model_id,
sampling_params={
"max_tokens": 50,
},
)
assert len(response.content) > 10
# assert "blue" in response.content.lower().strip()
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:sanity",
],
)
def test_text_completion_streaming(client_with_models, text_model_id, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=True,
model_id=text_model_id,
sampling_params={
"max_tokens": 50,
},
)
streamed_content = [chunk.delta for chunk in response]
content_str = "".join(streamed_content).lower().strip()
# assert "blue" in content_str
assert len(content_str) > 10
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:stop_sequence",
],
)
def test_text_completion_stop_sequence(client_with_models, text_model_id, inference_provider_type, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
# This is only supported/tested for remote vLLM: https://github.com/meta-llama/llama-stack/issues/1771
if inference_provider_type != "remote::vllm":
pytest.xfail(f"{inference_provider_type} doesn't support 'stop' parameter yet")
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=True,
model_id=text_model_id,
sampling_params={
"max_tokens": 50,
"stop": ["1963"],
},
)
streamed_content = [chunk.delta for chunk in response]
content_str = "".join(streamed_content).lower().strip()
assert "1963" not in content_str
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:log_probs",
],
)
def test_text_completion_log_probs_non_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=False,
model_id=text_model_id,
sampling_params={
"max_tokens": 5,
},
logprobs={
"top_k": 1,
},
)
assert response.logprobs, "Logprobs should not be empty"
assert 1 <= len(response.logprobs) <= 5 # each token has 1 logprob and here max_tokens=5
assert all(len(logprob.logprobs_by_token) == 1 for logprob in response.logprobs)
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:log_probs",
],
)
def test_text_completion_log_probs_streaming(client_with_models, text_model_id, inference_provider_type, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
if inference_provider_type not in PROVIDER_LOGPROBS_TOP_K:
pytest.xfail(f"{inference_provider_type} doesn't support log probs yet")
tc = TestCase(test_case)
response = client_with_models.inference.completion(
content=tc["content"],
stream=True,
model_id=text_model_id,
sampling_params={
"max_tokens": 5,
},
logprobs={
"top_k": 1,
},
)
streamed_content = list(response)
for chunk in streamed_content:
if chunk.delta: # if there's a token, we expect logprobs
assert chunk.logprobs, "Logprobs should not be empty"
assert all(len(logprob.logprobs_by_token) == 1 for logprob in chunk.logprobs)
else: # no token, no logprobs
assert not chunk.logprobs, "Logprobs should be empty"
@pytest.mark.parametrize(
"test_case",
[
"inference:completion:structured_output",
],
)
def test_text_completion_structured_output(client_with_models, text_model_id, test_case):
skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
skip_if_model_doesnt_support_json_schema_structured_output(client_with_models, text_model_id)
class AnswerFormat(BaseModel):
name: str
year_born: str
year_retired: str
tc = TestCase(test_case)
user_input = tc["user_input"]
response = client_with_models.inference.completion(
model_id=text_model_id,
content=user_input,
stream=False,
sampling_params={
"max_tokens": 50,
},
response_format={
"type": "json_schema",
"json_schema": AnswerFormat.model_json_schema(),
},
)
answer = AnswerFormat.model_validate_json(response.content)
expected = tc["expected"]
assert answer.name == expected["name"]
assert answer.year_born == expected["year_born"]
assert answer.year_retired == expected["year_retired"]
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:non_streaming_01",
"inference:chat_completion:non_streaming_02",
],
)
def test_text_chat_completion_non_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
question = tc["question"]
expected = tc["expected"]
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=[
{
"role": "user",
"content": question,
}
],
stream=False,
)
message_content = response.completion_message.content.lower().strip()
assert len(message_content) > 0
assert expected.lower() in message_content
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:streaming_01",
"inference:chat_completion:streaming_02",
],
)
def test_text_chat_completion_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
question = tc["question"]
expected = tc["expected"]
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=[{"role": "user", "content": question}],
stream=True,
timeout=120, # Increase timeout to 2 minutes for large conversation history
)
streamed_content = [str(chunk.event.delta.text.lower().strip()) for chunk in response]
assert len(streamed_content) > 0
assert expected.lower() in "".join(streamed_content)
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_calling_and_non_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_choice="auto",
stream=False,
)
# some models can return content for the response in addition to the tool call
assert response.completion_message.role == "assistant"
assert len(response.completion_message.tool_calls) == 1
assert response.completion_message.tool_calls[0].tool_name == tc["tools"][0]["tool_name"]
assert response.completion_message.tool_calls[0].arguments == tc["expected"]
# Will extract streamed text and separate it from tool invocation content
# The returned tool inovcation content will be a string so it's easy to comapare with expected value
# e.g. "[get_weather, {'location': 'San Francisco, CA'}]"
def extract_tool_invocation_content(response):
tool_invocation_content: str = ""
for chunk in response:
delta = chunk.event.delta
if delta.type == "tool_call" and delta.parse_status == "succeeded":
call = delta.tool_call
tool_invocation_content += f"[{call.tool_name}, {call.arguments}]"
return tool_invocation_content
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_calling_and_streaming(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_choice="auto",
stream=True,
)
tool_invocation_content = extract_tool_invocation_content(response)
expected_tool_name = tc["tools"][0]["tool_name"]
expected_argument = tc["expected"]
assert tool_invocation_content == f"[{expected_tool_name}, {expected_argument}]"
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_choice_required(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_config={
"tool_choice": "required",
},
stream=True,
)
tool_invocation_content = extract_tool_invocation_content(response)
expected_tool_name = tc["tools"][0]["tool_name"]
expected_argument = tc["expected"]
assert tool_invocation_content == f"[{expected_tool_name}, {expected_argument}]"
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling",
],
)
def test_text_chat_completion_with_tool_choice_none(client_with_models, text_model_id, test_case):
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
tools=tc["tools"],
tool_config={"tool_choice": "none"},
stream=True,
)
tool_invocation_content = extract_tool_invocation_content(response)
assert tool_invocation_content == ""
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:structured_output",
],
)
def test_text_chat_completion_structured_output(client_with_models, text_model_id, test_case):
skip_if_model_doesnt_support_json_schema_structured_output(client_with_models, text_model_id)
class NBAStats(BaseModel):
year_for_draft: int
num_seasons_in_nba: int
class AnswerFormat(BaseModel):
first_name: str
last_name: str
year_of_birth: int
nba_stats: NBAStats
tc = TestCase(test_case)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=tc["messages"],
response_format={
"type": "json_schema",
"json_schema": AnswerFormat.model_json_schema(),
},
stream=False,
)
answer = AnswerFormat.model_validate_json(response.completion_message.content)
expected = tc["expected"]
assert answer.first_name == expected["first_name"]
assert answer.last_name == expected["last_name"]
assert answer.year_of_birth == expected["year_of_birth"]
assert answer.nba_stats.num_seasons_in_nba == expected["num_seasons_in_nba"]
assert answer.nba_stats.year_for_draft == expected["year_for_draft"]
@pytest.mark.parametrize("streaming", [True, False])
@pytest.mark.parametrize(
"test_case",
[
"inference:chat_completion:tool_calling_tools_absent",
],
)
def test_text_chat_completion_tool_calling_tools_not_in_request(
client_with_models, text_model_id, test_case, streaming
):
tc = TestCase(test_case)
# TODO: more dynamic lookup on tool_prompt_format for model family
tool_prompt_format = "json" if "3.1" in text_model_id else "python_list"
request = {
"model_id": text_model_id,
"messages": tc["messages"],
"tools": tc["tools"],
"tool_choice": "auto",
"tool_prompt_format": tool_prompt_format,
"stream": streaming,
}
response = client_with_models.inference.chat_completion(**request)
if streaming:
for chunk in response:
delta = chunk.event.delta
if delta.type == "tool_call" and delta.parse_status == "succeeded":
assert delta.tool_call.tool_name == "get_object_namespace_list"
if delta.type == "tool_call" and delta.parse_status == "failed":
# expect raw message that failed to parse in tool_call
assert isinstance(delta.tool_call, str)
assert len(delta.tool_call) > 0
else:
for tc in response.completion_message.tool_calls:
assert tc.tool_name == "get_object_namespace_list"
@pytest.mark.parametrize(
"test_case",
[
# Tests if the model can handle simple messages like "Hi" or
# a message unrelated to one of the tool calls
"inference:chat_completion:text_then_tool",
# Tests if the model can do full tool call with responses correctly
"inference:chat_completion:tool_then_answer",
# Tests if model can generate multiple params and
# read outputs correctly
"inference:chat_completion:array_parameter",
],
)
def test_text_chat_completion_with_multi_turn_tool_calling(client_with_models, text_model_id, test_case):
"""This test tests the model's tool calling loop in various scenarios"""
if "llama-4" not in text_model_id.lower() and "llama4" not in text_model_id.lower():
pytest.xfail("Not tested for non-llama4 models yet")
tc = TestCase(test_case)
messages = []
# keep going until either
# 1. we have messages to test in multi-turn
# 2. no messages bust last message is tool response
while len(tc["messages"]) > 0 or (len(messages) > 0 and messages[-1]["role"] == "tool"):
# do not take new messages if last message is tool response
if len(messages) == 0 or messages[-1]["role"] != "tool":
new_messages = tc["messages"].pop(0)
messages += new_messages
# pprint(messages)
response = client_with_models.inference.chat_completion(
model_id=text_model_id,
messages=messages,
tools=tc["tools"],
stream=False,
sampling_params={
"strategy": {
"type": "top_p",
"top_p": 0.9,
"temperature": 0.6,
}
},
)
op_msg = response.completion_message
messages.append(op_msg.model_dump())
# print(op_msg)
assert op_msg.role == "assistant"
expected = tc["expected"].pop(0)
assert len(op_msg.tool_calls) == expected["num_tool_calls"]
if expected["num_tool_calls"] > 0:
assert op_msg.tool_calls[0].tool_name == expected["tool_name"]
assert op_msg.tool_calls[0].arguments == expected["tool_arguments"]
tool_response = tc["tool_responses"].pop(0)
messages.append(
# Tool Response Message
{
"role": "tool",
"call_id": op_msg.tool_calls[0].call_id,
"content": tool_response["response"],
}
)
else:
actual_answer = op_msg.content.lower()
# pprint(actual_answer)
assert expected["answer"] in actual_answer
# sleep to avoid rate limit
sleep(1)

86
tests/integration/inference/test_vision_inference.py
View file

@ -25,16 +25,19 @@ def base64_image_data(image_path):
return base64.b64encode(image_path.read_bytes()).decode("utf-8")
@pytest.fixture
def base64_image_url(base64_image_data):
return f"data:image/png;base64,{base64_image_data}"
def test_image_chat_completion_non_streaming(client_with_models, vision_model_id):
message = {
"role": "user",
"content": [
{
"type": "image",
"image": {
"url": {
"uri": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
},
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
},
},
{
@ -43,12 +46,12 @@ def test_image_chat_completion_non_streaming(client_with_models, vision_model_id
},
],
}
response = client_with_models.inference.chat_completion(
model_id=vision_model_id,
response = client_with_models.chat.completions.create(
model=vision_model_id,
messages=[message],
stream=False,
)
message_content = response.completion_message.content.lower().strip()
message_content = response.choices[0].message.content.lower().strip()
assert len(message_content) > 0
assert any(expected in message_content for expected in {"dog", "puppy", "pup"})
@ -68,8 +71,13 @@ def multi_image_data():
return encoded_files
@pytest.fixture
def multi_image_url(multi_image_data):
return [f"data:image/jpeg;base64,{data}" for data in multi_image_data]
@pytest.mark.parametrize("stream", [True, False])
def test_image_chat_completion_multiple_images(client_with_models, vision_model_id, multi_image_data, stream):
def test_image_chat_completion_multiple_images(client_with_models, vision_model_id, multi_image_url, stream):
supported_models = ["llama-4", "gpt-4o", "llama4"]
if not any(model in vision_model_id.lower() for model in supported_models):
pytest.skip(
@ -81,15 +89,15 @@ def test_image_chat_completion_multiple_images(client_with_models, vision_model_
"role": "user",
"content": [
{
"type": "image",
"image": {
"data": multi_image_data[0],
"type": "image_url",
"image_url": {
"url": multi_image_url[0],
},
},
{
"type": "image",
"image": {
"data": multi_image_data[1],
"type": "image_url",
"image_url": {
"url": multi_image_url[1],
},
},
{
@ -99,17 +107,17 @@ def test_image_chat_completion_multiple_images(client_with_models, vision_model_
],
},
]
response = client_with_models.inference.chat_completion(
model_id=vision_model_id,
response = client_with_models.chat.completions.create(
model=vision_model_id,
messages=messages,
stream=stream,
)
if stream:
message_content = ""
for chunk in response:
message_content += chunk.event.delta.text
message_content += chunk.choices[0].delta.content
else:
message_content = response.completion_message.content
message_content = response.choices[0].message.content
assert len(message_content) > 0
assert any(expected in message_content.lower().strip() for expected in {"bedroom"}), message_content
@ -125,17 +133,17 @@ def test_image_chat_completion_multiple_images(client_with_models, vision_model_
"role": "user",
"content": [
{
"type": "image",
"image": {
"data": multi_image_data[2],
"type": "image_url",
"image_url": {
"url": multi_image_data[2],
},
},
{"type": "text", "text": "How about this one?"},
],
},
)
response = client_with_models.inference.chat_completion(
model_id=vision_model_id,
response = client_with_models.chat.completions.create(
model=vision_model_id,
messages=messages,
stream=stream,
)
@ -144,7 +152,7 @@ def test_image_chat_completion_multiple_images(client_with_models, vision_model_
for chunk in response:
message_content += chunk.event.delta.text
else:
message_content = response.completion_message.content
message_content = response.choices[0].message.content
assert len(message_content) > 0
assert any(expected in message_content.lower().strip() for expected in {"sword", "shield"}), message_content
@ -154,11 +162,9 @@ def test_image_chat_completion_streaming(client_with_models, vision_model_id):
"role": "user",
"content": [
{
"type": "image",
"image": {
"url": {
"uri": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
},
"type": "image_url",
"image_url": {
"url": "https://raw.githubusercontent.com/meta-llama/llama-stack/main/tests/integration/inference/dog.png"
},
},
{
@ -167,23 +173,23 @@ def test_image_chat_completion_streaming(client_with_models, vision_model_id):
},
],
}
response = client_with_models.inference.chat_completion(
model_id=vision_model_id,
response = client_with_models.chat.completions.create(
model=vision_model_id,
messages=[message],
stream=True,
)
streamed_content = ""
for chunk in response:
streamed_content += chunk.event.delta.text.lower()
streamed_content += chunk.choices[0].delta.content.lower()
assert len(streamed_content) > 0
assert any(expected in streamed_content for expected in {"dog", "puppy", "pup"})
def test_image_chat_completion_base64(client_with_models, vision_model_id, base64_image_data):
def test_image_chat_completion_base64(client_with_models, vision_model_id, base64_image_url):
image_spec = {
"type": "image",
"image": {
"data": base64_image_data,
"type": "image_url",
"image_url": {
"url": base64_image_url,
},
}
@ -197,10 +203,10 @@ def test_image_chat_completion_base64(client_with_models, vision_model_id, base6
},
],
}
response = client_with_models.inference.chat_completion(
model_id=vision_model_id,
response = client_with_models.chat.completions.create(
model=vision_model_id,
messages=[message],
stream=False,
)
message_content = response.completion_message.content.lower().strip()
message_content = response.choices[0].message.content.lower().strip()
assert len(message_content) > 0

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"created": 1754089426,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": true,
"context_length": 131072
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "accounts/fireworks/models/deepseek-r1",
"created": 1737397673,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": false,
"context_length": 163840
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "accounts/fireworks/models/llama-v3p1-8b-instruct",
"created": 1721692808,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": false,
"context_length": 131072
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "accounts/fireworks/models/deepseek-r1-basic",
"created": 1742306746,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": false,
"context_length": 163840
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "accounts/fireworks/models/deepseek-v3p1",
"created": 1755758988,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": true,
"context_length": 163840
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "accounts/fireworks/models/flux-1-schnell-fp8",
"created": 1729535376,
"object": "model",
"owned_by": "fireworks",
"kind": "FLUMINA_BASE_MODEL",
"supports_chat": false,
"supports_image_input": false,
"supports_tools": false
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "accounts/fireworks/models/glm-4p5",
"created": 1753809636,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": true,
"context_length": 131072
}
},
{
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"__data__": {
"id": "accounts/fireworks/models/kimi-k2-instruct-0905",
"created": 1757018994,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": true,
"context_length": 262144
}
},
{
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"id": "accounts/fireworks/models/llama-v3p1-405b-instruct",
"created": 1721428386,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": true,
"context_length": 131072
}
},
{
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"id": "accounts/fireworks/models/llama4-scout-instruct-basic",
"created": 1743878279,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": true,
"supports_tools": true,
"context_length": 1048576
}
},
{
"__type__": "openai.types.model.Model",
"__data__": {
"id": "accounts/fireworks/models/qwen3-30b-a3b",
"created": 1745878133,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": true,
"context_length": 131072
}
},
{
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"__data__": {
"id": "accounts/fireworks/models/llama-v3p1-70b-instruct",
"created": 1721287357,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
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}
},
{
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"__data__": {
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"created": 1748456377,
"object": "model",
"owned_by": "fireworks",
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"supports_tools": true,
"context_length": 163840
}
},
{
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"id": "accounts/fireworks/models/mixtral-8x22b-instruct",
"created": 1713375508,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": true,
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}
},
{
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"created": 1753808388,
"object": "model",
"owned_by": "fireworks",
"kind": "HF_BASE_MODEL",
"supports_chat": true,
"supports_image_input": false,
"supports_tools": false,
"context_length": 262144
}
}
],
"is_streaming": false
}
}

5
tests/integration/suites.py
View file

@ -127,9 +127,8 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
name="fireworks",
description="Fireworks provider with a text model",
defaults={
"text_model": "accounts/fireworks/models/llama-v3p1-8b-instruct",
"vision_model": "accounts/fireworks/models/llama-v3p2-90b-vision-instruct",
"embedding_model": "nomic-ai/nomic-embed-text-v1.5",
"text_model": "fireworks/accounts/fireworks/models/llama-v3p1-8b-instruct",
"embedding_model": "fireworks/accounts/fireworks/models/qwen3-embedding-8b",
},
),
}

4
tests/integration/telemetry/test_telemetry.py
View file

@ -32,8 +32,8 @@ def setup_telemetry_data(llama_stack_client, text_model_id):
)
for i in range(2):
llama_stack_client.inference.chat_completion(
model_id=text_model_id, messages=[{"role": "user", "content": f"Test trace {i}"}]
llama_stack_client.chat.completions.create(
model=text_model_id, messages=[{"role": "user", "content": f"Test trace {i}"}]
)
start_time = time.time()

166
tests/integration/test_cases/inference/chat_completion.json
View file

@ -83,12 +83,19 @@
],
"tools": [
{
"tool_name": "get_weather",
"description": "Get the current weather",
"parameters": {
"location": {
"param_type": "string",
"description": "The city and state (both required), e.g. San Francisco, CA."
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state (both required), e.g. San Francisco, CA."
}
},
"required": ["location"]
}
}
}
@ -116,12 +123,19 @@
],
"tools": [
{
"tool_name": "get_weather",
"description": "Get the current weather",
"parameters": {
"location": {
"param_type": "string",
"description": "The city and state (both required), e.g. San Francisco, CA."
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state (both required), e.g. San Francisco, CA."
}
},
"required": ["location"]
}
}
}
@ -162,12 +176,19 @@
],
"tools": [
{
"tool_name": "get_weather",
"description": "Get the current weather",
"parameters": {
"location": {
"param_type": "string",
"description": "The city and state (both required), e.g. San Francisco, CA."
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state (both required), e.g. San Francisco, CA."
}
},
"required": ["location"]
}
}
}
@ -192,66 +213,6 @@
]
}
},
"array_parameter": {
"data": {
"messages": [
[
{
"role": "user",
"content": "Please add a new product with name 'Widget', price 19.99, in stock, and tags ['new', 'sale'] and give me the product id."
}
]
],
"tools": [
{
"tool_name": "addProduct",
"description": "Get the current weather",
"parameters": {
"name": {
"param_type": "string",
"description": "Name of the product"
},
"price": {
"param_type": "number",
"description": "Price of the product"
},
"inStock": {
"param_type": "boolean",
"description": "Availability status of the product."
},
"tags": {
"param_type": "list[str]",
"description": "List of product tags"
}
}
}
],
"tool_responses": [
{
"response": "{'response': 'Successfully added product with id: 123'}"
}
],
"expected": [
{
"num_tool_calls": 1,
"tool_name": "addProduct",
"tool_arguments": {
"name": "Widget",
"price": 19.99,
"inStock": true,
"tags": [
"new",
"sale"
]
}
},
{
"num_tool_calls": 0,
"answer": "123"
}
]
}
},
"sample_messages_tool_calling": {
"data": {
"messages": [
@ -270,13 +231,19 @@
],
"tools": [
{
"tool_name": "get_weather",
"description": "Get the current weather",
"parameters": {
"location": {
"param_type": "string",
"description": "The city and state, e.g. San Francisco, CA",
"required": true
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state (both required), e.g. San Francisco, CA."
}
},
"required": ["location"]
}
}
}
@ -343,18 +310,23 @@
],
"tools": [
{
"tool_name": "get_object_namespace_list",
"description": "Get the list of objects in a namespace",
"parameters": {
"kind": {
"param_type": "string",
"description": "the type of object",
"required": true
},
"namespace": {
"param_type": "string",
"description": "the name of the namespace",
"required": true
"type": "function",
"function": {
"name": "get_object_namespace_list",
"description": "Get the list of objects in a namespace",
"parameters": {
"type": "object",
"properties": {
"kind": {
"type": "string",
"description": "the type of object"
},
"namespace": {
"type": "string",
"description": "the name of the namespace"
}
},
"required": ["kind", "namespace"]
}
}
}