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Merge branch 'main' into feature/dpo-training

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Ashwin Bharambe 2025-07-30 23:33:00 -07:00 • committed by GitHub
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419 changed files with 44853 additions and 1661 deletions
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2
tests/integration/agents/test_openai_responses.py
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@ -6,7 +6,7 @@
import pytest
from openai import BadRequestError, OpenAI
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
from llama_stack.core.library_client import LlamaStackAsLibraryClient
@pytest.fixture

4
tests/integration/files/test_files.py
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@ -10,8 +10,8 @@ from unittest.mock import patch
import pytest
from openai import OpenAI
from llama_stack.distribution.datatypes import User
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
from llama_stack.core.datatypes import User
from llama_stack.core.library_client import LlamaStackAsLibraryClient
def test_openai_client_basic_operations(compat_client, client_with_models):

2
tests/integration/fixtures/common.py
View file

@ -20,7 +20,7 @@ from llama_stack_client import LlamaStackClient
from openai import OpenAI
from llama_stack import LlamaStackAsLibraryClient
from llama_stack.distribution.stack import run_config_from_adhoc_config_spec
from llama_stack.core.stack import run_config_from_adhoc_config_spec
from llama_stack.env import get_env_or_fail
DEFAULT_PORT = 8321

6
tests/integration/inference/test_openai_completion.py
View file

@ -14,7 +14,7 @@ from openai import OpenAI
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
from llama_stack.core.library_client import LlamaStackAsLibraryClient
from ..test_cases.test_case import TestCase
@ -345,7 +345,7 @@ def test_inference_store(compat_client, client_with_models, text_model_id, strea
response_id = response.id
content = response.choices[0].message.content
responses = client.chat.completions.list()
responses = client.chat.completions.list(limit=1000)
assert response_id in [r.id for r in responses.data]
retrieved_response = client.chat.completions.retrieve(response_id)
@ -410,7 +410,7 @@ def test_inference_store_tool_calls(compat_client, client_with_models, text_mode
response_id = response.id
content = response.choices[0].message.content
responses = client.chat.completions.list()
responses = client.chat.completions.list(limit=1000)
assert response_id in [r.id for r in responses.data]
retrieved_response = client.chat.completions.retrieve(response_id)

2
tests/integration/inference/test_openai_embeddings.py
View file

@ -10,7 +10,7 @@ import struct
import pytest
from openai import OpenAI
from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
from llama_stack.core.library_client import LlamaStackAsLibraryClient
def decode_base64_to_floats(base64_string: str) -> list[float]:

10
tests/integration/post_training/test_post_training.py
View file

@ -25,6 +25,15 @@ logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(
logger = logging.getLogger(__name__)
skip_because_resource_intensive = pytest.mark.skip(
reason="""
Post training tests are extremely resource intensive. They download large models and partly as a result,
are very slow to run. We cannot run them on every single PR update. CI should be considered
a scarce resource and properly utilitized.
"""
)
@pytest.fixture(autouse=True)
def capture_output(capsys):
"""Fixture to capture and display output during test execution."""
@ -61,6 +70,7 @@ class TestPostTraining:
],
)
@pytest.mark.timeout(360) # 6 minutes timeout
@skip_because_resource_intensive
def test_supervised_fine_tune(self, llama_stack_client, purpose, source):
logger.info("Starting supervised fine-tuning test")

6
tests/integration/providers/utils/sqlstore/test_authorized_sqlstore.py
View file

@ -10,8 +10,8 @@ from unittest.mock import patch
import pytest
from llama_stack.distribution.access_control.access_control import default_policy
from llama_stack.distribution.datatypes import User
from llama_stack.core.access_control.access_control import default_policy
from llama_stack.core.datatypes import User
from llama_stack.providers.utils.sqlstore.api import ColumnType
from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig, SqliteSqlStoreConfig, sqlstore_impl
@ -186,7 +186,7 @@ async def test_authorized_store_attributes(mock_get_authenticated_user, authoriz
@patch("llama_stack.providers.utils.sqlstore.authorized_sqlstore.get_authenticated_user")
async def test_user_ownership_policy(mock_get_authenticated_user, authorized_store, request):
"""Test that 'user is owner' policies work correctly with record ownership"""
from llama_stack.distribution.access_control.datatypes import AccessRule, Action, Scope
from llama_stack.core.access_control.datatypes import AccessRule, Action, Scope
backend_name = request.node.callspec.id

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tests/integration/recordings/index.sqlite Normal file
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39
tests/integration/recordings/responses/00ba04f74a96.json Normal file
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@ -0,0 +1,39 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama-guard3:1b",
"raw": true,
"prompt": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nTask: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Write a very short paragraph of a romantic story happening on a tropical island\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST User message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
"options": {
"temperature": 0.0
},
"stream": false
},
"endpoint": "/api/generate",
"model": "llama-guard3:1b"
},
"response": {
"body": {
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama-guard3:1b",
"created_at": "2025-07-29T20:32:56.173703Z",
"done": true,
"done_reason": "stop",
"total_duration": 136508333,
"load_duration": 65819417,
"prompt_eval_count": 216,
"prompt_eval_duration": 58491125,
"eval_count": 2,
"eval_duration": 11513166,
"response": "safe",
"thinking": null,
"context": null
}
},
"is_streaming": false
}
}

421
tests/integration/recordings/responses/011f70e24ce4.json Normal file
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@ -0,0 +1,421 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/embeddings",
"headers": {},
"body": {
"model": "all-minilm:l6-v2",
"input": [
"What is Python programming language?"
]
},
"endpoint": "/api/embeddings",
"model": "all-minilm:l6-v2"
},
"response": {
"body": {
"__type__": "ollama._types.EmbedResponse",
"__data__": {
"model": "all-minilm:l6-v2",
"created_at": null,
"done": null,
"done_reason": null,
"total_duration": 36244625,
"load_duration": 29784250,
"prompt_eval_count": 6,
"prompt_eval_duration": null,
"eval_count": null,
"eval_duration": null,
"embeddings": [
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}
},
"is_streaming": false
}
}

347
tests/integration/recordings/responses/04172112ffbb.json Normal file
View file

@ -0,0 +1,347 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"raw": true,
"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is 2 + 2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nThe answer to 2 + 2 is 4.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTell me a short joke<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
"options": {
"temperature": 0.0
},
"stream": true
},
"endpoint": "/api/generate",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": [
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.453648Z",
"done": false,
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"prompt_eval_duration": null,
"eval_count": null,
"eval_duration": null,
"response": "Here",
"thinking": null,
"context": null
}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.498568Z",
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"eval_count": null,
"eval_duration": null,
"response": "'s",
"thinking": null,
"context": null
}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.542162Z",
"done": false,
"done_reason": null,
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"eval_count": null,
"eval_duration": null,
"response": " one",
"thinking": null,
"context": null
}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.583708Z",
"done": false,
"done_reason": null,
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"prompt_eval_duration": null,
"eval_count": null,
"eval_duration": null,
"response": ":\n\n",
"thinking": null,
"context": null
}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.624941Z",
"done": false,
"done_reason": null,
"total_duration": null,
"load_duration": null,
"prompt_eval_count": null,
"prompt_eval_duration": null,
"eval_count": null,
"eval_duration": null,
"response": "What",
"thinking": null,
"context": null
}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.666128Z",
"done": false,
"done_reason": null,
"total_duration": null,
"load_duration": null,
"prompt_eval_count": null,
"prompt_eval_duration": null,
"eval_count": null,
"eval_duration": null,
"response": " do",
"thinking": null,
"context": null
}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.707429Z",
"done": false,
"done_reason": null,
"total_duration": null,
"load_duration": null,
"prompt_eval_count": null,
"prompt_eval_duration": null,
"eval_count": null,
"eval_duration": null,
"response": " you",
"thinking": null,
"context": null
}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.748343Z",
"done": false,
"done_reason": null,
"total_duration": null,
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"eval_count": null,
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"response": " call",
"thinking": null,
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}
},
{
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-07-29T21:56:43.789526Z",
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tests/integration/recordings/responses/17253d7cc667.json Normal file
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tests/integration/recordings/responses/1b92be674e2a.json Normal file
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{
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tests/integration/recordings/responses/1f48f4b2ae33.json Normal file
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{
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39
tests/integration/recordings/responses/211b1562d4e6.json Normal file
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258
tests/integration/recordings/responses/2afe3b38ca01.json Normal file
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{
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39
tests/integration/recordings/responses/44fb9cf5875f.json Normal file
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185
tests/integration/recordings/responses/4597743bcd2a.json Normal file
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421
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86
tests/integration/recordings/responses/48d2fb183a2a.json Normal file
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{
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132
tests/integration/recordings/responses/4a3a4447b16b.json Normal file
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tests/integration/recordings/responses/50340cd4d253.json Normal file
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{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama-guard3:1b",
"raw": true,
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{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
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366
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383
tests/integration/recordings/responses/6cc063bbd7d3.json Normal file
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258
tests/integration/recordings/responses/6d35c91287e2.json Normal file
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@ -0,0 +1,258 @@
{
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366
tests/integration/recordings/responses/6fbea1abca7c.json Normal file
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39
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{
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}

39
tests/integration/recordings/responses/7354ec181984.json Normal file
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@ -0,0 +1,39 @@
{
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64
tests/integration/recordings/responses/75d0dd9d0fa3.json Normal file
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{
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421
tests/integration/recordings/responses/76b89a84cd6f.json Normal file
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{
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366
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257
tests/integration/recordings/responses/7e6806cba34a.json Normal file
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39
tests/integration/recordings/responses/840fbb380b73.json Normal file
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366
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{
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347
tests/integration/recordings/responses/9c28ec9ac338.json Normal file
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@ -0,0 +1,347 @@
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39
tests/integration/recordings/responses/a59d0d7c1485.json Normal file
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@ -0,0 +1,39 @@
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799
tests/integration/recordings/responses/a6810c23eda8.json Normal file
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@ -0,0 +1,799 @@
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421
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39
tests/integration/recordings/responses/ae1c22f18ecc.json Normal file
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39
tests/integration/recordings/responses/ae6835cfe70e.json Normal file
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tests/integration/recordings/responses/afb33182f365.json Normal file
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39
tests/integration/recordings/responses/b14ff438ca99.json Normal file
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}
}

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{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"raw": true,
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"stream": true
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"endpoint": "/api/generate",
"model": "llama3.2:3b-instruct-fp16"
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"response": {
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}

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{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama-guard3:1b",
"raw": true,
"prompt": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nTask: Check if there is unsafe content in 'Assistant' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: What is the boiling point of the liquid polyjuice in celsius?\n\nAssistant: \n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST Assistant message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
"options": {
"temperature": 0.0
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"stream": false
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"endpoint": "/api/generate",
"model": "llama-guard3:1b"
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"response": {
"body": {
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"done": true,
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}
},
"is_streaming": false
}
}

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tests/integration/recordings/responses/d0ac68cbde69.json Normal file
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@ -0,0 +1,19 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/ps",
"headers": {},
"body": {},
"endpoint": "/api/ps",
"model": ""
},
"response": {
"body": {
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"__data__": {
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"is_streaming": false
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}

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tests/integration/recordings/responses/d4c86ac355fb.json Normal file
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{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama-guard3:1b",
"raw": true,
"prompt": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nTask: Check if there is unsafe content in 'User' messages in conversations according our safety policy with the below categories.\n\n<BEGIN UNSAFE CONTENT CATEGORIES>\nS1: Violent Crimes.\nS2: Non-Violent Crimes.\nS3: Sex Crimes.\nS4: Child Exploitation.\nS5: Defamation.\nS6: Specialized Advice.\nS7: Privacy.\nS8: Intellectual Property.\nS9: Indiscriminate Weapons.\nS10: Hate.\nS11: Self-Harm.\nS12: Sexual Content.\nS13: Elections.\nS14: Code Interpreter Abuse.\n<END UNSAFE CONTENT CATEGORIES>\n\n<BEGIN CONVERSATION>\n\nUser: Quels sont les principaux bienfaits de l'alimentation m\u00e9diterran\u00e9enne?\n\n<END CONVERSATION>\n\nProvide your safety assessment for ONLY THE LAST User message in the above conversation:\n - First line must read 'safe' or 'unsafe'.\n - If unsafe, a second line must include a comma-separated list of violated categories.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
"options": {
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"stream": false
},
"endpoint": "/api/generate",
"model": "llama-guard3:1b"
},
"response": {
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"done": true,
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}
},
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}
}

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{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"raw": true,
"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the capital of France?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
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"stream": false
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"model": "llama3.2:3b-instruct-fp16"
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}
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}

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tests/integration/recordings/responses/dd226d71f844.json Normal file
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@ -0,0 +1,258 @@
{
"request": {
"method": "POST",
"url": "http://localhost:11434/api/generate",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"raw": true,
"prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n {\n \"name\": \"get_boiling_point\",\n \"description\": \"Returns the boiling point of a liquid in Celcius or Fahrenheit.\",\n \"parameters\": {\n \"type\": \"dict\",\n \"required\": [\"liquid_name\"],\n \"properties\": {\n \"liquid_name\": {\n \"type\": \"str\",\n \"description\": \"The name of the liquid\"\n },\n \"celcius\": {\n \"type\": \"bool\",\n \"description\": \"Whether to return the boiling point in Celcius\",\n \"default\": \"True\"\n }\n }\n }\n }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nCall get_boiling_point tool and answer What is the boiling point of polyjuice?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[get_boiling_point(liquid_name=\"polyjuice\", celcius=True)]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\n-100<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
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59
tests/integration/recordings/responses/dd9e7d5913e9.json Normal file
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@ -0,0 +1,59 @@
{
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421
tests/integration/recordings/responses/e08848bfcd28.json Normal file
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{
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tests/integration/recordings/responses/e29300494763.json Normal file
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{
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"headers": {},
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39
tests/integration/recordings/responses/e96152610712.json Normal file
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{
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"headers": {},
"body": {
"model": "llama-guard3:1b",
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39
tests/integration/recordings/responses/ed9e9b34008d.json Normal file
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{
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366
tests/integration/recordings/responses/eee47930e3ae.json Normal file
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421
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}
}

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