mirror of
https://github.com/meta-llama/llama-stack.git
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Merge remote-tracking branch 'origin/main' into dependabot/uv/openai-2.5.0
This commit is contained in:
Ashwin Bharambe
commit
13450c1a68
317 changed files with 86802 additions and 18957 deletions
18
tests/external/run-byoa.yaml
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18
tests/external/run-byoa.yaml
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|
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|
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|
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|
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|
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|
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|
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|
||||
}
|
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],
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|
||||
"object": "chat.completion.chunk",
|
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|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
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}
|
||||
},
|
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{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
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"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": " liquid",
|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
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{
|
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
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"__data__": {
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|
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"delta": {
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|
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|
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|
||||
}
|
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],
|
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"created": 0,
|
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|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
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"id": "rec-b4a47451a2af",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "ju",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "ice",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
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"choices": [
|
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{
|
||||
"delta": {
|
||||
"content": " is",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
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"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
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"index": 0,
|
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"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
||||
"choices": [
|
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{
|
||||
"delta": {
|
||||
"content": " -",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "100",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "\u00b0C",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": ".",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-b4a47451a2af",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": "",
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": null
|
||||
},
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama3.2:3b-instruct-fp16",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": null
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -66,7 +66,7 @@
|
|||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_qryqpevz",
|
||||
"id": "call_9x4z21g1",
|
||||
"function": {
|
||||
"arguments": "{\"celcius\":\"true\",\"liquid_name\":\"polyjuice\"}",
|
||||
"name": "get_boiling_point"
|
||||
|
|
@ -116,5 +116,6 @@
|
|||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -54,5 +54,6 @@
|
|||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -54,5 +54,6 @@
|
|||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -54,5 +54,6 @@
|
|||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,59 @@
|
|||
{
|
||||
"test_id": "tests/integration/agents/test_agents.py::test_custom_tool[ollama/llama3.2:3b-instruct-fp16]",
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://0.0.0.0:11434/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "llama-guard3:1b",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Task: 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\nTool: -100\n\nAssistant: The boiling point of liquid polyjuice is -100\u00b0C.\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."
|
||||
}
|
||||
],
|
||||
"stream": false,
|
||||
"temperature": 0.0
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "llama-guard3:1b"
|
||||
},
|
||||
"response": {
|
||||
"body": {
|
||||
"__type__": "openai.types.chat.chat_completion.ChatCompletion",
|
||||
"__data__": {
|
||||
"id": "rec-da6fc54bb65d",
|
||||
"choices": [
|
||||
{
|
||||
"finish_reason": "stop",
|
||||
"index": 0,
|
||||
"logprobs": null,
|
||||
"message": {
|
||||
"content": "safe",
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"annotations": null,
|
||||
"audio": null,
|
||||
"function_call": null,
|
||||
"tool_calls": null
|
||||
}
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "llama-guard3:1b",
|
||||
"object": "chat.completion",
|
||||
"service_tier": null,
|
||||
"system_fingerprint": "fp_ollama",
|
||||
"usage": {
|
||||
"completion_tokens": 2,
|
||||
"prompt_tokens": 421,
|
||||
"total_tokens": 423,
|
||||
"completion_tokens_details": null,
|
||||
"prompt_tokens_details": null
|
||||
}
|
||||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -71,7 +71,7 @@
|
|||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_ur5tbdbt",
|
||||
"id": "call_5qverjg6",
|
||||
"function": {
|
||||
"arguments": "{\"celcius\":true,\"liquid_name\":\"polyjuice\"}",
|
||||
"name": "get_boiling_point"
|
||||
|
|
@ -121,5 +121,6 @@
|
|||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -54,5 +54,6 @@
|
|||
}
|
||||
},
|
||||
"is_streaming": false
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -66,7 +66,7 @@
|
|||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_rq1pcgq7",
|
||||
"id": "call_z1rt0qb1",
|
||||
"function": {
|
||||
"arguments": "{\"celcius\":true,\"liquid_name\":\"polyjuice\"}",
|
||||
"name": "get_boiling_point"
|
||||
|
|
@ -116,5 +116,6 @@
|
|||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -466,3 +466,53 @@ def test_guardrails_with_tools(compat_client, text_model_id):
|
|||
# Response should be either a function call or a message
|
||||
output_type = response.output[0].type
|
||||
assert output_type in ["function_call", "message"]
|
||||
|
||||
|
||||
def test_response_with_instructions(openai_client, client_with_models, text_model_id):
|
||||
"""Test instructions parameter in the responses object."""
|
||||
if isinstance(client_with_models, LlamaStackAsLibraryClient):
|
||||
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
|
||||
|
||||
client = openai_client
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the capital of France?",
|
||||
}
|
||||
]
|
||||
|
||||
# First create a response without instructions parameter
|
||||
response_w_o_instructions = client.responses.create(
|
||||
model=text_model_id,
|
||||
input=messages,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# Verify we have None in the instructions field
|
||||
assert response_w_o_instructions.instructions is None
|
||||
|
||||
# Next create a response and pass instructions parameter
|
||||
instructions = "You are a helpful assistant."
|
||||
response_with_instructions = client.responses.create(
|
||||
model=text_model_id,
|
||||
instructions=instructions,
|
||||
input=messages,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# Verify we have a valid instructions field
|
||||
assert response_with_instructions.instructions == instructions
|
||||
|
||||
# Finally test instructions parameter with a previous response id
|
||||
instructions2 = "You are a helpful assistant and speak in pirate language."
|
||||
response_with_instructions2 = client.responses.create(
|
||||
model=text_model_id,
|
||||
instructions=instructions2,
|
||||
input=messages,
|
||||
previous_response_id=response_with_instructions.id,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# Verify instructions from previous response was not carried over to the next response
|
||||
assert response_with_instructions2.instructions == instructions2
|
||||
|
|
|
|||
|
|
@ -0,0 +1,44 @@
|
|||
{
|
||||
"test_id": null,
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "http://0.0.0.0:11434/v1/v1/models",
|
||||
"headers": {},
|
||||
"body": {},
|
||||
"endpoint": "/v1/models",
|
||||
"model": ""
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "llama-guard3:1b",
|
||||
"created": 1753937098,
|
||||
"object": "model",
|
||||
"owned_by": "library"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "all-minilm:l6-v2",
|
||||
"created": 1753936935,
|
||||
"object": "model",
|
||||
"owned_by": "library"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.model.Model",
|
||||
"__data__": {
|
||||
"id": "llama3.2:3b-instruct-fp16",
|
||||
"created": 1753936925,
|
||||
"object": "model",
|
||||
"owned_by": "library"
|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": false
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -5,6 +5,7 @@
|
|||
# the root directory of this source tree.
|
||||
import inspect
|
||||
import itertools
|
||||
import logging # allow-direct-logging
|
||||
import os
|
||||
import tempfile
|
||||
import textwrap
|
||||
|
|
@ -37,6 +38,9 @@ def pytest_sessionstart(session):
|
|||
if "LLAMA_STACK_TEST_INFERENCE_MODE" not in os.environ:
|
||||
os.environ["LLAMA_STACK_TEST_INFERENCE_MODE"] = "replay"
|
||||
|
||||
if "LLAMA_STACK_LOGGING" not in os.environ:
|
||||
os.environ["LLAMA_STACK_LOGGING"] = "all=warning"
|
||||
|
||||
if "SQLITE_STORE_DIR" not in os.environ:
|
||||
os.environ["SQLITE_STORE_DIR"] = tempfile.mkdtemp()
|
||||
|
||||
|
|
@ -54,6 +58,12 @@ def pytest_sessionstart(session):
|
|||
patch_httpx_for_test_id()
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def suppress_httpx_logs(caplog):
|
||||
"""Suppress httpx INFO logs for all integration tests"""
|
||||
caplog.set_level(logging.WARNING, logger="httpx")
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def _track_test_context(request):
|
||||
"""Automatically track current test context for isolated recordings.
|
||||
|
|
@ -317,3 +327,72 @@ def pytest_ignore_collect(path: str, config: pytest.Config) -> bool:
|
|||
if p.is_relative_to(rp):
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def get_vector_io_provider_ids(client):
|
||||
"""Get all available vector_io provider IDs."""
|
||||
providers = [p for p in client.providers.list() if p.api == "vector_io"]
|
||||
return [p.provider_id for p in providers]
|
||||
|
||||
|
||||
def vector_provider_wrapper(func):
|
||||
"""Decorator to run a test against all available vector_io providers."""
|
||||
import functools
|
||||
import os
|
||||
|
||||
@functools.wraps(func)
|
||||
def wrapper(*args, **kwargs):
|
||||
# Get the vector_io_provider_id from the test arguments
|
||||
import inspect
|
||||
|
||||
sig = inspect.signature(func)
|
||||
bound_args = sig.bind(*args, **kwargs)
|
||||
bound_args.apply_defaults()
|
||||
|
||||
vector_io_provider_id = bound_args.arguments.get("vector_io_provider_id")
|
||||
if not vector_io_provider_id:
|
||||
pytest.skip("No vector_io_provider_id provided")
|
||||
|
||||
# Get client_with_models to check available providers
|
||||
client_with_models = bound_args.arguments.get("client_with_models")
|
||||
if client_with_models:
|
||||
available_providers = get_vector_io_provider_ids(client_with_models)
|
||||
if vector_io_provider_id not in available_providers:
|
||||
pytest.skip(f"Provider '{vector_io_provider_id}' not available. Available: {available_providers}")
|
||||
|
||||
return func(*args, **kwargs)
|
||||
|
||||
# For replay tests, only use providers that are available in ci-tests environment
|
||||
if os.environ.get("LLAMA_STACK_TEST_INFERENCE_MODE") == "replay":
|
||||
all_providers = ["faiss", "sqlite-vec"]
|
||||
else:
|
||||
# For live tests, try all providers (they'll skip if not available)
|
||||
all_providers = [
|
||||
"faiss",
|
||||
"sqlite-vec",
|
||||
"milvus",
|
||||
"chromadb",
|
||||
"pgvector",
|
||||
"weaviate",
|
||||
"qdrant",
|
||||
]
|
||||
|
||||
return pytest.mark.parametrize("vector_io_provider_id", all_providers)(wrapper)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def vector_io_provider_id(request, client_with_models):
|
||||
"""Fixture that provides a specific vector_io provider ID, skipping if not available."""
|
||||
if hasattr(request, "param"):
|
||||
requested_provider = request.param
|
||||
available_providers = get_vector_io_provider_ids(client_with_models)
|
||||
|
||||
if requested_provider not in available_providers:
|
||||
pytest.skip(f"Provider '{requested_provider}' not available. Available: {available_providers}")
|
||||
|
||||
return requested_provider
|
||||
else:
|
||||
provider_ids = get_vector_io_provider_ids(client_with_models)
|
||||
if not provider_ids:
|
||||
pytest.skip("No vector_io providers available")
|
||||
return provider_ids[0]
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ from llama_stack_client import LlamaStackClient
|
|||
from openai import OpenAI
|
||||
|
||||
from llama_stack import LlamaStackAsLibraryClient
|
||||
from llama_stack.core.datatypes import VectorStoresConfig
|
||||
from llama_stack.core.stack import run_config_from_adhoc_config_spec
|
||||
from llama_stack.env import get_env_or_fail
|
||||
|
||||
|
|
@ -39,7 +40,7 @@ def is_port_available(port: int, host: str = "localhost") -> bool:
|
|||
|
||||
def start_llama_stack_server(config_name: str) -> subprocess.Popen:
|
||||
"""Start a llama stack server with the given config."""
|
||||
cmd = f"uv run --with llama-stack llama stack build --distro {config_name} --image-type venv --run"
|
||||
cmd = f"uv run llama stack run {config_name}"
|
||||
devnull = open(os.devnull, "w")
|
||||
process = subprocess.Popen(
|
||||
shlex.split(cmd),
|
||||
|
|
@ -236,9 +237,16 @@ def instantiate_llama_stack_client(session):
|
|||
|
||||
if "=" in config:
|
||||
run_config = run_config_from_adhoc_config_spec(config)
|
||||
|
||||
# --stack-config bypasses template so need this to set default embedding model
|
||||
if "vector_io" in config and "inference" in config:
|
||||
run_config.vector_stores = VectorStoresConfig(
|
||||
embedding_model_id="inline::sentence-transformers/nomic-ai/nomic-embed-text-v1.5"
|
||||
)
|
||||
|
||||
run_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".yaml")
|
||||
with open(run_config_file.name, "w") as f:
|
||||
yaml.dump(run_config.model_dump(), f)
|
||||
yaml.dump(run_config.model_dump(mode="json"), f)
|
||||
config = run_config_file.name
|
||||
|
||||
client = LlamaStackAsLibraryClient(
|
||||
|
|
|
|||
|
|
@ -12,9 +12,15 @@ import pytest
|
|||
|
||||
from llama_stack.core.access_control.access_control import default_policy
|
||||
from llama_stack.core.datatypes import User
|
||||
from llama_stack.core.storage.datatypes import SqlStoreReference
|
||||
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
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import (
|
||||
PostgresSqlStoreConfig,
|
||||
SqliteSqlStoreConfig,
|
||||
register_sqlstore_backends,
|
||||
sqlstore_impl,
|
||||
)
|
||||
|
||||
|
||||
def get_postgres_config():
|
||||
|
|
@ -55,8 +61,9 @@ def authorized_store(backend_config):
|
|||
config_func = backend_config
|
||||
|
||||
config = config_func()
|
||||
|
||||
base_sqlstore = sqlstore_impl(config)
|
||||
backend_name = f"sql_{type(config).__name__.lower()}"
|
||||
register_sqlstore_backends({backend_name: config})
|
||||
base_sqlstore = sqlstore_impl(SqlStoreReference(backend=backend_name, table_name="authorized_store"))
|
||||
authorized_store = AuthorizedSqlStore(base_sqlstore, default_policy())
|
||||
|
||||
yield authorized_store
|
||||
|
|
|
|||
|
|
@ -0,0 +1,660 @@
|
|||
{
|
||||
"test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[openai_client-txt=openai/gpt-4o:emb=sentence-transformers/nomic-ai/nomic-embed-text-v1.5:dim=768-llama_experts_pdf]",
|
||||
"request": {
|
||||
"method": "POST",
|
||||
"url": "https://api.openai.com/v1/v1/chat/completions",
|
||||
"headers": {},
|
||||
"body": {
|
||||
"model": "gpt-4o",
|
||||
"messages": [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "How many experts does the Llama 4 Maverick model have?"
|
||||
}
|
||||
],
|
||||
"stream": true,
|
||||
"stream_options": {
|
||||
"include_usage": true
|
||||
},
|
||||
"tools": [
|
||||
{
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "knowledge_search",
|
||||
"description": "Search for information in a database.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": {
|
||||
"type": "string",
|
||||
"description": "The query to search for. Can be a natural language sentence or keywords."
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"query"
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
]
|
||||
},
|
||||
"endpoint": "/v1/chat/completions",
|
||||
"model": "gpt-4o"
|
||||
},
|
||||
"response": {
|
||||
"body": [
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-00913934356f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": "assistant",
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": "call_M8gyYiB39MwYdJKc4aHIGbfA",
|
||||
"function": {
|
||||
"arguments": "",
|
||||
"name": "knowledge_search"
|
||||
},
|
||||
"type": "function"
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "8anw"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-00913934356f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "{\"",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "99M"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-00913934356f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "query",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "I"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-00913934356f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "\":\"",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "9"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-00913934356f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "L",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "ptMnH"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-00913934356f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": "lama",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
|
||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "Ue"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
|
||||
"id": "rec-00913934356f",
|
||||
"choices": [
|
||||
{
|
||||
"delta": {
|
||||
"content": null,
|
||||
"function_call": null,
|
||||
"refusal": null,
|
||||
"role": null,
|
||||
"tool_calls": [
|
||||
{
|
||||
"index": 0,
|
||||
"id": null,
|
||||
"function": {
|
||||
"arguments": " ",
|
||||
"name": null
|
||||
},
|
||||
"type": null
|
||||
}
|
||||
]
|
||||
},
|
||||
"finish_reason": null,
|
||||
"index": 0,
|
||||
"logprobs": null
|
||||
}
|
||||
],
|
||||
"created": 0,
|
||||
"model": "gpt-4o-2024-08-06",
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6723
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2098
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2098
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Load diff
1901
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1901
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1091
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Normal file
1091
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generated
Normal file
File diff suppressed because it is too large
Load diff
1834
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Normal file
1834
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Normal file
File diff suppressed because it is too large
Load diff
|
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|
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|
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"logprobs": null
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"service_tier": "default",
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"system_fingerprint": "fp_f64f290af2",
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"usage": null,
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"obfuscation": ""
|
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},
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"refusal": null,
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"role": null,
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"tool_calls": [
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"finish_reason": null,
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"logprobs": null
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],
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"model": "gpt-4o-2024-08-06",
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"service_tier": "default",
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"system_fingerprint": "fp_f64f290af2",
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"usage": null,
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"created": 0,
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"service_tier": "default",
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"system_fingerprint": "fp_f64f290af2",
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"usage": null,
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"obfuscation": "tzlHgv0gPZ2Y7be"
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}
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},
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "rec-f80d49a32ea5",
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"model": "gpt-4o-2024-08-06",
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"object": "chat.completion.chunk",
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"service_tier": "default",
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"system_fingerprint": "fp_f64f290af2",
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}
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},
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"system_fingerprint": "fp_f64f290af2",
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{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "rec-f80d49a32ea5",
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|
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|
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{
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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],
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"created": 0,
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||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "CRw"
|
||||
}
|
||||
},
|
||||
{
|
||||
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
|
||||
"__data__": {
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"id": "rec-f80d49a32ea5",
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"choices": [
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{
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|
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|
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|
||||
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|
||||
"finish_reason": "tool_calls",
|
||||
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|
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|
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|
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|
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||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": null,
|
||||
"obfuscation": "jPOO"
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|
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{
|
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"model": "gpt-4o-2024-08-06",
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||||
"object": "chat.completion.chunk",
|
||||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f64f290af2",
|
||||
"usage": {
|
||||
"completion_tokens": 24,
|
||||
"prompt_tokens": 74,
|
||||
"total_tokens": 98,
|
||||
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|
||||
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|
||||
"audio_tokens": 0,
|
||||
"reasoning_tokens": 0,
|
||||
"rejected_prediction_tokens": 0
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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"obfuscation": "I4x1yzasaOQSFKk"
|
||||
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|
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||||
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|
||||
"is_streaming": true
|
||||
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|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
2239
tests/integration/responses/recordings/f970cb793e3a68879444e3b218cfbe37829f7ed7ca4706ff0cbb6113f9a3253b.json
generated
Normal file
2239
tests/integration/responses/recordings/f970cb793e3a68879444e3b218cfbe37829f7ed7ca4706ff0cbb6113f9a3253b.json
generated
Normal file
File diff suppressed because it is too large
Load diff
71
tests/integration/test_persistence_integration.py
Normal file
71
tests/integration/test_persistence_integration.py
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
# 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.
|
||||
|
||||
import yaml
|
||||
|
||||
from llama_stack.core.datatypes import StackRunConfig
|
||||
from llama_stack.core.storage.datatypes import (
|
||||
PostgresKVStoreConfig,
|
||||
PostgresSqlStoreConfig,
|
||||
SqliteKVStoreConfig,
|
||||
SqliteSqlStoreConfig,
|
||||
)
|
||||
|
||||
|
||||
def test_starter_distribution_config_loads_and_resolves():
|
||||
"""Integration: Actual starter config should parse and have correct storage structure."""
|
||||
with open("llama_stack/distributions/starter/run.yaml") as f:
|
||||
config_dict = yaml.safe_load(f)
|
||||
|
||||
config = StackRunConfig(**config_dict)
|
||||
|
||||
# Config should have named backends and explicit store references
|
||||
assert config.storage is not None
|
||||
assert "kv_default" in config.storage.backends
|
||||
assert "sql_default" in config.storage.backends
|
||||
assert isinstance(config.storage.backends["kv_default"], SqliteKVStoreConfig)
|
||||
assert isinstance(config.storage.backends["sql_default"], SqliteSqlStoreConfig)
|
||||
|
||||
stores = config.storage.stores
|
||||
assert stores.metadata is not None
|
||||
assert stores.metadata.backend == "kv_default"
|
||||
assert stores.metadata.namespace == "registry"
|
||||
|
||||
assert stores.inference is not None
|
||||
assert stores.inference.backend == "sql_default"
|
||||
assert stores.inference.table_name == "inference_store"
|
||||
assert stores.inference.max_write_queue_size > 0
|
||||
assert stores.inference.num_writers > 0
|
||||
|
||||
assert stores.conversations is not None
|
||||
assert stores.conversations.backend == "sql_default"
|
||||
assert stores.conversations.table_name == "openai_conversations"
|
||||
|
||||
|
||||
def test_postgres_demo_distribution_config_loads():
|
||||
"""Integration: Postgres demo should use Postgres backend for all stores."""
|
||||
with open("llama_stack/distributions/postgres-demo/run.yaml") as f:
|
||||
config_dict = yaml.safe_load(f)
|
||||
|
||||
config = StackRunConfig(**config_dict)
|
||||
|
||||
# Should have postgres backend
|
||||
assert config.storage is not None
|
||||
assert "kv_default" in config.storage.backends
|
||||
assert "sql_default" in config.storage.backends
|
||||
postgres_backend = config.storage.backends["sql_default"]
|
||||
assert isinstance(postgres_backend, PostgresSqlStoreConfig)
|
||||
assert postgres_backend.host == "${env.POSTGRES_HOST:=localhost}"
|
||||
|
||||
kv_backend = config.storage.backends["kv_default"]
|
||||
assert isinstance(kv_backend, PostgresKVStoreConfig)
|
||||
|
||||
stores = config.storage.stores
|
||||
# Stores target the Postgres backends explicitly
|
||||
assert stores.metadata is not None
|
||||
assert stores.metadata.backend == "kv_default"
|
||||
assert stores.inference is not None
|
||||
assert stores.inference.backend == "sql_default"
|
||||
|
|
@ -8,14 +8,15 @@ import time
|
|||
from io import BytesIO
|
||||
|
||||
import pytest
|
||||
from llama_stack_client import BadRequestError, NotFoundError
|
||||
from llama_stack_client import BadRequestError
|
||||
from openai import BadRequestError as OpenAIBadRequestError
|
||||
from openai import NotFoundError as OpenAINotFoundError
|
||||
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
from llama_stack.core.library_client import LlamaStackAsLibraryClient
|
||||
from llama_stack.log import get_logger
|
||||
|
||||
from ..conftest import vector_provider_wrapper
|
||||
|
||||
logger = get_logger(name=__name__, category="vector_io")
|
||||
|
||||
|
||||
|
|
@ -133,8 +134,9 @@ def compat_client_with_empty_stores(compat_client):
|
|||
clear_files()
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_create_vector_store(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test creating a vector store using OpenAI API."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -146,6 +148,7 @@ def test_openai_create_vector_store(
|
|||
metadata={"purpose": "testing", "environment": "integration"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -159,14 +162,18 @@ def test_openai_create_vector_store(
|
|||
assert hasattr(vector_store, "created_at")
|
||||
|
||||
|
||||
def test_openai_create_vector_store_default(compat_client_with_empty_stores, client_with_models):
|
||||
@vector_provider_wrapper
|
||||
def test_openai_create_vector_store_default(compat_client_with_empty_stores, client_with_models, vector_io_provider_id):
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
vector_store = compat_client_with_empty_stores.vector_stores.create()
|
||||
vector_store = compat_client_with_empty_stores.vector_stores.create(
|
||||
extra_body={"provider_id": vector_io_provider_id}
|
||||
)
|
||||
assert vector_store.id
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_list_vector_stores(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test listing vector stores using OpenAI API."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -179,6 +186,7 @@ def test_openai_list_vector_stores(
|
|||
metadata={"type": "test"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
store2 = client.vector_stores.create(
|
||||
|
|
@ -186,6 +194,7 @@ def test_openai_list_vector_stores(
|
|||
metadata={"type": "test"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -206,8 +215,9 @@ def test_openai_list_vector_stores(
|
|||
assert len(limited_response.data) == 1
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_retrieve_vector_store(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test retrieving a specific vector store using OpenAI API."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -220,6 +230,7 @@ def test_openai_retrieve_vector_store(
|
|||
metadata={"purpose": "retrieval_test"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -233,8 +244,9 @@ def test_openai_retrieve_vector_store(
|
|||
assert retrieved_store.object == "vector_store"
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_update_vector_store(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test modifying a vector store using OpenAI API."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -247,6 +259,7 @@ def test_openai_update_vector_store(
|
|||
metadata={"version": "1.0"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
time.sleep(1)
|
||||
|
|
@ -264,8 +277,9 @@ def test_openai_update_vector_store(
|
|||
assert modified_store.last_active_at > created_store.last_active_at
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_delete_vector_store(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test deleting a vector store using OpenAI API."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -278,6 +292,7 @@ def test_openai_delete_vector_store(
|
|||
metadata={"purpose": "deletion_test"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -294,8 +309,9 @@ def test_openai_delete_vector_store(
|
|||
client.vector_stores.retrieve(vector_store_id=created_store.id)
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_search_empty(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test searching an empty vector store using OpenAI API."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -308,6 +324,7 @@ def test_openai_vector_store_search_empty(
|
|||
metadata={"purpose": "search_testing"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -323,8 +340,14 @@ def test_openai_vector_store_search_empty(
|
|||
assert search_response.has_more is False
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_with_chunks(
|
||||
compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores,
|
||||
client_with_models,
|
||||
sample_chunks,
|
||||
embedding_model_id,
|
||||
embedding_dimension,
|
||||
vector_io_provider_id,
|
||||
):
|
||||
"""Test vector store functionality with actual chunks using both OpenAI and native APIs."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -338,6 +361,7 @@ def test_openai_vector_store_with_chunks(
|
|||
metadata={"purpose": "chunks_testing"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -380,6 +404,7 @@ def test_openai_vector_store_with_chunks(
|
|||
("What inspires neural networks?", "doc4", "ai"),
|
||||
],
|
||||
)
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_search_relevance(
|
||||
compat_client_with_empty_stores,
|
||||
client_with_models,
|
||||
|
|
@ -387,6 +412,7 @@ def test_openai_vector_store_search_relevance(
|
|||
test_case,
|
||||
embedding_model_id,
|
||||
embedding_dimension,
|
||||
vector_io_provider_id,
|
||||
):
|
||||
"""Test that OpenAI vector store search returns relevant results for different queries."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -402,6 +428,7 @@ def test_openai_vector_store_search_relevance(
|
|||
metadata={"purpose": "relevance_testing"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -430,8 +457,14 @@ def test_openai_vector_store_search_relevance(
|
|||
assert top_result.score > 0
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_search_with_ranking_options(
|
||||
compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores,
|
||||
client_with_models,
|
||||
sample_chunks,
|
||||
embedding_model_id,
|
||||
embedding_dimension,
|
||||
vector_io_provider_id,
|
||||
):
|
||||
"""Test OpenAI vector store search with ranking options."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -445,6 +478,7 @@ def test_openai_vector_store_search_with_ranking_options(
|
|||
metadata={"purpose": "ranking_testing"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -483,8 +517,14 @@ def test_openai_vector_store_search_with_ranking_options(
|
|||
assert result.score >= threshold
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_search_with_high_score_filter(
|
||||
compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores,
|
||||
client_with_models,
|
||||
sample_chunks,
|
||||
embedding_model_id,
|
||||
embedding_dimension,
|
||||
vector_io_provider_id,
|
||||
):
|
||||
"""Test that searching with text very similar to a document and high score threshold returns only that document."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -498,6 +538,7 @@ def test_openai_vector_store_search_with_high_score_filter(
|
|||
metadata={"purpose": "high_score_filtering"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -542,8 +583,14 @@ def test_openai_vector_store_search_with_high_score_filter(
|
|||
assert "python" in top_content.lower() or "programming" in top_content.lower()
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_search_with_max_num_results(
|
||||
compat_client_with_empty_stores, client_with_models, sample_chunks, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores,
|
||||
client_with_models,
|
||||
sample_chunks,
|
||||
embedding_model_id,
|
||||
embedding_dimension,
|
||||
vector_io_provider_id,
|
||||
):
|
||||
"""Test OpenAI vector store search with max_num_results."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -557,6 +604,7 @@ def test_openai_vector_store_search_with_max_num_results(
|
|||
metadata={"purpose": "max_num_results_testing"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -577,8 +625,9 @@ def test_openai_vector_store_search_with_max_num_results(
|
|||
assert len(search_response.data) == 2
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_attach_file(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store attach file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -591,6 +640,7 @@ def test_openai_vector_store_attach_file(
|
|||
name="test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -637,8 +687,9 @@ def test_openai_vector_store_attach_file(
|
|||
assert "foobazbar" in top_content.lower()
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_attach_files_on_creation(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store attach files on creation."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -668,6 +719,7 @@ def test_openai_vector_store_attach_files_on_creation(
|
|||
file_ids=file_ids,
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -700,8 +752,9 @@ def test_openai_vector_store_attach_files_on_creation(
|
|||
assert updated_vector_store.file_counts.failed == 0
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_list_files(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store list files."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -714,6 +767,7 @@ def test_openai_vector_store_list_files(
|
|||
name="test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -773,8 +827,9 @@ def test_openai_vector_store_list_files(
|
|||
assert updated_vector_store.file_counts.in_progress == 0
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_list_files_invalid_vector_store(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store list files with invalid vector store ID."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -783,14 +838,15 @@ def test_openai_vector_store_list_files_invalid_vector_store(
|
|||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
errors = ValueError
|
||||
else:
|
||||
errors = (NotFoundError, OpenAINotFoundError)
|
||||
errors = (BadRequestError, OpenAIBadRequestError)
|
||||
|
||||
with pytest.raises(errors):
|
||||
compat_client.vector_stores.files.list(vector_store_id="abc123")
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_retrieve_file_contents(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store retrieve file contents."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -803,6 +859,7 @@ def test_openai_vector_store_retrieve_file_contents(
|
|||
name="test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -848,8 +905,9 @@ def test_openai_vector_store_retrieve_file_contents(
|
|||
assert file_contents.attributes == attributes
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_delete_file(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store delete file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -862,6 +920,7 @@ def test_openai_vector_store_delete_file(
|
|||
name="test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -912,8 +971,9 @@ def test_openai_vector_store_delete_file(
|
|||
assert updated_vector_store.file_counts.in_progress == 0
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_delete_file_removes_from_vector_store(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store delete file removes from vector store."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -926,6 +986,7 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(
|
|||
name="test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -962,8 +1023,9 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(
|
|||
assert not search_response.data
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_update_file(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test OpenAI vector store update file."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -976,6 +1038,7 @@ def test_openai_vector_store_update_file(
|
|||
name="test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1017,8 +1080,9 @@ def test_openai_vector_store_update_file(
|
|||
assert retrieved_file.attributes["foo"] == "baz"
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_create_vector_store_files_duplicate_vector_store_name(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""
|
||||
This test confirms that client.vector_stores.create() creates a unique ID
|
||||
|
|
@ -1044,6 +1108,7 @@ def test_create_vector_store_files_duplicate_vector_store_name(
|
|||
name="test_store_with_files",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
assert vector_store.file_counts.completed == 0
|
||||
|
|
@ -1056,6 +1121,7 @@ def test_create_vector_store_files_duplicate_vector_store_name(
|
|||
name="test_store_with_files",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1086,8 +1152,15 @@ def test_create_vector_store_files_duplicate_vector_store_name(
|
|||
|
||||
|
||||
@pytest.mark.parametrize("search_mode", ["vector", "keyword", "hybrid"])
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_search_modes(
|
||||
llama_stack_client, client_with_models, sample_chunks, search_mode, embedding_model_id, embedding_dimension
|
||||
llama_stack_client,
|
||||
client_with_models,
|
||||
sample_chunks,
|
||||
search_mode,
|
||||
embedding_model_id,
|
||||
embedding_dimension,
|
||||
vector_io_provider_id,
|
||||
):
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_models, search_mode)
|
||||
|
|
@ -1097,6 +1170,7 @@ def test_openai_vector_store_search_modes(
|
|||
metadata={"purpose": "search_mode_testing"},
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1115,8 +1189,9 @@ def test_openai_vector_store_search_modes(
|
|||
assert search_response is not None
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_file_batch_create_and_retrieve(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test creating and retrieving a vector store file batch."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -1128,6 +1203,7 @@ def test_openai_vector_store_file_batch_create_and_retrieve(
|
|||
name="batch_test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1178,8 +1254,9 @@ def test_openai_vector_store_file_batch_create_and_retrieve(
|
|||
assert retrieved_batch.status == "completed" # Should be completed after processing
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_file_batch_list_files(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test listing files in a vector store file batch."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -1191,6 +1268,7 @@ def test_openai_vector_store_file_batch_list_files(
|
|||
name="batch_list_test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1271,8 +1349,9 @@ def test_openai_vector_store_file_batch_list_files(
|
|||
assert first_page_ids.isdisjoint(second_page_ids)
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_file_batch_cancel(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test cancelling a vector store file batch."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -1284,6 +1363,7 @@ def test_openai_vector_store_file_batch_cancel(
|
|||
name="batch_cancel_test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1326,8 +1406,9 @@ def test_openai_vector_store_file_batch_cancel(
|
|||
assert final_batch.status in ["completed", "cancelled"]
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_file_batch_retrieve_contents(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test retrieving file contents after file batch processing."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -1339,6 +1420,7 @@ def test_openai_vector_store_file_batch_retrieve_contents(
|
|||
name="batch_contents_test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1399,8 +1481,9 @@ def test_openai_vector_store_file_batch_retrieve_contents(
|
|||
assert file_data[i][1].decode("utf-8") in content_text
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_file_batch_error_handling(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test error handling for file batch operations."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -1412,6 +1495,7 @@ def test_openai_vector_store_file_batch_error_handling(
|
|||
name="batch_error_test_store",
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -1443,11 +1527,11 @@ def test_openai_vector_store_file_batch_error_handling(
|
|||
batch_id="non_existent_batch_id",
|
||||
)
|
||||
|
||||
# Test operations on non-existent vector store (returns NotFoundError)
|
||||
# Test operations on non-existent vector store (returns BadRequestError)
|
||||
if isinstance(compat_client, LlamaStackAsLibraryClient):
|
||||
vector_store_errors = ValueError
|
||||
else:
|
||||
vector_store_errors = (NotFoundError, OpenAINotFoundError)
|
||||
vector_store_errors = (BadRequestError, OpenAIBadRequestError)
|
||||
|
||||
with pytest.raises(vector_store_errors): # Should raise an error for non-existent vector store
|
||||
compat_client.vector_stores.file_batches.create(
|
||||
|
|
@ -1456,8 +1540,9 @@ def test_openai_vector_store_file_batch_error_handling(
|
|||
)
|
||||
|
||||
|
||||
@vector_provider_wrapper
|
||||
def test_openai_vector_store_embedding_config_from_metadata(
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension
|
||||
compat_client_with_empty_stores, client_with_models, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
"""Test that embedding configuration works from metadata source."""
|
||||
skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
|
||||
|
|
@ -1471,6 +1556,9 @@ def test_openai_vector_store_embedding_config_from_metadata(
|
|||
"embedding_dimension": str(embedding_dimension),
|
||||
"test_source": "metadata",
|
||||
},
|
||||
extra_body={
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
assert vector_store_metadata is not None
|
||||
|
|
@ -1489,6 +1577,7 @@ def test_openai_vector_store_embedding_config_from_metadata(
|
|||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"embedding_dimension": int(embedding_dimension), # Ensure same type/value
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -8,6 +8,8 @@ import pytest
|
|||
|
||||
from llama_stack.apis.vector_io import Chunk
|
||||
|
||||
from ..conftest import vector_provider_wrapper
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
def sample_chunks():
|
||||
|
|
@ -46,45 +48,51 @@ def client_with_empty_registry(client_with_models):
|
|||
clear_registry()
|
||||
|
||||
|
||||
def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embedding_dimension):
|
||||
vector_db_name = "test_vector_db"
|
||||
@vector_provider_wrapper
|
||||
def test_vector_store_retrieve(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
vector_store_name = "test_vector_store"
|
||||
create_response = client_with_empty_registry.vector_stores.create(
|
||||
name=vector_db_name,
|
||||
name=vector_store_name,
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
actual_vector_db_id = create_response.id
|
||||
actual_vector_store_id = create_response.id
|
||||
|
||||
# Retrieve the vector store and validate its properties
|
||||
response = client_with_empty_registry.vector_stores.retrieve(vector_store_id=actual_vector_db_id)
|
||||
response = client_with_empty_registry.vector_stores.retrieve(vector_store_id=actual_vector_store_id)
|
||||
assert response is not None
|
||||
assert response.id == actual_vector_db_id
|
||||
assert response.name == vector_db_name
|
||||
assert response.id == actual_vector_store_id
|
||||
assert response.name == vector_store_name
|
||||
assert response.id.startswith("vs_")
|
||||
|
||||
|
||||
def test_vector_db_register(client_with_empty_registry, embedding_model_id, embedding_dimension):
|
||||
vector_db_name = "test_vector_db"
|
||||
@vector_provider_wrapper
|
||||
def test_vector_store_register(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
vector_store_name = "test_vector_store"
|
||||
response = client_with_empty_registry.vector_stores.create(
|
||||
name=vector_db_name,
|
||||
name=vector_store_name,
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
actual_vector_db_id = response.id
|
||||
assert actual_vector_db_id.startswith("vs_")
|
||||
assert actual_vector_db_id != vector_db_name
|
||||
actual_vector_store_id = response.id
|
||||
assert actual_vector_store_id.startswith("vs_")
|
||||
assert actual_vector_store_id != vector_store_name
|
||||
|
||||
vector_stores = client_with_empty_registry.vector_stores.list()
|
||||
assert len(vector_stores.data) == 1
|
||||
vector_store = vector_stores.data[0]
|
||||
assert vector_store.id == actual_vector_db_id
|
||||
assert vector_store.name == vector_db_name
|
||||
assert vector_store.id == actual_vector_store_id
|
||||
assert vector_store.name == vector_store_name
|
||||
|
||||
client_with_empty_registry.vector_stores.delete(vector_store_id=actual_vector_db_id)
|
||||
client_with_empty_registry.vector_stores.delete(vector_store_id=actual_vector_store_id)
|
||||
|
||||
vector_stores = client_with_empty_registry.vector_stores.list()
|
||||
assert len(vector_stores.data) == 0
|
||||
|
|
@ -100,24 +108,27 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe
|
|||
("How does machine learning improve over time?", "doc2"),
|
||||
],
|
||||
)
|
||||
def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case):
|
||||
vector_db_name = "test_vector_db"
|
||||
@vector_provider_wrapper
|
||||
def test_insert_chunks(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, sample_chunks, test_case, vector_io_provider_id
|
||||
):
|
||||
vector_store_name = "test_vector_store"
|
||||
create_response = client_with_empty_registry.vector_stores.create(
|
||||
name=vector_db_name,
|
||||
name=vector_store_name,
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
actual_vector_db_id = create_response.id
|
||||
actual_vector_store_id = create_response.id
|
||||
|
||||
client_with_empty_registry.vector_io.insert(
|
||||
vector_db_id=actual_vector_db_id,
|
||||
vector_db_id=actual_vector_store_id,
|
||||
chunks=sample_chunks,
|
||||
)
|
||||
|
||||
response = client_with_empty_registry.vector_io.query(
|
||||
vector_db_id=actual_vector_db_id,
|
||||
vector_db_id=actual_vector_store_id,
|
||||
query="What is the capital of France?",
|
||||
)
|
||||
assert response is not None
|
||||
|
|
@ -126,7 +137,7 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
|
|||
|
||||
query, expected_doc_id = test_case
|
||||
response = client_with_empty_registry.vector_io.query(
|
||||
vector_db_id=actual_vector_db_id,
|
||||
vector_db_id=actual_vector_store_id,
|
||||
query=query,
|
||||
)
|
||||
assert response is not None
|
||||
|
|
@ -135,21 +146,24 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
|
|||
assert top_match.metadata["document_id"] == expected_doc_id, f"Query '{query}' should match {expected_doc_id}"
|
||||
|
||||
|
||||
def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, embedding_model_id, embedding_dimension):
|
||||
@vector_provider_wrapper
|
||||
def test_insert_chunks_with_precomputed_embeddings(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
vector_io_provider_params_dict = {
|
||||
"inline::milvus": {"score_threshold": -1.0},
|
||||
"inline::qdrant": {"score_threshold": -1.0},
|
||||
"remote::qdrant": {"score_threshold": -1.0},
|
||||
}
|
||||
vector_db_name = "test_precomputed_embeddings_db"
|
||||
vector_store_name = "test_precomputed_embeddings_db"
|
||||
register_response = client_with_empty_registry.vector_stores.create(
|
||||
name=vector_db_name,
|
||||
name=vector_store_name,
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
actual_vector_db_id = register_response.id
|
||||
actual_vector_store_id = register_response.id
|
||||
|
||||
chunks_with_embeddings = [
|
||||
Chunk(
|
||||
|
|
@ -160,13 +174,13 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
|
|||
]
|
||||
|
||||
client_with_empty_registry.vector_io.insert(
|
||||
vector_db_id=actual_vector_db_id,
|
||||
vector_db_id=actual_vector_store_id,
|
||||
chunks=chunks_with_embeddings,
|
||||
)
|
||||
|
||||
provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
|
||||
response = client_with_empty_registry.vector_io.query(
|
||||
vector_db_id=actual_vector_db_id,
|
||||
vector_db_id=actual_vector_store_id,
|
||||
query="precomputed embedding test",
|
||||
params=vector_io_provider_params_dict.get(provider, None),
|
||||
)
|
||||
|
|
@ -181,23 +195,25 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
|
|||
|
||||
|
||||
# expect this test to fail
|
||||
@vector_provider_wrapper
|
||||
def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
vector_io_provider_params_dict = {
|
||||
"inline::milvus": {"score_threshold": 0.0},
|
||||
"remote::qdrant": {"score_threshold": 0.0},
|
||||
"inline::qdrant": {"score_threshold": 0.0},
|
||||
}
|
||||
vector_db_name = "test_precomputed_embeddings_db"
|
||||
vector_store_name = "test_precomputed_embeddings_db"
|
||||
register_response = client_with_empty_registry.vector_stores.create(
|
||||
name=vector_db_name,
|
||||
name=vector_store_name,
|
||||
extra_body={
|
||||
"embedding_model": embedding_model_id,
|
||||
"provider_id": vector_io_provider_id,
|
||||
},
|
||||
)
|
||||
|
||||
actual_vector_db_id = register_response.id
|
||||
actual_vector_store_id = register_response.id
|
||||
|
||||
chunks_with_embeddings = [
|
||||
Chunk(
|
||||
|
|
@ -208,13 +224,13 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
|
|||
]
|
||||
|
||||
client_with_empty_registry.vector_io.insert(
|
||||
vector_db_id=actual_vector_db_id,
|
||||
vector_db_id=actual_vector_store_id,
|
||||
chunks=chunks_with_embeddings,
|
||||
)
|
||||
|
||||
provider = [p.provider_id for p in client_with_empty_registry.providers.list() if p.api == "vector_io"][0]
|
||||
response = client_with_empty_registry.vector_io.query(
|
||||
vector_db_id=actual_vector_db_id,
|
||||
vector_db_id=actual_vector_store_id,
|
||||
query="duplicate",
|
||||
params=vector_io_provider_params_dict.get(provider, None),
|
||||
)
|
||||
|
|
@ -226,33 +242,44 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
|
|||
assert response.chunks[0].metadata["source"] == "precomputed"
|
||||
|
||||
|
||||
def test_auto_extract_embedding_dimension(client_with_empty_registry, embedding_model_id):
|
||||
@vector_provider_wrapper
|
||||
def test_auto_extract_embedding_dimension(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
# This test specifically tests embedding model override, so we keep embedding_model
|
||||
vs = client_with_empty_registry.vector_stores.create(
|
||||
name="test_auto_extract", extra_body={"embedding_model": embedding_model_id}
|
||||
name="test_auto_extract",
|
||||
extra_body={"embedding_model": embedding_model_id, "provider_id": vector_io_provider_id},
|
||||
)
|
||||
assert vs.id is not None
|
||||
|
||||
|
||||
def test_provider_auto_selection_single_provider(client_with_empty_registry, embedding_model_id):
|
||||
@vector_provider_wrapper
|
||||
def test_provider_auto_selection_single_provider(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
|
||||
if len(providers) != 1:
|
||||
pytest.skip(f"Test requires exactly one vector_io provider, found {len(providers)}")
|
||||
|
||||
vs = client_with_empty_registry.vector_stores.create(
|
||||
name="test_auto_provider", extra_body={"embedding_model": embedding_model_id}
|
||||
)
|
||||
# Test that when only one provider is available, it's auto-selected (no provider_id needed)
|
||||
vs = client_with_empty_registry.vector_stores.create(name="test_auto_provider")
|
||||
assert vs.id is not None
|
||||
|
||||
|
||||
def test_provider_id_override(client_with_empty_registry, embedding_model_id):
|
||||
@vector_provider_wrapper
|
||||
def test_provider_id_override(
|
||||
client_with_empty_registry, embedding_model_id, embedding_dimension, vector_io_provider_id
|
||||
):
|
||||
providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
|
||||
if len(providers) != 1:
|
||||
pytest.skip(f"Test requires exactly one vector_io provider, found {len(providers)}")
|
||||
|
||||
provider_id = providers[0].provider_id
|
||||
|
||||
# Test explicit provider_id specification (using default embedding model)
|
||||
vs = client_with_empty_registry.vector_stores.create(
|
||||
name="test_provider_override", extra_body={"embedding_model": embedding_model_id, "provider_id": provider_id}
|
||||
name="test_provider_override", extra_body={"provider_id": provider_id}
|
||||
)
|
||||
assert vs.id is not None
|
||||
assert vs.metadata.get("provider_id") == provider_id
|
||||
|
|
|
|||
|
|
@ -23,6 +23,27 @@ def config_with_image_name_int():
|
|||
image_name: 1234
|
||||
apis_to_serve: []
|
||||
built_at: {datetime.now().isoformat()}
|
||||
storage:
|
||||
backends:
|
||||
kv_default:
|
||||
type: kv_sqlite
|
||||
db_path: /tmp/test_kv.db
|
||||
sql_default:
|
||||
type: sql_sqlite
|
||||
db_path: /tmp/test_sql.db
|
||||
stores:
|
||||
metadata:
|
||||
backend: kv_default
|
||||
namespace: metadata
|
||||
inference:
|
||||
backend: sql_default
|
||||
table_name: inference
|
||||
conversations:
|
||||
backend: sql_default
|
||||
table_name: conversations
|
||||
responses:
|
||||
backend: sql_default
|
||||
table_name: responses
|
||||
providers:
|
||||
inference:
|
||||
- provider_id: provider1
|
||||
|
|
@ -54,6 +75,27 @@ def up_to_date_config():
|
|||
image_name: foo
|
||||
apis_to_serve: []
|
||||
built_at: {datetime.now().isoformat()}
|
||||
storage:
|
||||
backends:
|
||||
kv_default:
|
||||
type: kv_sqlite
|
||||
db_path: /tmp/test_kv.db
|
||||
sql_default:
|
||||
type: sql_sqlite
|
||||
db_path: /tmp/test_sql.db
|
||||
stores:
|
||||
metadata:
|
||||
backend: kv_default
|
||||
namespace: metadata
|
||||
inference:
|
||||
backend: sql_default
|
||||
table_name: inference
|
||||
conversations:
|
||||
backend: sql_default
|
||||
table_name: conversations
|
||||
responses:
|
||||
backend: sql_default
|
||||
table_name: responses
|
||||
providers:
|
||||
inference:
|
||||
- provider_id: provider1
|
||||
|
|
|
|||
|
|
@ -20,7 +20,14 @@ from llama_stack.core.conversations.conversations import (
|
|||
ConversationServiceConfig,
|
||||
ConversationServiceImpl,
|
||||
)
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
|
||||
from llama_stack.core.datatypes import StackRunConfig
|
||||
from llama_stack.core.storage.datatypes import (
|
||||
ServerStoresConfig,
|
||||
SqliteSqlStoreConfig,
|
||||
SqlStoreReference,
|
||||
StorageConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -28,7 +35,18 @@ async def service():
|
|||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
db_path = Path(tmpdir) / "test_conversations.db"
|
||||
|
||||
config = ConversationServiceConfig(conversations_store=SqliteSqlStoreConfig(db_path=str(db_path)), policy=[])
|
||||
storage = StorageConfig(
|
||||
backends={
|
||||
"sql_test": SqliteSqlStoreConfig(db_path=str(db_path)),
|
||||
},
|
||||
stores=ServerStoresConfig(
|
||||
conversations=SqlStoreReference(backend="sql_test", table_name="openai_conversations"),
|
||||
),
|
||||
)
|
||||
register_sqlstore_backends({"sql_test": storage.backends["sql_test"]})
|
||||
run_config = StackRunConfig(image_name="test", apis=[], providers={}, storage=storage)
|
||||
|
||||
config = ConversationServiceConfig(run_config=run_config, policy=[])
|
||||
service = ConversationServiceImpl(config, {})
|
||||
await service.initialize()
|
||||
yield service
|
||||
|
|
@ -121,9 +139,18 @@ async def test_policy_configuration():
|
|||
AccessRule(forbid=Scope(principal="test_user", actions=[Action.CREATE, Action.READ], resource="*"))
|
||||
]
|
||||
|
||||
config = ConversationServiceConfig(
|
||||
conversations_store=SqliteSqlStoreConfig(db_path=str(db_path)), policy=restrictive_policy
|
||||
storage = StorageConfig(
|
||||
backends={
|
||||
"sql_test": SqliteSqlStoreConfig(db_path=str(db_path)),
|
||||
},
|
||||
stores=ServerStoresConfig(
|
||||
conversations=SqlStoreReference(backend="sql_test", table_name="openai_conversations"),
|
||||
),
|
||||
)
|
||||
register_sqlstore_backends({"sql_test": storage.backends["sql_test"]})
|
||||
run_config = StackRunConfig(image_name="test", apis=[], providers={}, storage=storage)
|
||||
|
||||
config = ConversationServiceConfig(run_config=run_config, policy=restrictive_policy)
|
||||
service = ConversationServiceImpl(config, {})
|
||||
await service.initialize()
|
||||
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ async def test_single_provider_auto_selection():
|
|||
Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
|
||||
]
|
||||
)
|
||||
mock_routing_table.register_vector_db = AsyncMock(
|
||||
mock_routing_table.register_vector_store = AsyncMock(
|
||||
return_value=Mock(identifier="vs_123", provider_id="inline::faiss", provider_resource_id="vs_123")
|
||||
)
|
||||
mock_routing_table.get_provider_impl = AsyncMock(
|
||||
|
|
|
|||
|
|
@ -4,90 +4,64 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
"""
|
||||
Unit tests for Stack validation functions.
|
||||
"""
|
||||
"""Unit tests for Stack validation functions."""
|
||||
|
||||
from unittest.mock import AsyncMock
|
||||
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.models import Model, ModelType
|
||||
from llama_stack.core.stack import validate_default_embedding_model
|
||||
from llama_stack.apis.models import ListModelsResponse, Model, ModelType
|
||||
from llama_stack.core.datatypes import QualifiedModel, StackRunConfig, StorageConfig, VectorStoresConfig
|
||||
from llama_stack.core.stack import validate_vector_stores_config
|
||||
from llama_stack.providers.datatypes import Api
|
||||
|
||||
|
||||
class TestStackValidation:
|
||||
"""Test Stack validation functions."""
|
||||
class TestVectorStoresValidation:
|
||||
async def test_validate_missing_model(self):
|
||||
"""Test validation fails when model not found."""
|
||||
run_config = StackRunConfig(
|
||||
image_name="test",
|
||||
providers={},
|
||||
storage=StorageConfig(backends={}, stores={}),
|
||||
vector_stores=VectorStoresConfig(
|
||||
default_provider_id="faiss",
|
||||
default_embedding_model=QualifiedModel(
|
||||
provider_id="p",
|
||||
model_id="missing",
|
||||
),
|
||||
),
|
||||
)
|
||||
mock_models = AsyncMock()
|
||||
mock_models.list_models.return_value = ListModelsResponse(data=[])
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"models,should_raise",
|
||||
[
|
||||
([], False), # No models
|
||||
(
|
||||
[
|
||||
Model(
|
||||
identifier="emb1",
|
||||
model_type=ModelType.embedding,
|
||||
metadata={"default_configured": True},
|
||||
provider_id="p",
|
||||
provider_resource_id="emb1",
|
||||
)
|
||||
],
|
||||
False,
|
||||
), # Single default
|
||||
(
|
||||
[
|
||||
Model(
|
||||
identifier="emb1",
|
||||
model_type=ModelType.embedding,
|
||||
metadata={"default_configured": True},
|
||||
provider_id="p",
|
||||
provider_resource_id="emb1",
|
||||
),
|
||||
Model(
|
||||
identifier="emb2",
|
||||
model_type=ModelType.embedding,
|
||||
metadata={"default_configured": True},
|
||||
provider_id="p",
|
||||
provider_resource_id="emb2",
|
||||
),
|
||||
],
|
||||
True,
|
||||
), # Multiple defaults
|
||||
(
|
||||
[
|
||||
Model(
|
||||
identifier="emb1",
|
||||
model_type=ModelType.embedding,
|
||||
metadata={"default_configured": True},
|
||||
provider_id="p",
|
||||
provider_resource_id="emb1",
|
||||
),
|
||||
Model(
|
||||
identifier="llm1",
|
||||
model_type=ModelType.llm,
|
||||
metadata={"default_configured": True},
|
||||
provider_id="p",
|
||||
provider_resource_id="llm1",
|
||||
),
|
||||
],
|
||||
False,
|
||||
), # Ignores non-embedding
|
||||
],
|
||||
)
|
||||
async def test_validate_default_embedding_model(self, models, should_raise):
|
||||
"""Test validation with various model configurations."""
|
||||
mock_models_impl = AsyncMock()
|
||||
mock_models_impl.list_models.return_value = models
|
||||
impls = {Api.models: mock_models_impl}
|
||||
with pytest.raises(ValueError, match="not found"):
|
||||
await validate_vector_stores_config(run_config.vector_stores, {Api.models: mock_models})
|
||||
|
||||
if should_raise:
|
||||
with pytest.raises(ValueError, match="Multiple embedding models marked as default_configured=True"):
|
||||
await validate_default_embedding_model(impls)
|
||||
else:
|
||||
await validate_default_embedding_model(impls)
|
||||
async def test_validate_success(self):
|
||||
"""Test validation passes with valid model."""
|
||||
run_config = StackRunConfig(
|
||||
image_name="test",
|
||||
providers={},
|
||||
storage=StorageConfig(backends={}, stores={}),
|
||||
vector_stores=VectorStoresConfig(
|
||||
default_provider_id="faiss",
|
||||
default_embedding_model=QualifiedModel(
|
||||
provider_id="p",
|
||||
model_id="valid",
|
||||
),
|
||||
),
|
||||
)
|
||||
mock_models = AsyncMock()
|
||||
mock_models.list_models.return_value = ListModelsResponse(
|
||||
data=[
|
||||
Model(
|
||||
identifier="p/valid", # Must match provider_id/model_id format
|
||||
model_type=ModelType.embedding,
|
||||
metadata={"embedding_dimension": 768},
|
||||
provider_id="p",
|
||||
provider_resource_id="valid",
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
async def test_validate_default_embedding_model_no_models_api(self):
|
||||
"""Test validation when models API is not available."""
|
||||
await validate_default_embedding_model({})
|
||||
await validate_vector_stores_config(run_config.vector_stores, {Api.models: mock_models})
|
||||
|
|
|
|||
84
tests/unit/core/test_storage_references.py
Normal file
84
tests/unit/core/test_storage_references.py
Normal file
|
|
@ -0,0 +1,84 @@
|
|||
# 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.
|
||||
|
||||
"""Unit tests for storage backend/reference validation."""
|
||||
|
||||
import pytest
|
||||
from pydantic import ValidationError
|
||||
|
||||
from llama_stack.core.datatypes import (
|
||||
LLAMA_STACK_RUN_CONFIG_VERSION,
|
||||
StackRunConfig,
|
||||
)
|
||||
from llama_stack.core.storage.datatypes import (
|
||||
InferenceStoreReference,
|
||||
KVStoreReference,
|
||||
ServerStoresConfig,
|
||||
SqliteKVStoreConfig,
|
||||
SqliteSqlStoreConfig,
|
||||
SqlStoreReference,
|
||||
StorageConfig,
|
||||
)
|
||||
|
||||
|
||||
def _base_run_config(**overrides):
|
||||
metadata_reference = overrides.pop(
|
||||
"metadata_reference",
|
||||
KVStoreReference(backend="kv_default", namespace="registry"),
|
||||
)
|
||||
inference_reference = overrides.pop(
|
||||
"inference_reference",
|
||||
InferenceStoreReference(backend="sql_default", table_name="inference"),
|
||||
)
|
||||
conversations_reference = overrides.pop(
|
||||
"conversations_reference",
|
||||
SqlStoreReference(backend="sql_default", table_name="conversations"),
|
||||
)
|
||||
storage = overrides.pop(
|
||||
"storage",
|
||||
StorageConfig(
|
||||
backends={
|
||||
"kv_default": SqliteKVStoreConfig(db_path="/tmp/kv.db"),
|
||||
"sql_default": SqliteSqlStoreConfig(db_path="/tmp/sql.db"),
|
||||
},
|
||||
stores=ServerStoresConfig(
|
||||
metadata=metadata_reference,
|
||||
inference=inference_reference,
|
||||
conversations=conversations_reference,
|
||||
),
|
||||
),
|
||||
)
|
||||
return StackRunConfig(
|
||||
version=LLAMA_STACK_RUN_CONFIG_VERSION,
|
||||
image_name="test-distro",
|
||||
apis=[],
|
||||
providers={},
|
||||
storage=storage,
|
||||
**overrides,
|
||||
)
|
||||
|
||||
|
||||
def test_references_require_known_backend():
|
||||
with pytest.raises(ValidationError, match="unknown backend 'missing'"):
|
||||
_base_run_config(metadata_reference=KVStoreReference(backend="missing", namespace="registry"))
|
||||
|
||||
|
||||
def test_references_must_match_backend_family():
|
||||
with pytest.raises(ValidationError, match="kv_.* is required"):
|
||||
_base_run_config(metadata_reference=KVStoreReference(backend="sql_default", namespace="registry"))
|
||||
|
||||
with pytest.raises(ValidationError, match="sql_.* is required"):
|
||||
_base_run_config(
|
||||
inference_reference=InferenceStoreReference(backend="kv_default", table_name="inference"),
|
||||
)
|
||||
|
||||
|
||||
def test_valid_configuration_passes_validation():
|
||||
config = _base_run_config()
|
||||
stores = config.storage.stores
|
||||
assert stores.metadata is not None and stores.metadata.backend == "kv_default"
|
||||
assert stores.inference is not None and stores.inference.backend == "sql_default"
|
||||
assert stores.conversations is not None and stores.conversations.backend == "sql_default"
|
||||
|
|
@ -1,40 +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 pathlib import Path
|
||||
|
||||
from llama_stack.cli.stack._build import (
|
||||
_run_stack_build_command_from_build_config,
|
||||
)
|
||||
from llama_stack.core.datatypes import BuildConfig, DistributionSpec
|
||||
from llama_stack.core.utils.image_types import LlamaStackImageType
|
||||
|
||||
|
||||
def test_container_build_passes_path(monkeypatch, tmp_path):
|
||||
called_with = {}
|
||||
|
||||
def spy_build_image(build_config, image_name, distro_or_config, run_config=None):
|
||||
called_with["path"] = distro_or_config
|
||||
called_with["run_config"] = run_config
|
||||
return 0
|
||||
|
||||
monkeypatch.setattr(
|
||||
"llama_stack.cli.stack._build.build_image",
|
||||
spy_build_image,
|
||||
raising=True,
|
||||
)
|
||||
|
||||
cfg = BuildConfig(
|
||||
image_type=LlamaStackImageType.CONTAINER.value,
|
||||
distribution_spec=DistributionSpec(providers={}, description=""),
|
||||
)
|
||||
|
||||
_run_stack_build_command_from_build_config(cfg, image_name="dummy")
|
||||
|
||||
assert "path" in called_with
|
||||
assert isinstance(called_with["path"], str)
|
||||
assert Path(called_with["path"]).exists()
|
||||
assert called_with["run_config"] is None
|
||||
|
|
@ -13,6 +13,15 @@ from pydantic import BaseModel, Field, ValidationError
|
|||
|
||||
from llama_stack.core.datatypes import Api, Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import INTERNAL_APIS, get_provider_registry, providable_apis
|
||||
from llama_stack.core.storage.datatypes import (
|
||||
InferenceStoreReference,
|
||||
KVStoreReference,
|
||||
ServerStoresConfig,
|
||||
SqliteKVStoreConfig,
|
||||
SqliteSqlStoreConfig,
|
||||
SqlStoreReference,
|
||||
StorageConfig,
|
||||
)
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
|
||||
|
||||
|
|
@ -29,6 +38,32 @@ class SampleConfig(BaseModel):
|
|||
}
|
||||
|
||||
|
||||
def _default_storage() -> StorageConfig:
|
||||
return StorageConfig(
|
||||
backends={
|
||||
"kv_default": SqliteKVStoreConfig(db_path=":memory:"),
|
||||
"sql_default": SqliteSqlStoreConfig(db_path=":memory:"),
|
||||
},
|
||||
stores=ServerStoresConfig(
|
||||
metadata=KVStoreReference(backend="kv_default", namespace="registry"),
|
||||
inference=InferenceStoreReference(backend="sql_default", table_name="inference_store"),
|
||||
conversations=SqlStoreReference(backend="sql_default", table_name="conversations"),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def make_stack_config(**overrides) -> StackRunConfig:
|
||||
storage = overrides.pop("storage", _default_storage())
|
||||
defaults = dict(
|
||||
image_name="test_image",
|
||||
apis=[],
|
||||
providers={},
|
||||
storage=storage,
|
||||
)
|
||||
defaults.update(overrides)
|
||||
return StackRunConfig(**defaults)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_providers():
|
||||
"""Mock the available_providers function to return test providers."""
|
||||
|
|
@ -47,8 +82,8 @@ def mock_providers():
|
|||
@pytest.fixture
|
||||
def base_config(tmp_path):
|
||||
"""Create a base StackRunConfig with common settings."""
|
||||
return StackRunConfig(
|
||||
image_name="test_image",
|
||||
return make_stack_config(
|
||||
apis=["inference"],
|
||||
providers={
|
||||
"inference": [
|
||||
Provider(
|
||||
|
|
@ -222,8 +257,8 @@ class TestProviderRegistry:
|
|||
|
||||
def test_missing_directory(self, mock_providers):
|
||||
"""Test handling of missing external providers directory."""
|
||||
config = StackRunConfig(
|
||||
image_name="test_image",
|
||||
config = make_stack_config(
|
||||
apis=["inference"],
|
||||
providers={
|
||||
"inference": [
|
||||
Provider(
|
||||
|
|
@ -278,7 +313,6 @@ pip_packages:
|
|||
"""Test loading an external provider from a module (success path)."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.providers.datatypes import Api, ProviderSpec
|
||||
|
||||
# Simulate a provider module with get_provider_spec
|
||||
|
|
@ -293,7 +327,7 @@ pip_packages:
|
|||
import_module_side_effect = make_import_module_side_effect(external_module=fake_module)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_module_side_effect) as mock_import:
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -317,12 +351,11 @@ pip_packages:
|
|||
|
||||
def test_external_provider_from_module_not_found(self, mock_providers):
|
||||
"""Test handling ModuleNotFoundError for missing provider module."""
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
|
||||
import_module_side_effect = make_import_module_side_effect(raise_for_external=True)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_module_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -341,12 +374,11 @@ pip_packages:
|
|||
|
||||
def test_external_provider_from_module_missing_get_provider_spec(self, mock_providers):
|
||||
"""Test handling missing get_provider_spec in provider module (should raise ValueError)."""
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
|
||||
import_module_side_effect = make_import_module_side_effect(missing_get_provider_spec=True)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_module_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -399,13 +431,12 @@ class TestGetExternalProvidersFromModule:
|
|||
|
||||
def test_stackrunconfig_provider_without_module(self, mock_providers):
|
||||
"""Test that providers without module attribute are skipped."""
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
|
||||
import_module_side_effect = make_import_module_side_effect()
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_module_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -426,7 +457,6 @@ class TestGetExternalProvidersFromModule:
|
|||
"""Test provider with module containing version spec (e.g., package==1.0.0)."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
|
||||
|
|
@ -444,7 +474,7 @@ class TestGetExternalProvidersFromModule:
|
|||
raise ModuleNotFoundError(name)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -564,7 +594,6 @@ class TestGetExternalProvidersFromModule:
|
|||
"""Test when get_provider_spec returns a list of specs."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
|
||||
|
|
@ -589,7 +618,7 @@ class TestGetExternalProvidersFromModule:
|
|||
raise ModuleNotFoundError(name)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -613,7 +642,6 @@ class TestGetExternalProvidersFromModule:
|
|||
"""Test that list return filters specs by provider_type."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
|
||||
|
|
@ -638,7 +666,7 @@ class TestGetExternalProvidersFromModule:
|
|||
raise ModuleNotFoundError(name)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -662,7 +690,6 @@ class TestGetExternalProvidersFromModule:
|
|||
"""Test that list return adds multiple different provider_types when config requests them."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
|
||||
|
|
@ -688,7 +715,7 @@ class TestGetExternalProvidersFromModule:
|
|||
raise ModuleNotFoundError(name)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -718,7 +745,6 @@ class TestGetExternalProvidersFromModule:
|
|||
|
||||
def test_module_not_found_raises_value_error(self, mock_providers):
|
||||
"""Test that ModuleNotFoundError raises ValueError with helpful message."""
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
|
||||
def import_side_effect(name):
|
||||
|
|
@ -727,7 +753,7 @@ class TestGetExternalProvidersFromModule:
|
|||
raise ModuleNotFoundError(name)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -751,7 +777,6 @@ class TestGetExternalProvidersFromModule:
|
|||
"""Test that generic exceptions are properly raised."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
|
||||
def bad_spec():
|
||||
|
|
@ -765,7 +790,7 @@ class TestGetExternalProvidersFromModule:
|
|||
raise ModuleNotFoundError(name)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
@ -787,10 +812,9 @@ class TestGetExternalProvidersFromModule:
|
|||
|
||||
def test_empty_provider_list(self, mock_providers):
|
||||
"""Test with empty provider list."""
|
||||
from llama_stack.core.datatypes import StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={},
|
||||
)
|
||||
|
|
@ -805,7 +829,6 @@ class TestGetExternalProvidersFromModule:
|
|||
"""Test multiple APIs with providers."""
|
||||
from types import SimpleNamespace
|
||||
|
||||
from llama_stack.core.datatypes import Provider, StackRunConfig
|
||||
from llama_stack.core.distribution import get_external_providers_from_module
|
||||
from llama_stack.providers.datatypes import ProviderSpec
|
||||
|
||||
|
|
@ -830,7 +853,7 @@ class TestGetExternalProvidersFromModule:
|
|||
raise ModuleNotFoundError(name)
|
||||
|
||||
with patch("importlib.import_module", side_effect=import_side_effect):
|
||||
config = StackRunConfig(
|
||||
config = make_stack_config(
|
||||
image_name="test_image",
|
||||
providers={
|
||||
"inference": [
|
||||
|
|
|
|||
|
|
@ -11,11 +11,12 @@ from llama_stack.apis.common.errors import ResourceNotFoundError
|
|||
from llama_stack.apis.common.responses import Order
|
||||
from llama_stack.apis.files import OpenAIFilePurpose
|
||||
from llama_stack.core.access_control.access_control import default_policy
|
||||
from llama_stack.core.storage.datatypes import SqliteSqlStoreConfig, SqlStoreReference
|
||||
from llama_stack.providers.inline.files.localfs import (
|
||||
LocalfsFilesImpl,
|
||||
LocalfsFilesImplConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
|
||||
|
||||
class MockUploadFile:
|
||||
|
|
@ -36,8 +37,11 @@ async def files_provider(tmp_path):
|
|||
storage_dir = tmp_path / "files"
|
||||
db_path = tmp_path / "files_metadata.db"
|
||||
|
||||
backend_name = "sql_localfs_test"
|
||||
register_sqlstore_backends({backend_name: SqliteSqlStoreConfig(db_path=db_path.as_posix())})
|
||||
config = LocalfsFilesImplConfig(
|
||||
storage_dir=storage_dir.as_posix(), metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix())
|
||||
storage_dir=storage_dir.as_posix(),
|
||||
metadata_store=SqlStoreReference(backend=backend_name, table_name="files_metadata"),
|
||||
)
|
||||
|
||||
provider = LocalfsFilesImpl(config, default_policy())
|
||||
|
|
|
|||
|
|
@ -9,7 +9,16 @@ import random
|
|||
import pytest
|
||||
|
||||
from llama_stack.core.prompts.prompts import PromptServiceConfig, PromptServiceImpl
|
||||
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
|
||||
from llama_stack.core.storage.datatypes import (
|
||||
InferenceStoreReference,
|
||||
KVStoreReference,
|
||||
ServerStoresConfig,
|
||||
SqliteKVStoreConfig,
|
||||
SqliteSqlStoreConfig,
|
||||
SqlStoreReference,
|
||||
StorageConfig,
|
||||
)
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl, register_kvstore_backends
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -19,12 +28,28 @@ async def temp_prompt_store(tmp_path_factory):
|
|||
db_path = str(temp_dir / f"{unique_id}.db")
|
||||
|
||||
from llama_stack.core.datatypes import StackRunConfig
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl
|
||||
|
||||
mock_run_config = StackRunConfig(image_name="test-distribution", apis=[], providers={})
|
||||
storage = StorageConfig(
|
||||
backends={
|
||||
"kv_test": SqliteKVStoreConfig(db_path=db_path),
|
||||
"sql_test": SqliteSqlStoreConfig(db_path=str(temp_dir / f"{unique_id}_sql.db")),
|
||||
},
|
||||
stores=ServerStoresConfig(
|
||||
metadata=KVStoreReference(backend="kv_test", namespace="registry"),
|
||||
inference=InferenceStoreReference(backend="sql_test", table_name="inference"),
|
||||
conversations=SqlStoreReference(backend="sql_test", table_name="conversations"),
|
||||
),
|
||||
)
|
||||
mock_run_config = StackRunConfig(
|
||||
image_name="test-distribution",
|
||||
apis=[],
|
||||
providers={},
|
||||
storage=storage,
|
||||
)
|
||||
config = PromptServiceConfig(run_config=mock_run_config)
|
||||
store = PromptServiceImpl(config, deps={})
|
||||
|
||||
store.kvstore = await kvstore_impl(SqliteKVStoreConfig(db_path=db_path))
|
||||
register_kvstore_backends({"kv_test": storage.backends["kv_test"]})
|
||||
store.kvstore = await kvstore_impl(KVStoreReference(backend="kv_test", namespace="prompts"))
|
||||
|
||||
yield store
|
||||
|
|
|
|||
|
|
@ -26,6 +26,20 @@ from llama_stack.providers.inline.agents.meta_reference.config import MetaRefere
|
|||
from llama_stack.providers.inline.agents.meta_reference.persistence import AgentInfo
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def setup_backends(tmp_path):
|
||||
"""Register KV and SQL store backends for testing."""
|
||||
from llama_stack.core.storage.datatypes import SqliteKVStoreConfig, SqliteSqlStoreConfig
|
||||
from llama_stack.providers.utils.kvstore.kvstore import register_kvstore_backends
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
|
||||
kv_path = str(tmp_path / "test_kv.db")
|
||||
sql_path = str(tmp_path / "test_sql.db")
|
||||
|
||||
register_kvstore_backends({"kv_default": SqliteKVStoreConfig(db_path=kv_path)})
|
||||
register_sqlstore_backends({"sql_default": SqliteSqlStoreConfig(db_path=sql_path)})
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_apis():
|
||||
return {
|
||||
|
|
@ -40,15 +54,20 @@ def mock_apis():
|
|||
|
||||
@pytest.fixture
|
||||
def config(tmp_path):
|
||||
from llama_stack.core.storage.datatypes import KVStoreReference, ResponsesStoreReference
|
||||
from llama_stack.providers.inline.agents.meta_reference.config import AgentPersistenceConfig
|
||||
|
||||
return MetaReferenceAgentsImplConfig(
|
||||
persistence_store={
|
||||
"type": "sqlite",
|
||||
"db_path": str(tmp_path / "test.db"),
|
||||
},
|
||||
responses_store={
|
||||
"type": "sqlite",
|
||||
"db_path": str(tmp_path / "test.db"),
|
||||
},
|
||||
persistence=AgentPersistenceConfig(
|
||||
agent_state=KVStoreReference(
|
||||
backend="kv_default",
|
||||
namespace="agents",
|
||||
),
|
||||
responses=ResponsesStoreReference(
|
||||
backend="sql_default",
|
||||
table_name="responses",
|
||||
),
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ from llama_stack.apis.inference import (
|
|||
)
|
||||
from llama_stack.apis.tools.tools import ListToolDefsResponse, ToolDef, ToolGroups, ToolInvocationResult, ToolRuntime
|
||||
from llama_stack.core.access_control.access_control import default_policy
|
||||
from llama_stack.core.datatypes import ResponsesStoreConfig
|
||||
from llama_stack.core.storage.datatypes import ResponsesStoreReference, SqliteSqlStoreConfig
|
||||
from llama_stack.providers.inline.agents.meta_reference.responses.openai_responses import (
|
||||
OpenAIResponsesImpl,
|
||||
)
|
||||
|
|
@ -50,7 +50,7 @@ from llama_stack.providers.utils.responses.responses_store import (
|
|||
ResponsesStore,
|
||||
_OpenAIResponseObjectWithInputAndMessages,
|
||||
)
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
from tests.unit.providers.agents.meta_reference.fixtures import load_chat_completion_fixture
|
||||
|
||||
|
||||
|
|
@ -814,6 +814,69 @@ async def test_create_openai_response_with_instructions_and_previous_response(
|
|||
assert sent_messages[3].content == "Which is the largest?"
|
||||
|
||||
|
||||
async def test_create_openai_response_with_previous_response_instructions(
|
||||
openai_responses_impl, mock_responses_store, mock_inference_api
|
||||
):
|
||||
"""Test prepending instructions and previous response with instructions."""
|
||||
|
||||
input_item_message = OpenAIResponseMessage(
|
||||
id="123",
|
||||
content="Name some towns in Ireland",
|
||||
role="user",
|
||||
)
|
||||
response_output_message = OpenAIResponseMessage(
|
||||
id="123",
|
||||
content="Galway, Longford, Sligo",
|
||||
status="completed",
|
||||
role="assistant",
|
||||
)
|
||||
response = _OpenAIResponseObjectWithInputAndMessages(
|
||||
created_at=1,
|
||||
id="resp_123",
|
||||
model="fake_model",
|
||||
output=[response_output_message],
|
||||
status="completed",
|
||||
text=OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")),
|
||||
input=[input_item_message],
|
||||
messages=[
|
||||
OpenAIUserMessageParam(content="Name some towns in Ireland"),
|
||||
OpenAIAssistantMessageParam(content="Galway, Longford, Sligo"),
|
||||
],
|
||||
instructions="You are a helpful assistant.",
|
||||
)
|
||||
mock_responses_store.get_response_object.return_value = response
|
||||
|
||||
model = "meta-llama/Llama-3.1-8B-Instruct"
|
||||
instructions = "You are a geography expert. Provide concise answers."
|
||||
|
||||
mock_inference_api.openai_chat_completion.return_value = fake_stream()
|
||||
|
||||
# Execute
|
||||
await openai_responses_impl.create_openai_response(
|
||||
input="Which is the largest?", model=model, instructions=instructions, previous_response_id="123"
|
||||
)
|
||||
|
||||
# Verify
|
||||
mock_inference_api.openai_chat_completion.assert_called_once()
|
||||
call_args = mock_inference_api.openai_chat_completion.call_args
|
||||
params = call_args.args[0]
|
||||
sent_messages = params.messages
|
||||
|
||||
# Check that instructions were prepended as a system message
|
||||
# and that the previous response instructions were not carried over
|
||||
assert len(sent_messages) == 4, sent_messages
|
||||
assert sent_messages[0].role == "system"
|
||||
assert sent_messages[0].content == instructions
|
||||
|
||||
# Check the rest of the messages were converted correctly
|
||||
assert sent_messages[1].role == "user"
|
||||
assert sent_messages[1].content == "Name some towns in Ireland"
|
||||
assert sent_messages[2].role == "assistant"
|
||||
assert sent_messages[2].content == "Galway, Longford, Sligo"
|
||||
assert sent_messages[3].role == "user"
|
||||
assert sent_messages[3].content == "Which is the largest?"
|
||||
|
||||
|
||||
async def test_list_openai_response_input_items_delegation(openai_responses_impl, mock_responses_store):
|
||||
"""Test that list_openai_response_input_items properly delegates to responses_store with correct parameters."""
|
||||
# Setup
|
||||
|
|
@ -854,8 +917,10 @@ async def test_responses_store_list_input_items_logic():
|
|||
|
||||
# Create mock store and response store
|
||||
mock_sql_store = AsyncMock()
|
||||
backend_name = "sql_responses_test"
|
||||
register_sqlstore_backends({backend_name: SqliteSqlStoreConfig(db_path="mock_db_path")})
|
||||
responses_store = ResponsesStore(
|
||||
ResponsesStoreConfig(sql_store_config=SqliteSqlStoreConfig(db_path="mock_db_path")), policy=default_policy()
|
||||
ResponsesStoreReference(backend=backend_name, table_name="responses"), policy=default_policy()
|
||||
)
|
||||
responses_store.sql_store = mock_sql_store
|
||||
|
||||
|
|
|
|||
|
|
@ -12,10 +12,10 @@ from unittest.mock import AsyncMock
|
|||
|
||||
import pytest
|
||||
|
||||
from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
|
||||
from llama_stack.providers.inline.batches.reference.batches import ReferenceBatchesImpl
|
||||
from llama_stack.providers.inline.batches.reference.config import ReferenceBatchesImplConfig
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl
|
||||
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
|
||||
from llama_stack.providers.utils.kvstore import kvstore_impl, register_kvstore_backends
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -23,8 +23,10 @@ async def provider():
|
|||
"""Create a test provider instance with temporary database."""
|
||||
with tempfile.TemporaryDirectory() as tmpdir:
|
||||
db_path = Path(tmpdir) / "test_batches.db"
|
||||
backend_name = "kv_batches_test"
|
||||
kvstore_config = SqliteKVStoreConfig(db_path=str(db_path))
|
||||
config = ReferenceBatchesImplConfig(kvstore=kvstore_config)
|
||||
register_kvstore_backends({backend_name: kvstore_config})
|
||||
config = ReferenceBatchesImplConfig(kvstore=KVStoreReference(backend=backend_name, namespace="batches"))
|
||||
|
||||
# Create kvstore and mock APIs
|
||||
kvstore = await kvstore_impl(config.kvstore)
|
||||
|
|
|
|||
|
|
@ -8,8 +8,9 @@ import boto3
|
|||
import pytest
|
||||
from moto import mock_aws
|
||||
|
||||
from llama_stack.core.storage.datatypes import SqliteSqlStoreConfig, SqlStoreReference
|
||||
from llama_stack.providers.remote.files.s3 import S3FilesImplConfig, get_adapter_impl
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
|
||||
from llama_stack.providers.utils.sqlstore.sqlstore import register_sqlstore_backends
|
||||
|
||||
|
||||
class MockUploadFile:
|
||||
|
|
@ -38,11 +39,13 @@ def sample_text_file2():
|
|||
def s3_config(tmp_path):
|
||||
db_path = tmp_path / "s3_files_metadata.db"
|
||||
|
||||
backend_name = f"sql_s3_{tmp_path.name}"
|
||||
register_sqlstore_backends({backend_name: SqliteSqlStoreConfig(db_path=db_path.as_posix())})
|
||||
return S3FilesImplConfig(
|
||||
bucket_name=f"test-bucket-{tmp_path.name}",
|
||||
region="not-a-region",
|
||||
auto_create_bucket=True,
|
||||
metadata_store=SqliteSqlStoreConfig(db_path=db_path.as_posix()),
|
||||
metadata_store=SqlStoreReference(backend=backend_name, table_name="s3_files_metadata"),
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -38,6 +38,28 @@ class OpenAIMixinWithEmbeddingsImpl(OpenAIMixinImpl):
|
|||
}
|
||||
|
||||
|
||||
class OpenAIMixinWithCustomModelConstruction(OpenAIMixinImpl):
|
||||
"""Test implementation that uses construct_model_from_identifier to add rerank models"""
|
||||
|
||||
embedding_model_metadata: dict[str, dict[str, int]] = {
|
||||
"text-embedding-3-small": {"embedding_dimension": 1536, "context_length": 8192},
|
||||
"text-embedding-ada-002": {"embedding_dimension": 1536, "context_length": 8192},
|
||||
}
|
||||
|
||||
# Adds rerank models via construct_model_from_identifier
|
||||
rerank_model_ids: set[str] = {"rerank-model-1", "rerank-model-2"}
|
||||
|
||||
def construct_model_from_identifier(self, identifier: str) -> Model:
|
||||
if identifier in self.rerank_model_ids:
|
||||
return Model(
|
||||
provider_id=self.__provider_id__, # type: ignore[attr-defined]
|
||||
provider_resource_id=identifier,
|
||||
identifier=identifier,
|
||||
model_type=ModelType.rerank,
|
||||
)
|
||||
return super().construct_model_from_identifier(identifier)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mixin():
|
||||
"""Create a test instance of OpenAIMixin with mocked model_store"""
|
||||
|
|
@ -62,6 +84,13 @@ def mixin_with_embeddings():
|
|||
return OpenAIMixinWithEmbeddingsImpl(config=config)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mixin_with_custom_model_construction():
|
||||
"""Create a test instance using custom construct_model_from_identifier"""
|
||||
config = RemoteInferenceProviderConfig()
|
||||
return OpenAIMixinWithCustomModelConstruction(config=config)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def mock_models():
|
||||
"""Create multiple mock OpenAI model objects"""
|
||||
|
|
@ -113,6 +142,19 @@ def mock_client_context():
|
|||
return _mock_client_context
|
||||
|
||||
|
||||
def _assert_models_match_expected(actual_models, expected_models):
|
||||
"""Verify the models match expected attributes.
|
||||
|
||||
Args:
|
||||
actual_models: List of models to verify
|
||||
expected_models: Mapping of model identifier to expected attribute values
|
||||
"""
|
||||
for identifier, expected_attrs in expected_models.items():
|
||||
model = next(m for m in actual_models if m.identifier == identifier)
|
||||
for attr_name, expected_value in expected_attrs.items():
|
||||
assert getattr(model, attr_name) == expected_value
|
||||
|
||||
|
||||
class TestOpenAIMixinListModels:
|
||||
"""Test cases for the list_models method"""
|
||||
|
||||
|
|
@ -342,21 +384,71 @@ class TestOpenAIMixinEmbeddingModelMetadata:
|
|||
assert result is not None
|
||||
assert len(result) == 2
|
||||
|
||||
# Find the models in the result
|
||||
embedding_model = next(m for m in result if m.identifier == "text-embedding-3-small")
|
||||
llm_model = next(m for m in result if m.identifier == "gpt-4")
|
||||
expected_models = {
|
||||
"text-embedding-3-small": {
|
||||
"model_type": ModelType.embedding,
|
||||
"metadata": {"embedding_dimension": 1536, "context_length": 8192},
|
||||
"provider_id": "test-provider",
|
||||
"provider_resource_id": "text-embedding-3-small",
|
||||
},
|
||||
"gpt-4": {
|
||||
"model_type": ModelType.llm,
|
||||
"metadata": {},
|
||||
"provider_id": "test-provider",
|
||||
"provider_resource_id": "gpt-4",
|
||||
},
|
||||
}
|
||||
|
||||
# Check embedding model
|
||||
assert embedding_model.model_type == ModelType.embedding
|
||||
assert embedding_model.metadata == {"embedding_dimension": 1536, "context_length": 8192}
|
||||
assert embedding_model.provider_id == "test-provider"
|
||||
assert embedding_model.provider_resource_id == "text-embedding-3-small"
|
||||
_assert_models_match_expected(result, expected_models)
|
||||
|
||||
# Check LLM model
|
||||
assert llm_model.model_type == ModelType.llm
|
||||
assert llm_model.metadata == {} # No metadata for LLMs
|
||||
assert llm_model.provider_id == "test-provider"
|
||||
assert llm_model.provider_resource_id == "gpt-4"
|
||||
|
||||
class TestOpenAIMixinCustomModelConstruction:
|
||||
"""Test cases for mixed model types (LLM, embedding, rerank) through construct_model_from_identifier"""
|
||||
|
||||
async def test_mixed_model_types_identification(self, mixin_with_custom_model_construction, mock_client_context):
|
||||
"""Test that LLM, embedding, and rerank models are correctly identified with proper types and metadata"""
|
||||
# Create mock models: 1 embedding, 1 rerank, 1 LLM
|
||||
mock_embedding_model = MagicMock(id="text-embedding-3-small")
|
||||
mock_rerank_model = MagicMock(id="rerank-model-1")
|
||||
mock_llm_model = MagicMock(id="gpt-4")
|
||||
mock_models = [mock_embedding_model, mock_rerank_model, mock_llm_model]
|
||||
|
||||
mock_client = MagicMock()
|
||||
|
||||
async def mock_models_list():
|
||||
for model in mock_models:
|
||||
yield model
|
||||
|
||||
mock_client.models.list.return_value = mock_models_list()
|
||||
|
||||
with mock_client_context(mixin_with_custom_model_construction, mock_client):
|
||||
result = await mixin_with_custom_model_construction.list_models()
|
||||
|
||||
assert result is not None
|
||||
assert len(result) == 3
|
||||
|
||||
expected_models = {
|
||||
"text-embedding-3-small": {
|
||||
"model_type": ModelType.embedding,
|
||||
"metadata": {"embedding_dimension": 1536, "context_length": 8192},
|
||||
"provider_id": "test-provider",
|
||||
"provider_resource_id": "text-embedding-3-small",
|
||||
},
|
||||
"rerank-model-1": {
|
||||
"model_type": ModelType.rerank,
|
||||
"metadata": {},
|
||||
"provider_id": "test-provider",
|
||||
"provider_resource_id": "rerank-model-1",
|
||||
},
|
||||
"gpt-4": {
|
||||
"model_type": ModelType.llm,
|
||||
"metadata": {},
|
||||
"provider_id": "test-provider",
|
||||
"provider_resource_id": "gpt-4",
|
||||
},
|
||||
}
|
||||
|
||||
_assert_models_match_expected(result, expected_models)
|
||||
|
||||
|
||||
class TestOpenAIMixinAllowedModels:
|
||||
|
|
|
|||
|
|
@ -10,15 +10,16 @@ from unittest.mock import AsyncMock, MagicMock, patch
|
|||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from llama_stack.apis.vector_dbs import VectorDB
|
||||
from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
|
||||
from llama_stack.apis.vector_stores import VectorStore
|
||||
from llama_stack.core.storage.datatypes import KVStoreReference, SqliteKVStoreConfig
|
||||
from llama_stack.providers.inline.vector_io.faiss.config import FaissVectorIOConfig
|
||||
from llama_stack.providers.inline.vector_io.faiss.faiss import FaissIndex, FaissVectorIOAdapter
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec import SQLiteVectorIOConfig
|
||||
from llama_stack.providers.inline.vector_io.sqlite_vec.sqlite_vec import SQLiteVecIndex, SQLiteVecVectorIOAdapter
|
||||
from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
|
||||
from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter
|
||||
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
|
||||
from llama_stack.providers.utils.kvstore import register_kvstore_backends
|
||||
|
||||
EMBEDDING_DIMENSION = 768
|
||||
COLLECTION_PREFIX = "test_collection"
|
||||
|
|
@ -30,7 +31,7 @@ def vector_provider(request):
|
|||
|
||||
|
||||
@pytest.fixture
|
||||
def vector_db_id() -> str:
|
||||
def vector_store_id() -> str:
|
||||
return f"test-vector-db-{random.randint(1, 100)}"
|
||||
|
||||
|
||||
|
|
@ -112,8 +113,9 @@ async def unique_kvstore_config(tmp_path_factory):
|
|||
unique_id = f"test_kv_{np.random.randint(1e6)}"
|
||||
temp_dir = tmp_path_factory.getbasetemp()
|
||||
db_path = str(temp_dir / f"{unique_id}.db")
|
||||
|
||||
return SqliteKVStoreConfig(db_path=db_path)
|
||||
backend_name = f"kv_vector_{unique_id}"
|
||||
register_kvstore_backends({backend_name: SqliteKVStoreConfig(db_path=db_path)})
|
||||
return KVStoreReference(backend=backend_name, namespace=f"vector_io::{unique_id}")
|
||||
|
||||
|
||||
@pytest.fixture(scope="session")
|
||||
|
|
@ -138,18 +140,17 @@ async def sqlite_vec_vec_index(embedding_dimension, tmp_path_factory):
|
|||
async def sqlite_vec_adapter(sqlite_vec_db_path, unique_kvstore_config, mock_inference_api, embedding_dimension):
|
||||
config = SQLiteVectorIOConfig(
|
||||
db_path=sqlite_vec_db_path,
|
||||
kvstore=unique_kvstore_config,
|
||||
persistence=unique_kvstore_config,
|
||||
)
|
||||
adapter = SQLiteVecVectorIOAdapter(
|
||||
config=config,
|
||||
inference_api=mock_inference_api,
|
||||
files_api=None,
|
||||
models_api=None,
|
||||
)
|
||||
collection_id = f"sqlite_test_collection_{np.random.randint(1e6)}"
|
||||
await adapter.initialize()
|
||||
await adapter.register_vector_db(
|
||||
VectorDB(
|
||||
await adapter.register_vector_store(
|
||||
VectorStore(
|
||||
identifier=collection_id,
|
||||
provider_id="test_provider",
|
||||
embedding_model="test_model",
|
||||
|
|
@ -177,17 +178,16 @@ async def faiss_vec_index(embedding_dimension):
|
|||
@pytest.fixture
|
||||
async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding_dimension):
|
||||
config = FaissVectorIOConfig(
|
||||
kvstore=unique_kvstore_config,
|
||||
persistence=unique_kvstore_config,
|
||||
)
|
||||
adapter = FaissVectorIOAdapter(
|
||||
config=config,
|
||||
inference_api=mock_inference_api,
|
||||
files_api=None,
|
||||
models_api=None,
|
||||
)
|
||||
await adapter.initialize()
|
||||
await adapter.register_vector_db(
|
||||
VectorDB(
|
||||
await adapter.register_vector_store(
|
||||
VectorStore(
|
||||
identifier=f"faiss_test_collection_{np.random.randint(1e6)}",
|
||||
provider_id="test_provider",
|
||||
embedding_model="test_model",
|
||||
|
|
@ -215,7 +215,7 @@ def mock_psycopg2_connection():
|
|||
async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection):
|
||||
connection, cursor = mock_psycopg2_connection
|
||||
|
||||
vector_db = VectorDB(
|
||||
vector_store = VectorStore(
|
||||
identifier="test-vector-db",
|
||||
embedding_model="test-model",
|
||||
embedding_dimension=embedding_dimension,
|
||||
|
|
@ -225,7 +225,7 @@ async def pgvector_vec_index(embedding_dimension, mock_psycopg2_connection):
|
|||
|
||||
with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.psycopg2"):
|
||||
with patch("llama_stack.providers.remote.vector_io.pgvector.pgvector.execute_values"):
|
||||
index = PGVectorIndex(vector_db, embedding_dimension, connection, distance_metric="COSINE")
|
||||
index = PGVectorIndex(vector_store, embedding_dimension, connection, distance_metric="COSINE")
|
||||
index._test_chunks = []
|
||||
original_add_chunks = index.add_chunks
|
||||
|
||||
|
|
@ -253,7 +253,7 @@ async def pgvector_vec_adapter(unique_kvstore_config, mock_inference_api, embedd
|
|||
db="test_db",
|
||||
user="test_user",
|
||||
password="test_password",
|
||||
kvstore=unique_kvstore_config,
|
||||
persistence=unique_kvstore_config,
|
||||
)
|
||||
|
||||
adapter = PGVectorVectorIOAdapter(config, mock_inference_api, None)
|
||||
|
|
@ -281,30 +281,30 @@ async def pgvector_vec_adapter(unique_kvstore_config, mock_inference_api, embedd
|
|||
await adapter.initialize()
|
||||
adapter.conn = mock_conn
|
||||
|
||||
async def mock_insert_chunks(vector_db_id, chunks, ttl_seconds=None):
|
||||
index = await adapter._get_and_cache_vector_db_index(vector_db_id)
|
||||
async def mock_insert_chunks(vector_store_id, chunks, ttl_seconds=None):
|
||||
index = await adapter._get_and_cache_vector_store_index(vector_store_id)
|
||||
if not index:
|
||||
raise ValueError(f"Vector DB {vector_db_id} not found")
|
||||
raise ValueError(f"Vector DB {vector_store_id} not found")
|
||||
await index.insert_chunks(chunks)
|
||||
|
||||
adapter.insert_chunks = mock_insert_chunks
|
||||
|
||||
async def mock_query_chunks(vector_db_id, query, params=None):
|
||||
index = await adapter._get_and_cache_vector_db_index(vector_db_id)
|
||||
async def mock_query_chunks(vector_store_id, query, params=None):
|
||||
index = await adapter._get_and_cache_vector_store_index(vector_store_id)
|
||||
if not index:
|
||||
raise ValueError(f"Vector DB {vector_db_id} not found")
|
||||
raise ValueError(f"Vector DB {vector_store_id} not found")
|
||||
return await index.query_chunks(query, params)
|
||||
|
||||
adapter.query_chunks = mock_query_chunks
|
||||
|
||||
test_vector_db = VectorDB(
|
||||
test_vector_store = VectorStore(
|
||||
identifier=f"pgvector_test_collection_{random.randint(1, 1_000_000)}",
|
||||
provider_id="test_provider",
|
||||
embedding_model="test_model",
|
||||
embedding_dimension=embedding_dimension,
|
||||
)
|
||||
await adapter.register_vector_db(test_vector_db)
|
||||
adapter.test_collection_id = test_vector_db.identifier
|
||||
await adapter.register_vector_store(test_vector_store)
|
||||
adapter.test_collection_id = test_vector_store.identifier
|
||||
|
||||
yield adapter
|
||||
await adapter.shutdown()
|
||||
|
|
|
|||
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Reference in a new issue