Merge branch 'main' into remove-deprecated-completion

This commit is contained in:
Matthew Farrellee 2025-10-01 08:56:33 -04:00
commit 0a0e02a0ac
66 changed files with 13976 additions and 5631 deletions

View file

@ -16,14 +16,14 @@ Meta's reference implementation of telemetry and observability using OpenTelemet
|-------|------|----------|---------|-------------|
| `otel_exporter_otlp_endpoint` | `str \| None` | No | | The OpenTelemetry collector endpoint URL (base URL for traces, metrics, and logs). If not set, the SDK will use OTEL_EXPORTER_OTLP_ENDPOINT environment variable. |
| `service_name` | `<class 'str'>` | No | | The service name to use for telemetry |
| `sinks` | `list[inline.telemetry.meta_reference.config.TelemetrySink` | No | [&lt;TelemetrySink.CONSOLE: 'console'&gt;, &lt;TelemetrySink.SQLITE: 'sqlite'&gt;] | List of telemetry sinks to enable (possible values: otel_trace, otel_metric, sqlite, console) |
| `sinks` | `list[inline.telemetry.meta_reference.config.TelemetrySink` | No | [&lt;TelemetrySink.SQLITE: 'sqlite'&gt;] | List of telemetry sinks to enable (possible values: otel_trace, otel_metric, sqlite, console) |
| `sqlite_db_path` | `<class 'str'>` | No | ~/.llama/runtime/trace_store.db | The path to the SQLite database to use for storing traces |
## Sample Configuration
```yaml
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
```

View file

@ -159,7 +159,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
post_training:

View file

@ -50,7 +50,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:

View file

@ -46,7 +46,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/dell}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:

View file

@ -61,7 +61,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:

View file

@ -51,7 +51,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/meta-reference-gpu}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:

View file

@ -53,7 +53,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:

View file

@ -48,7 +48,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/nvidia}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:

View file

@ -81,7 +81,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/open-benchmark}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
eval:

View file

@ -159,7 +159,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
post_training:

View file

@ -159,7 +159,7 @@ providers:
provider_type: inline::meta-reference
config:
service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
sinks: ${env.TELEMETRY_SINKS:=sqlite}
sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/trace_store.db
otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
post_training:

View file

@ -129,13 +129,16 @@ class StreamingResponseOrchestrator:
messages = self.ctx.messages.copy()
while True:
# Text is the default response format for chat completion so don't need to pass it
# (some providers don't support non-empty response_format when tools are present)
response_format = None if self.ctx.response_format.type == "text" else self.ctx.response_format
completion_result = await self.inference_api.openai_chat_completion(
model=self.ctx.model,
messages=messages,
tools=self.ctx.chat_tools,
stream=True,
temperature=self.ctx.temperature,
response_format=self.ctx.response_format,
response_format=response_format,
)
# Process streaming chunks and build complete response
@ -352,8 +355,11 @@ class StreamingResponseOrchestrator:
# Emit arguments.done events for completed tool calls (differentiate between MCP and function calls)
for tool_call_index in sorted(chat_response_tool_calls.keys()):
tool_call = chat_response_tool_calls[tool_call_index]
# Ensure that arguments, if sent back to the inference provider, are not None
tool_call.function.arguments = tool_call.function.arguments or "{}"
tool_call_item_id = tool_call_item_ids[tool_call_index]
final_arguments = chat_response_tool_calls[tool_call_index].function.arguments or ""
final_arguments = tool_call.function.arguments
tool_call_name = chat_response_tool_calls[tool_call_index].function.name
# Check if this is an MCP tool call

View file

@ -30,7 +30,7 @@ class TelemetryConfig(BaseModel):
description="The service name to use for telemetry",
)
sinks: list[TelemetrySink] = Field(
default=[TelemetrySink.CONSOLE, TelemetrySink.SQLITE],
default=[TelemetrySink.SQLITE],
description="List of telemetry sinks to enable (possible values: otel_trace, otel_metric, sqlite, console)",
)
sqlite_db_path: str = Field(
@ -49,7 +49,7 @@ class TelemetryConfig(BaseModel):
def sample_run_config(cls, __distro_dir__: str, db_name: str = "trace_store.db") -> dict[str, Any]:
return {
"service_name": "${env.OTEL_SERVICE_NAME:=\u200b}",
"sinks": "${env.TELEMETRY_SINKS:=console,sqlite}",
"sinks": "${env.TELEMETRY_SINKS:=sqlite}",
"sqlite_db_path": "${env.SQLITE_STORE_DIR:=" + __distro_dir__ + "}/" + db_name,
"otel_exporter_otlp_endpoint": "${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}",
}

View file

@ -264,3 +264,36 @@ def test_function_call_output_response(openai_client, client_with_models, text_m
assert (
"sunny" in response2.output[0].content[0].text.lower() or "warm" in response2.output[0].content[0].text.lower()
)
def test_function_call_output_response_with_none_arguments(openai_client, client_with_models, text_model_id):
"""Test handling of function call outputs in responses when function does not accept arguments."""
if isinstance(client_with_models, LlamaStackAsLibraryClient):
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
client = openai_client
# First create a response that triggers a function call
response = client.responses.create(
model=text_model_id,
input=[
{
"role": "user",
"content": "what's the current time? You MUST call the `get_current_time` function to find out.",
}
],
tools=[
{
"type": "function",
"name": "get_current_time",
"description": "Get the current time",
"parameters": {},
}
],
stream=False,
)
# Verify we got a function call
assert response.output[0].type == "function_call"
assert response.output[0].arguments == "{}"
_ = response.output[0].call_id

View file

@ -21,7 +21,7 @@
"body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": {
"id": "chatcmpl-618",
"id": "chatcmpl-447",
"choices": [
{
"finish_reason": "stop",
@ -38,7 +38,7 @@
}
}
],
"created": 1759245078,
"created": 1759282456,
"model": "llama-guard3:1b",
"object": "chat.completion",
"service_tier": null,

View file

@ -20,15 +20,15 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama-guard3:1b",
"created_at": "2025-09-03T17:37:47.461886Z",
"created_at": "2025-09-30T17:37:24.035083658Z",
"done": true,
"done_reason": "stop",
"total_duration": 338927833,
"load_duration": 100895125,
"total_duration": 2990785181,
"load_duration": 52933018,
"prompt_eval_count": 223,
"prompt_eval_duration": 221583042,
"prompt_eval_duration": 2884018743,
"eval_count": 2,
"eval_duration": 12341416,
"eval_duration": 53216446,
"response": "safe",
"thinking": null,
"context": null

View file

@ -24,7 +24,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -39,7 +39,7 @@
"logprobs": null
}
],
"created": 1756921333,
"created": 1759254065,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -50,7 +50,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -65,7 +65,7 @@
"logprobs": null
}
],
"created": 1756921333,
"created": 1759254066,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -76,7 +76,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -91,7 +91,7 @@
"logprobs": null
}
],
"created": 1756921333,
"created": 1759254066,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -102,7 +102,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -117,7 +117,7 @@
"logprobs": null
}
],
"created": 1756921333,
"created": 1759254066,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -128,7 +128,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -143,7 +143,7 @@
"logprobs": null
}
],
"created": 1756921334,
"created": 1759254066,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -154,7 +154,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -169,7 +169,7 @@
"logprobs": null
}
],
"created": 1756921334,
"created": 1759254066,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -180,7 +180,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -195,7 +195,7 @@
"logprobs": null
}
],
"created": 1756921334,
"created": 1759254067,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -206,7 +206,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-414",
"id": "chatcmpl-106",
"choices": [
{
"delta": {
@ -221,7 +221,7 @@
"logprobs": null
}
],
"created": 1756921334,
"created": 1759254067,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,

View file

@ -40,7 +40,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -55,7 +55,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253815,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -66,7 +66,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -81,7 +81,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253815,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -92,7 +92,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -107,7 +107,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253815,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -118,7 +118,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -133,7 +133,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253816,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -144,7 +144,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -159,7 +159,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253816,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -170,7 +170,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -185,7 +185,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253816,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -196,7 +196,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -211,7 +211,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253816,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,
@ -222,7 +222,7 @@
{
"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
"__data__": {
"id": "chatcmpl-921",
"id": "chatcmpl-629",
"choices": [
{
"delta": {
@ -237,7 +237,7 @@
"logprobs": null
}
],
"created": 1756920971,
"created": 1759253816,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion.chunk",
"service_tier": null,

View file

@ -21,7 +21,7 @@
"body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": {
"id": "chatcmpl-438",
"id": "chatcmpl-478",
"choices": [
{
"finish_reason": "stop",
@ -38,7 +38,7 @@
}
}
],
"created": 1759245073,
"created": 1759282396,
"model": "llama-guard3:1b",
"object": "chat.completion",
"service_tier": null,

View file

@ -22,7 +22,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.436472Z",
"created_at": "2025-10-01T01:34:06.144961341Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -40,7 +40,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.478138Z",
"created_at": "2025-10-01T01:34:06.3373667Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -58,7 +58,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.519952Z",
"created_at": "2025-10-01T01:34:06.532942727Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -76,7 +76,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.561433Z",
"created_at": "2025-10-01T01:34:06.728352251Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -94,7 +94,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.603624Z",
"created_at": "2025-10-01T01:34:06.924985367Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -112,7 +112,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.645851Z",
"created_at": "2025-10-01T01:34:07.121349528Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -130,7 +130,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.688403Z",
"created_at": "2025-10-01T01:34:07.318123626Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -148,7 +148,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.72991Z",
"created_at": "2025-10-01T01:34:07.51621183Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -166,7 +166,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.771635Z",
"created_at": "2025-10-01T01:34:07.715339999Z",
"done": false,
"done_reason": null,
"total_duration": null,
@ -184,7 +184,7 @@
"__type__": "ollama._types.GenerateResponse",
"__data__": {
"model": "llama3.2:3b-instruct-fp16",
"created_at": "2025-09-03T17:37:50.813711Z",
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View file

@ -37,7 +37,6 @@ from llama_stack.apis.inference import (
OpenAIJSONSchema,
OpenAIResponseFormatJSONObject,
OpenAIResponseFormatJSONSchema,
OpenAIResponseFormatText,
OpenAIUserMessageParam,
)
from llama_stack.apis.tools.tools import Tool, ToolGroups, ToolInvocationResult, ToolParameter, ToolRuntime
@ -148,7 +147,7 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
mock_inference_api.openai_chat_completion.assert_called_once_with(
model=model,
messages=[OpenAIUserMessageParam(role="user", content="What is the capital of Ireland?", name=None)],
response_format=OpenAIResponseFormatText(),
response_format=None,
tools=None,
stream=True,
temperature=0.1,
@ -328,6 +327,132 @@ async def test_create_openai_response_with_tool_call_type_none(openai_responses_
assert chunks[5].response.output[0].name == "get_weather"
async def test_create_openai_response_with_tool_call_function_arguments_none(openai_responses_impl, mock_inference_api):
"""Test creating an OpenAI response with a tool call response that has a function that does not accept arguments, or arguments set to None when they are not mandatory."""
# Setup
input_text = "What is the time right now?"
model = "meta-llama/Llama-3.1-8B-Instruct"
async def fake_stream_toolcall():
yield ChatCompletionChunk(
id="123",
choices=[
Choice(
index=0,
delta=ChoiceDelta(
tool_calls=[
ChoiceDeltaToolCall(
index=0,
id="tc_123",
function=ChoiceDeltaToolCallFunction(name="get_current_time", arguments=None),
type=None,
)
]
),
),
],
created=1,
model=model,
object="chat.completion.chunk",
)
mock_inference_api.openai_chat_completion.return_value = fake_stream_toolcall()
# Function does not accept arguments
result = await openai_responses_impl.create_openai_response(
input=input_text,
model=model,
stream=True,
temperature=0.1,
tools=[
OpenAIResponseInputToolFunction(
name="get_current_time",
description="Get current time for system's timezone",
parameters={},
)
],
)
# Check that we got the content from our mocked tool execution result
chunks = [chunk async for chunk in result]
# Verify event types
# Should have: response.created, output_item.added, function_call_arguments.delta,
# function_call_arguments.done, output_item.done, response.completed
assert len(chunks) == 5
# Verify inference API was called correctly (after iterating over result)
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
assert first_call.kwargs["messages"][0].content == input_text
assert first_call.kwargs["tools"] is not None
assert first_call.kwargs["temperature"] == 0.1
# Check response.created event (should have empty output)
assert chunks[0].type == "response.created"
assert len(chunks[0].response.output) == 0
# Check streaming events
assert chunks[1].type == "response.output_item.added"
assert chunks[2].type == "response.function_call_arguments.done"
assert chunks[3].type == "response.output_item.done"
# Check response.completed event (should have the tool call with arguments set to "{}")
assert chunks[4].type == "response.completed"
assert len(chunks[4].response.output) == 1
assert chunks[4].response.output[0].type == "function_call"
assert chunks[4].response.output[0].name == "get_current_time"
assert chunks[4].response.output[0].arguments == "{}"
mock_inference_api.openai_chat_completion.return_value = fake_stream_toolcall()
# Function accepts optional arguments
result = await openai_responses_impl.create_openai_response(
input=input_text,
model=model,
stream=True,
temperature=0.1,
tools=[
OpenAIResponseInputToolFunction(
name="get_current_time",
description="Get current time for system's timezone",
parameters={
"timezone": "string",
},
)
],
)
# Check that we got the content from our mocked tool execution result
chunks = [chunk async for chunk in result]
# Verify event types
# Should have: response.created, output_item.added, function_call_arguments.delta,
# function_call_arguments.done, output_item.done, response.completed
assert len(chunks) == 5
# Verify inference API was called correctly (after iterating over result)
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
assert first_call.kwargs["messages"][0].content == input_text
assert first_call.kwargs["tools"] is not None
assert first_call.kwargs["temperature"] == 0.1
# Check response.created event (should have empty output)
assert chunks[0].type == "response.created"
assert len(chunks[0].response.output) == 0
# Check streaming events
assert chunks[1].type == "response.output_item.added"
assert chunks[2].type == "response.function_call_arguments.done"
assert chunks[3].type == "response.output_item.done"
# Check response.completed event (should have the tool call with arguments set to "{}")
assert chunks[4].type == "response.completed"
assert len(chunks[4].response.output) == 1
assert chunks[4].response.output[0].type == "function_call"
assert chunks[4].response.output[0].name == "get_current_time"
assert chunks[4].response.output[0].arguments == "{}"
async def test_create_openai_response_with_multiple_messages(openai_responses_impl, mock_inference_api):
"""Test creating an OpenAI response with multiple messages."""
# Setup
@ -823,16 +948,16 @@ async def test_store_response_uses_rehydrated_input_with_previous_response(
@pytest.mark.parametrize(
"text_format, response_format",
[
(OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")), OpenAIResponseFormatText()),
(OpenAIResponseText(format=OpenAIResponseTextFormat(type="text")), None),
(
OpenAIResponseText(format=OpenAIResponseTextFormat(name="Test", schema={"foo": "bar"}, type="json_schema")),
OpenAIResponseFormatJSONSchema(json_schema=OpenAIJSONSchema(name="Test", schema={"foo": "bar"})),
),
(OpenAIResponseText(format=OpenAIResponseTextFormat(type="json_object")), OpenAIResponseFormatJSONObject()),
# ensure text param with no format specified defaults to text
(OpenAIResponseText(format=None), OpenAIResponseFormatText()),
# ensure text param of None defaults to text
(None, OpenAIResponseFormatText()),
# ensure text param with no format specified defaults to None
(OpenAIResponseText(format=None), None),
# ensure text param of None defaults to None
(None, None),
],
)
async def test_create_openai_response_with_text_format(
@ -855,7 +980,6 @@ async def test_create_openai_response_with_text_format(
# Verify
first_call = mock_inference_api.openai_chat_completion.call_args_list[0]
assert first_call.kwargs["messages"][0].content == input_text
assert first_call.kwargs["response_format"] is not None
assert first_call.kwargs["response_format"] == response_format