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feat(responses): implement usage tracking in streaming responses (#3771)
Implementats usage accumulation to StreamingResponseOrchestrator.
The most important part was to pass `stream_options = { "include_usage":
true }` to the chat_completion call. This means I will have to record
all responses tests again because request hash will change :)
Test changes:
- Add usage assertions to streaming and non-streaming tests
- Update test recordings with actual usage data from OpenAI
This commit is contained in:
Ashwin Bharambe
GitHub
parent
e7d21e1ee3
commit
1394403360
21 changed files with 15099 additions and 612 deletions
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@ -39,12 +39,16 @@ from llama_stack.apis.agents.openai_responses import (
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OpenAIResponseOutputMessageFunctionToolCall,
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OpenAIResponseOutputMessageMCPListTools,
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OpenAIResponseText,
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OpenAIResponseUsage,
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OpenAIResponseUsageInputTokensDetails,
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OpenAIResponseUsageOutputTokensDetails,
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WebSearchToolTypes,
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)
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from llama_stack.apis.inference import (
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Inference,
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OpenAIAssistantMessageParam,
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OpenAIChatCompletion,
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OpenAIChatCompletionChunk,
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OpenAIChatCompletionToolCall,
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OpenAIChoice,
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OpenAIMessageParam,
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@ -104,6 +108,8 @@ class StreamingResponseOrchestrator:
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self.final_messages: list[OpenAIMessageParam] = []
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# mapping for annotations
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self.citation_files: dict[str, str] = {}
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# Track accumulated usage across all inference calls
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self.accumulated_usage: OpenAIResponseUsage | None = None
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def _clone_outputs(self, outputs: list[OpenAIResponseOutput]) -> list[OpenAIResponseOutput]:
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cloned: list[OpenAIResponseOutput] = []
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@ -131,6 +137,7 @@ class StreamingResponseOrchestrator:
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text=self.text,
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tools=self.ctx.available_tools(),
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error=error,
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usage=self.accumulated_usage,
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)
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async def create_response(self) -> AsyncIterator[OpenAIResponseObjectStream]:
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@ -168,6 +175,9 @@ class StreamingResponseOrchestrator:
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stream=True,
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temperature=self.ctx.temperature,
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response_format=response_format,
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stream_options={
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"include_usage": True,
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},
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)
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# Process streaming chunks and build complete response
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@ -298,6 +308,51 @@ class StreamingResponseOrchestrator:
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return function_tool_calls, non_function_tool_calls, approvals, next_turn_messages
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def _accumulate_chunk_usage(self, chunk: OpenAIChatCompletionChunk) -> None:
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"""Accumulate usage from a streaming chunk into the response usage format."""
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if not chunk.usage:
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return
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if self.accumulated_usage is None:
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# Convert from chat completion format to response format
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self.accumulated_usage = OpenAIResponseUsage(
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input_tokens=chunk.usage.prompt_tokens,
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output_tokens=chunk.usage.completion_tokens,
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total_tokens=chunk.usage.total_tokens,
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input_tokens_details=(
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OpenAIResponseUsageInputTokensDetails(cached_tokens=chunk.usage.prompt_tokens_details.cached_tokens)
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if chunk.usage.prompt_tokens_details
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else None
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),
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output_tokens_details=(
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OpenAIResponseUsageOutputTokensDetails(
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reasoning_tokens=chunk.usage.completion_tokens_details.reasoning_tokens
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)
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if chunk.usage.completion_tokens_details
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else None
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),
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)
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else:
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# Accumulate across multiple inference calls
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self.accumulated_usage = OpenAIResponseUsage(
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input_tokens=self.accumulated_usage.input_tokens + chunk.usage.prompt_tokens,
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output_tokens=self.accumulated_usage.output_tokens + chunk.usage.completion_tokens,
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total_tokens=self.accumulated_usage.total_tokens + chunk.usage.total_tokens,
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# Use latest non-null details
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input_tokens_details=(
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OpenAIResponseUsageInputTokensDetails(cached_tokens=chunk.usage.prompt_tokens_details.cached_tokens)
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if chunk.usage.prompt_tokens_details
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else self.accumulated_usage.input_tokens_details
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),
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output_tokens_details=(
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OpenAIResponseUsageOutputTokensDetails(
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reasoning_tokens=chunk.usage.completion_tokens_details.reasoning_tokens
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)
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if chunk.usage.completion_tokens_details
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else self.accumulated_usage.output_tokens_details
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),
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)
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async def _process_streaming_chunks(
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self, completion_result, output_messages: list[OpenAIResponseOutput]
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) -> AsyncIterator[OpenAIResponseObjectStream | ChatCompletionResult]:
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@ -323,6 +378,10 @@ class StreamingResponseOrchestrator:
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chat_response_id = chunk.id
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chunk_created = chunk.created
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chunk_model = chunk.model
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# Accumulate usage from chunks (typically in final chunk with stream_options)
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self._accumulate_chunk_usage(chunk)
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for chunk_choice in chunk.choices:
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# Emit incremental text content as delta events
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if chunk_choice.delta.content:
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File diff suppressed because it is too large
Load diff
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@ -0,0 +1,130 @@
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{
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"test_id": "tests/integration/agents/test_openai_responses.py::test_responses_store[openai_client-txt=ollama/llama3.2:3b-instruct-fp16-tools1-True]",
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"request": {
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"method": "POST",
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"url": "http://0.0.0.0:11434/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "llama3.2:3b-instruct-fp16",
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"messages": [
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{
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"role": "user",
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"content": "What's the weather in Tokyo? YOU MUST USE THE get_weather function to get the weather."
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}
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],
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"stream": true,
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"stream_options": {
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"include_usage": true
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},
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"tools": [
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{
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"type": "function",
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"function": {
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"type": "function",
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"name": "get_weather",
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"description": "Get the weather in a given city",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "The city to get the weather for"
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}
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}
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},
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"strict": null
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}
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}
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]
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},
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"endpoint": "/v1/chat/completions",
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"model": "llama3.2:3b-instruct-fp16"
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},
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"response": {
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"body": [
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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-2172059863d4",
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"choices": [
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{
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"delta": {
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"content": "",
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"function_call": null,
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"refusal": null,
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"role": "assistant",
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"tool_calls": [
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{
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"index": 0,
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"id": "call_os3xa9go",
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"function": {
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"arguments": "{\"city\":\"Tokyo\"}",
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"name": "get_weather"
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},
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"type": "function"
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}
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]
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},
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"finish_reason": null,
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}
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],
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"created": 0,
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_ollama",
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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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"choices": [
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"role": "assistant",
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"finish_reason": "tool_calls",
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}
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],
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"created": 0,
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"object": "chat.completion.chunk",
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"system_fingerprint": "fp_ollama",
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"usage": null
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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-2172059863d4",
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"choices": [],
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"created": 0,
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"model": "llama3.2:3b-instruct-fp16",
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"object": "chat.completion.chunk",
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"service_tier": null,
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"system_fingerprint": "fp_ollama",
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"usage": {
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"completion_tokens": 15,
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"prompt_tokens": 179,
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"total_tokens": 194,
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"completion_tokens_details": null,
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"prompt_tokens_details": null
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}
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}
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}
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],
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"is_streaming": true
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},
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"id_normalization_mapping": {}
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}
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@ -0,0 +1,272 @@
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{
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"test_id": "tests/integration/agents/test_openai_responses.py::test_list_response_input_items_with_limit_and_order[txt=ollama/llama3.2:3b-instruct-fp16]",
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"request": {
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"method": "POST",
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"url": "http://0.0.0.0:11434/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "llama3.2:3b-instruct-fp16",
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"messages": [
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{
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"role": "user",
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"content": "Message A: What is the capital of France?"
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},
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{
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"role": "assistant",
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"content": "The capital of France is Paris."
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},
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{
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"role": "user",
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"content": "Message B: What about Spain?"
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},
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{
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"role": "assistant",
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"content": "The capital of Spain is Madrid."
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},
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{
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"role": "user",
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"content": "Message C: And Italy?"
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}
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],
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"stream": true,
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"stream_options": {
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"include_usage": true
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}
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},
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"endpoint": "/v1/chat/completions",
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"model": "llama3.2:3b-instruct-fp16"
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},
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"response": {
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"body": [
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"service_tier": "default",
|
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"system_fingerprint": "fp_cbf1785567",
|
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"usage": {
|
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"completion_tokens": 6,
|
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"prompt_tokens": 14,
|
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"total_tokens": 20,
|
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"completion_tokens_details": {
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"accepted_prediction_tokens": 0,
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"audio_tokens": 0,
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"reasoning_tokens": 0,
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"rejected_prediction_tokens": 0
|
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},
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"prompt_tokens_details": {
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"audio_tokens": 0,
|
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|
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}
|
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},
|
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"obfuscation": ""
|
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}
|
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}
|
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],
|
||||
"is_streaming": true
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
@ -43,7 +43,7 @@
|
|||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
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"usage": null,
|
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"obfuscation": "zz0dWhiq9Lc"
|
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"obfuscation": "zV8s7dKVA0e"
|
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}
|
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},
|
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{
|
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|
|
@ -70,7 +70,7 @@
|
|||
"service_tier": "default",
|
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"system_fingerprint": "fp_f33640a400",
|
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"usage": null,
|
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"obfuscation": "apZyVste9l"
|
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"obfuscation": "DoxBbL71uu"
|
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}
|
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},
|
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{
|
||||
|
|
@ -97,7 +97,7 @@
|
|||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
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"usage": null,
|
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"obfuscation": "bKzudmyiZd"
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"obfuscation": "3kPw1tJtBy"
|
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}
|
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},
|
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{
|
||||
|
|
@ -124,7 +124,7 @@
|
|||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "OgfYmGnf"
|
||||
"obfuscation": "GYMVbcCI"
|
||||
}
|
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},
|
||||
{
|
||||
|
|
@ -151,7 +151,7 @@
|
|||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "nWM9P08zaL"
|
||||
"obfuscation": "0alfiwg16c"
|
||||
}
|
||||
},
|
||||
{
|
||||
|
|
@ -178,7 +178,7 @@
|
|||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "gno9CMj"
|
||||
"obfuscation": "Wug4Phl"
|
||||
}
|
||||
},
|
||||
{
|
||||
|
|
@ -205,7 +205,7 @@
|
|||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "iheIwJKTdAVV"
|
||||
"obfuscation": "cIZ0eTIUOj4x"
|
||||
}
|
||||
},
|
||||
{
|
||||
|
|
@ -232,10 +232,11 @@
|
|||
"service_tier": "default",
|
||||
"system_fingerprint": "fp_f33640a400",
|
||||
"usage": null,
|
||||
"obfuscation": "X6N8Z47"
|
||||
"obfuscation": "FbdywkT"
|
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}
|
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}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
},
|
||||
"id_normalization_mapping": {}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -23,6 +23,14 @@ def test_response_non_streaming_basic(compat_client, text_model_id, case):
|
|||
assert len(output_text) > 0
|
||||
assert case.expected.lower() in output_text
|
||||
|
||||
# Verify usage is reported
|
||||
assert response.usage is not None, "Response should include usage information"
|
||||
assert response.usage.input_tokens > 0, "Input tokens should be greater than 0"
|
||||
assert response.usage.output_tokens > 0, "Output tokens should be greater than 0"
|
||||
assert response.usage.total_tokens == response.usage.input_tokens + response.usage.output_tokens, (
|
||||
"Total tokens should equal input + output tokens"
|
||||
)
|
||||
|
||||
retrieved_response = compat_client.responses.retrieve(response_id=response.id)
|
||||
assert retrieved_response.output_text == response.output_text
|
||||
|
||||
|
|
@ -73,6 +81,15 @@ def test_response_streaming_basic(compat_client, text_model_id, case):
|
|||
assert len(output_text) > 0, "Response should have content"
|
||||
assert case.expected.lower() in output_text, f"Expected '{case.expected}' in response"
|
||||
|
||||
# Verify usage is reported in final response
|
||||
assert chunk.response.usage is not None, "Completed response should include usage information"
|
||||
assert chunk.response.usage.input_tokens > 0, "Input tokens should be greater than 0"
|
||||
assert chunk.response.usage.output_tokens > 0, "Output tokens should be greater than 0"
|
||||
assert (
|
||||
chunk.response.usage.total_tokens
|
||||
== chunk.response.usage.input_tokens + chunk.response.usage.output_tokens
|
||||
), "Total tokens should equal input + output tokens"
|
||||
|
||||
# Use validator for common checks
|
||||
validator = StreamingValidator(events)
|
||||
validator.assert_basic_event_sequence()
|
||||
|
|
|
|||
|
|
@ -167,6 +167,9 @@ async def test_create_openai_response_with_string_input(openai_responses_impl, m
|
|||
tools=None,
|
||||
stream=True,
|
||||
temperature=0.1,
|
||||
stream_options={
|
||||
"include_usage": True,
|
||||
},
|
||||
)
|
||||
|
||||
# Should have content part events for text streaming
|
||||
|
|
|
|||
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Add table
Add a link
Reference in a new issue