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https://github.com/meta-llama/llama-stack.git
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7c63aebd64
Implements missing streaming events from OpenAI Responses API spec: - reasoning text/summary events for o1/o3 models, - refusal events for safety moderation - annotation events for citations, - and file search streaming events. Added optional reasoning_content field to chat completion chunks to support non-standard provider extensions. **NOTE:** OpenAI does _not_ fill reasoning_content when users use the chat_completion APIs. This means there is no way for us to implement Responses (with reasoning) by using OpenAI chat completions! We'd need to transparently punt to OpenAI's responses endpoints if we wish to do that. For others though (vLLM, etc.) we can use it. ## Test Plan File search streaming test passes: ``` ./scripts/integration-tests.sh --stack-config server:ci-tests \ --suite responses --setup gpt --inference-mode replay --pattern test_response_file_search_streaming_events ``` Need more complex setup and validation for reasoning tests (need a vLLM powered OSS model maybe gpt-oss which can return reasoning_content). I will do that in a followup PR.
1153 lines
35 KiB
JSON
1153 lines
35 KiB
JSON
{
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"test_id": "tests/integration/responses/test_file_search.py::test_response_file_search_streaming_events[openai_client-txt=openai/gpt-4o]",
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"request": {
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"method": "POST",
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"url": "https://api.openai.com/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "gpt-4o",
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"messages": [
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"role": "user",
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"content": "What are the marketing updates?"
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},
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"role": "assistant",
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"content": "",
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"index": 0,
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"id": "call_Vdxnbo2D8ds3BuKCon8XUt9P",
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"arguments": "{\"query\":\"marketing updates October 2023\"}"
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}
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}
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]
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{
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"role": "tool",
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"tool_call_id": "call_Vdxnbo2D8ds3BuKCon8XUt9P",
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"content": [
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{
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"type": "text",
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"text": "knowledge_search tool found 4 chunks:\nBEGIN of knowledge_search tool results.\n"
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},
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{
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"type": "text",
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"text": "[1] document_id: file-284290576317, score: 1.1939300430192143, attributes: {'region': 'us', 'category': 'engineering', 'date': 1680307200.0, 'filename': 'us_engineering_q2.txt', 'document_id': 'file-284290576317', 'token_count': 18.0, 'metadata_token_count': 32.0} (cite as <|file-284290576317|>)\nUS technical updates for Q2 2023. New features deployed in the US region.\n"
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},
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{
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"type": "text",
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"text": "[2] document_id: file-284290576316, score: 0.9860226028240189, attributes: {'region': 'us', 'category': 'marketing', 'date': 1672531200.0, 'filename': 'us_marketing_q1.txt', 'document_id': 'file-284290576316', 'token_count': 21.0, 'metadata_token_count': 32.0} (cite as <|file-284290576316|>)\nUS promotional campaigns for Q1 2023. Revenue increased by 15% in the US region.\n"
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},
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{
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"type": "text",
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"text": "[3] document_id: file-284290576318, score: 0.9676964172910248, attributes: {'region': 'eu', 'category': 'marketing', 'date': 1672531200.0, 'filename': 'eu_marketing_q1.txt', 'document_id': 'file-284290576318', 'token_count': 17.0, 'metadata_token_count': 32.0} (cite as <|file-284290576318|>)\nEuropean advertising campaign results for Q1 2023. Strong growth in EU markets.\n"
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},
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{
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"type": "text",
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"text": "[4] document_id: file-284290576319, score: 0.7632951871120561, attributes: {'region': 'asia', 'category': 'sales', 'date': 1688169600.0, 'filename': 'asia_sales_q3.txt', 'document_id': 'file-284290576319', 'token_count': 17.0, 'metadata_token_count': 31.0} (cite as <|file-284290576319|>)\nAsia Pacific revenue figures for Q3 2023. Record breaking quarter in Asia.\n"
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},
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{
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"type": "text",
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"text": "END of knowledge_search tool results.\n"
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},
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{
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"type": "text",
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"text": "The above results were retrieved to help answer the user's query: \"marketing updates October 2023\". Use them as supporting information only in answering this query. Cite sources immediately at the end of sentences before punctuation, using `<|file-id|>` format (e.g., 'This is a fact <|file-Cn3MSNn72ENTiiq11Qda4A|>.'). Do not add extra punctuation. Use only the file IDs provided (do not invent new ones).\n"
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{
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"type": "function",
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"function": {
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"name": "knowledge_search",
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"description": "Search for information in a database.",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "The query to search for. Can be a natural language sentence or keywords."
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"required": [
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