mirror of
https://github.com/meta-llama/llama-stack.git
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f50ce11a3b
Renames `inference_recorder.py` to `api_recorder.py` and extends it to support recording/replaying tool invocations in addition to inference calls. This allows us to record web-search, etc. tool calls and thereafter apply recordings for `tests/integration/responses` ## Test Plan ``` export OPENAI_API_KEY=... export TAVILY_SEARCH_API_KEY=... ./scripts/integration-tests.sh --stack-config ci-tests \ --suite responses --inference-mode record-if-missing ```
2075 lines
61 KiB
JSON
2075 lines
61 KiB
JSON
{
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"test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536]",
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"name": "knowledge_search",
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"arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}"
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"role": "tool",
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"content": [
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"text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
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"text": "[1] document_id: file-5efa8a5a4b414ce98726c4e133a18747, score: 2.5781234969335522, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-5efa8a5a4b414ce98726c4e133a18747', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-5efa8a5a4b414ce98726c4e133a18747|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
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"text": "END of knowledge_search tool results.\n"
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"type": "text",
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"text": "The above results were retrieved to help answer the user's query: \"Llama 4 Maverick model number of experts\". 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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"role": "assistant",
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"content": "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture <|file-5efa8a5a4b414ce98726c4e133a18747|>."
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"role": "user",
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"content": "Can you tell me more about the architecture?"
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"role": "assistant",
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"content": "",
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"tool_calls": [
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"id": "call_ySLmLmkOHqFkNCZrQhrXtOqO",
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"content": [
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"type": "text",
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"text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n"
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"text": "[1] document_id: file-5efa8a5a4b414ce98726c4e133a18747, score: 1.932386575539943, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-5efa8a5a4b414ce98726c4e133a18747', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-5efa8a5a4b414ce98726c4e133a18747|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n"
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"text": "END of knowledge_search tool results.\n"
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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: \"Llama 4 Maverick model architecture details\". 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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