diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 7f50e866f..012a99894 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -166,14 +166,3 @@ jobs: echo "::error::Full mypy failed. Reproduce locally with 'uv run pre-commit run mypy-full --hook-stage manual --all-files'." fi exit $status - - - name: Check if any unused recordings - run: | - set -e - PYTHONPATH=$PWD uv run ./scripts/cleanup_recordings.py --delete - changes=$(git status --short tests/integration | grep 'recordings' || true) - if [ -n "$changes" ]; then - echo "::error::Unused integration recordings detected. Run 'PYTHONPATH=$(pwd) uv run ./scripts/cleanup_recordings.py --delete' locally and commit the deletions." - echo "$changes" - exit 1 - fi diff --git a/tests/integration/batches/recordings/1d782697e7261920e2c21babd28d5126609fdc27c61a1d793792e713a48f6368.json b/tests/integration/batches/recordings/1d782697e7261920e2c21babd28d5126609fdc27c61a1d793792e713a48f6368.json new file mode 100644 index 000000000..c96b0bde8 --- /dev/null +++ b/tests/integration/batches/recordings/1d782697e7261920e2c21babd28d5126609fdc27c61a1d793792e713a48f6368.json @@ -0,0 +1,1206 @@ +{ + "test_id": "tests/integration/batches/test_batches.py::TestBatchesIntegration::test_batch_e2e_embeddings[emb=ollama/all-minilm:l6-v2]", + "request": { + "method": "POST", + "url": "http://0.0.0.0:11434/v1/v1/embeddings", + "headers": {}, + "body": { + "model": "all-minilm:l6-v2", + "input": [ + "How are you?", + "Good morning", + "Have a great day" + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": 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"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?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_aPe1vS1v5bIwPgl789D5bfmW", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_aPe1vS1v5bIwPgl789D5bfmW", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-690481987689, score: 2.5781234969335522, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-690481987689', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-690481987689|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n" + }, + { + "type": "text", + "text": "END of knowledge_search tool results.\n" + }, + { + "type": "text", + "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. 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\nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. \n" + }, + { + "type": "text", + "text": "[2] document_id: file-de7f28ce49b04e58ae6e21a63745e339, score: 1.1417523389560924, attributes: {'filename': 'llama_stack_and_models.pdf', 'document_id': 'file-de7f28ce49b04e58ae6e21a63745e339', 'token_count': 498.0, 'metadata_token_count': 11.0} (cite as <|file-de7f28ce49b04e58ae6e21a63745e339|>)\nLlama Stack \nLlama Stack Overview \nLlama Stack standardizes the core building blocks that simplify AI application development. 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b/tests/integration/responses/recordings/19a995f94d68e53d09f378afe7f4a27af4f029b679de5b986157dfd7fe7a4ec2.json @@ -0,0 +1,1645 @@ +{ + "test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[client_with_models-txt=openai/gpt-4o:emb=Nomic-v1.5:dim=768]", + "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?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_uy4RL1d6RPGFQ0dV63O5lqgM", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_uy4RL1d6RPGFQ0dV63O5lqgM", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-562027679042, score: 0.1388247736002883, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-562027679042', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-562027679042|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n" + }, + { + "type": "text", + "text": "END of knowledge_search tool results.\n" + }, + { + "type": "text", + "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. 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It codifies best \npractices\n \nacross\n \nthe\n \nLlama\n \necosystem.\n \nMore\n \nspecifically,\n \nit\n \nprovides\n \u25cf Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. \u25cf Plugin architecture to support the rich ecosystem of different API implementations in various \nenvironments,\n \nincluding\n \nlocal\n \ndevelopment,\n \non-premises,\n \ncloud,\n \nand\n \nmobile.\n \u25cf Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly \nand\n \nreliably\n \nin\n \nany\n \nenvironment.\n \u25cf Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android. \u25cf Standalone applications as examples for how to build production-grade AI applications with Llama \nStack.\n \nLlama Stack Benefits \n\u25cf Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy \nflexible\n \ndeployment\n \nchoices.\n \u25cf Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI \napplications\n \nwith\n \nconsistent\n \napplication\n \nbehavior.\n \u25cf Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. ", + ", \nhardware\n \nvendors,\n \nand\n \nAI-focused\n \ncompanies)\n \nthat\n \noffer\n \ntailored\n \ninfrastructure,\n \nsoftware,\n \nand\n \nservices\n \nfor\n \ndeploying\n \nLlama\n \nmodels.\n \nLlama 4 Maverick \n Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts. " + ], + "encoding_format": "float" + }, + "endpoint": "/v1/embeddings", + "model": "text-embedding-3-small" + }, + "response": { + "body": { + "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse", + "__data__": { + "data": [ + { + "embedding": [ + 0.030830158, + -0.03666073, + 0.03208147, + -0.0040933825, + 0.024933025, + -0.0059004608, + -0.02555868, + -0.010436461, + -0.012300115, + 0.013531458, + -0.0095978165, + -0.041665975, + -0.023561908, + -0.021977803, + -0.011627868, + 0.028833386, + -0.025146013, + -0.014642994, + -0.008040335, + 0.057826515, + -0.003554254, + 0.0070951963, + 0.010702698, + 0.016213788, + 0.009045376, + 0.0122269, + -0.026410636, + 0.007188379, + 0.003203155, + -0.039642576, + 0.07209678, + -0.044914056, + -0.01666639, + 0.029658718, + -0.02233722, + 0.0072016907, + -0.02864702, + -0.004895419, + 0.0062132883, + 0.009311613, + -0.033465896, + -0.034104865, + 0.0013719486, + 0.014989101, + 0.015108908, + -0.011807578, + 0.0028836713, 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"embedding" + } + ], + "model": "text-embedding-3-small", + "object": "list", + "usage": { + "prompt_tokens": 10, + "total_tokens": 10 + } + } + }, + "is_streaming": false + } +} diff --git a/tests/integration/responses/recordings/b6484b82fd6d6cd548e7b31637994cf2bf1d630699b33ff6e5161b880156e840.json b/tests/integration/responses/recordings/b6484b82fd6d6cd548e7b31637994cf2bf1d630699b33ff6e5161b880156e840.json new file mode 100644 index 000000000..86bec634b --- /dev/null +++ b/tests/integration/responses/recordings/b6484b82fd6d6cd548e7b31637994cf2bf1d630699b33ff6e5161b880156e840.json @@ -0,0 +1,291 @@ +{ + "test_id": "tests/integration/responses/test_tool_responses.py::test_response_sequential_file_search[client_with_models-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536]", + "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?" + }, + { + "role": "assistant", + "content": "", + "tool_calls": [ + { + "index": 0, + "id": "call_zS2WxgXWetjnlPt2MzH9Asrc", + "type": "function", + "function": { + "name": "knowledge_search", + "arguments": "{\"query\":\"Llama 4 Maverick model number of experts\"}" + } + } + ] + }, + { + "role": "tool", + "tool_call_id": "call_zS2WxgXWetjnlPt2MzH9Asrc", + "content": [ + { + "type": "text", + "text": "knowledge_search tool found 1 chunks:\nBEGIN of knowledge_search tool results.\n" + }, + { + "type": "text", + "text": "[1] document_id: file-5217982280, score: 2.57802841833685, attributes: {'filename': 'test_sequential_file_search.txt', 'document_id': 'file-5217982280', 'token_count': 19.0, 'metadata_token_count': 11.0} (cite as <|file-5217982280|>)\nThe Llama 4 Maverick model has 128 experts in its mixture of experts architecture.\n" + }, + { + "type": "text", + "text": "END of knowledge_search tool results.\n" + }, + { + "type": "text", + "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. 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