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llama-stack-mirror/tests/integration/responses/recordings/22f0ad06b093be67d13574bd2396040875395d346873bc80e68ecb5c603bc697.json
Ashwin Bharambe f50ce11a3b
feat(tests): make inference_recorder into api_recorder (include tool_invoke) (#3403) 
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
```
2025-10-09 14:27:51 -07:00

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{
"test_id": "tests/integration/responses/test_tool_responses.py::test_response_non_streaming_file_search[openai_client-txt=openai/gpt-4o:emb=openai/text-embedding-3-small:dim=1536-llama_experts_pdf]",
"request": {
"method": "POST",
"url": "https://api.openai.com/v1/v1/embeddings",
"headers": {},
"body": {
"model": "text-embedding-3-small",
"input": [
"Llama Stack \nLlama Stack Overview \nLlama Stack standardizes the core building blocks that simplify AI application development. 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",
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