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This is an initial working prototype of wiring up the `file_search` builtin tool for the Responses API to our existing rag knowledge search tool. I stubbed in a new test (that uses a hardcoded url hybrid of the OpenAI and Llama Stack clients for now, only until we finish landing the vector store APIs and insertion support). Note that this is currently under tests/verification only because it sometimes flakes with tool calling of the small Llama-3.2-3B model we run in CI (and that I use as an example below). We'd want to make the test a bit more robust in some way if we moved this over to tests/integration and ran it in CI. ``` ollama run llama3.2:3b INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \ llama stack run ./llama_stack/templates/ollama/run.yaml \ --image-type venv \ --env OLLAMA_URL="http://0.0.0.0:11434" pytest -sv 'tests/verifications/openai_api/test_responses.py::test_response_non_streaming_file_search' \ --base-url=http://localhost:8321/v1/openai/v1 \ --model meta-llama/Llama-3.2-3B-Instruct ``` Signed-off-by: Ben Browning <bbrownin@redhat.com> |
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| .. | ||
| agents | ||
| batch_inference | ||
| benchmarks | ||
| common | ||
| datasetio | ||
| datasets | ||
| eval | ||
| files | ||
| inference | ||
| inspect | ||
| models | ||
| post_training | ||
| providers | ||
| safety | ||
| scoring | ||
| scoring_functions | ||
| shields | ||
| synthetic_data_generation | ||
| telemetry | ||
| tools | ||
| vector_dbs | ||
| vector_io | ||
| __init__.py | ||
| datatypes.py | ||
| resource.py | ||
| version.py | ||