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# What does this PR do? Adds input validation for mode in RagQueryConfig This will prevent users from inputting search modes other than `vector` and `keyword` for the time being with `hybrid` to follow when that functionality is implemented. ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] ``` # Check out this PR and enter the LS directory uv sync --extra dev ``` Run the quickstart [example](https://llama-stack.readthedocs.io/en/latest/getting_started/#step-3-run-the-demo) Alter the Agent to include a query_config ``` agent = Agent( client, model=model_id, instructions="You are a helpful assistant", tools=[ { "name": "builtin::rag/knowledge_search", "args": { "vector_db_ids": [vector_db_id], "query_config": { "mode": "i-am-not-vector", # Test for non valid search mode "max_chunks": 6 } }, } ], ) ``` Ensure you get the following error: ``` 400: {'errors': [{'loc': ['mode'], 'msg': "Value error, mode must be either 'vector' or 'keyword' if supported by the vector_io provider", 'type': 'value_error'}]} ``` ## Running unit tests ``` uv sync --extra dev uv run pytest tests/unit/rag/test_rag_query.py -v ``` [//]: # (## Documentation) |
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| 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 | ||