forked from phoenix-oss/llama-stack-mirror
229f0d5f7c
# What does this PR do? Add response format for agents structured output. - [ ] Using structured output for agents (interior_design app as an example) (#issue) https://github.com/meta-llama/llama-stack-apps/issues/122 ## Test Plan E2E test plan with llama-stack-apps interior_design Please describe: Test ran: - provide instructions so it can be reproduced. Start your distro: llama stack run llama_stack/templates/fireworks/run.yaml --env FIREWORKS_API_KEY=<API_KEY> Run api test: ```PYTHONPATH=. python examples/interior_design_assistant/api.py localhost 5000 examples/interior_design_assistant/resources/documents/ examples/interior_design_assistant/resources/images/fireplaces``` ## Sources Results: https://github.com/meta-llama/llama-stack-client-python/pull/72 ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [x] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests. |
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| .. | ||
| agents | ||
| batch_inference | ||
| common | ||
| datasetio | ||
| datasets | ||
| eval | ||
| eval_tasks | ||
| inference | ||
| inspect | ||
| models | ||
| post_training | ||
| safety | ||
| scoring | ||
| scoring_functions | ||
| shields | ||
| synthetic_data_generation | ||
| telemetry | ||
| tools | ||
| vector_dbs | ||
| vector_io | ||
| __init__.py | ||
| datatypes.py | ||
| resource.py | ||
| version.py | ||