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# What does this PR do? This PR fixes a blocking issue in the detailed RAG tutorial where the code fails with a 400 Bad Request error. The root cause is that recent versions of Llama-Stack ignore the client-generated vector_db_id and assign a new server-side ID. The tutorial was not updated to reflect this, causing the rag_tool.insert call to fail. This change updates the code to capture the authoritative ID from the .identifier attribute of the register() method's response. This ensures the tutorial code runs successfully and reflects the current API behavior. ## Test Plan The fix can be verified by running the Python code snippet from the detailed tutorial page. Run the original code (Before this change): Result: The script fails with a 400 Bad Request error on the rag_tool.insert step. Run the updated code (After this change): Result: The script runs successfully to completion. Co-authored-by: Adam Young <adam.young@redhat.com> |
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| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
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| original_rfc.md | ||
| quick_start.ipynb | ||
| README.md | ||
Llama Stack Documentation
Here's a collection of comprehensive guides, examples, and resources for building AI applications with Llama Stack. For the complete documentation, visit our Github page.
Render locally
From the llama-stack root directory, run the following command to render the docs locally:
uv run --group docs sphinx-autobuild docs/source docs/build/html --write-all
You can open up the docs in your browser at http://localhost:8000
Content
Try out Llama Stack's capabilities through our detailed Jupyter notebooks:
- Building AI Applications Notebook - A comprehensive guide to building production-ready AI applications using Llama Stack
- Benchmark Evaluations Notebook - Detailed performance evaluations and benchmarking results
- Zero-to-Hero Guide - Step-by-step guide for getting started with Llama Stack