mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-12-03 09:53:45 +00:00
f18870a221
# Fix for Issue #3797 ## Problem Vector store search failed with Pydantic ValidationError when chunk metadata contained list-type values. **Error:** ``` ValidationError: 3 validation errors for VectorStoreSearchResponse attributes.tags.str: Input should be a valid string attributes.tags.float: Input should be a valid number attributes.tags.bool: Input should be a valid boolean ``` **Root Cause:** - `Chunk.metadata` accepts `dict[str, Any]` (any type allowed) - `VectorStoreSearchResponse.attributes` requires `dict[str, str | float | bool]` (primitives only) - Direct assignment at line 641 caused validation failure for non-primitive types ## Solution Added utility function to filter metadata to primitive types before creating search response. ## Impact **Fixed:** - Vector search works with list metadata (e.g., `tags: ["transformers", "gpu"]`) - Lists become searchable as comma-separated strings - No ValidationError on search responses **Preserved:** - Full metadata still available in `VectorStoreContent.metadata` - No API schema changes - Backward compatible with existing primitive metadata **Affected:** All vector store providers using `OpenAIVectorStoreMixin`: FAISS, Chroma, Qdrant, Milvus, Weaviate, PGVector, SQLite-vec ## Testing tests/unit/providers/vector_io/test_vector_utils.py::test_sanitize_metadata_for_attributes --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> Co-authored-by: Francisco Arceo <arceofrancisco@gmail.com> |
||
|---|---|---|
| .. | ||
| docs | ||
| notebooks | ||
| scripts | ||
| src | ||
| static | ||
| supplementary | ||
| zero_to_hero_guide | ||
| docusaurus.config.ts | ||
| dog.jpg | ||
| getting_started.ipynb | ||
| getting_started_llama4.ipynb | ||
| getting_started_llama_api.ipynb | ||
| license_header.txt | ||
| original_rfc.md | ||
| package-lock.json | ||
| package.json | ||
| quick_start.ipynb | ||
| README.md | ||
| sidebars.ts | ||
| tsconfig.json | ||
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 docs/ directory, run the following commands to render the docs locally:
npm install
npm run gen-api-docs all
npm run build
npm run serve
You can open up the docs in your browser at http://localhost:3000
File Import System
This documentation uses remark-code-import to import files directly from the repository, eliminating copy-paste maintenance. Files are automatically embedded during build time.
Importing Code Files
To import Python code (or any code files) with syntax highlighting, use this syntax in .mdx files:
```python file=./demo_script.py title="demo_script.py"
This automatically imports the file content and displays it as a formatted code block with Python syntax highlighting.
**Note:** Paths are relative to the current `.mdx` file location, not the repository root.
### Importing Markdown Files as Content
For importing and rendering markdown files (like CONTRIBUTING.md), use the raw-loader approach:
```jsx
import Contributing from '!!raw-loader!../../../CONTRIBUTING.md';
import ReactMarkdown from 'react-markdown';
<ReactMarkdown>{Contributing}</ReactMarkdown>
Requirements:
- Install dependencies:
npm install --save-dev raw-loader react-markdown
Path Resolution:
- For
remark-code-import: Paths are relative to the current.mdxfile location - For
raw-loader: Paths are relative to the current.mdxfile location - Use
../to navigate up directories as needed
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