fix: Pydantic validation error with list-type metadata in vector search (#3797) (#4173)

# 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>

tests/unit/providers/vector_io/test_vector_utils.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 
 from llama_stack.providers.utils.vector_io.vector_utils import generate_chunk_id
-from llama_stack_api import Chunk, ChunkMetadata
+from llama_stack_api import Chunk, ChunkMetadata, VectorStoreFileObject
 
 # This test is a unit test for the chunk_utils.py helpers. This should only contain
 # tests which are specific to this file. More general (API-level) tests should be placed in
@@ -78,3 +78,77 @@ def test_chunk_serialization():
     serialized_chunk = chunk.model_dump()
     assert serialized_chunk["chunk_id"] == "test-chunk-id"
     assert "chunk_id" in serialized_chunk
+
+
+def test_vector_store_file_object_attributes_validation():
+    """Test VectorStoreFileObject validates and sanitizes attributes at input boundary."""
+    # Test with metadata containing lists, nested dicts, and primitives
+    from llama_stack_api.vector_io import VectorStoreChunkingStrategyAuto
+
+    file_obj = VectorStoreFileObject(
+        id="file-123",
+        attributes={
+            "tags": ["transformers", "h100-compatible", "region:us"],  # List -> string
+            "model_name": "granite-3.3-8b",  # String preserved
+            "score": 0.95,  # Float preserved
+            "active": True,  # Bool preserved
+            "count": 42,  # Int -> float
+            "nested": {"key": "value"},  # Dict filtered out
+        },
+        chunking_strategy=VectorStoreChunkingStrategyAuto(),
+        created_at=1234567890,
+        status="completed",
+        vector_store_id="vs-123",
+    )
+
+    # Lists converted to comma-separated strings
+    assert file_obj.attributes["tags"] == "transformers, h100-compatible, region:us"
+    # Primitives preserved
+    assert file_obj.attributes["model_name"] == "granite-3.3-8b"
+    assert file_obj.attributes["score"] == 0.95
+    assert file_obj.attributes["active"] is True
+    assert file_obj.attributes["count"] == 42.0  # int -> float
+    # Complex types filtered out
+    assert "nested" not in file_obj.attributes
+
+
+def test_vector_store_file_object_attributes_constraints():
+    """Test VectorStoreFileObject enforces OpenAPI constraints on attributes."""
+    from llama_stack_api.vector_io import VectorStoreChunkingStrategyAuto
+
+    # Test max 16 properties
+    many_attrs = {f"key{i}": f"value{i}" for i in range(20)}
+    file_obj = VectorStoreFileObject(
+        id="file-123",
+        attributes=many_attrs,
+        chunking_strategy=VectorStoreChunkingStrategyAuto(),
+        created_at=1234567890,
+        status="completed",
+        vector_store_id="vs-123",
+    )
+    assert len(file_obj.attributes) == 16  # Max 16 properties
+
+    # Test max 64 char keys are filtered
+    long_key_attrs = {"a" * 65: "value", "valid_key": "value"}
+    file_obj = VectorStoreFileObject(
+        id="file-124",
+        attributes=long_key_attrs,
+        chunking_strategy=VectorStoreChunkingStrategyAuto(),
+        created_at=1234567890,
+        status="completed",
+        vector_store_id="vs-123",
+    )
+    assert "a" * 65 not in file_obj.attributes
+    assert "valid_key" in file_obj.attributes
+
+    # Test max 512 char string values are truncated
+    long_value_attrs = {"key": "x" * 600}
+    file_obj = VectorStoreFileObject(
+        id="file-125",
+        attributes=long_value_attrs,
+        chunking_strategy=VectorStoreChunkingStrategyAuto(),
+        created_at=1234567890,
+        status="completed",
+        vector_store_id="vs-123",
+    )
+    assert len(file_obj.attributes["key"]) == 512