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feat: Add ChunkMetadata to Chunk (#2497)

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# What does this PR do?
Adding `ChunkMetadata` so we can properly delete embeddings later.

More specifically, this PR refactors and extends the chunk metadata
handling in the vector database and introduces a distinction between
metadata used for model context and backend-only metadata required for
chunk management, storage, and retrieval. It also improves chunk ID
generation and propagation throughout the stack, enhances test coverage,
and adds new utility modules.

```python
class ChunkMetadata(BaseModel):
    """
    `ChunkMetadata` is backend metadata for a `Chunk` that is used to store additional information about the chunk that
        will NOT be inserted into the context during inference, but is required for backend functionality.
        Use `metadata` in `Chunk` for metadata that will be used during inference.
    """
    document_id: str | None = None
    chunk_id: str | None = None
    source: str | None = None
    created_timestamp: int | None = None
    updated_timestamp: int | None = None
    chunk_window: str | None = None
    chunk_tokenizer: str | None = None
    chunk_embedding_model: str | None = None
    chunk_embedding_dimension: int | None = None
    content_token_count: int | None = None
    metadata_token_count: int | None = None
```
Eventually we can migrate the document_id out of the `metadata` field.
I've introduced the changes so that `ChunkMetadata` is backwards
compatible with `metadata`.

<!-- If resolving an issue, uncomment and update the line below -->
Closes https://github.com/meta-llama/llama-stack/issues/2501 

## Test Plan
Added unit tests

---------

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Arceo 2025-06-25 13:55:23 -06:00 • committed by GitHub
parent fa0b0c13d4
commit 82f13fe83e
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14 changed files with 490 additions and 218 deletions
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16
tests/unit/providers/vector_io/conftest.py
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@ -9,7 +9,7 @@ import random
import numpy as np
import pytest
from llama_stack.apis.vector_io import Chunk
from llama_stack.apis.vector_io import Chunk, ChunkMetadata
EMBEDDING_DIMENSION = 384
@ -33,6 +33,20 @@ def sample_chunks():
for j in range(k)
for i in range(n)
]
sample.extend(
[
Chunk(
content=f"Sentence {i} from document {j + k}",
chunk_metadata=ChunkMetadata(
document_id=f"document-{j + k}",
chunk_id=f"document-{j}-chunk-{i}",
source=f"example source-{j + k}-{i}",
),
)
for j in range(k)
for i in range(n)
]
)
return sample