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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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llama_stack/providers/remote/vector_io/qdrant/qdrant.py
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@ -70,8 +70,8 @@ class QdrantIndex(EmbeddingIndex):
)
points = []
for i, (chunk, embedding) in enumerate(zip(chunks, embeddings, strict=False)):
chunk_id = f"{chunk.metadata['document_id']}:chunk-{i}"
for _i, (chunk, embedding) in enumerate(zip(chunks, embeddings, strict=False)):
chunk_id = chunk.chunk_id
points.append(
PointStruct(
id=convert_id(chunk_id),