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feat: Adding ChunkMetadata

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-06-23 14:59:11 -04:00
parent 6fde601765
commit f90fce218e
13 changed files with 416 additions and 206 deletions
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37
llama_stack/apis/vector_io/vector_io.py
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@ -19,17 +19,52 @@ from llama_stack.schema_utils import json_schema_type, webmethod
from llama_stack.strong_typing.schema import register_schema
@json_schema_type
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.
:param document_id: The ID of the document this chunk belongs to.
:param source: The source of the content, such as a URL or file path.
:param created_timestamp: An optional timestamp indicating when the chunk was created.
:param updated_timestamp: An optional timestamp indicating when the chunk was last updated.
:param chunk_window: The window of the chunk, which can be used to group related chunks together.
:param chunk_tokenizer: The tokenizer used to create the chunk. Default is Tiktoken.
:param chunk_embedding_model: The embedding model used to create the chunk's embedding.
:param chunk_embedding_dimension: The dimension of the embedding vector for the chunk.
:param content_token_count: The number of tokens in the content of the chunk.
:param metadata_token_count: The number of tokens in the metadata of the chunk.
"""
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
@json_schema_type
class Chunk(BaseModel):
"""
A chunk of content that can be inserted into a vector database.
:param content: The content of the chunk, which can be interleaved text, images, or other types.
:param embedding: Optional embedding for the chunk. If not provided, it will be computed later.
:param metadata: Metadata associated with the chunk, such as document ID, source, or other relevant information.
:param metadata: Metadata associated with the chunk that will be used during inference.
:param chunk_metadata: Metadata for the chunk that will NOT be inserted into the context during inference
that is required backend functionality.
"""
content: InterleavedContent
metadata: dict[str, Any] = Field(default_factory=dict)
embedding: list[float] | None = None
chunk_metadata: ChunkMetadata | None = None
@json_schema_type