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feat: Enable ingestion of precomputed embeddings (#2317)

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Francisco Arceo 2025-05-31 04:03:37 -06:00 • committed by GitHub
parent 31ce208bda
commit f328436831
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9 changed files with 366 additions and 15 deletions
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llama_stack/apis/vector_io/vector_io.py
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@ -19,8 +19,16 @@ from llama_stack.schema_utils import json_schema_type, webmethod
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.
"""
content: InterleavedContent
metadata: dict[str, Any] = Field(default_factory=dict)
embedding: list[float] | None = None
@json_schema_type
@ -50,7 +58,10 @@ class VectorIO(Protocol):
"""Insert chunks into a vector database.
:param vector_db_id: The identifier of the vector database to insert the chunks into.
:param chunks: The chunks to insert.
:param chunks: The chunks to insert. Each `Chunk` should contain content which can be interleaved text, images, or other types.
`metadata`: `dict[str, Any]` and `embedding`: `List[float]` are optional.
If `metadata` is provided, you configure how Llama Stack formats the chunk during generation.
If `embedding` is not provided, it will be computed later.
:param ttl_seconds: The time to live of the chunks.
"""
...