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feat: Enable ingestion of custom embeddings

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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Francisco Javier Arceo 2025-05-29 20:58:41 -04:00 • committed by Francisco Arceo
parent 2603f10f95
commit 73456878e5
8 changed files with 224 additions and 15 deletions
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13
llama_stack/providers/utils/memory/vector_store.py
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@ -199,11 +199,16 @@ class VectorDBWithIndex:
self,
chunks: list[Chunk],
) -> None:
embeddings_response = await self.inference_api.embeddings(
self.vector_db.embedding_model, [x.content for x in chunks]
)
embeddings = np.array(embeddings_response.embeddings)
chunks_to_embed = [c for c in chunks if c.embedding is None]
if chunks_to_embed:
resp = await self.inference_api.embeddings(
self.vector_db.embedding_model,
[c.content for c in chunks_to_embed],
)
for c, embedding in zip(chunks_to_embed, resp.embeddings, strict=False):
c.embedding = embedding
embeddings = np.array([c.embedding for c in chunks], dtype=np.float32)
await self.index.add_chunks(chunks, embeddings)
async def query_chunks(