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chore(migrate apis): move VectorDBWithIndex from embeddings to openai_embeddings

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note: query_chunks has no unit test coverage
This commit is contained in:
Matthew Farrellee 2025-08-31 08:48:10 -04:00
parent b12cd528ef
commit 486cd8679a
2 changed files with 12 additions and 10 deletions
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8
llama_stack/providers/utils/memory/vector_store.py
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@ -294,11 +294,11 @@ class VectorDBWithIndex:
_validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
if chunks_to_embed:
resp = await self.inference_api.embeddings(
resp = await self.inference_api.openai_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):
for c, embedding in zip(chunks_to_embed, resp.data, strict=False):
c.embedding = embedding
embeddings = np.array([c.embedding for c in chunks], dtype=np.float32)
@ -334,8 +334,8 @@ class VectorDBWithIndex:
if mode == "keyword":
return await self.index.query_keyword(query_string, k, score_threshold)
embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string])
query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32)
embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string])
query_vector = np.array(embeddings_response.data[0], dtype=np.float32)
if mode == "hybrid":
return await self.index.query_hybrid(
query_vector, query_string, k, score_threshold, reranker_type, reranker_params