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chore(migrate apis): move VectorDBWithIndex from embeddings to openai_embeddings (#3294)
# What does this PR do? migrates VectorDBWithIndex to use openai_embeddings part of #2365 ## Test Plan existing unit tests
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20 changed files with 8376 additions and 13 deletions
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@ -294,12 +294,12 @@ class VectorDBWithIndex:
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_validate_embedding(c.embedding, i, self.vector_db.embedding_dimension)
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if chunks_to_embed:
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resp = await self.inference_api.embeddings(
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resp = await self.inference_api.openai_embeddings(
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self.vector_db.embedding_model,
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[c.content for c in chunks_to_embed],
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)
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for c, embedding in zip(chunks_to_embed, resp.embeddings, strict=False):
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c.embedding = embedding
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for c, data in zip(chunks_to_embed, resp.data, strict=False):
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c.embedding = data.embedding
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embeddings = np.array([c.embedding for c in chunks], dtype=np.float32)
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await self.index.add_chunks(chunks, embeddings)
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@ -334,8 +334,8 @@ class VectorDBWithIndex:
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if mode == "keyword":
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return await self.index.query_keyword(query_string, k, score_threshold)
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embeddings_response = await self.inference_api.embeddings(self.vector_db.embedding_model, [query_string])
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query_vector = np.array(embeddings_response.embeddings[0], dtype=np.float32)
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embeddings_response = await self.inference_api.openai_embeddings(self.vector_db.embedding_model, [query_string])
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query_vector = np.array(embeddings_response.data[0].embedding, dtype=np.float32)
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if mode == "hybrid":
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return await self.index.query_hybrid(
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query_vector, query_string, k, score_threshold, reranker_type, reranker_params
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