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@ -324,7 +324,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
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input=tokens[i : i + _chunk_size], **self._invocation_params
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)
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if not isinstance(response, dict):
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response = response.dict()
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response = response.model_dump()
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batched_embeddings.extend(r["embedding"] for r in response["data"])
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results: List[List[List[float]]] = [[] for _ in range(len(texts))]
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@ -343,7 +343,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
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input="", **self._invocation_params
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)
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if not isinstance(average_embedded, dict):
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average_embedded = average_embedded.dict()
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average_embedded = average_embedded.model_dump()
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average = average_embedded["data"][0]["embedding"]
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else:
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average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
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@ -436,7 +436,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
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)
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if not isinstance(response, dict):
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response = response.dict()
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response = response.model_dump()
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batched_embeddings.extend(r["embedding"] for r in response["data"])
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results: List[List[List[float]]] = [[] for _ in range(len(texts))]
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@ -453,7 +453,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
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input="", **self._invocation_params
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)
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if not isinstance(average_embedded, dict):
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average_embedded = average_embedded.dict()
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average_embedded = average_embedded.model_dump()
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average = average_embedded["data"][0]["embedding"]
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else:
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average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
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