Created
February 26, 2024 23:33
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Hacking AzureOpenAI for lancedb
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@register("azure_openai") | |
class AzureOpenAIEmbeddings(TextEmbeddingFunction): | |
""" | |
An embedding function that uses the Azure OpenAI API | |
""" | |
name: str = "text-embedding-ada-002" | |
azure_api_key: str | |
azure_endpoint: str | |
azure_deployment: str | |
azure_api_version: str | |
def ndims(self): | |
return self._ndims | |
@cached_property | |
def _ndims(self): | |
if self.name == "text-embedding-ada-002": | |
return 1536 | |
else: | |
raise ValueError(f"Unknown model name {self.name}") | |
def generate_embeddings( | |
self, texts: Union[List[str], np.ndarray] | |
) -> List[np.array]: | |
""" | |
Get the embeddings for the given texts | |
Parameters | |
---------- | |
texts: list[str] or np.ndarray (of str) | |
The texts to embed | |
""" | |
# TODO retry, rate limit, token limit | |
if self.name == "text-embedding-ada-002": | |
rs = self._openai_client.embeddings.create(input=texts, model=self.name) | |
else: | |
rs = self._openai_client.embeddings.create( | |
input=texts, model=self.name, dimensions=self.ndims() | |
) | |
return [v.embedding for v in rs.data] | |
@cached_property | |
def _openai_client(self): | |
openai = attempt_import_or_raise("openai") | |
if not os.environ.get("OPENAI_API_KEY"): | |
api_key_not_found_help("openai") | |
return openai.AzureOpenAI( | |
azure_endpoint=self.azure_endpoint, | |
api_key=self.azure_api_key, | |
api_version=self.azure_api_version, | |
azure_deployment=self.azure_deployment | |
) |
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