fix: fullfill openai params when embedding (#5821)

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Fixes #5822 
I upgrade my langchain lib by execute `pip install -U langchain`, and
the verion is 0.0.192。But i found that openai.api_base not working. I
use azure openai service as openai backend, the openai.api_base is very
import for me. I hava compared tag/0.0.192 and tag/0.0.191, and figure
out that:

![image](https://github.com/hwchase17/langchain/assets/6478745/e183fdb2-8224-45c9-b3b4-26d62823999a)
openai params is moved inside `_invocation_params` function,and used in
some openai invoke:

![image](https://github.com/hwchase17/langchain/assets/6478745/5a55a048-5fa9-4bf4-aaef-3902226bec5e)

![image](https://github.com/hwchase17/langchain/assets/6478745/85b8cebc-eeb8-4538-a525-814719c8f8df)
but still some case not covered like:

![image](https://github.com/hwchase17/langchain/assets/6478745/e0297620-f2b2-4f4f-98bd-d0ed19022dac)

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---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This commit is contained in:
warjiang 2023-06-07 22:32:57 +08:00 committed by GitHub
parent b3ae6bcd3f
commit 5a207cce8f
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@ -97,8 +97,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
embeddings = OpenAIEmbeddings( embeddings = OpenAIEmbeddings(
deployment="your-embeddings-deployment-name", deployment="your-embeddings-deployment-name",
model="your-embeddings-model-name", model="your-embeddings-model-name",
api_base="https://your-endpoint.openai.azure.com/", openai_api_base="https://your-endpoint.openai.azure.com/",
api_type="azure", openai_api_type="azure",
) )
text = "This is a test query." text = "This is a test query."
query_result = embeddings.embed_query(text) query_result = embeddings.embed_query(text)
@ -257,10 +257,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
average = embed_with_retry( average = embed_with_retry(
self, self,
input="", input="",
engine=self.deployment, **self._invocation_params,
request_timeout=self.request_timeout, )[
headers=self.headers, "data"
)["data"][0]["embedding"] ][0]["embedding"]
else: else:
average = np.average(_result, axis=0, weights=num_tokens_in_batch[i]) average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
embeddings[i] = (average / np.linalg.norm(average)).tolist() embeddings[i] = (average / np.linalg.norm(average)).tolist()
@ -280,10 +280,10 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
return embed_with_retry( return embed_with_retry(
self, self,
input=[text], input=[text],
engine=engine, **self._invocation_params,
request_timeout=self.request_timeout, )[
headers=self.headers, "data"
)["data"][0]["embedding"] ][0]["embedding"]
def embed_documents( def embed_documents(
self, texts: List[str], chunk_size: Optional[int] = 0 self, texts: List[str], chunk_size: Optional[int] = 0