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>

langchain/embeddings/openai.py

@@ -97,8 +97,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
             embeddings = OpenAIEmbeddings(
                 deployment="your-embeddings-deployment-name",
                 model="your-embeddings-model-name",
-                api_base="https://your-endpoint.openai.azure.com/",
-                api_type="azure",
+                openai_api_base="https://your-endpoint.openai.azure.com/",
+                openai_api_type="azure",
             )
             text = "This is a test query."
             query_result = embeddings.embed_query(text)
@@ -257,10 +257,10 @@ def _get_len_safe_embeddings(
                 average = embed_with_retry(
                     self,
                     input="",
-                    engine=self.deployment,
-                    request_timeout=self.request_timeout,
-                    headers=self.headers,
-                )["data"][0]["embedding"]
+                    **self._invocation_params,
+                )[
+                    "data"
+                ][0]["embedding"]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
             embeddings[i] = (average / np.linalg.norm(average)).tolist()
@@ -280,10 +280,10 @@ def _embedding_func(self, text: str, *, engine: str) -> List[float]:
             return embed_with_retry(
                 self,
                 input=[text],
-                engine=engine,
-                request_timeout=self.request_timeout,
-                headers=self.headers,
-            )["data"][0]["embedding"]
+                **self._invocation_params,
+            )[
+                "data"
+            ][0]["embedding"]
 
     def embed_documents(
         self, texts: List[str], chunk_size: Optional[int] = 0