Issues with Azure OpenAI

### System Info

OS = MACOS
langchain=0.0.179 (also tried 0.0.174 and 0.0.178)

### Who can help?

@hwchase17 @agola11 

The full code below is single file.  imports and other information not added to keep it crisp.

The following works with no issues:
```
llm = AzureOpenAI(openai_api_base=openai_api_base , model="text-davinci-003",  engine="text-davinci-003", temperature=0.1, verbose=True, deployment_name="text-davinci-003", deployment_id="text-davinci-003", openai_api_key=openai_api_key)

resp = llm("Tell me pub joke")
print(resp)
```

The following does not work.  
```
#get document store
store = getfromstore(collection_name="sou_coll")

# Create vectorstore info object - metadata repo?
vectorstore_info = VectorStoreInfo(
    name="sou",
    description="sou folder",
    vectorstore=store
)
# Convert the document store into a langchain toolkit
toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)

# Add the toolkit to an end-to-end LC
agent_executor = create_vectorstore_agent(
    llm=llm,
    toolkit=toolkit,
    verbose=True
)
response = agent_executor.run(prompt)
print(response)
```

I can confirm the document store exists and the same code with appropriate OpenAI (not Azure OpenAI) works as expected with no issue.   Azure OpenAI gives the following error -  
```
File "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 83, in __prepare_create_request
    raise error.InvalidRequestError(
openai.error.InvalidRequestError: Must provide an 'engine' or 'deployment_id' parameter to create a <class 'openai.api_resources.completion.Completion'>
```
Observation LLM is correct since the first part (write a joke) works.  The agent does not.  Please help!

### Information

- [ ] The official example notebooks/scripts
- [ ] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

#!/usr/bin/env python3
import sys
from dotenv import load_dotenv

# Load default environment variables (.env)
load_dotenv()

# Import os to set API key
import os
# Import OpenAI as main LLM service
from langchain.llms import AzureOpenAI
from langchain.callbacks import get_openai_callback

# Bring in streamlit for UI/app interface
import streamlit as st

# Import PDF document loaders...there's other ones as well!
from langchain.document_loaders import PyPDFLoader
# Import chroma as the vector store 
from langchain.vectorstores import Chroma

from common.funs import getfromstore

# Import vector store stuff
from langchain.agents.agent_toolkits import (
    create_vectorstore_agent,
    VectorStoreToolkit,
    VectorStoreInfo
)

# Set this to `azure`
openai_api_type = os.environ["OPENAI_API_TYPE"] ="azure"
openai_api_version = os.environ["OPENAI_API_VERSION"] = os.environ["AOAI_OPENAI_API_VERSION"]
openai_api_base = os.environ["OPENAI_API_BASE"] = os.environ["AOAI_OPENAI_API_BASE"]
openai_api_key = os.environ["OPENAI_API_KEY"] = os.environ["AOAI_OPENAI_API_KEY"]

# Create instance of OpenAI LLM
#llm = AzureOpenAI(openai_api_base=openai_api_base , model="text-davinci-003", temperature=0.1, verbose=True, deployment_name="text-davinci-003", openai_api_key=openai_api_key)
llm = AzureOpenAI(openai_api_base=openai_api_base , model="text-davinci-003",  engine="text-davinci-003", temperature=0.1, verbose=True, deployment_name="text-davinci-003", deployment_id="text-davinci-003", openai_api_key=openai_api_key)

resp = llm("Tell me pub joke")
print(resp)
print("------------")
st.write(resp)
st.write("----------------------")

#get document store
store = getfromstore(collection_name="sou_coll")
#print(store1.get(["metadatas"]))

# Create vectorstore info object - metadata repo?
vectorstore_info = VectorStoreInfo(
    name="sou",
    description="sou folder",
    vectorstore=store
)
# Convert the document store into a langchain toolkit
toolkit = VectorStoreToolkit(vectorstore_info=vectorstore_info)

# Add the toolkit to an end-to-end LC
agent_executor = create_vectorstore_agent(
    llm=llm,
    toolkit=toolkit,
    verbose=True
)

st.title("🦜🔗🤗 What would you like to know?")
st.write("This sample uses Azure OpenAI")
# Create a text input box for the user
prompt = st.text_input('Input your prompt here:')

# If the user hits enter
if prompt:
    with get_openai_callback() as cb:
        #try:
            # Then pass the prompt to the LLM
            response = agent_executor.run(prompt)
            # ...and write it out to the screen
            st.write(response)
            st.write(cb)
        #except Exception as e:
        #    st.warning
        #    st.write("That was a difficult question!  I choked on it!!  Can you please try again with rephrasing it a bit?")
        #    st.write(cb)
        #    print(e)
    
    # Find the relevant pages
    search = store.similarity_search_with_score(prompt)
    # Write out the first 
    try:
        st.write("This information was found in:")
        for doc in search:
            score = doc[1]
            try:
                page_num = doc[0].metadata['page']
            except:
                page_num = "txt snippets"
            source = doc[0].metadata['source']
            # With a streamlit expander  
            with st.expander("Source: " + str(source) + " - Page: " + str(page_num) + "; Similarity Score: " + str(score) ):
                st.write(doc[0].page_content)
    except:
        print("unable to get source document detail")


### Expected behavior

The video shows the expected output - https://www.youtube.com/watch?v=q27RbxcfGvE

The OpenAI code in this sample is exact except for changes to LLM and env variables - file https://github.com/ushakrishnan/SearchWithOpenAI/blob/main/pages/6_Q%26A_with_Open_AI.py.