Unable to utilize AzureCognitiveSearch retriever without error.

### System Info

Python 3
Langchain: 0.0.199

### Who can help?

@hwchase17 
@agola11 

### Information

- [ ] The official example notebooks/scripts
- [X] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

I am attempting to create a chatbot as a customer service assistant for a hotel agency. This is so I can experiment with azure cognitive search's sample data.

However, I keep running into issues utilizing the retriever.

```
from langchain import OpenAI, PromptTemplate
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferWindowMemory

from sandbox.hotels_demo.hotels_retreiver import HotelRetriever
from sandbox.hotels_demo.intent_classification import get_customer_intent

template = """
Assistant is a large language model trained by OpenAI.

Assistant is to act as a customer service agent for a hotel agency.

Assistant will describe hotel and discuss pricing if user is attempting to book a hotel.

General questions will be answered with summarization.

{history}
Human: {human_input}
Assistant:"""
hotel_retriever = HotelRetriever()

while True:
    user_input = input("You: ")
    if user_input == "EXIT":
        print("Exiting...")
        break
    customer_intent = get_customer_intent(user_input).strip()
    if customer_intent == "book_hotel" or customer_intent == "new_hotel_question":
        hotel_retriever.refresh_relevant_hotels(user_input)
    chatgpt_chain = RetrievalQA.from_chain_type(
        llm=OpenAI(temperature=0),
        prompt=PromptTemplate(
            input_variables=["history", "human_input"], template=template
        ),
        retriever=hotel_retriever.vectordb.as_retriever(),
        memory=ConversationBufferWindowMemory(k=2),
    )
    print(
        f"AI: {chatgpt_chain.run(human_input=user_input)}"
    )
```

```
from langchain.embeddings import OpenAIEmbeddings
from langchain.retrievers import AzureCognitiveSearchRetriever
from langchain.vectorstores import Chroma


class HotelRetriever:
    def __init__(self):
        self.vectordb = None
        self.retriever = AzureCognitiveSearchRetriever(content_key="Description")

    def refresh_relevant_hotels(self, prompt):
        docs = self.retriever.get_relevant_documents(prompt)

        for document in docs:
            for key, value in document.metadata.items():
                if not isinstance(value, (int, float, str)):
                    document.metadata[key] = str(value)
        self.vectordb = Chroma.from_documents(
            documents=docs,
            embedding=OpenAIEmbeddings(),
            persist_directory="hotels-store",
        )

```

The error that I keep getting is this or some variation of this:

```
Traceback (most recent call last):
  File "C:\Users\naste\PycharmProjects\altairgpt\sandbox\hotels_demo\app.py", line 30, in <module>
    chatgpt_chain = RetrievalQA.from_chain_type(
  File "C:\Users\naste\PycharmProjects\altairgpt\venv\lib\site-packages\langchain\chains\retrieval_qa\base.py", line 94, in from_chain_type
    return cls(combine_documents_chain=combine_documents_chain, **kwargs)
  File "C:\Users\naste\PycharmProjects\altairgpt\venv\lib\site-packages\langchain\load\serializable.py", line 61, in __init__
    super().__init__(**kwargs)
  File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for RetrievalQA
prompt
  extra fields not permitted (type=value_error.extra)
  ```
  

### Expected behavior

My goal is to be able to create a gpt chatbot that can query Azure cognitive search data for it's responses in summarizing and providing information about booking hotels based off of Azure's sample data.