SelfQueryRetriever, ValueError: Expected where operand value to be a str, int, float, or list of those type
Labels: name: auto:bug, name: area: models, name: integration: chroma

### System Info

Chroma 0.4.22
Langchain 0.0.354

### Who can help?

@agola11 



### Information

- [ ] The official example notebooks/scripts
- [X] 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

1. Create a SelfQueryRetriever
2. Create AttributeInfo metadata list in preparation for filtering based off metadata.

```python
self_query_retriever = SelfQueryRetriever.from_llm(
        llm,
        vectorstore,
        "Information about when document was published and where it originated from",
        metadata_field_info
    )

    # retriever = MergerRetriever(retrievers=[parent_retriever, self_query_retriever])

    retriever = self_query_retriever
    
    template = """
    ### Instruction: You're an assistant who knows the following information:
    ### {context}
    
    If you don't know the answer, then say you don't know and refer the user to the respective department for extra information.        
    Absolutely do not mention you are an AI language model. Use only the chat history and the following information. 
    
    ### {chat_history}
    
    ### Input: {question}
    ### Response:
    """.strip()
    
    prompt = PromptTemplate(input_variables=["context", "chat_history", "question"], template=template)
    
    chain = ConversationalRetrievalChain.from_llm(
        llm,
        chain_type="stuff",
        retriever=retriever,
        combine_docs_chain_kwargs={"prompt": prompt},#, "metadata_weights": metadata_weights},
        return_source_documents=True,
        verbose=False,
        rephrase_question=True,
        max_tokens_limit=16000,
        response_if_no_docs_found="""I'm sorry, but I was not able to find the answer to your question based on the information I know. You may have to reach out to the respective internal department for more details regarding your inquiry."""
    )
    
    return chain

def score_unstructured(model, data, query, **kwargs) -> str:
    """Custom model hook for making completions with our knowledge base.

    When requesting predictions from the deployment, pass a dictionary
    with the following keys:
    - 'question' the question to be passed to the retrieval chain
    - 'chat_history' (optional) a list of two-element lists corresponding to
      preceding dialogue between the Human and AI, respectively

    datarobot-user-models (DRUM) handles loading the model and calling
    this function with the appropriate parameters.
    
    Returns:
    --------
    rv : str
        Json dictionary with keys:
            - 'question' user's original question
            - 'chat_history' chat history that was provided with the original question
            - 'answer' the generated answer to the question
            - 'references' list of references that were used to generate the answer
            - 'error' - error message if exception in handling request
    """
    import json
        
    try:
        chain = model
        data_dict = json.loads(data)

        if 'chat_history' in data_dict:
            chat_history = [(human, ai,) for human, ai in data_dict['chat_history']]
        else:
            chat_history = []# model.chat_history
        rv = chain(
                inputs={
                    'question': data_dict['question'],
                    'chat_history': chat_history,
                },
             )

        source_docs = rv.pop('source_documents')
        rv['references'] = [doc.metadata['source'] for doc in source_docs]

        if len(source_docs) > 0:
            rv["top_reference_text"] = [doc.page_content for doc in source_docs]
        else:
            rv["top_reference_text"] = ""
    except Exception as e:
        rv = {'error': f"{e.__class__.__name__}: {str(e)}"}
    return json.dumps(rv)

model = load_model(".")
```

I asked the following question:

```python
questions = ["What is the minimum opening deposit for each account as of January 2023?"]

os.environ["TOKENIZERS_PARALLELISM"] = "false"

for question in questions:
    rv = score_unstructured(model, json.dumps(
            {
                "question": question
                # "chat_history": []
            }
    	), 
        None)
    print(rv)
    print(question.upper())
    print(json.loads(rv)["answer"])
    print(json.loads(rv))
    print("------------------------------------------------")
```

The issue I got was ```ValueError: Expected where operand value to be a str, int, float, or list of those type, got {'date': '2023-01-01', 'type': 'date'}```

It looks like the SelfQueryRetriever converted my question that had January 2023 to a date object. This date object throws an error. I'm not sure how to resolve this issue on my end.

### Expected behavior

Query with a date and receive an answer from the SelfQueryRetriever.