Error when using Self Query Retriever with Weaviate 
Labels: name: area: vector store, name: auto:bug, name: auto:documentation

### System Info

LangChain version: 0.0.286
Python version: 3.11.2
Platform: MacOS Ventura 13.5.1 M1 chip
Weaviate 1.21.2 as vectorstore

### Who can help?

_No response_

### Information

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

### Related Components

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

### Reproduction

When following LangChain's documentation for [ Weaviate Self-Query Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/weaviate_self_query),
I get the following Warning:

```
/opt/homebrew/lib/python3.11/site-packages/langchain/chains/llm.py:278: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
  warnings.warn(
```
and the following errors
```
ValueError: Received disallowed comparator gte. Allowed comparators are [<Comparator.EQ: 'eq'>]

...
... stack trace
...

File "/opt/homebrew/lib/python3.11/site-packages/langchain/chains/query_constructor/base.py", line 52, in parse
    raise OutputParserException(
langchain.schema.output_parser.OutputParserException: Parsing text
``json
{
    "query": "natural disasters",
    "filter": "and(gte(\"published_at\", \"2022-10-01\"), lte(\"published_at\", \"2022-10-07\"))"
}
``
 raised following error:
Received disallowed comparator gte. Allowed comparators are [<Comparator.EQ: 'eq'>]
```

The following code led to the errors

```
import os, openai, weaviate, logging
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Weaviate
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.weaviate import WeaviateTranslator

openai.api_key  = os.environ['OPENAI_API_KEY']

embeddings = OpenAIEmbeddings()

client = weaviate.Client(
    url = WEAVIATE_URL,
    additional_headers = {
        "X-OpenAI-Api-Key": openai.api_key
    }
)

weaviate = Weaviate(
    client = client,
    index_name = INDEX_NAME,
    text_key = "article_body"
)

metadata_field_info = [ # Shortened for brevity
   AttributeInfo(
        name="published_at",
        description="Date article was published",
        type="date",
    ),
    AttributeInfo(
        name="weblink",
        description="The URL where the document was taken from.",
        type="string",
    ),
    AttributeInfo(
        name="keywords",
        description="A list of keywords from the piece of text.",
        type="string",
    ),
]

logging.basicConfig()
logging.getLogger('langchain.retrievers.self_query').setLevel(logging.INFO)

document_content_description = "News articles"

llm = OpenAI(temperature=0)

retriever = SelfQueryRetriever.from_llm(
    llm,
    weaviate,
    document_content_description,
    metadata_field_info,
    enable_limit = True,
    verbose=True,
)

returned_docs_selfq = retriever.get_relevant_documents(question)
```

### Expected behavior

No warnings or errors, or documentation stating what output parser replicates the existing functionality. Specifically picking up date range filters from user queries