Repository: langchain
Issue: ValueError: Self query retriever with Vector Store type <class 'langchain_weaviate.vectorstores.WeaviateVectorStore'> not supported.
Labels: Ɑ: vector store

### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

```
import weaviate
from langchain.schema import Document
from langchain_weaviate.vectorstores import WeaviateVectorStore
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
from weaviate.classes.init import Auth

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=weaviate_url,
    auth_credentials=Auth.api_key(weaviate_api_key),
)

documents = [
    Document(
        page_content="Weavite is a powerful vector database for embeddings.",
        metadata={"author": "John Doe", "category": "Technology"}
    ),
    Document(
        page_content="Python is a versatile programming language used for various applications.",
        metadata={"author": "Jane Smith", "category": "Programming"}
    ),
    Document(
        page_content="Machine learning enables computers to learn from data.",
        metadata={"author": "Alice Johnson", "category": "AI"}
    )
]

db_from_docs = WeaviateVectorStore.from_documents(documents, embeddings, client=client)

pdf_metadata_field_info = [
    AttributeInfo(name="source", description="The PDF file the chunk is from", type="string"),
    AttributeInfo(name="page", description="The page number from the PDF", type="integer")
]

search_kwargs = {"k": 6}

data_retriever = SelfQueryRetriever.from_llm(
    llm, db_from_docs, "Company information from PDF files", pdf_metadata_field_info, search_kwargs=search_kwargs
)
```

### Error Message and Stack Trace (if applicable)

File /opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:333, in SelfQueryRetriever.from_llm(cls, llm, vectorstore, document_contents, metadata_field_info, structured_query_translator, chain_kwargs, enable_limit, use_original_query, **kwargs)
    [319](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:319) @classmethod
    [320](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:320) def from_llm(
    [321](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:321)     cls,
   (...)
    [330](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:330)     **kwargs: Any,
    [331](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:331) ) -> "SelfQueryRetriever":
    [332](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:332)     if structured_query_translator is None:
--> [333](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:333)         structured_query_translator = _get_builtin_translator(vectorstore)
    [334](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:334)     chain_kwargs = chain_kwargs or {}
    [336](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:336)     if (
    [337](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:337)         "allowed_comparators" not in chain_kwargs
    [338](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:338)         and structured_query_translator.allowed_comparators is not None
...
    [208](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:208)     f"Self query retriever with Vector Store type {vectorstore.__class__}"
    [209](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:209)     f" not supported."
    [210](https://file+.vscode-resource.vscode-cdn.net/opt/conda/lib/python3.11/site-packages/langchain/retrievers/self_query/base.py:210) )

ValueError: Self query retriever with Vector Store type <class 'langchain_weaviate.vectorstores.WeaviateVectorStore'> not supported.

### Description

Is this related to the fact that Weavite is now in V4, creating a necessity to update the SelfQueryRetriever to support WeaviateVectorStore? 

The current Weavite support is targeted at Weavite V3.

### System Info

langchain==0.3.9
langchain-community==0.3.9
langchain-weaviate==0.0.3
langchain-openai==0.2.11