Repository: langchain
Issue: DOC: Neo4jVector class => many parameters not documented
Labels: Ɑ: vector store,🤖:docs,🔌: neo4j

### URL

https://github.com/langchain-ai/langchain/blob/c4e149d4f18319bef6f1d4c409250c4a0ad21dac/libs/community/langchain_community/vectorstores/neo4j_vector.py

### Checklist

- [X] I added a very descriptive title to this issue.
- [X] I included a link to the documentation page I am referring to (if applicable).

### Issue with current documentation:

Many parameters in the [Neo4jVector class](https://github.com/langchain-ai/langchain/blob/c4e149d4f18319bef6f1d4c409250c4a0ad21dac/libs/community/langchain_community/vectorstores/neo4j_vector.py#L429) are not documented. Some examples:

```python
class Neo4jVector(VectorStore):
    """`Neo4j` vector index.

    To use, you should have the ``neo4j`` python package installed.

    Args:
        url: Neo4j connection url
        username: Neo4j username.
        password: Neo4j password
        database: Optionally provide Neo4j database
                  Defaults to "neo4j"
        embedding: Any embedding function implementing
            `langchain.embeddings.base.Embeddings` interface.
        distance_strategy: The distance strategy to use. (default: COSINE)
        pre_delete_collection: If True, will delete existing data if it exists.
            (default: False). Useful for testing.

    Example:
        .. code-block:: python

            from langchain_community.vectorstores.neo4j_vector import Neo4jVector
            from langchain_community.embeddings.openai import OpenAIEmbeddings

            url="bolt://localhost:7687"
            username="neo4j"
            password="pleaseletmein"
            embeddings = OpenAIEmbeddings()
            vectorestore = Neo4jVector.from_documents(
                embedding=embeddings,
                documents=docs,
                url=url
                username=username,
                password=password,
            )


    """

    def __init__(
        self,
        embedding: Embeddings,
        *,
        search_type: SearchType = SearchType.VECTOR,
        username: Optional[str] = None,
        password: Optional[str] = None,
        url: Optional[str] = None,
        keyword_index_name: Optional[str] = "keyword",
        database: Optional[str] = None,
        index_name: str = "vector",
        node_label: str = "Chunk",
        embedding_node_property: str = "embedding",
        text_node_property: str = "text",
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        logger: Optional[logging.Logger] = None,
        pre_delete_collection: bool = False,
        retrieval_query: str = "",
        relevance_score_fn: Optional[Callable[[float], float]] = None,
        index_type: IndexType = DEFAULT_INDEX_TYPE,
        graph: Optional[Neo4jGraph] = None,
    ) -> None:
```

```python
   def retrieve_existing_fts_index(
        self, text_node_properties: List[str] = []
    ) -> Optional[str]:
        """
        Check if the fulltext index exists in the Neo4j database

        This method queries the Neo4j database for existing fts indexes
        with the specified name.

        Returns:
            (Tuple): keyword index information
        """
```

```python
    def similarity_search(
        self,
        query: str,
        k: int = 4,
        params: Dict[str, Any] = {},
        filter: Optional[Dict[str, Any]] = None,
        **kwargs: Any,
    ) -> List[Document]:
        """Run similarity search with Neo4jVector.

        Args:
            query (str): Query text to search for.
            k (int): Number of results to return. Defaults to 4.

        Returns:
            List of Documents most similar to the query.
        """
```

```python
    def from_texts(
        cls: Type[Neo4jVector],
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> Neo4jVector:
        """
        Return Neo4jVector initialized from texts and embeddings.
        Neo4j credentials are required in the form of `url`, `username`,
        and `password` and optional `database` parameters.
        """
```

### Idea or request for content:

At least include all parameters in the `Args` sections of the function docs.