Elastisearch 'fields' parameter not usable in similarity_search* methods

[krauhen]

Problem

If I use some of the similarity_search* methods i can not retrieve all fields of my Elasticsearch Index. The custom doc_builder method has no effect because the fields i would like to return are already not in the hits that get returned by the underlying search call.

I investigated and found out that the fields parameter of the underlying search method is not correctly treated.
This is for example one of the places where this happens:

langchain-elastic/libs/elasticsearch/langchain_elasticsearch/_sync/vectorstores.py

Line 567 in 03da5e1

hits = self._store.search(

I tested it out, if you copy all the content of the similarity_search* method and provide the field list and also specify a custom doc_builder you can retrieve other fields then just your text_field and metadata.

Example code

...
vector_store = ...
query_embedding = ...
k = ...

response = vector_store.similarity_search_by_vector_with_relevance_scores(
    embedding=query_embedding,
    k=k
)

Hot fix

...
def custom_doc_builder(hit: Dict) -> Document:
    doc = Document(
        page_content=hit["_source"].get("text", ""),
        metadata=hit["_source"].get("metadata", {}),
    )
    doc.metadata["EMBEDDING_VECTOR"] = hit["_source"].get("EMBEDDING_VECTOR", "")
    return doc

vector_store = ...
query_embedding = ...
k = ...
filter = ...
custom_query = ...

fields = ["text", "metadata", "EMBEDDING_VECTOR"]

hits = vector_store._store.search(
    query=None,
    query_vector=query_embedding,
    k=k,
    filter=filter,
    fields=fields
    custom_query=custom_query,
)
docs = _hits_to_docs_scores(
    hits=hits,
    content_field=vector_store.query_field,
    doc_builder=custom_doc_builder,
)

Soution/Fix

So i would suggest to update this line:

langchain-elastic/libs/elasticsearch/langchain_elasticsearch/_sync/vectorstores.py

Line 567 in 03da5e1

hits = self._store.search(

to this:

...
def similarity_search_by_vector_with_relevance_scores(
    self,
    embedding: List[float],
    k: int = 4,
    filter: Optional[List[Dict]] = None,
    fields: List[str] = None,                     # <===
    *,
    custom_query: Optional[
        Callable[[Dict[str, Any], Optional[str]], Dict[str, Any]]
    ] = None,
    doc_builder: Optional[Callable[[Dict], Document]] = None,
    **kwargs: Any,
) -> List[Tuple[Document, float]]:
...
hits = self._store.search(
    query=None,
    query_vector=embedding,
    k=k,
    filter=filter,
    fields=fields,                                      # <===
    custom_query=custom_query,
)
...

in every similarity_search* occurence in this file.

If you see that the same I can create the PR with the fix.

EDIT

I thought about it and i think in general there should be a standard document builder that looks more like this:

# Specify in user code
fields = ["text", "metadata", "EMBEDDING_VECTOR"]
....
def doc_builder(hit: Dict, fields: List[str]) -> Document:
    doc = Document(
        page_content=hit["_source"].get(content_field, ""),
        metadata=hit["_source"].get("metadata", {}),
    )
    for field_key in fields:
        doc.metadata[field_key]  = hit["_source"].get(field_key, None)
    return doc

Then one can simply specify the fields and the documents come out with all fields. Also I think a this point some how the id of the elements should also be specified to be returned.
Now this is the implementation:

langchain-elastic/libs/elasticsearch/langchain_elasticsearch/_utilities.py

Line 72 in 03da5e1

def default_doc_builder(hit: Dict) -> Document:

[krauhen]

The changes are online .