Source code for langchain_community.retrievers.vespa_retriever
from __future__ import annotations
import json
from typing import Any, Dict, List, Literal, Optional, Sequence, Union
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
[docs]class VespaRetriever(BaseRetriever):
"""`Vespa` retriever."""
app: Any
"""Vespa application to query."""
body: Dict
"""Body of the query."""
content_field: str
"""Name of the content field."""
metadata_fields: Sequence[str]
"""Names of the metadata fields."""
def _query(self, body: Dict) -> List[Document]:
response = self.app.query(body)
if not str(response.status_code).startswith("2"):
raise RuntimeError(
"Could not retrieve data from Vespa. Error code: {}".format(
response.status_code
)
)
root = response.json["root"]
if "errors" in root:
raise RuntimeError(json.dumps(root["errors"]))
docs = []
for child in response.hits:
page_content = child["fields"].pop(self.content_field, "")
if self.metadata_fields == "*":
metadata = child["fields"]
else:
metadata = {mf: child["fields"].get(mf) for mf in self.metadata_fields}
metadata["id"] = child["id"]
docs.append(Document(page_content=page_content, metadata=metadata))
return docs
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
body = self.body.copy()
body["query"] = query
return self._query(body)
[docs] def get_relevant_documents_with_filter(
self, query: str, *, _filter: Optional[str] = None
) -> List[Document]:
body = self.body.copy()
_filter = f" and {_filter}" if _filter else ""
body["yql"] = body["yql"] + _filter
body["query"] = query
return self._query(body)
[docs] @classmethod
def from_params(
cls,
url: str,
content_field: str,
*,
k: Optional[int] = None,
metadata_fields: Union[Sequence[str], Literal["*"]] = (),
sources: Union[Sequence[str], Literal["*"], None] = None,
_filter: Optional[str] = None,
yql: Optional[str] = None,
**kwargs: Any,
) -> VespaRetriever:
"""Instantiate retriever from params.
Args:
url (str): Vespa app URL.
content_field (str): Field in results to return as Document page_content.
k (Optional[int]): Number of Documents to return. Defaults to None.
metadata_fields(Sequence[str] or "*"): Fields in results to include in
document metadata. Defaults to empty tuple ().
sources (Sequence[str] or "*" or None): Sources to retrieve
from. Defaults to None.
_filter (Optional[str]): Document filter condition expressed in YQL.
Defaults to None.
yql (Optional[str]): Full YQL query to be used. Should not be specified
if _filter or sources are specified. Defaults to None.
kwargs (Any): Keyword arguments added to query body.
Returns:
VespaRetriever: Instantiated VespaRetriever.
"""
try:
from vespa.application import Vespa
except ImportError:
raise ImportError(
"pyvespa is not installed, please install with `pip install pyvespa`"
)
app = Vespa(url)
body = kwargs.copy()
if yql and (sources or _filter):
raise ValueError(
"yql should only be specified if both sources and _filter are not "
"specified."
)
else:
if metadata_fields == "*":
_fields = "*"
body["summary"] = "short"
else:
_fields = ", ".join([content_field] + list(metadata_fields or []))
_sources = ", ".join(sources) if isinstance(sources, Sequence) else "*"
_filter = f" and {_filter}" if _filter else ""
yql = f"select {_fields} from sources {_sources} where userQuery(){_filter}"
body["yql"] = yql
if k:
body["hits"] = k
return cls(
app=app,
body=body,
content_field=content_field,
metadata_fields=metadata_fields,
)