From 9f4e1ecbb7d0864e9ec6842e61d32949bcb64a16 Mon Sep 17 00:00:00 2001
From: Sebastian Weisshaar
 <76220851+sebastian-weisshaar@users.noreply.github.com>
Date: Mon, 19 Feb 2024 12:54:16 +0100
Subject: [PATCH] Feat: Add filters to run function in retrievers of
 elasticsearch (#440)

* feat: add filters to run function of bm_25 retriever in elastic search

* feat: add filters to run function of embedding retriever in elastic search

* docs: add docstring for filters in run function of retrievers in elasticsearch
---
 .../components/retrievers/elasticsearch/bm25_retriever.py    | 5 +++--
 .../retrievers/elasticsearch/embedding_retriever.py          | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py
index bd96a5fd8..0416389a2 100644
--- a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py
+++ b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/bm25_retriever.py
@@ -89,17 +89,18 @@ def from_dict(cls, data: Dict[str, Any]) -> "ElasticsearchBM25Retriever":
         return default_from_dict(cls, data)
 
     @component.output_types(documents=List[Document])
-    def run(self, query: str, top_k: Optional[int] = None):
+    def run(self, query: str, filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None):
         """
         Retrieve documents using the BM25 keyword-based algorithm.
 
         :param query: String to search in Documents' text.
+        :param filters: Filters applied to the retrieved Documents.
         :param top_k: Maximum number of Documents to return.
         :return: List of Documents that match the query.
         """
         docs = self._document_store._bm25_retrieval(
             query=query,
-            filters=self._filters,
+            filters=filters or self._filters,
             fuzziness=self._fuzziness,
             top_k=top_k or self._top_k,
             scale_score=self._scale_score,
diff --git a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py
index a2c825d66..2ba68916f 100644
--- a/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py
+++ b/integrations/elasticsearch/src/haystack_integrations/components/retrievers/elasticsearch/embedding_retriever.py
@@ -63,17 +63,18 @@ def from_dict(cls, data: Dict[str, Any]) -> "ElasticsearchEmbeddingRetriever":
         return default_from_dict(cls, data)
 
     @component.output_types(documents=List[Document])
-    def run(self, query_embedding: List[float], top_k: Optional[int] = None):
+    def run(self, query_embedding: List[float], filters: Optional[Dict[str, Any]] = None, top_k: Optional[int] = None):
         """
         Retrieve documents using a vector similarity metric.
 
         :param query_embedding: Embedding of the query.
+        :param filters: Filters applied to the retrieved Documents.
         :param top_k: Maximum number of Documents to return.
         :return: List of Documents similar to `query_embedding`.
         """
         docs = self._document_store._embedding_retrieval(
             query_embedding=query_embedding,
-            filters=self._filters,
+            filters=filters or self._filters,
             top_k=top_k or self._top_k,
             num_candidates=self._num_candidates,
         )