Harrison/faiss score (#6341)

Co-authored-by: Frank Stein <16441059+simonfromla@users.noreply.github.com>
Co-authored-by: Sims Juju <sims@Ju.lan>

langchain/retrievers/contextual_compression.py

@@ -34,8 +34,11 @@ class ContextualCompressionRetriever(BaseRetriever, BaseModel):
             Sequence of relevant documents
         """
         docs = self.base_retriever.get_relevant_documents(query)
-        compressed_docs = self.base_compressor.compress_documents(docs, query)
+        if docs:
-        return list(compressed_docs)
+            compressed_docs = self.base_compressor.compress_documents(docs, query)
+            return list(compressed_docs)
+        else:
+            return []
 
     async def aget_relevant_documents(self, query: str) -> List[Document]:
         """Get documents relevant for a query.
@@ -47,5 +50,10 @@ class ContextualCompressionRetriever(BaseRetriever, BaseModel):
             List of relevant documents
         """
         docs = await self.base_retriever.aget_relevant_documents(query)
-        compressed_docs = await self.base_compressor.acompress_documents(docs, query)
+        if docs:
-        return list(compressed_docs)
+            compressed_docs = await self.base_compressor.acompress_documents(
+                docs, query
+            )
+            return list(compressed_docs)
+        else:
+            return []

langchain/vectorstores/base.py

@@ -159,8 +159,8 @@ class VectorStore(ABC):
             ]
             if len(docs_and_similarities) == 0:
                 warnings.warn(
-                    f"No relevant docs were retrieved using the relevance score\
+                    "No relevant docs were retrieved using the relevance score"
-                          threshold {score_threshold}"
+                    f" threshold {score_threshold}"
                 )
         return docs_and_similarities
 

langchain/vectorstores/faiss.py

@@ -185,6 +185,7 @@ class FAISS(VectorStore):
         k: int = 4,
         filter: Optional[Dict[str, Any]] = None,
         fetch_k: int = 20,
+        **kwargs: Any,
     ) -> List[Tuple[Document, float]]:
         """Return docs most similar to query.
 
@@ -194,6 +195,9 @@ class FAISS(VectorStore):
             filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
             fetch_k: (Optional[int]) Number of Documents to fetch before filtering.
                       Defaults to 20.
+            **kwargs: kwargs to be passed to similarity search. Can include:
+                score_threshold: Optional, a floating point value between 0 to 1 to
+                    filter the resulting set of retrieved docs
 
         Returns:
             List of documents most similar to the query text and L2 distance
@@ -218,6 +222,14 @@ class FAISS(VectorStore):
                     docs.append((doc, scores[0][j]))
             else:
                 docs.append((doc, scores[0][j]))
+
+        score_threshold = kwargs.get("score_threshold")
+        if score_threshold is not None:
+            docs = [
+                (doc, similarity)
+                for doc, similarity in docs
+                if similarity >= score_threshold
+            ]
         return docs[:k]
 
     def similarity_search_with_score(