add similarity score method to faiss (#574)

adds `similarity_search_with_score` to faiss wrapper

langchain/vectorstores/faiss.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import uuid
-from typing import Any, Callable, Dict, Iterable, List, Optional
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
 
 import numpy as np
 
@@ -77,7 +77,9 @@ class FAISS(VectorStore):
         self.index_to_docstore_id.update(index_to_id)
         return [_id for _, _id, _ in full_info]
 
-    def similarity_search(self, query: str, k: int = 4) -> List[Document]:
+    def similarity_search_with_score(
+        self, query: str, k: int = 4
+    ) -> List[Tuple[Document, float]]:
         """Return docs most similar to query.
 
         Args:
@@ -85,12 +87,12 @@ class FAISS(VectorStore):
             k: Number of Documents to return. Defaults to 4.
 
         Returns:
-            List of Documents most similar to the query.
+            List of Documents most similar to the query and score for each
         """
         embedding = self.embedding_function(query)
-        _, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
+        scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
         docs = []
-        for i in indices[0]:
+        for j, i in enumerate(indices[0]):
             if i == -1:
                 # This happens when not enough docs are returned.
                 continue
@@ -98,9 +100,22 @@ class FAISS(VectorStore):
             doc = self.docstore.search(_id)
             if not isinstance(doc, Document):
                 raise ValueError(f"Could not find document for id {_id}, got {doc}")
-            docs.append(doc)
+            docs.append((doc, scores[0][j]))
         return docs
 
+    def similarity_search(self, query: str, k: int = 4) -> List[Document]:
+        """Return docs most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query.
+        """
+        docs_and_scores = self.similarity_search_with_score(query, k)
+        return [doc for doc, _ in docs_and_scores]
+
     def max_marginal_relevance_search(
         self, query: str, k: int = 4, fetch_k: int = 20
     ) -> List[Document]: