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
 
@@ -77,6 +77,32 @@ class FAISS(VectorStore):
         self.index_to_docstore_id.update(index_to_id)
         return [_id for _, _id, _ in full_info]
 
+    def similarity_search_with_score(
+        self, query: str, k: int = 4
+    ) -> List[Tuple[Document, float]]:
+        """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 and score for each
+        """
+        embedding = self.embedding_function(query)
+        scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
+        docs = []
+        for j, i in enumerate(indices[0]):
+            if i == -1:
+                # This happens when not enough docs are returned.
+                continue
+            _id = self.index_to_docstore_id[i]
+            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, scores[0][j]))
+        return docs
+
     def similarity_search(self, query: str, k: int = 4) -> List[Document]:
         """Return docs most similar to query.
 
@@ -87,19 +113,8 @@ class FAISS(VectorStore):
         Returns:
             List of Documents most similar to the query.
         """
-        embedding = self.embedding_function(query)
+        docs_and_scores = self.similarity_search_with_score(query, k)
-        _, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
+        return [doc for doc, _ in docs_and_scores]
-        docs = []
-        for i in indices[0]:
-            if i == -1:
-                # This happens when not enough docs are returned.
-                continue
-            _id = self.index_to_docstore_id[i]
-            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)
-        return docs
 
     def max_marginal_relevance_search(
         self, query: str, k: int = 4, fetch_k: int = 20