Harrison/faiss norm (#4903)

Co-authored-by: Jiaxin Shan <seedjeffwan@gmail.com>

langchain/vectorstores/faiss.py

@@ -81,6 +81,7 @@ class FAISS(VectorStore):
         relevance_score_fn: Optional[
             Callable[[float], float]
         ] = _default_relevance_score_fn,
+        normalize_L2: bool = False,
     ):
         """Initialize with necessary components."""
         self.embedding_function = embedding_function
@@ -88,6 +89,7 @@ class FAISS(VectorStore):
         self.docstore = docstore
         self.index_to_docstore_id = index_to_docstore_id
         self.relevance_score_fn = relevance_score_fn
+        self._normalize_L2 = normalize_L2
 
     def __add(
         self,
@@ -107,7 +109,11 @@ class FAISS(VectorStore):
             documents.append(Document(page_content=text, metadata=metadata))
         # Add to the index, the index_to_id mapping, and the docstore.
         starting_len = len(self.index_to_docstore_id)
-        self.index.add(np.array(embeddings, dtype=np.float32))
+        faiss = dependable_faiss_import()
+        vector = np.array(embeddings, dtype=np.float32)
+        if self._normalize_L2:
+            faiss.normalize_L2(vector)
+        self.index.add(vector)
         # Get list of index, id, and docs.
         full_info = [
             (starting_len + i, str(uuid.uuid4()), doc)
@@ -182,7 +188,11 @@ class FAISS(VectorStore):
         Returns:
             List of Documents most similar to the query and score for each
         """
-        scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
+        faiss = dependable_faiss_import()
+        vector = np.array([embedding], dtype=np.float32)
+        if self._normalize_L2:
+            faiss.normalize_L2(vector)
+        scores, indices = self.index.search(vector, k)
         docs = []
         for j, i in enumerate(indices[0]):
             if i == -1:
@@ -356,11 +366,15 @@ class FAISS(VectorStore):
         embeddings: List[List[float]],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
+        normalize_L2: bool = False,
         **kwargs: Any,
     ) -> FAISS:
         faiss = dependable_faiss_import()
         index = faiss.IndexFlatL2(len(embeddings[0]))
-        index.add(np.array(embeddings, dtype=np.float32))
+        vector = np.array(embeddings, dtype=np.float32)
+        if normalize_L2:
+            faiss.normalize_L2(vector)
+        index.add(vector)
         documents = []
         for i, text in enumerate(texts):
             metadata = metadatas[i] if metadatas else {}
@@ -369,7 +383,14 @@ class FAISS(VectorStore):
         docstore = InMemoryDocstore(
             {index_to_id[i]: doc for i, doc in enumerate(documents)}
         )
-        return cls(embedding.embed_query, index, docstore, index_to_id, **kwargs)
+        return cls(
+            embedding.embed_query,
+            index,
+            docstore,
+            index_to_id,
+            normalize_L2=normalize_L2,
+            **kwargs,
+        )
 
     @classmethod
     def from_texts(