Harrison/from methods (#1912)

Co-authored-by: shibuiwilliam <shibuiyusuke@gmail.com>

langchain/vectorstores/faiss.py

@@ -55,6 +55,36 @@ class FAISS(VectorStore):
         self.docstore = docstore
         self.index_to_docstore_id = index_to_docstore_id
 
+    def __add(
+        self,
+        texts: Iterable[str],
+        embeddings: Iterable[List[float]],
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        if not isinstance(self.docstore, AddableMixin):
+            raise ValueError(
+                "If trying to add texts, the underlying docstore should support "
+                f"adding items, which {self.docstore} does not"
+            )
+        documents = []
+        for i, text in enumerate(texts):
+            metadata = metadatas[i] if metadatas else {}
+            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))
+        # Get list of index, id, and docs.
+        full_info = [
+            (starting_len + i, str(uuid.uuid4()), doc)
+            for i, doc in enumerate(documents)
+        ]
+        # Add information to docstore and index.
+        self.docstore.add({_id: doc for _, _id, doc in full_info})
+        index_to_id = {index: _id for index, _id, _ in full_info}
+        self.index_to_docstore_id.update(index_to_id)
+        return [_id for _, _id, _ in full_info]
+
     def add_texts(
         self,
         texts: Iterable[str],
@@ -77,23 +107,34 @@ class FAISS(VectorStore):
             )
         # Embed and create the documents.
         embeddings = [self.embedding_function(text) for text in texts]
-        documents = []
-        for i, text in enumerate(texts):
-            metadata = metadatas[i] if metadatas else {}
-            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))
-        # Get list of index, id, and docs.
-        full_info = [
-            (starting_len + i, str(uuid.uuid4()), doc)
-            for i, doc in enumerate(documents)
-        ]
-        # Add information to docstore and index.
-        self.docstore.add({_id: doc for _, _id, doc in full_info})
-        index_to_id = {index: _id for index, _id, _ in full_info}
-        self.index_to_docstore_id.update(index_to_id)
-        return [_id for _, _id, _ in full_info]
+        return self.__add(texts, embeddings, metadatas, **kwargs)
+
+    def add_embeddings(
+        self,
+        text_embeddings: Iterable[Tuple[str, List[float]]],
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            text_embeddings: Iterable pairs of string and embedding to
+                add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+        if not isinstance(self.docstore, AddableMixin):
+            raise ValueError(
+                "If trying to add texts, the underlying docstore should support "
+                f"adding items, which {self.docstore} does not"
+            )
+        # Embed and create the documents.
+
+        texts = [te[0] for te in text_embeddings]
+        embeddings = [te[1] for te in text_embeddings]
+        return self.__add(texts, embeddings, metadatas, **kwargs)
 
     def similarity_search_with_score_by_vector(
         self, embedding: List[float], k: int = 4
@@ -253,6 +294,28 @@ class FAISS(VectorStore):
         index_to_id = {index: _id for index, _id, _ in full_info}
         self.index_to_docstore_id.update(index_to_id)
 
+    @classmethod
+    def __from(
+        cls,
+        texts: List[str],
+        embeddings: List[List[float]],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> FAISS:
+        faiss = dependable_faiss_import()
+        index = faiss.IndexFlatL2(len(embeddings[0]))
+        index.add(np.array(embeddings, dtype=np.float32))
+        documents = []
+        for i, text in enumerate(texts):
+            metadata = metadatas[i] if metadatas else {}
+            documents.append(Document(page_content=text, metadata=metadata))
+        index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
+        docstore = InMemoryDocstore(
+            {index_to_id[i]: doc for i, doc in enumerate(documents)}
+        )
+        return cls(embedding.embed_query, index, docstore, index_to_id)
+
     @classmethod
     def from_texts(
         cls,
@@ -278,19 +341,37 @@ class FAISS(VectorStore):
                 embeddings = OpenAIEmbeddings()
                 faiss = FAISS.from_texts(texts, embeddings)
         """
-        faiss = dependable_faiss_import()
         embeddings = embedding.embed_documents(texts)
-        index = faiss.IndexFlatL2(len(embeddings[0]))
-        index.add(np.array(embeddings, dtype=np.float32))
-        documents = []
-        for i, text in enumerate(texts):
-            metadata = metadatas[i] if metadatas else {}
-            documents.append(Document(page_content=text, metadata=metadata))
-        index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
-        docstore = InMemoryDocstore(
-            {index_to_id[i]: doc for i, doc in enumerate(documents)}
-        )
-        return cls(embedding.embed_query, index, docstore, index_to_id)
+        return cls.__from(texts, embeddings, embedding, metadatas, **kwargs)
+
+    @classmethod
+    def from_embeddings(
+        cls,
+        text_embeddings: List[Tuple[str, List[float]]],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> FAISS:
+        """Construct FAISS wrapper from raw documents.
+
+        This is a user friendly interface that:
+            1. Embeds documents.
+            2. Creates an in memory docstore
+            3. Initializes the FAISS database
+
+        This is intended to be a quick way to get started.
+
+        Example:
+            .. code-block:: python
+
+                from langchain import FAISS
+                from langchain.embeddings import OpenAIEmbeddings
+                embeddings = OpenAIEmbeddings()
+                faiss = FAISS.from_texts(texts, embeddings)
+        """
+        texts = [t[0] for t in text_embeddings]
+        embeddings = [t[1] for t in text_embeddings]
+        return cls.__from(texts, embeddings, embedding, metadatas, **kwargs)
 
     def save_local(self, folder_path: str) -> None:
         """Save FAISS index, docstore, and index_to_docstore_id to disk.