Harrison/from keys redis (#4653)

Co-authored-by: Christoph Kahl <christoph@zauberware.com>

langchain/vectorstores/redis.py

@@ -358,7 +358,7 @@ def _similarity_search_with_relevance_scores(
         return [(doc, self.relevance_score_fn(score)) for doc, score in docs_and_scores]
 
     @classmethod
-    def from_texts(
+    def from_texts_return_keys(
         cls: Type[Redis],
         texts: List[str],
         embedding: Embeddings,
@@ -369,7 +369,7 @@ def from_texts(
         vector_key: str = "content_vector",
         distance_metric: REDIS_DISTANCE_METRICS = "COSINE",
         **kwargs: Any,
-    ) -> Redis:
+    ) -> Tuple[Redis, List[str]]:
         """Create a Redis vectorstore from raw documents.
         This is a user-friendly interface that:
             1. Embeds documents.
@@ -414,7 +414,49 @@ def from_texts(
         instance._create_index(dim=len(embeddings[0]), distance_metric=distance_metric)
 
         # Add data to Redis
-        instance.add_texts(texts, metadatas, embeddings)
+        keys = instance.add_texts(texts, metadatas, embeddings)
+        return instance, keys
+
+    @classmethod
+    def from_texts(
+        cls: Type[Redis],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        index_name: Optional[str] = None,
+        content_key: str = "content",
+        metadata_key: str = "metadata",
+        vector_key: str = "content_vector",
+        **kwargs: Any,
+    ) -> Redis:
+        """Create a Redis vectorstore from raw documents.
+        This is a user-friendly interface that:
+            1. Embeds documents.
+            2. Creates a new index for the embeddings in Redis.
+            3. Adds the documents to the newly created Redis index.
+        This is intended to be a quick way to get started.
+        Example:
+            .. code-block:: python
+                from langchain.vectorstores import Redis
+                from langchain.embeddings import OpenAIEmbeddings
+                embeddings = OpenAIEmbeddings()
+                redisearch = RediSearch.from_texts(
+                    texts,
+                    embeddings,
+                    redis_url="redis://username:password@localhost:6379"
+                )
+        """
+        instance, _ = cls.from_texts_return_keys(
+            cls=cls,
+            texts=texts,
+            embedding=embedding,
+            metadatas=metadatas,
+            index_name=index_name,
+            content_key=content_key,
+            metadata_key=metadata_key,
+            vector_key=vector_key,
+            kwargs=kwargs,
+        )
         return instance
 
     @staticmethod