Add mmr support to redis retriever (#10556)

docs/extras/integrations/vectorstores/redis.ipynb

@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {
     "tags": []
    },
@@ -253,7 +253,7 @@
     "rds = Redis.from_texts(\n",
     "    texts,\n",
     "    embeddings,\n",
-    "    metadatas=metadats,\n",
+    "    metadatas=metadata,\n",
     "    redis_url=\"redis://localhost:6379\",\n",
     "    index_name=\"users\"\n",
     ")"
@@ -597,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -607,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1110,6 +1110,38 @@
     "retriever.get_relevant_documents(\"foo\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever = rds.as_retriever(search_type=\"mmr\",  search_kwargs={\"fetch_k\": 20, \"k\": 4, \"lambda_mult\": 0.1})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='foo', metadata={'id': 'doc:users:8f6b673b390647809d510112cde01a27', 'user': 'john', 'job': 'engineer', 'credit_score': 'high', 'age': '18'}),\n",
+       " Document(page_content='bar', metadata={'id': 'doc:users:93521560735d42328b48c9c6f6418d6a', 'user': 'tyler', 'job': 'engineer', 'credit_score': 'high', 'age': '100'}),\n",
+       " Document(page_content='foo', metadata={'id': 'doc:users:125ecd39d07845eabf1a699d44134a5b', 'user': 'nancy', 'job': 'doctor', 'credit_score': 'high', 'age': '94'}),\n",
+       " Document(page_content='foo', metadata={'id': 'doc:users:d6200ab3764c466082fde3eaab972a2a', 'user': 'derrick', 'job': 'doctor', 'credit_score': 'low', 'age': '45'})]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retriever.get_relevant_documents(\"foo\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1227,7 +1259,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

libs/langchain/langchain/vectorstores/redis/base.py

@@ -1425,6 +1425,7 @@ class RedisVectorStoreRetriever(VectorStoreRetriever):
         "similarity",
         "similarity_distance_threshold",
         "similarity_score_threshold",
+        "mmr",
     ]
     """Allowed search types."""
 
@@ -1438,7 +1439,6 @@ class RedisVectorStoreRetriever(VectorStoreRetriever):
     ) -> List[Document]:
         if self.search_type == "similarity":
             docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
-
         elif self.search_type == "similarity_distance_threshold":
             if self.search_kwargs["distance_threshold"] is None:
                 raise ValueError(
@@ -1454,6 +1454,10 @@ class RedisVectorStoreRetriever(VectorStoreRetriever):
                 )
             )
             docs = [doc for doc, _ in docs_and_similarities]
+        elif self.search_type == "mmr":
+            docs = self.vectorstore.max_marginal_relevance_search(
+                query, **self.search_kwargs
+            )
         else:
             raise ValueError(f"search_type of {self.search_type} not allowed.")
         return docs