diff --git a/docs/modules/indexes/chain_examples/chat_vector_db.ipynb b/docs/modules/indexes/chain_examples/chat_vector_db.ipynb
index 62a007b9..d5b7148d 100644
--- a/docs/modules/indexes/chain_examples/chat_vector_db.ipynb
+++ b/docs/modules/indexes/chain_examples/chat_vector_db.ipynb
@@ -268,48 +268,44 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4f49beab",
+   "metadata": {},
    "source": [
     "## Chat Vector DB with `search_distance`\n",
     "If you are using a vector store that supports filtering by search distance, you can add a threshold value parameter."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "5ed8d612",
+   "metadata": {},
    "outputs": [],
    "source": [
     "vectordbkwargs = {\"search_distance\": 0.9}"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "6a7b3459",
+   "metadata": {},
    "outputs": [],
    "source": [
     "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore, return_source_documents=True)\n",
     "chat_history = []\n",
     "query = \"What did the president say about Ketanji Brown Jackson\"\n",
     "result = qa({\"question\": query, \"chat_history\": chat_history, \"vectordbkwargs\": vectordbkwargs})"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "id": "99b96dae",
+   "metadata": {},
    "source": [
     "## Chat Vector DB with `map_reduce`\n",
     "We can also use different types of combine document chains with the Chat Vector DB chain."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -524,6 +520,71 @@
     "query = \"Did he mention who she suceeded\"\n",
     "result = qa({\"question\": query, \"chat_history\": chat_history})\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f793d56b",
+   "metadata": {},
+   "source": [
+    "## get_chat_history Function\n",
+    "You can also specify a `get_chat_history` function, which can be used to format the chat_history string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a7ba9d8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_chat_history(inputs) -> str:\n",
+    "    res = []\n",
+    "    for human, ai in inputs:\n",
+    "        res.append(f\"Human:{human}\\nAI:{ai}\")\n",
+    "    return \"\\n\".join(res)\n",
+    "qa = ChatVectorDBChain.from_llm(OpenAI(temperature=0), vectorstore, get_chat_history=get_chat_history)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a3e33c0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat_history = []\n",
+    "query = \"What did the president say about Ketanji Brown Jackson\"\n",
+    "result = qa({\"question\": query, \"chat_history\": chat_history})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "936dc62f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.\""
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result['answer']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8c26901",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/langchain/chains/chat_vector_db/base.py b/langchain/chains/chat_vector_db/base.py
index 22cbc5dd..4dd18b68 100644
--- a/langchain/chains/chat_vector_db/base.py
+++ b/langchain/chains/chat_vector_db/base.py
@@ -1,7 +1,8 @@
 """Chain for chatting with a vector database."""
 from __future__ import annotations
 
-from typing import Any, Dict, List, Optional, Tuple
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 from pydantic import BaseModel
 
@@ -33,6 +34,7 @@ class ChatVectorDBChain(Chain, BaseModel):
     output_key: str = "answer"
     return_source_documents: bool = False
     top_k_docs_for_context: int = 4
+    get_chat_history: Optional[Callable[[Tuple[str, str]], str]] = None
     """Return the source documents."""
 
     @property
@@ -81,7 +83,8 @@ class ChatVectorDBChain(Chain, BaseModel):
 
     def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         question = inputs["question"]
-        chat_history_str = _get_chat_history(inputs["chat_history"])
+        get_chat_history = self.get_chat_history or _get_chat_history
+        chat_history_str = get_chat_history(inputs["chat_history"])
         vectordbkwargs = inputs.get("vectordbkwargs", {})
         if chat_history_str:
             new_question = self.question_generator.run(
@@ -103,7 +106,8 @@ class ChatVectorDBChain(Chain, BaseModel):
 
     async def _acall(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         question = inputs["question"]
-        chat_history_str = _get_chat_history(inputs["chat_history"])
+        get_chat_history = self.get_chat_history or _get_chat_history
+        chat_history_str = get_chat_history(inputs["chat_history"])
         vectordbkwargs = inputs.get("vectordbkwargs", {})
         if chat_history_str:
             new_question = await self.question_generator.arun(
@@ -123,3 +127,8 @@ class ChatVectorDBChain(Chain, BaseModel):
             return {self.output_key: answer, "source_documents": docs}
         else:
             return {self.output_key: answer}
+
+    def save(self, file_path: Union[Path, str]) -> None:
+        if self.get_chat_history:
+            raise ValueError("Chain not savable when `get_chat_history` is not None.")
+        super().save(file_path)