diff --git a/docs/docs/modules/data_connection/retrievers/self_query.ipynb b/docs/docs/modules/data_connection/retrievers/self_query.ipynb
index 22ab1e46fa6..2b44db886f0 100644
--- a/docs/docs/modules/data_connection/retrievers/self_query.ipynb
+++ b/docs/docs/modules/data_connection/retrievers/self_query.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to its underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documents but to also extract filters from the user query on the metadata of stored documents and to execute those filters.\n",
     "\n",
-    "![](https://drive.google.com/uc?id=1OQUN-0MJcDUxmPXofgS7MqReEs720pqS)\n",
+    "![](../../../../static/img/self_querying.jpg)\n",
     "\n",
     "## Get started\n",
     "For demonstration purposes we'll use a `Chroma` vector store. We've created a small demo set of documents that contain summaries of movies.\n",
@@ -561,7 +561,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
diff --git a/docs/static/img/self_querying.jpg b/docs/static/img/self_querying.jpg
index 7970423415d..1f3055b53bd 100644
Binary files a/docs/static/img/self_querying.jpg and b/docs/static/img/self_querying.jpg differ