add fake embeddings class (#1503)

docs/modules/indexes/examples/embeddings.ipynb

@@ -463,6 +463,64 @@
    "source": [
     "query_result = embeddings.embed_query(text)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9c02c78",
+   "metadata": {},
+   "source": [
+    "## Fake Embeddings\n",
+    "\n",
+    "LangChain also provides a fake embedding class. You can use this to test your pipelines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ffc2e4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import FakeEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "80777571",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings = FakeEmbeddings(size=1352)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3ec9d8f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(\"foo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3b9ae9e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_results = embeddings.embed_documents([\"foo\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88d366bd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -481,7 +539,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {