docs for batch size (#1082)

docs/modules/chains/combine_docs_examples/qa_with_sources.ipynb

@@ -365,6 +365,20 @@
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d943c6c1",
+   "metadata": {},
+   "source": [
+    "**Batch Size**\n",
+    "\n",
+    "When using the `map_reduce` chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:\n",
+    "\n",
+    "```python\n",
+    "llm = OpenAI(batch_size=5, temperature=0)\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5bf0e1ab",

docs/modules/chains/combine_docs_examples/question_answering.ipynb

@@ -356,6 +356,20 @@
     "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6391b7ab",
+   "metadata": {},
+   "source": [
+    "**Batch Size**\n",
+    "\n",
+    "When using the `map_reduce` chain, one thing to keep in mind is the batch size you are using during the map step. If this is too high, it could cause rate limiting errors. You can control this by setting the batch size on the LLM used. Note that this only applies for LLMs with this parameter. Below is an example of doing so:\n",
+    "\n",
+    "```python\n",
+    "llm = OpenAI(batch_size=5, temperature=0)\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6ea50ad0",