Add how-to guide on runnable generators (#12135)

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docs/docs/expression_language/how_to/generators.ipynb

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Custom generator functions\n",
+    "\n",
+    "You can use generator functions (ie. functions that use the `yield` keyword, and behave like iterators) in a LCEL pipeline.\n",
+    "\n",
+    "The signature of these generators should be `Iterator[Input] -> Iterator[Output]`. Or for async generators: `AsyncIterator[Input] -> AsyncIterator[Output]`.\n",
+    "\n",
+    "These are useful for:\n",
+    "- implementing a custom output parser\n",
+    "- modifying the output of a previous step, while preserving streaming capabilities\n",
+    "\n",
+    "Let's implement a custom output parser for comma-separated lists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lion, tiger, wolf, gorilla, panda\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import Iterator, List\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts.chat import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\n",
+    "    \"Write a comma-separated list of 5 animals similar to: {animal}\"\n",
+    ")\n",
+    "model = ChatOpenAI(temperature=0.0)\n",
+    "\n",
+    "\n",
+    "str_chain = prompt | model | StrOutputParser()\n",
+    "\n",
+    "print(str_chain.invoke({\"animal\": \"bear\"}))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is a custom parser that splits an iterator of llm tokens\n",
+    "# into a list of strings separated by commas\n",
+    "def split_into_list(input: Iterator[str]) -> Iterator[List[str]]:\n",
+    "    # hold partial input until we get a comma\n",
+    "    buffer = \"\"\n",
+    "    for chunk in input:\n",
+    "        # add current chunk to buffer\n",
+    "        buffer += chunk\n",
+    "        # while there are commas in the buffer\n",
+    "        while \",\" in buffer:\n",
+    "            # split buffer on comma\n",
+    "            comma_index = buffer.index(\",\")\n",
+    "            # yield everything before the comma\n",
+    "            yield [buffer[:comma_index].strip()]\n",
+    "            # save the rest for the next iteration\n",
+    "            buffer = buffer[comma_index + 1 :]\n",
+    "    # yield the last chunk\n",
+    "    yield [buffer.strip()]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['lion', 'tiger', 'wolf', 'gorilla', 'panda']\n"
+     ]
+    }
+   ],
+   "source": [
+    "list_chain = str_chain | split_into_list\n",
+    "\n",
+    "print(list_chain.invoke({\"animal\": \"bear\"}))\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}