docs: Add example using TypedDict in structured outputs how-to guide (

#27415) For me, the [Pydantic example](https://python.langchain.com/docs/how_to/structured_output/#choosing-between-multiple-schemas) does not work (tested on various Python versions from 3.10 to 3.12, and `Pydantic` versions from 2.7 to 2.9). The `TypedDict` example (added in this PR) does. ---- Additionally, fixed an error in [Using PydanticOutputParser example](https://python.langchain.com/docs/how_to/structured_output/#using-pydanticoutputparser). Was: ```python query = "Anna is 23 years old and she is 6 feet tall" print(prompt.invoke(query).to_string()) ``` Corrected to: ```python query = "Anna is 23 years old and she is 6 feet tall" print(prompt.invoke({"query": query}).to_string()) ``` --------- Co-authored-by: Eugene Yurtsev <[email protected]>
langchain-ai · Nov 13, 2024 · 2ab5673 · 2ab5673
1 parent 3e972fa
commit 2ab5673
Showing 1 changed file with 114 additions and 29 deletions.
diff --git a/docs/docs/how_to/structured_output.ipynb b/docs/docs/how_to/structured_output.ipynb
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "id": "6d55008f",
    "metadata": {},
    "outputs": [],
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "070bf702",
    "metadata": {},
    "outputs": [
@@ -91,7 +91,7 @@
        "Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "id": "70d82891-42e8-424a-919e-07d83bcfec61",
    "metadata": {},
    "outputs": [
@@ -159,7 +159,7 @@
        " 'rating': 7}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -199,19 +199,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "6700994a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'setup': 'Why was the cat sitting on the computer?',\n",
-       " 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
-       " 'rating': 7}"
+       " 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -250,12 +249,14 @@
    "source": [
     "### Choosing between multiple schemas\n",
     "\n",
-    "The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute:"
+    "The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute.\n",
+    "\n",
+    "#### Using Pydantic"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 7,
    "id": "9194bcf2",
    "metadata": {},
    "outputs": [
@@ -265,7 +266,7 @@
        "FinalResponse(final_output=Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7))"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -274,7 +275,6 @@
     "from typing import Union\n",
     "\n",
     "\n",
-    "# Pydantic\n",
     "class Joke(BaseModel):\n",
     "    \"\"\"Joke to tell user.\"\"\"\n",
     "\n",
@@ -302,17 +302,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "id": "84d86132",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a bunch of code, so I don't have feelings, but I'm here and ready to help you! How can I assist you today?\"))"
+       "FinalResponse(final_output=ConversationalResponse(response=\"I'm just a computer program, so I don't have feelings, but I'm here and ready to help you with whatever you need!\"))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "structured_llm.invoke(\"How are you today?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b087112c23bafcd",
+   "metadata": {},
+   "source": [
+    "#### Using TypedDict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "eb0d5855-feba-48fb-84ea-9acb0edb238b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'final_output': {'setup': 'Why was the cat sitting on the computer?',\n",
+       "  'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
+       "  'rating': 7}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from typing import Optional, Union\n",
+    "\n",
+    "from typing_extensions import Annotated, TypedDict\n",
+    "\n",
+    "\n",
+    "class Joke(TypedDict):\n",
+    "    \"\"\"Joke to tell user.\"\"\"\n",
+    "\n",
+    "    setup: Annotated[str, ..., \"The setup of the joke\"]\n",
+    "    punchline: Annotated[str, ..., \"The punchline of the joke\"]\n",
+    "    rating: Annotated[Optional[int], None, \"How funny the joke is, from 1 to 10\"]\n",
+    "\n",
+    "\n",
+    "class ConversationalResponse(TypedDict):\n",
+    "    \"\"\"Respond in a conversational manner. Be kind and helpful.\"\"\"\n",
+    "\n",
+    "    response: Annotated[str, ..., \"A conversational response to the user's query\"]\n",
+    "\n",
+    "\n",
+    "class FinalResponse(TypedDict):\n",
+    "    final_output: Union[Joke, ConversationalResponse]\n",
+    "\n",
+    "\n",
+    "structured_llm = llm.with_structured_output(FinalResponse)\n",
+    "\n",
+    "structured_llm.invoke(\"Tell me a joke about cats\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ec753809-c2c1-41c0-a3c5-69855d65475b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'final_output': {'response': \"I'm just a computer program, so I don't have feelings, but I'm here and ready to help you with whatever you need!\"}}"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,6 +398,14 @@
     "structured_llm.invoke(\"How are you today?\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "dd22149ac9d41d57",
+   "metadata": {},
+   "source": [
+    "Responses shall be identical to the ones shown in the Pydantic example."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e28c14d3",
@@ -347,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "id": "aff89877-28a3-472f-a1aa-eff893fe7736",
    "metadata": {},
    "outputs": [
@@ -415,19 +500,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "283ba784-2072-47ee-9b2c-1119e3c69e8e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'setup': 'Woodpecker',\n",
-       " 'punchline': \"Woodpecker who? Woodpecker who can't find a tree is just a bird with a headache!\",\n",
+       " 'punchline': \"Woodpecker you a joke, but I'm afraid it might be too 'hole-some'!\",\n",
        " 'rating': 7}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "d7381cb0-b2c3-4302-a319-ed72d0b9e43f",
    "metadata": {},
    "outputs": [
@@ -474,10 +559,10 @@
       "text/plain": [
        "{'setup': 'Crocodile',\n",
        " 'punchline': 'Crocodile be seeing you later, alligator!',\n",
-       " 'rating': 7}"
+       " 'rating': 6}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -579,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "df0370e3",
    "metadata": {},
    "outputs": [
@@ -590,7 +675,7 @@
        " 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -733,7 +818,7 @@
    "source": [
     "query = \"Anna is 23 years old and she is 6 feet tall\"\n",
     "\n",
-    "print(prompt.invoke(query).to_string())"
+    "print(prompt.invoke({\"query\": query}).to_string())"
    ]
   },
   {
@@ -913,9 +998,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv-2",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv-2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -927,7 +1012,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,