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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 <eyurtsev@gmail.com>
This commit is contained in:
Artur Barseghyan
GitHub
parent
3e972faf81
commit
2ab5673eb1
@ -56,7 +56,7 @@
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 1,
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"id": "6d55008f",
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"metadata": {},
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"outputs": [],
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@ -81,7 +81,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 2,
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"id": "070bf702",
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"metadata": {},
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"outputs": [
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@ -91,7 +91,7 @@
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"Joke(setup='Why was the cat sitting on the computer?', punchline='Because it wanted to keep an eye on the mouse!', rating=7)"
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]
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},
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"execution_count": 4,
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"execution_count": 2,
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"output_type": "execute_result"
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@ -147,7 +147,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 3,
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"id": "70d82891-42e8-424a-919e-07d83bcfec61",
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"metadata": {},
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"outputs": [
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@ -159,7 +159,7 @@
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" 'rating': 7}"
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]
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},
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"execution_count": 8,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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@ -199,7 +199,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 4,
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"id": "6700994a",
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"metadata": {},
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"outputs": [
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@ -207,11 +207,10 @@
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"data": {
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"text/plain": [
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"{'setup': 'Why was the cat sitting on the computer?',\n",
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" 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
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" 'rating': 7}"
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" 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
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]
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},
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"execution_count": 6,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -250,12 +249,14 @@
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"source": [
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"### Choosing between multiple schemas\n",
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"\n",
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"The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute:"
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"The simplest way to let the model choose from multiple schemas is to create a parent schema that has a Union-typed attribute.\n",
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"\n",
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"#### Using Pydantic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 7,
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"id": "9194bcf2",
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"metadata": {},
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"outputs": [
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@ -265,7 +266,7 @@
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"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))"
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]
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},
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"execution_count": 19,
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -274,7 +275,6 @@
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"from typing import Union\n",
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"\n",
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"\n",
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"# Pydantic\n",
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"class Joke(BaseModel):\n",
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" \"\"\"Joke to tell user.\"\"\"\n",
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"\n",
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@ -302,17 +302,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 8,
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"id": "84d86132",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"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?\"))"
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"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!\"))"
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]
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},
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"execution_count": 20,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -321,6 +321,91 @@
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"structured_llm.invoke(\"How are you today?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8b087112c23bafcd",
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"metadata": {},
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"source": [
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"#### Using TypedDict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "eb0d5855-feba-48fb-84ea-9acb0edb238b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'final_output': {'setup': 'Why was the cat sitting on the computer?',\n",
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" 'punchline': 'Because it wanted to keep an eye on the mouse!',\n",
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" 'rating': 7}}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from typing import Optional, Union\n",
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"\n",
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"from typing_extensions import Annotated, TypedDict\n",
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"\n",
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"\n",
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"class Joke(TypedDict):\n",
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" \"\"\"Joke to tell user.\"\"\"\n",
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"\n",
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" setup: Annotated[str, ..., \"The setup of the joke\"]\n",
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" punchline: Annotated[str, ..., \"The punchline of the joke\"]\n",
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" rating: Annotated[Optional[int], None, \"How funny the joke is, from 1 to 10\"]\n",
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"\n",
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"\n",
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"class ConversationalResponse(TypedDict):\n",
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" \"\"\"Respond in a conversational manner. Be kind and helpful.\"\"\"\n",
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"\n",
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" response: Annotated[str, ..., \"A conversational response to the user's query\"]\n",
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"\n",
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"\n",
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"class FinalResponse(TypedDict):\n",
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" final_output: Union[Joke, ConversationalResponse]\n",
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"\n",
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"\n",
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"structured_llm = llm.with_structured_output(FinalResponse)\n",
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"\n",
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"structured_llm.invoke(\"Tell me a joke about cats\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "ec753809-c2c1-41c0-a3c5-69855d65475b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'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!\"}}"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"structured_llm.invoke(\"How are you today?\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dd22149ac9d41d57",
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"metadata": {},
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"source": [
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"Responses shall be identical to the ones shown in the Pydantic example."
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]
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},
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{
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"cell_type": "markdown",
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"id": "e28c14d3",
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@ -347,7 +432,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 11,
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"id": "aff89877-28a3-472f-a1aa-eff893fe7736",
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"metadata": {},
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"outputs": [
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@ -415,7 +500,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"id": "283ba784-2072-47ee-9b2c-1119e3c69e8e",
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"metadata": {},
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"outputs": [
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@ -423,11 +508,11 @@
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"data": {
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"text/plain": [
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"{'setup': 'Woodpecker',\n",
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" 'punchline': \"Woodpecker who? Woodpecker who can't find a tree is just a bird with a headache!\",\n",
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" 'punchline': \"Woodpecker you a joke, but I'm afraid it might be too 'hole-some'!\",\n",
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" 'rating': 7}"
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]
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},
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"execution_count": 11,
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -465,7 +550,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 13,
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"id": "d7381cb0-b2c3-4302-a319-ed72d0b9e43f",
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"metadata": {},
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"outputs": [
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@ -474,10 +559,10 @@
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"text/plain": [
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"{'setup': 'Crocodile',\n",
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" 'punchline': 'Crocodile be seeing you later, alligator!',\n",
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" 'rating': 7}"
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" 'rating': 6}"
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]
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},
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"execution_count": 12,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -579,7 +664,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 14,
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"id": "df0370e3",
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"metadata": {},
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"outputs": [
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@ -590,7 +675,7 @@
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" 'punchline': 'Because it wanted to keep an eye on the mouse!'}"
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]
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},
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"execution_count": 15,
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -733,7 +818,7 @@
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"source": [
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"query = \"Anna is 23 years old and she is 6 feet tall\"\n",
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"\n",
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"print(prompt.invoke(query).to_string())"
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"print(prompt.invoke({\"query\": query}).to_string())"
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]
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},
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{
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@ -913,9 +998,9 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "poetry-venv-2",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "poetry-venv-2"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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@ -927,7 +1012,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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