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trying to add docs
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```python
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from langchain.output_parsers import CommaSeparatedListOutputParser
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from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
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from langchain.llms import OpenAI
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from langchain.chat_models import ChatOpenAI
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output_parser = CommaSeparatedListOutputParser()
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```
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```python
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format_instructions = output_parser.get_format_instructions()
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prompt = PromptTemplate(
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template="List five {subject}.\n{format_instructions}",
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input_variables=["subject"],
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partial_variables={"format_instructions": format_instructions}
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)
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```
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```python
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model = OpenAI(temperature=0)
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```
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```python
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_input = prompt.format(subject="ice cream flavors")
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output = model(_input)
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```
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```python
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output_parser.parse(output)
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```
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<CodeOutputBlock lang="python">
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```
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['Vanilla',
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'Chocolate',
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'Strawberry',
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'Mint Chocolate Chip',
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'Cookies and Cream']
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```
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</CodeOutputBlock>
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---
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sidebar_position: 2
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---
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Below we go over the main type of output parser, the `PydanticOutputParser`.
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```python
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from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
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from langchain.llms import OpenAI
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from langchain.chat_models import ChatOpenAI
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from langchain.output_parsers import PydanticOutputParser
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from pydantic import BaseModel, Field, validator
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from typing import List
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```
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```python
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model_name = 'text-davinci-003'
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temperature = 0.0
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model = OpenAI(model_name=model_name, temperature=temperature)
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```
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```python
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# Define your desired data structure.
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class Joke(BaseModel):
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setup: str = Field(description="question to set up a joke")
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punchline: str = Field(description="answer to resolve the joke")
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# You can add custom validation logic easily with Pydantic.
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@validator('setup')
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def question_ends_with_question_mark(cls, field):
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if field[-1] != '?':
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raise ValueError("Badly formed question!")
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return field
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```
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```python
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# Set up a parser + inject instructions into the prompt template.
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parser = PydanticOutputParser(pydantic_object=Joke)
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```
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```python
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prompt = PromptTemplate(
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template="Answer the user query.\n{format_instructions}\n{query}\n",
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input_variables=["query"],
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partial_variables={"format_instructions": parser.get_format_instructions()}
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)
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```
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```python
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# And a query intended to prompt a language model to populate the data structure.
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joke_query = "Tell me a joke."
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_input = prompt.format_prompt(query=joke_query)
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```
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```python
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output = model(_input.to_string())
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```
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```python
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parser.parse(output)
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```
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<CodeOutputBlock lang="python">
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```
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Joke(setup='Why did the chicken cross the road?', punchline='To get to the other side!')
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```
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</CodeOutputBlock>
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For this example, we'll use the above Pydantic output parser. Here's what happens if we pass it a result that does not comply with the schema:
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```python
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from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
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from langchain.llms import OpenAI
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from langchain.chat_models import ChatOpenAI
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from langchain.output_parsers import PydanticOutputParser
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from pydantic import BaseModel, Field, validator
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from typing import List
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```
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```python
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class Actor(BaseModel):
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name: str = Field(description="name of an actor")
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film_names: List[str] = Field(description="list of names of films they starred in")
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actor_query = "Generate the filmography for a random actor."
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parser = PydanticOutputParser(pydantic_object=Actor)
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```
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```python
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misformatted = "{'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}"
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```
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```python
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parser.parse(misformatted)
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```
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<CodeOutputBlock lang="python">
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```
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---------------------------------------------------------------------------
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JSONDecodeError Traceback (most recent call last)
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File ~/workplace/langchain/langchain/output_parsers/pydantic.py:23, in PydanticOutputParser.parse(self, text)
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22 json_str = match.group()
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---> 23 json_object = json.loads(json_str)
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24 return self.pydantic_object.parse_obj(json_object)
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File ~/.pyenv/versions/3.9.1/lib/python3.9/json/__init__.py:346, in loads(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
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343 if (cls is None and object_hook is None and
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344 parse_int is None and parse_float is None and
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345 parse_constant is None and object_pairs_hook is None and not kw):
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--> 346 return _default_decoder.decode(s)
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347 if cls is None:
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File ~/.pyenv/versions/3.9.1/lib/python3.9/json/decoder.py:337, in JSONDecoder.decode(self, s, _w)
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333 """Return the Python representation of ``s`` (a ``str`` instance
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334 containing a JSON document).
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335
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336 """
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--> 337 obj, end = self.raw_decode(s, idx=_w(s, 0).end())
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338 end = _w(s, end).end()
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File ~/.pyenv/versions/3.9.1/lib/python3.9/json/decoder.py:353, in JSONDecoder.raw_decode(self, s, idx)
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352 try:
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--> 353 obj, end = self.scan_once(s, idx)
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354 except StopIteration as err:
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JSONDecodeError: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
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During handling of the above exception, another exception occurred:
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OutputParserException Traceback (most recent call last)
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Cell In[6], line 1
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----> 1 parser.parse(misformatted)
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File ~/workplace/langchain/langchain/output_parsers/pydantic.py:29, in PydanticOutputParser.parse(self, text)
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27 name = self.pydantic_object.__name__
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28 msg = f"Failed to parse {name} from completion {text}. Got: {e}"
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---> 29 raise OutputParserException(msg)
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OutputParserException: Failed to parse Actor from completion {'name': 'Tom Hanks', 'film_names': ['Forrest Gump']}. Got: Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
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```
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</CodeOutputBlock>
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Now we can construct and use a `OutputFixingParser`. This output parser takes as an argument another output parser but also an LLM with which to try to correct any formatting mistakes.
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```python
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from langchain.output_parsers import OutputFixingParser
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new_parser = OutputFixingParser.from_llm(parser=parser, llm=ChatOpenAI())
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```
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```python
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new_parser.parse(misformatted)
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```
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<CodeOutputBlock lang="python">
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```
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Actor(name='Tom Hanks', film_names=['Forrest Gump'])
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```
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</CodeOutputBlock>
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```python
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from langchain.output_parsers import StructuredOutputParser, ResponseSchema
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from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
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from langchain.llms import OpenAI
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from langchain.chat_models import ChatOpenAI
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```
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Here we define the response schema we want to receive.
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```python
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response_schemas = [
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ResponseSchema(name="answer", description="answer to the user's question"),
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ResponseSchema(name="source", description="source used to answer the user's question, should be a website.")
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]
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output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
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```
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We now get a string that contains instructions for how the response should be formatted, and we then insert that into our prompt.
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```python
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format_instructions = output_parser.get_format_instructions()
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prompt = PromptTemplate(
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template="answer the users question as best as possible.\n{format_instructions}\n{question}",
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input_variables=["question"],
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partial_variables={"format_instructions": format_instructions}
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)
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```
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We can now use this to format a prompt to send to the language model, and then parse the returned result.
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```python
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model = OpenAI(temperature=0)
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```
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```python
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_input = prompt.format_prompt(question="what's the capital of france?")
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output = model(_input.to_string())
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```
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```python
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output_parser.parse(output)
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```
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<CodeOutputBlock lang="python">
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```
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{'answer': 'Paris',
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'source': 'https://www.worldatlas.com/articles/what-is-the-capital-of-france.html'}
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```
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</CodeOutputBlock>
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And here's an example of using this in a chat model
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```python
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chat_model = ChatOpenAI(temperature=0)
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```
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```python
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prompt = ChatPromptTemplate(
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messages=[
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HumanMessagePromptTemplate.from_template("answer the users question as best as possible.\n{format_instructions}\n{question}")
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],
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input_variables=["question"],
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partial_variables={"format_instructions": format_instructions}
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)
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```
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```python
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_input = prompt.format_prompt(question="what's the capital of france?")
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output = chat_model(_input.to_messages())
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```
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```python
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output_parser.parse(output.content)
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```
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<CodeOutputBlock lang="python">
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```
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{'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Paris'}
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```
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</CodeOutputBlock>
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