mirror of
https://github.com/hwchase17/langchain
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0d92a7f357
Probably the most boring PR to review ;) Individual commits might be easier to digest --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
77 lines
1.8 KiB
Plaintext
77 lines
1.8 KiB
Plaintext
---
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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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