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https://github.com/kennethreitz/instructor.git
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Hongnan Gao
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
from typing import List
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from openai import OpenAI
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from chain_of_density import summarize_article
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import csv
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import logging
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import instructor
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from pydantic import BaseModel, Field
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logging.basicConfig(level=logging.INFO)
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client = instructor.patch(OpenAI())
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instructions = instructor.Instructions(
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name="Chain Of Density",
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finetune_format="messages",
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# log handler is used to save the data to a file
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# you can imagine saving it to a database or other storage
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# based on your needs!
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log_handlers=[logging.FileHandler("generated.jsonl")],
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openai_client=client,
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)
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class GeneratedSummary(BaseModel):
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"""
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This represents a highly concise summary that includes as many entities as possible from the original source article.
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An Entity is a real-world object that's assigned a name - for example, a person, country a product or a book title.
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Guidelines
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- Make every word count
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- The new summary should be highly dense and concise yet self-contained, eg., easily understood without the Article.
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- Make space with fusion, compression, and removal of uninformative phrases like "the article discusses"
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"""
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summary: str = Field(
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...,
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description="This represents the final summary generated that captures the meaning of the original article which is as concise as possible. ",
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)
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@instructions.distil
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def distil_summarization(text: str) -> GeneratedSummary:
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summary_chain: List[str] = summarize_article(text)
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return GeneratedSummary(summary=summary_chain[-1])
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with open("test.csv", "r") as file:
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reader = csv.reader(file)
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next(reader) # Skip the header
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for article, _summary in reader:
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distil_summarization(article)
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