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https://github.com/hwchase17/langchain
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108 lines
4.9 KiB
Python
108 lines
4.9 KiB
Python
import logging
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from langchain.output_parsers.openai_tools import JsonOutputToolsParser
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from langchain_community.chat_models import ChatOpenAI
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnableLambda
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Modified from the paper to be more robust to benign prompt injection
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# https://arxiv.org/abs/2312.06648
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# @misc{chen2023dense,
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# title={Dense X Retrieval: What Retrieval Granularity Should We Use?},
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# author={Tong Chen and Hongwei Wang and Sihao Chen and Wenhao Yu and Kaixin Ma
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# and Xinran Zhao and Hongming Zhang and Dong Yu},
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# year={2023},
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# eprint={2312.06648},
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# archivePrefix={arXiv},
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# primaryClass={cs.CL}
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# }
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PROMPT = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"""Decompose the "Content" into clear and simple propositions, ensuring they are interpretable out of
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context.
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1. Split compound sentence into simple sentences. Maintain the original phrasing from the input
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whenever possible.
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2. For any named entity that is accompanied by additional descriptive information, separate this
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information into its own distinct proposition.
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3. Decontextualize the proposition by adding necessary modifier to nouns or entire sentences
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and replacing pronouns (e.g., "it", "he", "she", "they", "this", "that") with the full name of the
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entities they refer to.
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4. Present the results as a list of strings, formatted in JSON.
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Example:
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Input: Title: ¯Eostre. Section: Theories and interpretations, Connection to Easter Hares. Content:
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The earliest evidence for the Easter Hare (Osterhase) was recorded in south-west Germany in
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1678 by the professor of medicine Georg Franck von Franckenau, but it remained unknown in
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other parts of Germany until the 18th century. Scholar Richard Sermon writes that "hares were
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frequently seen in gardens in spring, and thus may have served as a convenient explanation for the
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origin of the colored eggs hidden there for children. Alternatively, there is a European tradition
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that hares laid eggs, since a hare’s scratch or form and a lapwing’s nest look very similar, and
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both occur on grassland and are first seen in the spring. In the nineteenth century the influence
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of Easter cards, toys, and books was to make the Easter Hare/Rabbit popular throughout Europe.
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German immigrants then exported the custom to Britain and America where it evolved into the
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Easter Bunny."
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Output: [ "The earliest evidence for the Easter Hare was recorded in south-west Germany in
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1678 by Georg Franck von Franckenau.", "Georg Franck von Franckenau was a professor of
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medicine.", "The evidence for the Easter Hare remained unknown in other parts of Germany until
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the 18th century.", "Richard Sermon was a scholar.", "Richard Sermon writes a hypothesis about
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the possible explanation for the connection between hares and the tradition during Easter", "Hares
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were frequently seen in gardens in spring.", "Hares may have served as a convenient explanation
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for the origin of the colored eggs hidden in gardens for children.", "There is a European tradition
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that hares laid eggs.", "A hare’s scratch or form and a lapwing’s nest look very similar.", "Both
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hares and lapwing’s nests occur on grassland and are first seen in the spring.", "In the nineteenth
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century the influence of Easter cards, toys, and books was to make the Easter Hare/Rabbit popular
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throughout Europe.", "German immigrants exported the custom of the Easter Hare/Rabbit to
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Britain and America.", "The custom of the Easter Hare/Rabbit evolved into the Easter Bunny in
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Britain and America."]""", # noqa
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),
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("user", "Decompose the following:\n{input}"),
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]
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)
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def get_propositions(tool_calls: list) -> list:
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if not tool_calls:
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raise ValueError("No tool calls found")
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return tool_calls[0]["args"]["propositions"]
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def empty_proposals(x):
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# Model couldn't generate proposals
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return []
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proposition_chain = (
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PROMPT
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| ChatOpenAI(model="gpt-3.5-turbo-16k").bind(
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tools=[
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{
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"type": "function",
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"function": {
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"name": "decompose_content",
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"description": "Return the decomposed propositions",
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"parameters": {
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"type": "object",
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"properties": {
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"propositions": {
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"type": "array",
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"items": {"type": "string"},
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}
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},
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"required": ["propositions"],
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},
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},
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}
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],
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tool_choice={"type": "function", "function": {"name": "decompose_content"}},
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)
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| JsonOutputToolsParser()
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| get_propositions
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).with_fallbacks([RunnableLambda(empty_proposals)])
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