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52 lines
1.6 KiB
Python
52 lines
1.6 KiB
Python
from langchain.output_parsers import OutputFixingParser
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from loguru import logger
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from open_notebook.config import DEFAULT_MODELS
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from open_notebook.models import get_model
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from open_notebook.prompter import Prompter
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from open_notebook.utils import token_count
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def run_pattern(
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pattern_name: str,
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model_name=None,
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messages=[],
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state: dict = {},
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parser=None,
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output_fixing_model_name=None,
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) -> dict:
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system_prompt = Prompter(prompt_template=pattern_name, parser=parser).render(
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data=state
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)
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tokens = token_count(str(system_prompt) + str(messages))
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if tokens > 105_000 and DEFAULT_MODELS.large_context_model:
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model_name = DEFAULT_MODELS.large_context_model
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logger.debug(
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f"Using large context model ({model_name}) because the content has {tokens} tokens"
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)
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logger.warning(system_prompt)
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elif tokens > 105_000 and not DEFAULT_MODELS.large_context_model:
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logger.critical(
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f"Content has {tokens} tokens, but no large context model is configured"
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)
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elif not model_name:
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model_name = DEFAULT_MODELS.default_transformation_model
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chain = get_model(model_name, model_type="language")
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if parser:
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chain = chain | parser
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if output_fixing_model_name and parser:
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output_fix_model = get_model(output_fixing_model_name, model_type="language")
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chain = chain | OutputFixingParser.from_llm(
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parser=parser,
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llm=output_fix_model,
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)
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if len(messages) > 0:
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response = chain.invoke([system_prompt] + messages)
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else:
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response = chain.invoke(system_prompt)
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return response
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