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54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
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from loguru import logger
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from open_notebook.domain.models import model_manager
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from open_notebook.models.llms import LanguageModel
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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 provision_langchain_model(
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content, model_id, default_type, **kwargs
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) -> BaseChatModel:
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"""
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Returns the best model to use based on the context size and on whether there is a specific model being requested in Config.
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If context > 105_000, returns the large_context_model
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If model_id is specified in Config, returns that model
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Otherwise, returns the default model for the given type
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"""
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tokens = token_count(content)
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if tokens > 105_000:
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logger.debug(
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f"Using large context model because the content has {tokens} tokens"
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)
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model = model_manager.get_default_model("large_context", **kwargs)
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elif model_id:
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model = model_manager.get_model(model_id, **kwargs)
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else:
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model = model_manager.get_default_model(default_type, **kwargs)
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assert isinstance(model, LanguageModel), f"Model is not a LanguageModel: {model}"
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return model.to_langchain()
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# todo: turn into a graph
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def run_pattern(
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pattern_name: str,
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config,
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state: dict = {},
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parser=None,
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) -> BaseMessage:
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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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payload = [SystemMessage(content=system_prompt)] + [
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HumanMessage(content=state["input_text"])
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]
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chain = provision_langchain_model(
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str(payload), config.get("configurable", {}).get("model_id"), "transformation"
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)
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response = chain.invoke(payload)
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return response
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