d79b3744cb
This change remove `BaseComponent`'s: - run_raw - run_batch_raw - run_document - run_batch_document - is_document - is_batch Each component is expected to support multiple types of inputs and a single type of output. Since we want the component to work out-of-the-box with both standardized and customized use cases, supporting multiple types of inputs are expected. At the same time, to reduce the complexity of understanding how to use a component, we restrict a component to only have a single output type. To accommodate these changes, we also refactor some components to remove their run_raw, run_batch_raw... methods, and to decide the common output interface for those components. Tests are updated accordingly. Commit changes: * Add kwargs to vector store's query * Simplify the BaseComponent * Update tests * Remove support for Python 3.8 and 3.9 * Bump version 0.3.0 * Fix github PR caching still use old environment after bumping version --------- Co-authored-by: ian <ian@cinnamon.is>
71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
import logging
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from typing import Type
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from langchain.llms.base import BaseLLM
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from theflow.base import Param
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from ...base import BaseComponent
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from ..base import LLMInterface
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logger = logging.getLogger(__name__)
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class LLM(BaseComponent):
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pass
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class LangchainLLM(LLM):
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_lc_class: Type[BaseLLM]
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def __init__(self, **params):
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if self._lc_class is None:
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raise AttributeError(
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"Should set _lc_class attribute to the LLM class from Langchain "
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"if using LLM from Langchain"
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)
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self._kwargs: dict = {}
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for param in list(params.keys()):
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if param in self._lc_class.__fields__:
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self._kwargs[param] = params.pop(param)
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super().__init__(**params)
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@Param.auto(cache=False)
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def agent(self):
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return self._lc_class(**self._kwargs)
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def run(self, text: str) -> LLMInterface:
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pred = self.agent.generate([text])
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all_text = [each.text for each in pred.generations[0]]
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completion_tokens, total_tokens, prompt_tokens = 0, 0, 0
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try:
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if pred.llm_output is not None:
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completion_tokens = pred.llm_output["token_usage"]["completion_tokens"]
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total_tokens = pred.llm_output["token_usage"]["total_tokens"]
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prompt_tokens = pred.llm_output["token_usage"]["prompt_tokens"]
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except Exception:
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logger.warning(
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f"Cannot get token usage from LLM output for {self._lc_class.__name__}"
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)
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return LLMInterface(
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text=all_text[0] if len(all_text) > 0 else "",
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candidates=all_text,
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completion_tokens=completion_tokens,
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total_tokens=total_tokens,
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prompt_tokens=prompt_tokens,
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logits=[],
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)
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def __setattr__(self, name, value):
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if name in self._lc_class.__fields__:
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self._kwargs[name] = value
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setattr(self.agent, name, value)
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else:
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super().__setattr__(name, value)
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class LLMChat(BaseComponent):
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pass
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