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120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
"""The summary operator."""
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from typing import Any, Optional
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from dbgpt.core import LLMClient
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from dbgpt.core.awel.flow import IOField, OperatorCategory, Parameter, ViewMetadata
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from dbgpt.rag.assembler.summary import SummaryAssembler
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from dbgpt.rag.knowledge.base import Knowledge
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from dbgpt.rag.operators.assembler import AssemblerOperator
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from dbgpt.util.i18n_utils import _
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class SummaryAssemblerOperator(AssemblerOperator[Any, Any]):
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"""The summary assembler operator."""
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metadata = ViewMetadata(
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label=_("Summary Operator"),
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name="summary_assembler_operator",
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category=OperatorCategory.RAG,
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description=_("The summary assembler operator."),
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inputs=[
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IOField.build_from(
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_("Knowledge"), "knowledge", Knowledge, _("Knowledge datasource")
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)
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],
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outputs=[
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IOField.build_from(
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_("Document summary"),
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"summary",
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str,
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description="document summary",
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)
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],
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parameters=[
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Parameter.build_from(
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_("LLM Client"),
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"llm_client",
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LLMClient,
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optional=True,
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default=None,
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description=_("The LLM Client."),
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),
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Parameter.build_from(
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label=_("Model name"),
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name="model_name",
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type=str,
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optional=True,
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default="gpt-3.5-turbo",
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description=_("LLM model name"),
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),
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Parameter.build_from(
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label=_("prompt language"),
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name="language",
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type=str,
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optional=True,
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default="en",
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description=_("prompt language"),
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),
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Parameter.build_from(
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label=_("Max iteration with LLM"),
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name="max_iteration_with_llm",
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type=int,
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optional=True,
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default=5,
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description=_("prompt language"),
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),
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Parameter.build_from(
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label=_("Concurrency limit with LLM"),
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name="concurrency_limit_with_llm",
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type=int,
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optional=True,
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default=3,
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description=_("The concurrency limit with llm"),
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),
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],
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documentation_url="https://github.com/openai/openai-python",
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)
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def __init__(
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self,
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llm_client: Optional[LLMClient],
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model_name: Optional[str] = "gpt-3.5-turbo",
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language: Optional[str] = "en",
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max_iteration_with_llm: Optional[int] = 5,
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concurrency_limit_with_llm: Optional[int] = 3,
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**kwargs
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):
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"""Create the summary assemble operator.
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Args:
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llm_client: (Optional[LLMClient]) The LLM client.
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model_name: (Optional[str]) The model name.
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language: (Optional[str]) The prompt language.
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max_iteration_with_llm: (Optional[int]) The max iteration with llm.
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concurrency_limit_with_llm: (Optional[int]) The concurrency limit with
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llm.
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"""
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super().__init__(**kwargs)
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self._llm_client = llm_client
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self._model_name = model_name
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self._language = language
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self._max_iteration_with_llm = max_iteration_with_llm
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self._concurrency_limit_with_llm = concurrency_limit_with_llm
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async def map(self, knowledge: Knowledge) -> str:
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"""Assemble the summary."""
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assembler = SummaryAssembler.load_from_knowledge(
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knowledge=knowledge,
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llm_client=self._llm_client,
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model_name=self._model_name,
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language=self._language,
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max_iteration_with_llm=self._max_iteration_with_llm,
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concurrency_limit_with_llm=self._concurrency_limit_with_llm,
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)
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return await assembler.generate_summary()
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def assemble(self, knowledge: Knowledge) -> Any:
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"""Assemble the summary."""
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pass
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