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93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
"""SchemaLinking by LLM."""
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from functools import reduce
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from typing import List, Optional, cast
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from dbgpt.core import (
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Chunk,
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LLMClient,
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ModelMessage,
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ModelMessageRoleType,
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ModelRequest,
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)
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from dbgpt.datasource.base import BaseConnector
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from dbgpt.rag.index.base import IndexStoreBase
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from dbgpt.rag.schemalinker.base_linker import BaseSchemaLinker
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from dbgpt.rag.summary.rdbms_db_summary import _parse_db_summary
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from dbgpt.util.chat_util import run_async_tasks
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INSTRUCTION = """
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You need to filter out the most relevant database table schema information (it may be a
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single table or multiple tables) required to generate the SQL of the question query
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from the given database schema information. First, I will show you an example of an
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instruction followed by the correct schema response. Then, I will give you a new
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instruction, and you should write the schema response that appropriately completes the
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request.
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### Example1 Instruction:
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['job(id, name, age)', 'user(id, name, age)', 'student(id, name, age, info)']
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### Example1 Input:
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Find the age of student table
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### Example1 Response:
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['student(id, name, age, info)']
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###New Instruction:
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{}
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"""
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INPUT_PROMPT = "\n###New Input:\n{}\n###New Response:"
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class SchemaLinking(BaseSchemaLinker):
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"""SchemaLinking by LLM."""
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def __init__(
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self,
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connector: BaseConnector,
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model_name: str,
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llm: LLMClient,
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top_k: int = 5,
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index_store: Optional[IndexStoreBase] = None,
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):
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"""Create the schema linking instance.
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Args:
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connection (Optional[BaseConnector]): BaseConnector connection.
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llm (Optional[LLMClient]): base llm
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"""
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self._top_k = top_k
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self._connector = connector
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self._llm = llm
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self._model_name = model_name
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self._index_store = index_store
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def _schema_linking(self, query: str) -> List:
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"""Get all db schema info."""
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table_summaries = _parse_db_summary(self._connector)
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chunks = [Chunk(content=table_summary) for table_summary in table_summaries]
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chunks_content = [chunk.content for chunk in chunks]
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return chunks_content
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def _schema_linking_with_vector_db(self, query: str) -> List[Chunk]:
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queries = [query]
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if not self._index_store:
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raise ValueError("Vector store connector is not provided.")
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candidates = [
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self._index_store.similar_search(query, self._top_k) for query in queries
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]
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return cast(List[Chunk], reduce(lambda x, y: x + y, candidates))
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async def _schema_linking_with_llm(self, query: str) -> List:
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chunks_content = self.schema_linking(query)
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schema_prompt = INSTRUCTION.format(
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str(chunks_content) + INPUT_PROMPT.format(query)
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)
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messages = [
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ModelMessage(role=ModelMessageRoleType.SYSTEM, content=schema_prompt)
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]
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request = ModelRequest(model=self._model_name, messages=messages)
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tasks = [self._llm.generate(request)]
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# get accurate schem info by llm
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schema = await run_async_tasks(tasks=tasks, concurrency_limit=1)
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schema_text = schema[0].text
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return schema_text
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