feat: add schema-linking awel example (#1081)

dbgpt/rag/schemalinker/schema_linking.py

@@ -0,0 +1,78 @@
+from functools import reduce
+from typing import List, Optional
+
+from dbgpt.core import LLMClient, ModelMessage, ModelMessageRoleType, ModelRequest
+from dbgpt.datasource.rdbms.base import RDBMSDatabase
+from dbgpt.rag.chunk import Chunk
+from dbgpt.rag.schemalinker.base_linker import BaseSchemaLinker
+from dbgpt.rag.summary.rdbms_db_summary import _parse_db_summary
+from dbgpt.storage.vector_store.connector import VectorStoreConnector
+from dbgpt.util.chat_util import run_async_tasks
+
+INSTRUCTION = (
+    "You need to filter out the most relevant database table schema information (it may be a single "
+    "table or multiple tables) required to generate the SQL of the question query from the given "
+    "database schema information. First, I will show you an example of an instruction followed by "
+    "the correct schema response. Then, I will give you a new instruction, and you should write "
+    "the schema response that appropriately completes the request.\n### Example1 Instruction:\n"
+    "['job(id, name, age)', 'user(id, name, age)', 'student(id, name, age, info)']\n### Example1 "
+    "Input:\nFind the age of student table\n### Example1 Response:\n['student(id, name, age, info)']"
+    "\n###New Instruction:\n{}"
+)
+INPUT_PROMPT = "\n###New Input:\n{}\n###New Response:"
+
+
+class SchemaLinking(BaseSchemaLinker):
+    """SchemaLinking by LLM"""
+
+    def __init__(
+        self,
+        top_k: int = 5,
+        connection: Optional[RDBMSDatabase] = None,
+        llm: Optional[LLMClient] = None,
+        model_name: Optional[str] = None,
+        vector_store_connector: Optional[VectorStoreConnector] = None,
+        **kwargs
+    ):
+        """
+        Args:
+           connection (Optional[RDBMSDatabase]): RDBMSDatabase connection.
+           llm (Optional[LLMClient]): base llm
+        """
+        super().__init__(**kwargs)
+        self._top_k = top_k
+        self._connection = connection
+        self._llm = llm
+        self._model_name = model_name
+        self._vector_store_connector = vector_store_connector
+
+    def _schema_linking(self, query: str) -> List:
+        """get all db schema info"""
+        table_summaries = _parse_db_summary(self._connection)
+        chunks = [Chunk(content=table_summary) for table_summary in table_summaries]
+        chunks_content = [chunk.content for chunk in chunks]
+        return chunks_content
+
+    def _schema_linking_with_vector_db(self, query: str) -> List:
+        queries = [query]
+        candidates = [
+            self._vector_store_connector.similar_search(query, self._top_k)
+            for query in queries
+        ]
+        candidates = reduce(lambda x, y: x + y, candidates)
+        return candidates
+
+    async def _schema_linking_with_llm(self, query: str) -> List:
+        chunks_content = self.schema_linking(query)
+        schema_prompt = INSTRUCTION.format(
+            str(chunks_content) + INPUT_PROMPT.format(query)
+        )
+        messages = [
+            ModelMessage(role=ModelMessageRoleType.SYSTEM, content=schema_prompt)
+        ]
+        request = ModelRequest(model=self._model_name, messages=messages)
+        tasks = [self._llm.generate(request)]
+        # get accurate schem info by llm
+        schema = await run_async_tasks(tasks=tasks, concurrency_limit=1)
+        schema_text = schema[0].text
+        return schema_text