feat: add GraphRAG framework and integrate TuGraph (#1506)

Co-authored-by: KingSkyLi <15566300566@163.com>
Co-authored-by: aries_ckt <916701291@qq.com>
Co-authored-by: Fangyin Cheng <staneyffer@gmail.com>

dbgpt/rag/transformer/llm_extractor.py

@@ -0,0 +1,50 @@
+"""TripletExtractor class."""
+import logging
+from abc import ABC, abstractmethod
+from typing import List, Optional
+
+from dbgpt.core import HumanPromptTemplate, LLMClient, ModelMessage, ModelRequest
+from dbgpt.rag.transformer.base import ExtractorBase
+
+logger = logging.getLogger(__name__)
+
+
+class LLMExtractor(ExtractorBase, ABC):
+    """LLMExtractor class."""
+
+    def __init__(self, llm_client: LLMClient, model_name: str, prompt_template: str):
+        """Initialize the LLMExtractor."""
+        self._llm_client = llm_client
+        self._model_name = model_name
+        self._prompt_template = prompt_template
+
+    async def extract(self, text: str, limit: Optional[int] = None) -> List:
+        """Extract by LLm."""
+        template = HumanPromptTemplate.from_template(self._prompt_template)
+        messages = template.format_messages(text=text)
+
+        # use default model if needed
+        if not self._model_name:
+            models = await self._llm_client.models()
+            if not models:
+                raise Exception("No models available")
+            self._model_name = models[0].model
+            logger.info(f"Using model {self._model_name} to extract")
+
+        model_messages = ModelMessage.from_base_messages(messages)
+        request = ModelRequest(model=self._model_name, messages=model_messages)
+        response = await self._llm_client.generate(request=request)
+
+        if not response.success:
+            code = str(response.error_code)
+            reason = response.text
+            logger.error(f"request llm failed ({code}) {reason}")
+            return []
+
+        if limit and limit < 1:
+            ValueError("optional argument limit >= 1")
+        return self._parse_response(response.text, limit)
+
+    @abstractmethod
+    def _parse_response(self, text: str, limit: Optional[int] = None) -> List:
+        """Parse llm response."""