feat(workflow): add parameter extraction node

- Implemented ParameterExtractorNode to extract structured parameters from input text using LLM.
- Supports dynamic Jinja2 prompt rendering with field descriptions and types.
- Integrates with RedBearLLM and ModelConfigService for model retrieval.
- Handles JSON repair and raises clear BusinessException on parsing errors.

api/app/core/workflow/nodes/__init__.py

@@ -16,6 +16,7 @@ from app.core.workflow.nodes.llm import LLMNode
 from app.core.workflow.nodes.node_factory import NodeFactory, WorkflowNode
 from app.core.workflow.nodes.start import StartNode
 from app.core.workflow.nodes.transform import TransformNode
+from app.core.workflow.nodes.parameter_extractor import ParameterExtractorNode
 
 __all__ = [
     "BaseNode",
@@ -32,4 +33,5 @@ __all__ = [
     "AssignerNode",
     "HttpRequestNode",
     "JinjaRenderNode",
+    "ParameterExtractorNode"
 ]

api/app/core/workflow/nodes/enums.py

@@ -26,6 +26,7 @@ class NodeType(StrEnum):
     ASSIGNER = "assigner"
     JINJARENDER = "jinja-render"
     VAR_AGGREGATOR = "var-aggregator"
+    PARAMETER_EXTRACTOR = "parameter-extractor"
 
 
 class ComparisonOperator(StrEnum):

api/app/core/workflow/nodes/node_factory.py

@@ -17,6 +17,7 @@ from app.core.workflow.nodes.if_else import IfElseNode
 from app.core.workflow.nodes.jinja_render import JinjaRenderNode
 from app.core.workflow.nodes.knowledge import KnowledgeRetrievalNode
 from app.core.workflow.nodes.llm import LLMNode
+from app.core.workflow.nodes.parameter_extractor import ParameterExtractorNode
 from app.core.workflow.nodes.start import StartNode
 from app.core.workflow.nodes.transform import TransformNode
 from app.core.workflow.nodes.variable_aggregator import VariableAggregatorNode
@@ -35,7 +36,8 @@ WorkflowNode = Union[
     HttpRequestNode,
     KnowledgeRetrievalNode,
     JinjaRenderNode,
-    VariableAggregatorNode
+    VariableAggregatorNode,
+    ParameterExtractorNode
 ]
 
 
@@ -57,7 +59,8 @@ class NodeFactory:
         NodeType.ASSIGNER: AssignerNode,
         NodeType.HTTP_REQUEST: HttpRequestNode,
         NodeType.JINJARENDER: JinjaRenderNode,
-        NodeType.VAR_AGGREGATOR: VariableAggregatorNode
+        NodeType.VAR_AGGREGATOR: VariableAggregatorNode,
+        NodeType.PARAMETER_EXTRACTOR: ParameterExtractorNode,
     }
 
     @classmethod

api/app/core/workflow/nodes/parameter_extractor/__init__.py

@@ -0,0 +1,4 @@
+from app.core.workflow.nodes.parameter_extractor.config import ParameterExtractorNodeConfig
+from app.core.workflow.nodes.parameter_extractor.node import ParameterExtractorNode
+
+__all__ = ["ParameterExtractorNode", "ParameterExtractorNodeConfig"]

api/app/core/workflow/nodes/parameter_extractor/config.py

@@ -0,0 +1,54 @@
+import uuid
+
+from pydantic import Field, BaseModel
+from enum import StrEnum
+
+from app.core.workflow.nodes.base_config import BaseNodeConfig
+
+
+class ParamVariableType(StrEnum):
+    """
+    Enum for variable types that can be extracted as parameters.
+    Each member represents a type that can be used in parameter extraction.
+    """
+    STRING = "string"
+    NUMBER = "number"
+    BOOLEAN = "boolean"
+    ARRAY_STRING = "array[string]"
+    ARRAY_NUMBER = "array[number]"
+    ARRAY_BOOLEAN = "array[boolean]"
+    ARRAY_OBJECT = "array[object]"
+
+
+class ParamsConfig(BaseModel):
+    name: str = Field(
+        ...,
+        description="Name of the parameter"
+    )
+
+    type: ParamVariableType = Field(
+        ...,
+        description="Type of the parameter"
+    )
+
+    desc: str = Field(
+        ...,
+        description="Description of the parameter"
+    )
+
+
+class ParameterExtractorNodeConfig(BaseNodeConfig):
+    model_id: uuid.UUID = Field(
+        ...,
+        description="Unique identifier for the model"
+    )
+
+    text: str = Field(
+        ...,
+        description="The string to be extracted as a parameter"
+    )
+
+    params: list[ParamsConfig] = Field(
+        ...,
+        description="List of parameters"
+    )

api/app/core/workflow/nodes/parameter_extractor/node.py

@@ -0,0 +1,165 @@
+import os
+
+import json_repair
+from typing import Any
+
+from jinja2 import Template
+
+from app.core.error_codes import BizCode
+from app.core.exceptions import BusinessException
+from app.core.models import RedBearLLM, RedBearModelConfig
+from app.core.workflow.nodes import WorkflowState
+from app.core.workflow.nodes.base_node import BaseNode
+from app.core.workflow.nodes.parameter_extractor.config import ParameterExtractorNodeConfig
+from app.db import get_db_read
+from app.models import ModelType
+from app.services.model_service import ModelConfigService
+
+
+class ParameterExtractorNode(BaseNode):
+    def __init__(self, node_config: dict[str, Any], workflow_config: dict[str, Any]):
+        super().__init__(node_config, workflow_config)
+        self.typed_config = ParameterExtractorNodeConfig(**self.config)
+
+    @staticmethod
+    def _get_prompt():
+        """
+        Load system and user prompt templates from local prompt files.
+
+        Notes:
+        - Templates are expected to be Jinja2 files.
+        - Reading from disk each time ensures the latest template is used (could be cached if performance-critical).
+        - Both templates must exist, otherwise an exception will be raised.
+
+        Returns:
+            Tuple[str, str]: system_prompt, user_prompt
+        """
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        with open(
+                os.path.join(current_dir, "prompt", "system_prompt.jinja2"),
+                encoding='utf-8'
+        ) as f:
+            system_prompt = f.read()
+        with open(os.path.join(
+                current_dir, "prompt", "user_prompt.jinja2"),
+                encoding='utf-8'
+        ) as f:
+            user_prompt = f.read()
+        return system_prompt, user_prompt
+
+    def _get_llm_instance(self) -> RedBearLLM:
+        """
+        Retrieve a configured LLM instance based on the model ID from database.
+
+        Responsibilities:
+        - Validate that the model exists and has at least one API key configured.
+        - Construct RedBearLLM instance with proper credentials and model type.
+        - Raise clear BusinessException if configuration is invalid.
+
+        Returns:
+            RedBearLLM: Configured LLM instance ready to be invoked.
+
+        Raises:
+            BusinessException: If the model is missing or lacks valid API key.
+        """
+        model_id = self.typed_config.model_id
+
+        with get_db_read() as db:
+            config = ModelConfigService.get_model_by_id(db=db, model_id=model_id)
+
+            if not config:
+                raise BusinessException("配置的模型不存在", BizCode.NOT_FOUND)
+
+            if not config.api_keys or len(config.api_keys) == 0:
+                raise BusinessException("模型配置缺少 API Key", BizCode.INVALID_PARAMETER)
+
+            api_config = config.api_keys[0]
+            model_name = api_config.model_name
+            provider = api_config.provider
+            api_key = api_config.api_key
+            api_base = api_config.api_base
+            model_type = config.type
+
+        llm = RedBearLLM(
+            RedBearModelConfig(
+                model_name=model_name,
+                provider=provider,
+                api_key=api_key,
+                base_url=api_base,
+            ),
+            type=ModelType(model_type)
+        )
+        return llm
+
+    def _get_field_desc(self) -> dict[str, str]:
+        """
+        Build a dictionary mapping each parameter name to its description.
+        Useful for dynamically generating prompts for LLM.
+
+        Returns:
+            dict[str, str]: Mapping of parameter names to descriptions.
+        """
+        field_desc = {}
+        for param in self.typed_config.params:
+            field_desc[param.name] = param.desc
+        return field_desc
+
+    def _get_field_type(self) -> dict[str, str]:
+        """
+        Build a dictionary mapping each parameter name to its description.
+        Useful for dynamically generating prompts for LLM.
+
+        Returns:
+            dict[str, str]: Mapping of parameter names to descriptions.
+        """
+        field_type = {}
+        for param in self.typed_config.params:
+            field_type[param.name] = param.type
+        return field_type
+
+    async def execute(self, state: WorkflowState) -> Any:
+        """
+        Main execution function for this node.
+
+        Workflow:
+        1. Retrieve LLM instance with valid credentials.
+        2. Render user prompt template with field descriptions, types, and input text.
+        3. Send system and user prompts to LLM asynchronously.
+        4. Repair LLM JSON output safely.
+        5. Return output dictionary.
+
+        Notes:
+        - JSON repair is used to handle minor formatting errors in LLM output.
+        - Exceptions are raised explicitly if parsing fails, to prevent silent workflow failures.
+        - Rendering uses self._render_template for dynamic substitution from workflow state.
+
+        Args:
+            state (WorkflowState): Current state of the workflow, used for template rendering.
+
+        Returns:
+            dict[str, Any]: Dictionary containing extracted parameters under the "output" key.
+
+        Raises:
+            BusinessException: If LLM output cannot be parsed as valid JSON.
+        """
+        llm = self._get_llm_instance()
+        system_prompt, user_prompt = self._get_prompt()
+
+        user_prompt_teplate = Template(user_prompt)
+        rendered_user_prompt = user_prompt_teplate.render(
+            field_descriptions=str(self._get_field_desc()),
+            field_type=str(self._get_field_type()),
+            text_input=self._render_template(self.typed_config.text, state)
+        )
+
+        messages = [
+            ("system", system_prompt),
+            ("user", rendered_user_prompt),
+        ]
+
+        model_resp = await llm.ainvoke(messages)
+        result = json_repair.repair_json(model_resp.content)
+
+        return {
+            "output": result,
+        }

api/app/core/workflow/nodes/parameter_extractor/prompt/system_prompt.jinja2

@@ -0,0 +1,10 @@
+You are an information extraction engine.
+
+Your task is to extract structured data from text and output a valid JSON object (json).
+
+Rules:
+- Output MUST be a single valid JSON object.
+- No explanations, markdown, code blocks, or extra text.
+- Never invent information.
+- If a field is not found, use null.
+- Follow the output structure exactly.

api/app/core/workflow/nodes/parameter_extractor/prompt/user_prompt.jinja2

@@ -0,0 +1,12 @@
+Extract structured information from the following text.
+
+Field Descriptions:
+{{field_descriptions}}
+
+Output Structure:
+{{field_type}}
+
+Input Text:
+{{text_input}}
+
+Output:

api/app/db.py

@@ -23,6 +23,7 @@ SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
 
 Base = declarative_base()
 
+
 # Dependency to get a DB session
 def get_db():
     db = SessionLocal()
@@ -35,6 +36,7 @@ def get_db():
         finally:
             db.close()
 
+
 @contextmanager
 def get_db_context() -> Generator[Session, None, None]:
     """
@@ -54,12 +56,16 @@ def get_db_context() -> Generator[Session, None, None]:
             db.rollback()
         db.close()
 
+
 @contextmanager
 def get_db_read() -> Generator[Session, None, None]:
     """只读场景专用，出上下文自动 rollback，绝不留下 idle in transaction"""
     with get_db_context() as db:
-        yield db
+        try:
-        db.rollback()   # 只读任务无需 commit
+            yield db
+        finally:
+            db.rollback()  # 只读任务无需 commit
+
 
 def get_pool_status():
     """获取连接池状态（用于监控）"""
@@ -70,5 +76,6 @@ def get_pool_status():
         "checked_out": pool.checkedout(),
         "overflow": pool.overflow(),
         "total": pool.size() + pool.overflow(),
-        "usage_percent": round(pool.checkedout() / (pool.size() + pool.overflow()) * 100, 2) if (pool.size() + pool.overflow()) > 0 else 0
+        "usage_percent": round(pool.checkedout() / (pool.size() + pool.overflow()) * 100, 2) if (
+                                                                                                            pool.size() + pool.overflow()) > 0 else 0
     }