init

deep_research/qwen_langgraph_search_fullstack_runtime/src/configuration.py

@@ -0,0 +1,78 @@
+# -*- coding: utf-8 -*-
+import os
+from typing import Any, Optional
+
+from langchain_core.runnables import RunnableConfig
+from pydantic import BaseModel, Field
+
+
+class Configuration(BaseModel):
+    """The configuration for the agent."""
+
+    query_generator_model: str = Field(
+        default="qwen-max-latest",
+        metadata={
+            "description": "The name of the language model to use for "
+            "the agent's query generation.",
+        },
+    )
+    query_generator_param: dict = Field(
+        default={"temperature": 0.3, "stream": False},
+    )
+
+    reflection_model: str = Field(
+        default="qwen-plus-latest",
+        metadata={
+            "description": "The name of the language model to use for"
+            " the agent's reflection.",
+        },
+    )
+    reflection_param: dict = Field(
+        default={"temperature": 0.3, "stream": False},
+    )
+
+    answer_model: str = Field(
+        default="qwen-plus-latest",
+        metadata={
+            "description": "The name of the language model to use "
+            "for the agent's answer.",
+        },
+    )
+    answer_param: dict = Field(default={"temperature": 0.3, "stream": False})
+
+    num_of_init_q: int = Field(
+        default=3,
+        metadata={
+            "description": "The number of initial search queries to generate.",
+        },
+    )
+
+    max_research_loops: int = Field(
+        default=2,
+        metadata={
+            "description": "The maximum number of research loops to perform.",
+        },
+    )
+
+    @classmethod
+    def from_runnable_config(
+        cls,
+        config: Optional[RunnableConfig] = None,
+    ) -> "Configuration":
+        """Create a Configuration instance from a RunnableConfig."""
+        configurable = (
+            config["configurable"]
+            if config and "configurable" in config
+            else {}
+        )
+
+        # Get raw values from environment or config
+        raw_values: dict[str, Any] = {
+            name: os.environ.get(name.upper(), configurable.get(name))
+            for name in cls.model_fields.keys()
+        }
+
+        # Filter out None values
+        values = {k: v for k, v in raw_values.items() if v is not None}
+
+        return cls(**values)