feat: lite deep researcher implementation

src/llms/llm.py

@@ -0,0 +1,49 @@
+from langchain_openai import ChatOpenAI
+from src.config import load_yaml_config
+from pathlib import Path
+from typing import Dict, Any
+from src.config.agents import LLMType
+
+# Cache for LLM instances
+_llm_cache: dict[LLMType, ChatOpenAI] = {}
+
+
+def _create_llm_use_conf(llm_type: LLMType, conf: Dict[str, Any]) -> ChatOpenAI:
+    llm_type_map = {
+        "reasoning": conf.get("REASONING_MODEL"),
+        "basic": conf.get("BASIC_MODEL"),
+        "vision": conf.get("VISION_MODEL"),
+    }
+    llm_conf = llm_type_map.get(llm_type)
+    if not llm_conf:
+        raise ValueError(f"Unknown LLM type: {llm_type}")
+    if not isinstance(llm_conf, dict):
+        raise ValueError(f"Invalid LLM Conf: {llm_type}")
+    return ChatOpenAI(**llm_conf)
+
+
+def get_llm_by_type(
+    llm_type: LLMType,
+) -> ChatOpenAI:
+    """
+    Get LLM instance by type. Returns cached instance if available.
+    """
+    if llm_type in _llm_cache:
+        return _llm_cache[llm_type]
+
+    conf = load_yaml_config(
+        str((Path(__file__).parent.parent.parent / "conf.yaml").resolve())
+    )
+    llm = _create_llm_use_conf(llm_type, conf)
+    _llm_cache[llm_type] = llm
+    return llm
+
+
+# Initialize LLMs for different purposes - now these will be cached
+reasoning_llm = get_llm_by_type("reasoning")
+basic_llm = get_llm_by_type("basic")
+vl_llm = get_llm_by_type("vision")
+
+
+if __name__ == "__main__":
+    print(basic_llm.invoke("Hello"))