feat: add deep think feature (#311)

* feat: implement backend logic

* feat: implement api/config endpoint

* rename the symbol

* feat: re-implement configuration at client-side

* feat: add client-side of deep thinking

* fix backend bug

* feat: add reasoning block

* docs: update readme

* fix: translate into English

* fix: change icon to lightbulb

* feat: ignore more bad cases

* feat: adjust thinking layout, and implement auto scrolling

* docs: add comments

---------

Co-authored-by: Henry Li <henry1943@163.com>

src/llms/llm.py

@@ -6,6 +6,8 @@ from typing import Any, Dict
 import os
 
 from langchain_openai import ChatOpenAI
+from langchain_deepseek import ChatDeepSeek
+from typing import get_args
 
 from src.config import load_yaml_config
 from src.config.agents import LLMType
@@ -14,6 +16,20 @@ from src.config.agents import LLMType
 _llm_cache: dict[LLMType, ChatOpenAI] = {}
 
 
+def _get_config_file_path() -> str:
+    """Get the path to the configuration file."""
+    return str((Path(__file__).parent.parent.parent / "conf.yaml").resolve())
+
+
+def _get_llm_type_config_keys() -> dict[str, str]:
+    """Get mapping of LLM types to their configuration keys."""
+    return {
+        "reasoning": "REASONING_MODEL",
+        "basic": "BASIC_MODEL",
+        "vision": "VISION_MODEL",
+    }
+
+
 def _get_env_llm_conf(llm_type: str) -> Dict[str, Any]:
     """
     Get LLM configuration from environment variables.
@@ -29,15 +45,20 @@ def _get_env_llm_conf(llm_type: str) -> Dict[str, Any]:
     return conf
 
 
-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)
+def _create_llm_use_conf(
+    llm_type: LLMType, conf: Dict[str, Any]
+) -> ChatOpenAI | ChatDeepSeek:
+    """Create LLM instance using configuration."""
+    llm_type_config_keys = _get_llm_type_config_keys()
+    config_key = llm_type_config_keys.get(llm_type)
+
+    if not config_key:
+        raise ValueError(f"Unknown LLM type: {llm_type}")
+
+    llm_conf = conf.get(config_key, {})
     if not isinstance(llm_conf, dict):
-        raise ValueError(f"Invalid LLM Conf: {llm_type}")
+        raise ValueError(f"Invalid LLM configuration for {llm_type}: {llm_conf}")
+
     # Get configuration from environment variables
     env_conf = _get_env_llm_conf(llm_type)
 
@@ -45,9 +66,16 @@ def _create_llm_use_conf(llm_type: LLMType, conf: Dict[str, Any]) -> ChatOpenAI:
     merged_conf = {**llm_conf, **env_conf}
 
     if not merged_conf:
-        raise ValueError(f"Unknown LLM Conf: {llm_type}")
+        raise ValueError(f"No configuration found for LLM type: {llm_type}")
 
-    return ChatOpenAI(**merged_conf)
+    if llm_type == "reasoning":
+        merged_conf["api_base"] = merged_conf.pop("base_url", None)
+
+    return (
+        ChatOpenAI(**merged_conf)
+        if llm_type != "reasoning"
+        else ChatDeepSeek(**merged_conf)
+    )
 
 
 def get_llm_by_type(
@@ -59,14 +87,49 @@ def get_llm_by_type(
     if llm_type in _llm_cache:
         return _llm_cache[llm_type]
 
-    conf = load_yaml_config(
-        str((Path(__file__).parent.parent.parent / "conf.yaml").resolve())
-    )
+    conf = load_yaml_config(_get_config_file_path())
     llm = _create_llm_use_conf(llm_type, conf)
     _llm_cache[llm_type] = llm
     return llm
 
 
+def get_configured_llm_models() -> dict[str, list[str]]:
+    """
+    Get all configured LLM models grouped by type.
+
+    Returns:
+        Dictionary mapping LLM type to list of configured model names.
+    """
+    try:
+        conf = load_yaml_config(_get_config_file_path())
+        llm_type_config_keys = _get_llm_type_config_keys()
+
+        configured_models: dict[str, list[str]] = {}
+
+        for llm_type in get_args(LLMType):
+            # Get configuration from YAML file
+            config_key = llm_type_config_keys.get(llm_type, "")
+            yaml_conf = conf.get(config_key, {}) if config_key else {}
+
+            # Get configuration from environment variables
+            env_conf = _get_env_llm_conf(llm_type)
+
+            # Merge configurations, with environment variables taking precedence
+            merged_conf = {**yaml_conf, **env_conf}
+
+            # Check if model is configured
+            model_name = merged_conf.get("model")
+            if model_name:
+                configured_models.setdefault(llm_type, []).append(model_name)
+
+        return configured_models
+
+    except Exception as e:
+        # Log error and return empty dict to avoid breaking the application
+        print(f"Warning: Failed to load LLM configuration: {e}")
+        return {}
+
+
 # In the future, we will use reasoning_llm and vl_llm for different purposes
 # reasoning_llm = get_llm_by_type("reasoning")
 # vl_llm = get_llm_by_type("vision")