Chat History and multi model

chatgraph.py

@@ -1,29 +1,17 @@
-from typing import TypedDict
-from langgraph.graph import StateGraph, START
-from langgraph.checkpoint.memory import MemorySaver
 from langchain_openai import ChatOpenAI
+from langchain_core.messages import HumanMessage, AIMessage
 from os import getenv
 from dotenv import load_dotenv
 
 load_dotenv()
 
-class State(TypedDict):
-    messages: list
+def get_llm(provider: str):
+    """Return a LangChain chat model for the requested provider."""
+    return ChatOpenAI(
+      openai_api_key=getenv("OPENROUTER_API_KEY"),
+      openai_api_base=getenv("OPENROUTER_BASE_URL"),
+      model_name=provider,
+    )
 
-llm = ChatOpenAI(
-  openai_api_key=getenv("OPENROUTER_API_KEY"),
-  openai_api_base=getenv("OPENROUTER_BASE_URL"),
-  model_name="qwen/qwen3-235b-a22b-07-25",
-)
-
-def call_model(state: State):
-    # simple memory trim example: keep last 20 messages
-    trimmed = state["messages"][-20:]
-    response = llm.invoke(trimmed)
-    return {"messages": [response]}
-
-workflow = StateGraph(State)
-workflow.add_node("model", call_model)
-workflow.add_edge(START, "model")
-memory = MemorySaver()
-app_graph = workflow.compile(checkpointer=memory)
+def get_messages(chats, chat_id):
+    return [HumanMessage(**m) if m["role"] == "human" else AIMessage(**m) for m in chats[chat_id]]