pulling nextjs chat settings from config

backend/chat/chat.py

@@ -28,20 +28,36 @@ def __init__(
 
     def create_agent(self):
         """Create React Agent Graph"""
-        #If not vector store, split and talk to the report
-        llm_provider_name = getattr(self.config, "llm_provider")
-        fast_llm_model = getattr(self.config, "fast_llm_model")
-        temperature = getattr(self.config, "temperature")
-        fast_token_limit = getattr(self.config, "fast_token_limit")
+        cfg = Config()
 
-        provider = get_llm(llm_provider_name, model=fast_llm_model, temperature=temperature, max_tokens=fast_token_limit, **self.config.llm_kwargs).llm
+        # Retrieve LLM using get_llm with settings from config
+        provider = get_llm(
+            llm_provider=cfg.smart_llm_provider,
+            model=cfg.smart_llm_model,
+            temperature=0.35,
+            max_tokens=cfg.smart_token_limit,
+            **self.config.llm_kwargs
+        ).llm
+
+        # If vector_store is not initialized, process documents and add to vector_store
         if not self.vector_store:
             documents = self._process_document(self.report)
             self.chat_config = {"configurable": {"thread_id": str(uuid.uuid4())}}
-            self.embedding = Memory(getattr(self.config, 'embedding_provider', None), self.headers).get_embeddings()
+            self.embedding = Memory(
+                cfg.embedding_provider,
+                cfg.embedding_model,
+                **cfg.embedding_kwargs
+            ).get_embeddings()
             self.vector_store = InMemoryVectorStore(self.embedding)
             self.vector_store.add_texts(documents)
-        graph = create_react_agent(provider, tools=[self.vector_store_tool(self.vector_store)], checkpointer=MemorySaver())
+
+        # Create the React Agent Graph with the configured provider
+        graph = create_react_agent(
+            provider,
+            tools=[self.vector_store_tool(self.vector_store)],
+            checkpointer=MemorySaver()
+        )
+
         return graph
 
     def vector_store_tool(self, vector_store) -> Tool: