fix: model in agents via api

application/worker.py

@@ -146,14 +146,6 @@ def upload_index(full_path, file_data):
 
 def run_agent_logic(agent_config, input_data):
     try:
-        from application.core.model_utils import (
-            get_api_key_for_provider,
-            get_default_model_id,
-            get_provider_from_model_id,
-            validate_model_id,
-        )
-        from application.utils import calculate_doc_token_budget
-
         source = agent_config.get("source")
         retriever = agent_config.get("retriever", "classic")
         if isinstance(source, DBRef):
@@ -168,62 +160,31 @@ def run_agent_logic(agent_config, input_data):
         user_api_key = agent_config["key"]
         agent_type = agent_config.get("agent_type", "classic")
         decoded_token = {"sub": agent_config.get("user")}
-        json_schema = agent_config.get("json_schema")
         prompt = get_prompt(prompt_id, db["prompts"])
-
-        # Determine model_id: check agent's default_model_id, fallback to system default
-        agent_default_model = agent_config.get("default_model_id", "")
-        if agent_default_model and validate_model_id(agent_default_model):
-            model_id = agent_default_model
-        else:
-            model_id = get_default_model_id()
-
-        # Get provider and API key for the selected model
-        provider = get_provider_from_model_id(model_id) if model_id else settings.LLM_PROVIDER
-        system_api_key = get_api_key_for_provider(provider or settings.LLM_PROVIDER)
-
-        # Calculate proper doc_token_limit based on model's context window
-        history_token_limit = 2000  # Default for webhooks
-        doc_token_limit = calculate_doc_token_budget(
-            model_id=model_id, history_token_limit=history_token_limit
+        agent = AgentCreator.create_agent(
+            agent_type,
+            endpoint="webhook",
+            llm_name=settings.LLM_PROVIDER,
+            model_id=settings.LLM_NAME,
+            api_key=settings.API_KEY,
+            user_api_key=user_api_key,
+            prompt=prompt,
+            chat_history=[],
+            decoded_token=decoded_token,
+            attachments=[],
         )
-
         retriever = RetrieverCreator.create_retriever(
             retriever,
             source=source,
             chat_history=[],
             prompt=prompt,
             chunks=chunks,
-            doc_token_limit=doc_token_limit,
-            model_id=model_id,
+            token_limit=settings.DEFAULT_MAX_HISTORY,
+            model_id=settings.LLM_NAME,
             user_api_key=user_api_key,
             decoded_token=decoded_token,
         )
-
-        # Pre-fetch documents using the retriever
-        retrieved_docs = []
-        try:
-            docs = retriever.search(input_data)
-            if docs:
-                retrieved_docs = docs
-        except Exception as e:
-            logging.warning(f"Failed to retrieve documents: {e}")
-
-        agent = AgentCreator.create_agent(
-            agent_type,
-            endpoint="webhook",
-            llm_name=provider or settings.LLM_PROVIDER,
-            model_id=model_id,
-            api_key=system_api_key,
-            user_api_key=user_api_key,
-            prompt=prompt,
-            chat_history=[],
-            retrieved_docs=retrieved_docs,
-            decoded_token=decoded_token,
-            attachments=[],
-            json_schema=json_schema,
-        )
-        answer = agent.gen(query=input_data)
+        answer = agent.gen(query=input_data, retriever=retriever)
         response_full = ""
         thought = ""
         source_log_docs = []