mf1

libs/runnables.py

@@ -1,8 +1,8 @@
 from types import NoneType
 from langchain_ollama.chat_models import ChatOllama
-from libs.ollama_functions import OllamaFunctions
+from libs.ollama_functions import OllamaFunctionsLSM, OllamaFunctionsT2S
 from langchain_core.messages import AIMessage, SystemMessage, HumanMessage, ToolMessage
-from libs.classes import Test, Model
+from libs.classes import Technique, Test, Model
 from langchain.tools import Tool
 from typing import Literal
 
@@ -10,22 +10,31 @@ from langgraph.graph import StateGraph, MessagesState
 import json
 from pydantic import ValidationError
 
-def _get_llm(model: Model, base_url: str, seed: int, tools: list[Tool]|NoneType = None):
-    if model.supports_tools:
+from suite_settings.techniques import techniques
+
+def _get_llm(model: Model, base_url: str, seed: int, technique: Technique, tools: list[Tool]|NoneType = None):
+    if technique == techniques[1]: # Native
         llm = ChatOllama(
             model=model.identifier,
             seed=seed,
             base_url=base_url
         )
-    else:
-        llm = OllamaFunctions(
+    elif technique == techniques[903]: # Long System Message
+        llm = OllamaFunctionsLSM(
             model=model.identifier,
             seed=seed,
             base_url=base_url,
             format="json",
-            max_tool_call_fails=3,
-            temperature=0.0
         )
+    elif technique == techniques[572]: # ToolMessages to SystemMessages
+        llm = OllamaFunctionsT2S(
+            model=model.identifier,
+            seed=seed,
+            base_url=base_url,
+            format="json",
+        )
+    else:
+        raise ValueError("Unkown Technique in _get_llm()")
 
     if tools:
         llm = llm.bind_tools(tools=tools)
@@ -33,7 +42,7 @@ def _get_llm(model: Model, base_url: str, seed: int, tools: list[Tool]|NoneType
     return llm
 
 
-def basic_prompt(model: Model, seed: int, test: Test, base_url: str) -> str:
+def basic_prompt(model: Model, seed: int, test: Test, technique: Technique, base_url: str) -> str:
 
     messages = [SystemMessage(test.runnable_input['system_msg'])]
     try: 
@@ -42,20 +51,20 @@ def basic_prompt(model: Model, seed: int, test: Test, base_url: str) -> str:
         pass 
     messages += [ HumanMessage(test.runnable_input['human_msg']) ]
 
-    llm = _get_llm(model=model, base_url=base_url, seed=seed)
+    llm = _get_llm(model=model, base_url=base_url, technique=technique, seed=seed)
     ai_msg = llm.invoke(messages)
     assert isinstance(ai_msg.content, str)
     return ai_msg.content
 
 
 
-def one_tool_call_answer(model: Model, seed: int, test: Test, base_url: str) -> dict:
+def one_tool_call_answer(model: Model, seed: int, test: Test, technique: Technique, base_url: str) -> dict:
 
     tools_dict = test.runnable_input['tools']
     tools = []
     for key in tools_dict:
         tools.append(tools_dict[key])
-    llm = _get_llm(model=model, base_url=base_url, seed=seed, tools=tools)
+    llm = _get_llm(model=model, base_url=base_url, seed=seed, technique=technique, tools=tools)
 
     messages = [SystemMessage(test.runnable_input['system_msg'])]
     try: 
@@ -108,7 +117,7 @@ def one_tool_call_answer(model: Model, seed: int, test: Test, base_url: str) ->
         "tool_calls": tool_calls,
     }
 
-def agent_with_tools(model: Model, seed: int, test: Test, base_url: str) -> dict[str, str|list]:
+def agent_with_tools(model: Model, seed: int, test: Test, technique: Technique, base_url: str) -> dict[str, str|list]:
     
     tool_calls = []
     index = -1
@@ -173,7 +182,7 @@ def agent_with_tools(model: Model, seed: int, test: Test, base_url: str) -> dict
     for key in tools_dict:
         tools.append(tools_dict[key])
     tool_node = NxToolNode(tools)
-    llm = _get_llm(model=model, base_url=base_url, seed=seed, tools=tools)
+    llm = _get_llm(model=model, base_url=base_url, seed=seed, technique=technique, tools=tools)
 
     workflow = StateGraph(MessagesState)