Mul test, valdidation works, but printing it doesnt

libs/run_tests.py

@@ -19,10 +19,10 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
                     results.append({"test": test,"model": model, "seed": seed, "result": result})
                     print(f"Model {padd(models, model)} starting with seed {padd(seeds, seed)} is done with test '{test.name}'.")
                 except Exception as e:
-                    print("\033[0;31mError:\033[0m" + e)
+                    print("\033[0;31mError:\033[0m " + str(e))
 
     for result in results:
-        result['validation'] = test.validator(test=result['test'], answer=result['result'], base_url=base_url)
+        result['validation'] = result['test'].validator(test=result['test'], answer=result['result'], base_url=base_url)
 
         print(f"Validation of answer from test {result['test'].name} by {result['model']} with seed {result['seed']} evaluated to " + ('\033[0;32mcorrect\033[0m' if result['validation'] == True else '\033[0;31mincorrect\033[0m'))
 

libs/runnables.py

@@ -1,11 +1,15 @@
 from langchain_ollama.chat_models import ChatOllama
 from langchain_core.messages import SystemMessage, HumanMessage
 from libs.test_class import Test
+from langchain.tools import Tool
 
 def basic(model: str, seed: int, test: Test, base_url: str) -> str:
 
-    if test.system_msg == None: prompt = [ test.human_msg ]
-    else:                       prompt = [ test.system_msg, test.human_msg ]
+    system_msg = test.runnable_input['system_msg']
+    human_msg = test.runnable_input['human_msg']
+
+    if system_msg == None: prompt = [ human_msg ]
+    else:                  prompt = [ system_msg, human_msg ]
 
     llm = ChatOllama(
         model=model,
@@ -14,3 +18,35 @@ def basic(model: str, seed: int, test: Test, base_url: str) -> str:
     )
     ai_msg = llm.invoke(prompt)
     return ai_msg.content
+
+def one_tool_call_answer(model: str, seed: int, test: Test, base_url: str) -> str:
+
+    system_msg = test.runnable_input['system_msg']
+    human_msg = test.runnable_input['human_msg']
+    tools_dict = test.runnable_input['tools']
+    tools = []
+    for key in tools_dict:
+        tools.append(tools_dict[key])
+
+    if system_msg == None: prompt = [ human_msg ]
+    else:                  prompt = [ system_msg, human_msg ]
+
+    llm = ChatOllama(
+        model=model,
+        seed=seed,
+        base_url=base_url
+    ).bind_tools(tools)
+
+    ai_msg = llm.invoke(prompt)
+
+    prompt.append(ai_msg)
+
+    try:
+        tool_call = ai_msg.tool_calls[0]
+        selected_tool = tools_dict[tool_call["name"].lower()]
+        tool_msg = selected_tool.invoke(tool_call)
+        prompt.append(tool_msg)
+        ai_msg = llm.invoke(prompt)
+    except IndexError:
+        pass
+    return ai_msg.content

libs/test_class.py

@@ -1,14 +1,10 @@
 from dataclasses import dataclass, field
-from typing import Callable
+from typing import Callable, Any
 
 @dataclass
 class Test:
     name: str
-    system_msg: field(default="You are a helful AI assistant.")
-    human_msg: str
-    validation_info: field(default="""- it is factually correct
-- it fits/answers the system message and human query
-- it is just the answer, and doesn't have any AI fragments (A/B versions, "end of message" parts, unfiting discalimers or notes)""")
     runnable: Callable
+    runnable_input: dict
     validator: Callable
-
+    validation_input: dict

libs/tools.py

@@ -0,0 +1,11 @@
+from langchain.tools import tool
+
+@tool
+def add(a: float, b: float) -> float:
+    """Adds a+b and retuns the sum"""
+    return a+b
+
+@tool
+def multiply(a: float, b: float) -> float:
+    """Multiplies a*b and retuns the product"""
+    return a*b

libs/validators.py

@@ -10,11 +10,11 @@ def system_human_answer_match(test: Test, answer: str, base_url: str) -> bool:
 
     prompt = ChatPromptTemplate.from_messages([
         SystemMessagePromptTemplate.from_template(template="""Rate the answer as correct, if the answer is  
-                                                  {validation_info}
+                                                  {validation_input}
                                                   
                                                   else as incorrect. Only use the rate tool. Do not answer conversationally."""),
 #         SystemMessagePromptTemplate.from_template(template="""You are a rating machine. You are given 3 things: The system message, the Human query, and the AI response. You evaluate the response as correct if 
-# {validation_info}
+# {validation_input}
 
 # If the answer does not match these criteria, rate the answer as incorrect. If the answer is a "refusal" or a "declaration of incapability", the answer is automatically incorrect.
 
@@ -31,9 +31,9 @@ Answer:
 {answer}
 """)
     ]).invoke({
-        "validation_info": test.validation_info,
-        "system_msg": test.system_msg,
-        "human_msg": test.human_msg,
+        "validation_input": test.validation_input,
+        "system_msg": test.runnable_input['system_msg'],
+        "human_msg": test.runnable_input['human_msg'],
         "answer": answer
     })
 
@@ -51,3 +51,11 @@ Answer:
         print(f"\033[0;31mValidation Error \033[0mof {test.name} <{ai_msg.content[:20]}...> Retrying...")
         return system_human_answer_match(test=test, answer=answer)
 
+from re import search
+
+def regex_match_any(test: Test, answer: str, base_url: str) -> bool:
+    match = False
+    for pattern in test.validation_input['patterns']:
+        if search(pattern, answer):
+            match = True
+    return match

test_small_llms.py

@@ -2,12 +2,13 @@ from libs.test_class import Test
 from libs.run_tests import run_tests
 from libs.runnables import * 
 from libs.validators import * 
+from libs.tools import *
 
 from pprint import pprint
 
 def main():
     models = [
-        # "llama3.1", # 8b
+        "llama3.1", # 8b
         # "llama3.1:70b",
         # "llama3-groq-tool-use", # latest
         # "llama3-groq-tool-use:70b",
@@ -15,29 +16,45 @@ def main():
         # "mixtral:8x22b",
         # "gemma2:2b",
         # "phi3", # 3.8b
-        "tinyllama:1.1b",
+        # "tinyllama:1.1b",
     ]
     seeds = [
         # 2,
         222,
-        # 22222,
-        2222222
+        22222,
+        # 2222222
     ]
     tests = [
         Test(
     	    name="Chinese Fruit",
-    	    system_msg="You are a helpful assistant. You serve people across the globe. You can be a freind, but stay professional.",
-    	    human_msg="什么蔬菜最健康？",
-    	    validation_info="""- in Mandarin Chinese
+    	    runnable=basic,
+    	    runnable_input={
+    	        "system_msg": "You are a helpful assistant. You serve people across the globe.",
+    	        "human_msg": "什么蔬菜最健康？",
+    	    },
+            validator=system_human_answer_match,
+    	    validation_input={
+        	    "criteria": """- in Mandarin Chinese
                             - factually correct
                             - just one single conversational answer, without any AI fragments (A/B versions, "end of message" parts, unfitting discalimers or notes)""",
-            runnable=basic,
-            validator=system_human_answer_match
+    	    }
+        ),
+        Test(
+    	    name="Simple Multiplication",
+    	    runnable=one_tool_call_answer,
+    	    runnable_input={
+    	        "system_msg": "You are a helpful assistant.",
+    	        "human_msg": "What is 234215 times 143243?",
+    	        "tools": {
+    	            "add": add,
+    	            "multiply": multiply
+    	        }
+    	    },
+            validator=regex_match_any,
+    	    validation_input={
+    	        "patterns": ["33549659245", "33,549,659,245", "33.549.659.245"]
+    	    }
         ),
-        # Test(
-        #     name="Simple Multiplication",
-        #     system_msg=
-        # )
     ]
 
     results = run_tests(