mega commit

libs/classes.py

@@ -0,0 +1,18 @@
+from dataclasses import dataclass
+from typing import Callable 
+
+@dataclass
+class Test:
+    name: str
+    runnable: Callable
+    runnable_input: dict
+    validator: Callable
+    validation_input: dict
+
+@dataclass
+class Model:
+    display_name: str
+    identifier: str
+    supports_tools: bool
+    parameter_count_in_b: float
+

libs/functions.py

@@ -0,0 +1,5 @@
+def nxhash(text:str) -> str: # @BenVida StackOverflow
+    hash=0
+    for ch in text:
+        hash = ( hash*281  ^ ord(ch)*997) & 0xFFFFFFFF
+    return str(hex(hash)[2:].upper().zfill(8))

libs/ollama_functions.py

@@ -0,0 +1,322 @@
+import json
+import uuid
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Type,
+    TypeVar,
+    Union,
+    Tuple,
+)
+from types import NoneType
+
+from langchain_ollama.chat_models import ChatOllama
+from langchain_core.callbacks import CallbackManagerForLLMRun 
+from langchain_core.language_models import LanguageModelInput
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage, BaseMessage, ToolCall
+from langchain_core.outputs import ChatGeneration, ChatResult 
+from langchain_core.prompts import SystemMessagePromptTemplate
+from langchain_core.pydantic_v1 import BaseModel
+from langchain_core.runnables import Runnable
+from langchain_core.tools import BaseTool, Tool
+from langchain_core.utils.pydantic import is_basemodel_instance, is_basemodel_subclass
+
+from libs.functions import nxhash
+
+DEFAULT_SYTEM_PROMPT = """You have access to the following tools:
+
+{tools}
+
+You must always select one of the above tools and respond with only a JSON object matching the following schema:
+
+{{
+  "tool": <name of the selected tool>,
+  "tool_input": <parameters for the selected tool, matching the tool's JSON schema>
+}}
+"""
+
+
+DEFAULT_SYTEM_PROMPT_WITH_HISTORY = """{system_msg}
+
+You continue a chat history either conversationally or with a tool call.
+
+You have access to the following tools:
+
+{tools}
+
+You must either select one of the above tools and respond with only a JSON object matching the following schema:
+
+{{
+  "tool": <name of the selected tool>,
+  "tool_input": <parameters for the selected tool, matching the tool's JSON schema>
+}}
+
+or answer conversationally normally.
+
+The conversation before consisted of the following messages:
+
+{history}
+
+Now you must answer accordingly either conversationally or with another tool call.
+
+For conversational answers: Answer as if it was a continuous conversation. The Human only sees the conversational responses, and not anything about the tools. Do not mention the tools or the process of using them.
+"""
+CONVERSATIONAL_RESPONSE_TOOL = {
+    "name": "__conversational_response",
+    "description": (
+        "Respond conversationally if no other tools should be called for a given query."
+    ),
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "response": {
+                "type": "string",
+                "description": "Conversational response to the user.",
+            },
+        },
+        "required": ["response"],
+    },
+}
+
+
+_BM = TypeVar("_BM", bound=BaseModel)
+_DictOrPydantic = Union[Dict, _BM]
+
+
+def _is_pydantic_class(obj: Any) -> bool:
+    return isinstance(obj, type) and (
+        is_basemodel_subclass(obj) or BaseModel in obj.__bases__
+    )
+
+
+def convert_to_ollama_tool(tool: Any) -> Dict:
+    """Convert a tool to an Ollama tool."""
+    description = None
+    if _is_pydantic_class(tool):
+        schema = tool.construct().schema()
+        name = schema["title"]
+    elif isinstance(tool, BaseTool):
+        schema = tool.tool_call_schema.schema()
+        name = tool.get_name()
+        description = tool.description
+    elif is_basemodel_instance(tool):
+        schema = tool.get_input_schema().schema()
+        name = tool.get_name()
+        description = tool.description
+    elif isinstance(tool, dict) and "name" in tool and "parameters" in tool:
+        return tool.copy()
+    else:
+        raise ValueError(
+            f"""Cannot convert {tool} to an Ollama tool. 
+            {tool} needs to be a Pydantic class, model, or a dict."""
+        )
+    definition = {"name": name, "parameters": schema}
+    if description:
+        definition["description"] = description
+
+    return definition
+
+def parse_response(message: BaseMessage) -> str:
+    """Extract `function_call` from `AIMessage`."""
+    if isinstance(message, AIMessage):
+        kwargs = message.additional_kwargs
+        tool_calls = message.tool_calls
+        if len(tool_calls) > 0:
+            tool_call = tool_calls[-1]
+            args = tool_call.get("args")
+            return json.dumps(args)
+        elif "function_call" in kwargs:
+            if "arguments" in kwargs["function_call"]:
+                return kwargs["function_call"]["arguments"]
+            raise ValueError(f"`arguments` missing from `function_call` within AIMessage: {message}")
+        else:
+            raise ValueError("`tool_calls` missing from AIMessage: {message}")
+    raise ValueError(f"`message` is not an instance of `AIMessage`: {message}")
+
+    
+
+class OllamaFunctions(ChatOllama):
+    """Function chat model that uses Ollama API."""
+
+    tool_system_prompt_template: str = DEFAULT_SYTEM_PROMPT
+    tool_system_prompt_template_with_history: str = DEFAULT_SYTEM_PROMPT_WITH_HISTORY
+
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+
+    def bind_tools(
+        self,
+        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
+        **kwargs: Any,
+    ) -> Runnable[LanguageModelInput, BaseMessage]:
+        return self.bind(functions=tools, **kwargs)
+
+
+    def _generate(self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) -> ChatResult:
+
+        def _get_system_msg_and_formatted_history(self, messages: list) -> Tuple[str, str]:
+            def _format_tools_for_history(tool_calls: list[ToolCall]) -> str:
+                call_list = []
+                for c in tool_calls:
+                    call_list.append({
+                      "id": nxhash(c['id'])[-4:],
+                      "tool": c['name'],
+                      "args": c['args']
+                    })
+                if len(call_list) == 1:
+                    return json.dumps(obj=call_list[0], ensure_ascii=False, indent=2)
+                else:
+                    return json.dumps(obj=call_list, ensure_ascii=False, indent=2) 
+            formated_history = ""
+            system_msg = ""
+            for m in messages:
+
+                if formated_history != "":
+                    formated_history += "\n\n"
+
+                if isinstance(m, SystemMessage):
+                    system_msg += str(m.content)
+                elif isinstance(m, HumanMessage):
+                    formated_history += "The Human said:\n" + str(m.content)
+                elif isinstance(m, AIMessage) and m.tool_calls: 
+                    formated_history += "So you called the tool" + (":\n" if len(m.tool_calls) == 1 else "s:\n") + _format_tools_for_history(m.tool_calls)
+                elif isinstance(m, ToolMessage): 
+                    formated_history += "To which the tool (" + nxhash(m.tool_call_id)[-4:] + ") replied with:\n" + str(m.content)
+                elif isinstance(m, AIMessage) and not m.tool_calls: 
+                    formated_history += "You said:\n" + str(m.content)
+                else:
+                    raise TypeError("OllamaFunctions only supports SystemMessage HumanMessage ToolMessage AIMessage but got " + str(type(m)))
+
+            return system_msg, formated_history
+
+        
+        def _get_parsed_chat_result(self, chat_result_str: str) -> Union[dict, str]:
+            try:
+                parsed_chat_result = json.loads(chat_result_str)
+            except json.JSONDecodeError:
+                parsed_chat_result = chat_result_str
+            return parsed_chat_result
+
+        def _get_called_tool(self, d: dict, functions_list: list[dict]) -> dict|NoneType:
+            if not parsed_chat_result:
+                called_tool_name = None
+            elif "tool" in parsed_chat_result:
+                called_tool_name = d["tool"] # per spec
+            elif "name" in d:
+                called_tool_name = d["name"] # Phi3 often does this
+            elif "tool_name" in d:
+                called_tool_name = d["tool_name"] # Phi3 often does this
+            elif "action" in d:
+                called_tool_name = d["action"] # Phi3 does this
+            else:
+                return None
+
+            try:
+                called_tool = [tool for tool in functions_list if tool['name'] == called_tool_name][0] 
+            except IndexError:
+                return None # when a tool is called, but the tool doesnt exist
+
+            return called_tool
+
+        def _extract_conversaional_response(self, d: dict) -> str:
+            if ("tool_input" in d and "response" in d["tool_input"]):
+                response = d["tool_input"]["response"]
+            elif ("input" in d and "response" in d["input"]):
+                response = d["input"]["response"]
+            elif ("args" in d and "response" in d["args"]):
+                response = d["args"]["response"]
+            elif "response" in d:
+                response = d["response"]
+            elif "input" in d:
+                response = d["input"]
+            elif "args" in d:
+                response = d["args"]
+            elif "tool_input" in d:
+                response = d["tool_input"]
+            else:
+                raise ValueError(f"Failed to parse a response from {self.model} output: {chat_result}")
+
+            try: 
+                assert isinstance(response, str)
+            except AssertionError:
+                raise ValueError(f"Failed to parse a response from {self.model} output: {chat_result}")
+
+            return response
+        
+        def _extract_tool_args(self, d: dict) -> dict:
+            if "tool_input" in parsed_chat_result:
+                called_tool_args = d["tool_input"] # per spec
+            elif "input" in d:
+                called_tool_args = d["input"] # Phi3 often does this
+            elif "args" in d:
+                called_tool_args = d["args"]
+            else:
+                called_tool_args = {}
+            return called_tool_args
+
+        # prepare generation
+        functions_list = [convert_to_ollama_tool(fn) for fn in kwargs.get("functions", [])]
+        functions_list.append(CONVERSATIONAL_RESPONSE_TOOL)
+        functions_str = json.dumps(functions_list, indent=2)
+
+        # prepare generation with history
+        if True in [ isinstance(m, ToolMessage) for m in messages ]:
+            system_msg, formated_history = _get_system_msg_and_formatted_history(self, messages=messages)
+                    
+            system_message_prompt_template = SystemMessagePromptTemplate.from_template(self.tool_system_prompt_template_with_history)
+            system_message = system_message_prompt_template.format(
+                tools=functions_str, 
+                history=formated_history, 
+                system_msg=system_msg
+            )
+            final_messages = [ system_message ]
+
+        # prepare generation without history
+        else:
+            system_message_prompt_template = SystemMessagePromptTemplate.from_template(self.tool_system_prompt_template)
+            system_message = system_message_prompt_template.format(
+                tools=functions_str
+            )
+            final_messages = [ system_message ] + messages
+
+        # genrerate chat result
+        response_message = super()._generate(final_messages, stop=stop, run_manager=run_manager, **kwargs)
+        chat_result = response_message.generations[0].text
+
+        # chekc for validity
+        if not isinstance(chat_result, str):
+            raise ValueError("OllamaFunctions does not support non-string output.")
+
+        # make str to dict
+        parsed_chat_result = _get_parsed_chat_result(self, chat_result_str=chat_result)
+        # if model failed to return vailid json, just retrun the whole thing
+        if isinstance(parsed_chat_result, str): 
+            return ChatResult(generations=[ChatGeneration(message=AIMessage(content=parsed_chat_result))])
+
+
+        # get the called tool from the dict
+        called_tool = _get_called_tool(self, d=parsed_chat_result, functions_list=functions_list) 
+
+        if not called_tool:
+            response_msg = AIMessage(content=_extract_conversaional_response(self, d=parsed_chat_result))
+        elif called_tool == CONVERSATIONAL_RESPONSE_TOOL:
+            response_msg = AIMessage(content=_extract_conversaional_response(self, d=parsed_chat_result))
+        else:
+            response_msg = AIMessage(
+                content="",
+                tool_calls=[ToolCall(
+                    name=called_tool['name'],
+                    args=_extract_tool_args(self, d=parsed_chat_result),
+                    id=f"call_{str(uuid.uuid4()).replace('-', '')}",
+                )],
+            )
+
+        return ChatResult(generations=[ChatGeneration(message=response_msg)])
+    
+    @property
+    def _llm_type(self) -> str:
+        return "ollama_functions"

libs/run_tests.py

@@ -1,30 +1,19 @@
-from libs.test_class import Test
+from libs.classes import Test, Model
+from libs.functions import nxhash
 from typing import Union
 
 import json
 
-def padd(list, element):
-    longest = 0
-    for s in list:
-        longest = max(longest, len(str(s))) 
-    return str(element).ljust(longest)
-
-def nxhash(text:str): # @BenVida StackOverflow
-    hash=0
-    for ch in text:
-        hash = ( hash*281  ^ ord(ch)*997) & 0xFFFFFFFF
-    return hex(hash)[2:].upper().zfill(8)
-
 def get_len(collection: Union[list, dict]) -> int:
     maximum_length = 0
 
-    if isinstance(collection, dict):
+    if isinstance(collection, list):
-        collection_type = "tests"
-    elif isinstance(collection, list):
-        if isinstance(collection[0], str):
-                collection_type = "models"
-        elif isinstance(collection[0], int):
         collection_type = "seeds"
+    elif isinstance(collection, dict):
+        if isinstance(collection[list(collection.keys())[0]], Model):
+            collection_type = "models"
+        elif isinstance(collection[list(collection.keys())[0]], Test):
+            collection_type = "tests"
         else: 
             raise TypeError("get_len: unsupported collection_type")
     else:
@@ -32,8 +21,8 @@ def get_len(collection: Union[list, dict]) -> int:
 
     match collection_type:
         case "models":
-            for model_name in collection:
+            for model_id in collection:
-                maximum_length = max(maximum_length, len(model_name))
+                maximum_length = max(maximum_length, len(collection[model_id].display_name))
         case "seeds":
             for seed in collection:
                 maximum_length = max(maximum_length, len(str(seed)))
@@ -48,30 +37,32 @@ def get_len(collection: Union[list, dict]) -> int:
 
     
     
-def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url: str):
+def run_tests(models: dict[int, Model], seeds: list[int], tests: dict[int, Test], base_url: str):
     try:
         print("Trying to load saved_results.json")
         with open("./saved_results.json", "r") as f:
             saved_results = json.load(fp=f)
         print("Loaded.")
-    except: 
+    except FileNotFoundError: 
         print("saved_results.json not found. Initializing empty.")
         saved_results = {}
     # Get Results
     run_results = {}
     print("Starting to run Tests ... ")
-    for model in models:
+    for model_id in models:
+        model = models[model_id]
         for test_id in tests:
             test = tests[test_id]
             for seed in seeds:
                 # Init dict
                 combination = { 
                     'test_id': test_id,
-                    'model': model,
+                    'model_id': model_id,
                     'seed': seed,
                 }
                 hash_key = str(nxhash(json.dumps(combination, sort_keys=True)))
                 combination['test_name'] = test.name
+                combination['model_name'] = model.display_name
                     
                 # if hash_key == "DE3D137E":
                     # pass
@@ -79,9 +70,9 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
                 if hash_key not in saved_results.keys():
                     try: 
                         print("\033[0;35mModel '\033[0m" +
-                              model +
+                              model.display_name +
                               "\033[0;35m'" +
-                              (" " * (get_len(models) - len(model))) +
+                              (" " * (get_len(models) - len(model.display_name))) +
                               " with seed \033[0m\033[0;30m" +
                               ("0" * (get_len(seeds) - len(str(seed)))) +
                               "\033[0m" +
@@ -96,7 +87,7 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
                               end=""
                         )
                         answer = test.runnable(model=model, seed=seed, test=test, base_url=base_url)
-                        if isinstance(answer, str): # tool capabile return tools called as a list[dict]
+                        if isinstance(answer, str): 
                             combination['answer'] = answer
                             # combination['tool_calls'] = [] # no entry
                             del answer
@@ -105,15 +96,14 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
                             combination['tool_calls'] = answer['tool_calls']
                             del answer
                         else: 
-                            raise Exception(f"runnable returd unkown type {type(answer)}.")
+                            raise Exception(f"runnable returned unkown type {type(answer)}.")
-                            
                             
                         combination['test'] = test
                         run_results[hash_key] = combination
                         print("\r\033[0;32mModel '\033[0m" +
-                              model +
+                              model.display_name  +
                               "\033[0;32m'" +
-                              (" " * (get_len(models) - len(model))) +
+                              (" " * (get_len(models) - len(model.display_name))) +
                               " with seed \033[0m\033[0;30m" +
                               ("0" * (get_len(seeds) - len(str(seed)))) +
                               "\033[0m" +
@@ -127,12 +117,12 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
                               "\033[0;32m)\033[0m"
                         )
                     except Exception as e:
-                          print("\r\033[0;31mError: <\033[0m" + str(e) + "\033[0;31m> at (\033[0m" + hash_key + "\033[0;31m). Continuing...")
+                          print("\r\033[0;31mError: <\033[0m" + str(e) + "\033[0;31m> at (\033[0m" + hash_key + "\033[0;31m). Continuing...\033[0m                      ")
                 else: 
                     print("\r\033[0;34mModel '\033[0m" +
-                          model +
+                          model.display_name  +
                           "\033[0;34m'" +
-                          (" " * (get_len(models) - len(model))) +
+                          (" " * (get_len(models) - len(model.display_name))) +
                           " with seed \033[0m\033[0;30m" +
                           ("0" * (get_len(seeds) - len(str(seed)))) +
                           "\033[0m" +
@@ -148,7 +138,8 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
 
 
     # Validate Results
-    if run_results != {}: print("\nStarting validation of tests ...")
+    if run_results != {}:
+        print("\nStarting validation of tests ...")
     for hash_key in run_results:
         result = run_results[hash_key]
         
@@ -156,27 +147,28 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
             entry = {
                 'test_name':  result['test_name'],
                 'test_id':    result['test_id'],
-                'model':      result['model'],
+                'model_name': result['model_name'],
+                'model_id':   result['model_id'],
                 'seed':       result['seed'],
                 'answer':     result['answer'],
                 'validation': result['test'].validator(test=result['test'], answer=result['answer'], base_url=base_url),
             }
         except Exception as e:
-            print("\033[0;31mError validating entry (\033[0m" + hash_key + "\033[0;31m). <\033[0m" + str(e) + "\033[0;31m> Continuing...\033[0m")
+            print("\033[0;31mError validating entry (\033[0m" + hash_key + "\033[0;31m). <\033[0m" + str(e) + "\033[0;31m> Continuing...\033[0m                      ")
             continue
 
         try:
             entry['tool_calls'] = result['tool_calls'] 
-        except:
+        except KeyError:
             pass
             
 
         saved_results[hash_key] = entry # add result with validation to saved results
 
         print("\033[0;36mTest results of model '\033[0m" +
-              entry['model'] +
+              entry['model_name'] +
               "\033[0;36m'" +
-              (" " * (get_len(models) - len(entry['model']))) +
+              (" " * (get_len(models) - len(entry['model_name']))) +
               " with seed \033[0m\033[0;30m" +
               ("0" * (get_len(seeds) - len(str(entry['seed'])))) +
               "\033[0m" +
@@ -188,7 +180,7 @@ def run_tests(models: list[str], seeds: list[int], tests: list[Test], base_url:
               " (\033[0m" +
               hash_key +
               "\033[0;36m) evaluated to \033[0m" + 
-              ('\033[0;32mcorrect\033[0m' if entry['validation'] == True else '\033[0;31mincorrect\033[0m')
+              ('\033[0;32mcorrect\033[0m' if entry['validation'] else '\033[0;31mincorrect\033[0m')
         )
 
     with open("./saved_results.json", "w") as f:

libs/runnables.py

@@ -1,76 +1,100 @@
+from types import NoneType
 from langchain_ollama.chat_models import ChatOllama
-from langchain_core.messages import SystemMessage, HumanMessage, ToolMessage
+from libs.ollama_functions import OllamaFunctions
-from libs.test_class import Test
+from langchain_core.messages import AIMessage, SystemMessage, HumanMessage, ToolMessage
+from libs.classes import Test, Model
 from langchain.tools import Tool
 from typing import Literal
 
 from langgraph.graph import StateGraph, MessagesState
-# from langgraph.prebuilt import ToolNode
 import json
 from pydantic import ValidationError
 
-
+def _get_llm(model: Model, base_url: str, seed: int, tools: list[Tool]|NoneType = None):
-def basic(model: str, seed: int, test: Test, base_url: str) -> str:
+    if model.supports_tools:
-    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,
+            model=model.identifier,
             seed=seed,
             base_url=base_url
         )
-    ai_msg = llm.invoke(prompt)
+    else:
+        llm = OllamaFunctions(
+            model=model.identifier,
+            seed=seed,
+            base_url=base_url,
+            format="json"
+        )
+
+    if tools:
+        llm = llm.bind_tools(tools=tools)
+
+    return llm
+
+
+def basic_prompt(model: Model, seed: int, test: Test, base_url: str) -> str:
+
+    messages = [SystemMessage(test.runnable_input['system_msg'])]
+    try: 
+        messages += test.runnable_input['fsp_messages'] 
+    except KeyError:
+        pass 
+    messages += [ HumanMessage(test.runnable_input['human_msg']) ]
+
+    llm = _get_llm(model=model, base_url=base_url, seed=seed)
+    ai_msg = llm.invoke(messages)
+    assert isinstance(ai_msg.content, str)
     return ai_msg.content
 
 
 
-def one_tool_call_answer(model: str, seed: int, test: Test, base_url: str) -> str:
+def one_tool_call_answer(model: Model, seed: int, test: Test, base_url: str) -> dict:
-    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])
+    llm = _get_llm(model=model, base_url=base_url, seed=seed, tools=tools)
 
-    if system_msg == None: prompt = [ human_msg ]
+    messages = [SystemMessage(test.runnable_input['system_msg'])]
-    else:                  prompt = [ system_msg, human_msg ]
+    try: 
+        messages += test.runnable_input['fsp_messages'] 
+    except KeyError:
+        pass 
+    messages += [ HumanMessage(test.runnable_input['human_msg']) ]
 
-    llm = ChatOllama(
+    ai_msg = llm.invoke(messages)
-        model=model,
-        seed=seed,
-        base_url=base_url
-    ).bind_tools(tools)
 
-    ai_msg = llm.invoke(prompt)
+    messages += [ ai_msg ]
-
-    prompt.append(ai_msg)
 
     try:
         tool_calls = []
-        for i in range(len(ai_msg.tool_calls)):
+        assert isinstance(ai_msg, AIMessage)
-            tool_call = ai_msg.tool_calls[i]
+        calls = ai_msg.tool_calls
-            selected_tool = tools_dict[tool_call["name"].lower()]
+        for call in calls:
-            tool_msg = selected_tool.invoke(tool_call)
+            selected_tool = tools_dict[call["name"].lower()]
-            prompt.append(tool_msg)
+            tool_msg = selected_tool.invoke(call)
-            ai_msg = llm.invoke(prompt)
+            messages.append(tool_msg)
+            ai_msg = llm.invoke(messages)
             tool_calls.append({
-                "tool": tool_call["name"],
+                "tool": call["name"],
-                "args": tool_call["args"],
+                "args": call["args"],
-                "index": 0
             })
     except IndexError: # LLM didnt use a tool -> jsut return the content
         tool_calls = []
+    if len(ai_msg.tool_calls) > 0:
+        to_append_calls = []
+        for call in ai_msg.tool_calls:
+            to_append_calls.append({ "tool": call["name"], "args": call["args"] })
+        return {
+            "answer": ">>LLM did not respond conversationally<<",
+            "tool_calls": tool_calls + to_append_calls,
+        }
     return {
         "answer": ai_msg.content,
-        "tool_calls": tool_calls
+        "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: str, seed: int, test: Test, base_url: str) -> str:
     
     tool_calls = []
     index = -1
@@ -79,6 +103,7 @@ def agent_with_tools(model: str, seed: int, test: Test, base_url: str) -> str:
         messages = state["messages"]
         last_message = messages[-1]
         nonlocal index
+        assert isinstance(last_message, AIMessage) # this is just so the type checker is happy
         if last_message.tool_calls:
             index += 1
             return "tools"
@@ -113,10 +138,10 @@ def agent_with_tools(model: str, seed: int, test: Test, base_url: str) -> str:
 
                 try:
                     tool_result = self.tools_by_name[tool_call["name"]].invoke(tool_call["args"])
-                except KeyError as e:
+                except KeyError:
                     tool_result = f'Error: Tool with name `{tool_call["name"]}` does not exist. Available tools are: {[tool.name for tool in tools]}'
                 except ValidationError as e:
-                    tool_result = 'Tool got invalid input:\n' + e 
+                    tool_result = 'Tool got invalid input:\n' + str(e) 
                 except Exception as e:
                     tool_result = 'Error: ' + str(e)
 
@@ -135,11 +160,7 @@ def agent_with_tools(model: str, seed: int, test: Test, base_url: str) -> str:
     for key in tools_dict:
         tools.append(tools_dict[key])
     tool_node = NxToolNode(tools)
-    llm = ChatOllama(
+    llm = _get_llm(model=model, base_url=base_url, seed=seed, tools=tools)
-        model=model,
-        seed=seed,
-        base_url=base_url
-    ).bind_tools(tools)
 
     workflow = StateGraph(MessagesState)
 
@@ -156,124 +177,21 @@ def agent_with_tools(model: str, seed: int, test: Test, base_url: str) -> str:
 
     graph = workflow.compile()
 
-    # example with a single tool call
+    # compose "history" supprts few shot prompting
-    start_messages = [
+    start_messages = [SystemMessage(test.runnable_input['system_msg'])]
-        SystemMessage(content=test.runnable_input['system_msg']),
-        HumanMessage(content=test.runnable_input['human_msg'])
-    ]
-
-    chunks = []
-    
-    for chunk in graph.stream(
-        {"messages": start_messages},
-        stream_mode="values",
-    ): chunks.append(chunk["messages"][-1])
-
-    return {
-        "answer": chunks[-1].content,
-        "tool_calls": tool_calls
-    }
-
-
-
-
-
-
-def agent_with_tools_fsp(model: str, seed: int, test: Test, base_url: str) -> str:
-    
-    tool_calls = []
-    index = -1
-
-    def should_continue(state: MessagesState) -> Literal["tools", "__end__"]:
-        messages = state["messages"]
-        last_message = messages[-1]
-        nonlocal index
-        if last_message.tool_calls:
-            index += 1
-            return "tools"
-        return "__end__"
-
-    def call_llm(state: MessagesState):
-        messages = state["messages"]
-        response = llm.invoke(messages)
-        return {"messages": [response]}
-
-    class NxToolNode:
-        """A node that runs the tools requested in the last AIMessage."""
-
-        def __init__(self, tools: list) -> None:
-            self.tools_by_name = {tool.name: tool for tool in tools}
-
-        def __call__(self, inputs: dict):
-            if messages := inputs.get("messages", []):
-                message = messages[-1]
-            else:
-                raise ValueError("No message found in input")
-            outputs = []
-            for tool_call in message.tool_calls:
-
-                nonlocal tool_calls
-                nonlocal index
-                tool_calls.append({
-                    "tool": tool_call["name"],
-                    "args": tool_call["args"],
-                    "index": index
-                })
-
     try: 
-                    tool_result = self.tools_by_name[tool_call["name"]].invoke(tool_call["args"])
+        start_messages += test.runnable_input['fsp_messages'] 
-                except KeyError as e:
+    except KeyError:
-                    tool_result = f'Error: Tool with name `{tool_call["name"]}` does not exist. Available tools are: {[tool.name for tool in tools]}'
+        pass 
-                except ValidationError as e:
+    start_messages += [ HumanMessage(test.runnable_input['human_msg']) ]
-                    tool_result = 'Tool got invalid input:\n' + e 
-                except Exception as e:
-                    tool_result = 'Error: ' + str(e)
-
-                outputs.append(
-                    ToolMessage(
-                        content=json.dumps(tool_result),
-                        name=tool_call["name"],
-                        tool_call_id=tool_call["id"],
-                    )
-                )
-            return {"messages": outputs}
-
-
-    tools_dict = test.runnable_input['tools']
-    tools = []
-    for key in tools_dict:
-        tools.append(tools_dict[key])
-    tool_node = NxToolNode(tools)
-    llm = ChatOllama(
-        model=model,
-        seed=seed,
-        base_url=base_url
-    ).bind_tools(tools)
-
-    workflow = StateGraph(MessagesState)
-
-    # Define the two nodes we will cycle between
-    workflow.add_node("agent", call_llm)
-    workflow.add_node("tools", tool_node)
-
-    workflow.add_edge("__start__", "agent")
-    workflow.add_conditional_edges(
-        "agent",
-        should_continue,
-    )
-    workflow.add_edge("tools", "agent")
-
-    graph = workflow.compile()
-
-    # example with a single tool call
-    start_messages = [ SystemMessage(test.runnable_input['system_msg']) ] + test.runnable_input['fsp_messages'] + [ HumanMessage(test.runnable_input['human_msg']) ]
 
     chunks = []
     
     for chunk in graph.stream(
         {"messages": start_messages},
         stream_mode="values",
-    ): chunks.append(chunk["messages"][-1])
+    ): 
+        chunks.append(chunk["messages"][-1])
 
     return {
         "answer": chunks[-1].content,

libs/test_class.py

@@ -1,10 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Callable, Any
-
-@dataclass
-class Test:
-    name: str
-    runnable: Callable
-    runnable_input: dict
-    validator: Callable
-    validation_input: dict

libs/tools.py

@@ -6,14 +6,14 @@ from typing import Union
 
 @tool
 def add(a: float, b: float) -> str:
-    """Adds a+b and retuns the sum"""
+    """Adds a+b and returns the sum"""
     af = float(a)
     bf = float(b)
     return f"{a} + {b} = {a+b}"
 
 @tool
 def multiply(a: float, b: float) -> str:
-    """Multiplies a*b and retuns the product"""
+    """Multiplies a*b and returns the product"""
     af = float(a)
     bf = float(b)
     return f"{a} * {b} = {a*b}"

libs/validators.py

@@ -1,7 +1,7 @@
 from langchain_ollama.chat_models import ChatOllama
 from langchain_core.prompts import HumanMessagePromptTemplate, ChatPromptTemplate, SystemMessagePromptTemplate
 from langchain.tools import tool
-from libs.test_class import Test
+from libs.classes import Test
 from re import search
 from textwrap import dedent
 
@@ -16,7 +16,7 @@ def system_human_answer_match(test: Test, answer: str, base_url: str) -> bool:
         SystemMessagePromptTemplate.from_template(template=dedent("""You evaluate LLMs. Rate the LLM answer as correct, if the answer is  
         {validation_input}
 
-        else as incorrect. Only use the rate tool. Do not answer conversationally.""")),
+        else as incorrect. Only use the `rate` tool. You do not have accesss to any other tools. Do not answer conversationally.""")),
         HumanMessagePromptTemplate.from_template(template=dedent("""System Message:
         {system_msg}
 
@@ -50,7 +50,10 @@ def system_human_answer_match(test: Test, answer: str, base_url: str) -> bool:
         elif ret_str.lower() == 'false': return False
         else: raise Exception(f"rate tool retured {ret_str}")
     except IndexError as e:
-        print(f"\033[0;31mValidation Error \033[0mof {test.name} <{ai_msg.content[:20]}...> Retrying...")
+        print(f"\033[0;31mValidation Error of\033[0m {test.name} \033[0;31m<\033[0m{ai_msg.content[:20]}\033[0;31m...> Retrying...\033[0m")
+        return system_human_answer_match(test=test, answer=answer, base_url=base_url)
+    except Exception as e:
+        print(f"\033[0;31mValidation Error \033[0mof {test.name} \033[0;31m<\033[0m{e}\033[0;31m> Retrying...\033[0m")
         return system_human_answer_match(test=test, answer=answer, base_url=base_url)
 
 def regex_match_any(test: Test, answer: str, base_url: str) -> bool:

requirements.txt

@@ -1,6 +1,7 @@
 langchain
 langchain-core
 langchain-ollama
+langchain-community
 langgraph
 seaborn
 pandas

suite_settings/models.py

@@ -1,13 +1,112 @@
-models = [
+from libs.classes import Model
-    "llama3.1", # 8b
+
-    "llama3.1:70b",
+models = {
-    "llama3-groq-tool-use", # latest
+    245: Model(
-    "llama3-groq-tool-use:70b",
+        display_name="llama3.1 8b", 
-    # "mixtral:8x7b",
+        identifier="llama3.1",
-    "mixtral:8x22b",
+        supports_tools=True,
-    # "gemma2:2b",
+        parameter_count_in_b=8
-    # "phi3", # 3.8b
+    ),
-    # "tinyllama:1.1b",
+    238: Model(
-    "mistral-nemo:12b",
+        display_name="llama3.1 70b",
-    "command-r-plus:104b",
+        identifier="llama3.1:70b",
-]
+        supports_tools=True,
+        parameter_count_in_b=70
+    ),
+    120: Model(
+        display_name="llama3 groq TU 8b", 
+        identifier="llama3-groq-tool-use",
+        supports_tools=True,
+        parameter_count_in_b=8
+    ),
+    890: Model(
+        display_name="llama3 groq TU 70b",
+        identifier="llama3-groq-tool-use:70b",
+        supports_tools=True,
+        parameter_count_in_b=70
+    ),
+    348: Model(
+        display_name="Mixtral MoE 8x7b",
+        identifier="mixtral:8x7b",
+        supports_tools=False,
+        parameter_count_in_b=13,
+    ),
+    789: Model(
+        display_name="Mixtral MoE 8x22b",
+        identifier="mixtral:8x22b",
+        supports_tools=True,
+        parameter_count_in_b=39
+    ),
+    445: Model(
+        display_name="Gemma2 2b",
+        identifier="gemma2:2b",
+        supports_tools=False,
+        parameter_count_in_b=2
+    ),
+    475: Model(
+        display_name="Gemma2 9b",
+        identifier="gemma2:2b",
+        supports_tools=False,
+        parameter_count_in_b=9
+    ),
+    626: Model(
+        display_name="Gemma2 27b",
+        identifier="gemma2:2b",
+        supports_tools=False,
+        parameter_count_in_b=27
+    ),
+    229: Model(
+        display_name="Phi3 3.8b", 
+        identifier="phi3",
+        supports_tools=False,
+        parameter_count_in_b=3.8
+    ),
+    903: Model(
+        display_name="Tinyllama 1.1b",
+        identifier="tinyllama:1.1b",
+        supports_tools=False,
+        parameter_count_in_b=1.1
+    ),
+    670: Model(
+        display_name="Mistral Nemo 12b",
+        identifier="mistral-nemo:12b",
+        supports_tools=True,
+        parameter_count_in_b=12
+    ),
+    404: Model(
+        display_name="Command R+ 104b",
+        identifier="command-r-plus:104b",
+        supports_tools=True,
+        parameter_count_in_b=104
+    ),
+    701: Model(
+        display_name="Yi 6b",
+        identifier="yi:7b",
+        supports_tools=False,
+        parameter_count_in_b=6
+    ),
+    704: Model(
+        display_name="Yi 6b",
+        identifier="yi:7b",
+        supports_tools=False,
+        parameter_count_in_b=6
+    ),
+    724: Model(
+        display_name="Yi 34b",
+        identifier="yi:34b",
+        supports_tools=False,
+        parameter_count_in_b=34
+    ),
+    129: Model(
+        display_name="Yi 34b",
+        identifier="yi:34b",
+        supports_tools=False,
+        parameter_count_in_b=34
+    ),
+    853: Model(
+        display_name="Qwen2 0.5b",
+        identifier="qwen2:0.5b",
+        supports_tools=False,
+        parameter_count_in_b=0.5
+    ),
+}

suite_settings/tests.py

@@ -1,14 +1,14 @@
-from libs.test_class import Test
+from libs.classes    import Test
-from libs.runnables  import * 
+from libs.runnables  import basic_prompt, one_tool_call_answer, agent_with_tools
-from libs.validators import * 
+from libs.validators import regex_match_any, system_human_answer_match 
-from libs.tools      import *
+from libs.tools      import add, multiply, get_current_date_and_time, get_notes_in_timespan, get_notes_containing, write_note
 from textwrap        import dedent
 from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage, AIMessage
 
 tests = {
 	607: Test(
 	    name="Healthy Vegetables in Chinese",
-	    runnable=basic,
+	    runnable=basic_prompt,
 	    runnable_input={
 	        "system_msg": "You are a helpful assistant. You serve people across the globe.",
 	        "human_msg": "什么蔬菜最健康？",
@@ -67,7 +67,7 @@ tests = {
 	            "get_current_date_and_time": get_current_date_and_time,
 	            "get_notes_in_timespan": get_notes_in_timespan,
 	            "get_notes_containing": get_notes_containing,
-	            "Write note": write_note
+	            "Write note": write_note,
 	        }
 	    },
 	    validator=system_human_answer_match,
@@ -96,7 +96,7 @@ tests = {
 	),
 	856: Test(
 	    name="Notes from last Saturday TSO FSP", 
-	    runnable=agent_with_tools_fsp,
+	    runnable=agent_with_tools,
 	    runnable_input={
 		    "system_msg": "You are a helpful assistant. You can use tools to accomplish tasks. Once you've called a tool, the resulting tool_message content can be taken into consideration again. With that you can do \"multiple rounds\" of tool calling. To know the date, use the tool get_current_date_and_time.",
 	        "fsp_messages": [

test_suite.py

@@ -3,13 +3,14 @@ from suite_settings.models import models
 from suite_settings.seeds import seeds
 from suite_settings.tests import tests
 
+
 def main():
 
-    results = run_tests(
+    run_tests(
         models=models,
         seeds=seeds,
         tests=tests,
-        base_url="http://bolt.hs-mittweida.de:11434"
+        base_url="http://bolt.hs-mittweida.de:11434",
     )
 
 if __name__ == "__main__":

visualize.py

@@ -3,7 +3,6 @@ import matplotlib.pyplot as plt
 import pandas as pd
 import numpy as np
 import seaborn as sns
-from math import pi
 
 # Load the JSON data
 with open('saved_results.json', 'r') as f:
@@ -14,7 +13,7 @@ results = []
 for test_hash, test_data in data.items():
     results.append({
         "hash": test_hash,
-        "model": test_data['model'],
+        "model": test_data['model_name'],
         "seed": test_data['seed'],
         "test_name": test_data['test_name'],
         "validation": test_data['validation']
@@ -61,52 +60,7 @@ plt.savefig('validation_results_by_test_name.png')
 
 
 ## 3rd Chart
-# Prepare data for the spider chart
-models = df['model'].unique()
-
-# Calculate the pass rate for each model on each test
 pass_rate = pd.pivot_table(df, values='validation', index='model', columns='test_name', aggfunc="mean", fill_value=0)
-tests = df['test_name'].unique().tolist()
-
-# Initialize the spider plot
-num_vars = len(pass_rate)-1
-angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
-angles += [ angles[0] ]
-
-fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
-
-# Plot each model's performance
-for model in models:
-    values = pass_rate.loc[model].tolist()
-    values += [ values[0] ]
-    ax.fill(angles, values, alpha=0.25)
-    ax.plot(angles, values, label=model)
-# 
-
-# Configure the spider chart
-ax.set_theta_offset(pi / 2)
-ax.set_theta_direction(-1)
-
-tests.append(tests[0])
-tests.pop(0)
-
-ax.set_xticks(angles[:-1])
-ax.set_xticklabels(tests)
-
-ax.set_yticks(np.linspace(0, 1, 5))
-ax.set_yticklabels([f'{int(i * 100)}%' for i in np.linspace(0, 1, 5)], color="grey", size=8)
-ax.set_ylim(0, 1)
-
-plt.title('Model Performance on Each Test')
-plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1.1))
-plt.tight_layout()
-plt.savefig('model_performance_spider_chart.png')
-
-
-
-
-
-# 4th chart
 # Create a heatmap
 plt.figure(figsize=(8, 8))
 sns.heatmap(pass_rate*100, annot=True, fmt=".0f", cmap=sns.color_palette("blend:#100,#255,#4a3", as_cmap=True), cbar=True, annot_kws={"size": 10})