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8fa0f12320943790c88096da97fe1e174eb8f682 Mon Sep 17 00:00:00 2001 From: Raggini Marco <93598699+MarkRagg@users.noreply.github.com> Date: Mon, 9 Feb 2026 13:48:26 +0100 Subject: [PATCH 02/94] feat: implement first version of custom compiled graph * chore: add langchaing library * chore: add lang graph and dotenv * chore: add mlflow * chore: add classes for testing * feat: implement tool calling in compiled graph * chore: add mlflow tracking * chore: implementing backtracking * feat: implement chat completition with new agent * feat: removing tools after the usage from agent * feat: implement a first version of Graph of Thought usign llm as a judge * fix: remove unused imports * fix: fix mypy problems * chore: ruff format --- GoT/__init__.py | 6 +- GoT/graph_model.py | 193 +++++++++++++++++++++++++++++++++++++++++ GoT/ollama_llm.py | 45 ++++++++++ GoT/runtime_graph.py | 97 +++++++++++++++++++++ GoT/tools/math_tool.py | 32 +++++++ GoT/utils.py | 68 +++++++++++++++ 6 files changed, 439 insertions(+), 2 deletions(-) create mode 100644 GoT/graph_model.py create mode 100644 GoT/ollama_llm.py create mode 100644 GoT/runtime_graph.py create mode 100644 GoT/tools/math_tool.py create mode 100644 GoT/utils.py diff --git a/GoT/__init__.py b/GoT/__init__.py index d6aecc1..8fffebe 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -1,5 +1,6 @@ import logging +from GoT.graph_model import invoke_graph logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("GoT") @@ -17,8 +18,9 @@ def main(): # this is the main module of your app # it is only required if your project must be runnable # this is the script to be executed whenever some users writes `python -m GoT` on the command line, eg. - x = MyClass().my_method() - print(x) + # x = MyClass().my_method() + # print(x) + invoke_graph() # let this be the last line of this file diff --git a/GoT/graph_model.py b/GoT/graph_model.py new file mode 100644 index 0000000..a972416 --- /dev/null +++ b/GoT/graph_model.py @@ -0,0 +1,193 @@ +from dotenv import load_dotenv +from langchain_core.messages import HumanMessage, SystemMessage, AIMessage +from langgraph.graph import StateGraph, MessagesState, START, END + +from GoT.ollama_llm import OllamaLLM +from GoT.runtime_graph import RuntimeGraph, RuntimeNode, TestNode +from GoT.utils import ( + parse_response, + parse_score, + parse_tool_list, + remove_tools_from_list, +) + +load_dotenv() + +# Defining agents +starting_agent = OllamaLLM().create_custom_agent( + OllamaLLM().get_tools(), + SystemMessage( + "You are an assistant specialized in tools. Your goal is not to resolve the problem," + " only to make list with the best tool to use. " + "The list MUST be in this format and it is not possible to format the tool_name in any way: " + "- tool_name " + "- tool_name " + "- tool_name " + ), +) + +chat_completition_agent = OllamaLLM().create_custom_agent([]) + +judge_agent = OllamaLLM().create_custom_agent( + [], + SystemMessage( + "You are an assistant specialized in validation of response, like an LLM-as-a-judge. " + "Your duty is to score, from 0 to 6, the response that user gives to you and assign to it a score. " + "Your response MUST be a description of the score and then the" + "output format should be: " + "Score: \n" + "0: The response is impossible to understand and completely wrong. " + "1: The response is near to be completely wrong. " + "2: The response is in the correct language but it doesn't follow the instruction. " + "3: The response try to resolve the problem but doesn't follow the instruction or the response is wrong. " + "4: The response follow the instruction but the result is wrong or the result is correct but doesn't follow the instruction. " + "5: The response follow the instruction and the result is near to the solution (If the task is hard, the solution should be near to the corrected one). " + "6: The response follow the instruction and the result is perfectly correct." + ), +) + +# Defining runtime graph +runtime_graph = RuntimeGraph() + + +def goal(prompt: MessagesState): + runtime_graph.goal = prompt + return prompt + + +def tool_expand(goal: MessagesState): + msg = parse_response(goal) + sys_msg = "Which tools that I have can I use to solve this problem? Please make a list using '-' to denote each tool in a probabilistic order, don't use this character for other reasons." + messages = [ + HumanMessage(msg), + SystemMessage(sys_msg), + ] + res = starting_agent.invoke({"messages": messages}) + str_res = parse_response(res) + tool_list = parse_tool_list(str_res) # Toglie elementi inutili + # add tool nodes in the runtime graph + for tool in tool_list: + tool_node = RuntimeNode( + SystemMessage(sys_msg), AIMessage(tool), type="tool", resolved=True + ) + call_node = RuntimeNode( + SystemMessage( + "Please, resolve the problem with the tool: " + + tool + + ". You can't use other tools!" + ), + AIMessage(""), + type="call_tool", + ) + runtime_graph.add_node(tool_node) + runtime_graph.add_node(call_node) + runtime_graph.add_edge(tool_node, call_node) + runtime_graph.add_tool_link(call_node, tool) + # extract a tool to call + runtime_graph.temp_node = runtime_graph.call_tool_node() + return runtime_graph.runtime_node_to_state(runtime_graph.temp_node) + + +def tool_call(messages: MessagesState): + # It calls the llm and it resolves the call node + call_node = runtime_graph.temp_node + tool_agent = OllamaLLM().create_custom_agent( + remove_tools_from_list( + OllamaLLM().get_tools(), runtime_graph.get_resolved_tools() + ), + SystemMessage( + "You are an assistant specialized in tools. Your goal is to resolve the problem with " + " the tool that the user indicates to you. You MUST use the tool that user indicates to you. " + ), + ) + res = parse_response(tool_agent.invoke(messages)) + runtime_graph.resolve_node(call_node, AIMessage(res)) + + # Add test node + test_node = TestNode( + SystemMessage("Score this solution: \n" + res), + AIMessage(""), + type="test", + score=0, + ) + runtime_graph.add_node(test_node) + runtime_graph.add_edge(call_node, test_node) + runtime_graph.temp_node = test_node + return runtime_graph.runtime_node_to_state(runtime_graph.temp_node) + + +def test_result(result_msg: MessagesState): # TODO: Pensare ad un modo per testare + n = runtime_graph.exist_tool_available() + + # Get the actual tool execution result from the resolved call_node + test_node = runtime_graph.temp_node + if not isinstance(test_node, TestNode): + raise TypeError("Expected TestNode for scoring") + call_node_response = test_node.prompt.content # The actual solution to judge + + # Create a proper message for the judge with the solution + judge_messages = [ + HumanMessage(content=call_node_response), + SystemMessage( + content="Score this solution based on correctness and following instructions. Remember that you can't verify the usage of the tools." + ), + ] + + score_res = parse_response(judge_agent.invoke({"messages": judge_messages})) + test_node.score = parse_score(score_res) + runtime_graph.resolve_node(test_node, AIMessage(score_res)) + + if test_node.score < 5 and n is True: + return "backtrack" + elif n is False: + chat_completition_node = RuntimeNode( + SystemMessage("Please, solve this problem"), + AIMessage(""), + type="chat_completition", + ) + runtime_graph.add_node(chat_completition_node) + runtime_graph.temp_node = chat_completition_node + return "chat_completition" + else: + return END + + +def backtrack(messages: MessagesState): + runtime_graph.temp_node = runtime_graph.call_tool_node() + return runtime_graph.runtime_node_to_state(runtime_graph.temp_node) + + +def chat_completition(messages: MessagesState): + new_messages_history = runtime_graph.goal + new_messages_history["messages"].append(runtime_graph.temp_node.prompt) + chat_completition_agent.invoke(new_messages_history) + return new_messages_history + + +# https://docs.langchain.com/oss/python/langgraph/overview + + +def invoke_graph(): + graph = StateGraph(MessagesState) + graph.add_node(goal) + graph.add_node(tool_expand) + graph.add_node(tool_call) + graph.add_node(backtrack) + graph.add_node(chat_completition) + graph.add_edge(START, "goal") + graph.add_edge("goal", "tool_expand") + graph.add_edge("tool_expand", "tool_call") + graph.add_conditional_edges("tool_call", test_result) + graph.add_edge("backtrack", "tool_call") + graph.add_edge("chat_completition", END) + + graph = graph.compile() + + res = graph.invoke( + {"messages": [{"role": "user", "content": "Solve 100 + 100 + 200 + 400"}]} + ) + + # logger.info(res) + # logger.info(graph.get_graph().draw_mermaid()) + + return res diff --git a/GoT/ollama_llm.py b/GoT/ollama_llm.py new file mode 100644 index 0000000..3561616 --- /dev/null +++ b/GoT/ollama_llm.py @@ -0,0 +1,45 @@ +from langchain_openai import ChatOpenAI +from langchain_core.messages import SystemMessage +from langchain.agents import create_agent +import mlflow + +from GoT.tools.math_tool import sum_four, sum_three, summing, minus, square_root + +mlflow.set_experiment("marcoraggini-experiment") +mlflow.openai.autolog() +mlflow.langchain.autolog() + +SYSTEM_PROMPT_GENERAL = """ + You are a helpful assistant able to address general problems. + ... + """ + + +class OllamaLLM: + def __init__(self): + with mlflow.set_active_model(name="ollama-agent-ministral-3"): + self.ollamaLLM = ChatOpenAI( + base_url="http://localhost:11434/v1", + api_key="dummy", + model="ministral-3:8b", + ) + + self.system_prompt = SystemMessage(SYSTEM_PROMPT_GENERAL) + + self.agent = create_agent( + model=self.ollamaLLM, + tools=[sum_four, summing, minus, sum_three, square_root], + system_prompt=self.system_prompt, + ) + + def get_tools(self): + return [sum_four, summing, minus, sum_three, square_root] + + def create_custom_agent( + self, tools, system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL) + ): + return create_agent( + model=self.ollamaLLM, + tools=tools, + system_prompt=system_prompt, + ) diff --git a/GoT/runtime_graph.py b/GoT/runtime_graph.py new file mode 100644 index 0000000..039230f --- /dev/null +++ b/GoT/runtime_graph.py @@ -0,0 +1,97 @@ +from typing import Dict, List +from langgraph.graph import MessagesState +from langchain_core.messages import AIMessage, SystemMessage, AnyMessage + + +class RuntimeNode: + _id_counter = 0 # Contatore globale per ID unici + + def __init__( + self, + prompt: SystemMessage, + response: AIMessage, + type: str, + resolved: bool = False, + ): + self.id = RuntimeNode._id_counter # ID unico per ogni nodo + RuntimeNode._id_counter += 1 + self.prompt = prompt + self.response = response + self.type = type + self.resolved = resolved + + def __hash__(self): + return hash(self.id) + + def __eq__(self, other): + if not isinstance(other, RuntimeNode): + return False + return self.id == other.id + + def __repr__(self): + return f"RuntimeNode(id={self.id}, type={self.type}, resolved={self.resolved})" + + +class TestNode(RuntimeNode): + def __init__( + self, + prompt: SystemMessage, + response: AIMessage, + type: str, + score: int, + resolved: bool = False, + ): + super().__init__(prompt, response, type, resolved) + self.score = score + + +class RuntimeGraph: + def __init__(self): + self.goal: MessagesState = MessagesState(messages=[]) + self.nodes: Dict[RuntimeNode, List[RuntimeNode]] = {} + self.tools_available: Dict[RuntimeNode, str] = {} + self.temp_node: RuntimeNode = RuntimeNode( + SystemMessage(""), AIMessage(""), type="goal" + ) + + def add_node(self, node: RuntimeNode): + self.nodes.setdefault(node, []) + + def add_edge(self, n1: RuntimeNode, n2: RuntimeNode): + self.nodes.setdefault(n1, []).append(n2) + self.nodes.setdefault(n2, []) + + def add_tool_link(self, call_node: RuntimeNode, tool_name: str): + self.tools_available.setdefault(call_node, tool_name) + + def resolve_node(self, node: RuntimeNode, response: AIMessage) -> None: + node.response = response + node.resolved = True + + def call_tool_node(self) -> RuntimeNode: # TODO change name + nodes = list(self.nodes.keys()) + call_nodes = [ + n for n in nodes if (n.type.startswith("call_tool") and not n.resolved) + ] + return call_nodes[0] + + def exist_tool_available(self) -> bool: + nodes = list(self.nodes.keys()) + call_nodes = [ + n for n in nodes if (n.type.startswith("call_tool") and not n.resolved) + ] + return True if call_nodes else False + + def get_resolved_tools(self): + resolved_nodes = [t for t in self.tools_available.keys() if t.resolved is True] + return [self.tools_available[n] for n in resolved_nodes] + + def runtime_node_to_state( + self, node: RuntimeNode + ) -> MessagesState: # TODO change name + messages: list[AnyMessage] = [] + + if node.prompt: + messages.append(node.prompt) + + return MessagesState(messages=messages) diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py new file mode 100644 index 0000000..5bba61b --- /dev/null +++ b/GoT/tools/math_tool.py @@ -0,0 +1,32 @@ +from math import sqrt +from langchain.tools import tool + + +@tool +def summing(x: int, y: int) -> int: + """sum of two integer""" + return x + y + + +@tool +def sum_three(x: int, y: int, z: int): + """sum of three integer""" + return x + y + z + + +@tool +def sum_four(x, y, z, a): + """sum of four integer""" + return x + y + z + a + + +@tool +def minus(x: int, y: int) -> int: + """minus of two integer""" + return x - y + + +@tool +def square_root(x): + """Calculate the square root of x""" + return sqrt(x) diff --git a/GoT/utils.py b/GoT/utils.py new file mode 100644 index 0000000..b35e4d6 --- /dev/null +++ b/GoT/utils.py @@ -0,0 +1,68 @@ +import re + + +def parse_response(res) -> str: + """ + Parse the LLm response using agent.invoke + + :param res: the MessagesState + :return: The response in string + """ + return res["messages"][-1].content + + +def parse_tool_list(response: str) -> list[str]: + """ + Parse tool names from LLM response containing bullet-pointed list. + Handles both * and - formats and extracts only the tool name, + ignoring any extra descriptive text. + + Args: + response: Raw text response from LLM + Returns: + List of tool names extracted from the response + """ + tool_list = [] + counter = 0 + for line in response.split("\n"): + line = line.strip() + if line.startswith("*") or line.startswith("-"): + # Remove the bullet point and extract the tool name + content = re.sub(r"^[\*\-]\s+", "", line) + # Extract just the first word (the tool name) + tool_name = content.split()[0] + if tool_name and counter <= 3: + counter += 1 + tool_list.append(tool_name) + + return tool_list + + +def parse_score(response: str) -> int: + """ + Parse LLM response to get the score in a format like Score: number + + :param response: The LLM response + :type response: str + :return: The score + :rtype: int + """ + # Look for pattern like "Score: " + match = re.search(r"Score:\s*(\d+)", response, re.IGNORECASE) + + if match: + return int(match.group(1)) + + raise ValueError(f"Could not find score in response: {response}") + + +def remove_tools_from_list(tool_list, tools_to_remove): + """ + Remove a list of tools and return the updated list + + :param tool_list: is a list of StructuredTool + :param tools_to_remove: list of string representing the names of the tools + """ + result = [tool for tool in tool_list if tool.func.__name__ not in tools_to_remove] + print(result) + return result From 8856bee61020802296db7ff0bdb8f71cca004ba5 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 9 Feb 2026 17:21:12 +0100 Subject: [PATCH 03/94] chore: add lm-eval --- poetry.lock | 1545 +++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 1545 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 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2001 From: Raggini Marco Date: Mon, 9 Feb 2026 17:22:14 +0100 Subject: [PATCH 04/94] fix: fix judge messages, added the first question --- GoT/graph_model.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/GoT/graph_model.py b/GoT/graph_model.py index a972416..6432d44 100644 --- a/GoT/graph_model.py +++ b/GoT/graph_model.py @@ -127,6 +127,7 @@ def test_result(result_msg: MessagesState): # TODO: Pensare ad un modo per test # Create a proper message for the judge with the solution judge_messages = [ + HumanMessage(content=parse_response(runtime_graph.goal)), HumanMessage(content=call_node_response), SystemMessage( content="Score this solution based on correctness and following instructions. Remember that you can't verify the usage of the tools." @@ -167,7 +168,7 @@ def chat_completition(messages: MessagesState): # https://docs.langchain.com/oss/python/langgraph/overview -def invoke_graph(): +def invoke_graph(content: str): graph = StateGraph(MessagesState) graph.add_node(goal) graph.add_node(tool_expand) @@ -184,7 +185,7 @@ def invoke_graph(): graph = graph.compile() res = graph.invoke( - {"messages": [{"role": "user", "content": "Solve 100 + 100 + 200 + 400"}]} + {"messages": [{"role": "user", "content": content}]} ) # logger.info(res) From 07f31ebc54d961542286374836e46a3ae75adc89 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Feb 2026 10:56:11 +0100 Subject: [PATCH 05/94] chore: add multiply --- GoT/ollama_llm.py | 4 ++-- GoT/tools/math_tool.py | 5 +++++ 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/GoT/ollama_llm.py b/GoT/ollama_llm.py index 3561616..092f13f 100644 --- a/GoT/ollama_llm.py +++ b/GoT/ollama_llm.py @@ -3,7 +3,7 @@ from langchain.agents import create_agent import mlflow -from GoT.tools.math_tool import sum_four, sum_three, summing, minus, square_root +from GoT.tools.math_tool import multiply, sum_four, sum_three, summing, minus, square_root mlflow.set_experiment("marcoraggini-experiment") mlflow.openai.autolog() @@ -33,7 +33,7 @@ def __init__(self): ) def get_tools(self): - return [sum_four, summing, minus, sum_three, square_root] + return [sum_four, summing, minus, sum_three, square_root, multiply] def create_custom_agent( self, tools, system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL) diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py index 5bba61b..fdd5b4c 100644 --- a/GoT/tools/math_tool.py +++ b/GoT/tools/math_tool.py @@ -25,6 +25,11 @@ def minus(x: int, y: int) -> int: """minus of two integer""" return x - y +@tool +def multiply(x: int, y: int) -> int: + """ multiply of two integer""" + return x * y + @tool def square_root(x): From 545447744678f914c2a777f452c7a0d28453f329 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Feb 2026 10:58:54 +0100 Subject: [PATCH 06/94] chore: create model folder --- GoT/{ => model}/graph_model.py | 10 +++++----- GoT/{ => model}/ollama_llm.py | 0 GoT/{ => model}/runtime_graph.py | 0 GoT/{ => model/utils}/utils.py | 0 4 files changed, 5 insertions(+), 5 deletions(-) rename GoT/{ => model}/graph_model.py (96%) rename GoT/{ => model}/ollama_llm.py (100%) rename GoT/{ => model}/runtime_graph.py (100%) rename GoT/{ => model/utils}/utils.py (100%) diff --git a/GoT/graph_model.py b/GoT/model/graph_model.py similarity index 96% rename from GoT/graph_model.py rename to GoT/model/graph_model.py index 6432d44..5937ee0 100644 --- a/GoT/graph_model.py +++ b/GoT/model/graph_model.py @@ -2,9 +2,9 @@ from langchain_core.messages import HumanMessage, SystemMessage, AIMessage from langgraph.graph import StateGraph, MessagesState, START, END -from GoT.ollama_llm import OllamaLLM -from GoT.runtime_graph import RuntimeGraph, RuntimeNode, TestNode -from GoT.utils import ( +from GoT.model.ollama_llm import OllamaLLM +from GoT.model.runtime_graph import RuntimeGraph, RuntimeNode, TestNode +from GoT.model.utils.utils import ( parse_response, parse_score, parse_tool_list, @@ -113,10 +113,10 @@ def tool_call(messages: MessagesState): runtime_graph.add_node(test_node) runtime_graph.add_edge(call_node, test_node) runtime_graph.temp_node = test_node - return runtime_graph.runtime_node_to_state(runtime_graph.temp_node) + return messages["messages"].append(AIMessage(res)) -def test_result(result_msg: MessagesState): # TODO: Pensare ad un modo per testare +def test_result(result_msg: MessagesState): n = runtime_graph.exist_tool_available() # Get the actual tool execution result from the resolved call_node diff --git a/GoT/ollama_llm.py b/GoT/model/ollama_llm.py similarity index 100% rename from GoT/ollama_llm.py rename to GoT/model/ollama_llm.py diff --git a/GoT/runtime_graph.py b/GoT/model/runtime_graph.py similarity index 100% rename from GoT/runtime_graph.py rename to GoT/model/runtime_graph.py diff --git a/GoT/utils.py b/GoT/model/utils/utils.py similarity index 100% rename from GoT/utils.py rename to GoT/model/utils/utils.py From fad35d15ceabcb9c0b3f0037f7802c4cd204cabb Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Feb 2026 11:27:19 +0100 Subject: [PATCH 07/94] fix: judge agent now return an object --- GoT/model/graph_model.py | 10 +++++----- GoT/model/ollama_llm.py | 3 ++- GoT/model/utils/utils.py | 13 +++++-------- 3 files changed, 12 insertions(+), 14 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 5937ee0..86bcf82 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -3,10 +3,9 @@ from langgraph.graph import StateGraph, MessagesState, START, END from GoT.model.ollama_llm import OllamaLLM -from GoT.model.runtime_graph import RuntimeGraph, RuntimeNode, TestNode +from GoT.model.runtime_graph import RuntimeGraph, RuntimeNode, Score, TestNode from GoT.model.utils.utils import ( parse_response, - parse_score, parse_tool_list, remove_tools_from_list, ) @@ -44,6 +43,7 @@ "5: The response follow the instruction and the result is near to the solution (If the task is hard, the solution should be near to the corrected one). " "6: The response follow the instruction and the result is perfectly correct." ), + response_format=Score ) # Defining runtime graph @@ -134,9 +134,9 @@ def test_result(result_msg: MessagesState): ), ] - score_res = parse_response(judge_agent.invoke({"messages": judge_messages})) - test_node.score = parse_score(score_res) - runtime_graph.resolve_node(test_node, AIMessage(score_res)) + score_res = judge_agent.invoke({"messages": judge_messages})["structured_response"] + test_node.score = score_res.score + runtime_graph.resolve_node(test_node, AIMessage(score_res.description)) if test_node.score < 5 and n is True: return "backtrack" diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 092f13f..01803e4 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -36,10 +36,11 @@ def get_tools(self): return [sum_four, summing, minus, sum_three, square_root, multiply] def create_custom_agent( - self, tools, system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL) + self, tools, system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL), response_format=None ): return create_agent( model=self.ollamaLLM, tools=tools, system_prompt=system_prompt, + response_format=response_format ) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index b35e4d6..b0da97e 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -1,5 +1,8 @@ import re +from GoT.model.runtime_graph import Score +from langgraph.graph import MessagesState + def parse_response(res) -> str: """ @@ -38,7 +41,7 @@ def parse_tool_list(response: str) -> list[str]: return tool_list -def parse_score(response: str) -> int: +def parse_score(response: MessagesState) -> Score: """ Parse LLM response to get the score in a format like Score: number @@ -47,13 +50,7 @@ def parse_score(response: str) -> int: :return: The score :rtype: int """ - # Look for pattern like "Score: " - match = re.search(r"Score:\s*(\d+)", response, re.IGNORECASE) - - if match: - return int(match.group(1)) - - raise ValueError(f"Could not find score in response: {response}") + return response["structured_response"] def remove_tools_from_list(tool_list, tools_to_remove): From 4dc96602b7c929b9182b7cff4301eec90a3eef91 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Feb 2026 11:27:37 +0100 Subject: [PATCH 08/94] chore: add Score BaseModel --- GoT/model/runtime_graph.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 039230f..ab849b8 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -1,6 +1,7 @@ from typing import Dict, List from langgraph.graph import MessagesState from langchain_core.messages import AIMessage, SystemMessage, AnyMessage +from pydantic import BaseModel class RuntimeNode: @@ -44,6 +45,15 @@ def __init__( super().__init__(prompt, response, type, resolved) self.score = score +class Score(BaseModel): + """ Rapresents a score for a test node. + + Attributes: + score: int - The score assigned to the test node. + description: str - A description or rationale for the assigned score. + """ + score: int + description: str class RuntimeGraph: def __init__(self): From 9ee51934c7f80682548efb552cd2b18b67655f72 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Feb 2026 14:42:44 +0100 Subject: [PATCH 09/94] chore: add util methods --- GoT/model/utils/utils.py | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index b0da97e..3f3c101 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -61,5 +61,25 @@ def remove_tools_from_list(tool_list, tools_to_remove): :param tools_to_remove: list of string representing the names of the tools """ result = [tool for tool in tool_list if tool.func.__name__ not in tools_to_remove] - print(result) return result + +def extract_output(result) -> str: + """ + Extracts the output from the result of invoke_graph. + Supports various formats. + """ + if isinstance(result, dict): + if "output" in result and result["output"]: + return str(result["output"]) + + if "messages" in result and result["messages"]: + messages = result["messages"] + last_msg = messages[-1] + + if hasattr(last_msg, 'content'): + return str(last_msg.content) + elif isinstance(last_msg, dict) and 'content' in last_msg: + return str(last_msg['content']) + + return str(result) if result else "" + From e711cee972e613a45830f4953b7b8d492563220e Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Feb 2026 14:43:34 +0100 Subject: [PATCH 10/94] feat: implement lm eval benchmark testing --- GoT/__init__.py | 46 ++++--- GoT/model/lm_wrapper.py | 265 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 293 insertions(+), 18 deletions(-) create mode 100644 GoT/model/lm_wrapper.py diff --git a/GoT/__init__.py b/GoT/__init__.py index 8fffebe..56d85a7 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -1,27 +1,37 @@ +import json import logging -from GoT.graph_model import invoke_graph +from lm_eval import evaluator, tasks +from GoT.model.lm_wrapper import LangGraphLMWrapper, LangGraphBigBenchWrapper logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("GoT") - -# this is the initial module of your app -# this is executed whenever some client-code is calling `import GoT` or `from GoT import ...` -# put your main classes here, eg: -class MyClass: - def my_method(self): - return "Hello World" - - def main(): - # this is the main module of your app - # it is only required if your project must be runnable - # this is the script to be executed whenever some users writes `python -m GoT` on the command line, eg. - # x = MyClass().my_method() - # print(x) - invoke_graph() - + # tm = tasks.TaskManager() + # all_tasks = tm.all_tasks + + # # Filtra solo i task di bigbench + # bigbench_tasks = [t for t in all_tasks if 'bigbench' in t.lower()] + + # print("Task di BigBench disponibili:") + # for task in sorted(bigbench_tasks): + # print(f" - {task}") + task_list = ["bigbench_arithmetic_generate_until"] + lm = LangGraphBigBenchWrapper() + task_dict = tasks.get_task_dict(task_list) + + results = evaluator.evaluate( + lm=lm, + task_dict=task_dict, + limit=1, # Limit to 3 samples for quick testing + log_samples=True, + verbosity="INFO" + ) + + # --- Salva in JSON --- + with open("graph_benchmark_results.json", "w") as f: + json.dump(results, f, indent=2) # let this be the last line of this file -logger.info("GoT loaded") +logger.info("GoT loaded") \ No newline at end of file diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py new file mode 100644 index 0000000..b295a6c --- /dev/null +++ b/GoT/model/lm_wrapper.py @@ -0,0 +1,265 @@ +import math +from GoT.model.graph_model import invoke_graph +from lm_eval.api.registry import register_model +from lm_eval.api.model import LM + +from GoT.model.utils.utils import extract_output + +class LangGraphLM: + def __init__(self): + pass + + def generate(self, requests, max_new_tokens=None): + """ + requests: list di dict {"prompt": ""} + Restituisce list di stringhe + """ + outputs = [] + for r in requests: + prompt = r["prompt"] + result = invoke_graph(prompt) # usa la tua funzione + outputs.append(result["output"]) + return outputs + +# Register your custom model with lm_eval +@register_model("langgraph_lm") +class LangGraphLMWrapper(LM): + + def __init__(self, model_args=""): + super().__init__() + self.lm = LangGraphLM() + + def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): + """ + Generate text until a stopping condition is met. + """ + outputs = [] + for i, request in enumerate(requests): + try: + # Extract the question directly from request.doc + # This is much simpler than parsing the full prompt + if hasattr(request, 'doc') and isinstance(request.doc, dict): + question = request.doc.get('question', '') + else: + # Fallback: try to extract from arguments + if hasattr(request, 'arguments') and request.arguments: + full_prompt = request.arguments[0][0] + # Just take everything after the last "Answer:" as the question + last_answer_idx = full_prompt.rfind("Answer:") + if last_answer_idx != -1: + question = full_prompt[last_answer_idx + 8:].strip() # 8 = len("Answer:") + else: + question = full_prompt + else: + question = str(request) + + # Call invoke_graph with just the question + result = invoke_graph(question) + + # Extract the final answer from the result + output = "" + if isinstance(result, dict) and "messages" in result: + messages = result["messages"] + if messages: + # The last message should be the response + last_msg = messages[-1] + + # Extract content from message + if hasattr(last_msg, 'content'): + output = last_msg.content + elif isinstance(last_msg, dict) and 'content' in last_msg: + output = last_msg['content'] + else: + output = str(last_msg) + else: + output = str(result) if result else "" + + outputs.append(output if output else "") + + except Exception as e: + print(f"Error in request {i}: {e}") + import traceback + traceback.print_exc() + outputs.append("") + + return outputs + + def loglikelihood(self, requests): + return super().loglikelihood(requests) + + def loglikelihood_rolling(self, requests): + return super().loglikelihood_rolling(requests) + + +@register_model("langgraph_bigbench") +class LangGraphBigBenchWrapper(LM): + """ + Wrapper specifico per BigBench tasks. + Supporta principalmente generate_until e loglikelihood per multiple choice. + """ + + def __init__(self, model_args=""): + super().__init__() + + def _extract_text_from_request(self, request): + """ + Estrae il testo dalla request BigBench. + BigBench passa il prompt completo in doc['inputs']. + """ + # Prova con request.doc - BigBench usa il campo 'inputs' + if hasattr(request, 'doc') and isinstance(request.doc, dict): + doc = request.doc + + # BigBench mette il prompt completo in 'inputs' + if 'inputs' in doc and doc['inputs']: + prompt = str(doc['inputs']).strip() + return prompt + + # Fallback per altri campi + for field in ['input', 'question', 'text', 'prompt', 'instruction']: + if field in doc and doc[field]: + return str(doc[field]) + + # Fallback: prova con request.arguments + if hasattr(request, 'arguments') and request.arguments: + try: + full_prompt = request.arguments[0][0] + if full_prompt and len(str(full_prompt).strip()) > 1: + return full_prompt + except (IndexError, TypeError): + pass + + return str(request) + + def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): + """ + Genera testo fino a una condizione di stop. + Usato dalla maggior parte dei task di BigBench. + """ + outputs = [] + + # Se until non è specificato, usa default sensati + if not until: + until = ["\n\n", "\n"] + elif isinstance(until, str): + until = [until] + + print(f"DEBUG - Using stopping tokens: {until}") + + for i, request in enumerate(requests): + try: + question = self._extract_text_from_request(request) + + # Chiama il tuo grafo LangGraph + result = invoke_graph(question) + + # Estrai l'output + output = extract_output(result) + + # Taglia l'output al primo stopping token + if output and isinstance(output, str): + for stop_seq in until: + if stop_seq in output: + output = output[:output.index(stop_seq)] + + outputs.append(output.strip() if output else "") + + except Exception as e: + print(f"Error in BigBench generate_until request {i}: {e}") + import traceback + traceback.print_exc() + outputs.append("") + + return outputs + + def loglikelihood(self, requests): + """ + Calcola la log-likelihood per BigBench multiple choice tasks. + + Riceve una lista di tuple: (context, continuation) + Restituisce lista di tuple: (log_likelihood_score, is_greedy) + + NOTA: Poiché invoke_graph non fornisce logits, usiamo un'euristica. + Se hai bisogno di precision massima, modifica invoke_graph per restituire logits. + """ + outputs = [] + + for i, request in enumerate(requests): + try: + # Estrai context e continuation + if isinstance(request, tuple) and len(request) == 2: + context, continuation = request + else: + # Fallback + context = "" + continuation = str(request) + + # Prepara il prompt per il modello + full_text = context + continuation + + # Chiama il grafo + result = invoke_graph(full_text) + generated_output = extract_output(result) + + # Calcola il score di likelihood + score = self._calculate_likelihood_score( + generated_output, + continuation, + context + ) + + outputs.append((score, False)) + + except Exception as e: + print(f"Error in BigBench loglikelihood request {i}: {e}") + import traceback + traceback.print_exc() + outputs.append((float('-inf'), False)) + + return outputs + + def loglikelihood_rolling(self, requests): + """ + Per task che richiedono rolling loglikelihood. + Di solito per task di perplexity. + """ + return self.loglikelihood(requests) + + def _calculate_likelihood_score(self, generated_text, target_continuation, context=""): + """ + Calcola un score di likelihood per BigBench. + + Strategie: + 1. Se la generazione contiene esattamente la continuation target -> score alto (0.0) + 2. Se contiene la continuation parzialmente -> score medio + 3. Altrimenti -> score basso (log della similarità) + + Questo è un workaround poiché non abbiamo i veri logits. + """ + gen_text = str(generated_text).strip().lower() + target = str(target_continuation).strip().lower() + + # Caso 1: Match esatto + if gen_text == target: + return 0.0 # log(1) = 0, massima likelihood + + # Caso 2: Target è contenuto nel testo generato + if target in gen_text: + # Parziale match, score moderato + return math.log(0.8) + + # Caso 3: Calcola sovrapposizione di parole + gen_words = set(gen_text.split()) + target_words = set(target.split()) + + if not target_words: + return 0.0 + + overlap = len(gen_words & target_words) / len(target_words) + + if overlap > 0: + # Maggiore è l'overlap, più alto lo score + return math.log(overlap) + else: + # Nessuna sovrapposizione + return float('-inf') From 433277b21c7d88a8795c6d0817ad39e8e9602a46 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 11 Feb 2026 11:10:36 +0100 Subject: [PATCH 11/94] chore: resolve mypy problems --- GoT/model/ollama_llm.py | 1 - GoT/model/utils/utils.py | 6 +++++- pyproject.toml | 4 ++++ tests/test_my_class.py | 13 +++++-------- 4 files changed, 14 insertions(+), 10 deletions(-) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 01803e4..59a2a51 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -14,7 +14,6 @@ ... """ - class OllamaLLM: def __init__(self): with mlflow.set_active_model(name="ollama-agent-ministral-3"): diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 3f3c101..0761812 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -50,7 +50,11 @@ def parse_score(response: MessagesState) -> Score: :return: The score :rtype: int """ - return response["structured_response"] + score = response.get("structured_response") + if isinstance(score, Score): + return score + else: + return Score(score=0, description="Failed to parse score") def remove_tools_from_list(tool_list, tools_to_remove): diff --git a/pyproject.toml b/pyproject.toml index 18eb24d..112d18f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,3 +46,7 @@ static-checks = [ [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" + +[[tool.mypy.overrides]] +module = ["lm_eval.*"] +follow_untyped_imports = true diff --git a/tests/test_my_class.py b/tests/test_my_class.py index 38170f8..cba2600 100644 --- a/tests/test_my_class.py +++ b/tests/test_my_class.py @@ -1,14 +1,11 @@ import pytest -from GoT import MyClass - - # can test using classes, name must begin with `Test` -class TestMyClass: - # test methods' names must begin with `test_` - def test_my_method(self): - x = MyClass().my_method() - assert "Hello World" == x +# class TestMyClass: +# # test methods' names must begin with `test_` +# def test_my_method(self): +# x = MyClass().my_method() +# assert "Hello World" == x # can also have standalone test functions, name must begin with `test_` From 3f5b531ef13b23fec3afb06d385c6c44e3ba988a Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 11 Feb 2026 11:14:38 +0100 Subject: [PATCH 12/94] chore; add tasks.txt, remove unused import --- GoT/__init__.py | 2 +- GoT/model/graph_model.py | 13 +- tasks.txt | 292 +++++++++++++++++++++++++++++++++++++++ 3 files changed, 301 insertions(+), 6 deletions(-) create mode 100644 tasks.txt diff --git a/GoT/__init__.py b/GoT/__init__.py index 56d85a7..0699ba8 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -2,7 +2,7 @@ import logging from lm_eval import evaluator, tasks -from GoT.model.lm_wrapper import LangGraphLMWrapper, LangGraphBigBenchWrapper +from GoT.model.lm_wrapper import LangGraphBigBenchWrapper logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("GoT") diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 86bcf82..d947288 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -1,3 +1,4 @@ +from venv import logger from dotenv import load_dotenv from langchain_core.messages import HumanMessage, SystemMessage, AIMessage from langgraph.graph import StateGraph, MessagesState, START, END @@ -6,6 +7,7 @@ from GoT.model.runtime_graph import RuntimeGraph, RuntimeNode, Score, TestNode from GoT.model.utils.utils import ( parse_response, + parse_score, parse_tool_list, remove_tools_from_list, ) @@ -134,7 +136,7 @@ def test_result(result_msg: MessagesState): ), ] - score_res = judge_agent.invoke({"messages": judge_messages})["structured_response"] + score_res = parse_score(judge_agent.invoke({"messages": judge_messages})) test_node.score = score_res.score runtime_graph.resolve_node(test_node, AIMessage(score_res.description)) @@ -168,7 +170,7 @@ def chat_completition(messages: MessagesState): # https://docs.langchain.com/oss/python/langgraph/overview -def invoke_graph(content: str): +def invoke_graph(): graph = StateGraph(MessagesState) graph.add_node(goal) graph.add_node(tool_expand) @@ -184,11 +186,12 @@ def invoke_graph(content: str): graph = graph.compile() + logger.info(graph.get_graph().draw_mermaid()) + res = graph.invoke( - {"messages": [{"role": "user", "content": content}]} + {"messages": [{"role": "user", "content": "Please, solve this problem: What is the capital of France?"}]} ) - # logger.info(res) - # logger.info(graph.get_graph().draw_mermaid()) + logger.info(res) return res diff --git a/tasks.txt b/tasks.txt new file mode 100644 index 0000000..323f6d2 --- /dev/null +++ b/tasks.txt @@ -0,0 +1,292 @@ +gsm8k + +### BIGBENCH ### +- bigbench_abstract_narrative_understanding_generate_until + - bigbench_abstract_narrative_understanding_multiple_choice + - bigbench_anachronisms_generate_until + - bigbench_anachronisms_multiple_choice + - bigbench_analogical_similarity_generate_until + - bigbench_analogical_similarity_multiple_choice + - bigbench_analytic_entailment_generate_until + - bigbench_analytic_entailment_multiple_choice + - bigbench_arithmetic_generate_until + - bigbench_arithmetic_multiple_choice + - bigbench_ascii_word_recognition_generate_until + - bigbench_authorship_verification_generate_until + - bigbench_authorship_verification_multiple_choice + - bigbench_auto_categorization_generate_until + - bigbench_auto_debugging_generate_until + - bigbench_bbq_lite_json_generate_until + - bigbench_bbq_lite_json_multiple_choice + - bigbench_bridging_anaphora_resolution_barqa_generate_until + - bigbench_causal_judgment_generate_until + - bigbench_causal_judgment_multiple_choice + - bigbench_cause_and_effect_generate_until + - bigbench_cause_and_effect_multiple_choice + - bigbench_checkmate_in_one_generate_until + - bigbench_checkmate_in_one_multiple_choice + - bigbench_chess_state_tracking_generate_until + - bigbench_chinese_remainder_theorem_generate_until + - bigbench_cifar10_classification_generate_until + - bigbench_cifar10_classification_multiple_choice + - bigbench_code_line_description_generate_until + - bigbench_code_line_description_multiple_choice + - bigbench_codenames_generate_until + - bigbench_color_generate_until + - bigbench_color_multiple_choice + - bigbench_common_morpheme_generate_until + - bigbench_common_morpheme_multiple_choice + - bigbench_conceptual_combinations_generate_until + - bigbench_conceptual_combinations_multiple_choice + - bigbench_conlang_translation_generate_until + - bigbench_contextual_parametric_knowledge_conflicts_generate_until + - bigbench_contextual_parametric_knowledge_conflicts_multiple_choice + - bigbench_crash_blossom_generate_until + - bigbench_crash_blossom_multiple_choice + - bigbench_crass_ai_generate_until + - bigbench_crass_ai_multiple_choice + - bigbench_cryobiology_spanish_generate_until + - bigbench_cryobiology_spanish_multiple_choice + - bigbench_cryptonite_generate_until + - bigbench_cs_algorithms_generate_until + - bigbench_cs_algorithms_multiple_choice + - bigbench_dark_humor_detection_generate_until + - bigbench_dark_humor_detection_multiple_choice + - bigbench_date_understanding_generate_until + - bigbench_date_understanding_multiple_choice + - bigbench_disambiguation_qa_generate_until + - bigbench_disambiguation_qa_multiple_choice + - bigbench_discourse_marker_prediction_generate_until + - bigbench_discourse_marker_prediction_multiple_choice + - bigbench_disfl_qa_generate_until + - bigbench_dyck_languages_generate_until + - bigbench_dyck_languages_multiple_choice + - bigbench_elementary_math_qa_generate_until + - bigbench_elementary_math_qa_multiple_choice + - bigbench_emoji_movie_generate_until + - bigbench_emoji_movie_multiple_choice + - bigbench_emojis_emotion_prediction_generate_until + - bigbench_emojis_emotion_prediction_multiple_choice + - bigbench_empirical_judgments_generate_until + - bigbench_empirical_judgments_multiple_choice + - bigbench_english_proverbs_generate_until + - bigbench_english_proverbs_multiple_choice + - bigbench_english_russian_proverbs_generate_until + - bigbench_english_russian_proverbs_multiple_choice + - bigbench_entailed_polarity_generate_until + - bigbench_entailed_polarity_hindi_generate_until + - bigbench_entailed_polarity_hindi_multiple_choice + - bigbench_entailed_polarity_multiple_choice + - bigbench_epistemic_reasoning_generate_until + - bigbench_epistemic_reasoning_multiple_choice + - bigbench_evaluating_information_essentiality_generate_until + - bigbench_evaluating_information_essentiality_multiple_choice + - bigbench_fact_checker_generate_until + - bigbench_fact_checker_multiple_choice + - bigbench_fantasy_reasoning_generate_until + - bigbench_fantasy_reasoning_multiple_choice + - bigbench_few_shot_nlg_generate_until + - bigbench_figure_of_speech_detection_generate_until + - bigbench_figure_of_speech_detection_multiple_choice + - bigbench_formal_fallacies_syllogisms_negation_generate_until + - bigbench_formal_fallacies_syllogisms_negation_multiple_choice + - bigbench_gem_generate_until + - bigbench_gender_inclusive_sentences_german_generate_until + - bigbench_general_knowledge_generate_until + - bigbench_general_knowledge_multiple_choice + - bigbench_generate_until + - bigbench_geometric_shapes_generate_until + - bigbench_geometric_shapes_multiple_choice + - bigbench_goal_step_wikihow_generate_until + - bigbench_goal_step_wikihow_multiple_choice + - bigbench_gre_reading_comprehension_generate_until + - bigbench_gre_reading_comprehension_multiple_choice + - bigbench_hhh_alignment_generate_until + - bigbench_hhh_alignment_multiple_choice + - bigbench_hindi_question_answering_generate_until + - bigbench_hindu_knowledge_generate_until + - bigbench_hindu_knowledge_multiple_choice + - bigbench_hinglish_toxicity_generate_until + - bigbench_hinglish_toxicity_multiple_choice + - bigbench_human_organs_senses_generate_until + - bigbench_human_organs_senses_multiple_choice + - bigbench_hyperbaton_generate_until + - bigbench_hyperbaton_multiple_choice + - bigbench_identify_math_theorems_generate_until + - bigbench_identify_math_theorems_multiple_choice + - bigbench_identify_odd_metaphor_generate_until + - bigbench_identify_odd_metaphor_multiple_choice + - bigbench_implicatures_generate_until + - bigbench_implicatures_multiple_choice + - bigbench_implicit_relations_generate_until + - bigbench_implicit_relations_multiple_choice + - bigbench_intent_recognition_generate_until + - bigbench_intent_recognition_multiple_choice + - bigbench_international_phonetic_alphabet_nli_generate_until + - bigbench_international_phonetic_alphabet_nli_multiple_choice + - bigbench_international_phonetic_alphabet_transliterate_generate_until + - bigbench_intersect_geometry_generate_until + - bigbench_intersect_geometry_multiple_choice + - bigbench_irony_identification_generate_until + - bigbench_irony_identification_multiple_choice + - bigbench_kanji_ascii_generate_until + - bigbench_kanji_ascii_multiple_choice + - bigbench_kannada_generate_until + - bigbench_kannada_multiple_choice + - bigbench_key_value_maps_generate_until + - bigbench_key_value_maps_multiple_choice + - bigbench_known_unknowns_generate_until + - bigbench_known_unknowns_multiple_choice + - bigbench_language_games_generate_until + - bigbench_language_identification_generate_until + - bigbench_language_identification_multiple_choice + - bigbench_linguistic_mappings_generate_until + - bigbench_linguistics_puzzles_generate_until + - bigbench_list_functions_generate_until + - bigbench_logic_grid_puzzle_generate_until + - bigbench_logic_grid_puzzle_multiple_choice + - bigbench_logical_args_generate_until + - bigbench_logical_args_multiple_choice + - bigbench_logical_deduction_generate_until + - bigbench_logical_deduction_multiple_choice + - bigbench_logical_fallacy_detection_generate_until + - bigbench_logical_fallacy_detection_multiple_choice + - bigbench_logical_sequence_generate_until + - bigbench_logical_sequence_multiple_choice + - bigbench_mathematical_induction_generate_until + - bigbench_mathematical_induction_multiple_choice + - bigbench_matrixshapes_generate_until + - bigbench_metaphor_boolean_generate_until + - bigbench_metaphor_boolean_multiple_choice + - bigbench_metaphor_understanding_generate_until + - bigbench_metaphor_understanding_multiple_choice + - bigbench_minute_mysteries_qa_generate_until + - bigbench_misconceptions_generate_until + - bigbench_misconceptions_multiple_choice + - bigbench_misconceptions_russian_generate_until + - bigbench_misconceptions_russian_multiple_choice + - bigbench_mnist_ascii_generate_until + - bigbench_mnist_ascii_multiple_choice + - bigbench_modified_arithmetic_generate_until + - bigbench_moral_permissibility_generate_until + - bigbench_moral_permissibility_multiple_choice + - bigbench_movie_dialog_same_or_different_generate_until + - bigbench_movie_dialog_same_or_different_multiple_choice + - bigbench_movie_recommendation_generate_until + - bigbench_movie_recommendation_multiple_choice + - bigbench_mult_data_wrangling_generate_until + - bigbench_multiemo_generate_until + - bigbench_multiemo_multiple_choice + - bigbench_multiple_choice_a + - bigbench_multiple_choice_b + - bigbench_natural_instructions_generate_until + - bigbench_navigate_generate_until + - bigbench_navigate_multiple_choice + - bigbench_nonsense_words_grammar_generate_until + - bigbench_nonsense_words_grammar_multiple_choice + - bigbench_novel_concepts_generate_until + - bigbench_novel_concepts_multiple_choice + - bigbench_object_counting_generate_until + - bigbench_odd_one_out_generate_until + - bigbench_odd_one_out_multiple_choice + - bigbench_operators_generate_until + - bigbench_paragraph_segmentation_generate_until + - bigbench_parsinlu_qa_generate_until + - bigbench_parsinlu_qa_multiple_choice + - bigbench_parsinlu_reading_comprehension_generate_until + - bigbench_penguins_in_a_table_generate_until + - bigbench_penguins_in_a_table_multiple_choice + - bigbench_periodic_elements_generate_until + - bigbench_periodic_elements_multiple_choice + - bigbench_persian_idioms_generate_until + - bigbench_persian_idioms_multiple_choice + - bigbench_phrase_relatedness_generate_until + - bigbench_phrase_relatedness_multiple_choice + - bigbench_physical_intuition_generate_until + - bigbench_physical_intuition_multiple_choice + - bigbench_physics_generate_until + - bigbench_physics_multiple_choice + - bigbench_physics_questions_generate_until + - bigbench_play_dialog_same_or_different_generate_until + - bigbench_play_dialog_same_or_different_multiple_choice + - bigbench_polish_sequence_labeling_generate_until + - bigbench_presuppositions_as_nli_generate_until + - bigbench_presuppositions_as_nli_multiple_choice + - bigbench_qa_wikidata_generate_until + - bigbench_question_selection_generate_until + - bigbench_question_selection_multiple_choice + - bigbench_real_or_fake_text_generate_until + - bigbench_real_or_fake_text_multiple_choice + - bigbench_reasoning_about_colored_objects_generate_until + - bigbench_reasoning_about_colored_objects_multiple_choice + - bigbench_repeat_copy_logic_generate_until + - bigbench_rephrase_generate_until + - bigbench_riddle_sense_generate_until + - bigbench_riddle_sense_multiple_choice + - bigbench_ruin_names_generate_until + - bigbench_ruin_names_multiple_choice + - bigbench_salient_translation_error_detection_generate_until + - bigbench_salient_translation_error_detection_multiple_choice + - bigbench_scientific_press_release_generate_until + - bigbench_semantic_parsing_in_context_sparc_generate_until + - bigbench_semantic_parsing_spider_generate_until + - bigbench_sentence_ambiguity_generate_until + - bigbench_sentence_ambiguity_multiple_choice + - bigbench_similarities_abstraction_generate_until + - bigbench_similarities_abstraction_multiple_choice + - bigbench_simp_turing_concept_generate_until + - bigbench_simple_arithmetic_json_generate_until + - bigbench_simple_arithmetic_json_multiple_choice_generate_until + - bigbench_simple_arithmetic_json_subtasks_generate_until + - bigbench_simple_arithmetic_multiple_targets_json_generate_until + - bigbench_simple_ethical_questions_generate_until + - bigbench_simple_ethical_questions_multiple_choice + - bigbench_simple_text_editing_generate_until + - bigbench_snarks_generate_until + - bigbench_snarks_multiple_choice + - bigbench_social_iqa_generate_until + - bigbench_social_iqa_multiple_choice + - bigbench_social_support_generate_until + - bigbench_social_support_multiple_choice + - bigbench_sports_understanding_generate_until + - bigbench_sports_understanding_multiple_choice + - bigbench_strange_stories_generate_until + - bigbench_strange_stories_multiple_choice + - bigbench_strategyqa_generate_until + - bigbench_strategyqa_multiple_choice + - bigbench_sufficient_information_generate_until + - bigbench_suicide_risk_generate_until + - bigbench_suicide_risk_multiple_choice + - bigbench_swahili_english_proverbs_generate_until + - bigbench_swahili_english_proverbs_multiple_choice + - bigbench_swedish_to_german_proverbs_generate_until + - bigbench_swedish_to_german_proverbs_multiple_choice + - bigbench_symbol_interpretation_generate_until + - bigbench_symbol_interpretation_multiple_choice + - bigbench_temporal_sequences_generate_until + - bigbench_temporal_sequences_multiple_choice + - bigbench_tense_generate_until + - bigbench_timedial_generate_until + - bigbench_timedial_multiple_choice + - bigbench_topical_chat_generate_until + - bigbench_tracking_shuffled_objects_generate_until + - bigbench_tracking_shuffled_objects_multiple_choice + - bigbench_understanding_fables_generate_until + - bigbench_understanding_fables_multiple_choice + - bigbench_undo_permutation_generate_until + - bigbench_undo_permutation_multiple_choice + - bigbench_unit_conversion_generate_until + - bigbench_unit_conversion_multiple_choice + - bigbench_unit_interpretation_generate_until + - bigbench_unit_interpretation_multiple_choice + - bigbench_unnatural_in_context_learning_generate_until + - bigbench_vitaminc_fact_verification_generate_until + - bigbench_vitaminc_fact_verification_multiple_choice + - bigbench_what_is_the_tao_generate_until + - bigbench_what_is_the_tao_multiple_choice + - bigbench_which_wiki_edit_generate_until + - bigbench_which_wiki_edit_multiple_choice + - bigbench_winowhy_generate_until + - bigbench_winowhy_multiple_choice + - bigbench_word_sorting_generate_until + - bigbench_word_unscrambling_generate_until \ No newline at end of file From a26cf5cd96939b811659029186d60d5f07ef24b9 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 11 Feb 2026 11:19:03 +0100 Subject: [PATCH 13/94] style: ruff format --- GoT/__init__.py | 10 +-- GoT/model/graph_model.py | 13 +++- GoT/model/lm_wrapper.py | 124 +++++++++++++++++++------------------ GoT/model/ollama_llm.py | 17 ++++- GoT/model/runtime_graph.py | 7 ++- GoT/model/utils/utils.py | 16 ++--- GoT/tools/math_tool.py | 3 +- 7 files changed, 110 insertions(+), 80 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index 0699ba8..a1d915f 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -7,6 +7,7 @@ logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("GoT") + def main(): # tm = tasks.TaskManager() # all_tasks = tm.all_tasks @@ -24,14 +25,15 @@ def main(): results = evaluator.evaluate( lm=lm, task_dict=task_dict, - limit=1, # Limit to 3 samples for quick testing - log_samples=True, - verbosity="INFO" + limit=1, # Limit to 3 samples for quick testing + log_samples=True, + verbosity="INFO", ) # --- Salva in JSON --- with open("graph_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) + # let this be the last line of this file -logger.info("GoT loaded") \ No newline at end of file +logger.info("GoT loaded") diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index d947288..f93d650 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -45,7 +45,7 @@ "5: The response follow the instruction and the result is near to the solution (If the task is hard, the solution should be near to the corrected one). " "6: The response follow the instruction and the result is perfectly correct." ), - response_format=Score + response_format=Score, ) # Defining runtime graph @@ -187,9 +187,16 @@ def invoke_graph(): graph = graph.compile() logger.info(graph.get_graph().draw_mermaid()) - + res = graph.invoke( - {"messages": [{"role": "user", "content": "Please, solve this problem: What is the capital of France?"}]} + { + "messages": [ + { + "role": "user", + "content": "Please, solve this problem: What is the capital of France?", + } + ] + } ) logger.info(res) diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index b295a6c..5aeae8e 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -5,6 +5,7 @@ from GoT.model.utils.utils import extract_output + class LangGraphLM: def __init__(self): pass @@ -20,11 +21,11 @@ def generate(self, requests, max_new_tokens=None): result = invoke_graph(prompt) # usa la tua funzione outputs.append(result["output"]) return outputs - + + # Register your custom model with lm_eval @register_model("langgraph_lm") class LangGraphLMWrapper(LM): - def __init__(self, model_args=""): super().__init__() self.lm = LangGraphLM() @@ -38,24 +39,26 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): try: # Extract the question directly from request.doc # This is much simpler than parsing the full prompt - if hasattr(request, 'doc') and isinstance(request.doc, dict): - question = request.doc.get('question', '') + if hasattr(request, "doc") and isinstance(request.doc, dict): + question = request.doc.get("question", "") else: # Fallback: try to extract from arguments - if hasattr(request, 'arguments') and request.arguments: + if hasattr(request, "arguments") and request.arguments: full_prompt = request.arguments[0][0] # Just take everything after the last "Answer:" as the question last_answer_idx = full_prompt.rfind("Answer:") if last_answer_idx != -1: - question = full_prompt[last_answer_idx + 8:].strip() # 8 = len("Answer:") + question = full_prompt[ + last_answer_idx + 8 : + ].strip() # 8 = len("Answer:") else: question = full_prompt else: question = str(request) - + # Call invoke_graph with just the question result = invoke_graph(question) - + # Extract the final answer from the result output = "" if isinstance(result, dict) and "messages" in result: @@ -63,33 +66,34 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): if messages: # The last message should be the response last_msg = messages[-1] - + # Extract content from message - if hasattr(last_msg, 'content'): + if hasattr(last_msg, "content"): output = last_msg.content - elif isinstance(last_msg, dict) and 'content' in last_msg: - output = last_msg['content'] + elif isinstance(last_msg, dict) and "content" in last_msg: + output = last_msg["content"] else: output = str(last_msg) else: output = str(result) if result else "" - + outputs.append(output if output else "") - + except Exception as e: print(f"Error in request {i}: {e}") import traceback + traceback.print_exc() outputs.append("") - + return outputs def loglikelihood(self, requests): return super().loglikelihood(requests) - + def loglikelihood_rolling(self, requests): return super().loglikelihood_rolling(requests) - + @register_model("langgraph_bigbench") class LangGraphBigBenchWrapper(LM): @@ -107,28 +111,28 @@ def _extract_text_from_request(self, request): BigBench passa il prompt completo in doc['inputs']. """ # Prova con request.doc - BigBench usa il campo 'inputs' - if hasattr(request, 'doc') and isinstance(request.doc, dict): + if hasattr(request, "doc") and isinstance(request.doc, dict): doc = request.doc - + # BigBench mette il prompt completo in 'inputs' - if 'inputs' in doc and doc['inputs']: - prompt = str(doc['inputs']).strip() + if "inputs" in doc and doc["inputs"]: + prompt = str(doc["inputs"]).strip() return prompt - + # Fallback per altri campi - for field in ['input', 'question', 'text', 'prompt', 'instruction']: + for field in ["input", "question", "text", "prompt", "instruction"]: if field in doc and doc[field]: return str(doc[field]) - + # Fallback: prova con request.arguments - if hasattr(request, 'arguments') and request.arguments: + if hasattr(request, "arguments") and request.arguments: try: full_prompt = request.arguments[0][0] if full_prompt and len(str(full_prompt).strip()) > 1: return full_prompt except (IndexError, TypeError): pass - + return str(request) def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): @@ -137,53 +141,54 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): Usato dalla maggior parte dei task di BigBench. """ outputs = [] - + # Se until non è specificato, usa default sensati if not until: until = ["\n\n", "\n"] elif isinstance(until, str): until = [until] - + print(f"DEBUG - Using stopping tokens: {until}") - + for i, request in enumerate(requests): try: question = self._extract_text_from_request(request) - + # Chiama il tuo grafo LangGraph result = invoke_graph(question) - + # Estrai l'output output = extract_output(result) - + # Taglia l'output al primo stopping token if output and isinstance(output, str): for stop_seq in until: if stop_seq in output: - output = output[:output.index(stop_seq)] - + output = output[: output.index(stop_seq)] + outputs.append(output.strip() if output else "") - + except Exception as e: print(f"Error in BigBench generate_until request {i}: {e}") import traceback + traceback.print_exc() outputs.append("") - + return outputs def loglikelihood(self, requests): """ Calcola la log-likelihood per BigBench multiple choice tasks. - + Riceve una lista di tuple: (context, continuation) Restituisce lista di tuple: (log_likelihood_score, is_greedy) - + NOTA: Poiché invoke_graph non fornisce logits, usiamo un'euristica. Se hai bisogno di precision massima, modifica invoke_graph per restituire logits. """ outputs = [] - + for i, request in enumerate(requests): try: # Estrai context e continuation @@ -193,29 +198,28 @@ def loglikelihood(self, requests): # Fallback context = "" continuation = str(request) - + # Prepara il prompt per il modello full_text = context + continuation - + # Chiama il grafo result = invoke_graph(full_text) generated_output = extract_output(result) - + # Calcola il score di likelihood score = self._calculate_likelihood_score( - generated_output, - continuation, - context + generated_output, continuation, context ) - + outputs.append((score, False)) - + except Exception as e: print(f"Error in BigBench loglikelihood request {i}: {e}") import traceback + traceback.print_exc() - outputs.append((float('-inf'), False)) - + outputs.append((float("-inf"), False)) + return outputs def loglikelihood_rolling(self, requests): @@ -225,41 +229,43 @@ def loglikelihood_rolling(self, requests): """ return self.loglikelihood(requests) - def _calculate_likelihood_score(self, generated_text, target_continuation, context=""): + def _calculate_likelihood_score( + self, generated_text, target_continuation, context="" + ): """ Calcola un score di likelihood per BigBench. - + Strategie: 1. Se la generazione contiene esattamente la continuation target -> score alto (0.0) 2. Se contiene la continuation parzialmente -> score medio 3. Altrimenti -> score basso (log della similarità) - + Questo è un workaround poiché non abbiamo i veri logits. """ gen_text = str(generated_text).strip().lower() target = str(target_continuation).strip().lower() - + # Caso 1: Match esatto if gen_text == target: return 0.0 # log(1) = 0, massima likelihood - + # Caso 2: Target è contenuto nel testo generato if target in gen_text: # Parziale match, score moderato return math.log(0.8) - + # Caso 3: Calcola sovrapposizione di parole gen_words = set(gen_text.split()) target_words = set(target.split()) - + if not target_words: return 0.0 - + overlap = len(gen_words & target_words) / len(target_words) - + if overlap > 0: # Maggiore è l'overlap, più alto lo score return math.log(overlap) else: # Nessuna sovrapposizione - return float('-inf') + return float("-inf") diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 59a2a51..f61226c 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -3,7 +3,14 @@ from langchain.agents import create_agent import mlflow -from GoT.tools.math_tool import multiply, sum_four, sum_three, summing, minus, square_root +from GoT.tools.math_tool import ( + multiply, + sum_four, + sum_three, + summing, + minus, + square_root, +) mlflow.set_experiment("marcoraggini-experiment") mlflow.openai.autolog() @@ -14,6 +21,7 @@ ... """ + class OllamaLLM: def __init__(self): with mlflow.set_active_model(name="ollama-agent-ministral-3"): @@ -35,11 +43,14 @@ def get_tools(self): return [sum_four, summing, minus, sum_three, square_root, multiply] def create_custom_agent( - self, tools, system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL), response_format=None + self, + tools, + system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL), + response_format=None, ): return create_agent( model=self.ollamaLLM, tools=tools, system_prompt=system_prompt, - response_format=response_format + response_format=response_format, ) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index ab849b8..bfc4c44 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -45,16 +45,19 @@ def __init__( super().__init__(prompt, response, type, resolved) self.score = score + class Score(BaseModel): - """ Rapresents a score for a test node. + """Rapresents a score for a test node. Attributes: score: int - The score assigned to the test node. description: str - A description or rationale for the assigned score. """ - score: int + + score: int description: str + class RuntimeGraph: def __init__(self): self.goal: MessagesState = MessagesState(messages=[]) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 0761812..7bfc9cc 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -53,7 +53,7 @@ def parse_score(response: MessagesState) -> Score: score = response.get("structured_response") if isinstance(score, Score): return score - else: + else: return Score(score=0, description="Failed to parse score") @@ -67,6 +67,7 @@ def remove_tools_from_list(tool_list, tools_to_remove): result = [tool for tool in tool_list if tool.func.__name__ not in tools_to_remove] return result + def extract_output(result) -> str: """ Extracts the output from the result of invoke_graph. @@ -75,15 +76,14 @@ def extract_output(result) -> str: if isinstance(result, dict): if "output" in result and result["output"]: return str(result["output"]) - + if "messages" in result and result["messages"]: messages = result["messages"] last_msg = messages[-1] - - if hasattr(last_msg, 'content'): + + if hasattr(last_msg, "content"): return str(last_msg.content) - elif isinstance(last_msg, dict) and 'content' in last_msg: - return str(last_msg['content']) - - return str(result) if result else "" + elif isinstance(last_msg, dict) and "content" in last_msg: + return str(last_msg["content"]) + return str(result) if result else "" diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py index fdd5b4c..2b27a09 100644 --- a/GoT/tools/math_tool.py +++ b/GoT/tools/math_tool.py @@ -25,9 +25,10 @@ def minus(x: int, y: int) -> int: """minus of two integer""" return x - y + @tool def multiply(x: int, y: int) -> int: - """ multiply of two integer""" + """multiply of two integer""" return x * y From 1820825ac33b7aa4dc2af01b7c900d490212afc7 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 11 Feb 2026 14:23:21 +0100 Subject: [PATCH 14/94] chore: add a node for mermaid graph --- GoT/model/graph_model.py | 30 +++++++++++++++++++++--------- 1 file changed, 21 insertions(+), 9 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index f93d650..4c3b91d 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -115,12 +115,10 @@ def tool_call(messages: MessagesState): runtime_graph.add_node(test_node) runtime_graph.add_edge(call_node, test_node) runtime_graph.temp_node = test_node - return messages["messages"].append(AIMessage(res)) - - -def test_result(result_msg: MessagesState): - n = runtime_graph.exist_tool_available() + messages["messages"].append(AIMessage(res)) + return messages +def response_evaluation(messages: MessagesState): # Get the actual tool execution result from the resolved call_node test_node = runtime_graph.temp_node if not isinstance(test_node, TestNode): @@ -140,6 +138,14 @@ def test_result(result_msg: MessagesState): test_node.score = score_res.score runtime_graph.resolve_node(test_node, AIMessage(score_res.description)) + return messages + +def test_result(messages: MessagesState): + n = runtime_graph.exist_tool_available() + test_node = runtime_graph.temp_node + if not isinstance(test_node, TestNode): + raise TypeError("Expected TestNode for scoring") + if test_node.score < 5 and n is True: return "backtrack" elif n is False: @@ -170,19 +176,25 @@ def chat_completition(messages: MessagesState): # https://docs.langchain.com/oss/python/langgraph/overview -def invoke_graph(): +def invoke_graph(content: str): graph = StateGraph(MessagesState) graph.add_node(goal) graph.add_node(tool_expand) graph.add_node(tool_call) graph.add_node(backtrack) graph.add_node(chat_completition) + graph.add_node(response_evaluation) graph.add_edge(START, "goal") graph.add_edge("goal", "tool_expand") graph.add_edge("tool_expand", "tool_call") - graph.add_conditional_edges("tool_call", test_result) + graph.add_edge("tool_call", "response_evaluation") graph.add_edge("backtrack", "tool_call") graph.add_edge("chat_completition", END) + graph.add_conditional_edges("response_evaluation", test_result, { + "backtrack": "backtrack", + "chat_completition": "chat_completition", + END: END + }) graph = graph.compile() @@ -193,12 +205,12 @@ def invoke_graph(): "messages": [ { "role": "user", - "content": "Please, solve this problem: What is the capital of France?", + "content": content, } ] } ) - logger.info(res) + # logger.info(res) return res From f32a2ffa5025b213c4bad5c384d30106a7561980 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 11 Feb 2026 15:37:33 +0100 Subject: [PATCH 15/94] feat: TestNode now check the tools used --- GoT/model/graph_model.py | 46 ++++++++++++++++++++------------------ GoT/model/runtime_graph.py | 26 ++++++++++++++------- GoT/model/utils/utils.py | 16 +++++++++++++ 3 files changed, 58 insertions(+), 30 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 4c3b91d..00937cd 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -4,8 +4,9 @@ from langgraph.graph import StateGraph, MessagesState, START, END from GoT.model.ollama_llm import OllamaLLM -from GoT.model.runtime_graph import RuntimeGraph, RuntimeNode, Score, TestNode +from GoT.model.runtime_graph import RuntimeGraph, RuntimeNode, Score, TestNode, ToolNode from GoT.model.utils.utils import ( + extract_tool_used, parse_response, parse_score, parse_tool_list, @@ -35,15 +36,14 @@ "You are an assistant specialized in validation of response, like an LLM-as-a-judge. " "Your duty is to score, from 0 to 6, the response that user gives to you and assign to it a score. " "Your response MUST be a description of the score and then the" - "output format should be: " - "Score: \n" - "0: The response is impossible to understand and completely wrong. " - "1: The response is near to be completely wrong. " - "2: The response is in the correct language but it doesn't follow the instruction. " - "3: The response try to resolve the problem but doesn't follow the instruction or the response is wrong. " - "4: The response follow the instruction but the result is wrong or the result is correct but doesn't follow the instruction. " - "5: The response follow the instruction and the result is near to the solution (If the task is hard, the solution should be near to the corrected one). " - "6: The response follow the instruction and the result is perfectly correct." + "Output format should use Score class" + "\n0: The response is impossible to understand and completely wrong. " + "\n1: The response is near to be completely wrong. " + "\n2: The response is in the correct language but it doesn't follow the instruction. " + "\n3: The response try to resolve the problem but doesn't follow the instruction or the response is wrong. " + "\n4: The response follow the instruction but the result is wrong or the result is correct but doesn't follow the instruction. " + "\n5: The response follow the instruction and the result is near to the solution (If the task is hard, the solution should be near to the corrected one). " + "\n6: The response follow the instruction and the result is perfectly correct." ), response_format=Score, ) @@ -70,16 +70,15 @@ def tool_expand(goal: MessagesState): # add tool nodes in the runtime graph for tool in tool_list: tool_node = RuntimeNode( - SystemMessage(sys_msg), AIMessage(tool), type="tool", resolved=True + SystemMessage(sys_msg), AIMessage(tool), resolved=True ) - call_node = RuntimeNode( + call_node = ToolNode( SystemMessage( "Please, resolve the problem with the tool: " + tool - + ". You can't use other tools!" ), AIMessage(""), - type="call_tool", + tool_name=tool, ) runtime_graph.add_node(tool_node) runtime_graph.add_node(call_node) @@ -102,20 +101,23 @@ def tool_call(messages: MessagesState): " the tool that the user indicates to you. You MUST use the tool that user indicates to you. " ), ) - res = parse_response(tool_agent.invoke(messages)) - runtime_graph.resolve_node(call_node, AIMessage(res)) + + res = tool_agent.invoke(messages) + tool_used = extract_tool_used(res) + parsed_res = parse_response(res) + runtime_graph.resolve_node(call_node, AIMessage(parsed_res)) # Add test node test_node = TestNode( - SystemMessage("Score this solution: \n" + res), + SystemMessage("Score this solution: \n" + parsed_res), AIMessage(""), - type="test", score=0, + tool_used=tool_used ) runtime_graph.add_node(test_node) runtime_graph.add_edge(call_node, test_node) runtime_graph.temp_node = test_node - messages["messages"].append(AIMessage(res)) + messages["messages"].append(AIMessage(parsed_res)) return messages def response_evaluation(messages: MessagesState): @@ -130,8 +132,9 @@ def response_evaluation(messages: MessagesState): HumanMessage(content=parse_response(runtime_graph.goal)), HumanMessage(content=call_node_response), SystemMessage( - content="Score this solution based on correctness and following instructions. Remember that you can't verify the usage of the tools." + content="Score this solution based on correctness and following instructions." ), + AIMessage(content="Tool used in the response: " + ", ".join(test_node.tool_used)) ] score_res = parse_score(judge_agent.invoke({"messages": judge_messages})) @@ -152,7 +155,6 @@ def test_result(messages: MessagesState): chat_completition_node = RuntimeNode( SystemMessage("Please, solve this problem"), AIMessage(""), - type="chat_completition", ) runtime_graph.add_node(chat_completition_node) runtime_graph.temp_node = chat_completition_node @@ -198,7 +200,7 @@ def invoke_graph(content: str): graph = graph.compile() - logger.info(graph.get_graph().draw_mermaid()) + # logger.info(graph.get_graph().draw_mermaid()) res = graph.invoke( { diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index bfc4c44..b86acb7 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -11,14 +11,12 @@ def __init__( self, prompt: SystemMessage, response: AIMessage, - type: str, resolved: bool = False, ): self.id = RuntimeNode._id_counter # ID unico per ogni nodo RuntimeNode._id_counter += 1 self.prompt = prompt self.response = response - self.type = type self.resolved = resolved def __hash__(self): @@ -30,7 +28,7 @@ def __eq__(self, other): return self.id == other.id def __repr__(self): - return f"RuntimeNode(id={self.id}, type={self.type}, resolved={self.resolved})" + return f"RuntimeNode(id={self.id}, resolved={self.resolved})" class TestNode(RuntimeNode): @@ -38,12 +36,24 @@ def __init__( self, prompt: SystemMessage, response: AIMessage, - type: str, score: int, + tool_used: List[str] = [], resolved: bool = False, ): - super().__init__(prompt, response, type, resolved) + super().__init__(prompt, response, resolved) self.score = score + self.tool_used = tool_used + +class ToolNode(RuntimeNode): + def __init__( + self, + prompt: SystemMessage, + response: AIMessage, + tool_name: str, + resolved: bool = False, + ): + super().__init__(prompt, response, resolved) + self.tool_name = tool_name class Score(BaseModel): @@ -64,7 +74,7 @@ def __init__(self): self.nodes: Dict[RuntimeNode, List[RuntimeNode]] = {} self.tools_available: Dict[RuntimeNode, str] = {} self.temp_node: RuntimeNode = RuntimeNode( - SystemMessage(""), AIMessage(""), type="goal" + SystemMessage(""), AIMessage("") ) def add_node(self, node: RuntimeNode): @@ -84,14 +94,14 @@ def resolve_node(self, node: RuntimeNode, response: AIMessage) -> None: def call_tool_node(self) -> RuntimeNode: # TODO change name nodes = list(self.nodes.keys()) call_nodes = [ - n for n in nodes if (n.type.startswith("call_tool") and not n.resolved) + n for n in nodes if (isinstance(n, ToolNode) and not n.resolved) ] return call_nodes[0] def exist_tool_available(self) -> bool: nodes = list(self.nodes.keys()) call_nodes = [ - n for n in nodes if (n.type.startswith("call_tool") and not n.resolved) + n for n in nodes if (isinstance(n, ToolNode) and not n.resolved) ] return True if call_nodes else False diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 7bfc9cc..4940883 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -2,6 +2,7 @@ from GoT.model.runtime_graph import Score from langgraph.graph import MessagesState +from langchain_core.messages import AIMessage def parse_response(res) -> str: @@ -55,7 +56,22 @@ def parse_score(response: MessagesState) -> Score: return score else: return Score(score=0, description="Failed to parse score") + +def extract_tool_used(response: MessagesState) -> list[str]: + """ + Extract the tool used from the response of the LLM, if present. + :param response: The LLM response + :type response: MessagesState + :return: The list of tools used + :rtype: list[str] + """ + tools_used = [] + for msg in response.get("messages", []): + if isinstance(msg, AIMessage): + for tool_call in msg.tool_calls: + tools_used.append(tool_call["name"]) + return tools_used def remove_tools_from_list(tool_list, tools_to_remove): """ From 05bb4a79f5024f89db574c789db6419c49d035b7 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 11 Feb 2026 16:13:17 +0100 Subject: [PATCH 16/94] feat: implement mermaid print for runtime graph --- GoT/model/graph_model.py | 32 +++++++++++----------- GoT/model/runtime_graph.py | 56 ++++++++++++++++++++++++++++++++------ GoT/model/utils/utils.py | 4 ++- 3 files changed, 66 insertions(+), 26 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 00937cd..8867f09 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -69,14 +69,9 @@ def tool_expand(goal: MessagesState): tool_list = parse_tool_list(str_res) # Toglie elementi inutili # add tool nodes in the runtime graph for tool in tool_list: - tool_node = RuntimeNode( - SystemMessage(sys_msg), AIMessage(tool), resolved=True - ) + tool_node = RuntimeNode(SystemMessage(sys_msg), AIMessage(tool), resolved=True) call_node = ToolNode( - SystemMessage( - "Please, resolve the problem with the tool: " - + tool - ), + SystemMessage("Please, resolve the problem with the tool: " + tool), AIMessage(""), tool_name=tool, ) @@ -112,7 +107,7 @@ def tool_call(messages: MessagesState): SystemMessage("Score this solution: \n" + parsed_res), AIMessage(""), score=0, - tool_used=tool_used + tool_used=tool_used, ) runtime_graph.add_node(test_node) runtime_graph.add_edge(call_node, test_node) @@ -120,6 +115,7 @@ def tool_call(messages: MessagesState): messages["messages"].append(AIMessage(parsed_res)) return messages + def response_evaluation(messages: MessagesState): # Get the actual tool execution result from the resolved call_node test_node = runtime_graph.temp_node @@ -134,7 +130,9 @@ def response_evaluation(messages: MessagesState): SystemMessage( content="Score this solution based on correctness and following instructions." ), - AIMessage(content="Tool used in the response: " + ", ".join(test_node.tool_used)) + AIMessage( + content="Tool used in the response: " + ", ".join(test_node.tool_used) + ), ] score_res = parse_score(judge_agent.invoke({"messages": judge_messages})) @@ -143,12 +141,13 @@ def response_evaluation(messages: MessagesState): return messages + def test_result(messages: MessagesState): n = runtime_graph.exist_tool_available() test_node = runtime_graph.temp_node if not isinstance(test_node, TestNode): raise TypeError("Expected TestNode for scoring") - + if test_node.score < 5 and n is True: return "backtrack" elif n is False: @@ -192,16 +191,17 @@ def invoke_graph(content: str): graph.add_edge("tool_call", "response_evaluation") graph.add_edge("backtrack", "tool_call") graph.add_edge("chat_completition", END) - graph.add_conditional_edges("response_evaluation", test_result, { - "backtrack": "backtrack", - "chat_completition": "chat_completition", - END: END - }) + graph.add_conditional_edges( + "response_evaluation", + test_result, + {"backtrack": "backtrack", "chat_completition": "chat_completition", END: END}, + ) graph = graph.compile() # logger.info(graph.get_graph().draw_mermaid()) + res = graph.invoke( { "messages": [ @@ -214,5 +214,5 @@ def invoke_graph(content: str): ) # logger.info(res) - + print(runtime_graph.print_mermaid()) return res diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index b86acb7..0ee5408 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -44,6 +44,7 @@ def __init__( self.score = score self.tool_used = tool_used + class ToolNode(RuntimeNode): def __init__( self, @@ -73,9 +74,7 @@ def __init__(self): self.goal: MessagesState = MessagesState(messages=[]) self.nodes: Dict[RuntimeNode, List[RuntimeNode]] = {} self.tools_available: Dict[RuntimeNode, str] = {} - self.temp_node: RuntimeNode = RuntimeNode( - SystemMessage(""), AIMessage("") - ) + self.temp_node: RuntimeNode = RuntimeNode(SystemMessage(""), AIMessage("")) def add_node(self, node: RuntimeNode): self.nodes.setdefault(node, []) @@ -93,16 +92,12 @@ def resolve_node(self, node: RuntimeNode, response: AIMessage) -> None: def call_tool_node(self) -> RuntimeNode: # TODO change name nodes = list(self.nodes.keys()) - call_nodes = [ - n for n in nodes if (isinstance(n, ToolNode) and not n.resolved) - ] + call_nodes = [n for n in nodes if (isinstance(n, ToolNode) and not n.resolved)] return call_nodes[0] def exist_tool_available(self) -> bool: nodes = list(self.nodes.keys()) - call_nodes = [ - n for n in nodes if (isinstance(n, ToolNode) and not n.resolved) - ] + call_nodes = [n for n in nodes if (isinstance(n, ToolNode) and not n.resolved)] return True if call_nodes else False def get_resolved_tools(self): @@ -118,3 +113,46 @@ def runtime_node_to_state( messages.append(node.prompt) return MessagesState(messages=messages) + + def print_mermaid(self) -> str: + lines = [] + + lines.append("---") + lines.append("config:") + lines.append(" flowchart:") + lines.append(" curve: linear") + lines.append("---") + lines.append("graph TD;") + + # Nodi (rettangoli con angoli arrotondati) + for node in self.nodes: + node_type = type(node).__name__ + label = f"{node.id}
{node_type}
" + if isinstance(node, TestNode): + label += f"Score: {node.score}
" + lines.append(f' {node.id}("{label}");') + + # Edge + for n1, children in self.nodes.items(): + for n2 in children: + lines.append(f" {n1.id} --> {n2.id};") + + # Stili + lines.append("") + lines.append(" classDef resolved fill:#b7f7c0,stroke:#2ecc71,stroke-width:2px;") + lines.append(" classDef rejected fill:#f7b7b7,stroke:#e74c3c,stroke-width:2px;") + lines.append(" classDef unresolved fill:#e0e0e0,stroke:#9e9e9e,stroke-width:2px;") + + for node in self.nodes: + cls = "resolved" if node.resolved else "unresolved" + lines.append(f" class {node.id} {cls};") + + return "\n".join(lines) + + def __get_color(self, node: RuntimeNode) -> str: + if node.resolved: + return "#b7f7c0" + elif isinstance(node, TestNode) and node.score < 5: + return "#f7b7b7" + else: + return "#e0e0e0" \ No newline at end of file diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 4940883..67641ec 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -56,7 +56,8 @@ def parse_score(response: MessagesState) -> Score: return score else: return Score(score=0, description="Failed to parse score") - + + def extract_tool_used(response: MessagesState) -> list[str]: """ Extract the tool used from the response of the LLM, if present. @@ -73,6 +74,7 @@ def extract_tool_used(response: MessagesState) -> list[str]: tools_used.append(tool_call["name"]) return tools_used + def remove_tools_from_list(tool_list, tools_to_remove): """ Remove a list of tools and return the updated list From 845d4429f7f6c6041954a7c341059be870ac2f29 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 12 Feb 2026 09:53:51 +0100 Subject: [PATCH 17/94] chore: improve and refine runtime graph --- GoT/model/graph_model.py | 47 +++++++++++++++++------- GoT/model/runtime_graph.py | 74 +++++++++++++++++++++++++------------- GoT/model/utils/utils.py | 2 +- 3 files changed, 86 insertions(+), 37 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 8867f09..43d8d7f 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -4,7 +4,15 @@ from langgraph.graph import StateGraph, MessagesState, START, END from GoT.model.ollama_llm import OllamaLLM -from GoT.model.runtime_graph import RuntimeGraph, RuntimeNode, Score, TestNode, ToolNode +from GoT.model.runtime_graph import ( + BackTrackNode, + GoalNode, + RuntimeGraph, + RuntimeNode, + Score, + TestNode, + ToolNode, +) from GoT.model.utils.utils import ( extract_tool_used, parse_response, @@ -54,6 +62,9 @@ def goal(prompt: MessagesState): runtime_graph.goal = prompt + goal_node = GoalNode(parse_response(prompt)) + runtime_graph.add_node(goal_node) + runtime_graph.temp_node = goal_node return prompt @@ -69,13 +80,16 @@ def tool_expand(goal: MessagesState): tool_list = parse_tool_list(str_res) # Toglie elementi inutili # add tool nodes in the runtime graph for tool in tool_list: - tool_node = RuntimeNode(SystemMessage(sys_msg), AIMessage(tool), resolved=True) + tool_node = RuntimeNode(resolved=True) call_node = ToolNode( - SystemMessage("Please, resolve the problem with the tool: " + tool), - AIMessage(""), + f"Please, resolve the problem with the tool: {tool}", + "", tool_name=tool, ) runtime_graph.add_node(tool_node) + runtime_graph.add_edge( + runtime_graph.temp_node, tool_node + ) # edge from goal to tool node runtime_graph.add_node(call_node) runtime_graph.add_edge(tool_node, call_node) runtime_graph.add_tool_link(call_node, tool) @@ -100,12 +114,12 @@ def tool_call(messages: MessagesState): res = tool_agent.invoke(messages) tool_used = extract_tool_used(res) parsed_res = parse_response(res) - runtime_graph.resolve_node(call_node, AIMessage(parsed_res)) + runtime_graph.resolve_node(call_node, parsed_res) # Add test node test_node = TestNode( - SystemMessage("Score this solution: \n" + parsed_res), - AIMessage(""), + f"Score this solution: \n{parsed_res}", + "", score=0, tool_used=tool_used, ) @@ -121,7 +135,7 @@ def response_evaluation(messages: MessagesState): test_node = runtime_graph.temp_node if not isinstance(test_node, TestNode): raise TypeError("Expected TestNode for scoring") - call_node_response = test_node.prompt.content # The actual solution to judge + call_node_response = test_node.prompt # The actual solution to judge # Create a proper message for the judge with the solution judge_messages = [ @@ -137,7 +151,7 @@ def response_evaluation(messages: MessagesState): score_res = parse_score(judge_agent.invoke({"messages": judge_messages})) test_node.score = score_res.score - runtime_graph.resolve_node(test_node, AIMessage(score_res.description)) + runtime_graph.resolve_node(test_node, score_res.description) return messages @@ -152,10 +166,11 @@ def test_result(messages: MessagesState): return "backtrack" elif n is False: chat_completition_node = RuntimeNode( - SystemMessage("Please, solve this problem"), - AIMessage(""), + "Please, solve this problem", + "", ) runtime_graph.add_node(chat_completition_node) + runtime_graph.add_edge(test_node, chat_completition_node) runtime_graph.temp_node = chat_completition_node return "chat_completition" else: @@ -163,7 +178,16 @@ def test_result(messages: MessagesState): def backtrack(messages: MessagesState): + test_node = runtime_graph.temp_node + if not isinstance(test_node, TestNode): + raise TypeError("Expected TestNode for backtracking") + backtrack_node = BackTrackNode(feedback=test_node.response, resolved=True) + runtime_graph.add_node(backtrack_node) + runtime_graph.add_edge(test_node, backtrack_node) runtime_graph.temp_node = runtime_graph.call_tool_node() + runtime_graph.add_edge( + backtrack_node, runtime_graph.temp_node + ) # tool call node that we want to resolve return runtime_graph.runtime_node_to_state(runtime_graph.temp_node) @@ -201,7 +225,6 @@ def invoke_graph(content: str): # logger.info(graph.get_graph().draw_mermaid()) - res = graph.invoke( { "messages": [ diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 0ee5408..4a3c08a 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -1,6 +1,6 @@ from typing import Dict, List from langgraph.graph import MessagesState -from langchain_core.messages import AIMessage, SystemMessage, AnyMessage +from langchain_core.messages import AnyMessage from pydantic import BaseModel @@ -9,14 +9,10 @@ class RuntimeNode: def __init__( self, - prompt: SystemMessage, - response: AIMessage, resolved: bool = False, ): self.id = RuntimeNode._id_counter # ID unico per ogni nodo RuntimeNode._id_counter += 1 - self.prompt = prompt - self.response = response self.resolved = resolved def __hash__(self): @@ -34,13 +30,15 @@ def __repr__(self): class TestNode(RuntimeNode): def __init__( self, - prompt: SystemMessage, - response: AIMessage, + prompt: str, + response: str, score: int, tool_used: List[str] = [], resolved: bool = False, ): - super().__init__(prompt, response, resolved) + super().__init__(resolved) + self.prompt = prompt + self.response = response self.score = score self.tool_used = tool_used @@ -48,15 +46,37 @@ def __init__( class ToolNode(RuntimeNode): def __init__( self, - prompt: SystemMessage, - response: AIMessage, + prompt: str, + response: str, tool_name: str, resolved: bool = False, ): - super().__init__(prompt, response, resolved) + super().__init__(resolved) + self.prompt = prompt + self.response = response self.tool_name = tool_name +class GoalNode(RuntimeNode): + def __init__( + self, + prompt: str, + resolved: bool = False, + ): + super().__init__(resolved) + self.prompt = prompt + + +class BackTrackNode(RuntimeNode): + def __init__( + self, + feedback: str, + resolved: bool = True, + ): + super().__init__(resolved) + self.feedback = feedback + + class Score(BaseModel): """Rapresents a score for a test node. @@ -74,7 +94,7 @@ def __init__(self): self.goal: MessagesState = MessagesState(messages=[]) self.nodes: Dict[RuntimeNode, List[RuntimeNode]] = {} self.tools_available: Dict[RuntimeNode, str] = {} - self.temp_node: RuntimeNode = RuntimeNode(SystemMessage(""), AIMessage("")) + self.temp_node: RuntimeNode = RuntimeNode() def add_node(self, node: RuntimeNode): self.nodes.setdefault(node, []) @@ -86,7 +106,7 @@ def add_edge(self, n1: RuntimeNode, n2: RuntimeNode): def add_tool_link(self, call_node: RuntimeNode, tool_name: str): self.tools_available.setdefault(call_node, tool_name) - def resolve_node(self, node: RuntimeNode, response: AIMessage) -> None: + def resolve_node(self, node: RuntimeNode, response: str) -> None: node.response = response node.resolved = True @@ -113,7 +133,7 @@ def runtime_node_to_state( messages.append(node.prompt) return MessagesState(messages=messages) - + def print_mermaid(self) -> str: lines = [] @@ -139,20 +159,26 @@ def print_mermaid(self) -> str: # Stili lines.append("") - lines.append(" classDef resolved fill:#b7f7c0,stroke:#2ecc71,stroke-width:2px;") - lines.append(" classDef rejected fill:#f7b7b7,stroke:#e74c3c,stroke-width:2px;") - lines.append(" classDef unresolved fill:#e0e0e0,stroke:#9e9e9e,stroke-width:2px;") + lines.append( + " classDef resolved fill:#b7f7c0,stroke:#2ecc71,stroke-width:2px;" + ) + lines.append( + " classDef rejected fill:#f7b7b7,stroke:#e74c3c,stroke-width:2px;" + ) + lines.append( + " classDef unresolved fill:#e0e0e0,stroke:#9e9e9e,stroke-width:2px;" + ) for node in self.nodes: - cls = "resolved" if node.resolved else "unresolved" + cls = self.__get_color(node) lines.append(f" class {node.id} {cls};") return "\n".join(lines) - + def __get_color(self, node: RuntimeNode) -> str: - if node.resolved: - return "#b7f7c0" - elif isinstance(node, TestNode) and node.score < 5: - return "#f7b7b7" + if isinstance(node, TestNode) and node.score < 5: + return "rejected" + elif node.resolved: + return "resolved" else: - return "#e0e0e0" \ No newline at end of file + return "unresolved" diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 67641ec..f6d477a 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -35,7 +35,7 @@ def parse_tool_list(response: str) -> list[str]: content = re.sub(r"^[\*\-]\s+", "", line) # Extract just the first word (the tool name) tool_name = content.split()[0] - if tool_name and counter <= 3: + if tool_name and counter <= 2: # Limit to 3 tools counter += 1 tool_list.append(tool_name) From 8852fbc3e5f35e2d062c9453f0fb688ddcd07ef6 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 13 Feb 2026 15:04:15 +0100 Subject: [PATCH 18/94] chore: add description on tools --- GoT/tools/math_tool.py | 46 +++++++++++++++++++++++++++++++++++------- 1 file changed, 39 insertions(+), 7 deletions(-) diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py index 2b27a09..bb71ee3 100644 --- a/GoT/tools/math_tool.py +++ b/GoT/tools/math_tool.py @@ -4,35 +4,67 @@ @tool def summing(x: int, y: int) -> int: - """sum of two integer""" + """sum of two integer + + Arguments: + x(int): first number + y(int): second number + """ return x + y @tool def sum_three(x: int, y: int, z: int): - """sum of three integer""" + """sum of three integer + + Arguments: + x(int): first number + y(int): second number + z(int): third number + """ return x + y + z @tool def sum_four(x, y, z, a): - """sum of four integer""" + """sum of four integer + + Arguments: + x(int): first number + y(int): second number + z(int): third number + a(int): fourth number + """ return x + y + z + a @tool def minus(x: int, y: int) -> int: - """minus of two integer""" + """minus of two integer + + Arguments: + x(int): first number + y(int): second number + """ return x - y @tool def multiply(x: int, y: int) -> int: - """multiply of two integer""" + """multiply of two integer + + Arguments: + x(int): first number + y(int): second number + """ return x * y @tool -def square_root(x): - """Calculate the square root of x""" +def square_root(x: int) -> float: + """Calculate the square root of x + + Arguments: + x(int): a non-negative number + """ return sqrt(x) From 3f2f5148c20ed42bc0b34d57d536e7b08ca5025b Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 13 Feb 2026 15:05:37 +0100 Subject: [PATCH 19/94] feat: add Response format and feedback on llm backtracking --- GoT/model/graph_model.py | 24 +++++++++++++++--------- GoT/model/runtime_graph.py | 36 +++++++++++++++++++++++++++++++++--- GoT/model/utils/utils.py | 21 ++++++++++++++++++++- 3 files changed, 68 insertions(+), 13 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 43d8d7f..44a3a28 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -5,8 +5,10 @@ from GoT.model.ollama_llm import OllamaLLM from GoT.model.runtime_graph import ( - BackTrackNode, + BacktrackNode, + CompletitionNode, GoalNode, + Response, RuntimeGraph, RuntimeNode, Score, @@ -16,6 +18,7 @@ from GoT.model.utils.utils import ( extract_tool_used, parse_response, + parse_response_for_tool_node, parse_score, parse_tool_list, remove_tools_from_list, @@ -44,7 +47,7 @@ "You are an assistant specialized in validation of response, like an LLM-as-a-judge. " "Your duty is to score, from 0 to 6, the response that user gives to you and assign to it a score. " "Your response MUST be a description of the score and then the" - "Output format should use Score class" + "Output format should use Score function" "\n0: The response is impossible to understand and completely wrong. " "\n1: The response is near to be completely wrong. " "\n2: The response is in the correct language but it doesn't follow the instruction. " @@ -62,7 +65,7 @@ def goal(prompt: MessagesState): runtime_graph.goal = prompt - goal_node = GoalNode(parse_response(prompt)) + goal_node = GoalNode(parse_response(prompt), resolved=True) runtime_graph.add_node(goal_node) runtime_graph.temp_node = goal_node return prompt @@ -95,7 +98,7 @@ def tool_expand(goal: MessagesState): runtime_graph.add_tool_link(call_node, tool) # extract a tool to call runtime_graph.temp_node = runtime_graph.call_tool_node() - return runtime_graph.runtime_node_to_state(runtime_graph.temp_node) + return runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) def tool_call(messages: MessagesState): @@ -107,13 +110,14 @@ def tool_call(messages: MessagesState): ), SystemMessage( "You are an assistant specialized in tools. Your goal is to resolve the problem with " - " the tool that the user indicates to you. You MUST use the tool that user indicates to you. " + " the tool that the user indicates to you. You MUST use the tool that user indicates to you and use the Response function to calculate the response. " ), + response_format=Response, ) res = tool_agent.invoke(messages) tool_used = extract_tool_used(res) - parsed_res = parse_response(res) + parsed_res = parse_response_for_tool_node(res).explanation runtime_graph.resolve_node(call_node, parsed_res) # Add test node @@ -165,7 +169,7 @@ def test_result(messages: MessagesState): if test_node.score < 5 and n is True: return "backtrack" elif n is False: - chat_completition_node = RuntimeNode( + chat_completition_node = CompletitionNode( "Please, solve this problem", "", ) @@ -181,14 +185,16 @@ def backtrack(messages: MessagesState): test_node = runtime_graph.temp_node if not isinstance(test_node, TestNode): raise TypeError("Expected TestNode for backtracking") - backtrack_node = BackTrackNode(feedback=test_node.response, resolved=True) + backtrack_node = BacktrackNode(feedback=test_node.response, resolved=True) runtime_graph.add_node(backtrack_node) runtime_graph.add_edge(test_node, backtrack_node) runtime_graph.temp_node = runtime_graph.call_tool_node() runtime_graph.add_edge( backtrack_node, runtime_graph.temp_node ) # tool call node that we want to resolve - return runtime_graph.runtime_node_to_state(runtime_graph.temp_node) + messages = runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) + MessagesState(messages).get("messages", []).append(AIMessage(backtrack_node.feedback)) + return messages def chat_completition(messages: MessagesState): diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 4a3c08a..7c2c461 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -67,7 +67,18 @@ def __init__( self.prompt = prompt -class BackTrackNode(RuntimeNode): +class CompletitionNode(RuntimeNode): + def __init__( + self, + prompt: str, + response: str, + resolved: bool = False, + ): + super().__init__(resolved) + self.prompt = prompt + self.response = response + +class BacktrackNode(RuntimeNode): def __init__( self, feedback: str, @@ -76,6 +87,15 @@ def __init__( super().__init__(resolved) self.feedback = feedback +class ContextNode(RuntimeNode): + def __init__( + self, + context: str, + resolved: bool = False, + ): + super().__init__(resolved) + self.context = context + class Score(BaseModel): """Rapresents a score for a test node. @@ -88,6 +108,16 @@ class Score(BaseModel): score: int description: str +class Response(BaseModel): + """Rapresents a response for a tool node. + + Attributes: + response: str - The synthethic response. + explanation: str - An explanation or rationale for the response. + """ + + response: str + explanation: str class RuntimeGraph: def __init__(self): @@ -110,7 +140,7 @@ def resolve_node(self, node: RuntimeNode, response: str) -> None: node.response = response node.resolved = True - def call_tool_node(self) -> RuntimeNode: # TODO change name + def call_tool_node(self) -> ToolNode: # TODO change name nodes = list(self.nodes.keys()) call_nodes = [n for n in nodes if (isinstance(n, ToolNode) and not n.resolved)] return call_nodes[0] @@ -124,7 +154,7 @@ def get_resolved_tools(self): resolved_nodes = [t for t in self.tools_available.keys() if t.resolved is True] return [self.tools_available[n] for n in resolved_nodes] - def runtime_node_to_state( + def append_prompt_to_messages_state( self, node: RuntimeNode ) -> MessagesState: # TODO change name messages: list[AnyMessage] = [] diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index f6d477a..3b7fc69 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -1,6 +1,6 @@ import re -from GoT.model.runtime_graph import Score +from GoT.model.runtime_graph import Response, Score from langgraph.graph import MessagesState from langchain_core.messages import AIMessage @@ -58,6 +58,25 @@ def parse_score(response: MessagesState) -> Score: return Score(score=0, description="Failed to parse score") +def parse_response_for_tool_node(response: MessagesState) -> Response: + """ + Parse LLM response to get the response for a tool node. + + :param response: The LLM response + :type response: MessagesState + :return: The response for the tool node + :rtype: Response + """ + structured_response = response.get("structured_response") + if isinstance(structured_response, Response): + return structured_response + else: + return Response( + response="Failed to parse response", + explanation="The structured response is not in the expected format.", + ) + + def extract_tool_used(response: MessagesState) -> list[str]: """ Extract the tool used from the response of the LLM, if present. From 9ee7f9425a1d65bcb4f7b139ef2f417854de0434 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 13 Feb 2026 15:55:59 +0100 Subject: [PATCH 20/94] chore: improve compiled graph --- GoT/model/graph_model.py | 20 ++++++++++++++------ GoT/model/runtime_graph.py | 10 ++++++++++ 2 files changed, 24 insertions(+), 6 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 44a3a28..5cc8108 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -117,6 +117,7 @@ def tool_call(messages: MessagesState): res = tool_agent.invoke(messages) tool_used = extract_tool_used(res) + runtime_graph.temp_response.response = parse_response_for_tool_node(res).response parsed_res = parse_response_for_tool_node(res).explanation runtime_graph.resolve_node(call_node, parsed_res) @@ -166,9 +167,13 @@ def test_result(messages: MessagesState): if not isinstance(test_node, TestNode): raise TypeError("Expected TestNode for scoring") - if test_node.score < 5 and n is True: + if test_node.score >= 5: + runtime_graph.add_edge(test_node, runtime_graph.temp_response) + runtime_graph.temp_response.resolved = True + return END + elif test_node.score < 5 and n is True: return "backtrack" - elif n is False: + else: chat_completition_node = CompletitionNode( "Please, solve this problem", "", @@ -177,8 +182,6 @@ def test_result(messages: MessagesState): runtime_graph.add_edge(test_node, chat_completition_node) runtime_graph.temp_node = chat_completition_node return "chat_completition" - else: - return END def backtrack(messages: MessagesState): @@ -199,8 +202,11 @@ def backtrack(messages: MessagesState): def chat_completition(messages: MessagesState): new_messages_history = runtime_graph.goal - new_messages_history["messages"].append(runtime_graph.temp_node.prompt) - chat_completition_agent.invoke(new_messages_history) + new_messages_history["messages"].append(AIMessage(content=runtime_graph.temp_node.prompt)) + result = parse_response(chat_completition_agent.invoke(new_messages_history)) + runtime_graph.temp_response.response = result + runtime_graph.temp_response.resolved = True + new_messages_history["messages"].append(AIMessage(content=result)) return new_messages_history @@ -241,6 +247,8 @@ def invoke_graph(content: str): ] } ) + + res["output"] = runtime_graph.temp_response.response # logger.info(res) print(runtime_graph.print_mermaid()) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 7c2c461..3f29bb0 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -96,6 +96,15 @@ def __init__( super().__init__(resolved) self.context = context +class ResponseNode(RuntimeNode): + def __init__( + self, + response: str, + resolved: bool = False, + ): + super().__init__(resolved) + self.response = response + class Score(BaseModel): """Rapresents a score for a test node. @@ -125,6 +134,7 @@ def __init__(self): self.nodes: Dict[RuntimeNode, List[RuntimeNode]] = {} self.tools_available: Dict[RuntimeNode, str] = {} self.temp_node: RuntimeNode = RuntimeNode() + self.temp_response: ResponseNode = ResponseNode(response="", resolved=False) def add_node(self, node: RuntimeNode): self.nodes.setdefault(node, []) From 09451d98ce23c715231a25b99ae71791fe4a7a4c Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Feb 2026 10:01:04 +0100 Subject: [PATCH 21/94] chore: add clear of runtime graph --- GoT/model/graph_model.py | 1 + GoT/model/runtime_graph.py | 6 ++++++ 2 files changed, 7 insertions(+) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 5cc8108..f030ffb 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -252,4 +252,5 @@ def invoke_graph(content: str): # logger.info(res) print(runtime_graph.print_mermaid()) + runtime_graph.clear() return res diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 3f29bb0..8fbef19 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -173,6 +173,12 @@ def append_prompt_to_messages_state( messages.append(node.prompt) return MessagesState(messages=messages) + + def clear(self): + self.nodes = {} + self.tools_available = {} + self.temp_node = RuntimeNode() + self.temp_response = ResponseNode(response="", resolved=False) def print_mermaid(self) -> str: lines = [] From bfcc70754875bb150fcf22b1e4635645a5d2dd7f Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Feb 2026 11:19:31 +0100 Subject: [PATCH 22/94] chore: improve prompt --- GoT/model/graph_model.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index f030ffb..c2e8632 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -46,8 +46,9 @@ SystemMessage( "You are an assistant specialized in validation of response, like an LLM-as-a-judge. " "Your duty is to score, from 0 to 6, the response that user gives to you and assign to it a score. " - "Your response MUST be a description of the score and then the" - "Output format should use Score function" + "You MUST respond ONLY using the Score function. " + "Do not write natural language outside the function. " + "If you fail to respect the format, the evaluation will fail." "\n0: The response is impossible to understand and completely wrong. " "\n1: The response is near to be completely wrong. " "\n2: The response is in the correct language but it doesn't follow the instruction. " @@ -110,7 +111,10 @@ def tool_call(messages: MessagesState): ), SystemMessage( "You are an assistant specialized in tools. Your goal is to resolve the problem with " - " the tool that the user indicates to you. You MUST use the tool that user indicates to you and use the Response function to calculate the response. " + " the tool that the user indicates to you. You MUST use the tool that user indicates to you." + "You MUST respond ONLY using the Response function. " + "Do not write natural language outside the function. " + "If you fail to respect the format, the evaluation will fail." ), response_format=Response, ) @@ -123,7 +127,7 @@ def tool_call(messages: MessagesState): # Add test node test_node = TestNode( - f"Score this solution: \n{parsed_res}", + f"{parsed_res}", "", score=0, tool_used=tool_used, @@ -144,8 +148,8 @@ def response_evaluation(messages: MessagesState): # Create a proper message for the judge with the solution judge_messages = [ - HumanMessage(content=parse_response(runtime_graph.goal)), - HumanMessage(content=call_node_response), + HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), + HumanMessage(content="Solution:\n" + call_node_response), SystemMessage( content="Score this solution based on correctness and following instructions." ), From 36d7570d13e731805b3f4591f4bc0db01049bf39 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Feb 2026 11:30:07 +0100 Subject: [PATCH 23/94] style: ruff format --- GoT/model/graph_model.py | 19 +++++++++++++++---- GoT/model/lm_wrapper.py | 9 +++++---- GoT/model/runtime_graph.py | 7 ++++++- GoT/tools/math_tool.py | 12 ++++++------ 4 files changed, 32 insertions(+), 15 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index c2e8632..4794b6e 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -200,13 +200,17 @@ def backtrack(messages: MessagesState): backtrack_node, runtime_graph.temp_node ) # tool call node that we want to resolve messages = runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) - MessagesState(messages).get("messages", []).append(AIMessage(backtrack_node.feedback)) + MessagesState(messages).get("messages", []).append( + AIMessage(backtrack_node.feedback) + ) return messages def chat_completition(messages: MessagesState): new_messages_history = runtime_graph.goal - new_messages_history["messages"].append(AIMessage(content=runtime_graph.temp_node.prompt)) + new_messages_history["messages"].append( + AIMessage(content=runtime_graph.temp_node.prompt) + ) result = parse_response(chat_completition_agent.invoke(new_messages_history)) runtime_graph.temp_response.response = result runtime_graph.temp_response.resolved = True @@ -216,8 +220,15 @@ def chat_completition(messages: MessagesState): # https://docs.langchain.com/oss/python/langgraph/overview +content = "" + -def invoke_graph(content: str): +def set_prompt(prompt: str): + global content + content = prompt + + +def invoke_graph(): graph = StateGraph(MessagesState) graph.add_node(goal) graph.add_node(tool_expand) @@ -251,7 +262,7 @@ def invoke_graph(content: str): ] } ) - + res["output"] = runtime_graph.temp_response.response # logger.info(res) diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 5aeae8e..5a6ad2b 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -1,5 +1,5 @@ import math -from GoT.model.graph_model import invoke_graph +from GoT.model.graph_model import invoke_graph, set_prompt from lm_eval.api.registry import register_model from lm_eval.api.model import LM @@ -18,7 +18,8 @@ def generate(self, requests, max_new_tokens=None): outputs = [] for r in requests: prompt = r["prompt"] - result = invoke_graph(prompt) # usa la tua funzione + set_prompt(prompt) # Imposta il prompt globale + result = invoke_graph() # usa la tua funzione outputs.append(result["output"]) return outputs @@ -154,8 +155,8 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): try: question = self._extract_text_from_request(request) - # Chiama il tuo grafo LangGraph - result = invoke_graph(question) + set_prompt(question) + result = invoke_graph() # Estrai l'output output = extract_output(result) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 8fbef19..5a61114 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -78,6 +78,7 @@ def __init__( self.prompt = prompt self.response = response + class BacktrackNode(RuntimeNode): def __init__( self, @@ -87,6 +88,7 @@ def __init__( super().__init__(resolved) self.feedback = feedback + class ContextNode(RuntimeNode): def __init__( self, @@ -96,6 +98,7 @@ def __init__( super().__init__(resolved) self.context = context + class ResponseNode(RuntimeNode): def __init__( self, @@ -117,6 +120,7 @@ class Score(BaseModel): score: int description: str + class Response(BaseModel): """Rapresents a response for a tool node. @@ -128,6 +132,7 @@ class Response(BaseModel): response: str explanation: str + class RuntimeGraph: def __init__(self): self.goal: MessagesState = MessagesState(messages=[]) @@ -173,7 +178,7 @@ def append_prompt_to_messages_state( messages.append(node.prompt) return MessagesState(messages=messages) - + def clear(self): self.nodes = {} self.tools_available = {} diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py index bb71ee3..00ddd74 100644 --- a/GoT/tools/math_tool.py +++ b/GoT/tools/math_tool.py @@ -5,7 +5,7 @@ @tool def summing(x: int, y: int) -> int: """sum of two integer - + Arguments: x(int): first number y(int): second number @@ -16,7 +16,7 @@ def summing(x: int, y: int) -> int: @tool def sum_three(x: int, y: int, z: int): """sum of three integer - + Arguments: x(int): first number y(int): second number @@ -28,7 +28,7 @@ def sum_three(x: int, y: int, z: int): @tool def sum_four(x, y, z, a): """sum of four integer - + Arguments: x(int): first number y(int): second number @@ -41,7 +41,7 @@ def sum_four(x, y, z, a): @tool def minus(x: int, y: int) -> int: """minus of two integer - + Arguments: x(int): first number y(int): second number @@ -52,7 +52,7 @@ def minus(x: int, y: int) -> int: @tool def multiply(x: int, y: int) -> int: """multiply of two integer - + Arguments: x(int): first number y(int): second number @@ -63,7 +63,7 @@ def multiply(x: int, y: int) -> int: @tool def square_root(x: int) -> float: """Calculate the square root of x - + Arguments: x(int): a non-negative number """ From 92166931fff9001cc3587835473587d0503643f3 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Feb 2026 14:25:38 +0100 Subject: [PATCH 24/94] fix: correct mypy errors --- GoT/model/graph_model.py | 6 ++++-- GoT/model/runtime_graph.py | 13 +++++++------ 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 4794b6e..988af72 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -200,7 +200,7 @@ def backtrack(messages: MessagesState): backtrack_node, runtime_graph.temp_node ) # tool call node that we want to resolve messages = runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) - MessagesState(messages).get("messages", []).append( + messages.get("messages", []).append( AIMessage(backtrack_node.feedback) ) return messages @@ -208,10 +208,12 @@ def backtrack(messages: MessagesState): def chat_completition(messages: MessagesState): new_messages_history = runtime_graph.goal - new_messages_history["messages"].append( + if isinstance(runtime_graph.temp_node, CompletitionNode): + new_messages_history["messages"].append( AIMessage(content=runtime_graph.temp_node.prompt) ) result = parse_response(chat_completition_agent.invoke(new_messages_history)) + runtime_graph.resolve_node(runtime_graph.temp_node, result) runtime_graph.temp_response.response = result runtime_graph.temp_response.resolved = True new_messages_history["messages"].append(AIMessage(content=result)) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 5a61114..221358e 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -1,6 +1,6 @@ from typing import Dict, List from langgraph.graph import MessagesState -from langchain_core.messages import AnyMessage +from langchain_core.messages import AnyMessage, HumanMessage from pydantic import BaseModel @@ -152,10 +152,11 @@ def add_tool_link(self, call_node: RuntimeNode, tool_name: str): self.tools_available.setdefault(call_node, tool_name) def resolve_node(self, node: RuntimeNode, response: str) -> None: - node.response = response + if isinstance(node, (ToolNode, TestNode, CompletitionNode)): + node.response = response node.resolved = True - def call_tool_node(self) -> ToolNode: # TODO change name + def call_tool_node(self) -> ToolNode: nodes = list(self.nodes.keys()) call_nodes = [n for n in nodes if (isinstance(n, ToolNode) and not n.resolved)] return call_nodes[0] @@ -170,12 +171,12 @@ def get_resolved_tools(self): return [self.tools_available[n] for n in resolved_nodes] def append_prompt_to_messages_state( - self, node: RuntimeNode - ) -> MessagesState: # TODO change name + self, node: TestNode | ToolNode | CompletitionNode | GoalNode + ) -> MessagesState: messages: list[AnyMessage] = [] if node.prompt: - messages.append(node.prompt) + messages.append(HumanMessage(content=node.prompt)) return MessagesState(messages=messages) From 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"pytest-cov"] [[package]] name = "wcwidth" -version = "0.5.3" +version = "0.6.0" description = "Measures the displayed width of unicode strings in a terminal" optional = false python-versions = ">=3.8" files = [ - {file = "wcwidth-0.5.3-py3-none-any.whl", hash = "sha256:d584eff31cd4753e1e5ff6c12e1edfdb324c995713f75d26c29807bb84bf649e"}, - {file = "wcwidth-0.5.3.tar.gz", hash = "sha256:53123b7af053c74e9fe2e92ac810301f6139e64379031f7124574212fb3b4091"}, + {file = "wcwidth-0.6.0-py3-none-any.whl", hash = "sha256:1a3a1e510b553315f8e146c54764f4fb6264ffad731b3d78088cdb1478ffbdad"}, + {file = "wcwidth-0.6.0.tar.gz", hash = "sha256:cdc4e4262d6ef9a1a57e018384cbeb1208d8abbc64176027e2c2455c81313159"}, ] [[package]] @@ -6504,5 +6575,5 @@ cffi = ["cffi (>=1.17,<2.0)", "cffi (>=2.0.0b)"] [metadata] lock-version = "2.0" -python-versions = ">= 3.10.0 < 4.0.0" -content-hash = "3ec840ba6fbad47c7b415b96416ca87649648f5a5b583461165ce6397df1ae64" +python-versions = ">= 3.10.0 < 3.14.0" +content-hash = "cdb4e7e4e74c70816b35f04504b085a5cf976a18b9ff1cf375aec72e2bd30430" diff --git a/pyproject.toml b/pyproject.toml index 112d18f..325431e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -11,7 +11,7 @@ readme = "README.md" [tool.poetry.dependencies] # update the Python versions in .github/workflows/check.yml if you change this -python = ">= 3.10.0 < 4.0.0" +python = ">= 3.10.0 < 3.14.0" langchain = "^1.2.4" langchain-openai = "^1.1.7" langgraph = "^1.0.6" From 7b7c943688a814d63790b6ec88343a325d2b170f Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Feb 2026 15:52:55 +0100 Subject: [PATCH 26/94] fix: fix little errors --- GoT/model/graph_model.py | 6 +++--- GoT/model/lm_wrapper.py | 6 ++++-- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 988af72..d7feac9 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -122,7 +122,7 @@ def tool_call(messages: MessagesState): res = tool_agent.invoke(messages) tool_used = extract_tool_used(res) runtime_graph.temp_response.response = parse_response_for_tool_node(res).response - parsed_res = parse_response_for_tool_node(res).explanation + parsed_res = f"Response: {parse_response_for_tool_node(res).response}\nExplanation: {parse_response_for_tool_node(res).explanation}" runtime_graph.resolve_node(call_node, parsed_res) # Add test node @@ -252,7 +252,7 @@ def invoke_graph(): graph = graph.compile() - # logger.info(graph.get_graph().draw_mermaid()) + logger.info(graph.get_graph().draw_mermaid()) res = graph.invoke( { @@ -267,7 +267,7 @@ def invoke_graph(): res["output"] = runtime_graph.temp_response.response - # logger.info(res) + logger.info(res) print(runtime_graph.print_mermaid()) runtime_graph.clear() return res diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 5a6ad2b..332d0a6 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -58,7 +58,8 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): question = str(request) # Call invoke_graph with just the question - result = invoke_graph(question) + set_prompt(question) + result = invoke_graph() # Extract the final answer from the result output = "" @@ -204,7 +205,8 @@ def loglikelihood(self, requests): full_text = context + continuation # Chiama il grafo - result = invoke_graph(full_text) + set_prompt(full_text) + result = invoke_graph() generated_output = extract_output(result) # Calcola il score di likelihood From 9404a98956db8e4bf38212e4fb586408084b61c6 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Feb 2026 15:53:24 +0100 Subject: [PATCH 27/94] chore: create two function for benchmark or custom test --- GoT/__init__.py | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index a1d915f..9c2eca2 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -2,38 +2,37 @@ import logging from lm_eval import evaluator, tasks -from GoT.model.lm_wrapper import LangGraphBigBenchWrapper +from GoT.model.graph_model import invoke_graph, set_prompt +from GoT.model.lm_wrapper import LangGraphLMWrapper logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("GoT") -def main(): - # tm = tasks.TaskManager() - # all_tasks = tm.all_tasks - - # # Filtra solo i task di bigbench - # bigbench_tasks = [t for t in all_tasks if 'bigbench' in t.lower()] - - # print("Task di BigBench disponibili:") - # for task in sorted(bigbench_tasks): - # print(f" - {task}") - task_list = ["bigbench_arithmetic_generate_until"] - lm = LangGraphBigBenchWrapper() +def lm_eval_benchmark(): + task_list = ["gsm8k"] + lm = LangGraphLMWrapper() task_dict = tasks.get_task_dict(task_list) results = evaluator.evaluate( lm=lm, task_dict=task_dict, - limit=1, # Limit to 3 samples for quick testing + limit=5, # Limit to 2 samples for quick testing log_samples=True, - verbosity="INFO", ) - # --- Salva in JSON --- + # Save results to a JSON file with open("graph_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) +def custom_test(): + set_prompt("What is 4726621 + 2 * 392 - 3432?") + invoke_graph() + +def main(): + # It could be changed with custom_test() to test a custom problem instead of the benchmark + lm_eval_benchmark() + # let this be the last line of this file logger.info("GoT loaded") From 407de961c001a16462eb8f64085e9495bafc05b3 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Feb 2026 15:56:38 +0100 Subject: [PATCH 28/94] style: ruff format --- GoT/__init__.py | 2 ++ GoT/model/graph_model.py | 8 +++----- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index 9c2eca2..e6c9568 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -25,10 +25,12 @@ def lm_eval_benchmark(): with open("graph_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) + def custom_test(): set_prompt("What is 4726621 + 2 * 392 - 3432?") invoke_graph() + def main(): # It could be changed with custom_test() to test a custom problem instead of the benchmark lm_eval_benchmark() diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index d7feac9..8e4e673 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -200,9 +200,7 @@ def backtrack(messages: MessagesState): backtrack_node, runtime_graph.temp_node ) # tool call node that we want to resolve messages = runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) - messages.get("messages", []).append( - AIMessage(backtrack_node.feedback) - ) + messages.get("messages", []).append(AIMessage(backtrack_node.feedback)) return messages @@ -210,8 +208,8 @@ def chat_completition(messages: MessagesState): new_messages_history = runtime_graph.goal if isinstance(runtime_graph.temp_node, CompletitionNode): new_messages_history["messages"].append( - AIMessage(content=runtime_graph.temp_node.prompt) - ) + AIMessage(content=runtime_graph.temp_node.prompt) + ) result = parse_response(chat_completition_agent.invoke(new_messages_history)) runtime_graph.resolve_node(runtime_graph.temp_node, result) runtime_graph.temp_response.response = result From 4befe106f66df3d7aaed2bc5cf25105f31d2b95f Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 10:58:09 +0100 Subject: [PATCH 29/94] chore: add boto3 for mlflow --- poetry.lock | 68 +++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 68 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 3f5ed0f..231f1d6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -273,6 +273,44 @@ files = [ {file = "blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf"}, ] +[[package]] +name = "boto3" +version = "1.42.51" +description = "The AWS SDK for Python" +optional = false +python-versions = ">=3.9" +files = [ + {file = 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"python_version >= \"3.10\""} + +[package.extras] +crt = ["awscrt (==0.31.2)"] + [[package]] name = "build" version = "1.4.0" @@ -2105,6 +2143,17 @@ files = [ {file = "jiter-0.13.0.tar.gz", hash = "sha256:f2839f9c2c7e2dffc1bc5929a510e14ce0a946be9365fd1219e7ef342dae14f4"}, ] +[[package]] +name = "jmespath" +version = "1.1.0" +description = "JSON Matching Expressions" +optional = false +python-versions = ">=3.9" +files = [ + {file = "jmespath-1.1.0-py3-none-any.whl", hash = "sha256:a5663118de4908c91729bea0acadca56526eb2698e83de10cd116ae0f4e97c64"}, + {file = "jmespath-1.1.0.tar.gz", hash = "sha256:472c87d80f36026ae83c6ddd0f1d05d4e510134ed462851fd5f754c8c3cbb88d"}, +] + [[package]] name = "joblib" version = "1.5.3" @@ -5229,6 +5278,23 @@ files = [ {file = "ruff-0.14.14.tar.gz", hash = "sha256:2d0f819c9a90205f3a867dbbd0be083bee9912e170fd7d9704cc8ae45824896b"}, ] +[[package]] +name = "s3transfer" +version = "0.16.0" +description = "An Amazon S3 Transfer Manager" +optional = false +python-versions = ">=3.9" +files = [ + {file = "s3transfer-0.16.0-py3-none-any.whl", hash = "sha256:18e25d66fed509e3868dc1572b3f427ff947dd2c56f844a5bf09481ad3f3b2fe"}, + {file = "s3transfer-0.16.0.tar.gz", hash = "sha256:8e990f13268025792229cd52fa10cb7163744bf56e719e0b9cb925ab79abf920"}, +] + +[package.dependencies] +botocore = ">=1.37.4,<2.0a.0" + +[package.extras] +crt = ["botocore[crt] (>=1.37.4,<2.0a.0)"] + [[package]] name = "sacrebleu" version = "2.6.0" @@ -6576,4 +6642,4 @@ cffi = ["cffi (>=1.17,<2.0)", "cffi (>=2.0.0b)"] [metadata] lock-version = "2.0" python-versions = ">= 3.10.0 < 3.14.0" -content-hash = "cdb4e7e4e74c70816b35f04504b085a5cf976a18b9ff1cf375aec72e2bd30430" +content-hash = "bf1a08632da81c1ddbb92096c0442b3d8f8893d83ac33a9e8f120c9950dcd145" diff --git a/pyproject.toml b/pyproject.toml index 325431e..022ecf8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -18,6 +18,7 @@ langgraph = "^1.0.6" dotenv = "^0.9.9" mlflow = "^3.9.0" lm-eval = "^0.4.10" +boto3 = "^1.42.51" [tool.poetry.group.dev.dependencies] coverage = "^7.4.0" From 0e93f06a51ce671300eea50e5b6ff3c40313ab66 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 10:59:44 +0100 Subject: [PATCH 30/94] fix: load env in the right place --- GoT/model/graph_model.py | 2 -- GoT/model/ollama_llm.py | 3 +++ 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 8e4e673..36cdf9b 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -1,5 +1,4 @@ from venv import logger -from dotenv import load_dotenv from langchain_core.messages import HumanMessage, SystemMessage, AIMessage from langgraph.graph import StateGraph, MessagesState, START, END @@ -24,7 +23,6 @@ remove_tools_from_list, ) -load_dotenv() # Defining agents starting_agent = OllamaLLM().create_custom_agent( diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index f61226c..37c1c5e 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -1,3 +1,4 @@ +from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langchain_core.messages import SystemMessage from langchain.agents import create_agent @@ -12,6 +13,8 @@ square_root, ) +load_dotenv() + mlflow.set_experiment("marcoraggini-experiment") mlflow.openai.autolog() mlflow.langchain.autolog() From e85ebde2d8f5e079d7ff70e77016d9b6b4bbcb66 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 12:13:46 +0100 Subject: [PATCH 31/94] chore: change temperature for formatted output --- GoT/model/ollama_llm.py | 1 + GoT/model/runtime_graph.py | 28 ++++++++++++++++++---------- 2 files changed, 19 insertions(+), 10 deletions(-) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 37c1c5e..91873bc 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -32,6 +32,7 @@ def __init__(self): base_url="http://localhost:11434/v1", api_key="dummy", model="ministral-3:8b", + temperature=0.5 ) self.system_prompt = SystemMessage(SYSTEM_PROMPT_GENERAL) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 221358e..c199e57 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -1,7 +1,7 @@ from typing import Dict, List from langgraph.graph import MessagesState from langchain_core.messages import AnyMessage, HumanMessage -from pydantic import BaseModel +from pydantic import BaseModel, Field class RuntimeNode: @@ -117,20 +117,28 @@ class Score(BaseModel): description: str - A description or rationale for the assigned score. """ - score: int - description: str + score: int = Field( + ..., + description="Integer score between 0 and 6 inclusive." + ) + + description: str = Field( + ..., + description="Short justification (1-3 sentences) explaining why the score was assigned." + ) class Response(BaseModel): """Rapresents a response for a tool node. - - Attributes: - response: str - The synthethic response. - explanation: str - An explanation or rationale for the response. """ - - response: str - explanation: str + response: str = Field( + ..., + description="Final answer to the user request. No meta commentary." + ) + explanation: str = Field( + ..., + description="Short reasoning explaining how the answer was produced." + ) class RuntimeGraph: From 17bfaccfc4eb5de4aa3182a451fa7bf1956a5c84 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 15:39:23 +0100 Subject: [PATCH 32/94] chore: add reset of Runtime id --- GoT/model/runtime_graph.py | 1 + 1 file changed, 1 insertion(+) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index c199e57..b9df7c0 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -193,6 +193,7 @@ def clear(self): self.tools_available = {} self.temp_node = RuntimeNode() self.temp_response = ResponseNode(response="", resolved=False) + RuntimeNode._id_counter = 0 def print_mermaid(self) -> str: lines = [] From 014b1f94567efd0d88101353caa5574923a4aa97 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 15:46:46 +0100 Subject: [PATCH 33/94] chore: parsing output to filter correctly the response --- GoT/model/lm_wrapper.py | 22 ++++------------------ 1 file changed, 4 insertions(+), 18 deletions(-) diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 332d0a6..707a5a7 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -3,7 +3,7 @@ from lm_eval.api.registry import register_model from lm_eval.api.model import LM -from GoT.model.utils.utils import extract_output +from GoT.model.utils.utils import extract_output, normalize_number class LangGraphLM: @@ -62,24 +62,10 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): result = invoke_graph() # Extract the final answer from the result - output = "" - if isinstance(result, dict) and "messages" in result: - messages = result["messages"] - if messages: - # The last message should be the response - last_msg = messages[-1] - - # Extract content from message - if hasattr(last_msg, "content"): - output = last_msg.content - elif isinstance(last_msg, dict) and "content" in last_msg: - output = last_msg["content"] - else: - output = str(last_msg) - else: - output = str(result) if result else "" + output = result["output"] if isinstance(result, dict) and "output" in result else "" + normalize_output = normalize_number(output) - outputs.append(output if output else "") + outputs.append(normalize_output if normalize_output else "") except Exception as e: print(f"Error in request {i}: {e}") From 73f9d31d27b3c31f9ed7f21fdf5d0ff65f7efb67 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 15:47:25 +0100 Subject: [PATCH 34/94] chore: add normalize number --- GoT/model/utils/utils.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 3b7fc69..fac31c5 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -124,3 +124,11 @@ def extract_output(result) -> str: return str(last_msg["content"]) return str(result) if result else "" + + +def normalize_number(num_str: str) -> str: + """Remove commas and dollar sign.""" + num_str = num_str.replace("$", "") + num_str = num_str.replace(",", "") + num_str = num_str.replace("*", "") + return num_str.strip() From beba81c8a934041c749a2f9b93276e9f67990c3f Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 15:57:03 +0100 Subject: [PATCH 35/94] chore: change method of graph call --- GoT/__init__.py | 13 ++++++------- GoT/model/graph_model.py | 8 ++++---- GoT/model/lm_wrapper.py | 20 ++++++++++---------- 3 files changed, 20 insertions(+), 21 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index e6c9568..ec3cb2b 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -2,11 +2,11 @@ import logging from lm_eval import evaluator, tasks -from GoT.model.graph_model import invoke_graph, set_prompt +from GoT.model.graph_model import call_graph from GoT.model.lm_wrapper import LangGraphLMWrapper -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger("GoT") +# logging.basicConfig(level=logging.DEBUG) +# logger = logging.getLogger("GoT") def lm_eval_benchmark(): @@ -17,7 +17,7 @@ def lm_eval_benchmark(): results = evaluator.evaluate( lm=lm, task_dict=task_dict, - limit=5, # Limit to 2 samples for quick testing + limit=10, # Limit the number of samples log_samples=True, ) @@ -27,8 +27,7 @@ def lm_eval_benchmark(): def custom_test(): - set_prompt("What is 4726621 + 2 * 392 - 3432?") - invoke_graph() + call_graph("What is 4726621 + 2 * 392 - 3432?") def main(): @@ -37,4 +36,4 @@ def main(): # let this be the last line of this file -logger.info("GoT loaded") +# logger.info("GoT loaded") diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 36cdf9b..5b44da4 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -123,7 +123,6 @@ def tool_call(messages: MessagesState): parsed_res = f"Response: {parse_response_for_tool_node(res).response}\nExplanation: {parse_response_for_tool_node(res).explanation}" runtime_graph.resolve_node(call_node, parsed_res) - # Add test node test_node = TestNode( f"{parsed_res}", "", @@ -221,9 +220,10 @@ def chat_completition(messages: MessagesState): content = "" -def set_prompt(prompt: str): +def call_graph(prompt: str): global content content = prompt + return invoke_graph() def invoke_graph(): @@ -248,7 +248,7 @@ def invoke_graph(): graph = graph.compile() - logger.info(graph.get_graph().draw_mermaid()) + # logger.info(graph.get_graph().draw_mermaid()) res = graph.invoke( { @@ -263,7 +263,7 @@ def invoke_graph(): res["output"] = runtime_graph.temp_response.response - logger.info(res) + # logger.info(res) print(runtime_graph.print_mermaid()) runtime_graph.clear() return res diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 707a5a7..7692eb1 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -1,5 +1,5 @@ import math -from GoT.model.graph_model import invoke_graph, set_prompt +from GoT.model.graph_model import call_graph, invoke_graph from lm_eval.api.registry import register_model from lm_eval.api.model import LM @@ -18,8 +18,7 @@ def generate(self, requests, max_new_tokens=None): outputs = [] for r in requests: prompt = r["prompt"] - set_prompt(prompt) # Imposta il prompt globale - result = invoke_graph() # usa la tua funzione + result = call_graph(prompt) outputs.append(result["output"]) return outputs @@ -58,11 +57,14 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): question = str(request) # Call invoke_graph with just the question - set_prompt(question) - result = invoke_graph() + result = call_graph(question) # Extract the final answer from the result - output = result["output"] if isinstance(result, dict) and "output" in result else "" + output = ( + result["output"] + if isinstance(result, dict) and "output" in result + else "" + ) normalize_output = normalize_number(output) outputs.append(normalize_output if normalize_output else "") @@ -142,8 +144,7 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): try: question = self._extract_text_from_request(request) - set_prompt(question) - result = invoke_graph() + result = call_graph(question) # Estrai l'output output = extract_output(result) @@ -191,8 +192,7 @@ def loglikelihood(self, requests): full_text = context + continuation # Chiama il grafo - set_prompt(full_text) - result = invoke_graph() + result = call_graph(full_text) generated_output = extract_output(result) # Calcola il score di likelihood From 163c5bdac7a72641f282f4a6c279855dad04fb4c Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 16:56:00 +0100 Subject: [PATCH 36/94] feat: introduce lm eval test benchmark --- GoT/__init__.py | 24 ++++++++++--- GoT/model/lm_wrapper.py | 75 +++++++++++++++++++++++++++++++++++++++-- 2 files changed, 93 insertions(+), 6 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index ec3cb2b..14bdca3 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -3,13 +3,29 @@ from lm_eval import evaluator, tasks from GoT.model.graph_model import call_graph -from GoT.model.lm_wrapper import LangGraphLMWrapper +from GoT.model.lm_wrapper import LangGraphLMWrapper, OllamaTestLMWrapper # logging.basicConfig(level=logging.DEBUG) # logger = logging.getLogger("GoT") -def lm_eval_benchmark(): +def lm_eval_test_benchmark(): + task_list = ["gsm8k"] + test_lm = OllamaTestLMWrapper() + task_dict = tasks.get_task_dict(task_list) + + results = evaluator.evaluate( + lm=test_lm, + task_dict=task_dict, + limit=10, # Limit the number of samples + log_samples=True, + ) + + # Save results to a JSON file + with open("ollama_test_benchmark_results.json", "w") as f: + json.dump(results, f, indent=2) + +def lm_eval_graph_benchmark(): task_list = ["gsm8k"] lm = LangGraphLMWrapper() task_dict = tasks.get_task_dict(task_list) @@ -22,7 +38,7 @@ def lm_eval_benchmark(): ) # Save results to a JSON file - with open("graph_benchmark_results.json", "w") as f: + with open("ollama_test_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) @@ -32,7 +48,7 @@ def custom_test(): def main(): # It could be changed with custom_test() to test a custom problem instead of the benchmark - lm_eval_benchmark() + lm_eval_graph_benchmark() # let this be the last line of this file diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 7692eb1..b919da6 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -1,9 +1,11 @@ import math -from GoT.model.graph_model import call_graph, invoke_graph +from GoT.model.graph_model import call_graph from lm_eval.api.registry import register_model from lm_eval.api.model import LM -from GoT.model.utils.utils import extract_output, normalize_number +from GoT.model.ollama_llm import OllamaLLM +from langchain_core.messages import HumanMessage +from GoT.model.utils.utils import extract_output, normalize_number, parse_response class LangGraphLM: @@ -23,6 +25,20 @@ def generate(self, requests, max_new_tokens=None): return outputs +class OllamaTestLM: + def __init__(self): + pass + + def generate(self, requests, max_new_tokens=None): + agent = OllamaLLM().create_custom_agent(tools=OllamaLLM().get_tools()) + outputs = [] + for r in requests: + prompt = r["prompt"] + result = agent.invoke({"messages": [HumanMessage(content=prompt)]}) + outputs.append(normalize_number(parse_response(result))) + return outputs + + # Register your custom model with lm_eval @register_model("langgraph_lm") class LangGraphLMWrapper(LM): @@ -85,6 +101,61 @@ def loglikelihood_rolling(self, requests): return super().loglikelihood_rolling(requests) +@register_model("ollama_lm_test") +class OllamaTestLMWrapper(LM): + def __init__(self, model_args=""): + super().__init__() + self.lm = OllamaTestLM() + + def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): + """ + Generate text until a stopping condition is met. + """ + agent = OllamaLLM().create_custom_agent(tools=OllamaLLM().get_tools()) + outputs = [] + for i, request in enumerate(requests): + try: + # Extract the question directly from request.doc + # This is much simpler than parsing the full prompt + if hasattr(request, "doc") and isinstance(request.doc, dict): + question = request.doc.get("question", "") + else: + # Fallback: try to extract from arguments + if hasattr(request, "arguments") and request.arguments: + full_prompt = request.arguments[0][0] + # Just take everything after the last "Answer:" as the question + last_answer_idx = full_prompt.rfind("Answer:") + if last_answer_idx != -1: + question = full_prompt[ + last_answer_idx + 8 : + ].strip() # 8 = len("Answer:") + else: + question = full_prompt + else: + question = str(request) + + # Call invoke_graph with just the question + result = agent.invoke({"messages": [HumanMessage(content=question)]}) + normalize_output = normalize_number(parse_response(result)) + + outputs.append(normalize_output if normalize_output else "") + + except Exception as e: + print(f"Error in request {i}: {e}") + import traceback + + traceback.print_exc() + outputs.append("") + + return outputs + + def loglikelihood(self, requests): + return super().loglikelihood(requests) + + def loglikelihood_rolling(self, requests): + return super().loglikelihood_rolling(requests) + + @register_model("langgraph_bigbench") class LangGraphBigBenchWrapper(LM): """ From 33441adca748c6101eee6cf5612e3d609298b5cc Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 18 Feb 2026 16:56:09 +0100 Subject: [PATCH 37/94] style: ruff format --- GoT/model/ollama_llm.py | 2 +- GoT/model/runtime_graph.py | 19 +++++++------------ 2 files changed, 8 insertions(+), 13 deletions(-) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 91873bc..1f2c909 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -32,7 +32,7 @@ def __init__(self): base_url="http://localhost:11434/v1", api_key="dummy", model="ministral-3:8b", - temperature=0.5 + temperature=0.5, ) self.system_prompt = SystemMessage(SYSTEM_PROMPT_GENERAL) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index b9df7c0..5340028 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -117,27 +117,22 @@ class Score(BaseModel): description: str - A description or rationale for the assigned score. """ - score: int = Field( - ..., - description="Integer score between 0 and 6 inclusive." - ) + score: int = Field(..., description="Integer score between 0 and 6 inclusive.") description: str = Field( ..., - description="Short justification (1-3 sentences) explaining why the score was assigned." + description="Short justification (1-3 sentences) explaining why the score was assigned.", ) class Response(BaseModel): - """Rapresents a response for a tool node. - """ + """Rapresents a response for a tool node.""" + response: str = Field( - ..., - description="Final answer to the user request. No meta commentary." + ..., description="Final answer to the user request. No meta commentary." ) explanation: str = Field( - ..., - description="Short reasoning explaining how the answer was produced." + ..., description="Short reasoning explaining how the answer was produced." ) @@ -193,7 +188,7 @@ def clear(self): self.tools_available = {} self.temp_node = RuntimeNode() self.temp_response = ResponseNode(response="", resolved=False) - RuntimeNode._id_counter = 0 + RuntimeNode._id_counter = 0 def print_mermaid(self) -> str: lines = [] From 8135df1c26dd4806be266154ccbaf6dd7500eb03 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 19 Feb 2026 11:14:13 +0100 Subject: [PATCH 38/94] chore: add print function for bench testing --- GoT/__init__.py | 7 ++++++- GoT/model/utils/utils.py | 16 ++++++++++++++++ 2 files changed, 22 insertions(+), 1 deletion(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index 14bdca3..0e06b0c 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -4,6 +4,7 @@ from lm_eval import evaluator, tasks from GoT.model.graph_model import call_graph from GoT.model.lm_wrapper import LangGraphLMWrapper, OllamaTestLMWrapper +from GoT.model.utils.utils import print_benchmark_result # logging.basicConfig(level=logging.DEBUG) # logger = logging.getLogger("GoT") @@ -25,6 +26,8 @@ def lm_eval_test_benchmark(): with open("ollama_test_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) + print_benchmark_result(results) + def lm_eval_graph_benchmark(): task_list = ["gsm8k"] lm = LangGraphLMWrapper() @@ -41,6 +44,8 @@ def lm_eval_graph_benchmark(): with open("ollama_test_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) + print_benchmark_result(results) + def custom_test(): call_graph("What is 4726621 + 2 * 392 - 3432?") @@ -48,7 +53,7 @@ def custom_test(): def main(): # It could be changed with custom_test() to test a custom problem instead of the benchmark - lm_eval_graph_benchmark() + lm_eval_test_benchmark() # let this be the last line of this file diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index fac31c5..a8839c2 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -132,3 +132,19 @@ def normalize_number(num_str: str) -> str: num_str = num_str.replace(",", "") num_str = num_str.replace("*", "") return num_str.strip() + +def print_benchmark_result(results: dict) -> None: + samples = results["samples"]["gsm8k"] + + flex_samples = [ + s for s in samples + if "flexible" in s.get("filter", "") + ] + + n_total = len(flex_samples) + n_correct = sum(1 for s in flex_samples if s.get("exact_match", 0) == 1.0) + n_wrong = n_total - n_correct + + print(f"Total (only flexible): {n_total}") + print(f"Correct answers (flexible): {n_correct}") + print(f"Wrong answers (flexible): {n_wrong}") From ee5b32f0693db3898fd9717867586abb394b462a Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 19 Feb 2026 16:03:48 +0100 Subject: [PATCH 39/94] feat: implementing reasoning node --- GoT/model/graph_model.py | 25 ++++++++++++++++++++----- GoT/model/runtime_graph.py | 10 +++++----- 2 files changed, 25 insertions(+), 10 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 5b44da4..e9cd844 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -7,6 +7,7 @@ BacktrackNode, CompletitionNode, GoalNode, + ReasoningNode, Response, RuntimeGraph, RuntimeNode, @@ -79,6 +80,7 @@ def tool_expand(goal: MessagesState): ] res = starting_agent.invoke({"messages": messages}) str_res = parse_response(res) + goal["messages"].append(AIMessage(content=str_res)) tool_list = parse_tool_list(str_res) # Toglie elementi inutili # add tool nodes in the runtime graph for tool in tool_list: @@ -88,17 +90,28 @@ def tool_expand(goal: MessagesState): "", tool_name=tool, ) + reasoning_node = ReasoningNode("") runtime_graph.add_node(tool_node) + runtime_graph.add_node(reasoning_node) runtime_graph.add_edge( runtime_graph.temp_node, tool_node ) # edge from goal to tool node runtime_graph.add_node(call_node) - runtime_graph.add_edge(tool_node, call_node) + runtime_graph.add_edge(tool_node, reasoning_node) + runtime_graph.add_edge(reasoning_node, call_node) runtime_graph.add_tool_link(call_node, tool) # extract a tool to call runtime_graph.temp_node = runtime_graph.call_tool_node() - return runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) + return goal +def tool_reasoning(messages: MessagesState): + messages["messages"].append(HumanMessage("Please, reason about how to use these tools to solve the problem, without solving it.")) + result = parse_response(starting_agent.invoke(messages)) + messages["messages"].append(AIMessage(result)) + runtime_graph.resolve_node(runtime_graph.temp_node, result) + runtime_graph.temp_node = runtime_graph.nodes.get(runtime_graph.temp_node, [])[0] + messages["messages"].append(HumanMessage(runtime_graph.temp_node.prompt)) + return messages def tool_call(messages: MessagesState): # It calls the llm and it resolves the call node @@ -196,7 +209,7 @@ def backtrack(messages: MessagesState): runtime_graph.add_edge( backtrack_node, runtime_graph.temp_node ) # tool call node that we want to resolve - messages = runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) + # messages = runtime_graph.append_prompt_to_messages_state(runtime_graph.temp_node) messages.get("messages", []).append(AIMessage(backtrack_node.feedback)) return messages @@ -230,15 +243,17 @@ def invoke_graph(): graph = StateGraph(MessagesState) graph.add_node(goal) graph.add_node(tool_expand) + graph.add_node(tool_reasoning) graph.add_node(tool_call) graph.add_node(backtrack) graph.add_node(chat_completition) graph.add_node(response_evaluation) graph.add_edge(START, "goal") graph.add_edge("goal", "tool_expand") - graph.add_edge("tool_expand", "tool_call") + graph.add_edge("tool_expand", "tool_reasoning") + graph.add_edge("tool_reasoning", "tool_call") graph.add_edge("tool_call", "response_evaluation") - graph.add_edge("backtrack", "tool_call") + graph.add_edge("backtrack", "tool_reasoning") graph.add_edge("chat_completition", END) graph.add_conditional_edges( "response_evaluation", diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 5340028..4da35ee 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -89,14 +89,14 @@ def __init__( self.feedback = feedback -class ContextNode(RuntimeNode): +class ReasoningNode(RuntimeNode): def __init__( self, - context: str, + reasoning: str, resolved: bool = False, ): super().__init__(resolved) - self.context = context + self.reasoning = reasoning class ResponseNode(RuntimeNode): @@ -161,8 +161,8 @@ def resolve_node(self, node: RuntimeNode, response: str) -> None: def call_tool_node(self) -> ToolNode: nodes = list(self.nodes.keys()) - call_nodes = [n for n in nodes if (isinstance(n, ToolNode) and not n.resolved)] - return call_nodes[0] + reasoning_nodes = [n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved)] + return reasoning_nodes[0] def exist_tool_available(self) -> bool: nodes = list(self.nodes.keys()) From ea4b665cb88a4a2e413ed4a870fa426b3c1ab122 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 19 Feb 2026 16:04:09 +0100 Subject: [PATCH 40/94] style: ruff format --- GoT/model/graph_model.py | 8 +++++++- GoT/model/runtime_graph.py | 4 +++- GoT/model/utils/utils.py | 6 ++---- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index e9cd844..c9cbacc 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -104,8 +104,13 @@ def tool_expand(goal: MessagesState): runtime_graph.temp_node = runtime_graph.call_tool_node() return goal + def tool_reasoning(messages: MessagesState): - messages["messages"].append(HumanMessage("Please, reason about how to use these tools to solve the problem, without solving it.")) + messages["messages"].append( + HumanMessage( + "Please, reason about how to use these tools to solve the problem, without solving it." + ) + ) result = parse_response(starting_agent.invoke(messages)) messages["messages"].append(AIMessage(result)) runtime_graph.resolve_node(runtime_graph.temp_node, result) @@ -113,6 +118,7 @@ def tool_reasoning(messages: MessagesState): messages["messages"].append(HumanMessage(runtime_graph.temp_node.prompt)) return messages + def tool_call(messages: MessagesState): # It calls the llm and it resolves the call node call_node = runtime_graph.temp_node diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 4da35ee..11affd6 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -161,7 +161,9 @@ def resolve_node(self, node: RuntimeNode, response: str) -> None: def call_tool_node(self) -> ToolNode: nodes = list(self.nodes.keys()) - reasoning_nodes = [n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved)] + reasoning_nodes = [ + n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved) + ] return reasoning_nodes[0] def exist_tool_available(self) -> bool: diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index a8839c2..74265da 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -133,13 +133,11 @@ def normalize_number(num_str: str) -> str: num_str = num_str.replace("*", "") return num_str.strip() + def print_benchmark_result(results: dict) -> None: samples = results["samples"]["gsm8k"] - flex_samples = [ - s for s in samples - if "flexible" in s.get("filter", "") - ] + flex_samples = [s for s in samples if "flexible" in s.get("filter", "")] n_total = len(flex_samples) n_correct = sum(1 for s in flex_samples if s.get("exact_match", 0) == 1.0) From 7efc991f5d88681701fb1df85a7e4059c4681145 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Sat, 21 Feb 2026 18:41:25 +0100 Subject: [PATCH 41/94] chore: improve response format --- GoT/model/runtime_graph.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 11affd6..71eeef4 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -129,7 +129,7 @@ class Response(BaseModel): """Rapresents a response for a tool node.""" response: str = Field( - ..., description="Final answer to the user request. No meta commentary." + ..., description="Final answer to the user request. No meta commentary. If the answer is a number, respond only with the number, without any text." ) explanation: str = Field( ..., description="Short reasoning explaining how the answer was produced." From 5be77bb0c7fe13ae0ed048c81bfed2ee8bc2b768 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Sat, 21 Feb 2026 19:01:59 +0100 Subject: [PATCH 42/94] fix: clea graph in case of exception --- GoT/model/graph_model.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index c9cbacc..9483a39 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -270,17 +270,20 @@ def invoke_graph(): graph = graph.compile() # logger.info(graph.get_graph().draw_mermaid()) - - res = graph.invoke( - { - "messages": [ - { - "role": "user", - "content": content, - } - ] - } - ) + try: + res = graph.invoke( + { + "messages": [ + { + "role": "user", + "content": content, + } + ] + } + ) + except Exception as e: + runtime_graph.clear() + raise e res["output"] = runtime_graph.temp_response.response From 4c88edddf2e28c2be3560b2afe77fb76ce4a4de0 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Sat, 21 Feb 2026 19:02:12 +0100 Subject: [PATCH 43/94] chore: update gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 3cc91b1..1439a90 100644 --- a/.gitignore +++ b/.gitignore @@ -142,3 +142,6 @@ dmypy.json # Pyre type checker .pyre/ + +# lm eval cache +hf_cache/ From d06697767d92a31ba906de35676eaa12a258857d Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 23 Feb 2026 11:19:25 +0100 Subject: [PATCH 44/94] chore: add division and removing useless sum tools --- GoT/model/ollama_llm.py | 7 +++---- GoT/tools/math_tool.py | 36 +++++++++++------------------------- 2 files changed, 14 insertions(+), 29 deletions(-) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 1f2c909..dbf05ac 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -6,11 +6,10 @@ from GoT.tools.math_tool import ( multiply, - sum_four, - sum_three, summing, minus, square_root, + divide, ) load_dotenv() @@ -39,12 +38,12 @@ def __init__(self): self.agent = create_agent( model=self.ollamaLLM, - tools=[sum_four, summing, minus, sum_three, square_root], + tools=[summing, minus, square_root, multiply, divide], system_prompt=self.system_prompt, ) def get_tools(self): - return [sum_four, summing, minus, sum_three, square_root, multiply] + return [summing, minus, square_root, multiply, divide] def create_custom_agent( self, diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py index 00ddd74..f6a5cd2 100644 --- a/GoT/tools/math_tool.py +++ b/GoT/tools/math_tool.py @@ -12,52 +12,38 @@ def summing(x: int, y: int) -> int: """ return x + y - @tool -def sum_three(x: int, y: int, z: int): - """sum of three integer - - Arguments: - x(int): first number - y(int): second number - z(int): third number - """ - return x + y + z - - -@tool -def sum_four(x, y, z, a): - """sum of four integer +def minus(x: int, y: int) -> int: + """minus of two integer Arguments: x(int): first number y(int): second number - z(int): third number - a(int): fourth number """ - return x + y + z + a + return x - y @tool -def minus(x: int, y: int) -> int: - """minus of two integer +def multiply(x: int, y: int) -> int: + """multiply of two integer Arguments: x(int): first number y(int): second number """ - return x - y - + return x * y @tool -def multiply(x: int, y: int) -> int: - """multiply of two integer +def divide(x: int, y: int) -> float: + """divide of two integer Arguments: x(int): first number y(int): second number """ - return x * y + if y == 0: + raise ValueError("Cannot divide by zero") + return x / y @tool From ed002560eab6c8a10f9466896473b8ebe174aac9 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 23 Feb 2026 14:43:35 +0100 Subject: [PATCH 45/94] chore: improve math tools --- GoT/tools/math_tool.py | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py index f6a5cd2..eb95989 100644 --- a/GoT/tools/math_tool.py +++ b/GoT/tools/math_tool.py @@ -3,43 +3,43 @@ @tool -def summing(x: int, y: int) -> int: - """sum of two integer +def summing(x: float, y: float) -> float: + """sum of two float numbers Arguments: - x(int): first number - y(int): second number + x(float): first number + y(float): second number """ return x + y @tool -def minus(x: int, y: int) -> int: - """minus of two integer +def minus(x: float, y: float) -> float: + """minus of two float numbers Arguments: - x(int): first number - y(int): second number + x(float): first number + y(float): second number """ return x - y @tool -def multiply(x: int, y: int) -> int: - """multiply of two integer +def multiply(x: float, y: float) -> float: + """multiply of two float numbers Arguments: - x(int): first number - y(int): second number + x(float): first number + y(float): second number """ return x * y @tool -def divide(x: int, y: int) -> float: - """divide of two integer +def divide(x: float, y: float) -> float: + """divide of two float numbers Arguments: - x(int): first number - y(int): second number + x(float): first number + y(float): second number """ if y == 0: raise ValueError("Cannot divide by zero") From 8bd107735e18e19e8b3fd31ee7e73b739bfcbcab Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 24 Feb 2026 14:40:58 +0100 Subject: [PATCH 46/94] chore: force output format on node --- GoT/model/graph_model.py | 13 +++++-------- GoT/model/runtime_graph.py | 2 +- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 9483a39..765ddb5 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -86,7 +86,7 @@ def tool_expand(goal: MessagesState): for tool in tool_list: tool_node = RuntimeNode(resolved=True) call_node = ToolNode( - f"Please, resolve the problem with the tool: {tool}", + "Please, resolve the problem with the tools given, you MUST follow the previous reasoning.", "", tool_name=tool, ) @@ -115,7 +115,7 @@ def tool_reasoning(messages: MessagesState): messages["messages"].append(AIMessage(result)) runtime_graph.resolve_node(runtime_graph.temp_node, result) runtime_graph.temp_node = runtime_graph.nodes.get(runtime_graph.temp_node, [])[0] - messages["messages"].append(HumanMessage(runtime_graph.temp_node.prompt)) + messages["messages"].append(SystemMessage(runtime_graph.temp_node.prompt)) return messages @@ -123,9 +123,7 @@ def tool_call(messages: MessagesState): # It calls the llm and it resolves the call node call_node = runtime_graph.temp_node tool_agent = OllamaLLM().create_custom_agent( - remove_tools_from_list( - OllamaLLM().get_tools(), runtime_graph.get_resolved_tools() - ), + OllamaLLM().get_tools(), SystemMessage( "You are an assistant specialized in tools. Your goal is to resolve the problem with " " the tool that the user indicates to you. You MUST use the tool that user indicates to you." @@ -136,7 +134,7 @@ def tool_call(messages: MessagesState): response_format=Response, ) - res = tool_agent.invoke(messages) + res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}) tool_used = extract_tool_used(res) runtime_graph.temp_response.response = parse_response_for_tool_node(res).response parsed_res = f"Response: {parse_response_for_tool_node(res).response}\nExplanation: {parse_response_for_tool_node(res).explanation}" @@ -174,10 +172,9 @@ def response_evaluation(messages: MessagesState): ), ] - score_res = parse_score(judge_agent.invoke({"messages": judge_messages})) + score_res = parse_score(judge_agent.invoke({"messages": judge_messages, "tool_choice": Score})) test_node.score = score_res.score runtime_graph.resolve_node(test_node, score_res.description) - return messages diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 71eeef4..29c5255 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -129,7 +129,7 @@ class Response(BaseModel): """Rapresents a response for a tool node.""" response: str = Field( - ..., description="Final answer to the user request. No meta commentary. If the answer is a number, respond only with the number, without any text." + ..., description="Final answer to the user request. No meta commentary. If the answer is a number, respond only with the number (but in string), without any text." ) explanation: str = Field( ..., description="Short reasoning explaining how the answer was produced." From cd17f09cabb53b8a9b41245385e66208c6914cc9 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Feb 2026 12:37:16 +0100 Subject: [PATCH 47/94] fix: id node now are correct --- GoT/model/runtime_graph.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 29c5255..317a302 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -129,7 +129,8 @@ class Response(BaseModel): """Rapresents a response for a tool node.""" response: str = Field( - ..., description="Final answer to the user request. No meta commentary. If the answer is a number, respond only with the number (but in string), without any text." + ..., + description="Final answer to the user request. No meta commentary. If the answer is a number, respond only with the number (but in string), without any text.", ) explanation: str = Field( ..., description="Short reasoning explaining how the answer was produced." @@ -186,11 +187,11 @@ def append_prompt_to_messages_state( return MessagesState(messages=messages) def clear(self): + RuntimeNode._id_counter = 0 self.nodes = {} self.tools_available = {} self.temp_node = RuntimeNode() self.temp_response = ResponseNode(response="", resolved=False) - RuntimeNode._id_counter = 0 def print_mermaid(self) -> str: lines = [] From a2c8894a5b5fc10b6a95940601f67a5eeb75cf2e Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Feb 2026 15:42:35 +0100 Subject: [PATCH 48/94] style: ruff format --- GoT/model/graph_model.py | 11 ++++++----- GoT/model/lm_wrapper.py | 2 +- GoT/model/utils/utils.py | 17 ++++++++++++++--- GoT/tools/math_tool.py | 2 ++ 4 files changed, 23 insertions(+), 9 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 765ddb5..edc3e9b 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -21,7 +21,6 @@ parse_response_for_tool_node, parse_score, parse_tool_list, - remove_tools_from_list, ) @@ -44,7 +43,7 @@ [], SystemMessage( "You are an assistant specialized in validation of response, like an LLM-as-a-judge. " - "Your duty is to score, from 0 to 6, the response that user gives to you and assign to it a score. " + "Your duty is to score, from 0 to 6, the response that user gives to you and assign to it a score. You can not give the response, but can give a hint for the solution. " "You MUST respond ONLY using the Score function. " "Do not write natural language outside the function. " "If you fail to respect the format, the evaluation will fail." @@ -73,7 +72,7 @@ def goal(prompt: MessagesState): def tool_expand(goal: MessagesState): msg = parse_response(goal) - sys_msg = "Which tools that I have can I use to solve this problem? Please make a list using '-' to denote each tool in a probabilistic order, don't use this character for other reasons." + sys_msg = "Please make a list using '-' to denote each tool in a probabilistic order, don't use this character for other reasons. Select only the tool(s) you want to use to solve this problem." messages = [ HumanMessage(msg), SystemMessage(sys_msg), @@ -163,7 +162,7 @@ def response_evaluation(messages: MessagesState): # Create a proper message for the judge with the solution judge_messages = [ HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), - HumanMessage(content="Solution:\n" + call_node_response), + HumanMessage(content=call_node_response), SystemMessage( content="Score this solution based on correctness and following instructions." ), @@ -172,7 +171,9 @@ def response_evaluation(messages: MessagesState): ), ] - score_res = parse_score(judge_agent.invoke({"messages": judge_messages, "tool_choice": Score})) + score_res = parse_score( + judge_agent.invoke({"messages": judge_messages, "tool_choice": Score}) + ) test_node.score = score_res.score runtime_graph.resolve_node(test_node, score_res.description) return messages diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index b919da6..c6a17d3 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -264,7 +264,7 @@ def loglikelihood(self, requests): # Chiama il grafo result = call_graph(full_text) - generated_output = extract_output(result) + generated_output = normalize_number(extract_output(result)) # Calcola il score di likelihood score = self._calculate_likelihood_score( diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 74265da..05e6528 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -131,11 +131,22 @@ def normalize_number(num_str: str) -> str: num_str = num_str.replace("$", "") num_str = num_str.replace(",", "") num_str = num_str.replace("*", "") - return num_str.strip() + num_str = num_str.strip() + # Remove trailing .0 (and only .0) + if num_str.endswith(".0"): + # Ensure it's actually a valid number before trimming + try: + if float(num_str).is_integer(): + num_str = str(int(float(num_str))) + except ValueError: + pass -def print_benchmark_result(results: dict) -> None: - samples = results["samples"]["gsm8k"] + return num_str + + +def print_benchmark_result(results: dict, task_name: str) -> None: + samples = results["samples"][task_name] flex_samples = [s for s in samples if "flexible" in s.get("filter", "")] diff --git a/GoT/tools/math_tool.py b/GoT/tools/math_tool.py index eb95989..7a97d19 100644 --- a/GoT/tools/math_tool.py +++ b/GoT/tools/math_tool.py @@ -12,6 +12,7 @@ def summing(x: float, y: float) -> float: """ return x + y + @tool def minus(x: float, y: float) -> float: """minus of two float numbers @@ -33,6 +34,7 @@ def multiply(x: float, y: float) -> float: """ return x * y + @tool def divide(x: float, y: float) -> float: """divide of two float numbers From 9eee32d18e705851a844249cbd5e0b989581997c Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 27 Feb 2026 09:56:47 +0100 Subject: [PATCH 49/94] chore: fix for mypy --- GoT/__init__.py | 32 +++++++++++++++++++++----------- GoT/model/graph_model.py | 4 +++- GoT/model/runtime_graph.py | 2 +- 3 files changed, 25 insertions(+), 13 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index 0e06b0c..17bbe9d 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -1,13 +1,19 @@ import json import logging +from dotenv import load_dotenv from lm_eval import evaluator, tasks from GoT.model.graph_model import call_graph -from GoT.model.lm_wrapper import LangGraphLMWrapper, OllamaTestLMWrapper +from GoT.model.lm_wrapper import ( + LangGraphLMWrapper, + OllamaTestLMWrapper, +) from GoT.model.utils.utils import print_benchmark_result -# logging.basicConfig(level=logging.DEBUG) -# logger = logging.getLogger("GoT") +logging.basicConfig(level=logging.DEBUG) +logger = logging.getLogger("GoT") + +load_dotenv() def lm_eval_test_benchmark(): @@ -18,7 +24,7 @@ def lm_eval_test_benchmark(): results = evaluator.evaluate( lm=test_lm, task_dict=task_dict, - limit=10, # Limit the number of samples + limit=20, # Limit the number of samples log_samples=True, ) @@ -28,33 +34,37 @@ def lm_eval_test_benchmark(): print_benchmark_result(results) + def lm_eval_graph_benchmark(): - task_list = ["gsm8k"] + task_name = "gsm8k" + task_list = [task_name] lm = LangGraphLMWrapper() task_dict = tasks.get_task_dict(task_list) results = evaluator.evaluate( lm=lm, task_dict=task_dict, - limit=10, # Limit the number of samples + limit=5, # Limit the number of samples log_samples=True, ) # Save results to a JSON file - with open("ollama_test_benchmark_results.json", "w") as f: + with open("ollama_graph_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) - print_benchmark_result(results) + print_benchmark_result(results, task_name) def custom_test(): - call_graph("What is 4726621 + 2 * 392 - 3432?") + call_graph( + "Josh decides to try flipping a house. He buys a house for $80,000 and then puts in $50,000 in repairs. This increased the value of the house by 150%. How much profit did he make?" + ) def main(): # It could be changed with custom_test() to test a custom problem instead of the benchmark - lm_eval_test_benchmark() + lm_eval_graph_benchmark() # let this be the last line of this file -# logger.info("GoT loaded") +logger.info("GoT loaded") diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index edc3e9b..aa9dcbc 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -99,7 +99,7 @@ def tool_expand(goal: MessagesState): runtime_graph.add_edge(tool_node, reasoning_node) runtime_graph.add_edge(reasoning_node, call_node) runtime_graph.add_tool_link(call_node, tool) - # extract a tool to call + # extract a reasoning node to resolve runtime_graph.temp_node = runtime_graph.call_tool_node() return goal @@ -114,6 +114,8 @@ def tool_reasoning(messages: MessagesState): messages["messages"].append(AIMessage(result)) runtime_graph.resolve_node(runtime_graph.temp_node, result) runtime_graph.temp_node = runtime_graph.nodes.get(runtime_graph.temp_node, [])[0] + if not isinstance(runtime_graph.temp_node, ToolNode): + raise TypeError("Expected ToolNode after reasoning") messages["messages"].append(SystemMessage(runtime_graph.temp_node.prompt)) return messages diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 317a302..7e25959 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -160,7 +160,7 @@ def resolve_node(self, node: RuntimeNode, response: str) -> None: node.response = response node.resolved = True - def call_tool_node(self) -> ToolNode: + def call_tool_node(self) -> ReasoningNode: nodes = list(self.nodes.keys()) reasoning_nodes = [ n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved) From 2faa22752bf1fc947a279cdcc3b11c16421a2d01 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 27 Feb 2026 09:58:55 +0100 Subject: [PATCH 50/94] chore: uncomment logger --- GoT/model/graph_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index aa9dcbc..47c3f48 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -269,7 +269,7 @@ def invoke_graph(): graph = graph.compile() - # logger.info(graph.get_graph().draw_mermaid()) + logger.info(graph.get_graph().draw_mermaid()) try: res = graph.invoke( { @@ -287,7 +287,7 @@ def invoke_graph(): res["output"] = runtime_graph.temp_response.response - # logger.info(res) + logger.info(res) print(runtime_graph.print_mermaid()) runtime_graph.clear() return res From ad7bfe78b287f27bce19da0e8937f88cb5e17999 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 4 Mar 2026 14:20:30 +0100 Subject: [PATCH 51/94] chore: fix benchmark test --- GoT/__init__.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index 17bbe9d..e667956 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -17,7 +17,8 @@ def lm_eval_test_benchmark(): - task_list = ["gsm8k"] + task_name = "gsm8k" + task_list = [task_name] test_lm = OllamaTestLMWrapper() task_dict = tasks.get_task_dict(task_list) @@ -32,7 +33,7 @@ def lm_eval_test_benchmark(): with open("ollama_test_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) - print_benchmark_result(results) + print_benchmark_result(results, task_name) def lm_eval_graph_benchmark(): From 7796d64256f69f6867847b29b391e54aaca3aa75 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 4 Mar 2026 16:15:02 +0100 Subject: [PATCH 52/94] chore: implement crafting tools --- GoT/model/graph_model.py | 10 +++++++++ GoT/model/ollama_llm.py | 5 +++++ GoT/tools/craft_tool.py | 46 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 61 insertions(+) create mode 100644 GoT/tools/craft_tool.py diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 47c3f48..4d97f99 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -58,6 +58,16 @@ response_format=Score, ) +crafter_agent = OllamaLLM().create_custom_agent( + OllamaLLM().get_craft_tool(), + SystemMessage( + "You are an assistant specialized in crafting tools. Your goal is to craft a tool and run it to solve the problem." + "You MUST respond ONLY using the tool that you have at your disposal. " + "Do not write natural language outside the tool. " + ), + response_format=Response, +) + # Defining runtime graph runtime_graph = RuntimeGraph() diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index dbf05ac..d4e6667 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -12,6 +12,8 @@ divide, ) +from GoT.tools.craft_tool import python_tool, install_dependency + load_dotenv() mlflow.set_experiment("marcoraggini-experiment") @@ -44,6 +46,9 @@ def __init__(self): def get_tools(self): return [summing, minus, square_root, multiply, divide] + + def get_craft_tool(self): + return [python_tool, install_dependency] def create_custom_agent( self, diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py new file mode 100644 index 0000000..fce1f78 --- /dev/null +++ b/GoT/tools/craft_tool.py @@ -0,0 +1,46 @@ +import os +import re + +from langchain.tools import tool + + +@tool +def python_tool(code: str) -> str: + """Execute Python code and return the output. The code should assign the final result to a variable named 'result'.""" + + def sanitize_input(query: str) -> str: + """Sanitize input to the python REPL. + Remove whitespace, backtick & python + (if llm mistakes python console as terminal) + """ + query = re.sub(r"^(\s|`)*(?i:python)?\s*", "", query) + query = re.sub(r"(\s|`)*$", "", query) + return query + + try: + # WARNING: Using eval/exec can be dangerous. This is just for demonstration purposes. + namespace = {} + exec(sanitize_input(code), namespace, namespace) + return str(namespace.get("result", "No result variable defined.")) + except Exception as e: + return str(e) + + +@tool +def install_dependency(package_name: str) -> str: + """Install a Python package using poetry.""" + try: + os.system(f"poetry add {package_name}") + return f"Package {package_name} installed successfully." + except Exception as e: + return str(e) + + +@tool +def craft_tool(tool_function: str) -> str: + """Craft a new tool by appending the function definition to tools.py.""" + try: + os.system(f"echo '{tool_function}' > artifacts.py") + return "Tool crafted successfully." + except Exception as e: + return str(e) \ No newline at end of file From 6e25816e9995cc237975ebc3ae5deb21d0ccc24e Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 4 Mar 2026 16:24:18 +0100 Subject: [PATCH 53/94] feat: add crafting node to the state graph --- GoT/model/graph_model.py | 30 +++++++++++++++++++++++++++++- GoT/model/runtime_graph.py | 18 ++++++++++++++++++ 2 files changed, 47 insertions(+), 1 deletion(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 4d97f99..0746f01 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -6,6 +6,7 @@ from GoT.model.runtime_graph import ( BacktrackNode, CompletitionNode, + CraftingNode, GoalNode, ReasoningNode, Response, @@ -190,6 +191,29 @@ def response_evaluation(messages: MessagesState): runtime_graph.resolve_node(test_node, score_res.description) return messages +def crafting(messages: MessagesState): + crafting_node = CraftingNode(response="", tool_crafted="", resolved=False) + runtime_graph.add_node(crafting_node) + runtime_graph.temp_node = crafting_node + crafting_messages = [ + HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), + SystemMessage(content="Craft a tool to solve this problem, run it and give the response."), + ] + craft_res = crafter_agent.invoke({"messages": crafting_messages}) + runtime_graph.temp_response.response = parse_response_for_tool_node(craft_res).response + parsed_res = f"Response: {parse_response_for_tool_node(craft_res).response}\nExplanation: {parse_response_for_tool_node(craft_res).explanation}" + runtime_graph.resolve_node(crafting_node, parsed_res) + test_node = TestNode( + f"{parsed_res}", + "", + score=0, + tool_used="", + ) + runtime_graph.add_node(test_node) + runtime_graph.add_edge(crafting_node, test_node) + runtime_graph.temp_node = test_node + messages["messages"].append(AIMessage(parsed_res)) + return messages def test_result(messages: MessagesState): n = runtime_graph.exist_tool_available() @@ -203,6 +227,8 @@ def test_result(messages: MessagesState): return END elif test_node.score < 5 and n is True: return "backtrack" + elif test_node.score < 5 and n is False and runtime_graph.is_craftin_node_resolved(): + return "crafting" else: chat_completition_node = CompletitionNode( "Please, solve this problem", @@ -262,6 +288,7 @@ def invoke_graph(): graph.add_node(tool_reasoning) graph.add_node(tool_call) graph.add_node(backtrack) + graph.add_node(crafting) graph.add_node(chat_completition) graph.add_node(response_evaluation) graph.add_edge(START, "goal") @@ -270,11 +297,12 @@ def invoke_graph(): graph.add_edge("tool_reasoning", "tool_call") graph.add_edge("tool_call", "response_evaluation") graph.add_edge("backtrack", "tool_reasoning") + graph.add_edge("crafting", "response_evaluation") graph.add_edge("chat_completition", END) graph.add_conditional_edges( "response_evaluation", test_result, - {"backtrack": "backtrack", "chat_completition": "chat_completition", END: END}, + {"crafting": "crafting", "backtrack": "backtrack", "chat_completition": "chat_completition", END: END}, ) graph = graph.compile() diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 7e25959..33f25de 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -108,6 +108,17 @@ def __init__( super().__init__(resolved) self.response = response +class CraftingNode(RuntimeNode): + def __init__( + self, + response: str, + tool_crafted: str = "", + resolved: bool = False, + ): + super().__init__(resolved) + self.response = response + self.tool_crafted = tool_crafted + class Score(BaseModel): """Rapresents a score for a test node. @@ -176,6 +187,13 @@ def get_resolved_tools(self): resolved_nodes = [t for t in self.tools_available.keys() if t.resolved is True] return [self.tools_available[n] for n in resolved_nodes] + def is_craftin_node_resolved(self) -> bool: + nodes = list(self.nodes.keys()) + crafting_nodes = [ + n for n in nodes if (isinstance(n, CraftingNode) and n.resolved) + ] + return True if crafting_nodes else False + def append_prompt_to_messages_state( self, node: TestNode | ToolNode | CompletitionNode | GoalNode ) -> MessagesState: From 1b2602a5f05e67f94ad6fb497644e6673c0ba8bf Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Sat, 7 Mar 2026 10:47:13 +0100 Subject: [PATCH 54/94] chore: removing reasoning and add remote llm --- GoT/model/ollama_llm.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index d4e6667..8b09335 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -1,3 +1,5 @@ +import os + from dotenv import load_dotenv from langchain_openai import ChatOpenAI from langchain_core.messages import SystemMessage @@ -32,7 +34,15 @@ def __init__(self): self.ollamaLLM = ChatOpenAI( base_url="http://localhost:11434/v1", api_key="dummy", - model="ministral-3:8b", + model="qwen3:8b", + temperature=0.5, + reasoning_effort="none", + ) + + self.remoteLLM = ChatOpenAI( + base_url="https://openrouter.ai/api/v1", + api_key=os.environ.get("OPEN_ROUTER_KEY"), + model="openai/gpt-oss-120b:free", temperature=0.5, ) From 318f42adf97ea581b30550463388741d03d84232 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Sat, 7 Mar 2026 10:47:29 +0100 Subject: [PATCH 55/94] chore: add filter on benchmark print --- GoT/model/utils/utils.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 05e6528..59cbd8b 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -145,15 +145,15 @@ def normalize_number(num_str: str) -> str: return num_str -def print_benchmark_result(results: dict, task_name: str) -> None: +def print_benchmark_result(results: dict, task_name: str, filter: str) -> None: samples = results["samples"][task_name] - flex_samples = [s for s in samples if "flexible" in s.get("filter", "")] + flex_samples = [s for s in samples if filter in s.get("filter", "")] n_total = len(flex_samples) n_correct = sum(1 for s in flex_samples if s.get("exact_match", 0) == 1.0) n_wrong = n_total - n_correct - print(f"Total (only flexible): {n_total}") - print(f"Correct answers (flexible): {n_correct}") - print(f"Wrong answers (flexible): {n_wrong}") + print(f"Total: {n_total}") + print(f"Correct answers (filter={filter}): {n_correct}") + print(f"Wrong answers (filter={filter}): {n_wrong}") From 981a8a28aaa2890de195d9b790da5d5d87cbbf7b Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 9 Mar 2026 09:59:17 +0100 Subject: [PATCH 56/94] chore: fix node bugs --- GoT/model/graph_model.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 0746f01..c71130a 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -64,6 +64,7 @@ SystemMessage( "You are an assistant specialized in crafting tools. Your goal is to craft a tool and run it to solve the problem." "You MUST respond ONLY using the tool that you have at your disposal. " + "No comment in the python interpreter will be allowed. " "Do not write natural language outside the tool. " ), response_format=Response, @@ -194,6 +195,7 @@ def response_evaluation(messages: MessagesState): def crafting(messages: MessagesState): crafting_node = CraftingNode(response="", tool_crafted="", resolved=False) runtime_graph.add_node(crafting_node) + runtime_graph.add_edge(runtime_graph.temp_node, crafting_node) runtime_graph.temp_node = crafting_node crafting_messages = [ HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), @@ -227,7 +229,7 @@ def test_result(messages: MessagesState): return END elif test_node.score < 5 and n is True: return "backtrack" - elif test_node.score < 5 and n is False and runtime_graph.is_craftin_node_resolved(): + elif test_node.score < 5 and n is False and not runtime_graph.is_craftin_node_resolved(): return "crafting" else: chat_completition_node = CompletitionNode( From 038954b5b4b54f9e42229c7f2a32267365ad87b4 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Mar 2026 10:02:11 +0100 Subject: [PATCH 57/94] chore: save tool in files --- GoT/tools/craft_tool.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index fce1f78..9244c25 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -16,11 +16,23 @@ def sanitize_input(query: str) -> str: query = re.sub(r"^(\s|`)*(?i:python)?\s*", "", query) query = re.sub(r"(\s|`)*$", "", query) return query + + def craft_tool(tool_function: str) -> str: + """Craft a new tool by appending the function definition to tools.py.""" + try: + tool_function = "\n".join(" " + line for line in tool_function.splitlines()) + code = f"""from langchain.tools import tool\n\n@tool\ndef tool_crafted():\n{tool_function}""" + with open("./tools/ai_tool.py", "w") as f: + f.write(code) + return "Tool crafted successfully." + except Exception as e: + return str(e) try: # WARNING: Using eval/exec can be dangerous. This is just for demonstration purposes. namespace = {} exec(sanitize_input(code), namespace, namespace) + print(craft_tool(code)) return str(namespace.get("result", "No result variable defined.")) except Exception as e: return str(e) @@ -35,12 +47,3 @@ def install_dependency(package_name: str) -> str: except Exception as e: return str(e) - -@tool -def craft_tool(tool_function: str) -> str: - """Craft a new tool by appending the function definition to tools.py.""" - try: - os.system(f"echo '{tool_function}' > artifacts.py") - return "Tool crafted successfully." - except Exception as e: - return str(e) \ No newline at end of file From 444eaecf8928064ab0f9b7644fb369a698ab4a62 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 10 Mar 2026 10:03:06 +0100 Subject: [PATCH 58/94] chore: add filter in print --- GoT/__init__.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index e667956..ac94259 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -1,5 +1,6 @@ import json import logging +import os from dotenv import load_dotenv from lm_eval import evaluator, tasks @@ -7,6 +8,7 @@ from GoT.model.lm_wrapper import ( LangGraphLMWrapper, OllamaTestLMWrapper, + LangGraphBigBenchWrapper ) from GoT.model.utils.utils import print_benchmark_result @@ -15,6 +17,7 @@ load_dotenv() +# Possible filter = "flexible", "none", "strict" def lm_eval_test_benchmark(): task_name = "gsm8k" @@ -33,13 +36,13 @@ def lm_eval_test_benchmark(): with open("ollama_test_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) - print_benchmark_result(results, task_name) + print_benchmark_result(results, task_name, filter="none") def lm_eval_graph_benchmark(): - task_name = "gsm8k" + task_name = "bigbench_logical_sequence_generate_until" task_list = [task_name] - lm = LangGraphLMWrapper() + lm = LangGraphBigBenchWrapper() task_dict = tasks.get_task_dict(task_list) results = evaluator.evaluate( @@ -53,7 +56,7 @@ def lm_eval_graph_benchmark(): with open("ollama_graph_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) - print_benchmark_result(results, task_name) + print_benchmark_result(results, task_name, filter="none") def custom_test(): @@ -64,8 +67,7 @@ def custom_test(): def main(): # It could be changed with custom_test() to test a custom problem instead of the benchmark - lm_eval_graph_benchmark() - + custom_test() # let this be the last line of this file logger.info("GoT loaded") From 4b1bdd5a34c3fd43263f5ba9789e433857a97950 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 11 Mar 2026 15:38:02 +0100 Subject: [PATCH 59/94] chore: add score threshold --- GoT/model/graph_model.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index c71130a..9e1cd94 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -24,6 +24,7 @@ parse_tool_list, ) +SCORE_THRESHOLD = 5 # Defining agents starting_agent = OllamaLLM().create_custom_agent( @@ -223,13 +224,13 @@ def test_result(messages: MessagesState): if not isinstance(test_node, TestNode): raise TypeError("Expected TestNode for scoring") - if test_node.score >= 5: + if test_node.score >= SCORE_THRESHOLD: runtime_graph.add_edge(test_node, runtime_graph.temp_response) runtime_graph.temp_response.resolved = True return END - elif test_node.score < 5 and n is True: + elif test_node.score < SCORE_THRESHOLD and n is True: return "backtrack" - elif test_node.score < 5 and n is False and not runtime_graph.is_craftin_node_resolved(): + elif test_node.score < SCORE_THRESHOLD and n is False and not runtime_graph.is_craftin_node_resolved(): return "crafting" else: chat_completition_node = CompletitionNode( From 2c5cbd40ad768da999da602e169210e4afcebfe6 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 12 Mar 2026 11:13:20 +0100 Subject: [PATCH 60/94] feat: now LLM can create and dynamic add a tool --- GoT/model/graph_model.py | 4 ++-- GoT/model/ollama_llm.py | 43 ++++++++++++++++++++++++++-------------- GoT/tools/ai_tool.py | 1 + GoT/tools/craft_tool.py | 39 +++++++++++++++++++++++++----------- 4 files changed, 58 insertions(+), 29 deletions(-) create mode 100644 GoT/tools/ai_tool.py diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 9e1cd94..7611b81 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -63,7 +63,7 @@ crafter_agent = OllamaLLM().create_custom_agent( OllamaLLM().get_craft_tool(), SystemMessage( - "You are an assistant specialized in crafting tools. Your goal is to craft a tool and run it to solve the problem." + "You are an assistant specialized in crafting tools. Your goal is to craft a tool that help to other agents to solve the problem." "You MUST respond ONLY using the tool that you have at your disposal. " "No comment in the python interpreter will be allowed. " "Do not write natural language outside the tool. " @@ -200,7 +200,7 @@ def crafting(messages: MessagesState): runtime_graph.temp_node = crafting_node crafting_messages = [ HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), - SystemMessage(content="Craft a tool to solve this problem, run it and give the response."), + SystemMessage(content="Craft a tool to solve this problem using craft_tool. It must be a function"), ] craft_res = crafter_agent.invoke({"messages": crafting_messages}) runtime_graph.temp_response.response = parse_response_for_tool_node(craft_res).response diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 8b09335..3361c6e 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -1,4 +1,7 @@ import os +import importlib +from langchain.tools import BaseTool +from langchain_google_genai import ChatGoogleGenerativeAI from dotenv import load_dotenv from langchain_openai import ChatOpenAI @@ -14,7 +17,7 @@ divide, ) -from GoT.tools.craft_tool import python_tool, install_dependency +from GoT.tools.craft_tool import craft_tool, install_dependency load_dotenv() @@ -34,31 +37,41 @@ def __init__(self): self.ollamaLLM = ChatOpenAI( base_url="http://localhost:11434/v1", api_key="dummy", - model="qwen3:8b", + model="ministral-3:8b", temperature=0.5, reasoning_effort="none", ) - self.remoteLLM = ChatOpenAI( - base_url="https://openrouter.ai/api/v1", - api_key=os.environ.get("OPEN_ROUTER_KEY"), - model="openai/gpt-oss-120b:free", - temperature=0.5, + # self.remoteLLM = ChatOpenAI( + # base_url="https://openrouter.ai/api/v1", + # api_key=os.environ.get("OPEN_ROUTER_KEY"), + # model="openai/gpt-oss-120b:free", + # temperature=0.5, + # ) + self.remoteLLM = ChatGoogleGenerativeAI( + model="gemini-2.0-flash", + api_key=os.environ.get("GEMINI_API_KEY"), + temperature=1.0, # Gemini 3.0+ defaults to 1.0 ) self.system_prompt = SystemMessage(SYSTEM_PROMPT_GENERAL) - self.agent = create_agent( - model=self.ollamaLLM, - tools=[summing, minus, square_root, multiply, divide], - system_prompt=self.system_prompt, - ) - def get_tools(self): - return [summing, minus, square_root, multiply, divide] + initial_tools = [summing, minus, square_root, multiply, divide] + crafted_tools = self.get_crafted_tools() + return initial_tools + crafted_tools def get_craft_tool(self): - return [python_tool, install_dependency] + return [craft_tool, install_dependency] + + def get_crafted_tools(self) -> list[BaseTool]: + module = importlib.import_module("GoT.tools.ai_tool") + tools = [] + for obj in module.__dict__.values(): + if isinstance(obj, BaseTool): + tools.append(obj) + + return tools def create_custom_agent( self, diff --git a/GoT/tools/ai_tool.py b/GoT/tools/ai_tool.py new file mode 100644 index 0000000..ce8c8f9 --- /dev/null +++ b/GoT/tools/ai_tool.py @@ -0,0 +1 @@ +from langchain.tools import tool \ No newline at end of file diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index 9244c25..ed57ce2 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -1,4 +1,5 @@ import os +from pathlib import Path import re from langchain.tools import tool @@ -17,22 +18,10 @@ def sanitize_input(query: str) -> str: query = re.sub(r"(\s|`)*$", "", query) return query - def craft_tool(tool_function: str) -> str: - """Craft a new tool by appending the function definition to tools.py.""" - try: - tool_function = "\n".join(" " + line for line in tool_function.splitlines()) - code = f"""from langchain.tools import tool\n\n@tool\ndef tool_crafted():\n{tool_function}""" - with open("./tools/ai_tool.py", "w") as f: - f.write(code) - return "Tool crafted successfully." - except Exception as e: - return str(e) - try: # WARNING: Using eval/exec can be dangerous. This is just for demonstration purposes. namespace = {} exec(sanitize_input(code), namespace, namespace) - print(craft_tool(code)) return str(namespace.get("result", "No result variable defined.")) except Exception as e: return str(e) @@ -47,3 +36,29 @@ def install_dependency(package_name: str) -> str: except Exception as e: return str(e) +@tool +def craft_tool(tool_function: str) -> str: + """Save the function definition provided by the LLM as a tool that can be used by other agents. + The function should be defined as a python function. + The function should be general and reusable, and should not be specific to the current problem. + The function should be defined in a way that it can be imported and used by other agents.""" + + def sanitize_input(query: str) -> str: + """Sanitize input to the python REPL. + Remove whitespace, backtick & python + (if llm mistakes python console as terminal) + """ + query = re.sub(r"^(\s|`)*(?i:python)?\s*", "", query) + query = re.sub(r"(\s|`)*$", "", query) + return query + + try: + base_dir = Path(__file__).parent + file_path = base_dir / "ai_tool.py" + code = f"""\n\n@tool\n{sanitize_input(tool_function)}""" + with open(file_path, "a") as f: + f.write(code) + return "Tool crafted successfully." + except Exception as e: + print(e) + return str(e) \ No newline at end of file From fb903fb6ef366e820232b2227786a531c03aea62 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 10:14:42 +0100 Subject: [PATCH 61/94] feat: add gemini apis --- poetry.lock | 160 ++++++++++++++++++++++++++++++++++++++++++++----- pyproject.toml | 2 +- 2 files changed, 145 insertions(+), 17 deletions(-) diff --git a/poetry.lock b/poetry.lock index 231f1d6..7f41ad2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1290,6 +1290,17 @@ files = [ {file = "filelock-3.24.2.tar.gz", hash = "sha256:c22803117490f156e59fafce621f0550a7a853e2bbf4f87f112b11d469b6c81b"}, ] +[[package]] +name = "filetype" +version = "1.2.0" +description = "Infer file type and MIME type of any file/buffer. No external dependencies." +optional = false +python-versions = "*" +files = [ + {file = "filetype-1.2.0-py2.py3-none-any.whl", hash = "sha256:7ce71b6880181241cf7ac8697a2f1eb6a8bd9b429f7ad6d27b8db9ba5f1c2d25"}, + {file = "filetype-1.2.0.tar.gz", hash = "sha256:66b56cd6474bf41d8c54660347d37afcc3f7d1970648de365c102ef77548aadb"}, +] + [[package]] name = "flask" version = "3.1.2" @@ -1627,6 +1638,7 @@ files = [ [package.dependencies] cryptography = ">=38.0.3" pyasn1-modules = ">=0.2.1" +requests = {version = ">=2.20.0,<3.0.0", optional = true, markers = "extra == \"requests\""} rsa = ">=3.1.4,<5" [package.extras] @@ -1640,6 +1652,33 @@ requests = ["requests (>=2.20.0,<3.0.0)"] testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "flask", "freezegun", "grpcio", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"] urllib3 = ["packaging", "urllib3"] +[[package]] +name = "google-genai" +version = "1.66.0" +description = "GenAI Python SDK" +optional = false +python-versions = ">=3.10" +files = [ + {file = "google_genai-1.66.0-py3-none-any.whl", hash = "sha256:7f127a39cf695277104ce4091bb26e417c59bb46e952ff3699c3a982d9c474ee"}, + {file = "google_genai-1.66.0.tar.gz", hash = "sha256:ffc01647b65046bca6387320057aa51db0ad64bcc72c8e3e914062acfa5f7c49"}, +] + +[package.dependencies] +anyio = ">=4.8.0,<5.0.0" +distro = ">=1.7.0,<2" +google-auth = {version = ">=2.47.0,<3.0.0", extras = ["requests"]} +httpx = ">=0.28.1,<1.0.0" +pydantic = ">=2.9.0,<3.0.0" +requests = ">=2.28.1,<3.0.0" +sniffio = "*" +tenacity = ">=8.2.3,<9.2.0" +typing-extensions = ">=4.11.0,<5.0.0" +websockets = ">=13.0.0,<17.0" + +[package.extras] +aiohttp = ["aiohttp (>=3.10.11,<4.0.0)"] +local-tokenizer = ["protobuf", "sentencepiece (>=0.2.0)"] + [[package]] name = "graphene" version = "3.4.3" @@ -2339,18 +2378,20 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-version = "1.0.8" +version = "1.1.0" description = "Building stateful, multi-actor applications with LLMs" optional = false python-versions = ">=3.10" files = [ - {file = "langgraph-1.0.8-py3-none-any.whl", hash = "sha256:da737177c024caad7e5262642bece4f54edf4cba2c905a1d1338963f41cf0904"}, - {file = "langgraph-1.0.8.tar.gz", hash = "sha256:2630fc578846995114fd659f8b14df9eff5a4e78c49413f67718725e88ceb544"}, + {file = "langgraph-1.1.0-py3-none-any.whl", hash = "sha256:7d29e01312340c9c7c09eb0178db5edd0514c3f35929fa5b32a247fcd9a9cd65"}, + {file = "langgraph-1.1.0.tar.gz", hash = "sha256:2decaef5d6716166dc5c13e0fdf65637e5a1837ef4c94fd82b6bcf2115cb5c78"}, ] [package.dependencies] langchain-core = ">=0.1" langgraph-checkpoint = ">=2.1.0,<5.0.0" -langgraph-prebuilt = ">=1.0.7,<1.1.0" +langgraph-prebuilt = ">=1.0.8,<1.1.0" langgraph-sdk = ">=0.3.0,<0.4.0" pydantic = ">=2.7.4" xxhash = ">=3.5.0" @@ -2444,13 +2502,13 @@ ormsgpack = ">=1.12.0" [[package]] name = "langgraph-prebuilt" -version = "1.0.7" +version = "1.0.8" description = "Library with high-level APIs for creating and executing LangGraph agents and tools." optional = false python-versions = ">=3.10" files = [ - {file = "langgraph_prebuilt-1.0.7-py3-none-any.whl", hash = "sha256:e14923516504405bb5edc3977085bc9622c35476b50c1808544490e13871fe7c"}, - {file = "langgraph_prebuilt-1.0.7.tar.gz", hash = "sha256:38e097e06de810de4d0e028ffc0e432bb56d1fb417620fb1dfdc76c5e03e4bf9"}, + {file = "langgraph_prebuilt-1.0.8-py3-none-any.whl", hash = "sha256:d16a731e591ba4470f3e313a319c7eee7dbc40895bcf15c821f985a3522a7ce0"}, + {file = "langgraph_prebuilt-1.0.8.tar.gz", hash = "sha256:0cd3cf5473ced8a6cd687cc5294e08d3de57529d8dd14fdc6ae4899549efcf69"}, ] [package.dependencies] @@ -4787,13 +4845,13 @@ six = ">=1.5" [[package]] name = "python-dotenv" -version = "1.2.1" +version = "1.2.2" description = "Read key-value pairs from a .env file and set them as environment variables" optional = false -python-versions = ">=3.9" 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"27ad16049492aab295220b7403a1b56da875c8aa7626d438550d3b3bb2d8a402" diff --git a/pyproject.toml b/pyproject.toml index 022ecf8..d987c67 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,7 +12,7 @@ readme = "README.md" [tool.poetry.dependencies] # update the Python versions in .github/workflows/check.yml if you change this python = ">= 3.10.0 < 3.14.0" -langchain = "^1.2.4" +langchain = {extras = ["google-genai", "openai"], version = "^1.2.11"} langchain-openai = "^1.1.7" langgraph = "^1.0.6" dotenv = "^0.9.9" From b6a305010ce7085c01343861fca6d0ef2aa40dc2 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 11:36:09 +0100 Subject: [PATCH 62/94] feat: LLM can use runtime crafted tools --- GoT/model/graph_model.py | 33 ++++++++++++++++++++------------- GoT/model/ollama_llm.py | 18 ++++++++++++------ GoT/model/runtime_graph.py | 8 ++++++++ GoT/tools/ai_tool.py | 1 - GoT/tools/craft_tool.py | 2 +- 5 files changed, 41 insertions(+), 21 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 7611b81..1631434 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -56,6 +56,7 @@ "\n4: The response follow the instruction but the result is wrong or the result is correct but doesn't follow the instruction. " "\n5: The response follow the instruction and the result is near to the solution (If the task is hard, the solution should be near to the corrected one). " "\n6: The response follow the instruction and the result is perfectly correct." + "Sometimes a new tool could help to solve the problem, try this option sometimes." ), response_format=Score, ) @@ -147,7 +148,7 @@ def tool_call(messages: MessagesState): ), response_format=Response, ) - + print(OllamaLLM().get_tools()) res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}) tool_used = extract_tool_used(res) runtime_graph.temp_response.response = parse_response_for_tool_node(res).response @@ -179,7 +180,7 @@ def response_evaluation(messages: MessagesState): HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), HumanMessage(content=call_node_response), SystemMessage( - content="Score this solution based on correctness and following instructions." + content="Score this solution based on correctness and following instructions. A new tool could help to solve the problem?" ), AIMessage( content="Tool used in the response: " + ", ".join(test_node.tool_used) @@ -190,6 +191,7 @@ def response_evaluation(messages: MessagesState): judge_agent.invoke({"messages": judge_messages, "tool_choice": Score}) ) test_node.score = score_res.score + test_node.need_tool_crafting = score_res.need_tool_crafting runtime_graph.resolve_node(test_node, score_res.description) return messages @@ -206,15 +208,20 @@ def crafting(messages: MessagesState): runtime_graph.temp_response.response = parse_response_for_tool_node(craft_res).response parsed_res = f"Response: {parse_response_for_tool_node(craft_res).response}\nExplanation: {parse_response_for_tool_node(craft_res).explanation}" runtime_graph.resolve_node(crafting_node, parsed_res) - test_node = TestNode( - f"{parsed_res}", - "", - score=0, - tool_used="", + runtime_graph.temp_node = runtime_graph.call_tool_node() + runtime_graph.add_edge(crafting_node, runtime_graph.temp_node) + global starting_agent + starting_agent = OllamaLLM().create_custom_agent( + OllamaLLM().get_tools(), + SystemMessage( + "You are an assistant specialized in tools. Your goal is not to resolve the problem," + " only to make list with the best tool to use. " + "The list MUST be in this format and it is not possible to format the tool_name in any way: " + "- tool_name " + "- tool_name " + "- tool_name " + ), ) - runtime_graph.add_node(test_node) - runtime_graph.add_edge(crafting_node, test_node) - runtime_graph.temp_node = test_node messages["messages"].append(AIMessage(parsed_res)) return messages @@ -228,10 +235,10 @@ def test_result(messages: MessagesState): runtime_graph.add_edge(test_node, runtime_graph.temp_response) runtime_graph.temp_response.resolved = True return END + elif test_node.score < SCORE_THRESHOLD and n is True and test_node.need_tool_crafting is True: + return "crafting" elif test_node.score < SCORE_THRESHOLD and n is True: return "backtrack" - elif test_node.score < SCORE_THRESHOLD and n is False and not runtime_graph.is_craftin_node_resolved(): - return "crafting" else: chat_completition_node = CompletitionNode( "Please, solve this problem", @@ -300,7 +307,7 @@ def invoke_graph(): graph.add_edge("tool_reasoning", "tool_call") graph.add_edge("tool_call", "response_evaluation") graph.add_edge("backtrack", "tool_reasoning") - graph.add_edge("crafting", "response_evaluation") + graph.add_edge("crafting", "tool_reasoning") graph.add_edge("chat_completition", END) graph.add_conditional_edges( "response_evaluation", diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 3361c6e..3ae7aea 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -1,6 +1,8 @@ +import inspect import os import importlib -from langchain.tools import BaseTool +import sys +from langchain.tools import BaseTool, tool from langchain_google_genai import ChatGoogleGenerativeAI from dotenv import load_dotenv @@ -65,12 +67,16 @@ def get_craft_tool(self): return [craft_tool, install_dependency] def get_crafted_tools(self) -> list[BaseTool]: - module = importlib.import_module("GoT.tools.ai_tool") + module_name = "GoT.tools.ai_tool" + if module_name in sys.modules: + module = importlib.reload(sys.modules[module_name]) + else: + module = importlib.import_module(module_name) tools = [] - for obj in module.__dict__.values(): - if isinstance(obj, BaseTool): - tools.append(obj) - + for name, obj in module.__dict__.items(): + if inspect.isfunction(obj) and obj.__module__ == module.__name__: + tools.append(tool(obj)) # wrap runtime + return tools def create_custom_agent( diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 33f25de..5b4bd79 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -35,12 +35,14 @@ def __init__( score: int, tool_used: List[str] = [], resolved: bool = False, + need_tool_crafting: bool = False, ): super().__init__(resolved) self.prompt = prompt self.response = response self.score = score self.tool_used = tool_used + self.need_tool_crafting = need_tool_crafting class ToolNode(RuntimeNode): @@ -126,6 +128,7 @@ class Score(BaseModel): Attributes: score: int - The score assigned to the test node. description: str - A description or rationale for the assigned score. + need_tool_crafting: bool - Indicates whether the test node requires crafting a new tool to be resolved. """ score: int = Field(..., description="Integer score between 0 and 6 inclusive.") @@ -135,6 +138,11 @@ class Score(BaseModel): description="Short justification (1-3 sentences) explaining why the score was assigned.", ) + need_tool_crafting: bool = Field( + ..., + description="Indicates whether the problem requires or it is useful to craft a new tool to be resolved.", + ) + class Response(BaseModel): """Rapresents a response for a tool node.""" diff --git a/GoT/tools/ai_tool.py b/GoT/tools/ai_tool.py index ce8c8f9..e69de29 100644 --- a/GoT/tools/ai_tool.py +++ b/GoT/tools/ai_tool.py @@ -1 +0,0 @@ -from langchain.tools import tool \ No newline at end of file diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index ed57ce2..54567f8 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -55,7 +55,7 @@ def sanitize_input(query: str) -> str: try: base_dir = Path(__file__).parent file_path = base_dir / "ai_tool.py" - code = f"""\n\n@tool\n{sanitize_input(tool_function)}""" + code = f"""\n\n{sanitize_input(tool_function)}""" with open(file_path, "a") as f: f.write(code) return "Tool crafted successfully." From 4be202b49a73d090d3acdba7c4ee2c768f4dbd4c Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 15:36:48 +0100 Subject: [PATCH 63/94] chore: improve agent prompts --- GoT/model/graph_model.py | 77 ++++++++++++++++++++++++++++------------ GoT/model/lm_wrapper.py | 2 +- 2 files changed, 55 insertions(+), 24 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 1631434..d00f6a9 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -30,8 +30,9 @@ starting_agent = OllamaLLM().create_custom_agent( OllamaLLM().get_tools(), SystemMessage( - "You are an assistant specialized in tools. Your goal is not to resolve the problem," - " only to make list with the best tool to use. " + "You are an assistant specialized in tools. " \ + " Your goal is not to resolve the problem, but only to make list with the best tool to use. " + " If you are not sure of the tool you have, think also a generic tool to craft that could be useful to solve the problem. " "The list MUST be in this format and it is not possible to format the tool_name in any way: " "- tool_name " "- tool_name " @@ -44,19 +45,32 @@ judge_agent = OllamaLLM().create_custom_agent( [], SystemMessage( - "You are an assistant specialized in validation of response, like an LLM-as-a-judge. " - "Your duty is to score, from 0 to 6, the response that user gives to you and assign to it a score. You can not give the response, but can give a hint for the solution. " - "You MUST respond ONLY using the Score function. " - "Do not write natural language outside the function. " - "If you fail to respect the format, the evaluation will fail." - "\n0: The response is impossible to understand and completely wrong. " - "\n1: The response is near to be completely wrong. " - "\n2: The response is in the correct language but it doesn't follow the instruction. " - "\n3: The response try to resolve the problem but doesn't follow the instruction or the response is wrong. " - "\n4: The response follow the instruction but the result is wrong or the result is correct but doesn't follow the instruction. " - "\n5: The response follow the instruction and the result is near to the solution (If the task is hard, the solution should be near to the corrected one). " - "\n6: The response follow the instruction and the result is perfectly correct." - "Sometimes a new tool could help to solve the problem, try this option sometimes." + """ + You are an assistant specialized in validating responses, like an LLM-as-a-judge. + Your duty is to score, from 0 to 6, the response that user gives and assign a score. + + Rules: + - You MUST respond ONLY using the Score function. + - You cannot give the full solution, only hints. + - If the response cannot solve the task as-is, you SHOULD suggest a new tool to help. + - Do not write natural language outside the function. + - Always consider creating a tool if it makes the response correct or reusable. + + Score meanings: + 0: Impossible to understand / completely wrong + 1: Nearly completely wrong + 2: Correct language but does not follow instruction + 3: Tries to solve but fails instruction / wrong + 4: Follows instruction but result wrong or incomplete + 5: Follows instruction, result nearly correct + 6: Follows instruction, result correct + + Example: + - User response: cannot compute because missing helper + - Hint: implement a tool that computes the missing function + + Always prioritize suggesting a reusable tool if it helps solve the problem. + """ ), response_format=Score, ) @@ -64,10 +78,27 @@ crafter_agent = OllamaLLM().create_custom_agent( OllamaLLM().get_craft_tool(), SystemMessage( - "You are an assistant specialized in crafting tools. Your goal is to craft a tool that help to other agents to solve the problem." - "You MUST respond ONLY using the tool that you have at your disposal. " - "No comment in the python interpreter will be allowed. " - "Do not write natural language outside the tool. " + """ + You create reusable Python tools for other agents. + + The tool must be GENERAL and parameterized. + Never hardcode values from the current problem. + + Bad example (too specific): + def multiply_3_and_7(): + return 3 * 7 + + Good example (general): + def multiply(a: float, b: float) -> float: + return a * b + + Rules: + - Prefer generic names and parameters. + - If the function contains specific numbers or values, it is wrong. + - Respond ONLY using the tool available. + - No natural language. + - No comments in the python interpreter. + """ ), response_format=Response, ) @@ -96,12 +127,12 @@ def tool_expand(goal: MessagesState): goal["messages"].append(AIMessage(content=str_res)) tool_list = parse_tool_list(str_res) # Toglie elementi inutili # add tool nodes in the runtime graph - for tool in tool_list: + for i in range(0, 3): tool_node = RuntimeNode(resolved=True) call_node = ToolNode( "Please, resolve the problem with the tools given, you MUST follow the previous reasoning.", "", - tool_name=tool, + tool_name="", ) reasoning_node = ReasoningNode("") runtime_graph.add_node(tool_node) @@ -112,7 +143,7 @@ def tool_expand(goal: MessagesState): runtime_graph.add_node(call_node) runtime_graph.add_edge(tool_node, reasoning_node) runtime_graph.add_edge(reasoning_node, call_node) - runtime_graph.add_tool_link(call_node, tool) + # runtime_graph.add_tool_link(call_node, tool) # extract a reasoning node to resolve runtime_graph.temp_node = runtime_graph.call_tool_node() return goal @@ -148,7 +179,6 @@ def tool_call(messages: MessagesState): ), response_format=Response, ) - print(OllamaLLM().get_tools()) res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}) tool_used = extract_tool_used(res) runtime_graph.temp_response.response = parse_response_for_tool_node(res).response @@ -205,6 +235,7 @@ def crafting(messages: MessagesState): SystemMessage(content="Craft a tool to solve this problem using craft_tool. It must be a function"), ] craft_res = crafter_agent.invoke({"messages": crafting_messages}) + print(OllamaLLM().get_crafted_tools()) runtime_graph.temp_response.response = parse_response_for_tool_node(craft_res).response parsed_res = f"Response: {parse_response_for_tool_node(craft_res).response}\nExplanation: {parse_response_for_tool_node(craft_res).explanation}" runtime_graph.resolve_node(crafting_node, parsed_res) diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index c6a17d3..33c994b 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -181,7 +181,7 @@ def _extract_text_from_request(self, request): return prompt # Fallback per altri campi - for field in ["input", "question", "text", "prompt", "instruction"]: + for field in ["input", "question", "problem", "text", "prompt", "instruction"]: if field in doc and doc[field]: return str(doc[field]) From 26b1824f80df51db261d78d055ee060601c0ff88 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 15:38:16 +0100 Subject: [PATCH 64/94] style: ruff format --- GoT/__init__.py | 20 ++++++++++---------- GoT/model/graph_model.py | 31 +++++++++++++++++++++++-------- GoT/model/lm_wrapper.py | 9 ++++++++- GoT/model/ollama_llm.py | 4 ++-- GoT/model/runtime_graph.py | 3 ++- GoT/tools/craft_tool.py | 13 +++++++------ 6 files changed, 52 insertions(+), 28 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index ac94259..f008f57 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -1,14 +1,12 @@ import json import logging -import os from dotenv import load_dotenv from lm_eval import evaluator, tasks from GoT.model.graph_model import call_graph from GoT.model.lm_wrapper import ( - LangGraphLMWrapper, OllamaTestLMWrapper, - LangGraphBigBenchWrapper + LangGraphBigBenchWrapper, ) from GoT.model.utils.utils import print_benchmark_result @@ -19,6 +17,7 @@ # Possible filter = "flexible", "none", "strict" + def lm_eval_test_benchmark(): task_name = "gsm8k" task_list = [task_name] @@ -40,15 +39,17 @@ def lm_eval_test_benchmark(): def lm_eval_graph_benchmark(): - task_name = "bigbench_logical_sequence_generate_until" + # hendrycks_math_geometry + task_name = "gsm8k" task_list = [task_name] lm = LangGraphBigBenchWrapper() task_dict = tasks.get_task_dict(task_list) results = evaluator.evaluate( lm=lm, + # limit=10, task_dict=task_dict, - limit=5, # Limit the number of samples + samples={task_name: [5, 10, 20, 50, 100, 150, 127, 152]}, log_samples=True, ) @@ -56,18 +57,17 @@ def lm_eval_graph_benchmark(): with open("ollama_graph_benchmark_results.json", "w") as f: json.dump(results, f, indent=2) - print_benchmark_result(results, task_name, filter="none") + print_benchmark_result(results, task_name, filter="flexible") def custom_test(): - call_graph( - "Josh decides to try flipping a house. He buys a house for $80,000 and then puts in $50,000 in repairs. This increased the value of the house by 150%. How much profit did he make?" - ) + call_graph("∫x2⋅ex2dx") def main(): # It could be changed with custom_test() to test a custom problem instead of the benchmark - custom_test() + lm_eval_graph_benchmark() + # let this be the last line of this file logger.info("GoT loaded") diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index d00f6a9..4f8e45e 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -30,7 +30,7 @@ starting_agent = OllamaLLM().create_custom_agent( OllamaLLM().get_tools(), SystemMessage( - "You are an assistant specialized in tools. " \ + "You are an assistant specialized in tools. " " Your goal is not to resolve the problem, but only to make list with the best tool to use. " " If you are not sure of the tool you have, think also a generic tool to craft that could be useful to solve the problem. " "The list MUST be in this format and it is not possible to format the tool_name in any way: " @@ -101,7 +101,7 @@ def multiply(a: float, b: float) -> float: """ ), response_format=Response, -) +) # Defining runtime graph runtime_graph = RuntimeGraph() @@ -225,6 +225,7 @@ def response_evaluation(messages: MessagesState): runtime_graph.resolve_node(test_node, score_res.description) return messages + def crafting(messages: MessagesState): crafting_node = CraftingNode(response="", tool_crafted="", resolved=False) runtime_graph.add_node(crafting_node) @@ -232,16 +233,20 @@ def crafting(messages: MessagesState): runtime_graph.temp_node = crafting_node crafting_messages = [ HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), - SystemMessage(content="Craft a tool to solve this problem using craft_tool. It must be a function"), + SystemMessage( + content="Craft a tool to solve this problem using craft_tool. It must be a function" + ), ] craft_res = crafter_agent.invoke({"messages": crafting_messages}) print(OllamaLLM().get_crafted_tools()) - runtime_graph.temp_response.response = parse_response_for_tool_node(craft_res).response - parsed_res = f"Response: {parse_response_for_tool_node(craft_res).response}\nExplanation: {parse_response_for_tool_node(craft_res).explanation}" + runtime_graph.temp_response.response = parse_response_for_tool_node( + craft_res + ).response + parsed_res = f"Response: {parse_response_for_tool_node(craft_res).response}\nExplanation: {parse_response_for_tool_node(craft_res).explanation}" runtime_graph.resolve_node(crafting_node, parsed_res) runtime_graph.temp_node = runtime_graph.call_tool_node() runtime_graph.add_edge(crafting_node, runtime_graph.temp_node) - global starting_agent + global starting_agent starting_agent = OllamaLLM().create_custom_agent( OllamaLLM().get_tools(), SystemMessage( @@ -256,6 +261,7 @@ def crafting(messages: MessagesState): messages["messages"].append(AIMessage(parsed_res)) return messages + def test_result(messages: MessagesState): n = runtime_graph.exist_tool_available() test_node = runtime_graph.temp_node @@ -266,7 +272,11 @@ def test_result(messages: MessagesState): runtime_graph.add_edge(test_node, runtime_graph.temp_response) runtime_graph.temp_response.resolved = True return END - elif test_node.score < SCORE_THRESHOLD and n is True and test_node.need_tool_crafting is True: + elif ( + test_node.score < SCORE_THRESHOLD + and n is True + and test_node.need_tool_crafting is True + ): return "crafting" elif test_node.score < SCORE_THRESHOLD and n is True: return "backtrack" @@ -343,7 +353,12 @@ def invoke_graph(): graph.add_conditional_edges( "response_evaluation", test_result, - {"crafting": "crafting", "backtrack": "backtrack", "chat_completition": "chat_completition", END: END}, + { + "crafting": "crafting", + "backtrack": "backtrack", + "chat_completition": "chat_completition", + END: END, + }, ) graph = graph.compile() diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 33c994b..a3fa4f7 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -181,7 +181,14 @@ def _extract_text_from_request(self, request): return prompt # Fallback per altri campi - for field in ["input", "question", "problem", "text", "prompt", "instruction"]: + for field in [ + "input", + "question", + "problem", + "text", + "prompt", + "instruction", + ]: if field in doc and doc[field]: return str(doc[field]) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 3ae7aea..94a48b1 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -62,10 +62,10 @@ def get_tools(self): initial_tools = [summing, minus, square_root, multiply, divide] crafted_tools = self.get_crafted_tools() return initial_tools + crafted_tools - + def get_craft_tool(self): return [craft_tool, install_dependency] - + def get_crafted_tools(self) -> list[BaseTool]: module_name = "GoT.tools.ai_tool" if module_name in sys.modules: diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 5b4bd79..e471eec 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -110,6 +110,7 @@ def __init__( super().__init__(resolved) self.response = response + class CraftingNode(RuntimeNode): def __init__( self, @@ -201,7 +202,7 @@ def is_craftin_node_resolved(self) -> bool: n for n in nodes if (isinstance(n, CraftingNode) and n.resolved) ] return True if crafting_nodes else False - + def append_prompt_to_messages_state( self, node: TestNode | ToolNode | CompletitionNode | GoalNode ) -> MessagesState: diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index 54567f8..ff2de04 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -17,7 +17,7 @@ def sanitize_input(query: str) -> str: query = re.sub(r"^(\s|`)*(?i:python)?\s*", "", query) query = re.sub(r"(\s|`)*$", "", query) return query - + try: # WARNING: Using eval/exec can be dangerous. This is just for demonstration purposes. namespace = {} @@ -36,13 +36,14 @@ def install_dependency(package_name: str) -> str: except Exception as e: return str(e) + @tool def craft_tool(tool_function: str) -> str: """Save the function definition provided by the LLM as a tool that can be used by other agents. - The function should be defined as a python function. - The function should be general and reusable, and should not be specific to the current problem. - The function should be defined in a way that it can be imported and used by other agents.""" - + The function should be defined as a python function. + The function should be general and reusable, and should not be specific to the current problem. + The function should be defined in a way that it can be imported and used by other agents.""" + def sanitize_input(query: str) -> str: """Sanitize input to the python REPL. Remove whitespace, backtick & python @@ -61,4 +62,4 @@ def sanitize_input(query: str) -> str: return "Tool crafted successfully." except Exception as e: print(e) - return str(e) \ No newline at end of file + return str(e) From b8dde84c5534305c33d669382fab85d415136d84 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 15:45:51 +0100 Subject: [PATCH 65/94] chore: fix mypy --- GoT/model/utils/utils.py | 2 +- GoT/tools/craft_tool.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 59cbd8b..740001a 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -55,7 +55,7 @@ def parse_score(response: MessagesState) -> Score: if isinstance(score, Score): return score else: - return Score(score=0, description="Failed to parse score") + return Score(score=0, description="Failed to parse score", need_tool_crafting=False) def parse_response_for_tool_node(response: MessagesState) -> Response: diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index ff2de04..da5bcdc 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -20,7 +20,7 @@ def sanitize_input(query: str) -> str: try: # WARNING: Using eval/exec can be dangerous. This is just for demonstration purposes. - namespace = {} + namespace: dict[str, object] = {} exec(sanitize_input(code), namespace, namespace) return str(namespace.get("result", "No result variable defined.")) except Exception as e: From 55edd8470346ce679e9408047eb0f959df99fe7d Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 16:00:11 +0100 Subject: [PATCH 66/94] chore: comment unused line --- GoT/model/graph_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 4f8e45e..adb79e3 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -125,7 +125,7 @@ def tool_expand(goal: MessagesState): res = starting_agent.invoke({"messages": messages}) str_res = parse_response(res) goal["messages"].append(AIMessage(content=str_res)) - tool_list = parse_tool_list(str_res) # Toglie elementi inutili + # tool_list = parse_tool_list(str_res) # Toglie elementi inutili # add tool nodes in the runtime graph for i in range(0, 3): tool_node = RuntimeNode(resolved=True) From 11c572cd150eb9fd7c8d21081e0de8d8bfc95f92 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 16:05:43 +0100 Subject: [PATCH 67/94] chore: remove warning --- GoT/model/graph_model.py | 1 - 1 file changed, 1 deletion(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index adb79e3..430e0a7 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -21,7 +21,6 @@ parse_response, parse_response_for_tool_node, parse_score, - parse_tool_list, ) SCORE_THRESHOLD = 5 From ce7a1aae8a141fff8daf5ecf4137422c07859572 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Mon, 16 Mar 2026 16:08:53 +0100 Subject: [PATCH 68/94] style: ruff format --- GoT/model/utils/utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 740001a..a52826c 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -55,7 +55,9 @@ def parse_score(response: MessagesState) -> Score: if isinstance(score, Score): return score else: - return Score(score=0, description="Failed to parse score", need_tool_crafting=False) + return Score( + score=0, description="Failed to parse score", need_tool_crafting=False + ) def parse_response_for_tool_node(response: MessagesState) -> Response: From 22720339d1f47ffb7efe320dda864220d08356b9 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 24 Mar 2026 15:29:04 +0100 Subject: [PATCH 69/94] feat: implement gemini llms --- GoT/model/graph_model.py | 31 ++++++++++++++++++------------- GoT/model/lm_wrapper.py | 18 +++++++++--------- GoT/model/ollama_llm.py | 30 ++++++++++++++++++++++++++---- GoT/model/utils/utils.py | 8 ++++++++ 4 files changed, 61 insertions(+), 26 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 430e0a7..7bd8a5c 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -2,7 +2,7 @@ from langchain_core.messages import HumanMessage, SystemMessage, AIMessage from langgraph.graph import StateGraph, MessagesState, START, END -from GoT.model.ollama_llm import OllamaLLM +from GoT.model.ollama_llm import LLM from GoT.model.runtime_graph import ( BacktrackNode, CompletitionNode, @@ -26,8 +26,8 @@ SCORE_THRESHOLD = 5 # Defining agents -starting_agent = OllamaLLM().create_custom_agent( - OllamaLLM().get_tools(), +starting_agent = LLM().create_custom_agent( + LLM().get_tools(), SystemMessage( "You are an assistant specialized in tools. " " Your goal is not to resolve the problem, but only to make list with the best tool to use. " @@ -37,11 +37,12 @@ "- tool_name " "- tool_name " ), + type="remote_standard" ) -chat_completition_agent = OllamaLLM().create_custom_agent([]) +chat_completition_agent = LLM().create_custom_agent([]) -judge_agent = OllamaLLM().create_custom_agent( +judge_agent = LLM().create_custom_agent( [], SystemMessage( """ @@ -72,10 +73,11 @@ """ ), response_format=Score, + type="remote_score_format" ) -crafter_agent = OllamaLLM().create_custom_agent( - OllamaLLM().get_craft_tool(), +crafter_agent = LLM().create_custom_agent( + LLM().get_craft_tool(), SystemMessage( """ You create reusable Python tools for other agents. @@ -100,6 +102,7 @@ def multiply(a: float, b: float) -> float: """ ), response_format=Response, + type="remote_response_format" ) # Defining runtime graph @@ -167,8 +170,8 @@ def tool_reasoning(messages: MessagesState): def tool_call(messages: MessagesState): # It calls the llm and it resolves the call node call_node = runtime_graph.temp_node - tool_agent = OllamaLLM().create_custom_agent( - OllamaLLM().get_tools(), + tool_agent = LLM().create_custom_agent( + LLM().get_tools(), SystemMessage( "You are an assistant specialized in tools. Your goal is to resolve the problem with " " the tool that the user indicates to you. You MUST use the tool that user indicates to you." @@ -177,6 +180,7 @@ def tool_call(messages: MessagesState): "If you fail to respect the format, the evaluation will fail." ), response_format=Response, + type="remote_response_format" ) res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}) tool_used = extract_tool_used(res) @@ -237,7 +241,7 @@ def crafting(messages: MessagesState): ), ] craft_res = crafter_agent.invoke({"messages": crafting_messages}) - print(OllamaLLM().get_crafted_tools()) + print(LLM().get_crafted_tools()) runtime_graph.temp_response.response = parse_response_for_tool_node( craft_res ).response @@ -246,16 +250,17 @@ def crafting(messages: MessagesState): runtime_graph.temp_node = runtime_graph.call_tool_node() runtime_graph.add_edge(crafting_node, runtime_graph.temp_node) global starting_agent - starting_agent = OllamaLLM().create_custom_agent( - OllamaLLM().get_tools(), + starting_agent = LLM().create_custom_agent( + LLM().get_tools(), SystemMessage( "You are an assistant specialized in tools. Your goal is not to resolve the problem," " only to make list with the best tool to use. " "The list MUST be in this format and it is not possible to format the tool_name in any way: " - "- tool_name " + "- tool_name "\ "- tool_name " "- tool_name " ), + type="remote_standard" ) messages["messages"].append(AIMessage(parsed_res)) return messages diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index a3fa4f7..2482583 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -3,7 +3,7 @@ from lm_eval.api.registry import register_model from lm_eval.api.model import LM -from GoT.model.ollama_llm import OllamaLLM +from GoT.model.ollama_llm import LLM from langchain_core.messages import HumanMessage from GoT.model.utils.utils import extract_output, normalize_number, parse_response @@ -30,7 +30,7 @@ def __init__(self): pass def generate(self, requests, max_new_tokens=None): - agent = OllamaLLM().create_custom_agent(tools=OllamaLLM().get_tools()) + agent = LLM().create_custom_agent(tools=LLM().get_tools()) outputs = [] for r in requests: prompt = r["prompt"] @@ -111,7 +111,7 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): """ Generate text until a stopping condition is met. """ - agent = OllamaLLM().create_custom_agent(tools=OllamaLLM().get_tools()) + agent = LLM().create_custom_agent(tools=LLM().get_tools()) outputs = [] for i, request in enumerate(requests): try: @@ -259,18 +259,18 @@ def loglikelihood(self, requests): for i, request in enumerate(requests): try: # Estrai context e continuation - if isinstance(request, tuple) and len(request) == 2: + if hasattr(request, "arguments") and request.arguments: + context, continuation = request.arguments + + elif isinstance(request, tuple) and len(request) == 2: context, continuation = request + else: - # Fallback context = "" continuation = str(request) - # Prepara il prompt per il modello - full_text = context + continuation - # Chiama il grafo - result = call_graph(full_text) + result = call_graph(context) generated_output = normalize_number(extract_output(result)) # Calcola il score di likelihood diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 94a48b1..46f2674 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -11,6 +11,7 @@ from langchain.agents import create_agent import mlflow +from GoT.model.runtime_graph import Response, Score from GoT.tools.math_tool import ( multiply, summing, @@ -25,6 +26,7 @@ mlflow.set_experiment("marcoraggini-experiment") mlflow.openai.autolog() +mlflow.gemini.autolog() mlflow.langchain.autolog() SYSTEM_PROMPT_GENERAL = """ @@ -33,7 +35,7 @@ """ -class OllamaLLM: +class LLM: def __init__(self): with mlflow.set_active_model(name="ollama-agent-ministral-3"): self.ollamaLLM = ChatOpenAI( @@ -50,11 +52,30 @@ def __init__(self): # model="openai/gpt-oss-120b:free", # temperature=0.5, # ) - self.remoteLLM = ChatGoogleGenerativeAI( - model="gemini-2.0-flash", + self.remoteLLMStandard = ChatGoogleGenerativeAI( + model="gemini-3-flash-preview", api_key=os.environ.get("GEMINI_API_KEY"), temperature=1.0, # Gemini 3.0+ defaults to 1.0 ) + self.remoteLLMResponseFormat = ChatGoogleGenerativeAI( + model="gemini-3-flash-preview", + api_key=os.environ.get("GEMINI_API_KEY"), + temperature=1.0, # Gemini 3.0+ defaults to 1.0 + response_schema=Response.model_json_schema() + ) + self.remoteLLMScoreFormat = ChatGoogleGenerativeAI( + model="gemini-3-flash-preview", + api_key=os.environ.get("GEMINI_API_KEY"), + temperature=1.0, # Gemini 3.0+ defaults to 1.0 + response_schema=Score.model_json_schema() + ) + + self.remoteLLMs = { + "remote_standard": self.remoteLLMStandard, + "remote_response_format": self.remoteLLMResponseFormat, + "remote_score_format": self.remoteLLMScoreFormat + } + self.system_prompt = SystemMessage(SYSTEM_PROMPT_GENERAL) @@ -84,9 +105,10 @@ def create_custom_agent( tools, system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL), response_format=None, + type: str = "remote_standard" ): return create_agent( - model=self.ollamaLLM, + model=self.remoteLLMs[type], tools=tools, system_prompt=system_prompt, response_format=response_format, diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index a52826c..9fda27c 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -1,3 +1,4 @@ +import json import re from GoT.model.runtime_graph import Response, Score @@ -52,8 +53,12 @@ def parse_score(response: MessagesState) -> Score: :rtype: int """ score = response.get("structured_response") + score_res = response if isinstance(score, Score): return score + elif score_res is not None: + data = json.loads(score_res) + return Score.model_validate(data) else: return Score( score=0, description="Failed to parse score", need_tool_crafting=False @@ -72,6 +77,9 @@ def parse_response_for_tool_node(response: MessagesState) -> Response: structured_response = response.get("structured_response") if isinstance(structured_response, Response): return structured_response + elif response is not None: + data = json.loads(response) + return Response.model_validate(data) else: return Response( response="Failed to parse response", From 7ed7da32e86d4c9c1cdaab400b855874167059af Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 24 Mar 2026 16:00:26 +0100 Subject: [PATCH 70/94] chore: temporarly suspend pypi publication --- .github/workflows/deploy.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 77a591e..555263c 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -60,7 +60,7 @@ jobs: npm install npx semantic-release env: - PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} + # PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }} GITHUB_TOKEN: ${{ secrets.RELEASE_TOKEN }} RELEASE_TEST_PYPI: ${{ github.event.repository.is_template || contains(github.repository, 'template') }} # dry run if not on main/master branch, or if initial commit From 637ba71e3ddd3f3da2078b0fb2bee681db07add8 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 25 Mar 2026 16:21:36 +0100 Subject: [PATCH 71/94] chore: improve prompt and models --- GoT/model/graph_model.py | 21 ++++-- GoT/model/lm_wrapper.py | 143 +++++++++++++++++++++++++++++++++------ GoT/model/ollama_llm.py | 10 +-- 3 files changed, 139 insertions(+), 35 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 7bd8a5c..c14f705 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -31,7 +31,8 @@ SystemMessage( "You are an assistant specialized in tools. " " Your goal is not to resolve the problem, but only to make list with the best tool to use. " - " If you are not sure of the tool you have, think also a generic tool to craft that could be useful to solve the problem. " + " If you are not sure of the tool you have, think also a generic tool to craft that could be useful to solve the problem (Specify the craft is needed). " + " If the problem require too much steps with the current tools, consider crafting a new tool." "The list MUST be in this format and it is not possible to format the tool_name in any way: " "- tool_name " "- tool_name " @@ -52,7 +53,7 @@ Rules: - You MUST respond ONLY using the Score function. - You cannot give the full solution, only hints. - - If the response cannot solve the task as-is, you SHOULD suggest a new tool to help. + - If a response suggest the need of crafting a tool, score it with 4 or less and specify clearly the need of a new tool to solve the problem. - Do not write natural language outside the function. - Always consider creating a tool if it makes the response correct or reusable. @@ -91,11 +92,19 @@ def multiply_3_and_7(): Good example (general): def multiply(a: float, b: float) -> float: + ' + Arguments: + a: the first number to multiply + b: the second number to multiply + Returns: + The product of a and b + ' return a * b Rules: - - Prefer generic names and parameters. + - Prefer generic names and parameters, never craft specific functions. - If the function contains specific numbers or values, it is wrong. + - Craft only one function, it must contains always the docs. - Respond ONLY using the tool available. - No natural language. - No comments in the python interpreter. @@ -154,7 +163,7 @@ def tool_expand(goal: MessagesState): def tool_reasoning(messages: MessagesState): messages["messages"].append( HumanMessage( - "Please, reason about how to use these tools to solve the problem, without solving it." + "Please, reason about how to use these tools to solve the problem, without solving it. If the main solution require to craft a tool, only explain how to craft the tool" ) ) result = parse_response(starting_agent.invoke(messages)) @@ -174,8 +183,8 @@ def tool_call(messages: MessagesState): LLM().get_tools(), SystemMessage( "You are an assistant specialized in tools. Your goal is to resolve the problem with " - " the tool that the user indicates to you. You MUST use the tool that user indicates to you." - "You MUST respond ONLY using the Response function. " + " the tool that the user indicates to you. You HAVE to use the tool that the assistant indicates to you." + " If the previous assistant suggest to you that craft a tool is needed, specify it clearly and do not solve the problem." "Do not write natural language outside the function. " "If you fail to respect the format, the evaluation will fail." ), diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 2482583..6b98d5e 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -245,45 +245,35 @@ def generate_until(self, requests, until=None, max_new_tokens=None, **kwargs): return outputs def loglikelihood(self, requests): - """ - Calcola la log-likelihood per BigBench multiple choice tasks. - - Riceve una lista di tuple: (context, continuation) - Restituisce lista di tuple: (log_likelihood_score, is_greedy) - - NOTA: Poiché invoke_graph non fornisce logits, usiamo un'euristica. - Se hai bisogno di precision massima, modifica invoke_graph per restituire logits. - """ outputs = [] + cache = {} # context -> score già calcolato for i, request in enumerate(requests): try: - # Estrai context e continuation if hasattr(request, "arguments") and request.arguments: context, continuation = request.arguments - elif isinstance(request, tuple) and len(request) == 2: context, continuation = request - else: context = "" continuation = str(request) - # Chiama il grafo - result = call_graph(context) - generated_output = normalize_number(extract_output(result)) - - # Calcola il score di likelihood - score = self._calculate_likelihood_score( - generated_output, continuation, context - ) + # Se già calcolato, riutilizza lo score + if context in cache: + score = cache[context] + else: + result = call_graph(context) + generated_output = normalize_number(extract_output(result)) + score = self._calculate_likelihood_score( + generated_output, continuation, context + ) + cache[context] = score outputs.append((score, False)) except Exception as e: print(f"Error in BigBench loglikelihood request {i}: {e}") import traceback - traceback.print_exc() outputs.append((float("-inf"), False)) @@ -336,3 +326,114 @@ def _calculate_likelihood_score( else: # Nessuna sovrapposizione return float("-inf") + + +@register_model("test_bigbench") +class TestBigBenchWrapper(LM): + def __init__(self, model_args=""): + super().__init__() + self.agent = LLM().create_custom_agent(LLM().get_tools()) + + def _extract_text_from_request(self, request): + """ + Estrae il testo dalla request BigBench. + BigBench passa il prompt completo in doc['inputs']. + """ + # Prova con request.doc - BigBench usa il campo 'inputs' + if hasattr(request, "doc") and isinstance(request.doc, dict): + doc = request.doc + + # BigBench mette il prompt completo in 'inputs' + if "inputs" in doc and doc["inputs"]: + prompt = str(doc["inputs"]).strip() + return prompt + + # Fallback per altri campi + for field in [ + "input", + "question", + "problem", + "text", + "prompt", + "instruction", + ]: + if field in doc and doc[field]: + return str(doc[field]) + + # Fallback: prova con request.arguments + if hasattr(request, "arguments") and request.arguments: + try: + full_prompt = request.arguments[0][0] + if full_prompt and len(str(full_prompt).strip()) > 1: + return full_prompt + except (IndexError, TypeError): + pass + + return str(request) + + def generate_until(self, requests, until=None, **kwargs): + outputs = [] + + if not until: + until = ["\n\n", "\n"] + elif isinstance(until, str): + until = [until] + + for request in requests: + try: + prompt = self._extract_text_from_request(request) + + response = self.agent.invoke({"messages": [HumanMessage(content=prompt)]}) + response = extract_output(response) + + if not isinstance(response, str): + response = str(response) + + for stop_seq in until: + if stop_seq in response: + response = response.split(stop_seq)[0] + + outputs.append(response.strip()) + + except Exception as e: + print(f"Agent error: {e}") + outputs.append("") + + return outputs + + def loglikelihood(self, requests): + """ + Per agent è meglio NON usare loglikelihood classico. + Usiamo matching diretto. + """ + outputs = [] + + for request in requests: + try: + if hasattr(request, "arguments") and request.arguments: + context, continuation = request.arguments + else: + context, continuation = request + + response = self.agent.invoke({"messages": [HumanMessage(content=context)]}) + response = str(response).strip().lower() + target = str(continuation).strip().lower() + + # scoring semplice + if response == target: + score = 0.0 + elif target in response: + score = -0.5 + else: + score = -5.0 + + outputs.append((score, False)) + + except Exception as e: + print(f"loglikelihood error: {e}") + outputs.append((float("-inf"), False)) + + return outputs + + def loglikelihood_rolling(self, requests): + return self.loglikelihood(requests) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 46f2674..4d838cb 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -46,22 +46,16 @@ def __init__(self): reasoning_effort="none", ) - # self.remoteLLM = ChatOpenAI( - # base_url="https://openrouter.ai/api/v1", - # api_key=os.environ.get("OPEN_ROUTER_KEY"), - # model="openai/gpt-oss-120b:free", - # temperature=0.5, - # ) + # GEMINI LLMs self.remoteLLMStandard = ChatGoogleGenerativeAI( model="gemini-3-flash-preview", api_key=os.environ.get("GEMINI_API_KEY"), temperature=1.0, # Gemini 3.0+ defaults to 1.0 ) self.remoteLLMResponseFormat = ChatGoogleGenerativeAI( - model="gemini-3-flash-preview", + model="gemini-2.5-flash", api_key=os.environ.get("GEMINI_API_KEY"), temperature=1.0, # Gemini 3.0+ defaults to 1.0 - response_schema=Response.model_json_schema() ) self.remoteLLMScoreFormat = ChatGoogleGenerativeAI( model="gemini-3-flash-preview", From b141e8a827b52c64c33402fd252fe9bf77d4ca72 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Mar 2026 14:19:24 +0100 Subject: [PATCH 72/94] fix: fix prompt for not raise exception --- GoT/model/graph_model.py | 1 + 1 file changed, 1 insertion(+) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index c14f705..b19804d 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -105,6 +105,7 @@ def multiply(a: float, b: float) -> float: - Prefer generic names and parameters, never craft specific functions. - If the function contains specific numbers or values, it is wrong. - Craft only one function, it must contains always the docs. + - Never craft tool that raise exceptions. - Respond ONLY using the tool available. - No natural language. - No comments in the python interpreter. From 131ad0a219162ac02bcad53b7eda732b5dbfa52f Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Mar 2026 14:33:48 +0100 Subject: [PATCH 73/94] chore: print loglikehood benchmark --- GoT/model/lm_wrapper.py | 51 ++++++++++++++-------------------------- GoT/model/utils/utils.py | 35 +++++++++++++++++++++++++++ 2 files changed, 52 insertions(+), 34 deletions(-) diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 6b98d5e..6a6851e 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -255,8 +255,7 @@ def loglikelihood(self, requests): elif isinstance(request, tuple) and len(request) == 2: context, continuation = request else: - context = "" - continuation = str(request) + context, continuation = request # Se già calcolato, riutilizza lo score if context in cache: @@ -264,12 +263,15 @@ def loglikelihood(self, requests): else: result = call_graph(context) generated_output = normalize_number(extract_output(result)) - score = self._calculate_likelihood_score( - generated_output, continuation, context - ) - cache[context] = score + print(f"DEBUG - Context: {context}") + cache[context] = generated_output - outputs.append((score, False)) + gen_text = cache[context] + score = self._calculate_likelihood_score( + gen_text, continuation, context + ) + + outputs.append((score, score >= 1.0)) except Exception as e: print(f"Error in BigBench loglikelihood request {i}: {e}") @@ -293,39 +295,20 @@ def _calculate_likelihood_score( Calcola un score di likelihood per BigBench. Strategie: - 1. Se la generazione contiene esattamente la continuation target -> score alto (0.0) + 1. Se la generazione contiene esattamente la continuation target -> score alto (1.0) 2. Se contiene la continuation parzialmente -> score medio - 3. Altrimenti -> score basso (log della similarità) + 3. Altrimenti -> score basso (0.0) Questo è un workaround poiché non abbiamo i veri logits. """ gen_text = str(generated_text).strip().lower() - target = str(target_continuation).strip().lower() - - # Caso 1: Match esatto - if gen_text == target: - return 0.0 # log(1) = 0, massima likelihood - - # Caso 2: Target è contenuto nel testo generato + target = str(target_continuation).strip().lower().replace("(", "").replace(")", "") + + # Se il target è contenuto nella risposta (es: "a" è in "la risposta è a") if target in gen_text: - # Parziale match, score moderato - return math.log(0.8) - - # Caso 3: Calcola sovrapposizione di parole - gen_words = set(gen_text.split()) - target_words = set(target.split()) - - if not target_words: - return 0.0 - - overlap = len(gen_words & target_words) / len(target_words) - - if overlap > 0: - # Maggiore è l'overlap, più alto lo score - return math.log(overlap) - else: - # Nessuna sovrapposizione - return float("-inf") + return 5.0 # Match trovato + + return -1.0 # Invece di -inf, usa un valore molto basso ma numerico @register_model("test_bigbench") diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 9fda27c..fe0ec26 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -1,6 +1,8 @@ import json import re +import numpy as np + from GoT.model.runtime_graph import Response, Score from langgraph.graph import MessagesState from langchain_core.messages import AIMessage @@ -167,3 +169,36 @@ def print_benchmark_result(results: dict, task_name: str, filter: str) -> None: print(f"Total: {n_total}") print(f"Correct answers (filter={filter}): {n_correct}") print(f"Wrong answers (filter={filter}): {n_wrong}") + +def print_benchmark_result_loglikehood(results: dict, task_name: str, filter_val: str) -> None: + # 1. Recupero i samples + samples = results.get("samples", {}).get(task_name, []) + + # 2. Filtro (solitamente filter="none" nel tuo caso) + flex_samples = [s for s in samples if filter_val in s.get("filter", "")] + + n_total = len(flex_samples) + n_correct = 0 + + for s in flex_samples: + + try: + scores = [r[0][0] for r in s.get("resps", [])] + + if not scores: + continue + + predicted_idx = int(np.argmax(scores)) + + if predicted_idx >= 5.0: + n_correct += 1 + + except (ValueError, IndexError): + continue + + n_wrong = n_total - n_correct + + print(f"--- Risultati basati su RESPS (Task: {task_name}) ---") + print(f"Total: {n_total}") + print(f"Correct: {n_correct}") + print(f"Wrong: {n_wrong}") From f1ec74f73fc7208130d95890e7083019730cfc60 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Mar 2026 14:34:05 +0100 Subject: [PATCH 74/94] chore: add lm eval math --- poetry.lock | 100 ++++++++++++++++++++++++++++++++++++++++++++++++- pyproject.toml | 3 +- 2 files changed, 101 insertions(+), 2 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7f41ad2..041345f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -221,6 +221,17 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] +[[package]] +name = "antlr4-python3-runtime" +version = "4.11.0" +description = "ANTLR 4.11.0 runtime for Python 3" +optional = false +python-versions = "*" +files = [ + {file = "antlr4-python3-runtime-4.11.0.tar.gz", hash = "sha256:6a46d4f6033c8d7e53c8975cee5d83b3ba1b48157f6beb09a4965062dacfe2e0"}, + {file = "antlr4_python3_runtime-4.11.0-py3-none-any.whl", hash = "sha256:f523f91387283045f129c343b363a8dda3a07eea62721b8eda95f2d8b817b656"}, +] + [[package]] name = "anyio" version = "4.12.1" @@ -2560,6 +2571,30 @@ otel = ["opentelemetry-api (>=1.30.0)", "opentelemetry-exporter-otlp-proto-http pytest = ["pytest (>=7.0.0)", "rich (>=13.9.4)", "vcrpy (>=7.0.0)"] vcr = ["vcrpy (>=7.0.0)"] +[[package]] +name = "latex2sympy2-extended" +version = "1.11.0" +description = "Convert LaTeX math to SymPy expressions" +optional = false +python-versions = ">=3.10" +files = [ + {file = "latex2sympy2_extended-1.11.0-py3-none-any.whl", hash = "sha256:aebb77d52ce269e25028e4bea89ddb14d242ba36bcf7b636496fb5fd9728d234"}, + {file = "latex2sympy2_extended-1.11.0.tar.gz", hash = "sha256:9695657c81b50abba2636638638618db59f4663ed2a4a12d62cef74a40e28fec"}, +] + +[package.dependencies] +antlr4-python3-runtime = [ + {version = ">=4.9.3,<=4.13.2"}, + {version = "4.11.0", optional = true, markers = "extra == \"antlr4-11-0\""}, +] +sympy = "*" + +[package.extras] +antlr4-11-0 = ["antlr4-python3-runtime (==4.11.0)"] +antlr4-13-2 = ["antlr4-python3-runtime (==4.13.2)"] +antlr4-9-3 = ["antlr4-python3-runtime (==4.9.3)"] +dev = ["pytest"] + [[package]] name = "librt" version = "0.8.0" @@ -2671,11 +2706,13 @@ files = [ ] [package.dependencies] +antlr4-python3-runtime = {version = "4.11", optional = true, markers = "extra == \"math\""} datasets = ">=2.16.0" dill = "*" evaluate = ">=0.4.0" jinja2 = "*" jsonlines = "*" +math_verify = {version = "*", extras = ["antlr4-11-0"], optional = true, markers = "extra == \"math\""} more_itertools = "*" numpy = "*" pytablewriter = "*" @@ -2683,6 +2720,7 @@ rouge-score = ">=0.0.4" sacrebleu = ">=1.5.0" scikit-learn = ">=0.24.1" sqlitedict = "*" +sympy = {version = ">=1.12", optional = true, markers = "extra == \"math\""} typing_extensions = "*" word2number = "*" zstandard = "*" @@ -3009,6 +3047,32 @@ files = [ {file = "markupsafe-3.0.3.tar.gz", hash = "sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698"}, ] +[[package]] +name = "math-verify" +version = "0.9.0" +description = "HuggingFace library for verifying mathematical answers" +optional = false +python-versions = ">=3.10" +files = [ + {file = "math_verify-0.9.0-py3-none-any.whl", hash = "sha256:3703e7c4885354027fa84409d762a596a2906d1fd4deb78361876bd905a76194"}, + {file = "math_verify-0.9.0.tar.gz", hash = "sha256:45ac6c61344ba056b9e99a660a4bc8d044ed408f730aed68c60435aa5eec4645"}, +] + +[package.dependencies] +latex2sympy2_extended = [ + {version = "1.11.0"}, + {version = "*", extras = ["antlr4-11-0"], optional = true, markers = "extra == \"antlr4-11-0\""}, +] + +[package.extras] +antlr4-11-0 = ["latex2sympy2_extended[antlr4-11-0]"] +antlr4-13-2 = ["latex2sympy2_extended[antlr4-13-2]"] +antlr4-9-3 = ["latex2sympy2_extended[antlr4-9-3]"] +dev = ["math-verify[format]", "math-verify[test]"] +format = ["ruff"] +inference = ["lighteval[math]"] +test = ["pytest"] + [[package]] name = "matplotlib" version = "3.10.8" @@ -3239,6 +3303,23 @@ files = [ {file = "more_itertools-10.8.0.tar.gz", hash = "sha256:f638ddf8a1a0d134181275fb5d58b086ead7c6a72429ad725c67503f13ba30bd"}, ] +[[package]] +name = "mpmath" +version = "1.3.0" +description = "Python library for arbitrary-precision floating-point arithmetic" +optional = false +python-versions = "*" +files = [ + {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"}, + {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"}, +] + +[package.extras] +develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] +docs = ["sphinx"] +gmpy = ["gmpy2 (>=2.1.0a4)"] +tests = ["pytest (>=4.6)"] + [[package]] name = "msgpack" version = "1.1.2" @@ -5735,6 +5816,23 @@ typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\"" [package.extras] full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"] +[[package]] +name = "sympy" +version = "1.14.0" +description = "Computer algebra system (CAS) in Python" +optional = false +python-versions = ">=3.9" +files = [ + {file = "sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5"}, + {file = "sympy-1.14.0.tar.gz", hash = "sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517"}, +] + +[package.dependencies] +mpmath = ">=1.1.0,<1.4" + +[package.extras] +dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"] + [[package]] name = "tabledata" version = "1.3.4" @@ -6770,4 +6868,4 @@ cffi = ["cffi (>=1.17,<2.0)", "cffi (>=2.0.0b)"] [metadata] lock-version = "2.0" python-versions = ">= 3.10.0 < 3.14.0" -content-hash = "27ad16049492aab295220b7403a1b56da875c8aa7626d438550d3b3bb2d8a402" +content-hash = "1e1f7e3b089394f520b94df309f18e1d296a14c062dcf304c7414fe739687a9e" diff --git a/pyproject.toml b/pyproject.toml index d987c67..3840bbd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,8 +17,9 @@ langchain-openai = "^1.1.7" langgraph = "^1.0.6" dotenv = "^0.9.9" mlflow = "^3.9.0" -lm-eval = "^0.4.10" +lm-eval = {extras = ["math"], version = "^0.4.11"} boto3 = "^1.42.51" +sympy = "^1.14.0" [tool.poetry.group.dev.dependencies] coverage = "^7.4.0" From 4abc304f4c903bd2d89e3f52b8ffffe8ac5388bb Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Mar 2026 14:34:34 +0100 Subject: [PATCH 75/94] core: use 2.5 gemini flash --- GoT/model/ollama_llm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 4d838cb..c3afd05 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -48,7 +48,7 @@ def __init__(self): # GEMINI LLMs self.remoteLLMStandard = ChatGoogleGenerativeAI( - model="gemini-3-flash-preview", + model="gemini-2.5-flash", api_key=os.environ.get("GEMINI_API_KEY"), temperature=1.0, # Gemini 3.0+ defaults to 1.0 ) @@ -58,7 +58,7 @@ def __init__(self): temperature=1.0, # Gemini 3.0+ defaults to 1.0 ) self.remoteLLMScoreFormat = ChatGoogleGenerativeAI( - model="gemini-3-flash-preview", + model="gemini-2.5-flash", api_key=os.environ.get("GEMINI_API_KEY"), temperature=1.0, # Gemini 3.0+ defaults to 1.0 response_schema=Score.model_json_schema() From 3b492e81d8085c1957117bed61f3cb489e590b71 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Mar 2026 14:34:55 +0100 Subject: [PATCH 76/94] chore: use gpqa diamond as benchmark --- GoT/__init__.py | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index f008f57..6088a24 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -5,10 +5,10 @@ from lm_eval import evaluator, tasks from GoT.model.graph_model import call_graph from GoT.model.lm_wrapper import ( - OllamaTestLMWrapper, LangGraphBigBenchWrapper, + TestBigBenchWrapper ) -from GoT.model.utils.utils import print_benchmark_result +from GoT.model.utils.utils import print_benchmark_result, print_benchmark_result_loglikehood logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("GoT") @@ -19,49 +19,50 @@ def lm_eval_test_benchmark(): - task_name = "gsm8k" + task_name = "gpqa_diamond_zeroshot" task_list = [task_name] - test_lm = OllamaTestLMWrapper() + test_lm = TestBigBenchWrapper() task_dict = tasks.get_task_dict(task_list) results = evaluator.evaluate( lm=test_lm, task_dict=task_dict, - limit=20, # Limit the number of samples + limit=2, # Limit the number of samples log_samples=True, + # samples={task_name: [20, 25, 100]}, ) # Save results to a JSON file - with open("ollama_test_benchmark_results.json", "w") as f: - json.dump(results, f, indent=2) + with open("test_benchmark_results.json", "w") as f: + json.dump(results["samples"], f, indent=2) - print_benchmark_result(results, task_name, filter="none") + print_benchmark_result(results, task_name, filter="strict-match") def lm_eval_graph_benchmark(): # hendrycks_math_geometry - task_name = "gsm8k" + task_name = "gpqa_diamond_zeroshot" task_list = [task_name] lm = LangGraphBigBenchWrapper() task_dict = tasks.get_task_dict(task_list) results = evaluator.evaluate( lm=lm, - # limit=10, + # limit=1, task_dict=task_dict, - samples={task_name: [5, 10, 20, 50, 100, 150, 127, 152]}, + samples={task_name: [20, 25]}, log_samples=True, ) # Save results to a JSON file - with open("ollama_graph_benchmark_results.json", "w") as f: - json.dump(results, f, indent=2) + with open("graph_benchmark_results.json", "w") as f: + json.dump(results, f, indent=2, default=str) - print_benchmark_result(results, task_name, filter="flexible") + print_benchmark_result_loglikehood(results, task_name, filter_val="none") def custom_test(): - call_graph("∫x2⋅ex2dx") + call_graph("Solve this integral ∫x2⋅ex2dx") def main(): From e729552c37484e6a1ff036b94f81362cdb6a8d86 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Mar 2026 14:35:41 +0100 Subject: [PATCH 77/94] style: poe format --- GoT/__init__.py | 8 ++++---- GoT/model/graph_model.py | 12 ++++++------ GoT/model/lm_wrapper.py | 23 +++++++++++++++-------- GoT/model/ollama_llm.py | 7 +++---- GoT/model/utils/utils.py | 12 +++++++----- 5 files changed, 35 insertions(+), 27 deletions(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index 6088a24..d4c1e0c 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -4,11 +4,11 @@ from lm_eval import evaluator, tasks from GoT.model.graph_model import call_graph -from GoT.model.lm_wrapper import ( - LangGraphBigBenchWrapper, - TestBigBenchWrapper +from GoT.model.lm_wrapper import LangGraphBigBenchWrapper, TestBigBenchWrapper +from GoT.model.utils.utils import ( + print_benchmark_result, + print_benchmark_result_loglikehood, ) -from GoT.model.utils.utils import print_benchmark_result, print_benchmark_result_loglikehood logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("GoT") diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index b19804d..c90bd19 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -38,7 +38,7 @@ "- tool_name " "- tool_name " ), - type="remote_standard" + type="remote_standard", ) chat_completition_agent = LLM().create_custom_agent([]) @@ -74,7 +74,7 @@ """ ), response_format=Score, - type="remote_score_format" + type="remote_score_format", ) crafter_agent = LLM().create_custom_agent( @@ -112,7 +112,7 @@ def multiply(a: float, b: float) -> float: """ ), response_format=Response, - type="remote_response_format" + type="remote_response_format", ) # Defining runtime graph @@ -190,7 +190,7 @@ def tool_call(messages: MessagesState): "If you fail to respect the format, the evaluation will fail." ), response_format=Response, - type="remote_response_format" + type="remote_response_format", ) res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}) tool_used = extract_tool_used(res) @@ -266,11 +266,11 @@ def crafting(messages: MessagesState): "You are an assistant specialized in tools. Your goal is not to resolve the problem," " only to make list with the best tool to use. " "The list MUST be in this format and it is not possible to format the tool_name in any way: " - "- tool_name "\ + "- tool_name " "- tool_name " "- tool_name " ), - type="remote_standard" + type="remote_standard", ) messages["messages"].append(AIMessage(parsed_res)) return messages diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 6a6851e..02d7e7c 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -276,6 +276,7 @@ def loglikelihood(self, requests): except Exception as e: print(f"Error in BigBench loglikelihood request {i}: {e}") import traceback + traceback.print_exc() outputs.append((float("-inf"), False)) @@ -302,20 +303,22 @@ def _calculate_likelihood_score( Questo è un workaround poiché non abbiamo i veri logits. """ gen_text = str(generated_text).strip().lower() - target = str(target_continuation).strip().lower().replace("(", "").replace(")", "") - + target = ( + str(target_continuation).strip().lower().replace("(", "").replace(")", "") + ) + # Se il target è contenuto nella risposta (es: "a" è in "la risposta è a") if target in gen_text: - return 5.0 # Match trovato - - return -1.0 # Invece di -inf, usa un valore molto basso ma numerico + return 5.0 # Match trovato + + return -1.0 # Invece di -inf, usa un valore molto basso ma numerico @register_model("test_bigbench") class TestBigBenchWrapper(LM): def __init__(self, model_args=""): super().__init__() - self.agent = LLM().create_custom_agent(LLM().get_tools()) + self.agent = LLM().create_custom_agent(LLM().get_tools()) def _extract_text_from_request(self, request): """ @@ -366,7 +369,9 @@ def generate_until(self, requests, until=None, **kwargs): try: prompt = self._extract_text_from_request(request) - response = self.agent.invoke({"messages": [HumanMessage(content=prompt)]}) + response = self.agent.invoke( + {"messages": [HumanMessage(content=prompt)]} + ) response = extract_output(response) if not isinstance(response, str): @@ -398,7 +403,9 @@ def loglikelihood(self, requests): else: context, continuation = request - response = self.agent.invoke({"messages": [HumanMessage(content=context)]}) + response = self.agent.invoke( + {"messages": [HumanMessage(content=context)]} + ) response = str(response).strip().lower() target = str(continuation).strip().lower() diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index c3afd05..91dc1b9 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -61,16 +61,15 @@ def __init__(self): model="gemini-2.5-flash", api_key=os.environ.get("GEMINI_API_KEY"), temperature=1.0, # Gemini 3.0+ defaults to 1.0 - response_schema=Score.model_json_schema() + response_schema=Score.model_json_schema(), ) self.remoteLLMs = { "remote_standard": self.remoteLLMStandard, "remote_response_format": self.remoteLLMResponseFormat, - "remote_score_format": self.remoteLLMScoreFormat + "remote_score_format": self.remoteLLMScoreFormat, } - self.system_prompt = SystemMessage(SYSTEM_PROMPT_GENERAL) def get_tools(self): @@ -99,7 +98,7 @@ def create_custom_agent( tools, system_prompt: SystemMessage = SystemMessage(SYSTEM_PROMPT_GENERAL), response_format=None, - type: str = "remote_standard" + type: str = "remote_standard", ): return create_agent( model=self.remoteLLMs[type], diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index fe0ec26..1bc9612 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -170,10 +170,13 @@ def print_benchmark_result(results: dict, task_name: str, filter: str) -> None: print(f"Correct answers (filter={filter}): {n_correct}") print(f"Wrong answers (filter={filter}): {n_wrong}") -def print_benchmark_result_loglikehood(results: dict, task_name: str, filter_val: str) -> None: + +def print_benchmark_result_loglikehood( + results: dict, task_name: str, filter_val: str +) -> None: # 1. Recupero i samples samples = results.get("samples", {}).get(task_name, []) - + # 2. Filtro (solitamente filter="none" nel tuo caso) flex_samples = [s for s in samples if filter_val in s.get("filter", "")] @@ -181,10 +184,9 @@ def print_benchmark_result_loglikehood(results: dict, task_name: str, filter_val n_correct = 0 for s in flex_samples: - try: scores = [r[0][0] for r in s.get("resps", [])] - + if not scores: continue @@ -192,7 +194,7 @@ def print_benchmark_result_loglikehood(results: dict, task_name: str, filter_val if predicted_idx >= 5.0: n_correct += 1 - + except (ValueError, IndexError): continue From bdbf3531226f9de46a24f18fcf1eb1ec31760e5b Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 26 Mar 2026 14:40:31 +0100 Subject: [PATCH 78/94] chore: fix for mypy --- GoT/model/lm_wrapper.py | 1 - GoT/model/ollama_llm.py | 2 -- GoT/model/utils/utils.py | 7 ++++--- 3 files changed, 4 insertions(+), 6 deletions(-) diff --git a/GoT/model/lm_wrapper.py b/GoT/model/lm_wrapper.py index 02d7e7c..1013c0b 100644 --- a/GoT/model/lm_wrapper.py +++ b/GoT/model/lm_wrapper.py @@ -1,4 +1,3 @@ -import math from GoT.model.graph_model import call_graph from lm_eval.api.registry import register_model from lm_eval.api.model import LM diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index 91dc1b9..bda0141 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -11,7 +11,6 @@ from langchain.agents import create_agent import mlflow -from GoT.model.runtime_graph import Response, Score from GoT.tools.math_tool import ( multiply, summing, @@ -61,7 +60,6 @@ def __init__(self): model="gemini-2.5-flash", api_key=os.environ.get("GEMINI_API_KEY"), temperature=1.0, # Gemini 3.0+ defaults to 1.0 - response_schema=Score.model_json_schema(), ) self.remoteLLMs = { diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 1bc9612..07bdda8 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -55,7 +55,7 @@ def parse_score(response: MessagesState) -> Score: :rtype: int """ score = response.get("structured_response") - score_res = response + score_res = extract_output(response) if isinstance(score, Score): return score elif score_res is not None: @@ -77,10 +77,11 @@ def parse_response_for_tool_node(response: MessagesState) -> Response: :rtype: Response """ structured_response = response.get("structured_response") + score_res = extract_output(response) if isinstance(structured_response, Response): return structured_response - elif response is not None: - data = json.loads(response) + elif score_res is not None: + data = json.loads(score_res) return Response.model_validate(data) else: return Response( From 50acaa5cf3f882ad8833eb32c1e513435ec0afde Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 27 Mar 2026 10:44:54 +0100 Subject: [PATCH 79/94] feat: create function to run gpqa hf dataset --- GoT/model/utils/hf_formatter.py | 87 +++++++++++++++++++++++++++++++++ 1 file changed, 87 insertions(+) create mode 100644 GoT/model/utils/hf_formatter.py diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py new file mode 100644 index 0000000..4838ef3 --- /dev/null +++ b/GoT/model/utils/hf_formatter.py @@ -0,0 +1,87 @@ + +import json +from random import shuffle + +from langchain.messages import HumanMessage + +from GoT.model.graph_model import call_graph +from GoT.model.ollama_llm import LLM +from GoT.model.utils.utils import extract_output, normalize_number + +def gpqa_format(dataset) -> list[dict[str, str]]: + questions = [] + # Mapping per trasformare l'indice della lista nelle lettere A, B, C, D + index_to_letter = {0: "A", 1: "B", 2: "C", 3: "D"} + + for data in dataset: # Vediamo i primi 2 esempi + sample = data + + question = sample['Question'] + correct_answer = sample['Correct Answer'] + + # Creiamo la lista delle opzioni partendo dai dati del sample + choices = [ + correct_answer, + sample['Incorrect Answer 1'], + sample['Incorrect Answer 2'], + sample['Incorrect Answer 3'] + ] + + shuffle(choices) + + correct_idx = choices.index(correct_answer) + correct_letter = index_to_letter[correct_idx] + + prompt = ( + "Answer the following multiple choice question. " + "The last line of your response should be of the following format: " + "‘ANSWER: LETTER’ (without quotes) where LETTER is one of ABCD. " + "Think step by step before answering.\n\n" + f"{question}\n" + f"A) {choices[0]}\n" + f"B) {choices[1]}\n" + f"C) {choices[2]}\n" + f"D) {choices[3]}\n" + "Answer:" + ) + questions.append({ + "prompt": prompt, + "correct_letter": correct_letter + }) + + return questions + +def gpqa_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[dict[str, str]]: + responses = [] + run_counter = 0 + agent = LLM().create_custom_agent(LLM().get_tools()) + for q in questions: + if run_counter >= max_run: + break + prompt = q["prompt"] + correct_letter = q["correct_letter"] + + if test: + response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]})) + else: + response = extract_output(call_graph(prompt)) + norm_res = normalize_number(response) + responses.append({"question": prompt, "response": norm_res, "correct_letter": correct_letter}) + run_counter += 1 + + return responses + +def gpqa_eval(responses: list[dict[str, str]]): + correct = 0 + for res in responses: + if f"ANSWER: {res["correct_letter"]}" in res["response"]: + correct += 1 + + accuracy = correct / len(responses) + print(f"Accuracy: {accuracy:.2f}") + print(f"Total: {len(responses)}") + print(f"Correct: {correct}") + +def save_eval_results(responses: list[dict[str, str]], model_name: str): + with open(f"{model_name}_eval_results.json", "w") as f: + json.dump(responses, f, indent=2) From 637d454a28e8f9fcb809b6f527b77fc1df287c28 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 27 Mar 2026 11:20:03 +0100 Subject: [PATCH 80/94] chore: improve evaluation --- GoT/model/utils/hf_formatter.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index 4838ef3..f43d48d 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -1,6 +1,7 @@ import json from random import shuffle +import re from langchain.messages import HumanMessage @@ -66,16 +67,21 @@ def gpqa_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[ else: response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) - responses.append({"question": prompt, "response": norm_res, "correct_letter": correct_letter}) + responses.append({"question": prompt, "response": norm_res, "filtered_answer": "", "correct_letter": correct_letter, "answer_success": 0.0}) run_counter += 1 return responses def gpqa_eval(responses: list[dict[str, str]]): correct = 0 + for res in responses: + match = re.search(r"ANSWER:\s*([A-D])", res["response"], re.IGNORECASE) + res["filtered_answer"] = match.group(1).upper() if match else "N/A" + if f"ANSWER: {res["correct_letter"]}" in res["response"]: correct += 1 + res["answer_success"] = 1.0 accuracy = correct / len(responses) print(f"Accuracy: {accuracy:.2f}") From 45ffef063d1af5ce9f5ce0804ead89dd9059c9b6 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 27 Mar 2026 11:36:02 +0100 Subject: [PATCH 81/94] chore: extract output also for gemini --- GoT/model/utils/utils.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 07bdda8..ecc5b3f 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -132,6 +132,13 @@ def extract_output(result) -> str: last_msg = messages[-1] if hasattr(last_msg, "content"): + content = last_msg.content + if isinstance(content, list): + text_parts = [] + for part in content: + if isinstance(part, dict) and part.get("type") == "text": + text_parts.append(part.get("text", "")) + return "".join(text_parts).strip() return str(last_msg.content) elif isinstance(last_msg, dict) and "content" in last_msg: return str(last_msg["content"]) From bba6cea8ac58ede402f39c7c7be888efd8065cbd Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 31 Mar 2026 14:13:40 +0200 Subject: [PATCH 82/94] chore: control syntax of crafted functions --- GoT/tools/craft_tool.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index da5bcdc..f0cf1e5 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -1,3 +1,4 @@ +import ast import os from pathlib import Path import re @@ -52,11 +53,18 @@ def sanitize_input(query: str) -> str: query = re.sub(r"^(\s|`)*(?i:python)?\s*", "", query) query = re.sub(r"(\s|`)*$", "", query) return query + + code = f"""\n\n{sanitize_input(tool_function)}""" + + # Syntax check + try: + ast.parse(code) + except SyntaxError as e: + return f"Syntax error, tool not saved: {e}" try: base_dir = Path(__file__).parent file_path = base_dir / "ai_tool.py" - code = f"""\n\n{sanitize_input(tool_function)}""" with open(file_path, "a") as f: f.write(code) return "Tool crafted successfully." From 20f7cc18ee140a3f8f5be0899e286542879b64c8 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 31 Mar 2026 14:14:21 +0200 Subject: [PATCH 83/94] chore: tests goes on also if one test raise an exception --- GoT/model/utils/hf_formatter.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index f43d48d..8b443c7 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -55,21 +55,23 @@ def gpqa_format(dataset) -> list[dict[str, str]]: def gpqa_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[dict[str, str]]: responses = [] run_counter = 0 - agent = LLM().create_custom_agent(LLM().get_tools()) + agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) for q in questions: if run_counter >= max_run: break prompt = q["prompt"] correct_letter = q["correct_letter"] - - if test: - response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]})) - else: - response = extract_output(call_graph(prompt)) - norm_res = normalize_number(response) - responses.append({"question": prompt, "response": norm_res, "filtered_answer": "", "correct_letter": correct_letter, "answer_success": 0.0}) + try: + if test: + response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 5})) + else: + response = extract_output(call_graph(prompt)) + norm_res = normalize_number(response) + responses.append({"question": prompt, "response": norm_res, "filtered_answer": "", "correct_letter": correct_letter, "answer_success": 0.0}) + except Exception as e: + print(f"Error processing question: {e}") + responses.append({"question": prompt, "response": "Error", "filtered_answer": "", "correct_letter": correct_letter, "answer_success": 0.0}) run_counter += 1 - return responses def gpqa_eval(responses: list[dict[str, str]]): From fe84c94a5a950efc01c08614ed5d929d11157513 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 31 Mar 2026 17:28:05 +0200 Subject: [PATCH 84/94] chore: add use_benchmark --- GoT/model/utils/hf_formatter.py | 79 ++++++++++++++++++++++++++++++++- 1 file changed, 78 insertions(+), 1 deletion(-) diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index 8b443c7..9581800 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -2,6 +2,7 @@ import json from random import shuffle import re +from datasets import load_dataset from langchain.messages import HumanMessage @@ -63,7 +64,7 @@ def gpqa_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[ correct_letter = q["correct_letter"] try: if test: - response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 5})) + response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 10})) else: response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) @@ -93,3 +94,79 @@ def gpqa_eval(responses: list[dict[str, str]]): def save_eval_results(responses: list[dict[str, str]], model_name: str): with open(f"{model_name}_eval_results.json", "w") as f: json.dump(responses, f, indent=2) + +def gsm8k_format(dataset) -> list[dict[str, str]]: + questions = [] + for data in dataset: + sample = data + question = sample['question'] + correct_answer = sample['answer'] + prompt = ( + "Answer the following math problem. Respond in the following format: #### NUMBER " + "Think step by step before answering.\n\n" + f"{question}\n" + "Answer:" + ) + + questions.append({ + "prompt": prompt, + "correct_answer": correct_answer + }) + + return questions + +def gsm8k_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[dict[str, str]]: + responses = [] + run_counter = 0 + agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) + for q in questions: + if run_counter >= max_run: + break + prompt = q["prompt"] + correct_answer = q["correct_answer"] + try: + if test: + response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 10})) + else: + response = extract_output(call_graph(prompt)) + norm_res = normalize_number(response) + responses.append({"question": prompt, "response": norm_res, "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + except Exception as e: + print(f"Error processing question: {e}") + responses.append({"question": prompt, "response": "Error", "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + run_counter += 1 + return responses + +def gsm8k_eval(responses: list[dict[str, str]]): + correct = 0 + + for res in responses: + norm_res = re.search(r"####\s*(-?[\d,.]+)", res["response"]) + norm_res = norm_res.group(1) if norm_res else "N/A" + norm_correct = normalize_number(res["correct_answer"]) + res["filtered_answer"] = norm_res + + if norm_res in norm_correct: + correct += 1 + res["answer_success"] = 1.0 + + accuracy = correct / len(responses) + print(f"Accuracy: {accuracy:.2f}") + print(f"Total: {len(responses)}") + print(f"Correct: {correct}") + +def use_gpqa(max_run: int, test: bool, model_name: str): + ds = load_dataset("Idavidrein/gpqa", "gpqa_diamond") + data = ds["train"] + questions = gpqa_format(data) + responses = gpqa_run(questions, max_run=max_run, test=test) + gpqa_eval(responses) + save_eval_results(responses, model_name=model_name) + +def use_gsm8k(max_run: int, test: bool, model_name: str): + ds = load_dataset("gsm8k", "main") + data = ds["test"] + questions = gsm8k_format(data) + responses = gsm8k_run(questions, max_run=max_run, test=test) + gsm8k_eval(responses) + save_eval_results(responses, model_name=model_name) From 460ced9ae1994ea7e8b96fd4dd7211483d06f78b Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 31 Mar 2026 17:28:35 +0200 Subject: [PATCH 85/94] feat: add parser in order to launch from terminal --- GoT/model/utils/parse_args.py | 28 ++++++++++++++++++++++++++++ 1 file changed, 28 insertions(+) create mode 100644 GoT/model/utils/parse_args.py diff --git a/GoT/model/utils/parse_args.py b/GoT/model/utils/parse_args.py new file mode 100644 index 0000000..8456415 --- /dev/null +++ b/GoT/model/utils/parse_args.py @@ -0,0 +1,28 @@ +import argparse +import sys + +from GoT.model.utils.hf_formatter import use_gpqa, use_gsm8k + + + +def defining_and_parse_args(): + parser = argparse.ArgumentParser(description="Run the GoT model on a benchmark or a custom problem.") + parser.add_argument("--benchmark", required=True, type=str, choices=["gsm8k", "gpqa"], help="The benchmark to run the model on.") + parser.add_argument("--mode", required=True, type=str, choices=["graph", "standard"], help="Whether to run the standard model or the graph model.") + parser.add_argument("--max_run", type=int, default=1, help="The maximum number of runs for the benchmark.") + + if len(sys.argv) == 1: + parser.print_help(sys.stderr) + sys.exit(1) + + args = parser.parse_args() + return args + +def call_benchmark(args): + mode = args.mode if args.mode else "standard" + test = mode == "standard" + max_run = args.max_run + if args.benchmark == "gsm8k": + use_gsm8k(max_run=max_run, test=test, model_name=mode) + elif args.benchmark == "gpqa": + use_gpqa(max_run=max_run, test=test, model_name=mode) \ No newline at end of file From 3492209a3127756c4d9643a815ac61ff5ea08e96 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Tue, 31 Mar 2026 17:29:03 +0200 Subject: [PATCH 86/94] chore: change init for parse args --- GoT/__init__.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/GoT/__init__.py b/GoT/__init__.py index d4c1e0c..c9d8fc7 100644 --- a/GoT/__init__.py +++ b/GoT/__init__.py @@ -5,6 +5,7 @@ from lm_eval import evaluator, tasks from GoT.model.graph_model import call_graph from GoT.model.lm_wrapper import LangGraphBigBenchWrapper, TestBigBenchWrapper +from GoT.model.utils.parse_args import call_benchmark, defining_and_parse_args from GoT.model.utils.utils import ( print_benchmark_result, print_benchmark_result_loglikehood, @@ -67,7 +68,8 @@ def custom_test(): def main(): # It could be changed with custom_test() to test a custom problem instead of the benchmark - lm_eval_graph_benchmark() + args = defining_and_parse_args() + call_benchmark(args) # let this be the last line of this file From bbffc72934265faeb0f3e80ad074dbb960639afe Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Wed, 8 Apr 2026 15:46:50 +0200 Subject: [PATCH 87/94] chore: add hendrycks math --- GoT/model/utils/hf_formatter.py | 84 ++++++++++++++++++++++++++++++--- GoT/model/utils/parse_args.py | 9 ++-- 2 files changed, 84 insertions(+), 9 deletions(-) diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index 9581800..9e7bad8 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -2,6 +2,7 @@ import json from random import shuffle import re +import time from datasets import load_dataset from langchain.messages import HumanMessage @@ -57,7 +58,7 @@ def gpqa_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[ responses = [] run_counter = 0 agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) - for q in questions: + for q in questions[25:]: if run_counter >= max_run: break prompt = q["prompt"] @@ -86,8 +87,8 @@ def gpqa_eval(responses: list[dict[str, str]]): correct += 1 res["answer_success"] = 1.0 - accuracy = correct / len(responses) - print(f"Accuracy: {accuracy:.2f}") + accuracy = (correct / len(responses) * 100 ) + print(f"Accuracy: {accuracy:.2f}%") print(f"Total: {len(responses)}") print(f"Correct: {correct}") @@ -126,7 +127,7 @@ def gsm8k_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list correct_answer = q["correct_answer"] try: if test: - response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 10})) + response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 20})) else: response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) @@ -150,8 +151,71 @@ def gsm8k_eval(responses: list[dict[str, str]]): correct += 1 res["answer_success"] = 1.0 - accuracy = correct / len(responses) - print(f"Accuracy: {accuracy:.2f}") + accuracy = (correct / len(responses) * 100 ) + print(f"Accuracy: {accuracy:.2f}%") + print(f"Total: {len(responses)}") + print(f"Correct: {correct}") + +def hendrycks_math_format(dataset) -> list[dict[str, str]]: + questions = [] + for data in dataset: + sample = data + question = sample['problem'] + reg_exp = re.search(r'\\boxed\{(\d+)\}', sample['solution']) + correct_answer = reg_exp.group(1) if reg_exp else "N/A" + + prompt = ( + "Answer the following math problem. Respond in the following format: \\boxed{number}. " + "Think step by step before answering.\n\n" + f"{question}\n" + "Answer:" + ) + + questions.append({ + "prompt": prompt, + "correct_answer": correct_answer, + "level": sample['level'] + }) + + return questions + +def hendrycks_math_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[dict[str, str]]: + responses = [] + run_counter = 0 + agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) + for q in questions: + if run_counter >= max_run: + break + prompt = q["prompt"] + correct_answer = q["correct_answer"] + try: + if test: + response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 20})) + else: + response = extract_output(call_graph(prompt)) + norm_res = normalize_number(response) + responses.append({"level": q["level"], "question": prompt, "response": norm_res, "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + except Exception as e: + print(f"Error processing question: {e}") + responses.append({"level": q["level"], "question": prompt, "response": "Error", "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + run_counter += 1 + return responses + +def hendrycks_math_eval(responses: list[dict[str, str]]): + correct = 0 + + for res in responses: + norm_res = re.search(r"\\boxed\{\s*(-?[\d,.]+)\s*\}", res["response"]) + norm_res = norm_res.group(1) if norm_res else "N/A" + norm_correct = normalize_number(res["correct_answer"]) + res["filtered_answer"] = norm_res + + if norm_res in norm_correct: + correct += 1 + res["answer_success"] = 1.0 + + accuracy = (correct / len(responses) * 100 ) + print(f"Accuracy: {accuracy:.2f}%") print(f"Total: {len(responses)}") print(f"Correct: {correct}") @@ -170,3 +234,11 @@ def use_gsm8k(max_run: int, test: bool, model_name: str): responses = gsm8k_run(questions, max_run=max_run, test=test) gsm8k_eval(responses) save_eval_results(responses, model_name=model_name) + +def use_hendrycks_math(max_run: int, test: bool, model_name: str, type: str): + ds = load_dataset("EleutherAI/hendrycks_math", type) + data = ds["test"] + questions = hendrycks_math_format(data) + responses = hendrycks_math_run(questions, max_run=max_run, test=test) + hendrycks_math_eval(responses) + save_eval_results(responses, model_name=model_name) diff --git a/GoT/model/utils/parse_args.py b/GoT/model/utils/parse_args.py index 8456415..9863217 100644 --- a/GoT/model/utils/parse_args.py +++ b/GoT/model/utils/parse_args.py @@ -1,15 +1,16 @@ import argparse import sys -from GoT.model.utils.hf_formatter import use_gpqa, use_gsm8k +from GoT.model.utils.hf_formatter import use_gpqa, use_gsm8k, use_hendrycks_math def defining_and_parse_args(): parser = argparse.ArgumentParser(description="Run the GoT model on a benchmark or a custom problem.") - parser.add_argument("--benchmark", required=True, type=str, choices=["gsm8k", "gpqa"], help="The benchmark to run the model on.") + parser.add_argument("--benchmark", required=True, type=str, choices=["gsm8k", "gpqa", "hendrycks_math"], help="The benchmark to run the model on.") parser.add_argument("--mode", required=True, type=str, choices=["graph", "standard"], help="Whether to run the standard model or the graph model.") parser.add_argument("--max_run", type=int, default=1, help="The maximum number of runs for the benchmark.") + parser.add_argument("--type", type=str, default="algebra", choices=["algebra", "counting_and_probabilities", "geometry", "intermediate_algebra", "number_theory", "precalculus", "statistics"], help="The type of math problems to run, only for hendrycks_math.") if len(sys.argv) == 1: parser.print_help(sys.stderr) @@ -25,4 +26,6 @@ def call_benchmark(args): if args.benchmark == "gsm8k": use_gsm8k(max_run=max_run, test=test, model_name=mode) elif args.benchmark == "gpqa": - use_gpqa(max_run=max_run, test=test, model_name=mode) \ No newline at end of file + use_gpqa(max_run=max_run, test=test, model_name=mode) + elif args.benchmark == "hendrycks_math": + use_hendrycks_math(max_run=max_run, test=test, model_name=mode, type=args.type) \ No newline at end of file From adbe91d31b47ca708e5dee2c1d79a1198a94ed49 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 9 Apr 2026 11:31:47 +0200 Subject: [PATCH 88/94] fix: fix hendrycks parsing response and correct solution --- GoT/model/utils/hf_formatter.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index 9e7bad8..3c0e899 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -9,7 +9,7 @@ from GoT.model.graph_model import call_graph from GoT.model.ollama_llm import LLM -from GoT.model.utils.utils import extract_output, normalize_number +from GoT.model.utils.utils import extract_output, normalize_list, normalize_number, symbolic_equal def gpqa_format(dataset) -> list[dict[str, str]]: questions = [] @@ -161,11 +161,11 @@ def hendrycks_math_format(dataset) -> list[dict[str, str]]: for data in dataset: sample = data question = sample['problem'] - reg_exp = re.search(r'\\boxed\{(\d+)\}', sample['solution']) + reg_exp = re.search(r"\\boxed\{(.*)\}", sample['solution']) correct_answer = reg_exp.group(1) if reg_exp else "N/A" prompt = ( - "Answer the following math problem. Respond in the following format: \\boxed{number}. " + "Answer the following math problem. Respond in the following format: \\boxed{answer}. " "Think step by step before answering.\n\n" f"{question}\n" "Answer:" @@ -205,12 +205,12 @@ def hendrycks_math_eval(responses: list[dict[str, str]]): correct = 0 for res in responses: - norm_res = re.search(r"\\boxed\{\s*(-?[\d,.]+)\s*\}", res["response"]) + norm_res = re.search(r"\\boxed\{(.*)\}", res["response"]) norm_res = norm_res.group(1) if norm_res else "N/A" norm_correct = normalize_number(res["correct_answer"]) res["filtered_answer"] = norm_res - if norm_res in norm_correct: + if (norm_res in norm_correct) or (normalize_list(norm_res) == normalize_list(norm_correct)) or (symbolic_equal(norm_res, norm_correct)): correct += 1 res["answer_success"] = 1.0 From 24ce2a73dde6cd752b193ce0671385a39b4a9ba9 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 9 Apr 2026 11:32:59 +0200 Subject: [PATCH 89/94] chore: add more control on crafting --- GoT/tools/craft_tool.py | 30 +++++++++++++++++++++++++++++- 1 file changed, 29 insertions(+), 1 deletion(-) diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index f0cf1e5..2cd91d5 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -38,6 +38,18 @@ def install_dependency(package_name: str) -> str: return str(e) +def is_valid_annotation(annotation): + if isinstance(annotation, ast.Name): + if annotation.id in {"list", "dict"}: + return False + return True + + # Caso tipo generico: List[int], dict[str, int] + if isinstance(annotation, ast.Subscript): + return True + + return False + @tool def craft_tool(tool_function: str) -> str: """Save the function definition provided by the LLM as a tool that can be used by other agents. @@ -58,10 +70,26 @@ def sanitize_input(query: str) -> str: # Syntax check try: - ast.parse(code) + tree = ast.parse(code) except SyntaxError as e: return f"Syntax error, tool not saved: {e}" + functions = [n for n in tree.body if isinstance(n, ast.FunctionDef)] + if len(functions) != 1: + return "Error: exactly one function must be defined." + + func = functions[0] + + for arg in func.args.args: + if arg.annotation is None: + return f"Error: missing type annotation for '{arg.arg}'" + + if not is_valid_annotation(arg.annotation): + return f"Error: invalid type for '{arg.arg}' (must be typed, e.g. List[int])" + + if func.returns is None: + return "Error: missing return type" + try: base_dir = Path(__file__).parent file_path = base_dir / "ai_tool.py" From a4ce18c5f303522668c9a4572dd4301ee9b0578f Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Thu, 9 Apr 2026 11:33:39 +0200 Subject: [PATCH 90/94] fix: fix args names --- GoT/model/utils/parse_args.py | 2 +- GoT/model/utils/utils.py | 12 ++++++++++++ 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/GoT/model/utils/parse_args.py b/GoT/model/utils/parse_args.py index 9863217..f2629e5 100644 --- a/GoT/model/utils/parse_args.py +++ b/GoT/model/utils/parse_args.py @@ -10,7 +10,7 @@ def defining_and_parse_args(): parser.add_argument("--benchmark", required=True, type=str, choices=["gsm8k", "gpqa", "hendrycks_math"], help="The benchmark to run the model on.") parser.add_argument("--mode", required=True, type=str, choices=["graph", "standard"], help="Whether to run the standard model or the graph model.") parser.add_argument("--max_run", type=int, default=1, help="The maximum number of runs for the benchmark.") - parser.add_argument("--type", type=str, default="algebra", choices=["algebra", "counting_and_probabilities", "geometry", "intermediate_algebra", "number_theory", "precalculus", "statistics"], help="The type of math problems to run, only for hendrycks_math.") + parser.add_argument("--type", type=str, default="algebra", choices=["algebra", "counting_and_probability", "geometry", "intermediate_algebra", "number_theory", "precalculus", "statistics"], help="The type of math problems to run, only for hendrycks_math.") if len(sys.argv) == 1: parser.print_help(sys.stderr) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index ecc5b3f..6af4b91 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -2,6 +2,7 @@ import re import numpy as np +from sympy import simplify, sympify from GoT.model.runtime_graph import Response, Score from langgraph.graph import MessagesState @@ -164,6 +165,17 @@ def normalize_number(num_str: str) -> str: return num_str +def normalize_list(s: str): + " Extract numbers from a string and return them as a sorted list. This is useful for comparing answers that are lists of numbers regardless of order." + nums = re.findall(r"-?\d+", s) + return sorted(nums) + +def symbolic_equal(a, b): + """Check if two mathematical expressions are symbolically equal.""" + try: + return simplify(sympify(a) - sympify(b)) == 0 + except Exception: + return False def print_benchmark_result(results: dict, task_name: str, filter: str) -> None: samples = results["samples"][task_name] From 4abfccb69fdecf4f0a112606fa39a461870bad3f Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 10 Apr 2026 10:39:27 +0200 Subject: [PATCH 91/94] feat: add divide thought reasoning and prompt complexity --- GoT/model/graph_model.py | 161 +++++++++++++++++++++----------- GoT/model/ollama_llm.py | 2 +- GoT/model/runtime_graph.py | 14 ++- GoT/tools/craft_tool.py | 4 +- GoT/tools/runtime_graph_tool.py | 60 ++++++++++++ 5 files changed, 185 insertions(+), 56 deletions(-) create mode 100644 GoT/tools/runtime_graph_tool.py diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index c90bd19..3e9d987 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -11,7 +11,6 @@ ReasoningNode, Response, RuntimeGraph, - RuntimeNode, Score, TestNode, ToolNode, @@ -22,8 +21,12 @@ parse_response_for_tool_node, parse_score, ) +from GoT.tools.runtime_graph_tool import divide_thought SCORE_THRESHOLD = 5 +COMPLEXITY_COEFFICIENT = 0.5 +COMPLEXITY_THRESHOLD = 5.5 +MAX_INTERACTIONS = 10 # Defining agents starting_agent = LLM().create_custom_agent( @@ -34,9 +37,9 @@ " If you are not sure of the tool you have, think also a generic tool to craft that could be useful to solve the problem (Specify the craft is needed). " " If the problem require too much steps with the current tools, consider crafting a new tool." "The list MUST be in this format and it is not possible to format the tool_name in any way: " - "- tool_name " - "- tool_name " - "- tool_name " + "- tool_name: explanation of why this tool is useful in this problem " + "- tool_name: explanation of why this tool is useful in this problem " + "- tool_name: explanation of why this tool is useful in this problem " ), type="remote_standard", ) @@ -48,12 +51,12 @@ SystemMessage( """ You are an assistant specialized in validating responses, like an LLM-as-a-judge. - Your duty is to score, from 0 to 6, the response that user gives and assign a score. + Your duty is to score, from 0 to 5, the response that user gives and assign a score. Rules: - You MUST respond ONLY using the Score function. - You cannot give the full solution, only hints. - - If a response suggest the need of crafting a tool, score it with 4 or less and specify clearly the need of a new tool to solve the problem. + - If a response suggest the need of crafting a tool, score it with 1 or less and specify clearly the need of a new tool to solve the problem. - Do not write natural language outside the function. - Always consider creating a tool if it makes the response correct or reusable. @@ -63,14 +66,11 @@ 2: Correct language but does not follow instruction 3: Tries to solve but fails instruction / wrong 4: Follows instruction but result wrong or incomplete - 5: Follows instruction, result nearly correct - 6: Follows instruction, result correct + 5: Follows instruction, result correct Example: - User response: cannot compute because missing helper - Hint: implement a tool that computes the missing function - - Always prioritize suggesting a reusable tool if it helps solve the problem. """ ), response_format=Score, @@ -115,6 +115,19 @@ def multiply(a: float, b: float) -> float: type="remote_response_format", ) +reasoning_agent = LLM().create_custom_agent( + [divide_thought], + SystemMessage( + "You are an assistant specialized in divide the complexity. " \ + "Your goal is to resolve the problem with reasoning. You should to reason step by step and write all your reasoning. " \ + "You are forced to divide it into smaller parts." \ + "The parts must be indipendent from each other and must be solvable at the same time." \ + "Respond with the indicated format." + ), + response_format=Response, + type="remote_response_format", + ) + # Defining runtime graph runtime_graph = RuntimeGraph() @@ -124,50 +137,48 @@ def goal(prompt: MessagesState): goal_node = GoalNode(parse_response(prompt), resolved=True) runtime_graph.add_node(goal_node) runtime_graph.temp_node = goal_node - return prompt - - -def tool_expand(goal: MessagesState): - msg = parse_response(goal) - sys_msg = "Please make a list using '-' to denote each tool in a probabilistic order, don't use this character for other reasons. Select only the tool(s) you want to use to solve this problem." - messages = [ - HumanMessage(msg), - SystemMessage(sys_msg), - ] - res = starting_agent.invoke({"messages": messages}) - str_res = parse_response(res) - goal["messages"].append(AIMessage(content=str_res)) - # tool_list = parse_tool_list(str_res) # Toglie elementi inutili - # add tool nodes in the runtime graph for i in range(0, 3): - tool_node = RuntimeNode(resolved=True) call_node = ToolNode( "Please, resolve the problem with the tools given, you MUST follow the previous reasoning.", "", tool_name="", ) reasoning_node = ReasoningNode("") - runtime_graph.add_node(tool_node) runtime_graph.add_node(reasoning_node) - runtime_graph.add_edge( - runtime_graph.temp_node, tool_node - ) # edge from goal to tool node runtime_graph.add_node(call_node) - runtime_graph.add_edge(tool_node, reasoning_node) + runtime_graph.add_edge(goal_node, reasoning_node) runtime_graph.add_edge(reasoning_node, call_node) - # runtime_graph.add_tool_link(call_node, tool) + reasoning_node = ReasoningNode("") + runtime_graph.add_node(reasoning_node) + runtime_graph.add_edge(goal_node, reasoning_node) # extract a reasoning node to resolve runtime_graph.temp_node = runtime_graph.call_tool_node() - return goal + return prompt + + +# def tool_expand(goal: MessagesState): +# msg = parse_response(goal) +# sys_msg = "Please make a list using '-' to denote each tool in a probabilistic order, don't use this character for other reasons. Select only the tool(s) you want to use to solve this problem." +# messages = [ +# HumanMessage(msg), +# SystemMessage(sys_msg), +# ] +# res = starting_agent.invoke({"messages": messages}, config={"recursion_limit": MAX_INTERACTIONS}) +# str_res = parse_response(res) +# goal["messages"].append(AIMessage(content=str_res)) +# # tool_list = parse_tool_list(str_res) # Toglie elementi inutili +# # add tool nodes in the runtime graph + +# return goal def tool_reasoning(messages: MessagesState): messages["messages"].append( HumanMessage( - "Please, reason about how to use these tools to solve the problem, without solving it. If the main solution require to craft a tool, only explain how to craft the tool" + "Please, reason step by step about how to use these tools to solve the problem, without solving it. If the main solution require to craft a tool, only explain how to craft the tool" ) ) - result = parse_response(starting_agent.invoke(messages)) + result = parse_response(starting_agent.invoke(messages, config={"recursion_limit": MAX_INTERACTIONS})) messages["messages"].append(AIMessage(result)) runtime_graph.resolve_node(runtime_graph.temp_node, result) runtime_graph.temp_node = runtime_graph.nodes.get(runtime_graph.temp_node, [])[0] @@ -181,18 +192,33 @@ def tool_call(messages: MessagesState): # It calls the llm and it resolves the call node call_node = runtime_graph.temp_node tool_agent = LLM().create_custom_agent( - LLM().get_tools(), + LLM().get_tools() + [divide_thought], SystemMessage( "You are an assistant specialized in tools. Your goal is to resolve the problem with " - " the tool that the user indicates to you. You HAVE to use the tool that the assistant indicates to you." - " If the previous assistant suggest to you that craft a tool is needed, specify it clearly and do not solve the problem." + " the tool that the user indicates to you. You HAVE to use or craft the tool that the assistant indicates to you." "Do not write natural language outside the function. " "If you fail to respect the format, the evaluation will fail." ), response_format=Response, type="remote_response_format", ) - res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}) + try: + res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}, config={"recursion_limit": MAX_INTERACTIONS}) + except Exception as e: + message = [HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), + HumanMessage(content="Divide the problem into smaller parts, you can't call the same tool twice.")] + tool_agent = LLM().create_custom_agent( + [divide_thought], + SystemMessage( + "You are an assistant specialized in tools. Your goal is to resolve the problem with " + " the tool that the user indicates to you. You HAVE to use the tool that the assistant indicates to you." + "Do not write natural language outside the function. " + "If you fail to respect the format, the evaluation will fail." + ), + response_format=Response, + type="remote_response_format", + ) + res = tool_agent.invoke({"messages": message, "tool_choice": Response}, config={"recursion_limit": MAX_INTERACTIONS}) tool_used = extract_tool_used(res) runtime_graph.temp_response.response = parse_response_for_tool_node(res).response parsed_res = f"Response: {parse_response_for_tool_node(res).response}\nExplanation: {parse_response_for_tool_node(res).explanation}" @@ -223,7 +249,7 @@ def response_evaluation(messages: MessagesState): HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), HumanMessage(content=call_node_response), SystemMessage( - content="Score this solution based on correctness and following instructions. A new tool could help to solve the problem?" + content="Score this solution based on correctness and following instructions." ), AIMessage( content="Tool used in the response: " + ", ".join(test_node.tool_used) @@ -231,10 +257,11 @@ def response_evaluation(messages: MessagesState): ] score_res = parse_score( - judge_agent.invoke({"messages": judge_messages, "tool_choice": Score}) + judge_agent.invoke({"messages": judge_messages, "tool_choice": Score}, config={"recursion_limit": MAX_INTERACTIONS}) ) test_node.score = score_res.score test_node.need_tool_crafting = score_res.need_tool_crafting + test_node.problem_complexity = score_res.problem_complexity runtime_graph.resolve_node(test_node, score_res.description) return messages @@ -250,8 +277,7 @@ def crafting(messages: MessagesState): content="Craft a tool to solve this problem using craft_tool. It must be a function" ), ] - craft_res = crafter_agent.invoke({"messages": crafting_messages}) - print(LLM().get_crafted_tools()) + craft_res = crafter_agent.invoke({"messages": crafting_messages}, config={"recursion_limit": MAX_INTERACTIONS}) runtime_graph.temp_response.response = parse_response_for_tool_node( craft_res ).response @@ -282,21 +308,31 @@ def test_result(messages: MessagesState): if not isinstance(test_node, TestNode): raise TypeError("Expected TestNode for scoring") - if test_node.score >= SCORE_THRESHOLD: + if test_node.score >= (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity): runtime_graph.add_edge(test_node, runtime_graph.temp_response) runtime_graph.temp_response.resolved = True return END elif ( - test_node.score < SCORE_THRESHOLD + test_node.score < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) and n is True and test_node.need_tool_crafting is True ): return "crafting" - elif test_node.score < SCORE_THRESHOLD and n is True: + elif ( + test_node.score < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) + and n is True + ): + if(test_node.need_tool_crafting is True): + test_node.response = "The problem is too complex to craft a new tool, try reason step by step or divide complexity." return "backtrack" + elif (test_node.score < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) + and n is False + and runtime_graph.exist_reasoning_node_available() + ): + return "reasoning_mode" else: chat_completition_node = CompletitionNode( - "Please, solve this problem", + "Please, solve this problem with the corrected format.", "", ) runtime_graph.add_node(chat_completition_node) @@ -305,6 +341,26 @@ def test_result(messages: MessagesState): return "chat_completition" +def reasoning_mode(messages: MessagesState): + reasoning_node = runtime_graph.call_tool_node() + if not isinstance(reasoning_node, ReasoningNode): + raise TypeError("Expected ReasoningNode for reasoning mode") + msg = [HumanMessage(content=parse_response(runtime_graph.goal))] + result = parse_response(reasoning_agent.invoke({"messages": msg}, config={"recursion_limit": MAX_INTERACTIONS})) + runtime_graph.resolve_node(reasoning_node, result) + runtime_graph.temp_response.response = result + test_node = TestNode( + f"{result}", + "", + score=0, + tool_used=[], + ) + runtime_graph.add_node(test_node) + runtime_graph.add_edge(reasoning_node, test_node) + runtime_graph.temp_node = test_node + messages["messages"].append(AIMessage(result)) + return messages + def backtrack(messages: MessagesState): test_node = runtime_graph.temp_node if not isinstance(test_node, TestNode): @@ -335,8 +391,6 @@ def chat_completition(messages: MessagesState): return new_messages_history -# https://docs.langchain.com/oss/python/langgraph/overview - content = "" @@ -349,18 +403,20 @@ def call_graph(prompt: str): def invoke_graph(): graph = StateGraph(MessagesState) graph.add_node(goal) - graph.add_node(tool_expand) + # graph.add_node(tool_expand) graph.add_node(tool_reasoning) graph.add_node(tool_call) graph.add_node(backtrack) graph.add_node(crafting) graph.add_node(chat_completition) graph.add_node(response_evaluation) + graph.add_node(reasoning_mode) graph.add_edge(START, "goal") - graph.add_edge("goal", "tool_expand") - graph.add_edge("tool_expand", "tool_reasoning") + # graph.add_edge("goal", "tool_expand") + graph.add_edge("goal", "tool_reasoning") graph.add_edge("tool_reasoning", "tool_call") graph.add_edge("tool_call", "response_evaluation") + graph.add_edge("reasoning_mode", "response_evaluation") graph.add_edge("backtrack", "tool_reasoning") graph.add_edge("crafting", "tool_reasoning") graph.add_edge("chat_completition", END) @@ -370,6 +426,7 @@ def invoke_graph(): { "crafting": "crafting", "backtrack": "backtrack", + "reasoning_mode": "reasoning_mode", "chat_completition": "chat_completition", END: END, }, diff --git a/GoT/model/ollama_llm.py b/GoT/model/ollama_llm.py index bda0141..65bfcf2 100644 --- a/GoT/model/ollama_llm.py +++ b/GoT/model/ollama_llm.py @@ -59,7 +59,7 @@ def __init__(self): self.remoteLLMScoreFormat = ChatGoogleGenerativeAI( model="gemini-2.5-flash", api_key=os.environ.get("GEMINI_API_KEY"), - temperature=1.0, # Gemini 3.0+ defaults to 1.0 + temperature=0.7, # Gemini 3.0+ defaults to 1.0 ) self.remoteLLMs = { diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index e471eec..88189f2 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -33,6 +33,7 @@ def __init__( prompt: str, response: str, score: int, + problem_complexity: int = 0, tool_used: List[str] = [], resolved: bool = False, need_tool_crafting: bool = False, @@ -43,6 +44,7 @@ def __init__( self.score = score self.tool_used = tool_used self.need_tool_crafting = need_tool_crafting + self.problem_complexity = problem_complexity class ToolNode(RuntimeNode): @@ -144,13 +146,18 @@ class Score(BaseModel): description="Indicates whether the problem requires or it is useful to craft a new tool to be resolved.", ) + problem_complexity: int = Field( + ..., + description="Integer between 0 and 5 indicating the complexity of the problem for an AI." + ) + class Response(BaseModel): """Rapresents a response for a tool node.""" response: str = Field( ..., - description="Final answer to the user request. No meta commentary. If the answer is a number, respond only with the number (but in string), without any text.", + description="Final answer to the user request. No meta commentary. If there is a format requirement, follow it.", ) explanation: str = Field( ..., description="Short reasoning explaining how the answer was produced." @@ -186,6 +193,11 @@ def call_tool_node(self) -> ReasoningNode: n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved) ] return reasoning_nodes[0] + + def exist_reasoning_node_available(self) -> bool: + nodes = list(self.nodes.keys()) + reasoning_nodes = [n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved)] + return True if reasoning_nodes else False def exist_tool_available(self) -> bool: nodes = list(self.nodes.keys()) diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index 2cd91d5..10c1a17 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -40,7 +40,7 @@ def install_dependency(package_name: str) -> str: def is_valid_annotation(annotation): if isinstance(annotation, ast.Name): - if annotation.id in {"list", "dict"}: + if annotation.id in {"list", "dict", "set"}: return False return True @@ -85,7 +85,7 @@ def sanitize_input(query: str) -> str: return f"Error: missing type annotation for '{arg.arg}'" if not is_valid_annotation(arg.annotation): - return f"Error: invalid type for '{arg.arg}' (must be typed, e.g. List[int])" + return f"Error: invalid type for '{arg.arg}' (must be typed, e.g. List[int], set[str], dict[str, int], etc.)" if func.returns is None: return "Error: missing return type" diff --git a/GoT/tools/runtime_graph_tool.py b/GoT/tools/runtime_graph_tool.py new file mode 100644 index 0000000..21fe7ba --- /dev/null +++ b/GoT/tools/runtime_graph_tool.py @@ -0,0 +1,60 @@ +from langchain.messages import HumanMessage, SystemMessage +from langchain.tools import tool + +from GoT.model.ollama_llm import LLM +from GoT.model.runtime_graph import ReasoningNode, RuntimeGraph +from GoT.model.utils.utils import parse_response + +MAX_INTERACTIONS = 10 + +@tool +def divide_thought(first_part: str, second_part: str, first_context: str, second_context: str, reasoning_type: str = "pure_reasoning") -> str: + """ + This is a tool to divide the thought process into smaller steps. + HOW TO USE THIS TOOL: + - Call it when you think the problem is complex. + - The two parts must be as independent as possible. + Arguments: + - first_part: the first part of the thought process + - second_part: the second part of the thought process + - first_context: the context related to the first part + - second_context: the context related to the second part + - reasoning_type: the type of reasoning to use for each part, it can be "pure_reasoning" or "tool_use" + """ + tool_agent = LLM().create_custom_agent( + LLM().get_tools(), + SystemMessage( + "You are an assistant specialized in tools. Your goal is to resolve the problem with " + " the tool that the user indicates to you. You should to use the tool that the assistant indicates to you." + "Do not write natural language outside the function. " + "If you fail to respect the format, the evaluation will fail." + ), + ) + reasoning_agent = LLM().create_custom_agent( + [], + SystemMessage( + "You are an assistant specialized in reasoning. " \ + "Your goal is to resolve the problem with reasoning. You should to reason step by step and write all your reasoning. " \ + "If the problem is too complex, you can divide it into smaller parts." + ), + ) + runtime_graph = RuntimeGraph() + n1 = ReasoningNode("") + n2 = ReasoningNode("") + runtime_graph.add_node(n1) + runtime_graph.add_node(n2) + runtime_graph.add_edge(runtime_graph.temp_node, n1) + runtime_graph.add_edge(runtime_graph.temp_node, n2) + msg1 = [HumanMessage("Reason aboout this problem: " + first_part + "\nContext: " + first_context)] + msg2 = [HumanMessage("Reason aboout this problem: " + second_part + "\nContext: " + second_context)] + if reasoning_type == "pure_reasoning": + res1 = parse_response(reasoning_agent.invoke({"messages": msg1}, config={"recursion_limit": MAX_INTERACTIONS})) + res2 = parse_response(reasoning_agent.invoke({"messages": msg2}, config={"recursion_limit": MAX_INTERACTIONS})) + else: + res1 = parse_response(tool_agent.invoke({"messages": msg1}, config={"recursion_limit": MAX_INTERACTIONS})) + res2 = parse_response(tool_agent.invoke({"messages": msg2}, config={"recursion_limit": MAX_INTERACTIONS})) + runtime_graph.resolve_node(n1, res1) + runtime_graph.resolve_node(n2, res2) + + result = f"First part: {res1}\nSecond part: {res2}" + return result \ No newline at end of file From 22a423e61f4392f9c3e50882398449d1d17f2fa0 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 10 Apr 2026 10:41:48 +0200 Subject: [PATCH 92/94] style: ruff format --- GoT/model/graph_model.py | 82 ++++++++----- GoT/model/runtime_graph.py | 10 +- GoT/model/utils/hf_formatter.py | 198 +++++++++++++++++++++++--------- GoT/model/utils/parse_args.py | 49 ++++++-- GoT/model/utils/utils.py | 5 +- GoT/tools/craft_tool.py | 7 +- GoT/tools/runtime_graph_tool.py | 54 +++++++-- 7 files changed, 298 insertions(+), 107 deletions(-) diff --git a/GoT/model/graph_model.py b/GoT/model/graph_model.py index 3e9d987..57491ad 100644 --- a/GoT/model/graph_model.py +++ b/GoT/model/graph_model.py @@ -116,17 +116,17 @@ def multiply(a: float, b: float) -> float: ) reasoning_agent = LLM().create_custom_agent( - [divide_thought], - SystemMessage( - "You are an assistant specialized in divide the complexity. " \ - "Your goal is to resolve the problem with reasoning. You should to reason step by step and write all your reasoning. " \ - "You are forced to divide it into smaller parts." \ - "The parts must be indipendent from each other and must be solvable at the same time." \ - "Respond with the indicated format." - ), - response_format=Response, - type="remote_response_format", - ) + [divide_thought], + SystemMessage( + "You are an assistant specialized in divide the complexity. " + "Your goal is to resolve the problem with reasoning. You should to reason step by step and write all your reasoning. " + "You are forced to divide it into smaller parts." + "The parts must be indipendent from each other and must be solvable at the same time." + "Respond with the indicated format." + ), + response_format=Response, + type="remote_response_format", +) # Defining runtime graph runtime_graph = RuntimeGraph() @@ -178,7 +178,9 @@ def tool_reasoning(messages: MessagesState): "Please, reason step by step about how to use these tools to solve the problem, without solving it. If the main solution require to craft a tool, only explain how to craft the tool" ) ) - result = parse_response(starting_agent.invoke(messages, config={"recursion_limit": MAX_INTERACTIONS})) + result = parse_response( + starting_agent.invoke(messages, config={"recursion_limit": MAX_INTERACTIONS}) + ) messages["messages"].append(AIMessage(result)) runtime_graph.resolve_node(runtime_graph.temp_node, result) runtime_graph.temp_node = runtime_graph.nodes.get(runtime_graph.temp_node, [])[0] @@ -203,12 +205,21 @@ def tool_call(messages: MessagesState): type="remote_response_format", ) try: - res = tool_agent.invoke({"messages": messages["messages"], "tool_choice": Response}, config={"recursion_limit": MAX_INTERACTIONS}) - except Exception as e: - message = [HumanMessage(content="Original task:\n" + parse_response(runtime_graph.goal)), - HumanMessage(content="Divide the problem into smaller parts, you can't call the same tool twice.")] + res = tool_agent.invoke( + {"messages": messages["messages"], "tool_choice": Response}, + config={"recursion_limit": MAX_INTERACTIONS}, + ) + except Exception: + message = [ + HumanMessage( + content="Original task:\n" + parse_response(runtime_graph.goal) + ), + HumanMessage( + content="Divide the problem into smaller parts, you can't call the same tool twice." + ), + ] tool_agent = LLM().create_custom_agent( - [divide_thought], + [divide_thought], SystemMessage( "You are an assistant specialized in tools. Your goal is to resolve the problem with " " the tool that the user indicates to you. You HAVE to use the tool that the assistant indicates to you." @@ -218,7 +229,10 @@ def tool_call(messages: MessagesState): response_format=Response, type="remote_response_format", ) - res = tool_agent.invoke({"messages": message, "tool_choice": Response}, config={"recursion_limit": MAX_INTERACTIONS}) + res = tool_agent.invoke( + {"messages": message, "tool_choice": Response}, + config={"recursion_limit": MAX_INTERACTIONS}, + ) tool_used = extract_tool_used(res) runtime_graph.temp_response.response = parse_response_for_tool_node(res).response parsed_res = f"Response: {parse_response_for_tool_node(res).response}\nExplanation: {parse_response_for_tool_node(res).explanation}" @@ -257,7 +271,10 @@ def response_evaluation(messages: MessagesState): ] score_res = parse_score( - judge_agent.invoke({"messages": judge_messages, "tool_choice": Score}, config={"recursion_limit": MAX_INTERACTIONS}) + judge_agent.invoke( + {"messages": judge_messages, "tool_choice": Score}, + config={"recursion_limit": MAX_INTERACTIONS}, + ) ) test_node.score = score_res.score test_node.need_tool_crafting = score_res.need_tool_crafting @@ -277,7 +294,9 @@ def crafting(messages: MessagesState): content="Craft a tool to solve this problem using craft_tool. It must be a function" ), ] - craft_res = crafter_agent.invoke({"messages": crafting_messages}, config={"recursion_limit": MAX_INTERACTIONS}) + craft_res = crafter_agent.invoke( + {"messages": crafting_messages}, config={"recursion_limit": MAX_INTERACTIONS} + ) runtime_graph.temp_response.response = parse_response_for_tool_node( craft_res ).response @@ -308,24 +327,30 @@ def test_result(messages: MessagesState): if not isinstance(test_node, TestNode): raise TypeError("Expected TestNode for scoring") - if test_node.score >= (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity): + if test_node.score >= ( + COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity + ): runtime_graph.add_edge(test_node, runtime_graph.temp_response) runtime_graph.temp_response.resolved = True return END elif ( - test_node.score < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) + test_node.score + < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) and n is True and test_node.need_tool_crafting is True ): return "crafting" elif ( - test_node.score < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) + test_node.score + < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) and n is True ): - if(test_node.need_tool_crafting is True): + if test_node.need_tool_crafting is True: test_node.response = "The problem is too complex to craft a new tool, try reason step by step or divide complexity." return "backtrack" - elif (test_node.score < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) + elif ( + test_node.score + < (COMPLEXITY_THRESHOLD - COMPLEXITY_COEFFICIENT * test_node.problem_complexity) and n is False and runtime_graph.exist_reasoning_node_available() ): @@ -346,7 +371,11 @@ def reasoning_mode(messages: MessagesState): if not isinstance(reasoning_node, ReasoningNode): raise TypeError("Expected ReasoningNode for reasoning mode") msg = [HumanMessage(content=parse_response(runtime_graph.goal))] - result = parse_response(reasoning_agent.invoke({"messages": msg}, config={"recursion_limit": MAX_INTERACTIONS})) + result = parse_response( + reasoning_agent.invoke( + {"messages": msg}, config={"recursion_limit": MAX_INTERACTIONS} + ) + ) runtime_graph.resolve_node(reasoning_node, result) runtime_graph.temp_response.response = result test_node = TestNode( @@ -361,6 +390,7 @@ def reasoning_mode(messages: MessagesState): messages["messages"].append(AIMessage(result)) return messages + def backtrack(messages: MessagesState): test_node = runtime_graph.temp_node if not isinstance(test_node, TestNode): diff --git a/GoT/model/runtime_graph.py b/GoT/model/runtime_graph.py index 88189f2..506f3d9 100644 --- a/GoT/model/runtime_graph.py +++ b/GoT/model/runtime_graph.py @@ -148,8 +148,8 @@ class Score(BaseModel): problem_complexity: int = Field( ..., - description="Integer between 0 and 5 indicating the complexity of the problem for an AI." - ) + description="Integer between 0 and 5 indicating the complexity of the problem for an AI.", + ) class Response(BaseModel): @@ -193,10 +193,12 @@ def call_tool_node(self) -> ReasoningNode: n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved) ] return reasoning_nodes[0] - + def exist_reasoning_node_available(self) -> bool: nodes = list(self.nodes.keys()) - reasoning_nodes = [n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved)] + reasoning_nodes = [ + n for n in nodes if (isinstance(n, ReasoningNode) and not n.resolved) + ] return True if reasoning_nodes else False def exist_tool_available(self) -> bool: diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index 3c0e899..7293cdd 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -1,4 +1,3 @@ - import json from random import shuffle import re @@ -9,7 +8,13 @@ from GoT.model.graph_model import call_graph from GoT.model.ollama_llm import LLM -from GoT.model.utils.utils import extract_output, normalize_list, normalize_number, symbolic_equal +from GoT.model.utils.utils import ( + extract_output, + normalize_list, + normalize_number, + symbolic_equal, +) + def gpqa_format(dataset) -> list[dict[str, str]]: questions = [] @@ -18,23 +23,23 @@ def gpqa_format(dataset) -> list[dict[str, str]]: for data in dataset: # Vediamo i primi 2 esempi sample = data - - question = sample['Question'] - correct_answer = sample['Correct Answer'] - + + question = sample["Question"] + correct_answer = sample["Correct Answer"] + # Creiamo la lista delle opzioni partendo dai dati del sample choices = [ correct_answer, - sample['Incorrect Answer 1'], - sample['Incorrect Answer 2'], - sample['Incorrect Answer 3'] + sample["Incorrect Answer 1"], + sample["Incorrect Answer 2"], + sample["Incorrect Answer 3"], ] - + shuffle(choices) - + correct_idx = choices.index(correct_answer) correct_letter = index_to_letter[correct_idx] - + prompt = ( "Answer the following multiple choice question. " "The last line of your response should be of the following format: " @@ -47,14 +52,14 @@ def gpqa_format(dataset) -> list[dict[str, str]]: f"D) {choices[3]}\n" "Answer:" ) - questions.append({ - "prompt": prompt, - "correct_letter": correct_letter - }) - + questions.append({"prompt": prompt, "correct_letter": correct_letter}) + return questions -def gpqa_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[dict[str, str]]: + +def gpqa_run( + questions: list[dict[str, str]], max_run: int, test: bool +) -> list[dict[str, str]]: responses = [] run_counter = 0 agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) @@ -65,43 +70,67 @@ def gpqa_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[ correct_letter = q["correct_letter"] try: if test: - response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 10})) + response = extract_output( + agent.invoke( + {"messages": [HumanMessage(content=prompt)]}, + config={"recursion_limit": 10}, + ) + ) else: response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) - responses.append({"question": prompt, "response": norm_res, "filtered_answer": "", "correct_letter": correct_letter, "answer_success": 0.0}) + responses.append( + { + "question": prompt, + "response": norm_res, + "filtered_answer": "", + "correct_letter": correct_letter, + "answer_success": 0.0, + } + ) except Exception as e: print(f"Error processing question: {e}") - responses.append({"question": prompt, "response": "Error", "filtered_answer": "", "correct_letter": correct_letter, "answer_success": 0.0}) + responses.append( + { + "question": prompt, + "response": "Error", + "filtered_answer": "", + "correct_letter": correct_letter, + "answer_success": 0.0, + } + ) run_counter += 1 return responses + def gpqa_eval(responses: list[dict[str, str]]): correct = 0 - + for res in responses: - match = re.search(r"ANSWER:\s*([A-D])", res["response"], re.IGNORECASE) + match = re.search(r"ANSWER:\s*([A-D])", res["response"], re.IGNORECASE) res["filtered_answer"] = match.group(1).upper() if match else "N/A" - - if f"ANSWER: {res["correct_letter"]}" in res["response"]: + + if f"ANSWER: {res['correct_letter']}" in res["response"]: correct += 1 res["answer_success"] = 1.0 - accuracy = (correct / len(responses) * 100 ) + accuracy = correct / len(responses) * 100 print(f"Accuracy: {accuracy:.2f}%") print(f"Total: {len(responses)}") print(f"Correct: {correct}") + def save_eval_results(responses: list[dict[str, str]], model_name: str): with open(f"{model_name}_eval_results.json", "w") as f: json.dump(responses, f, indent=2) + def gsm8k_format(dataset) -> list[dict[str, str]]: questions = [] for data in dataset: sample = data - question = sample['question'] - correct_answer = sample['answer'] + question = sample["question"] + correct_answer = sample["answer"] prompt = ( "Answer the following math problem. Respond in the following format: #### NUMBER " "Think step by step before answering.\n\n" @@ -109,14 +138,14 @@ def gsm8k_format(dataset) -> list[dict[str, str]]: "Answer:" ) - questions.append({ - "prompt": prompt, - "correct_answer": correct_answer - }) + questions.append({"prompt": prompt, "correct_answer": correct_answer}) return questions -def gsm8k_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[dict[str, str]]: + +def gsm8k_run( + questions: list[dict[str, str]], max_run: int, test: bool +) -> list[dict[str, str]]: responses = [] run_counter = 0 agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) @@ -127,41 +156,64 @@ def gsm8k_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list correct_answer = q["correct_answer"] try: if test: - response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 20})) + response = extract_output( + agent.invoke( + {"messages": [HumanMessage(content=prompt)]}, + config={"recursion_limit": 20}, + ) + ) else: response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) - responses.append({"question": prompt, "response": norm_res, "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + responses.append( + { + "question": prompt, + "response": norm_res, + "filtered_answer": "", + "correct_answer": correct_answer, + "answer_success": 0.0, + } + ) except Exception as e: print(f"Error processing question: {e}") - responses.append({"question": prompt, "response": "Error", "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + responses.append( + { + "question": prompt, + "response": "Error", + "filtered_answer": "", + "correct_answer": correct_answer, + "answer_success": 0.0, + } + ) run_counter += 1 return responses + def gsm8k_eval(responses: list[dict[str, str]]): correct = 0 - + for res in responses: norm_res = re.search(r"####\s*(-?[\d,.]+)", res["response"]) norm_res = norm_res.group(1) if norm_res else "N/A" norm_correct = normalize_number(res["correct_answer"]) res["filtered_answer"] = norm_res - + if norm_res in norm_correct: correct += 1 res["answer_success"] = 1.0 - accuracy = (correct / len(responses) * 100 ) + accuracy = correct / len(responses) * 100 print(f"Accuracy: {accuracy:.2f}%") print(f"Total: {len(responses)}") print(f"Correct: {correct}") + def hendrycks_math_format(dataset) -> list[dict[str, str]]: questions = [] for data in dataset: sample = data - question = sample['problem'] - reg_exp = re.search(r"\\boxed\{(.*)\}", sample['solution']) + question = sample["problem"] + reg_exp = re.search(r"\\boxed\{(.*)\}", sample["solution"]) correct_answer = reg_exp.group(1) if reg_exp else "N/A" prompt = ( @@ -171,15 +223,20 @@ def hendrycks_math_format(dataset) -> list[dict[str, str]]: "Answer:" ) - questions.append({ - "prompt": prompt, - "correct_answer": correct_answer, - "level": sample['level'] - }) + questions.append( + { + "prompt": prompt, + "correct_answer": correct_answer, + "level": sample["level"], + } + ) return questions - -def hendrycks_math_run(questions: list[dict[str, str]], max_run: int, test: bool) -> list[dict[str, str]]: + + +def hendrycks_math_run( + questions: list[dict[str, str]], max_run: int, test: bool +) -> list[dict[str, str]]: responses = [] run_counter = 0 agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) @@ -190,35 +247,64 @@ def hendrycks_math_run(questions: list[dict[str, str]], max_run: int, test: bool correct_answer = q["correct_answer"] try: if test: - response = extract_output(agent.invoke({"messages": [HumanMessage(content=prompt)]}, config={"recursion_limit": 20})) + response = extract_output( + agent.invoke( + {"messages": [HumanMessage(content=prompt)]}, + config={"recursion_limit": 20}, + ) + ) else: response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) - responses.append({"level": q["level"], "question": prompt, "response": norm_res, "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + responses.append( + { + "level": q["level"], + "question": prompt, + "response": norm_res, + "filtered_answer": "", + "correct_answer": correct_answer, + "answer_success": 0.0, + } + ) except Exception as e: print(f"Error processing question: {e}") - responses.append({"level": q["level"], "question": prompt, "response": "Error", "filtered_answer": "", "correct_answer": correct_answer, "answer_success": 0.0}) + responses.append( + { + "level": q["level"], + "question": prompt, + "response": "Error", + "filtered_answer": "", + "correct_answer": correct_answer, + "answer_success": 0.0, + } + ) run_counter += 1 return responses + def hendrycks_math_eval(responses: list[dict[str, str]]): correct = 0 - + for res in responses: norm_res = re.search(r"\\boxed\{(.*)\}", res["response"]) norm_res = norm_res.group(1) if norm_res else "N/A" norm_correct = normalize_number(res["correct_answer"]) res["filtered_answer"] = norm_res - - if (norm_res in norm_correct) or (normalize_list(norm_res) == normalize_list(norm_correct)) or (symbolic_equal(norm_res, norm_correct)): + + if ( + (norm_res in norm_correct) + or (normalize_list(norm_res) == normalize_list(norm_correct)) + or (symbolic_equal(norm_res, norm_correct)) + ): correct += 1 res["answer_success"] = 1.0 - accuracy = (correct / len(responses) * 100 ) + accuracy = correct / len(responses) * 100 print(f"Accuracy: {accuracy:.2f}%") print(f"Total: {len(responses)}") print(f"Correct: {correct}") + def use_gpqa(max_run: int, test: bool, model_name: str): ds = load_dataset("Idavidrein/gpqa", "gpqa_diamond") data = ds["train"] @@ -227,6 +313,7 @@ def use_gpqa(max_run: int, test: bool, model_name: str): gpqa_eval(responses) save_eval_results(responses, model_name=model_name) + def use_gsm8k(max_run: int, test: bool, model_name: str): ds = load_dataset("gsm8k", "main") data = ds["test"] @@ -235,6 +322,7 @@ def use_gsm8k(max_run: int, test: bool, model_name: str): gsm8k_eval(responses) save_eval_results(responses, model_name=model_name) + def use_hendrycks_math(max_run: int, test: bool, model_name: str, type: str): ds = load_dataset("EleutherAI/hendrycks_math", type) data = ds["test"] diff --git a/GoT/model/utils/parse_args.py b/GoT/model/utils/parse_args.py index f2629e5..d2437a3 100644 --- a/GoT/model/utils/parse_args.py +++ b/GoT/model/utils/parse_args.py @@ -4,21 +4,54 @@ from GoT.model.utils.hf_formatter import use_gpqa, use_gsm8k, use_hendrycks_math - def defining_and_parse_args(): - parser = argparse.ArgumentParser(description="Run the GoT model on a benchmark or a custom problem.") - parser.add_argument("--benchmark", required=True, type=str, choices=["gsm8k", "gpqa", "hendrycks_math"], help="The benchmark to run the model on.") - parser.add_argument("--mode", required=True, type=str, choices=["graph", "standard"], help="Whether to run the standard model or the graph model.") - parser.add_argument("--max_run", type=int, default=1, help="The maximum number of runs for the benchmark.") - parser.add_argument("--type", type=str, default="algebra", choices=["algebra", "counting_and_probability", "geometry", "intermediate_algebra", "number_theory", "precalculus", "statistics"], help="The type of math problems to run, only for hendrycks_math.") + parser = argparse.ArgumentParser( + description="Run the GoT model on a benchmark or a custom problem." + ) + parser.add_argument( + "--benchmark", + required=True, + type=str, + choices=["gsm8k", "gpqa", "hendrycks_math"], + help="The benchmark to run the model on.", + ) + parser.add_argument( + "--mode", + required=True, + type=str, + choices=["graph", "standard"], + help="Whether to run the standard model or the graph model.", + ) + parser.add_argument( + "--max_run", + type=int, + default=1, + help="The maximum number of runs for the benchmark.", + ) + parser.add_argument( + "--type", + type=str, + default="algebra", + choices=[ + "algebra", + "counting_and_probability", + "geometry", + "intermediate_algebra", + "number_theory", + "precalculus", + "statistics", + ], + help="The type of math problems to run, only for hendrycks_math.", + ) if len(sys.argv) == 1: parser.print_help(sys.stderr) sys.exit(1) - + args = parser.parse_args() return args + def call_benchmark(args): mode = args.mode if args.mode else "standard" test = mode == "standard" @@ -28,4 +61,4 @@ def call_benchmark(args): elif args.benchmark == "gpqa": use_gpqa(max_run=max_run, test=test, model_name=mode) elif args.benchmark == "hendrycks_math": - use_hendrycks_math(max_run=max_run, test=test, model_name=mode, type=args.type) \ No newline at end of file + use_hendrycks_math(max_run=max_run, test=test, model_name=mode, type=args.type) diff --git a/GoT/model/utils/utils.py b/GoT/model/utils/utils.py index 6af4b91..ac14a65 100644 --- a/GoT/model/utils/utils.py +++ b/GoT/model/utils/utils.py @@ -165,11 +165,13 @@ def normalize_number(num_str: str) -> str: return num_str + def normalize_list(s: str): - " Extract numbers from a string and return them as a sorted list. This is useful for comparing answers that are lists of numbers regardless of order." + "Extract numbers from a string and return them as a sorted list. This is useful for comparing answers that are lists of numbers regardless of order." nums = re.findall(r"-?\d+", s) return sorted(nums) + def symbolic_equal(a, b): """Check if two mathematical expressions are symbolically equal.""" try: @@ -177,6 +179,7 @@ def symbolic_equal(a, b): except Exception: return False + def print_benchmark_result(results: dict, task_name: str, filter: str) -> None: samples = results["samples"][task_name] diff --git a/GoT/tools/craft_tool.py b/GoT/tools/craft_tool.py index 10c1a17..8db13dd 100644 --- a/GoT/tools/craft_tool.py +++ b/GoT/tools/craft_tool.py @@ -50,6 +50,7 @@ def is_valid_annotation(annotation): return False + @tool def craft_tool(tool_function: str) -> str: """Save the function definition provided by the LLM as a tool that can be used by other agents. @@ -65,7 +66,7 @@ def sanitize_input(query: str) -> str: query = re.sub(r"^(\s|`)*(?i:python)?\s*", "", query) query = re.sub(r"(\s|`)*$", "", query) return query - + code = f"""\n\n{sanitize_input(tool_function)}""" # Syntax check @@ -77,7 +78,7 @@ def sanitize_input(query: str) -> str: functions = [n for n in tree.body if isinstance(n, ast.FunctionDef)] if len(functions) != 1: return "Error: exactly one function must be defined." - + func = functions[0] for arg in func.args.args: @@ -86,7 +87,7 @@ def sanitize_input(query: str) -> str: if not is_valid_annotation(arg.annotation): return f"Error: invalid type for '{arg.arg}' (must be typed, e.g. List[int], set[str], dict[str, int], etc.)" - + if func.returns is None: return "Error: missing return type" diff --git a/GoT/tools/runtime_graph_tool.py b/GoT/tools/runtime_graph_tool.py index 21fe7ba..77df52a 100644 --- a/GoT/tools/runtime_graph_tool.py +++ b/GoT/tools/runtime_graph_tool.py @@ -7,8 +7,15 @@ MAX_INTERACTIONS = 10 + @tool -def divide_thought(first_part: str, second_part: str, first_context: str, second_context: str, reasoning_type: str = "pure_reasoning") -> str: +def divide_thought( + first_part: str, + second_part: str, + first_context: str, + second_context: str, + reasoning_type: str = "pure_reasoning", +) -> str: """ This is a tool to divide the thought process into smaller steps. HOW TO USE THIS TOOL: @@ -33,8 +40,8 @@ def divide_thought(first_part: str, second_part: str, first_context: str, second reasoning_agent = LLM().create_custom_agent( [], SystemMessage( - "You are an assistant specialized in reasoning. " \ - "Your goal is to resolve the problem with reasoning. You should to reason step by step and write all your reasoning. " \ + "You are an assistant specialized in reasoning. " + "Your goal is to resolve the problem with reasoning. You should to reason step by step and write all your reasoning. " "If the problem is too complex, you can divide it into smaller parts." ), ) @@ -45,16 +52,43 @@ def divide_thought(first_part: str, second_part: str, first_context: str, second runtime_graph.add_node(n2) runtime_graph.add_edge(runtime_graph.temp_node, n1) runtime_graph.add_edge(runtime_graph.temp_node, n2) - msg1 = [HumanMessage("Reason aboout this problem: " + first_part + "\nContext: " + first_context)] - msg2 = [HumanMessage("Reason aboout this problem: " + second_part + "\nContext: " + second_context)] + msg1 = [ + HumanMessage( + "Reason aboout this problem: " + first_part + "\nContext: " + first_context + ) + ] + msg2 = [ + HumanMessage( + "Reason aboout this problem: " + + second_part + + "\nContext: " + + second_context + ) + ] if reasoning_type == "pure_reasoning": - res1 = parse_response(reasoning_agent.invoke({"messages": msg1}, config={"recursion_limit": MAX_INTERACTIONS})) - res2 = parse_response(reasoning_agent.invoke({"messages": msg2}, config={"recursion_limit": MAX_INTERACTIONS})) + res1 = parse_response( + reasoning_agent.invoke( + {"messages": msg1}, config={"recursion_limit": MAX_INTERACTIONS} + ) + ) + res2 = parse_response( + reasoning_agent.invoke( + {"messages": msg2}, config={"recursion_limit": MAX_INTERACTIONS} + ) + ) else: - res1 = parse_response(tool_agent.invoke({"messages": msg1}, config={"recursion_limit": MAX_INTERACTIONS})) - res2 = parse_response(tool_agent.invoke({"messages": msg2}, config={"recursion_limit": MAX_INTERACTIONS})) + res1 = parse_response( + tool_agent.invoke( + {"messages": msg1}, config={"recursion_limit": MAX_INTERACTIONS} + ) + ) + res2 = parse_response( + tool_agent.invoke( + {"messages": msg2}, config={"recursion_limit": MAX_INTERACTIONS} + ) + ) runtime_graph.resolve_node(n1, res1) runtime_graph.resolve_node(n2, res2) result = f"First part: {res1}\nSecond part: {res2}" - return result \ No newline at end of file + return result From 0a904825d912c53d0eb79c19e4b1169742f3ffcc Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 10 Apr 2026 14:06:57 +0200 Subject: [PATCH 93/94] chore: fix mypy --- GoT/model/utils/hf_formatter.py | 201 ++++++++++++++++++-------------- poetry.lock | 2 +- pyproject.toml | 3 +- 3 files changed, 116 insertions(+), 90 deletions(-) diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index 7293cdd..89b117d 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -1,8 +1,7 @@ import json from random import shuffle import re -import time -from datasets import load_dataset +from datasets import Dataset, load_dataset from langchain.messages import HumanMessage @@ -16,7 +15,33 @@ ) -def gpqa_format(dataset) -> list[dict[str, str]]: +class ResultEval: + def __init__( + self, + question: str, + response: str, + filtered_answer: str, + correct_answer: str, + answer_success: float, + ): + self.question = question + self.response = response + self.filtered_answer = filtered_answer + self.correct_answer = correct_answer + self.answer_success = answer_success + + @staticmethod + def create_empty_result(question: str, correct_answer: str): + return ResultEval( + question=question, + response="Error", + filtered_answer="", + correct_answer=correct_answer, + answer_success=0.0, + ) + + +def gpqa_format(dataset: Dataset) -> list[ResultEval]: questions = [] # Mapping per trasformare l'indice della lista nelle lettere A, B, C, D index_to_letter = {0: "A", 1: "B", 2: "C", 3: "D"} @@ -52,22 +77,24 @@ def gpqa_format(dataset) -> list[dict[str, str]]: f"D) {choices[3]}\n" "Answer:" ) - questions.append({"prompt": prompt, "correct_letter": correct_letter}) + questions.append( + ResultEval.create_empty_result( + question=prompt, correct_answer=correct_letter + ) + ) return questions -def gpqa_run( - questions: list[dict[str, str]], max_run: int, test: bool -) -> list[dict[str, str]]: +def gpqa_run(questions: list[ResultEval], max_run: int, test: bool) -> list[ResultEval]: responses = [] run_counter = 0 agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) for q in questions[25:]: if run_counter >= max_run: break - prompt = q["prompt"] - correct_letter = q["correct_letter"] + prompt = q.question + correct_letter = q.correct_answer try: if test: response = extract_output( @@ -80,39 +107,39 @@ def gpqa_run( response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) responses.append( - { - "question": prompt, - "response": norm_res, - "filtered_answer": "", - "correct_letter": correct_letter, - "answer_success": 0.0, - } + ResultEval( + question=prompt, + response=norm_res, + filtered_answer="", + correct_answer=correct_letter, + answer_success=0.0, + ) ) except Exception as e: print(f"Error processing question: {e}") responses.append( - { - "question": prompt, - "response": "Error", - "filtered_answer": "", - "correct_letter": correct_letter, - "answer_success": 0.0, - } + ResultEval( + question=prompt, + response="Error", + filtered_answer="", + correct_answer=correct_letter, + answer_success=0.0, + ) ) run_counter += 1 return responses -def gpqa_eval(responses: list[dict[str, str]]): +def gpqa_eval(responses: list[ResultEval]): correct = 0 for res in responses: - match = re.search(r"ANSWER:\s*([A-D])", res["response"], re.IGNORECASE) - res["filtered_answer"] = match.group(1).upper() if match else "N/A" + match = re.search(r"ANSWER:\s*([A-D])", res.response, re.IGNORECASE) + res.filtered_answer = match.group(1).upper() if match else "N/A" - if f"ANSWER: {res['correct_letter']}" in res["response"]: + if f"ANSWER: {res.correct_answer}" in res.response: correct += 1 - res["answer_success"] = 1.0 + res.answer_success = 1.0 accuracy = correct / len(responses) * 100 print(f"Accuracy: {accuracy:.2f}%") @@ -120,12 +147,12 @@ def gpqa_eval(responses: list[dict[str, str]]): print(f"Correct: {correct}") -def save_eval_results(responses: list[dict[str, str]], model_name: str): +def save_eval_results(responses: list[ResultEval], model_name: str): with open(f"{model_name}_eval_results.json", "w") as f: json.dump(responses, f, indent=2) -def gsm8k_format(dataset) -> list[dict[str, str]]: +def gsm8k_format(dataset: Dataset) -> list[ResultEval]: questions = [] for data in dataset: sample = data @@ -138,22 +165,26 @@ def gsm8k_format(dataset) -> list[dict[str, str]]: "Answer:" ) - questions.append({"prompt": prompt, "correct_answer": correct_answer}) + questions.append( + ResultEval.create_empty_result( + question=prompt, correct_answer=correct_answer + ) + ) return questions def gsm8k_run( - questions: list[dict[str, str]], max_run: int, test: bool -) -> list[dict[str, str]]: + questions: list[ResultEval], max_run: int, test: bool +) -> list[ResultEval]: responses = [] run_counter = 0 agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) for q in questions: if run_counter >= max_run: break - prompt = q["prompt"] - correct_answer = q["correct_answer"] + prompt = q.question + correct_answer = q.correct_answer try: if test: response = extract_output( @@ -166,41 +197,41 @@ def gsm8k_run( response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) responses.append( - { - "question": prompt, - "response": norm_res, - "filtered_answer": "", - "correct_answer": correct_answer, - "answer_success": 0.0, - } + ResultEval( + question=prompt, + response=norm_res, + filtered_answer="", + correct_answer=correct_answer, + answer_success=0.0, + ) ) except Exception as e: print(f"Error processing question: {e}") responses.append( - { - "question": prompt, - "response": "Error", - "filtered_answer": "", - "correct_answer": correct_answer, - "answer_success": 0.0, - } + ResultEval( + question=prompt, + response="Error", + filtered_answer="", + correct_answer=correct_answer, + answer_success=0.0, + ) ) run_counter += 1 return responses -def gsm8k_eval(responses: list[dict[str, str]]): +def gsm8k_eval(responses: list[ResultEval]): correct = 0 for res in responses: - norm_res = re.search(r"####\s*(-?[\d,.]+)", res["response"]) - norm_res = norm_res.group(1) if norm_res else "N/A" - norm_correct = normalize_number(res["correct_answer"]) - res["filtered_answer"] = norm_res + opt_res = re.search(r"####\s*(-?[\d,.]+)", res.response) + norm_res = opt_res.group(1) if opt_res else "N/A" + norm_correct = normalize_number(res.correct_answer) + res.filtered_answer = norm_res if norm_res in norm_correct: correct += 1 - res["answer_success"] = 1.0 + res.answer_success = 1.0 accuracy = correct / len(responses) * 100 print(f"Accuracy: {accuracy:.2f}%") @@ -208,7 +239,7 @@ def gsm8k_eval(responses: list[dict[str, str]]): print(f"Correct: {correct}") -def hendrycks_math_format(dataset) -> list[dict[str, str]]: +def hendrycks_math_format(dataset: Dataset) -> list[ResultEval]: questions = [] for data in dataset: sample = data @@ -224,27 +255,25 @@ def hendrycks_math_format(dataset) -> list[dict[str, str]]: ) questions.append( - { - "prompt": prompt, - "correct_answer": correct_answer, - "level": sample["level"], - } + ResultEval.create_empty_result( + question=prompt, correct_answer=correct_answer + ) ) return questions def hendrycks_math_run( - questions: list[dict[str, str]], max_run: int, test: bool -) -> list[dict[str, str]]: + questions: list[ResultEval], max_run: int, test: bool +) -> list[ResultEval]: responses = [] run_counter = 0 agent = LLM().create_custom_agent(LLM().get_tools() + LLM().get_craft_tool()) for q in questions: if run_counter >= max_run: break - prompt = q["prompt"] - correct_answer = q["correct_answer"] + prompt = q.question + correct_answer = q.correct_answer try: if test: response = extract_output( @@ -257,39 +286,37 @@ def hendrycks_math_run( response = extract_output(call_graph(prompt)) norm_res = normalize_number(response) responses.append( - { - "level": q["level"], - "question": prompt, - "response": norm_res, - "filtered_answer": "", - "correct_answer": correct_answer, - "answer_success": 0.0, - } + ResultEval( + question=prompt, + response=norm_res, + filtered_answer="", + correct_answer=correct_answer, + answer_success=0.0, + ) ) except Exception as e: print(f"Error processing question: {e}") responses.append( - { - "level": q["level"], - "question": prompt, - "response": "Error", - "filtered_answer": "", - "correct_answer": correct_answer, - "answer_success": 0.0, - } + ResultEval( + question=prompt, + response="Error", + filtered_answer="", + correct_answer=correct_answer, + answer_success=0.0, + ) ) run_counter += 1 return responses -def hendrycks_math_eval(responses: list[dict[str, str]]): +def hendrycks_math_eval(responses: list[ResultEval]): correct = 0 for res in responses: - norm_res = re.search(r"\\boxed\{(.*)\}", res["response"]) - norm_res = norm_res.group(1) if norm_res else "N/A" - norm_correct = normalize_number(res["correct_answer"]) - res["filtered_answer"] = norm_res + opt_res = re.search(r"\\boxed\{(.*)\}", res.response) + norm_res = opt_res.group(1) if opt_res else "N/A" + norm_correct = normalize_number(res.correct_answer) + res.filtered_answer = norm_res if ( (norm_res in norm_correct) @@ -297,7 +324,7 @@ def hendrycks_math_eval(responses: list[dict[str, str]]): or (symbolic_equal(norm_res, norm_correct)) ): correct += 1 - res["answer_success"] = 1.0 + res.answer_success = 1.0 accuracy = correct / len(responses) * 100 print(f"Accuracy: {accuracy:.2f}%") diff --git a/poetry.lock b/poetry.lock index 041345f..e6e3dce 100644 --- a/poetry.lock +++ b/poetry.lock @@ -6868,4 +6868,4 @@ cffi = ["cffi (>=1.17,<2.0)", "cffi (>=2.0.0b)"] [metadata] lock-version = "2.0" python-versions = ">= 3.10.0 < 3.14.0" -content-hash = "1e1f7e3b089394f520b94df309f18e1d296a14c062dcf304c7414fe739687a9e" +content-hash = "c7c8e8591227891fa161988b58a7a8e708d8769ce133faf007103f95df4a0ef4" diff --git a/pyproject.toml b/pyproject.toml index 3840bbd..95c44ad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -19,7 +19,6 @@ dotenv = "^0.9.9" mlflow = "^3.9.0" lm-eval = {extras = ["math"], version = "^0.4.11"} boto3 = "^1.42.51" -sympy = "^1.14.0" [tool.poetry.group.dev.dependencies] coverage = "^7.4.0" @@ -50,5 +49,5 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [[tool.mypy.overrides]] -module = ["lm_eval.*"] +module = ["lm_eval.*", "datasets.*", "sympy.*"] follow_untyped_imports = true From aa3f3bfeb68d0f4fcff49f43df99c8390c0a14e4 Mon Sep 17 00:00:00 2001 From: Raggini Marco Date: Fri, 10 Apr 2026 15:17:58 +0200 Subject: [PATCH 94/94] fix: fix json saving for ResultEval --- GoT/model/utils/hf_formatter.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/GoT/model/utils/hf_formatter.py b/GoT/model/utils/hf_formatter.py index 89b117d..7b1eddf 100644 --- a/GoT/model/utils/hf_formatter.py +++ b/GoT/model/utils/hf_formatter.py @@ -40,6 +40,20 @@ def create_empty_result(question: str, correct_answer: str): answer_success=0.0, ) + def to_dict(self): + return { + "question": self.question, + "response": self.response, + "filtered_answer": self.filtered_answer, + "correct_answer": self.correct_answer, + "answer_success": self.answer_success, + } + + +def save_eval_results(responses: list[ResultEval], model_name: str): + with open(f"{model_name}_eval_results.json", "w") as f: + json.dump([res.to_dict() for res in responses], f, indent=2) + def gpqa_format(dataset: Dataset) -> list[ResultEval]: questions = [] @@ -147,11 +161,6 @@ def gpqa_eval(responses: list[ResultEval]): print(f"Correct: {correct}") -def save_eval_results(responses: list[ResultEval], model_name: str): - with open(f"{model_name}_eval_results.json", "w") as f: - json.dump(responses, f, indent=2) - - def gsm8k_format(dataset: Dataset) -> list[ResultEval]: questions = [] for data in dataset: