diff --git a/backend/Dockerfile b/backend/Dockerfile index f0c3ce6..75e502e 100644 --- a/backend/Dockerfile +++ b/backend/Dockerfile @@ -9,6 +9,7 @@ ENV \ RUN : \ && apt-get update \ && apt-get install -y --no-install-recommends \ + build-essential \ curl \ ca-certificates \ && apt-get clean \ diff --git a/backend/app/api/router.py b/backend/app/api/router.py index e9cc08a..004f0fa 100644 --- a/backend/app/api/router.py +++ b/backend/app/api/router.py @@ -1,7 +1,8 @@ -from app.api.routes import health, login, users +from app.api.routes import health, login, scan, users from app.core.utils import APIRouter api_router = APIRouter() api_router.include_router(health.router) api_router.include_router(login.router) +api_router.include_router(scan.router) api_router.include_router(users.router) diff --git a/backend/app/api/routes/scan.py b/backend/app/api/routes/scan.py new file mode 100644 index 0000000..8ca8f21 --- /dev/null +++ b/backend/app/api/routes/scan.py @@ -0,0 +1,29 @@ +from fastapi import HTTPException +from loguru import logger +from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR + +from app.api.deps import CurrentUser +from app.core.config import settings +from app.core.utils import APIRouter +from app.models.scan import AnalysisReport, Topic +from app.scan.graph import CustomGraph + +router = APIRouter(tags=["SCAN"], prefix=f"{settings.API_V1_PREFIX}/scan") + + +@router.post("/") +async def ask_question(*, topic: Topic, _: CurrentUser) -> AnalysisReport: + """Ask SCAN for help with a questions.""" + + graph = CustomGraph(topic.topic) + try: + logger.debug("Preparing analysis") + analysis = await graph.execute() + except Exception as e: + logger.error(f"An error occurred while answering question: {e}") + raise HTTPException( + status_code=HTTP_500_INTERNAL_SERVER_ERROR, + detail="An error occurred when getting an answer", + ) from e + + return analysis diff --git a/backend/app/models/scan.py b/backend/app/models/scan.py new file mode 100644 index 0000000..413b876 --- /dev/null +++ b/backend/app/models/scan.py @@ -0,0 +1,13 @@ +from camel_converter.pydantic_base import CamelBase + + +class AnalysisReport(CamelBase): + dlpfc_analysis: str | None = None + vmpfc_analysis: str | None = None + ofc_analysis: str | None = None + acc_analysis: str | None = None + mpfc_analysis: str | None = None + + +class Topic(CamelBase): + topic: str diff --git a/backend/app/scan/__init__.py b/backend/app/scan/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/scan/graph.py b/backend/app/scan/graph.py new file mode 100644 index 0000000..bda6bed --- /dev/null +++ b/backend/app/scan/graph.py @@ -0,0 +1,86 @@ +from collections.abc import Hashable +from typing import cast + +from langgraph.graph import END, StateGraph + +from app.models.scan import AnalysisReport +from app.scan.pfc_agents import AgentState, PFCAgents + + +class CustomGraph: + def __init__(self, topic: str) -> None: + self.topic = topic + self.agents = PFCAgents(topic=self.topic) + self.workflow = StateGraph(AgentState) + + def build_graph(self) -> None: + for role, agent_function in self.agents.agents.items(): + self.workflow.add_node(role, agent_function) + + def router(state: AgentState) -> str | None: + next_agent = state["next"] + if next_agent == END: + return None + + if next_agent is not None: + return next_agent + return END + + dlpfc_edges: dict[str, str] = { + "VMPFC": "VMPFC", + "OFC": "OFC", + "ACC": "ACC", + "MPFC": "MPFC", + END: END, + } + + self.workflow.add_conditional_edges("DLPFC", router, cast(dict[Hashable, str], dlpfc_edges)) + + for role in ("VMPFC", "OFC", "ACC", "MPFC"): + self.workflow.add_conditional_edges( + role, + router, + ) + + self.workflow.set_entry_point("DLPFC") + self.graph = self.workflow.compile() + + async def execute(self) -> AnalysisReport: + self.build_graph() + + initial_state = AgentState( + input=self.topic, + history=[], + next="DLPFC", + current_role="DLPFC", + ) + + final_state = await self.graph.ainvoke(initial_state) + return self.prepare_output(cast(AgentState, final_state)) + + def prepare_output(self, state: AgentState) -> AnalysisReport: + dlpfc_analysis = None + vmpfc_analysis = None + ofc_analysis = None + acc_analysis = None + mpfc_analysis = None + + for role, analysis in state["history"]: + if role == "DLPFC": + dlpfc_analysis = analysis if analysis else None + elif role == "VMPFC": + vmpfc_analysis = analysis if analysis else None + elif role == "OFC": + ofc_analysis = analysis if analysis else None + elif role == "ACC": + acc_analysis = analysis if analysis else None + elif role == "MPFC": + mpfc_analysis = analysis if analysis else None + + return AnalysisReport( + dlpfc_analysis=dlpfc_analysis, + vmpfc_analysis=vmpfc_analysis, + ofc_analysis=ofc_analysis, + acc_analysis=acc_analysis, + mpfc_analysis=mpfc_analysis, + ) diff --git a/backend/app/scan/openai.py b/backend/app/scan/openai.py new file mode 100644 index 0000000..f7d3288 --- /dev/null +++ b/backend/app/scan/openai.py @@ -0,0 +1,34 @@ +from collections.abc import Iterable + +from openai import AsyncOpenAI +from openai.types.chat import ChatCompletionMessageParam + +from app.core.config import settings + + +class OpenAIWrapper: + """Wrapper for OpenAI API interactions.""" + + def __init__(self, model_name: str): + self.model_name = model_name + self.client = AsyncOpenAI(api_key=settings.OPENAI_API_KEY) + + async def create_chat_completion( + self, + messages: Iterable[ChatCompletionMessageParam], + max_tokens: int = settings.MAX_TOKENS, + temperature: float = settings.TEMPERATURE, + ) -> str | None: + """Create a chat completion with error handling.""" + + response = await self.client.chat.completions.create( + model=self.model_name, + messages=messages, + max_tokens=max_tokens, + temperature=temperature, + ) + + if not response.choices[0].message.content: + return None + + return response.choices[0].message.content.strip() diff --git a/backend/app/scan/pfc_agents.py b/backend/app/scan/pfc_agents.py new file mode 100644 index 0000000..883bac1 --- /dev/null +++ b/backend/app/scan/pfc_agents.py @@ -0,0 +1,333 @@ +from collections.abc import Callable, Coroutine +from functools import cached_property +from typing import Annotated, Any, TypedDict, cast + +from langchain.tools import Tool +from langchain_core.messages import AIMessage +from langgraph.prebuilt import ToolNode + +from app.core.config import settings +from app.scan.openai import OpenAIWrapper +from app.types import RoleName + + +class AgentState(TypedDict): + current_role: RoleName + input: str + history: list[tuple[str, str]] # List of (role, message) tuples + next: str | None + + +class ToolInput(TypedDict): + input: str # The topic or content to analyze + history: list[tuple[str, str]] # List of (role, message) tuples + role: str # The PFC role performing the analysis + + +class AgentConfig(TypedDict): + role: str + model: str + backstory: str + goal: str + temperature: float + max_tokens: int + + +class RolesConfig(TypedDict): + DLPFC: AgentConfig + VMPFC: AgentConfig + OFC: AgentConfig + ACC: AgentConfig + MPFC: AgentConfig + + +class PFCAgents: + """Implements a network of PFC region agents that collaborate in cognitive processing. + + Each agent represents a distinct prefrontal cortex region with specialized functions. + """ + + def __init__(self, topic: str) -> None: + self.topic = topic + + @cached_property + def agents( + self, + ) -> dict[RoleName, Callable[[AgentState], Coroutine[None, None, AgentState]]]: + agents_dict = {} + for role in ("DLPFC", "VMPFC", "OFC", "ACC", "MPFC"): + agents_dict[cast(RoleName, role)] = self.create_agent_function(cast(RoleName, role)) + + return agents_dict + + def create_agent_function( + self, role_name: RoleName + ) -> Callable[[AgentState], Coroutine[None, None, AgentState]]: + config = self.create_agent(role_name) + + async def agent_function(state: AgentState) -> AgentState: + current_input = state["input"] + history = state["history"] + + context_messages = [] + for role, message in history: + context_messages.append(f"{role}:\n{message}\n") + context = "\n".join(context_messages) if context_messages else "" + + messages = [ + {"role": "system", "content": config["backstory"] + "\n" + config["goal"]}, + { + "role": "user", + "content": ( + f"Previous Analysis:\n{context}\n\n" + f"Current Topic: {current_input}\n\n" + f"Provide your analysis as {role_name}, considering the previous analyses " + f"and focusing on your specific role in the decision-making process." + ), + }, + ] + + llm_response = await config["llm"].create_chat_completion( + messages=messages, + max_tokens=settings.MAX_TOKENS, + temperature=settings.TEMPERATURE, + ) + + tool_response = await config["tool_node"].ainvoke( + {"messages": [AIMessage(content=llm_response, history=history, role=role_name)]} + ) + next_agent = _determine_next_agent(role_name) + + return AgentState( + current_role=role_name, + input=tool_response, + history=history + [(role_name, tool_response["messages"])], + next=next_agent, + ) + + return agent_function + + def create_agent(self, role_name: RoleName) -> dict[str, Any]: + """Creates base agent configuration with enhanced setup.""" + model_name = getattr(settings, f"{role_name}_MODEL") + client = OpenAIWrapper(model_name=model_name) + tools = _get_tools(role_name) + tool_node = ToolNode(tools) + backstory = _get_backstory(role_name) + goal = _get_goal(role_name) + + return { + "role": role_name, + "llm": client, + "tools": tools, + "tool_node": tool_node, + "backstory": f"You are the {role_name}, specializing in {backstory} for the topic '{self.topic}'.", + "goal": goal, + } + + def communicate(self, state: AgentState) -> AgentState: + state["next"] = _determine_next_agent(cast(RoleName, state["current_role"])) + + return state + + +def _get_prompt_templates(context: str, topic: str, role_name: RoleName) -> dict[str, str]: + return { + "analysis": f""" + Previous Analysis: + {context} + + Current Topic: {topic} + Role: {role_name} + + Instructions: + 1. Review previous analyses if available + 2. Analyze from your role's perspective + 3. Consider interactions with other PFC regions + 4. Provide structured insights + + Format your response with clear sections and bullet points. + """, + "integration": """ + As the DLPFC, integrate the following analyses: + {context} + + Focus on: + 1. Key patterns and insights + 2. Conflicts or contradictions + 3. Integrated recommendations + 4. Next steps + """, + "error": "Error in {role} processing: {error}", + } + + +async def _complex_analyzer( + tool_input: Annotated[ToolInput, "Input for complex analysis"], +) -> str: + return await _build_analyzer(tool_input, "analysis", settings.DLPFC_MODEL) + + +async def _integration_analyzer( + tool_input: Annotated[ToolInput, "Input for integration analysis"], +) -> str: + return await _build_analyzer(tool_input, "integration", settings.MPFC_MODEL) + + +async def _emotional_analyzer( + tool_input: Annotated[ToolInput, "Input for emotional analysis"], +) -> str: + return await _build_analyzer(tool_input, "emotional", settings.VMPFC_MODEL) + + +async def _reward_analyzer( + tool_input: Annotated[ToolInput, "Input for reward analysis"], +) -> str: + return await _build_analyzer(tool_input, "reward", settings.OFC_MODEL) + + +async def _conflict_analyzer( + tool_input: Annotated[ToolInput, "Input for conflict analysis"], +) -> str: + return await _build_analyzer(tool_input, "conflict", settings.ACC_MODEL) + + +async def _social_analyzer( + tool_input: Annotated[ToolInput, "Input for social analysis"], +) -> str: + return await _build_analyzer(tool_input, "social", settings.MPFC_MODEL) + + +async def _build_analyzer(tool_input: ToolInput, prompt_template: str, model: str) -> str: + topic = tool_input["input"] + role = tool_input["role"] + history = tool_input["history"] + context_messages = [f"{prev_role}:\n{message}\n" for prev_role, message in history] + context = "\n".join(context_messages) if context_messages else "" + prompt_template = _get_prompt_templates(context, topic, cast(RoleName, role))[prompt_template] + prompt = prompt_template.format(context=context, role=role, topic=topic) + openai_wrapper = OpenAIWrapper(model_name=model) + analysis_report = await openai_wrapper.create_chat_completion( + messages=[ + { + "role": "system", + "content": f"You are the {role}, focusing on specialized analysis. " + f"Format your response with clear sections and bullet points.", + }, + {"role": "user", "content": prompt}, + ], + max_tokens=settings.MAX_TOKENS, + temperature=settings.TEMPERATURE, + ) + return f"{role} Analysis:\n{analysis_report}" + + +def _get_tools(role_name: RoleName) -> list[Tool]: + tools = [ + Tool( + name="complex_analyzer", + description="Conducts comprehensive analysis based on role specialization", + func=None, # langchain forces you to set this to None when using a coroutine + coroutine=_complex_analyzer, + input_schema=ToolInput, + ) + ] + + if role_name == "DLPFC": + tools.append( + Tool( + name="integration_analyzer", + description="Integrates information from other PFC regions", + func=None, + coroutine=_integration_analyzer, + input_schema=ToolInput, + ) + ) + return tools + + if role_name == "VMPFC": + tools.append( + Tool( + name="emotional_analyzer", + description="Analyzes emotional implications", + func=None, + coroutine=_emotional_analyzer, + input_schema=ToolInput, + ) + ) + return tools + + if role_name == "OFC": + tools.append( + Tool( + name="reward_analyzer", + description="Analyzes reward implications", + func=None, + coroutine=_reward_analyzer, + input_schema=ToolInput, + ) + ) + return tools + + if role_name == "ACC": + tools.append( + Tool( + name="confict_analyzer", + description="Analyzes conflict implications", + func=None, + coroutine=_conflict_analyzer, + input_schema=ToolInput, + ) + ) + return tools + + tools.append( + Tool( + name="social_analyzer", + description="Analyzes social implications", + func=None, + coroutine=_social_analyzer, + input_schema=ToolInput, + ) + ) + + return tools + + +def _determine_next_agent(current_role: RoleName) -> str | None: + if current_role == "DLPFC": + return "VMPFC" + elif current_role == "VMPFC": + return "OFC" + elif current_role == "OFC": + return "ACC" + elif current_role == "ACC": + return "MPFC" + else: + return None + + +def _get_backstory(role_name: RoleName) -> str: + if role_name == "DLPFC": + return "executive functions including working memory, planning, and cognitive control. You integrate information to guide complex decision-making and regulate behavior." + elif role_name == "VMPFC": + return "processing emotional value and risk assessment. You evaluate the emotional significance of choices and predict their emotional outcomes." + elif role_name == "OFC": + return "reward processing and value-based decision making. You integrate sensory and emotional information to evaluate rewards and guide behavior optimization." + elif role_name == "ACC": + return "error detection, conflict monitoring, and emotional regulation. You identify conflicts between competing responses and help regulate emotional reactions." + else: + return "self-referential thinking and social cognition. You process information about self and others, supporting social decision-making and perspective-taking." + + +def _get_goal(role_name: RoleName) -> str: + if role_name == "DLPFC": + return "Analyze the situation using executive control, maintain relevant information in working memory, and develop strategic plans for optimal outcomes." + elif role_name == "VMPFC": + return "Evaluate the emotional implications and risks, considering how different choices might affect emotional wellbeing and social relationships." + elif role_name == "OFC": + return "Assess the reward value of different options, integrate sensory and emotional information, and optimize decision-making for maximum benefit." + elif role_name == "ACC": + return "Monitor for conflicts between competing options, detect potential errors, and help regulate emotional responses to support optimal choices." + else: + return "Consider social and self-relevant implications, integrate personal and social knowledge, and support perspective-taking in decision-making." diff --git a/backend/app/types.py b/backend/app/types.py new file mode 100644 index 0000000..a4fe358 --- /dev/null +++ b/backend/app/types.py @@ -0,0 +1,3 @@ +from typing import Literal + +type RoleName = Literal["DLPFC", "VMPFC", "OFC", "ACC", "MPFC"] diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 7e1da7a..a2128de 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -15,7 +15,11 @@ dependencies = [ "camel-converter[pydantic]==4.0.1", "fastapi==0.115.5", "httptools==0.6.4", + "httpx==0.27.2", + "langchain==0.3.7", + "langgraph==0.2.48", "loguru==0.7.2", + "openai==1.54.4", "orjson==3.10.11", "pwdlib[argon2]==0.2.1", "pydantic[email]==2.9.2", @@ -24,7 +28,6 @@ dependencies = [ "python-multipart==0.0.17", "uvicorn==0.32.0", "uvloop==0.21.0", - "httpx>=0.27.2", ] [dependency-groups] @@ -56,6 +59,7 @@ ignore_missing_imports = true minversion = "6.0" addopts = "--cov=app --cov-report term-missing --no-cov-on-fail" asyncio_mode = "auto" +asyncio_default_fixture_loop_scope = "function" [tool.coverage.report] exclude_lines = ["if __name__ == .__main__.:", "pragma: no cover"] diff --git a/backend/tests/scan/__init__.py b/backend/tests/scan/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/tests/scan/test_graph.py b/backend/tests/scan/test_graph.py new file mode 100644 index 0000000..7ac86e3 --- /dev/null +++ b/backend/tests/scan/test_graph.py @@ -0,0 +1,69 @@ +import pytest + +from app.models.scan import AnalysisReport +from app.scan.graph import CustomGraph +from app.scan.pfc_agents import AgentState + + +@pytest.mark.parametrize( + "state, expected", + ( + ( + AgentState( + current_role="DLPFC", + input="some input", + history=[ + ("DLPFC", "dlpfc"), + ("VMPFC", "vmpfc"), + ("OFC", "ofc"), + ("ACC", "acc"), + ("MPFC", "mpfc"), + ], + next="VMPFC", + ), + AnalysisReport( + dlpfc_analysis="dlpfc", + vmpfc_analysis="vmpfc", + ofc_analysis="ofc", + acc_analysis="acc", + mpfc_analysis="mpfc", + ), + ), + ( + AgentState( + current_role="DLPFC", + input="some input", + history=[ + ("DLPFC", "dlpfc"), + ], + next="VMPFC", + ), + AnalysisReport( + dlpfc_analysis="dlpfc", + vmpfc_analysis=None, + ofc_analysis=None, + acc_analysis=None, + mpfc_analysis=None, + ), + ), + ( + AgentState( + current_role="DLPFC", + input="some input", + history=[], + next="VMPFC", + ), + AnalysisReport( + dlpfc_analysis=None, + vmpfc_analysis=None, + ofc_analysis=None, + acc_analysis=None, + mpfc_analysis=None, + ), + ), + ), +) +def test_prepare_output(state, expected): + graph = CustomGraph("some topic") + + assert graph.prepare_output(state) == expected diff --git a/backend/tests/scan/test_pfc_agents.py b/backend/tests/scan/test_pfc_agents.py new file mode 100644 index 0000000..56d4b21 --- /dev/null +++ b/backend/tests/scan/test_pfc_agents.py @@ -0,0 +1,135 @@ +from typing import cast + +import pytest + +from app.scan.pfc_agents import ( + PFCAgents, + _determine_next_agent, + _get_backstory, + _get_goal, + _get_prompt_templates, +) +from app.types import RoleName + + +def test_agents(): + pfc_agents = PFCAgents("some topic") + + assert list(pfc_agents.agents.keys()) == ["DLPFC", "VMPFC", "OFC", "ACC", "MPFC"] + + +def test_create_agent(): + role = "DLPFC" + pfc_agents = PFCAgents("some topic") + + result = pfc_agents.create_agent(cast(RoleName, role)) + + assert result["role"] == role + assert ( + result["backstory"] + == f"You are the {role}, specializing in executive functions including working memory, planning, and cognitive control. You integrate information to guide complex decision-making and regulate behavior. for the topic 'some topic'." + ) + assert ( + result["goal"] + == "Analyze the situation using executive control, maintain relevant information in working memory, and develop strategic plans for optimal outcomes." + ) + + +@pytest.mark.parametrize( + "role, expected", + ( + ( + "DLPFC", + "executive functions including working memory, planning, and cognitive control. You integrate information to guide complex decision-making and regulate behavior.", + ), + ( + "VMPFC", + "processing emotional value and risk assessment. You evaluate the emotional significance of choices and predict their emotional outcomes.", + ), + ( + "ACC", + "error detection, conflict monitoring, and emotional regulation. You identify conflicts between competing responses and help regulate emotional reactions.", + ), + ( + "MPFC", + "self-referential thinking and social cognition. You process information about self and others, supporting social decision-making and perspective-taking.", + ), + ), +) +def test_get_backstory(role, expected): + assert _get_backstory(role) == expected + + +@pytest.mark.parametrize( + "role, expected", + ( + ( + "DLPFC", + "Analyze the situation using executive control, maintain relevant information in working memory, and develop strategic plans for optimal outcomes.", + ), + ( + "VMPFC", + "Evaluate the emotional implications and risks, considering how different choices might affect emotional wellbeing and social relationships.", + ), + ( + "OFC", + "Assess the reward value of different options, integrate sensory and emotional information, and optimize decision-making for maximum benefit.", + ), + ( + "ACC", + "Monitor for conflicts between competing options, detect potential errors, and help regulate emotional responses to support optimal choices.", + ), + ( + "MPFC", + "Consider social and self-relevant implications, integrate personal and social knowledge, and support perspective-taking in decision-making.", + ), + ), +) +def test_get_goal(role, expected): + assert _get_goal(role) == expected + + +@pytest.mark.parametrize( + "role, expected", + (("DLPFC", "VMPFC"), ("VMPFC", "OFC"), ("OFC", "ACC"), ("ACC", "MPFC"), ("MPFC", None)), +) +def test_determine_next_agent(role, expected): + result = _determine_next_agent(role) + + assert result == expected + + +def test_get_prompt_templates(): + context = "some context" + topic = "some topic" + role_name = "DLPFC" + result = _get_prompt_templates(context, topic, cast(RoleName, role_name)) + + assert result == { + "analysis": f""" + Previous Analysis: + {context} + + Current Topic: {topic} + Role: {role_name} + + Instructions: + 1. Review previous analyses if available + 2. Analyze from your role's perspective + 3. Consider interactions with other PFC regions + 4. Provide structured insights + + Format your response with clear sections and bullet points. + """, + "integration": """ + As the DLPFC, integrate the following analyses: + {context} + + Focus on: + 1. Key patterns and insights + 2. Conflicts or contradictions + 3. Integrated recommendations + 4. 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