workflow.py

# workflow.py

import time
from datetime import datetime
from typing import Dict, Any, Sequence

from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
from typing_extensions import TypedDict, Annotated

from processor import EnhancedCognitiveProcessor
from config import ResearchConfig

import logging
logger = logging.getLogger(__name__)

# Define the state schema
class AgentState(TypedDict):
    messages: Annotated[Sequence[AIMessage | HumanMessage | ToolMessage], add_messages]
    context: Dict[str, Any]
    metadata: Dict[str, Any]

class ResearchWorkflow:
    """
    A multi-step research workflow that leverages a Retrieval-Augmented Generation (RAG) strategy.
    It dynamically retrieves external data and integrates it with the raw query to generate domain-specific analyses.
    Supported domains include:
      - Biomedical Research
      - Legal Research
      - Environmental and Energy Studies
      - Competitive Programming and Theoretical Computer Science
      - Social Sciences
    """
    def __init__(self) -> None:
        self.processor = EnhancedCognitiveProcessor()
        # Provide the state schema to the StateGraph constructor.
        self.workflow = StateGraph(AgentState)
        self._build_workflow()
        self.app = self.workflow.compile()

    def _build_workflow(self) -> None:
        self.workflow.add_node("ingest", self.ingest_query)
        self.workflow.add_node("retrieve", self.retrieve_documents)
        self.workflow.add_node("analyze", self.analyze_content)
        self.workflow.add_node("validate", self.validate_output)
        self.workflow.add_node("refine", self.refine_results)
        self.workflow.set_entry_point("ingest")
        self.workflow.add_edge("ingest", "retrieve")
        self.workflow.add_edge("retrieve", "analyze")
        self.workflow.add_conditional_edges(
            "analyze",
            self._quality_check,
            {"valid": "validate", "invalid": "refine"}
        )
        self.workflow.add_edge("validate", END)
        self.workflow.add_edge("refine", "retrieve")
        # Extended node for multi-modal enhancement.
        self.workflow.add_node("enhance", self.enhance_analysis)
        self.workflow.add_edge("validate", "enhance")
        self.workflow.add_edge("enhance", END)

    def ingest_query(self, state: Dict) -> Dict:
        try:
            query = state["messages"][-1].content
            # Get the domain from state; default to Biomedical Research if not provided.
            domain = state.get("context", {}).get("domain", "Biomedical Research")
            new_context = {
                "raw_query": query,
                "domain": domain,
                "refine_count": 0,
                "refinement_history": []
            }
            logger.info(f"Query ingested. Domain: {domain}")
            return {
                "messages": [AIMessage(content="Query ingested successfully")],
                "context": new_context,
                "metadata": {"timestamp": datetime.now().isoformat()}
            }
        except Exception as e:
            logger.exception("Error during query ingestion.")
            return self._error_state(f"Ingestion Error: {str(e)}")

    def retrieve_documents(self, state: Dict) -> Dict:
        try:
            query = state["context"]["raw_query"]
            # For demonstration, we use an empty list to simulate retrieval failure.
            # In a full implementation, integrate a retriever (e.g., via LangChain, LlamaIndex, or a vector DB).
            docs = []  
            logger.info(f"Retrieved {len(docs)} documents for query.")
            return {
                "messages": [AIMessage(content=f"Retrieved {len(docs)} documents")],
                "context": {
                    "documents": docs,
                    "retrieval_time": time.time(),
                    "refine_count": state["context"].get("refine_count", 0),
                    "refinement_history": state["context"].get("refinement_history", []),
                    "domain": state["context"].get("domain", "Biomedical Research")
                }
            }
        except Exception as e:
            logger.exception("Error during document retrieval.")
            return self._error_state(f"Retrieval Error: {str(e)}")

    def analyze_content(self, state: Dict) -> Dict:
        try:
            domain = state["context"].get("domain", "Biomedical Research").strip().lower()
            docs = state["context"].get("documents", [])
            # If documents are present, use their content; otherwise, fall back to the raw query.
            if docs:
                docs_text = "\n\n".join([d.page_content for d in docs])
            else:
                docs_text = state["context"].get("raw_query", "")
                logger.info("No documents retrieved; switching to dynamic synthesis using RAG.")
            domain_prompt = ResearchConfig.DOMAIN_PROMPTS.get(domain, "")
            # Combine the domain prompt with either retrieved text or raw query.
            full_prompt = f"Domain: {state['context'].get('domain', 'Biomedical Research')}\n" \
                          f"{domain_prompt}\n\n" + \
                          ResearchConfig.ANALYSIS_TEMPLATE.format(context=docs_text)
            response = self.processor.process_query(full_prompt)
            if "error" in response:
                logger.error("Backend response error during analysis.")
                return self._error_state(response["error"])
            logger.info("Content analysis completed using RAG approach.")
            return {
                "messages": [AIMessage(content=response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
                "context": state["context"]
            }
        except Exception as e:
            logger.exception("Error during content analysis.")
            return self._error_state(f"Analysis Error: {str(e)}")

    def validate_output(self, state: Dict) -> Dict:
        try:
            analysis = state["messages"][-1].content
            validation_prompt = (
                f"Validate the following research analysis:\n{analysis}\n\n"
                "Check for:\n"
                "1. Technical accuracy\n"
                "2. Citation support (are claims backed by evidence?)\n"
                "3. Logical consistency\n"
                "4. Methodological soundness\n\n"
                "Respond with 'VALID: [brief justification]' or 'INVALID: [brief justification]'."
            )
            response = self.processor.process_query(validation_prompt)
            logger.info("Output validation completed.")
            return {
                "messages": [AIMessage(content=analysis + f"\n\nValidation: {response.get('choices', [{}])[0].get('message', {}).get('content', '')}")],
                "context": state["context"]
            }
        except Exception as e:
            logger.exception("Error during output validation.")
            return self._error_state(f"Validation Error: {str(e)}")

    def refine_results(self, state: Dict) -> Dict:
        try:
            current_count = state["context"].get("refine_count", 0)
            state["context"]["refine_count"] = current_count + 1
            refinement_history = state["context"].setdefault("refinement_history", [])
            current_analysis = state["messages"][-1].content
            refinement_history.append(current_analysis)
            difficulty_level = max(0, 3 - state["context"]["refine_count"])
            domain = state["context"].get("domain", "Biomedical Research")
            logger.info(f"Refinement iteration: {state['context']['refine_count']}, Difficulty level: {difficulty_level}")
            if state["context"]["refine_count"] >= 3:
                meta_prompt = (
                    f"Domain: {domain}\n"
                    "You are given the following series of refinement outputs:\n" +
                    "\n---\n".join(refinement_history) +
                    "\n\nSynthesize the above into a final, concise, and high-quality technical analysis report. "
                    "Focus on the key findings and improvements made across the iterations. Do not introduce new ideas; just synthesize the improvements. Ensure the report is well-structured and easy to understand."
                )
                meta_response = self.processor.process_query(meta_prompt)
                logger.info("Meta-refinement completed.")
                return {
                    "messages": [AIMessage(content=meta_response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
                    "context": state["context"]
                }
            else:
                refinement_prompt = (
                    f"Domain: {domain}\n"
                    f"Refine this analysis (current difficulty level: {difficulty_level}):\n{current_analysis}\n\n"
                    "First, critically evaluate the analysis and identify its weaknesses, such as inaccuracies, unsupported claims, or lack of clarity. Summarize these weaknesses in a short paragraph.\n\n"
                    "Then, improve the following aspects:\n"
                    "1. Technical precision\n"
                    "2. Empirical grounding\n"
                    "3. Theoretical coherence\n\n"
                    "Use a structured difficulty gradient approach to produce a simpler yet more accurate variant, addressing the identified weaknesses."
                )
                response = self.processor.process_query(refinement_prompt)
                logger.info("Refinement completed.")
                return {
                    "messages": [AIMessage(content=response.get('choices', [{}])[0].get('message', {}).get('content', ''))],
                    "context": state["context"]
                }
        except Exception as e:
            logger.exception("Error during refinement.")
            return self._error_state(f"Refinement Error: {str(e)}")

    def _quality_check(self, state: Dict) -> str:
        refine_count = state["context"].get("refine_count", 0)
        if refine_count >= 3:
            logger.warning("Refinement limit reached. Forcing valid outcome.")
            return "valid"
        content = state["messages"][-1].content
        quality = "valid" if "VALID" in content else "invalid"
        logger.info(f"Quality check returned: {quality}")
        return quality

    def _error_state(self, message: str) -> Dict:
        logger.error(message)
        return {
            "messages": [AIMessage(content=f"❌ {message}")],
            "context": {"error": True},
            "metadata": {"status": "error"}
        }

    def enhance_analysis(self, state: Dict) -> Dict:
        try:
            analysis = state["messages"][-1].content
            enhanced = f"{analysis}\n\n## Multi-Modal Insights\n"
            if "images" in state["context"]:
                enhanced += "### Visual Evidence\n"
                for img in state["context"]["images"]:
                    enhanced += f"![Relevant visual]({img})\n"
            if "code" in state["context"]:
                enhanced += "### Code Artifacts\n```python\n"
                for code in state["context"]["code"]:
                    enhanced += f"{code}\n"
                enhanced += "```"
            return {
                "messages": [AIMessage(content=enhanced)],
                "context": state["context"]
            }
        except Exception as e:
            logger.exception("Error during multi-modal enhancement.")
            return self._error_state(f"Enhancement Error: {str(e)}")