opro_demo/_qwen_xinference_demo/opro/session_state.py

import uuid
from typing import List, Tuple, Dict, Any

# Legacy session storage (for query rewriting)
SESSIONS = {}
USER_FEEDBACK_LOG = []

# OPRO session storage (for system instruction optimization)
OPRO_RUNS = {}
OPRO_RUN_LOG = []

def create_session(query: str) -> str:
    sid = uuid.uuid4().hex
    SESSIONS[sid] = {
        "original_query": query,
        "round": 0,
        "history_candidates": [],
        "user_feedback": [],
        "rejected": [],
        "selected_prompt": None,
        "chat_history": [],
        "model_name": None
    }
    return sid

def get_session(sid: str):
    return SESSIONS.get(sid)

def update_session_add_candidates(sid: str, candidates: list):
    s = SESSIONS[sid]
    s["round"] += 1
    s["history_candidates"].extend(candidates)

def log_user_choice(sid: str, choice: str):
    SESSIONS[sid]["user_feedback"].append(
        {"round": SESSIONS[sid]["round"], "choice": choice}
    )
    USER_FEEDBACK_LOG.append({
        "session_id": sid,
        "round": SESSIONS[sid]["round"],
        "choice": choice
    })

def log_user_reject(sid: str, candidate: str, reason: str | None = None):
    SESSIONS[sid]["rejected"].append(candidate)
    USER_FEEDBACK_LOG.append({
        "session_id": sid,
        "round": SESSIONS[sid]["round"],
        "reject": candidate,
        "reason": reason or ""
    })

def set_selected_prompt(sid: str, prompt: str):
    SESSIONS[sid]["selected_prompt"] = prompt

def log_chat_message(sid: str, role: str, content: str):
    SESSIONS[sid]["chat_history"].append({"role": role, "content": content})

def set_session_model(sid: str, model_name: str | None):
    s = SESSIONS.get(sid)
    if s is not None:
        s["model_name"] = model_name


# ============================================================================
# TRUE OPRO SESSION MANAGEMENT
# ============================================================================

def create_opro_run(
    task_description: str,
    test_cases: List[Tuple[str, str]] = None,
    model_name: str = None
) -> str:
    """
    Create a new OPRO optimization run.

    Args:
        task_description: Description of the task to optimize for
        test_cases: List of (input, expected_output) tuples for evaluation
        model_name: Optional model name to use

    Returns:
        run_id: Unique identifier for this OPRO run
    """
    run_id = uuid.uuid4().hex
    OPRO_RUNS[run_id] = {
        "task_description": task_description,
        "test_cases": test_cases or [],
        "model_name": model_name,
        "iteration": 0,
        "trajectory": [],  # List of (instruction, score) tuples
        "best_instruction": None,
        "best_score": 0.0,
        "current_candidates": [],
        "created_at": uuid.uuid1().time,
        "status": "active"  # active, completed, failed
    }
    return run_id


def get_opro_run(run_id: str) -> Dict[str, Any]:
    """Get OPRO run by ID."""
    return OPRO_RUNS.get(run_id)


def update_opro_iteration(
    run_id: str,
    candidates: List[str],
    scores: List[float] = None
):
    """
    Update OPRO run with new iteration results.

    Args:
        run_id: OPRO run identifier
        candidates: List of system instruction candidates
        scores: Optional list of scores (if evaluated)
    """
    run = OPRO_RUNS.get(run_id)
    if not run:
        return

    run["iteration"] += 1
    run["current_candidates"] = candidates

    # If scores provided, update trajectory
    if scores and len(scores) == len(candidates):
        for candidate, score in zip(candidates, scores):
            run["trajectory"].append((candidate, score))

            # Update best if this is better
            if score > run["best_score"]:
                run["best_score"] = score
                run["best_instruction"] = candidate

    # Log the iteration
    OPRO_RUN_LOG.append({
        "run_id": run_id,
        "iteration": run["iteration"],
        "num_candidates": len(candidates),
        "best_score": run["best_score"]
    })


def add_opro_evaluation(
    run_id: str,
    instruction: str,
    score: float
):
    """
    Add a single evaluation result to OPRO run.

    Args:
        run_id: OPRO run identifier
        instruction: System instruction that was evaluated
        score: Performance score
    """
    run = OPRO_RUNS.get(run_id)
    if not run:
        return

    # Add to trajectory
    run["trajectory"].append((instruction, score))

    # Update best if this is better
    if score > run["best_score"]:
        run["best_score"] = score
        run["best_instruction"] = instruction


def get_opro_trajectory(run_id: str) -> List[Tuple[str, float]]:
    """
    Get the performance trajectory for an OPRO run.

    Returns:
        List of (instruction, score) tuples sorted by score (highest first)
    """
    run = OPRO_RUNS.get(run_id)
    if not run:
        return []

    trajectory = run["trajectory"]
    return sorted(trajectory, key=lambda x: x[1], reverse=True)


def set_opro_test_cases(
    run_id: str,
    test_cases: List[Tuple[str, str]]
):
    """
    Set or update test cases for an OPRO run.

    Args:
        run_id: OPRO run identifier
        test_cases: List of (input, expected_output) tuples
    """
    run = OPRO_RUNS.get(run_id)
    if run:
        run["test_cases"] = test_cases


def complete_opro_run(run_id: str):
    """Mark an OPRO run as completed."""
    run = OPRO_RUNS.get(run_id)
    if run:
        run["status"] = "completed"


def list_opro_runs() -> List[Dict[str, Any]]:
    """
    List all OPRO runs with summary information.

    Returns:
        List of run summaries
    """
    return [
        {
            "run_id": run_id,
            "task_description": run["task_description"][:100] + "..." if len(run["task_description"]) > 100 else run["task_description"],
            "iteration": run["iteration"],
            "best_score": run["best_score"],
            "num_test_cases": len(run["test_cases"]),
            "status": run["status"]
        }
        for run_id, run in OPRO_RUNS.items()
    ]