project-lyra/cortex/intake/intake.py

import os
from datetime import datetime
from typing import List, Dict, Any

from llm.llm_router import call_llm  # use Cortex's shared router

# ─────────────────────────────
# Config
# ─────────────────────────────

INTAKE_LLM = os.getenv("INTAKE_LLM", "PRIMARY").upper()

SUMMARY_MAX_TOKENS = int(os.getenv("SUMMARY_MAX_TOKENS", "200"))
SUMMARY_TEMPERATURE = float(os.getenv("SUMMARY_TEMPERATURE", "0.3"))

NEOMEM_API = os.getenv("NEOMEM_API")
NEOMEM_KEY = os.getenv("NEOMEM_KEY")

# ─────────────────────────────
# Internal history for L10/L20/L30
# ─────────────────────────────

L10_HISTORY: Dict[str, list[str]] = {}   # session_id → list of L10 blocks
L20_HISTORY: Dict[str, list[str]] = {}   # session_id → list of merged overviews


# ─────────────────────────────
# LLM helper (via Cortex router)
# ─────────────────────────────

async def _llm(prompt: str) -> str:
    """
    Use Cortex's llm_router to run a summary prompt.
    """
    try:
        text = await call_llm(
            prompt,
            backend=INTAKE_LLM,
            temperature=SUMMARY_TEMPERATURE,
            max_tokens=SUMMARY_MAX_TOKENS,
        )
        return (text or "").strip()
    except Exception as e:
        return f"[Error summarizing: {e}]"


# ─────────────────────────────
# Formatting helpers
# ─────────────────────────────

def _format_exchanges(exchanges: List[Dict[str, Any]]) -> str:
    """
    Expect each exchange to look like:
      { "user_msg": "...", "assistant_msg": "..." }
    """
    chunks = []
    for e in exchanges:
        user = e.get("user_msg", "")
        assistant = e.get("assistant_msg", "")
        chunks.append(f"User: {user}\nAssistant: {assistant}\n")
    return "\n".join(chunks)


# ─────────────────────────────
# Base factual summary
# ─────────────────────────────

async def summarize_simple(exchanges: List[Dict[str, Any]]) -> str:
    """
    Simple factual summary of recent exchanges.
    """
    if not exchanges:
        return ""

    text = _format_exchanges(exchanges)

    prompt = f"""
Summarize the following conversation between Brian (user) and Lyra (assistant).
Focus only on factual content. Avoid names, examples, story tone, or invented details.

{text}

Summary:
"""
    return await _llm(prompt)


# ─────────────────────────────
# Multilevel Summaries (L1, L5, L10, L20, L30)
# ─────────────────────────────

async def summarize_L1(buf: List[Dict[str, Any]]) -> str:
    # Last ~5 exchanges
    return await summarize_simple(buf[-5:])


async def summarize_L5(buf: List[Dict[str, Any]]) -> str:
    # Last ~10 exchanges
    return await summarize_simple(buf[-10:])


async def summarize_L10(session_id: str, buf: List[Dict[str, Any]]) -> str:
    # “Reality Check” for last 10 exchanges
    text = _format_exchanges(buf[-10:])

    prompt = f"""
You are Lyra Intake performing a short 'Reality Check'.
Summarize the last block of conversation (up to 10 exchanges)
in one clear paragraph focusing on tone, intent, and direction.

{text}

Reality Check:
"""
    summary = await _llm(prompt)

    # Track history for this session
    L10_HISTORY.setdefault(session_id, [])
    L10_HISTORY[session_id].append(summary)

    return summary


async def summarize_L20(session_id: str) -> str:
    """
    Merge all L10 Reality Checks into a 'Session Overview'.
    """
    history = L10_HISTORY.get(session_id, [])
    joined = "\n\n".join(history) if history else ""

    if not joined:
        return ""

    prompt = f"""
You are Lyra Intake creating a 'Session Overview'.
Merge the following Reality Check paragraphs into one short summary
capturing progress, themes, and the direction of the conversation.

{joined}

Overview:
"""
    summary = await _llm(prompt)

    L20_HISTORY.setdefault(session_id, [])
    L20_HISTORY[session_id].append(summary)

    return summary


async def summarize_L30(session_id: str) -> str:
    """
    Merge all L20 session overviews into a 'Continuity Report'.
    """
    history = L20_HISTORY.get(session_id, [])
    joined = "\n\n".join(history) if history else ""

    if not joined:
        return ""

    prompt = f"""
You are Lyra Intake generating a 'Continuity Report'.
Condense these session overviews into one high-level reflection,
noting major themes, persistent goals, and shifts.

{joined}

Continuity Report:
"""
    return await _llm(prompt)


# ─────────────────────────────
# NeoMem push
# ─────────────────────────────

def push_to_neomem(summary: str, session_id: str, level: str) -> None:
    """
    Fire-and-forget push of a summary into NeoMem.
    """
    if not NEOMEM_API or not summary:
        return

    headers = {"Content-Type": "application/json"}
    if NEOMEM_KEY:
        headers["Authorization"] = f"Bearer {NEOMEM_KEY}"

    payload = {
        "messages": [{"role": "assistant", "content": summary}],
        "user_id": "brian",
        "metadata": {
            "source": "intake",
            "session_id": session_id,
            "level": level,
        },
    }

    try:
        import requests
        requests.post(
            f"{NEOMEM_API}/memories",
            json=payload,
            headers=headers,
            timeout=20,
        ).raise_for_status()
        print(f"🧠 NeoMem updated ({level}) for {session_id}")
    except Exception as e:
        print(f"NeoMem push failed ({level}, {session_id}): {e}")


# ─────────────────────────────
# Main entrypoint for Cortex
# ─────────────────────────────

async def summarize_context(
    session_id: str,
    exchanges: List[Dict[str, Any]],
) -> Dict[str, Any]:
    """
    Main API used by Cortex:

        summaries = await summarize_context(session_id, exchanges)

    `exchanges` should be the recent conversation buffer for that session.
    """
    buf = list(exchanges)
    if not buf:
        return {
            "session_id": session_id,
            "exchange_count": 0,
            "L1": "",
            "L5": "",
            "L10": "",
            "L20": "",
            "L30": "",
            "last_updated": None,
        }

    # Base levels
    L1 = await summarize_L1(buf)
    L5 = await summarize_L5(buf)
    L10 = await summarize_L10(session_id, buf)
    L20 = await summarize_L20(session_id)
    L30 = await summarize_L30(session_id)

    # Push the "interesting" tiers into NeoMem
    push_to_neomem(L10, session_id, "L10")
    push_to_neomem(L20, session_id, "L20")
    push_to_neomem(L30, session_id, "L30")

    return {
        "session_id": session_id,
        "exchange_count": len(buf),
        "L1": L1,
        "L5": L5,
        "L10": L10,
        "L20": L20,
        "L30": L30,
        "last_updated": datetime.now().isoformat(),
    }