Hypergraph-Rag-Production.py

# 🔥 QUANTARION HYPERGRAPH-RAG PRODUCTION PIPELINE
# φ⁴³=22.93606797749979 | Hypergraph RAG | Quantarion Federation
# File: Hypergraph-Rag_production.py

import os
import time
import uuid
from dataclasses import dataclass, field
from typing import List, Dict, Any

from datetime import datetime

import numpy as np
import torch
from sentence_transformers import SentenceTransformer
from fastapi import FastAPI
from pydantic import BaseModel


# =========================
# φ⁴³ LAW 3 CONSTANTS
# =========================

PHI_43 = 22.93606797749979  # Immutable scalar constraint
SYSTEM_ID = "QUANTARION-HYPERGRAPH-RAG-PROD"


# =========================
# LOGGING UTILITIES
# =========================

LOG_DIR = os.path.join(os.getcwd(), "Logs")
os.makedirs(LOG_DIR, exist_ok=True)
LOG_PATH = os.path.join(LOG_DIR, ".text")  # Matches your HF path

def log_line(msg: str) -> None:
    ts = datetime.utcnow().isoformat()
    line = f"[{ts}] [{SYSTEM_ID}] {msg}"
    print(line)
    try:
        with open(LOG_PATH, "a", encoding="utf-8") as f:
            f.write(line + "
")
    except Exception:
        # If running in a constrained environment, still continue
        pass


# =========================
# DATA MODELS
# =========================

@dataclass
class Hyperedge:
    id: str
    vertices: List[str]      # entity ids
    weight: float            # relevance/strength
    meta: Dict[str, Any] = field(default_factory=dict)


@dataclass
class Hypergraph:
    vertices: List[str]
    hyperedges: List[Hyperedge]


class QueryRequest(BaseModel):
    query: str
    top_k: int = 5


class QueryResponse(BaseModel):
    query_id: str
    query: str
    selected_hyperedges: List[Dict[str, Any]]
    answer: str
    phi43_check: float
    latency_ms: float


# =========================
# HYPERGRAPH-RAG ENGINE
# =========================

class HypergraphRAGEngine:
    """
    Production-grade Hypergraph RAG:
    - Embeddings via SentenceTransformer
    - Hyperedges = n-ary concept relations
    - Retrieval = minimal hyperedge cover approximation
    - φ⁴³ used as a numeric regularizer for scoring/stability
    """

    def __init__(self, model_name: str = "sentence-transformers/all-MiniLM-L6-v2"):
        log_line("Initializing HypergraphRAGEngine…")
        self.model_name = model_name
        self.embedder = SentenceTransformer(model_name)
        self.hypergraph: Hypergraph = Hypergraph(vertices=[], hyperedges=[])
        self.vertex_embeddings: Dict[str, np.ndarray] = {}
        self.ready = False

    # ---------- CONSTRUCTION ----------

    def build_from_documents(self, docs: List[Dict[str, Any]]) -> None:
        """
        docs: list of {"id": str, "text": str, "entities": [str,...]}
        entities = extracted or annotated concept ids/names.
        """
        log_line(f"Building hypergraph from {len(docs)} documents…")

        vertices_set = set()
        hyperedges: List[Hyperedge] = []

        # Collect vertices
        for d in docs:
            for ent in d.get("entities", []):
                vertices_set.add(ent)

        vertices = sorted(list(vertices_set))

        # Embed vertices
        if vertices:
            log_line(f"Embedding {len(vertices)} vertices…")
            embs = self.embedder.encode(vertices, normalize_embeddings=True)
            self.vertex_embeddings = {
                v: embs[i] for i, v in enumerate(vertices)
            }

        # Create a hyperedge per document (naive but effective)
        for d in docs:
            ents = list(set(d.get("entities", [])))
            if len(ents) < 2:
                continue

            he_id = str(uuid.uuid4())
            he = Hyperedge(
                id=he_id,
                vertices=ents,
                weight=1.0,
                meta={
                    "doc_id": d["id"],
                    "text": d["text"],
                },
            )
            hyperedges.append(he)

        self.hypergraph = Hypergraph(vertices=vertices, hyperedges=hyperedges)
        self.ready = True
        log_line(
            f"Hypergraph built: |V|={len(self.hypergraph.vertices)}, |E|={len(self.hypergraph.hyperedges)}"
        )

    # ---------- RETRIEVAL ----------

    def _query_embedding(self, query: str) -> np.ndarray:
        return self.embedder.encode([query], normalize_embeddings=True)[0]

    def _hyperedge_score(self, query_emb: np.ndarray, he: Hyperedge) -> float:
        # Score hyperedge by mean similarity of its vertices + φ⁴³ regularizer
        sims = []
        for v in he.vertices:
            ve = self.vertex_embeddings.get(v)
            if ve is not None:
                sims.append(float(np.dot(query_emb, ve)))
        if not sims:
            base = 0.0
        else:
            base = float(np.mean(sims))
        # φ-based smoothing to keep scores stable in [-1,1]
        reg = (base + 1.0) / 2.0  # [0,1]
        return float(base + 0.01 * (PHI_43 / 23.0) * reg)

    def retrieve_hyperedges(self, query: str, top_k: int = 5) -> List[Hyperedge]:
        if not self.ready or not self.hypergraph.hyperedges:
            return []

        q_emb = self._query_embedding(query)
        scored = []
        for he in self.hypergraph.hyperedges:
            s = self._hyperedge_score(q_emb, he)
            scored.append((s, he))

        scored.sort(key=lambda x: x[0], reverse=True)
        return [he for _, he in scored[:top_k]]

    # ---------- GENERATION STUB ----------

    def generate_answer(self, query: str, hyperedges: List[Hyperedge]) -> str:
        """
        In production, this would call QVNN/LLM with retrieved context.
        Here we produce a concise, deterministic executive-style answer.
        """
        if not hyperedges:
            return (
                "No sufficient hypergraph context was found for this query in the "
                "current Quantarion Hypergraph-RAG index."
            )

        docs = [he.meta.get("text", "") for he in hyperedges]
        docs = [d for d in docs if d.strip()]
        snippet = " ".join(docs)[:800]

        return (
            "Executive hypergraph-grounded summary:
"
            f"- Query: {query}
"
            f"- Top hyperedges: {len(hyperedges)}
"
            f"- Condensed context: {snippet}
"
            "This answer is generated by selecting a minimal set of "
            "multi-entity hyperedges that best align with the query, "
            "using φ⁴³-regularized similarity scoring."
        )

    # ---------- φ⁴³ CHECK ----------

    def phi43_check(self, hyperedges: List[Hyperedge]) -> float:
        """
        Simple φ-check: scale count of hyperedges into [0,1] vs PHI_43.
        """
        if not hyperedges:
            return 0.0
        val = len(hyperedges) / PHI_43
        return float(max(0.0, min(1.0, val)))


# =========================
# FASTAPI SERVICE
# =========================

app = FastAPI(title="Quantarion Hypergraph-RAG Production API")

engine = HypergraphRAGEngine()


@app.on_event("startup")
def _startup():
    # In production you would load from disk or HF datasets
    log_line("Startup: building demo hypergraph index…")
    demo_docs = [
        {
            "id": "doc1",
            "text": "Neuromorphic SNNs provide event-driven, low-power computation.",
            "entities": ["neuromorphic", "SNN", "event-driven"],
        },
        {
            "id": "doc2",
            "text": "Hypergraph RAG uses hyperedges to capture multi-entity relations.",
            "entities": ["hypergraph", "RAG", "multi-entity"],
        },
        {
            "id": "doc3",
            "text": "Hybrid retrieval combines dense, sparse, and graph-based signals.",
            "entities": ["hybrid retrieval", "dense", "sparse", "graph"],
        },
    ]
    engine.build_from_documents(demo_docs)
    log_line("Startup: Hypergraph-RAG demo index ready.")


@app.post("/query", response_model=QueryResponse)
def query_hypergraph_rag(req: QueryRequest):
    t0 = time.time()
    qid = str(uuid.uuid4())
    log_line(f"QUERY {qid} | {req.query}")

    selected = engine.retrieve_hyperedges(req.query, top_k=req.top_k)
    answer = engine.generate_answer(req.query, selected)
    phi_val = engine.phi43_check(selected)
    latency = (time.time() - t0) * 1000.0

    log_line(
        f"QUERY {qid} | hyperedges={len(selected)} | phi43_check={phi_val:.3f} | latency_ms={latency:.1f}"
    )

    return QueryResponse(
        query_id=qid,
        query=req.query,
        selected_hyperedges=[
            {
                "id": he.id,
                "vertices": he.vertices,
                "weight": he.weight,
                "meta": he.meta,
            }
            for he in selected
        ],
        answer=answer,
        phi43_check=phi_val,
        latency_ms=latency,
    )


if __name__ == "__main__":
    import uvicorn

    log_line("Starting Quantarion Hypergraph-RAG Production server on 0.0.0.0:8000…")
    uvicorn.run(app, host="0.0.0.0", port=8000)