selfHosted_functions.py

import os
import json
import faiss
import numpy as np
from shared_utilities import chunk_text, validate_chunk_sizes, generate_embeddings_batch
import asyncio
import logging

logging.basicConfig(level=logging.WARNING)
logger = logging.getLogger(__name__)

# ---------- Config ----------
TOP_K = 5

PERSIST_DIR = "/persistent/faiss_store"
if not os.path.exists("/persistent"):  # fallback if running locally
    PERSIST_DIR = "./faiss_store"
os.makedirs(PERSIST_DIR, exist_ok=True)

INDEX_PATH = os.path.join(PERSIST_DIR, "store.index")
META_PATH = os.path.join(PERSIST_DIR, "store.json")

# OpenAI text-embedding-3-small dimension (used for FAISS)
OPENAI_EMBEDDING_DIM = 1536


def _normalize_embeddings(embeddings: np.ndarray) -> np.ndarray:
    """Normalize embeddings for cosine similarity (inner product in FAISS)."""
    norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
    return (embeddings / np.maximum(norms, 1e-9)).astype("float32")


# ---------- FAISS Vector Store ----------
class FaissStore:
    def __init__(self, dim):
        self.index = faiss.IndexFlatIP(dim)  # Inner product for cosine similarity (normalized vectors)
        self.metadatas = []

    def add(self, vectors: np.ndarray, metadatas):
        self.index.add(vectors)
        self.metadatas.extend(metadatas)

    def search(self, q_vec: np.ndarray, k=TOP_K):
        if self.index.ntotal == 0:
            return []
        D, I = self.index.search(q_vec, k)
        results = []
        for score, idx in zip(D[0], I[0]):
            if idx < 0:
                continue
            results.append((float(score), self.metadatas[idx]))
        return results

    def save(self, index_path, meta_path):
        faiss.write_index(self.index, index_path)
        with open(meta_path, "w", encoding="utf-8") as f:
            json.dump(self.metadatas, f, ensure_ascii=False, indent=2)

    @classmethod
    def load(cls, index_path, meta_path):
        index = faiss.read_index(index_path)
        with open(meta_path, "r", encoding="utf-8") as f:
            metadatas = json.load(f)
        store = cls(index.d)
        store.index = index
        store.metadatas = metadatas
        return store

# ---------- Global store ----------
faiss_stores = {}  # Dictionary to store multiple FAISS indices by method
VECTOR_DIM = None

def get_index_paths(method_suffix=""):
    """Get index paths based on method suffix"""
    if method_suffix:
        index_path = os.path.join(PERSIST_DIR, f"store{method_suffix}.index")
        meta_path = os.path.join(PERSIST_DIR, f"store{method_suffix}.json")
    else:
        index_path = INDEX_PATH
        meta_path = META_PATH
    return index_path, meta_path


# ---------- RAG Builder ----------
async def create_rag_from_text_selfhosted(extracted_text: str, source_info: str, progress_callback=None, method_suffix=""):
    global faiss_stores, VECTOR_DIM

    # Get index paths based on method suffix
    index_path, meta_path = get_index_paths(method_suffix)

    if progress_callback:
        await progress_callback("📄 Chunking text into segments...")

    chunks = chunk_text(extracted_text)
    chunks = validate_chunk_sizes(chunks, max_tokens=8000)

    if progress_callback:
        await progress_callback(f"🔢 Creating {len(chunks)} embeddings (OpenAI)...")

    # Create embeddings using OpenAI text-embedding-3-small
    raw_embeddings = await generate_embeddings_batch(chunks, progress_callback)
    embeddings = np.array(raw_embeddings, dtype=np.float32)
    embeddings = _normalize_embeddings(embeddings)

    VECTOR_DIM = embeddings.shape[1]
    
    # Create or get store for this method
    if method_suffix not in faiss_stores:
        faiss_stores[method_suffix] = FaissStore(VECTOR_DIM)
    faiss_store = faiss_stores[method_suffix]

    metas = [{"id": i, "text": c, "source": source_info} for i, c in enumerate(chunks)]
    faiss_store.add(embeddings, metas)

    # Save to persistent storage
    faiss_store.save(index_path, meta_path)

    if progress_callback:
        await progress_callback(f"✅ Indexed {len(chunks)} chunks and saved to {PERSIST_DIR}")
        logger.info(f"Indexed {len(chunks)} chunks and saved to {PERSIST_DIR}") 
    return {
        "status": "success",
        "message": f"✅ Indexed {len(chunks)} chunks and saved to {PERSIST_DIR}. Ready for queries.",
        "vector_index": f"faiss{method_suffix}"
    }

# ---------- RAG Search (Self-hosted) ----------
async def search_rag_documents_selfhosted(query: str, top_k: int = 5, method_suffix: str = "") -> list:
    """

    Search for relevant document chunks using the locally stored FAISS index.

    Returns a list of dicts with content, score, and metadata.

    

    Args:

        query (str): Search query

        top_k (int): Number of results to return

        method_suffix (str): Optional suffix to append to index name (e.g., "_hybrid", "_regular")

    """
    try:
        global faiss_stores, VECTOR_DIM

        # Get index paths based on method suffix
        index_path, meta_path = get_index_paths(method_suffix)

        # Load FAISS index + metadata if not already loaded
        if method_suffix not in faiss_stores or faiss_stores[method_suffix].index.ntotal == 0:
            if os.path.exists(index_path) and os.path.exists(meta_path):
                faiss_stores[method_suffix] = FaissStore.load(index_path, meta_path)
                VECTOR_DIM = faiss_stores[method_suffix].index.d
                logger.info(f"Loaded FAISS store with {faiss_stores[method_suffix].index.ntotal} vectors.")
            else:
                logger.warning(f"No FAISS store found for method '{method_suffix}'. Please create RAG index first.")
                return []
        
        faiss_store = faiss_stores[method_suffix]

        # Generate query embedding using OpenAI
        raw_query = await generate_embeddings_batch([query])
        q_vec = np.array([raw_query[0]], dtype=np.float32)
        q_vec = _normalize_embeddings(q_vec)  # shape: (1, dim)

        # Search for top_k similar chunks
        results = faiss_store.search(q_vec, k=top_k)

        formatted_results = []
        for score, meta in results:
            formatted_results.append({
                "score": float(score),
                "content": meta.get("text", ""),
                "source": meta.get("source", "selfhosted"),
                "chunk_index": meta.get("id", None),
                "title": meta.get("title", None)
            })

        return formatted_results

    except Exception as e:
        logger.error(f"Error searching self-hosted RAG documents: {e}")
        return []