notes/services/rag_service.py

"""
RAG service using ChromaDB + LM Studio's /v1/embeddings endpoint.
No local ML libraries (torch, sentence-transformers, onnxruntime) needed —
embeddings are generated by the same LM Studio instance used for chat.
Each chunk is stored with metadata: user_id, source_id, source_type (doc|url).
"""

import re
from typing import Optional

from chromadb import EmbeddingFunction, Documents, Embeddings
from flask import current_app
from openai import OpenAI

_chroma_client = None
_collection = None


class LMStudioEmbeddingFunction(EmbeddingFunction):
    """ChromaDB-compatible embedding function that calls LM Studio's /v1/embeddings."""

    def __init__(self, base_url: str, model: str):
        self._client = OpenAI(
            base_url=f"{base_url.rstrip('/')}/v1",
            api_key="lm-studio",
            timeout=60.0,
        )
        self._model = model

    def __call__(self, input: Documents) -> Embeddings:
        response = self._client.embeddings.create(model=self._model, input=input)
        return [item.embedding for item in response.data]


def _get_collection():
    global _chroma_client, _collection
    if _collection is None:
        import chromadb
        path = current_app.config["VECTORDB_PATH"]
        base_url = current_app.config["LM_STUDIO_URL"]
        model = current_app.config["LM_STUDIO_EMBEDDING_MODEL"]
        _chroma_client = chromadb.PersistentClient(path=path)
        _collection = _chroma_client.get_or_create_collection(
            name="ki_context",
            embedding_function=LMStudioEmbeddingFunction(base_url, model),
            metadata={"hnsw:space": "cosine"},
        )
    return _collection


def chunk_text(text: str, chunk_size: int, overlap: int) -> list[str]:
    """
    Paragraph-aware chunking:
    1. Split on blank lines to keep paragraphs intact.
    2. Paragraphs shorter than chunk_size are merged together.
    3. Paragraphs longer than chunk_size are split by sentence, then by word.
    Overlap is applied by re-including the last <overlap> words of the previous chunk.
    """
    import re as _re

    # Split into paragraphs (one or more blank lines)
    paragraphs = [p.strip() for p in _re.split(r'\n{2,}', text) if p.strip()]

    # Further split very long paragraphs at sentence boundaries
    sentences: list[str] = []
    for para in paragraphs:
        # Split on ". ", "! ", "? " followed by uppercase or end-of-string
        parts = _re.split(r'(?<=[.!?])\s+(?=[A-ZÜÖÄ\u00C0-\u00FF"])', para)
        sentences.extend([s.strip() for s in parts if s.strip()])

    chunks: list[str] = []
    current_words: list[str] = []

    for sentence in sentences:
        words = sentence.split()
        # If adding this sentence would exceed chunk_size, flush current chunk
        if current_words and len(current_words) + len(words) > chunk_size:
            chunks.append(" ".join(current_words))
            # Keep last <overlap> words as context for the next chunk
            current_words = current_words[-overlap:] if overlap else []
        current_words.extend(words)
        # If a single sentence is already longer than chunk_size, flush immediately
        if len(current_words) >= chunk_size:
            chunks.append(" ".join(current_words))
            current_words = current_words[-overlap:] if overlap else []

    if current_words:
        chunks.append(" ".join(current_words))

    return [c for c in chunks if len(c.split()) >= 10]  # drop near-empty chunks


def index_source(
    text: str,
    user_id: int,
    source_id: int,
    source_type: str,  # "doc" | "url"
    chunk_size: int = 500,
    chunk_overlap: int = 50,
):
    """Chunk, embed via LM Studio and store in ChromaDB. Replaces existing chunks."""
    collection = _get_collection()

    delete_source(user_id, source_id, source_type)

    chunks = chunk_text(text, chunk_size, chunk_overlap)
    if not chunks:
        return

    ids = [f"{source_type}_{source_id}_chunk_{i}" for i in range(len(chunks))]
    metadatas = [
        {"user_id": str(user_id), "source_id": str(source_id), "source_type": source_type}
        for _ in chunks
    ]

    collection.add(documents=chunks, ids=ids, metadatas=metadatas)


def delete_source(user_id: int, source_id: int, source_type: str):
    """Remove all chunks belonging to a source from ChromaDB."""
    collection = _get_collection()
    try:
        collection.delete(
            where={
                "$and": [
                    {"user_id": {"$eq": str(user_id)}},
                    {"source_id": {"$eq": str(source_id)}},
                    {"source_type": {"$eq": source_type}},
                ]
            }
        )
    except Exception:
        pass


def similarity_search(
    query: str,
    user_id: int,
    source_ids: Optional[list[int]] = None,
    source_type: Optional[str] = None,
    top_k: int = 5,
) -> list[str]:
    """Search for relevant chunks via LM Studio embeddings."""
    collection = _get_collection()

    conditions = [{"user_id": {"$eq": str(user_id)}}]

    if source_ids is not None and len(source_ids) > 0:
        conditions.append(
            {"source_id": {"$in": [str(sid) for sid in source_ids]}}
        )

    if source_type:
        conditions.append({"source_type": {"$eq": source_type}})

    where = {"$and": conditions} if len(conditions) > 1 else conditions[0]

    try:
        results = collection.query(
            query_texts=[query],
            n_results=top_k,
            where=where,
            include=["documents", "distances"],
        )
        docs = results["documents"][0] if results["documents"] else []
        distances = results["distances"][0] if results.get("distances") else []
        # Cosine distance: 0 = identical, 2 = opposite. Filter out poor matches (> 0.6).
        if distances:
            docs = [d for d, dist in zip(docs, distances) if dist < 0.6]
        return docs
    except Exception:
        return []