"""
src/retrieval/vector_store.py
------------------------------
Extended ChromaStore with metadata filtering for scientific papers.
"""
from pathlib import Path
from typing import Optional


class ScientificChromaStore:
    def __init__(self, persist_dir="data/chroma_scientific", collection="scientific_corpus"):
        import chromadb
        Path(persist_dir).mkdir(parents=True, exist_ok=True)
        self.client = chromadb.PersistentClient(path=persist_dir)
        self.col = self.client.get_or_create_collection(
            name=collection, metadata={"hnsw:space": "cosine"}
        )

    def index_chunks(self, chunks, embeddings):
        ids = [f"chunk_{i}" for i in range(len(chunks))]
        documents = [c.text for c in chunks]
        metadatas = [c.metadata for c in chunks]
        emb_list = embeddings.tolist() if hasattr(embeddings, "tolist") else embeddings
        batch_size = 500
        for start in range(0, len(ids), batch_size):
            end = start + batch_size
            self.col.upsert(
                ids=ids[start:end], documents=documents[start:end],
                embeddings=emb_list[start:end], metadatas=metadatas[start:end],
            )
        print(f"✅ Indexed {len(ids)} chunks. Total: {self.col.count()}")

    def query(self, query_embedding, k=5, where=None):
        kwargs = dict(query_embeddings=[query_embedding], n_results=min(k, self.col.count()))
        if where:
            kwargs["where"] = where
        res = self.col.query(**kwargs)
        return {"documents": res["documents"][0], "metadatas": res["metadatas"][0], "distances": res["distances"][0]}

    def query_with_metadata_filter(self, query_embedding, k=5, year_min=None, sections=None, paper_ids=None):
        where_clauses = []
        if year_min:
            where_clauses.append({"year": {"$gte": year_min}})
        if sections:
            where_clauses.append({"section": {"$in": sections}})
        if paper_ids:
            where_clauses.append({"paper_id": {"$in": paper_ids}})
        where = None
        if len(where_clauses) == 1:
            where = where_clauses[0]
        elif len(where_clauses) > 1:
            where = {"$and": where_clauses}
        return self.query(query_embedding, k=k, where=where)

    def count(self):
        return self.col.count()

    def list_papers(self):
        all_meta = self.col.get(include=["metadatas"])["metadatas"]
        seen = set()
        papers = []
        for m in all_meta:
            pid = m.get("paper_id", "")
            if pid not in seen:
                seen.add(pid)
                papers.append({"paper_id": pid, "title": m.get("title", ""),
                               "authors_str": m.get("authors_str", ""), "year": m.get("year", "")})
        return sorted(papers, key=lambda x: x["year"], reverse=True)

ChromaStore = ScientificChromaStore