"""
retriever.py
Performs cosine-similarity search against the FAISS index.
"""

from __future__ import annotations
import numpy as np
import faiss
from rag.embedder import VectorIndex

DEFAULT_TOP_K = 5

# Chunks with a cosine similarity below this threshold are considered
# too dissimilar to the query and are dropped before reaching the LLM.
# This prevents low-quality context from polluting the answer.
# Range: 0.0 (no filtering) → 1.0 (exact match only). 0.30 is a safe default.
MIN_SCORE = 0.30


def retrieve(query: str, vector_index: VectorIndex, top_k: int = DEFAULT_TOP_K) -> list[dict]:
    """
    Embed the query and return top_k most similar chunks above MIN_SCORE.
    Each result: {"source": str, "text": str, "score": float}
    Scores are cosine similarities (higher = more relevant).
    """
    if vector_index is None or vector_index.index is None:
        return []

    query_embedding = vector_index.embedder.encode([query], show_progress_bar=False)
    query_embedding = np.array(query_embedding, dtype="float32")
    faiss.normalize_L2(query_embedding)  # Must match IndexFlatIP cosine index

    n_results = min(top_k, vector_index.index.ntotal)
    scores, indices = vector_index.index.search(query_embedding, n_results)

    results = []
    for score, idx in zip(scores[0], indices[0]):
        if idx == -1:
            continue
        if float(score) < MIN_SCORE:
            continue  # Drop chunks below relevance threshold
        chunk = vector_index.chunks[idx]
        results.append({
            "source": chunk["source"],
            "text": chunk["text"],
            "score": float(score),   # cosine similarity (0–1 range)
        })
    return results