from .pdf_utils import extract_text_from_pdf, chunk_text
import numpy as np
import json
import requests


# Parâmetros Azure OpenAI
ENDPOINT = "https://botdall3.openai.azure.com/openai/deployments/text-embedding-3-large/embeddings?api-version=2024-02-15-preview"
KEY = "d154d781aaf34410b502bb9be357e174"

def build_vector_index(pdf_path, index_path="my_vector_index.json"):
    text_chunks = chunk_text(extract_text_from_pdf(pdf_path), chunk_size=1500, overlap=200)
    embeddings = []
    headers = {
        "Content-Type": "application/json",
        "api-key": KEY
    }
    for chunk in text_chunks:
        if len(chunk) > 2000:
            chunk = chunk[:2000]
        payload = {"input": [chunk]}
        try:
            response = requests.post(ENDPOINT, headers=headers, json=payload)
            if response.status_code == 200:
                data = response.json()
                if "data" in data and data["data"] and "embedding" in data["data"][0]:
                    embeddings.append(data["data"][0]["embedding"])
                else:
                    print(f"[ERRO] Resposta inesperada da API para chunk: {chunk[:50]}...")
                    embeddings.append([0.0]*1536)
            else:
                print(f"[ERRO] Falha na requisição: {response.status_code} - {response.text}")
                embeddings.append([0.0]*1536)
        except Exception as e:
            print(f"[ERRO] Falha ao embedar chunk: {e}")
            embeddings.append([0.0]*1536)
    # Salva os embeddings e os textos em um arquivo JSON
    with open(index_path, 'w', encoding='utf-8') as f:
        json.dump({"chunks": text_chunks, "embeddings": embeddings}, f)
    return index_path

def semantic_search(query, index_path="my_vector_index.json", top_k=3):
    headers = {
        "Content-Type": "application/json",
        "api-key": KEY
    }
    with open(index_path, 'r', encoding='utf-8') as f:
        data = json.load(f)
    chunks = data["chunks"]
    embeddings = np.array(data["embeddings"])
    payload = {"input": [query]}
    response = requests.post(ENDPOINT, headers=headers, json=payload)
    if response.status_code == 200:
        data_emb = response.json()
        if "data" in data_emb and data_emb["data"] and "embedding" in data_emb["data"][0]:
            query_emb = np.array(data_emb["data"][0]["embedding"])
        else:
            print("[ERRO] Resposta inesperada da API para query.")
            return []
    else:
        print(f"[ERRO] Falha na requisição de embedding da query: {response.status_code} - {response.text}")
        return []
    # Similaridade por produto escalar
    sims = np.dot(embeddings, query_emb) / (np.linalg.norm(embeddings, axis=1) * np.linalg.norm(query_emb) + 1e-8)
    top_idx = sims.argsort()[-top_k:][::-1]
    return [chunks[i] for i in top_idx]


# Exemplo de uso direto (teste rápido)
if __name__ == "__main__":
    # Gere o índice a partir de um PDF
    index_path = build_vector_index("data/exemplo.pdf")
    # Faça uma busca semântica
    resultados = semantic_search("Resumo do documento", index_path=index_path)
    print("Resultados mais relevantes:")
    for r in resultados:
        print(r)