import hashlib
import logging
import os
from pathlib import Path

from elasticsearch import Elasticsearch
from langchain_elasticsearch import ElasticsearchStore
from langchain_ollama import OllamaEmbeddings
from scripts.pipelines.tasks.chunks import build_chunks_from_folder

logger = logging.getLogger(__name__)

ELASTICSEARCH_LOCAL_URL = os.getenv("ELASTICSEARCH_LOCAL_URL")
OLLAMA_LOCAL_URL = os.getenv("OLLAMA_LOCAL_URL")
ELASTICSEARCH_INDEX = os.getenv("ELASTICSEARCH_INDEX")
OLLAMA_URL = os.getenv("OLLAMA_URL")
OLLAMA_EMB_MODEL_NAME = os.getenv("OLLAMA_EMB_MODEL_NAME")
PROJ_ROOT = Path(__file__).resolve().parents[3]


def elasticsearch_ingestion(
    docs_folder_path: str = "ingestion",
    es_request_timeout: int = 120,
    es_max_retries: int = 5,
    es_retry_on_timeout: bool = True,
    distance_strategy: str = "COSINE",
):
    logger.info("Starting Elasticsearch ingestion pipeline...")
    logger.info(f"Using docs folder path: {PROJ_ROOT / docs_folder_path}")
    chunks = build_chunks_from_folder(folder_path=PROJ_ROOT / docs_folder_path)

    logger.info("Connecting to Elasticsearch...")
    try:
        es = Elasticsearch(
            ELASTICSEARCH_LOCAL_URL,
            request_timeout=es_request_timeout,
            max_retries=es_max_retries,
            retry_on_timeout=es_retry_on_timeout,
        )
    except:
        logger.exception("Failed to connect to Elasticsearch.")
        raise

    logger.info("Instantiating embeddings model...")
    try:
        embeddings = OllamaEmbeddings(
            base_url=OLLAMA_LOCAL_URL, model=OLLAMA_EMB_MODEL_NAME
        )
    except:
        logger.exception("Failed to instantiate embeddings model.")
        raise

    logger.info(f"Uploading documents to index {ELASTICSEARCH_INDEX}...")
    ElasticsearchStore.from_documents(
        chunks,
        embeddings,
        client=es,
        index_name=ELASTICSEARCH_INDEX,
        distance_strategy=distance_strategy,
    )
    logger.info(f"Finished uploading documents to index {ELASTICSEARCH_INDEX}.")
    logger.info(f"Total documents uploaded: {len(chunks)}.")


if __name__ == "__main__":
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
    )
    try:
        elasticsearch_ingestion()
    except Exception as exc:
        logger.exception(exc)
        raise