from enum import Enum
import typer
import logging
import os

from loguru import logger
from elasticsearch import Elasticsearch
from langchain_elasticsearch import ElasticsearchStore
from chonkie import SemanticChunker, MarkdownChef
from transformers import AutoTokenizer

from src.utils.emb_factory import create_embedding_model
from scripts.pipelines.tasks.chunk import (
    read_files, 
    get_chunk_docs, 
    convert_chunks_to_document
)

app = typer.Typer()

ELASTICSEARCH_LOCAL_URL = os.getenv("ELASTICSEARCH_LOCAL_URL")
OLLAMA_LOCAL_URL = os.getenv("OLLAMA_LOCAL_URL")
OLLAMA_URL = os.getenv("OLLAMA_URL")
OLLAMA_EMB_MODEL_NAME = os.getenv("OLLAMA_EMB_MODEL_NAME")
AVAP_WEB_DOCS_URL = os.getenv("AVAP_WEB_DOCS_URL")
HF_EMB_MODEL_NAME = os.getenv("HF_EMB_MODEL_NAME")

class DistanceStrategy(str, Enum):
    euclidean = "EUCLIDEAN_DISTANCE"
    max_inner_product = "MAX_INNER_PRODUCT"
    dot_product = "DOT_PRODUCT"
    jaccard = "JACCARD"
    cosine = "COSINE"


@app.command()
def elasticsearch_ingestion(
    docs_folder_path: str = "docs",
    es_index: str = "avap-docs-test-v2",
    es_request_timeout: int = 120,
    es_max_retries: int = 5,
    es_retry_on_timeout: bool = True,
    distance_strategy: DistanceStrategy = DistanceStrategy.cosine,
    chunk_size: int = 2048,
    chunk_threshold: float = 0.5,
    chunk_similarity_window: int = 3,
    chunk_skip_window: int = 1,
):  
    logger.info("Starting Elasticsearch ingestion pipeline...")
    logger.info(f"Reading files from folder: {docs_folder_path}/LRM and {docs_folder_path}/samples...")
    avap_code_docs = read_files(f"{docs_folder_path}/samples")
    avap_language_docs = read_files(f"{docs_folder_path}/LRM")

    logger.info("Instantiating semantic chunker and chef...")
    custom_tokenizer = AutoTokenizer.from_pretrained(HF_EMB_MODEL_NAME)
    chef = MarkdownChef(tokenizer=custom_tokenizer)
    chunker = SemanticChunker(
        embedding_model=HF_EMB_MODEL_NAME,
        chunk_size=chunk_size,
        threshold=chunk_threshold,
        similarity_window=chunk_similarity_window,
        skip_window=chunk_skip_window
    )
    logger.info("Processing Markdown docs with chef...")
    doc = chef.process(f"{docs_folder_path}/LRM/avap.md")

    logger.info("Chunking AVAP Language docs...")
    avap_language_docs_chunks = get_chunk_docs(avap_language_docs, chunker)

    logger.info("Creating Langchain Document to index...")
    avap_language_langchain_docs = convert_chunks_to_document(avap_language_docs_chunks)
    avap_code_langchain_docs = convert_chunks_to_document(avap_code_docs)
    avap_documents = avap_language_langchain_docs + avap_code_langchain_docs

    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 = create_embedding_model(
            provider="ollama",
            model=OLLAMA_EMB_MODEL_NAME,
            base_url=OLLAMA_LOCAL_URL, 
        )
    except:
        logger.exception("Failed to instantiate embeddings model.")
        raise
    
    logger.info(f"Checking if index {es_index} exists and deleting if it does...")
    if es.indices.exists(index=es_index):
        es.indices.delete(index=es_index)

    logger.info(f"Uploading documents to index {es_index}...")
    ElasticsearchStore.from_documents(
        avap_documents,
        embeddings,
        client=es,
        index_name=es_index,
        distance_strategy=distance_strategy.value,
    )
    logger.info(f"Finished uploading documents to index {es_index}.")


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