import threading

import chromadb
import llama_index.embeddings.openai.base as llama_openai
from llama_index.core import Document, VectorStoreIndex
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.vector_stores.chroma import ChromaVectorStore
from openai._exceptions import APIConnectionError, InternalServerError, RateLimitError
from tenacity import (
    retry,
    retry_if_exception_type,
    stop_after_attempt,
    wait_random_exponential,
)

from opendevin.core.config import config
from opendevin.core.logger import opendevin_logger as logger
from opendevin.core.utils import json

num_retries = config.llm.num_retries
retry_min_wait = config.llm.retry_min_wait
retry_max_wait = config.llm.retry_max_wait

# llama-index includes a retry decorator around openai.get_embeddings() function
# it is initialized with hard-coded values and errors
# this non-customizable behavior is creating issues when it's retrying faster than providers' rate limits
# this block attempts to banish it and replace it with our decorator, to allow users to set their own limits

if hasattr(llama_openai.get_embeddings, '__wrapped__'):
    original_get_embeddings = llama_openai.get_embeddings.__wrapped__
else:
    logger.warning('Cannot set custom retry limits.')
    num_retries = 1
    original_get_embeddings = llama_openai.get_embeddings


def attempt_on_error(retry_state):
    logger.error(
        f'{retry_state.outcome.exception()}. Attempt #{retry_state.attempt_number} | You can customize these settings in the configuration.',
        exc_info=False,
    )
    return None


@retry(
    reraise=True,
    stop=stop_after_attempt(num_retries),
    wait=wait_random_exponential(min=retry_min_wait, max=retry_max_wait),
    retry=retry_if_exception_type(
        (RateLimitError, APIConnectionError, InternalServerError)
    ),
    after=attempt_on_error,
)
def wrapper_get_embeddings(*args, **kwargs):
    return original_get_embeddings(*args, **kwargs)


llama_openai.get_embeddings = wrapper_get_embeddings


class EmbeddingsLoader:
    """Loader for embedding model initialization."""

    @staticmethod
    def get_embedding_model(strategy: str):
        supported_ollama_embed_models = [
            'llama2',
            'mxbai-embed-large',
            'nomic-embed-text',
            'all-minilm',
            'stable-code',
        ]
        if strategy in supported_ollama_embed_models:
            from llama_index.embeddings.ollama import OllamaEmbedding

            return OllamaEmbedding(
                model_name=strategy,
                base_url=config.llm.embedding_base_url,
                ollama_additional_kwargs={'mirostat': 0},
            )
        elif strategy == 'openai':
            from llama_index.embeddings.openai import OpenAIEmbedding

            return OpenAIEmbedding(
                model='text-embedding-ada-002',
                api_key=config.llm.api_key,
            )
        elif strategy == 'azureopenai':
            from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding

            return AzureOpenAIEmbedding(
                model='text-embedding-ada-002',
                deployment_name=config.llm.embedding_deployment_name,
                api_key=config.llm.api_key,
                azure_endpoint=config.llm.base_url,
                api_version=config.llm.api_version,
            )
        elif (strategy is not None) and (strategy.lower() == 'none'):
            # TODO: this works but is not elegant enough. The incentive is when
            # monologue agent is not used, there is no reason we need to initialize an
            # embedding model
            return None
        else:
            from llama_index.embeddings.huggingface import HuggingFaceEmbedding

            return HuggingFaceEmbedding(model_name='BAAI/bge-small-en-v1.5')


sema = threading.Semaphore(value=config.agent.memory_max_threads)


class LongTermMemory:
    """
    Handles storing information for the agent to access later, using chromadb.
    """

    def __init__(self):
        """
        Initialize the chromadb and set up ChromaVectorStore for later use.
        """
        db = chromadb.Client(chromadb.Settings(anonymized_telemetry=False))
        self.collection = db.get_or_create_collection(name='memories')
        vector_store = ChromaVectorStore(chroma_collection=self.collection)
        embedding_strategy = config.llm.embedding_model
        embed_model = EmbeddingsLoader.get_embedding_model(embedding_strategy)
        self.index = VectorStoreIndex.from_vector_store(vector_store, embed_model)
        self.thought_idx = 0
        self._add_threads = []

    def add_event(self, event: dict):
        """
        Adds a new event to the long term memory with a unique id.

        Parameters:
        - event (dict): The new event to be added to memory
        """
        id = ''
        t = ''
        if 'action' in event:
            t = 'action'
            id = event['action']
        elif 'observation' in event:
            t = 'observation'
            id = event['observation']
        doc = Document(
            text=json.dumps(event),
            doc_id=str(self.thought_idx),
            extra_info={
                'type': t,
                'id': id,
                'idx': self.thought_idx,
            },
        )
        self.thought_idx += 1
        logger.debug('Adding %s event to memory: %d', t, self.thought_idx)
        thread = threading.Thread(target=self._add_doc, args=(doc,))
        self._add_threads.append(thread)
        thread.start()  # We add the doc concurrently so we don't have to wait ~500ms for the insert

    def _add_doc(self, doc):
        with sema:
            self.index.insert(doc)

    def search(self, query: str, k: int = 10):
        """
        Searches through the current memory using VectorIndexRetriever

        Parameters:
        - query (str): A query to match search results to
        - k (int): Number of top results to return

        Returns:
        - list[str]: list of top k results found in current memory
        """
        retriever = VectorIndexRetriever(
            index=self.index,
            similarity_top_k=k,
        )
        results = retriever.retrieve(query)
        return [r.get_text() for r in results]
