import os
from typing import Dict, List, Any

import uuid
from copy import deepcopy
from langchain.embeddings import OpenAIEmbeddings

from chromadb import Client as ChromaClient

from aiflows.base_flows import AtomicFlow

import hydra

class ChromaDBFlow(AtomicFlow):
    """ A flow that uses the ChromaDB model to write and read memories stored in a database
    
    *Configuration Parameters*:
    
    - `name` (str): The name of the flow. Default: "chroma_db"
    - `description` (str): A description of the flow. This description is used to generate the help message of the flow. 
    Default: "ChromaDB is a document store that uses vector embeddings to store and retrieve documents."
    - `backend` (Dict[str, Any]): The configuration of the backend which is used to fetch api keys. Default: LiteLLMBackend with the
    default parameters of LiteLLMBackend (see aiflows.backends.LiteLLMBackend). Except for the following parameter whose default value is overwritten:
        - `api_infos` (List[Dict[str, Any]]): The list of api infos. Default: No default value, this parameter is required.
        - `model_name` (str): The name of the model. Default: "". In the current implementation, this parameter is not used.
    - `n_results` (int): The number of results to retrieve when reading from the database. Default: 5
    - Other parameters are inherited from the default configuration of AtomicFlow (see AtomicFlow)
    
    *Input Interface*:
    
    - `operation` (str): The operation to perform. It can be "write" or "read".
    - `content` (str or List[str]): The content to write or read. If operation is "write", it must be a string or a list of strings. If operation is "read", it must be a string.
        
    *Output Interface*:
    
    - `retrieved` (str or List[str]): The retrieved content. If operation is "write", it is an empty string. If operation is "read", it is a string or a list of strings.
    
    :param backend: The backend of the flow (used to retrieve the API key)
    :type backend: LiteLLMBackend
    :param \**kwargs: Additional arguments to pass to the flow.
    """
    def __init__(self, backend,**kwargs):
        super().__init__(**kwargs)
        self.client = ChromaClient()
        self.collection = self.client.get_or_create_collection(name=self.flow_config["name"])
        self.backend = backend

    @classmethod
    def _set_up_backend(cls, config):
        """ This instantiates the backend of the flow from a configuration file.
        
        :param config: The configuration of the backend.
        :type config: Dict[str, Any]
        :return: The backend of the flow.
        :rtype: Dict[str, LiteLLMBackend]
        """
        kwargs = {}

        kwargs["backend"] = \
            hydra.utils.instantiate(config['backend'], _convert_="partial")
        
        return kwargs
    
    @classmethod
    def instantiate_from_config(cls, config):
        """ This method instantiates the flow from a configuration file
        
        :param config: The configuration of the flow.
        :type config: Dict[str, Any]
        :return: The instantiated flow.
        :rtype: ChromaDBFlow
        """
        flow_config = deepcopy(config)

        kwargs = {"flow_config": flow_config}

        # ~~~ Set up backend ~~~
        kwargs.update(cls._set_up_backend(flow_config))

        # ~~~ Instantiate flow ~~~
        return cls(**kwargs)
    
    def get_input_keys(self) -> List[str]:
        """ This method returns the input keys of the flow.
        
        :return: The input keys of the flow.
        :rtype: List[str]
        """
        return self.flow_config["input_keys"]

    def get_output_keys(self) -> List[str]:
        """ This method returns the output keys of the flow.
        
        :return: The output keys of the flow.
        :rtype: List[str]
        """
        return self.flow_config["output_keys"]

    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """ This method runs the flow. It runs the ChromaDBFlow. It either writes or reads memories from the database.
        
        :param input_data: The input data of the flow.
        :type input_data: Dict[str, Any]
        :return: The output data of the flow.
        :rtype: Dict[str, Any]
        """
        api_information = self.backend.get_key()

        if api_information.backend_used == "openai":
            embeddings = OpenAIEmbeddings(openai_api_key=api_information.api_key)
        else:
            # ToDo: Add support for Azure
            embeddings = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY"))
        response = {}

        operation = input_data["operation"]
        if operation not in ["write", "read"]:
            raise ValueError(f"Operation '{operation}' not supported")

        content = input_data["content"]
        if operation == "read":
            if not isinstance(content, str):
                raise ValueError(f"content(query) must be a string during read, got {type(content)}: {content}")
            if content == "":
                response["retrieved"] = [[""]]
                return response
            query = content
            query_result = self.collection.query(
                query_embeddings=embeddings.embed_query(query),
                n_results=self.flow_config["n_results"]
            )

            response["retrieved"] = [doc for doc in query_result["documents"]]

        elif operation == "write":
            if content != "":
                if not isinstance(content, list):
                    content = [content]
                documents = content
                self.collection.add(
                    ids=[str(uuid.uuid4()) for _ in range(len(documents))],
                    embeddings=embeddings.embed_documents(documents),
                    documents=documents
                )
            response["retrieved"] = ""

        return response