import os
from typing import Dict, List, Any

import uuid

from langchain.embeddings import OpenAIEmbeddings

from chromadb import Client as ChromaClient

from flows.base_flows import AtomicFlow


class ChromaDBFlow(AtomicFlow):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.client = ChromaClient()
        self.collection = self.client.get_or_create_collection(name=self.flow_config["name"])

    def get_input_keys(self) -> List[str]:
        return self.flow_config["input_keys"]

    def get_output_keys(self) -> List[str]:
        return self.flow_config["output_keys"]

    def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:

        api_information = self._get_from_state("api_information")

        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