_val, exc_tb=exc_tb)

    async def get_version(self) -> str:
        """Gets version of the AlephAlpha HTTP API."""
        return await self._get_request_text("version")

    async def _get_request_text(self, endpoint: str) -> str:
        async with self.session.get(
            self.host + endpoint,
        ) as response:
            if not response.ok:
                _raise_for_status(response.status, await response.text())
            return await response.text()

    async def _get_request_json(
        self, endpoint: str
    ) -> Union[List[Mapping[str, Any]], Mapping[str, Any]]:
        async with self.session.get(
            self.host + endpoint,
        ) as response:
            if not response.ok:
                _raise_for_status(response.status, await response.text())
            return await response.json()

    async def _post_request(
        self,
        endpoint: str,
        request: AnyRequest,
        model: Optional[str] = None,
    ) -> Dict[str, Any]:
        json_body = self._build_json_body(request, model)

        query_params = self._build_query_parameters()

        async with self.session.post(
            self.host + endpoint, json=json_body, params=query_params
        ) as response:
            if not response.ok:
                _raise_for_status(response.status, await response.text())
            return await response.json()

    def _build_query_parameters(self) -> Mapping[str, str]:
        return {
            # cannot use str() here because we want lowercase true/false in query string
            # Also do not want to send the nice flag with every request if it is false
            **({"nice": "true"} if self.nice else {}),
        }

    def _build_json_body(
        self, request: AnyRequest, model: Optional[str]
    ) -> Mapping[str, Any]:
        json_body = dict(request.to_json())

        if model is not None:
            json_body["model"] = model
        if self.hosting is not None:
            json_body["hosting"] = self.hosting
        return json_body

    async def models(self) -> List[Mapping[str, Any]]:
        """
        Queries all models which are currently available.

        For documentation of the response, see https://docs.aleph-alpha.com/api/available-models/
        """
        return await self._get_request_json("models_available")  # type: ignore

    async def complete(
        self,
        request: CompletionRequest,
        model: str,
    ) -> CompletionResponse:
        """Generates completions given a prompt.

        Parameters:
            request (CompletionRequest, required):
                Parameters for the requested completion.

            model (string, required):
                Name of model to use. A model name refers to a model architecture (number of parameters among others).
                Always the latest version of model is used.

        Examples:
            >>> # create a prompt
            >>> prompt = Prompt.from_text("An apple a day, ")
            >>>
            >>> # create a completion request
            >>> request = CompletionRequest(
                    prompt=prompt,
                    maximum_tokens=32,
                    stop_sequences=["###","\\n"],
                    temperature=0.12
                )
            >>>
            >>> # complete the prompt
            >>> result = await client.complete(request, model=model_name)
        """
        response = await self._post_request(
            "complete",
            request,
            model,
        )
        return CompletionResponse.from_json(response)

    async def tokenize(
        self,
        request: TokenizationRequest,
        model: str,
    ) -> TokenizationResponse:
        """Tokenizes the given prompt for the given model.

        Parameters:
            request (TokenizationRequest, required):
                Parameters for the requested tokenization.

            model (string, required):
                Name of model to use. A model name refers to a model architecture (number of parameters among others).
                Always the latest version of model is used.

        Examples:
            >>> request = TokenizationRequest(prompt="hello", token_ids=True, tokens=True)
            >>> response = await client.tokenize(request, model=model_name)
        """
        response = await self._post_request(
            "tokenize",
            request,
            model,
        )
        return TokenizationResponse.from_json(response)

    async def detokenize(
        self,
        request: DetokenizationRequest,
        model: str,
    ) -> DetokenizationResponse:
        """Detokenizes the given prompt for the given model.

        Parameters:
            request (DetokenizationRequest, required):
                Parameters for the requested detokenization.

            model (string, required):
                Name of model to use. A model name refers to a model architecture (number of parameters among others).
                Always the latest version of model is used.

        Examples:
            >>> request = DetokenizationRequest(token_ids=[2, 3, 4])
            >>> response = await client.detokenize(request, model=model_name)
        """
        response = await self._post_request(
            "detokenize",
            request,
            model,
        )
        return DetokenizationResponse.from_json(response)

    async def embed(
        self,
        request: EmbeddingRequest,
        model: str,
    ) -> EmbeddingResponse:
        """Embeds a text and returns vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers).

        Parameters:
            request (EmbeddingRequest, required):
                Parameters for the requested embedding.

            model (string, required):
                Name of