langchain/libs/community/langchain_community/llms/sambanova.py

import json
from typing import Any, Dict, Generator, Iterator, List, Optional, Union

import requests
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env


class SVEndpointHandler:
    """
    SambaNova Systems Interface for Sambaverse endpoint.

    :param str host_url: Base URL of the DaaS API service
    """

    API_BASE_PATH = "/api/predict"

    def __init__(self, host_url: str):
        """
        Initialize the SVEndpointHandler.

        :param str host_url: Base URL of the DaaS API service
        """
        self.host_url = host_url
        self.http_session = requests.Session()

    @staticmethod
    def _process_response(response: requests.Response) -> Dict:
        """
        Processes the API response and returns the resulting dict.

        All resulting dicts, regardless of success or failure, will contain the
        `status_code` key with the API response status code.

        If the API returned an error, the resulting dict will contain the key
        `detail` with the error message.

        If the API call was successful, the resulting dict will contain the key
        `data` with the response data.

        :param requests.Response response: the response object to process
        :return: the response dict
        :rtype: dict
        """
        result: Dict[str, Any] = {}
        try:
            lines_result = response.text.strip().split("\n")
            text_result = lines_result[-1]
            if response.status_code == 200 and json.loads(text_result).get("error"):
                completion = ""
                for line in lines_result[:-1]:
                    completion += json.loads(line)["result"]["responses"][0][
                        "stream_token"
                    ]
                text_result = lines_result[-2]
                result = json.loads(text_result)
                result["result"]["responses"][0]["completion"] = completion
            else:
                result = json.loads(text_result)
        except Exception as e:
            result["detail"] = str(e)
        if "status_code" not in result:
            result["status_code"] = response.status_code
        return result

    @staticmethod
    def _process_streaming_response(
        response: requests.Response,
    ) -> Generator[Dict, None, None]:
        """Process the streaming response"""
        try:
            for line in response.iter_lines():
                chunk = json.loads(line)
                if "status_code" not in chunk:
                    chunk["status_code"] = response.status_code
                if chunk["status_code"] == 200 and chunk.get("error"):
                    chunk["result"] = {"responses": [{"stream_token": ""}]}
                    return chunk
                yield chunk
        except Exception as e:
            raise RuntimeError(f"Error processing streaming response: {e}")

    def _get_full_url(self) -> str:
        """
        Return the full API URL for a given path.
        :returns: the full API URL for the sub-path
        :rtype: str
        """
        return f"{self.host_url}{self.API_BASE_PATH}"

    def nlp_predict(
        self,
        key: str,
        sambaverse_model_name: Optional[str],
        input: Union[List[str], str],
        params: Optional[str] = "",
        stream: bool = False,
    ) -> Dict:
        """
        NLP predict using inline input string.

        :param str project: Project ID in which the endpoint exists
        :param str endpoint: Endpoint ID
        :param str key: API Key
        :param str input_str: Input string
        :param str params: Input params string
        :returns: Prediction results
        :rtype: dict
        """
        parsed_element = {
            "conversation_id": "sambaverse-conversation-id",
            "messages": [
                {
                    "message_id": 0,
                    "role": "user",
                    "content": input,
                }
            ],
        }
        parsed_input = json.dumps(parsed_element)
        if params:
            data = {"instance": parsed_input, "params": json.loads(params)}
        else:
            data = {"instance": parsed_input}
        response = self.http_session.post(
            self._get_full_url(),
            headers={
                "key": key,
                "Content-Type": "application/json",
                "modelName": sambaverse_model_name,
            },
            json=data,
        )
        return SVEndpointHandler._process_response(response)

    def nlp_predict_stream(
        self,
        key: str,
        sambaverse_model_name: Optional[str],
        input: Union[List[str], str],
        params: Optional[str] = "",
    ) -> Iterator[Dict]:
        """
        NLP predict using inline input string.

        :param str project: Project ID in which the endpoint exists
        :param str endpoint: Endpoint ID
        :param str key: API Key
        :param str input_str: Input string
        :param str params: Input params string
        :returns: Prediction results
        :rtype: dict
        """
        parsed_element = {
            "conversation_id": "sambaverse-conversation-id",
            "messages": [
                {
                    "message_id": 0,
                    "role": "user",
                    "content": input,
                }
            ],
        }
        parsed_input = json.dumps(parsed_element)
        if params:
            data = {"instance": parsed_input, "params": json.loads(params)}
        else:
            data = {"instance": parsed_input}
        # Streaming output
        response = self.http_session.post(
            self._get_full_url(),
            headers={
                "key": key,
                "Content-Type": "application/json",
                "modelName": sambaverse_model_name,
            },
            json=data,
            stream=True,
        )
        for chunk in SVEndpointHandler._process_streaming_response(response):
            yield chunk


class Sambaverse(LLM):
    """
    Sambaverse large language models.

    To use, you should have the environment variable ``SAMBAVERSE_API_KEY``
    set with your API key.

    get one in https://sambaverse.sambanova.ai
    read extra documentation in https://docs.sambanova.ai/sambaverse/latest/index.html


    Example:
    .. code-block:: python

        from langchain_community.llms.sambanova  import Sambaverse
        Sambaverse(
            sambaverse_url="https://sambaverse.sambanova.ai",
            sambaverse_api_key="your-sambaverse-api-key",
            sambaverse_model_name="Meta/llama-2-7b-chat-hf",
            streaming: = False
            model_kwargs={
                "select_expert": "llama-2-7b-chat-hf",
                "do_sample": False,
                "max_tokens_to_generate": 100,
                "temperature": 0.7,
                "top_p": 1.0,
                "repetition_penalty": 1.0,
                "top_k": 50,
            },
        )
    """

    sambaverse_url: str = ""
    """Sambaverse url to use"""

    sambaverse_api_key: str = ""
    """sambaverse api key"""

    sambaverse_model_name: Optional[str] = None
    """sambaverse expert model to use"""

    model_kwargs: Optional[dict] = None
    """Key word arguments to pass to the model."""

    streaming: Optional[bool] = False
    """Streaming flag to get streamed response."""

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid

    @classmethod
    def is_lc_serializable(cls) -> bool:
        return True

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key exists in environment."""
        values["sambaverse_url"] = get_from_dict_or_env(
            values,
            "sambaverse_url",
            "SAMBAVERSE_URL",
            default="https://sambaverse.sambanova.ai",
        )
        values["sambaverse_api_key"] = get_from_dict_or_env(
            values, "sambaverse_api_key", "SAMBAVERSE_API_KEY"
        )
        values["sambaverse_model_name"] = get_from_dict_or_env(
            values, "sambaverse_model_name", "SAMBAVERSE_MODEL_NAME"
        )
        return values

    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {**{"model_kwargs": self.model_kwargs}}

    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "Sambaverse LLM"

    def _get_tuning_params(self, stop: Optional[List[str]]) -> str:
        """
        Get the tuning parameters to use when calling the LLM.

        Args:
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of the stop substrings.

        Returns:
            The tuning parameters as a JSON string.
        """
        _model_kwargs = self.model_kwargs or {}
        _kwarg_stop_sequences = _model_kwargs.get("stop_sequences", [])
        _stop_sequences = stop or _kwarg_stop_sequences
        if not _kwarg_stop_sequences:
            _model_kwargs["stop_sequences"] = ",".join(
                f'"{x}"' for x in _stop_sequences
            )
        tuning_params_dict = {
            k: {"type": type(v).__name__, "value": str(v)}
            for k, v in (_model_kwargs.items())
        }
        _model_kwargs["stop_sequences"] = _kwarg_stop_sequences
        tuning_params = json.dumps(tuning_params_dict)
        return tuning_params

    def _handle_nlp_predict(
        self,
        sdk: SVEndpointHandler,
        prompt: Union[List[str], str],
        tuning_params: str,
    ) -> str:
        """
        Perform an NLP prediction using the Sambaverse endpoint handler.

        Args:
            sdk: The SVEndpointHandler to use for the prediction.
            prompt: The prompt to use for the prediction.
            tuning_params: The tuning parameters to use for the prediction.

        Returns:
            The prediction result.

        Raises:
            ValueError: If the prediction fails.
        """
        response = sdk.nlp_predict(
            self.sambaverse_api_key, self.sambaverse_model_name, prompt, tuning_params
        )
        if response["status_code"] != 200:
            error = response.get("error")
            if error:
                optional_code = error.get("code")
                optional_details = error.get("details")
                optional_message = error.get("message")
                raise RuntimeError(
                    f"Sambanova /complete call failed with status code "
                    f"{response['status_code']}.\n"
                    f"Message: {optional_message}\n"
                    f"Details: {optional_details}\n"
                    f"Code: {optional_code}\n"
                )
            else:
                raise RuntimeError(
                    f"Sambanova /complete call failed with status code "
                    f"{response['status_code']}."
                    f"{response}."
                )
        return response["result"]["responses"][0]["completion"]

    def _handle_completion_requests(
        self, prompt: Union[List[str], str], stop: Optional[List[str]]
    ) -> str:
        """
        Perform a prediction using the Sambaverse endpoint handler.

        Args:
            prompt: The prompt to use for the prediction.
            stop: stop sequences.

        Returns:
            The prediction result.

        Raises:
            ValueError: If the prediction fails.
        """
        ss_endpoint = SVEndpointHandler(self.sambaverse_url)
        tuning_params = self._get_tuning_params(stop)
        return self._handle_nlp_predict(ss_endpoint, prompt, tuning_params)

    def _handle_nlp_predict_stream(
        self, sdk: SVEndpointHandler, prompt: Union[List[str], str], tuning_params: str
    ) -> Iterator[GenerationChunk]:
        """
        Perform a streaming request to the LLM.

        Args:
            sdk: The SVEndpointHandler to use for the prediction.
            prompt: The prompt to use for the prediction.
            tuning_params: The tuning parameters to use for the prediction.

        Returns:
            An iterator of GenerationChunks.
        """
        for chunk in sdk.nlp_predict_stream(
            self.sambaverse_api_key, self.sambaverse_model_name, prompt, tuning_params
        ):
            if chunk["status_code"] != 200:
                error = chunk.get("error")
                if error:
                    optional_code = error.get("code")
                    optional_details = error.get("details")
                    optional_message = error.get("message")
                    raise ValueError(
                        f"Sambanova /complete call failed with status code "
                        f"{chunk['status_code']}.\n"
                        f"Message: {optional_message}\n"
                        f"Details: {optional_details}\n"
                        f"Code: {optional_code}\n"
                    )
                else:
                    raise RuntimeError(
                        f"Sambanova /complete call failed with status code "
                        f"{chunk['status_code']}."
                        f"{chunk}."
                    )
            text = chunk["result"]["responses"][0]["stream_token"]
            generated_chunk = GenerationChunk(text=text)
            yield generated_chunk

    def _stream(
        self,
        prompt: Union[List[str], str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[GenerationChunk]:
        """Stream the Sambaverse's LLM on the given prompt.

        Args:
            prompt: The prompt to pass into the model.
            stop: Optional list of stop words to use when generating.
            run_manager: Callback manager for the run.
            **kwargs: Additional keyword arguments. directly passed
                to the sambaverse model in API call.

        Returns:
            An iterator of GenerationChunks.
        """
        ss_endpoint = SVEndpointHandler(self.sambaverse_url)
        tuning_params = self._get_tuning_params(stop)
        try:
            if self.streaming:
                for chunk in self._handle_nlp_predict_stream(
                    ss_endpoint, prompt, tuning_params
                ):
                    if run_manager:
                        run_manager.on_llm_new_token(chunk.text)
                    yield chunk
            else:
                return
        except Exception as e:
            # Handle any errors raised by the inference endpoint
            raise ValueError(f"Error raised by the inference endpoint: {e}") from e

    def _handle_stream_request(
        self,
        prompt: Union[List[str], str],
        stop: Optional[List[str]],
        run_manager: Optional[CallbackManagerForLLMRun],
        kwargs: Dict[str, Any],
    ) -> str:
        """
        Perform a streaming request to the LLM.

        Args:
            prompt: The prompt to generate from.
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of the stop substrings.
            run_manager: Callback manager for the run.
            **kwargs: Additional keyword arguments. directly passed
                to the sambaverse model in API call.

        Returns:
            The model output as a string.
        """
        completion = ""
        for chunk in self._stream(
            prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
        ):
            completion += chunk.text
        return completion

    def _call(
        self,
        prompt: Union[List[str], str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Run the LLM on the given input.

        Args:
            prompt: The prompt to generate from.
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of the stop substrings.
            run_manager: Callback manager for the run.
            **kwargs: Additional keyword arguments. directly passed
                to the sambaverse model in API call.

        Returns:
            The model output as a string.
        """
        try:
            if self.streaming:
                return self._handle_stream_request(prompt, stop, run_manager, kwargs)
            return self._handle_completion_requests(prompt, stop)
        except Exception as e:
            # Handle any errors raised by the inference endpoint
            raise ValueError(f"Error raised by the inference endpoint: {e}") from e


class SSEndpointHandler:
    """
    SambaNova Systems Interface for SambaStudio model endpoints.

    :param str host_url: Base URL of the DaaS API service
    """

    def __init__(self, host_url: str, api_base_uri: str):
        """
        Initialize the SSEndpointHandler.

        :param str host_url: Base URL of the DaaS API service
        :param str api_base_uri: Base URI of the DaaS API service
        """
        self.host_url = host_url
        self.api_base_uri = api_base_uri
        self.http_session = requests.Session()

    def _process_response(self, response: requests.Response) -> Dict:
        """
        Processes the API response and returns the resulting dict.

        All resulting dicts, regardless of success or failure, will contain the
        `status_code` key with the API response status code.

        If the API returned an error, the resulting dict will contain the key
        `detail` with the error message.

        If the API call was successful, the resulting dict will contain the key
        `data` with the response data.

        :param requests.Response response: the response object to process
        :return: the response dict
        :rtype: dict
        """
        result: Dict[str, Any] = {}
        try:
            result = response.json()
        except Exception as e:
            result["detail"] = str(e)
        if "status_code" not in result:
            result["status_code"] = response.status_code
        return result

    def _process_streaming_response(
        self,
        response: requests.Response,
    ) -> Generator[Dict, None, None]:
        """Process the streaming response"""
        if "nlp" in self.api_base_uri:
            try:
                import sseclient
            except ImportError:
                raise ImportError(
                    "could not import sseclient library"
                    "Please install it with `pip install sseclient-py`."
                )
            client = sseclient.SSEClient(response)
            close_conn = False
            for event in client.events():
                if event.event == "error_event":
                    close_conn = True
                chunk = {
                    "event": event.event,
                    "data": event.data,
                    "status_code": response.status_code,
                }
                yield chunk
            if close_conn:
                client.close()
        elif "generic" in self.api_base_uri:
            try:
                for line in response.iter_lines():
                    chunk = json.loads(line)
                    if "status_code" not in chunk:
                        chunk["status_code"] = response.status_code
                    if chunk["status_code"] == 200 and chunk.get("error"):
                        chunk["result"] = {"responses": [{"stream_token": ""}]}
                    yield chunk
            except Exception as e:
                raise RuntimeError(f"Error processing streaming response: {e}")
        else:
            raise ValueError(
                f"handling of endpoint uri: {self.api_base_uri} not implemented"
            )

    def _get_full_url(self, path: str) -> str:
        """
        Return the full API URL for a given path.

        :param str path: the sub-path
        :returns: the full API URL for the sub-path
        :rtype: str
        """
        return f"{self.host_url}/{self.api_base_uri}/{path}"

    def nlp_predict(
        self,
        project: str,
        endpoint: str,
        key: str,
        input: Union[List[str], str],
        params: Optional[str] = "",
        stream: bool = False,
    ) -> Dict:
        """
        NLP predict using inline input string.

        :param str project: Project ID in which the endpoint exists
        :param str endpoint: Endpoint ID
        :param str key: API Key
        :param str input_str: Input string
        :param str params: Input params string
        :returns: Prediction results
        :rtype: dict
        """
        if isinstance(input, str):
            input = [input]
        if "nlp" in self.api_base_uri:
            if params:
                data = {"inputs": input, "params": json.loads(params)}
            else:
                data = {"inputs": input}
        elif "generic" in self.api_base_uri:
            if params:
                data = {"instances": input, "params": json.loads(params)}
            else:
                data = {"instances": input}
        else:
            raise ValueError(
                f"handling of endpoint uri: {self.api_base_uri} not implemented"
            )
        response = self.http_session.post(
            self._get_full_url(f"{project}/{endpoint}"),
            headers={"key": key},
            json=data,
        )
        return self._process_response(response)

    def nlp_predict_stream(
        self,
        project: str,
        endpoint: str,
        key: str,
        input: Union[List[str], str],
        params: Optional[str] = "",
    ) -> Iterator[Dict]:
        """
        NLP predict using inline input string.

        :param str project: Project ID in which the endpoint exists
        :param str endpoint: Endpoint ID
        :param str key: API Key
        :param str input_str: Input string
        :param str params: Input params string
        :returns: Prediction results
        :rtype: dict
        """
        if "nlp" in self.api_base_uri:
            if isinstance(input, str):
                input = [input]
            if params:
                data = {"inputs": input, "params": json.loads(params)}
            else:
                data = {"inputs": input}
        elif "generic" in self.api_base_uri:
            if isinstance(input, list):
                input = input[0]
            if params:
                data = {"instance": input, "params": json.loads(params)}
            else:
                data = {"instance": input}
        else:
            raise ValueError(
                f"handling of endpoint uri: {self.api_base_uri} not implemented"
            )
        # Streaming output
        response = self.http_session.post(
            self._get_full_url(f"stream/{project}/{endpoint}"),
            headers={"key": key},
            json=data,
            stream=True,
        )
        for chunk in self._process_streaming_response(response):
            yield chunk


class SambaStudio(LLM):
    """
    SambaStudio large language models.

    To use, you should have the environment variables
    ``SAMBASTUDIO_BASE_URL`` set with your SambaStudio environment URL.
    ``SAMBASTUDIO_BASE_URI`` set with your SambaStudio api base URI.
    ``SAMBASTUDIO_PROJECT_ID`` set with your SambaStudio project ID.
    ``SAMBASTUDIO_ENDPOINT_ID`` set with your SambaStudio endpoint ID.
    ``SAMBASTUDIO_API_KEY``  set with your SambaStudio endpoint API key.

    https://sambanova.ai/products/enterprise-ai-platform-sambanova-suite

    read extra documentation in https://docs.sambanova.ai/sambastudio/latest/index.html

    Example:
    .. code-block:: python

        from langchain_community.llms.sambanova  import Sambaverse
        SambaStudio(
            sambastudio_base_url="your-SambaStudio-environment-URL",
            sambastudio_base_uri="your-SambaStudio-base-URI",
            sambastudio_project_id="your-SambaStudio-project-ID",
            sambastudio_endpoint_id="your-SambaStudio-endpoint-ID",
            sambastudio_api_key="your-SambaStudio-endpoint-API-key,
            streaming=False
            model_kwargs={
                "do_sample": False,
                "max_tokens_to_generate": 1000,
                "temperature": 0.7,
                "top_p": 1.0,
                "repetition_penalty": 1,
                "top_k": 50,
            },
        )
    """

    sambastudio_base_url: str = ""
    """Base url to use"""

    sambastudio_base_uri: str = ""
    """endpoint base uri"""

    sambastudio_project_id: str = ""
    """Project id on sambastudio for model"""

    sambastudio_endpoint_id: str = ""
    """endpoint id on sambastudio for model"""

    sambastudio_api_key: str = ""
    """sambastudio api key"""

    model_kwargs: Optional[dict] = None
    """Key word arguments to pass to the model."""

    streaming: Optional[bool] = False
    """Streaming flag to get streamed response."""

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid

    @classmethod
    def is_lc_serializable(cls) -> bool:
        return True

    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {**{"model_kwargs": self.model_kwargs}}

    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "Sambastudio LLM"

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        values["sambastudio_base_url"] = get_from_dict_or_env(
            values, "sambastudio_base_url", "SAMBASTUDIO_BASE_URL"
        )
        values["sambastudio_base_uri"] = get_from_dict_or_env(
            values,
            "sambastudio_base_uri",
            "SAMBASTUDIO_BASE_URI",
            default="api/predict/nlp",
        )
        values["sambastudio_project_id"] = get_from_dict_or_env(
            values, "sambastudio_project_id", "SAMBASTUDIO_PROJECT_ID"
        )
        values["sambastudio_endpoint_id"] = get_from_dict_or_env(
            values, "sambastudio_endpoint_id", "SAMBASTUDIO_ENDPOINT_ID"
        )
        values["sambastudio_api_key"] = get_from_dict_or_env(
            values, "sambastudio_api_key", "SAMBASTUDIO_API_KEY"
        )
        return values

    def _get_tuning_params(self, stop: Optional[List[str]]) -> str:
        """
        Get the tuning parameters to use when calling the LLM.

        Args:
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of the stop substrings.

        Returns:
            The tuning parameters as a JSON string.
        """
        _model_kwargs = self.model_kwargs or {}
        _kwarg_stop_sequences = _model_kwargs.get("stop_sequences", [])
        _stop_sequences = stop or _kwarg_stop_sequences
        # if not _kwarg_stop_sequences:
        # _model_kwargs["stop_sequences"] = ",".join(
        #    f'"{x}"' for x in _stop_sequences
        # )
        tuning_params_dict = {
            k: {"type": type(v).__name__, "value": str(v)}
            for k, v in (_model_kwargs.items())
        }
        # _model_kwargs["stop_sequences"] = _kwarg_stop_sequences
        tuning_params = json.dumps(tuning_params_dict)
        return tuning_params

    def _handle_nlp_predict(
        self, sdk: SSEndpointHandler, prompt: Union[List[str], str], tuning_params: str
    ) -> str:
        """
        Perform an NLP prediction using the SambaStudio endpoint handler.

        Args:
            sdk: The SSEndpointHandler to use for the prediction.
            prompt: The prompt to use for the prediction.
            tuning_params: The tuning parameters to use for the prediction.

        Returns:
            The prediction result.

        Raises:
            ValueError: If the prediction fails.
        """
        response = sdk.nlp_predict(
            self.sambastudio_project_id,
            self.sambastudio_endpoint_id,
            self.sambastudio_api_key,
            prompt,
            tuning_params,
        )
        if response["status_code"] != 200:
            optional_detail = response.get("detail")
            if optional_detail:
                raise RuntimeError(
                    f"Sambanova /complete call failed with status code "
                    f"{response['status_code']}.\n Details: {optional_detail}"
                )
            else:
                raise RuntimeError(
                    f"Sambanova /complete call failed with status code "
                    f"{response['status_code']}.\n response {response}"
                )
        if "nlp" in self.sambastudio_base_uri:
            return response["data"][0]["completion"]
        elif "generic" in self.sambastudio_base_uri:
            return response["predictions"][0]["completion"]
        else:
            raise ValueError(
                f"handling of endpoint uri: {self.sambastudio_base_uri} not implemented"
            )

    def _handle_completion_requests(
        self, prompt: Union[List[str], str], stop: Optional[List[str]]
    ) -> str:
        """
        Perform a prediction using the SambaStudio endpoint handler.

        Args:
            prompt: The prompt to use for the prediction.
            stop: stop sequences.

        Returns:
            The prediction result.

        Raises:
            ValueError: If the prediction fails.
        """
        ss_endpoint = SSEndpointHandler(
            self.sambastudio_base_url, self.sambastudio_base_uri
        )
        tuning_params = self._get_tuning_params(stop)
        return self._handle_nlp_predict(ss_endpoint, prompt, tuning_params)

    def _handle_nlp_predict_stream(
        self, sdk: SSEndpointHandler, prompt: Union[List[str], str], tuning_params: str
    ) -> Iterator[GenerationChunk]:
        """
        Perform a streaming request to the LLM.

        Args:
            sdk: The SVEndpointHandler to use for the prediction.
            prompt: The prompt to use for the prediction.
            tuning_params: The tuning parameters to use for the prediction.

        Returns:
            An iterator of GenerationChunks.
        """
        for chunk in sdk.nlp_predict_stream(
            self.sambastudio_project_id,
            self.sambastudio_endpoint_id,
            self.sambastudio_api_key,
            prompt,
            tuning_params,
        ):
            if chunk["status_code"] != 200:
                error = chunk.get("error")
                if error:
                    optional_code = error.get("code")
                    optional_details = error.get("details")
                    optional_message = error.get("message")
                    raise ValueError(
                        f"Sambanova /complete call failed with status code "
                        f"{chunk['status_code']}.\n"
                        f"Message: {optional_message}\n"
                        f"Details: {optional_details}\n"
                        f"Code: {optional_code}\n"
                    )
                else:
                    raise RuntimeError(
                        f"Sambanova /complete call failed with status code "
                        f"{chunk['status_code']}."
                        f"{chunk}."
                    )
            if "nlp" in self.sambastudio_base_uri:
                text = json.loads(chunk["data"])["stream_token"]
            elif "generic" in self.sambastudio_base_uri:
                text = chunk["result"]["responses"][0]["stream_token"]
            else:
                raise ValueError(
                    f"handling of endpoint uri: {self.sambastudio_base_uri}"
                    f"not implemented"
                )
            generated_chunk = GenerationChunk(text=text)
            yield generated_chunk

    def _stream(
        self,
        prompt: Union[List[str], str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[GenerationChunk]:
        """Call out to Sambanova's complete endpoint.

        Args:
            prompt: The prompt to pass into the model.
            stop: Optional list of stop words to use when generating.

        Returns:
            The string generated by the model.
        """
        ss_endpoint = SSEndpointHandler(
            self.sambastudio_base_url, self.sambastudio_base_uri
        )
        tuning_params = self._get_tuning_params(stop)
        try:
            if self.streaming:
                for chunk in self._handle_nlp_predict_stream(
                    ss_endpoint, prompt, tuning_params
                ):
                    if run_manager:
                        run_manager.on_llm_new_token(chunk.text)
                    yield chunk
            else:
                return
        except Exception as e:
            # Handle any errors raised by the inference endpoint
            raise ValueError(f"Error raised by the inference endpoint: {e}") from e

    def _handle_stream_request(
        self,
        prompt: Union[List[str], str],
        stop: Optional[List[str]],
        run_manager: Optional[CallbackManagerForLLMRun],
        kwargs: Dict[str, Any],
    ) -> str:
        """
        Perform a streaming request to the LLM.

        Args:
            prompt: The prompt to generate from.
            stop: Stop words to use when generating. Model output is cut off at the
                first occurrence of any of the stop substrings.
            run_manager: Callback manager for the run.
            **kwargs: Additional keyword arguments. directly passed
                to the sambaverse model in API call.

        Returns:
            The model output as a string.
        """
        completion = ""
        for chunk in self._stream(
            prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
        ):
            completion += chunk.text
        return completion

    def _call(
        self,
        prompt: Union[List[str], str],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Call out to Sambanova's complete endpoint.

        Args:
            prompt: The prompt to pass into the model.
            stop: Optional list of stop words to use when generating.

        Returns:
            The string generated by the model.
        """
        if stop is not None:
            raise Exception("stop not implemented")
        try:
            if self.streaming:
                return self._handle_stream_request(prompt, stop, run_manager, kwargs)
            return self._handle_completion_requests(prompt, stop)
        except Exception as e:
            # Handle any errors raised by the inference endpoint
            raise ValueError(f"Error raised by the inference endpoint: {e}") from e