Source code for langchain.llms.sagemaker_endpoint
"""Wrapper around Sagemaker InvokeEndpoint API."""
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Mapping, Optional, TypeVar, Union
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
INPUT_TYPE = TypeVar("INPUT_TYPE", bound=Union[str, List[str]])
OUTPUT_TYPE = TypeVar("OUTPUT_TYPE", bound=Union[str, List[List[float]]])
class ContentHandlerBase(Generic[INPUT_TYPE, OUTPUT_TYPE]):
    """A handler class to transform input from LLM to a
    format that SageMaker endpoint expects. Similarily,
    the class also handles transforming output from the
    SageMaker endpoint to a format that LLM class expects.
    """
    """
    Example:
        .. code-block:: python
            class ContentHandler(ContentHandlerBase):
                content_type = "application/json"
                accepts = "application/json"
                def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
                    input_str = json.dumps({prompt: prompt, **model_kwargs})
                    return input_str.encode('utf-8')

                def transform_output(self, output: bytes) -> str:
                    response_json = json.loads(output.read().decode("utf-8"))
                    return response_json[0]["generated_text"]
    """
    content_type: Optional[str] = "text/plain"
    """The MIME type of the input data passed to endpoint"""
    accepts: Optional[str] = "text/plain"
    """The MIME type of the response data returned from endpoint"""
    @abstractmethod
    def transform_input(self, prompt: INPUT_TYPE, model_kwargs: Dict) -> bytes:
        """Transforms the input to a format that model can accept
        as the request Body. Should return bytes or seekable file
        like object in the format specified in the content_type
        request header.
        """
    @abstractmethod
    def transform_output(self, output: bytes) -> OUTPUT_TYPE:
        """Transforms the output from the model to string that
        the LLM class expects.
        """
class LLMContentHandler(ContentHandlerBase[str, str]):
    """Content handler for LLM class."""
[docs]class SagemakerEndpoint(LLM):
    """Wrapper around custom Sagemaker Inference Endpoints.
    To use, you must supply the endpoint name from your deployed
    Sagemaker model & the region where it is deployed.
    To authenticate, the AWS client uses the following methods to
    automatically load credentials:
    https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
    If a specific credential profile should be used, you must pass
    the name of the profile from the ~/.aws/credentials file that is to be used.
    Make sure the credentials / roles used have the required policies to
    access the Sagemaker endpoint.
    See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
    """
    """
    Example:
        .. code-block:: python
            from langchain import SagemakerEndpoint
            endpoint_name = (
                "my-endpoint-name"
            )
            region_name = (
                "us-west-2"
            )
            credentials_profile_name = (
                "default"
            )
            se = SagemakerEndpoint(
                endpoint_name=endpoint_name,
                region_name=region_name,
                credentials_profile_name=credentials_profile_name
            )
    """
    client: Any  #: :meta private:
    endpoint_name: str = ""
    """The name of the endpoint from the deployed Sagemaker model.
    Must be unique within an AWS Region."""
    region_name: str = ""
    """The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
    credentials_profile_name: Optional[str] = None
    """The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
    has either access keys or role information specified.
    If not specified, the default credential profile or, if on an EC2 instance,
    credentials from IMDS will be used.
    See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
    """
    content_handler: LLMContentHandler
    """The content handler class that provides an input and
    output transform functions to handle formats between LLM
    and the endpoint.
    """
    """
     Example:
        .. code-block:: python
        from langchain.llms.sagemaker_endpoint import LLMContentHandler
        class ContentHandler(LLMContentHandler):
                content_type = "application/json"
                accepts = "application/json"
                def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
                    input_str = json.dumps({prompt: prompt, **model_kwargs})
                    return input_str.encode('utf-8')

                def transform_output(self, output: bytes) -> str:
                    response_json = json.loads(output.read().decode("utf-8"))
                    return response_json[0]["generated_text"]
    """
    model_kwargs: Optional[Dict] = None
    """Key word arguments to pass to the model."""
    endpoint_kwargs: Optional[Dict] = None
    """Optional attributes passed to the invoke_endpoint
    function. See `boto3`_. docs for more info.
    .. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
    """
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that AWS credentials to and python package exists in environment."""
        try:
            import boto3
            try:
                if values["credentials_profile_name"] is not None:
                    session = boto3.Session(
                        profile_name=values["credentials_profile_name"]
                    )
                else:
                    # use default credentials
                    session = boto3.Session()
                values["client"] = session.client(
                    "sagemaker-runtime", region_name=values["region_name"]
                )
            except Exception as e:
                raise ValueError(
                    "Could not load credentials to authenticate with AWS client. "
                    "Please check that credentials in the specified "
                    "profile name are valid."
                ) from e
        except ImportError:
            raise ImportError(
                "Could not import boto3 python package. "
                "Please install it with `pip install boto3`."
            )
        return values
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        _model_kwargs = self.model_kwargs or {}
        return {
            **{"endpoint_name": self.endpoint_name},
            **{"model_kwargs": _model_kwargs},
        }
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "sagemaker_endpoint"
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Call out to Sagemaker inference 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.
        Example:
            .. code-block:: python
                response = se("Tell me a joke.")
        """
        _model_kwargs = self.model_kwargs or {}
        _model_kwargs = {**_model_kwargs, **kwargs}
        _endpoint_kwargs = self.endpoint_kwargs or {}
        body = self.content_handler.transform_input(prompt, _model_kwargs)
        content_type = self.content_handler.content_type
        accepts = self.content_handler.accepts
        # send request
        try:
            response = self.client.invoke_endpoint(
                EndpointName=self.endpoint_name,
                Body=body,
                ContentType=content_type,
                Accept=accepts,
                **_endpoint_kwargs,
            )
        except Exception as e:
            raise ValueError(f"Error raised by inference endpoint: {e}")
        text = self.content_handler.transform_output(response["Body"])
        if stop is not None:
            # This is a bit hacky, but I can't figure out a better way to enforce
            # stop tokens when making calls to the sagemaker endpoint.
            text = enforce_stop_tokens(text, stop)
        return text


Source code for langchain.llms.llamacpp
"""Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, Generator, List, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name__)
[docs]class LlamaCpp(LLM):
    """Wrapper around the llama.cpp model.
    To use, you should have the llama-cpp-python library installed, and provide the
    path to the Llama model as a named parameter to the constructor.
    Check out: https://github.com/abetlen/llama-cpp-python
    Example:
        .. code-block:: python
            from langchain.llms import LlamaCppEmbeddings
            llm = LlamaCppEmbeddings(model_path="/path/to/llama/model")
    """
    client: Any  #: :meta private:
    model_path: str
    """The path to the Llama model file."""
    lora_base: Optional[str] = None
    """The path to the Llama LoRA base model."""
    lora_path: Optional[str] = None
    """The path to the Llama LoRA. If None, no LoRa is loaded."""
    n_ctx: int = Field(512, alias="n_ctx")
    """Token context window."""
    n_parts: int = Field(-1, alias="n_parts")
    """Number of parts to split the model into.
    If -1, the number of parts is automatically determined."""
    seed: int = Field(-1, alias="seed")
    """Seed. If -1, a random seed is used."""
    f16_kv: bool = Field(True, alias="f16_kv")
    """Use half-precision for key/value cache."""
    logits_all: bool = Field(False, alias="logits_all")
    """Return logits for all tokens, not just the last token."""
    vocab_only: bool = Field(False, alias="vocab_only")
    """Only load the vocabulary, no weights."""
    use_mlock: bool = Field(False, alias="use_mlock")
    """Force system to keep model in RAM."""
    n_threads: Optional[int] = Field(None, alias="n_threads")
    """Number of threads to use.
    If None, the number of threads is automatically determined."""
    n_batch: Optional[int] = Field(8, alias="n_batch")
    """Number of tokens to process in parallel.
    Should be a number between 1 and n_ctx."""
    n_gpu_layers: Optional[int] = Field(None, alias="n_gpu_layers")
    """Number of layers to be loaded into gpu memory. Default None."""
    suffix: Optional[str] = Field(None)
    """A suffix to append to the generated text. If None, no suffix is appended."""
    max_tokens: Optional[int] = 256
    """The maximum number of tokens to generate."""
    temperature: Optional[float] = 0.8
    """The temperature to use for sampling."""
    top_p: Optional[float] = 0.95
    """The top-p value to use for sampling."""
    logprobs: Optional[int] = Field(None)
    """The number of logprobs to return. If None, no logprobs are returned."""
    echo: Optional[bool] = False
    """Whether to echo the prompt."""
    stop: Optional[List[str]] = []
    """A list of strings to stop generation when encountered."""
    repeat_penalty: Optional[float] = 1.1
    """The penalty to apply to repeated tokens."""
    top_k: Optional[int] = 40
    """The top-k value to use for sampling."""
    last_n_tokens_size: Optional[int] = 64
    """The number of tokens to look back when applying the repeat_penalty."""
    use_mmap: Optional[bool] = True
    """Whether to keep the model loaded in RAM"""
    streaming: bool = True
    """Whether to stream the results, token by token."""
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that llama-cpp-python library is installed."""
        model_path = values["model_path"]
        model_param_names = [
            "lora_path",
            "lora_base",
            "n_ctx",
            "n_parts",
            "seed",
            "f16_kv",
            "logits_all",
            "vocab_only",
            "use_mlock",
            "n_threads",
            "n_batch",
            "use_mmap",
            "last_n_tokens_size",
        ]
        model_params = {k: values[k] for k in model_param_names}
        # For backwards compatibility, only include if non-null.
        if values["n_gpu_layers"] is not None:
            model_params["n_gpu_layers"] = values["n_gpu_layers"]
        try:
            from llama_cpp import Llama
            values["client"] = Llama(model_path, **model_params)
        except ImportError:
            raise ModuleNotFoundError(
                "Could not import llama-cpp-python library. "
                "Please install the llama-cpp-python library to "
                "use this embedding model: pip install llama-cpp-python"
            )
        except Exception as e:
            raise ValueError(
                f"Could not load Llama model from path: {model_path}. "
                f"Received error {e}"
            )
        return values
    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling llama_cpp."""
        return {
            "suffix": self.suffix,
            "max_tokens": self.max_tokens,
            "temperature": self.temperature,
            "top_p": self.top_p,
            "logprobs": self.logprobs,
            "echo": self.echo,
            "stop_sequences": self.stop,  # key here is convention among LLM classes
            "repeat_penalty": self.repeat_penalty,
            "top_k": self.top_k,
        }
    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {**{"model_path": self.model_path}, **self._default_params}
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "llamacpp"
    def _get_parameters(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
        """
        Performs sanity check, preparing parameters in format needed by llama_cpp.
        Args:
            stop (Optional[List[str]]): List of stop sequences for llama_cpp.
        Returns:
            Dictionary containing the combined parameters.
        """
        # Raise error if stop sequences are in both input and default params
        if self.stop and stop is not None:
            raise ValueError("`stop` found in both the input and default params.")
        params = self._default_params
        # llama_cpp expects the "stop" key not this, so we remove it:
        params.pop("stop_sequences")
        # then sets it as configured, or default to an empty list:
        params["stop"] = self.stop or stop or []
        return params
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Call the Llama model and return the output.
        Args:
            prompt: The prompt to use for generation.
            stop: A list of strings to stop generation when encountered.
        Returns:
            The generated text.
        Example:
            .. code-block:: python
                from langchain.llms import LlamaCpp
                llm = LlamaCpp(model_path="/path/to/local/llama/model.bin")
                llm("This is a prompt.")
        """
        if self.streaming:
            # If streaming is enabled, we use the stream
            # method that yields as they are generated
            # and return the combined strings from the first choices's text:
            combined_text_output = ""
            for token in self.stream(prompt=prompt, stop=stop, run_manager=run_manager):
                combined_text_output += token["choices"][0]["text"]
            return combined_text_output
        else:
            params = self._get_parameters(stop)
            params = {**params, **kwargs}
            result = self.client(prompt=prompt, **params)
            return result["choices"][0]["text"]
[docs]    def stream(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
    ) -> Generator[Dict, None, None]:
        """Yields results objects as they are generated in real time.
        BETA: this is a beta feature while we figure out the right abstraction.
        Once that happens, this interface could change.
        It also calls the callback manager's on_llm_new_token event with
        similar parameters to the OpenAI LLM class method of the same name.
        Args:
            prompt: The prompts to pass into the model.
            stop: Optional list of stop words to use when generating.
        Returns:
            A generator representing the stream of tokens being generated.
        Yields:
            A dictionary like objects containing a string token and metadata.
            See llama-cpp-python docs and below for more.
        Example:
            .. code-block:: python
                from langchain.llms import LlamaCpp
                llm = LlamaCpp(
                    model_path="/path/to/local/model.bin",
                    temperature = 0.5
                )
                for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'",
                        stop=["'","\n"]):
                    result = chunk["choices"][0]
                    print(result["text"], end='', flush=True)
        """
        params = self._get_parameters(stop)
        result = self.client(prompt=prompt, stream=True, **params)
        for chunk in result:
            token = chunk["choices"][0]["text"]
            log_probs = chunk["choices"][0].get("logprobs", None)
            if run_manager:
                run_manager.on_llm_new_token(
                    token=token, verbose=self.verbose, log_probs=log_probs
                )
            yield chunk
[docs]    def get_num_tokens(self, text: str) -> int:
        tokenized_text = self.client.tokenize(text.encode("utf-8"))
        return len(tokenized_text)


Source code for langchain.llms.azureml_endpoint
"""Wrapper around AzureML Managed Online Endpoint API."""
import json
import urllib.request
from abc import abstractmethod
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
class AzureMLEndpointClient(object):
    """Wrapper around AzureML Managed Online Endpoint Client."""
    def __init__(
        self, endpoint_url: str, endpoint_api_key: str, deployment_name: str
    ) -> None:
        """Initialize the class."""
        if not endpoint_api_key:
            raise ValueError("A key should be provided to invoke the endpoint")
        self.endpoint_url = endpoint_url
        self.endpoint_api_key = endpoint_api_key
        self.deployment_name = deployment_name
    def call(self, body: bytes) -> bytes:
        """call."""
        # The azureml-model-deployment header will force the request to go to a
        # specific deployment. Remove this header to have the request observe the
        # endpoint traffic rules.
        headers = {
            "Content-Type": "application/json",
            "Authorization": ("Bearer " + self.endpoint_api_key),
            "azureml-model-deployment": self.deployment_name,
        }
        req = urllib.request.Request(self.endpoint_url, body, headers)
        response = urllib.request.urlopen(req, timeout=50)
        result = response.read()
        return result
class ContentFormatterBase:
    """A handler class to transform request and response of
    AzureML endpoint to match with required schema.
    """
    """
    Example:
        .. code-block:: python

            class ContentFormatter(ContentFormatterBase):
                content_type = "application/json"
                accepts = "application/json"

                def format_request_payload(
                    self,
                    prompt: str,
                    model_kwargs: Dict
                ) -> bytes:
                    input_str = json.dumps(
                        {
                            "inputs": {"input_string": [prompt]},
                            "parameters": model_kwargs,
                        }
                    )
                    return str.encode(input_str)

                def format_response_payload(self, output: str) -> str:
                    response_json = json.loads(output)
                    return response_json[0]["0"]
    """
    content_type: Optional[str] = "application/json"
    """The MIME type of the input data passed to the endpoint"""
    accepts: Optional[str] = "application/json"
    """The MIME type of the response data returned form the endpoint"""
    @abstractmethod
    def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
        """Formats the request body according to the input schema of
        the model. Returns bytes or seekable file like object in the
        format specified in the content_type request header.
        """
    @abstractmethod
    def format_response_payload(self, output: bytes) -> str:
        """Formats the response body according to the output
        schema of the model. Returns the data type that is
        received from the response.
        """
class OSSContentFormatter(ContentFormatterBase):
    """Content handler for LLMs from the OSS catalog."""
    def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
        input_str = json.dumps(
            {"inputs": {"input_string": [prompt]}, "parameters": model_kwargs}
        )
        return str.encode(input_str)
    def format_response_payload(self, output: bytes) -> str:
        response_json = json.loads(output)
        return response_json[0]["0"]
class HFContentFormatter(ContentFormatterBase):
    """Content handler for LLMs from the HuggingFace catalog."""
    def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
        input_str = json.dumps({"inputs": [prompt], "parameters": model_kwargs})
        return str.encode(input_str)
    def format_response_payload(self, output: bytes) -> str:
        response_json = json.loads(output)
        return response_json[0][0]["generated_text"]
class DollyContentFormatter(ContentFormatterBase):
    """Content handler for the Dolly-v2-12b model"""
    def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
        input_str = json.dumps(
            {"input_data": {"input_string": [prompt]}, "parameters": model_kwargs}
        )
        return str.encode(input_str)
    def format_response_payload(self, output: bytes) -> str:
        response_json = json.loads(output)
        return response_json[0]
[docs]class AzureMLOnlineEndpoint(LLM, BaseModel):
    """Wrapper around Azure ML Hosted models using Managed Online Endpoints.
    Example:
        .. code-block:: python
            azure_llm = AzureMLModel(
                endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/score",
                endpoint_api_key="my-api-key",
                deployment_name="my-deployment-name",
                content_formatter=content_formatter,
            )
    """  # noqa: E501
    endpoint_url: str = ""
    """URL of pre-existing Endpoint. Should be passed to constructor or specified as
        env var `AZUREML_ENDPOINT_URL`."""
    endpoint_api_key: str = ""
    """Authentication Key for Endpoint. Should be passed to constructor or specified as
        env var `AZUREML_ENDPOINT_API_KEY`."""
    deployment_name: str = ""
    """Deployment Name for Endpoint. Should be passed to constructor or specified as
        env var `AZUREML_DEPLOYMENT_NAME`."""
    http_client: Any = None  #: :meta private:
    content_formatter: Any = None
    """The content formatter that provides an input and output
    transform function to handle formats between the LLM and
    the endpoint"""
    model_kwargs: Optional[dict] = None
    """Key word arguments to pass to the model."""
    @validator("http_client", always=True, allow_reuse=True)
    @classmethod
    def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
        """Validate that api key and python package exists in environment."""
        endpoint_key = get_from_dict_or_env(
            values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY"
        )
        endpoint_url = get_from_dict_or_env(
            values, "endpoint_url", "AZUREML_ENDPOINT_URL"
        )
        deployment_name = get_from_dict_or_env(
            values, "deployment_name", "AZUREML_DEPLOYMENT_NAME"
        )
        http_client = AzureMLEndpointClient(endpoint_url, endpoint_key, deployment_name)
        return http_client
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        _model_kwargs = self.model_kwargs or {}
        return {
            **{"deployment_name": self.deployment_name},
            **{"model_kwargs": _model_kwargs},
        }
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "azureml_endpoint"
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any
    ) -> str:
        """Call out to an AzureML Managed Online 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.
        Example:
            .. code-block:: python
                response = azureml_model("Tell me a joke.")
        """
        _model_kwargs = self.model_kwargs or {}
        body = self.content_formatter.format_request_payload(prompt, _model_kwargs)
        endpoint_response = self.http_client.call(body)
        response = self.content_formatter.format_response_payload(endpoint_response)
        return response


Source code for langchain.llms.amazon_api_gateway
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class ContentHandlerAmazonAPIGateway:
    """Adapter class to prepare the inputs from Langchain to a format
    that LLM model expects. Also, provides helper function to extract
    the generated text from the model response."""
    @classmethod
    def transform_input(
        cls, prompt: str, model_kwargs: Dict[str, Any]
    ) -> Dict[str, Any]:
        return {"inputs": prompt, "parameters": model_kwargs}
    @classmethod
    def transform_output(cls, response: Any) -> str:
        return response.json()[0]["generated_text"]
[docs]class AmazonAPIGateway(LLM):
    """Wrapper around custom Amazon API Gateway"""
    api_url: str
    """API Gateway URL"""
    model_kwargs: Optional[Dict] = None
    """Key word arguments to pass to the model."""
    content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
    """The content handler class that provides an input and
    output transform functions to handle formats between LLM
    and the endpoint.
    """
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        _model_kwargs = self.model_kwargs or {}
        return {
            **{"endpoint_name": self.api_url},
            **{"model_kwargs": _model_kwargs},
        }
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "amazon_api_gateway"
    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Call out to Amazon API Gateway model.
        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.
        Example:
            .. code-block:: python
                response = se("Tell me a joke.")
        """
        _model_kwargs = self.model_kwargs or {}
        payload = self.content_handler.transform_input(prompt, _model_kwargs)
        try:
            response = requests.post(
                self.api_url,
                json=payload,
            )
            text = self.content_handler.transform_output(response)
        except Exception as error:
            raise ValueError(f"Error raised by the service: {error}")
        if stop is not None:
            text = enforce_stop_tokens(text, stop)
        return text


Source code for langchain.llms.beam
"""Wrapper around Beam API."""
import base64
import json
import logging
import subprocess
import textwrap
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
DEFAULT_NUM_TRIES = 10
DEFAULT_SLEEP_TIME = 4
[docs]class Beam(LLM):
    """Wrapper around Beam API for gpt2 large language model.
    To use, you should have the ``beam-sdk`` python package installed,
    and the environment variable ``BEAM_CLIENT_ID`` set with your client id
    and ``BEAM_CLIENT_SECRET`` set with your client secret. Information on how
    to get these is available here: https://docs.beam.cloud/account/api-keys.
    The wrapper can then be called as follows, where the name, cpu, memory, gpu,
    python version, and python packages can be updated accordingly. Once deployed,
    the instance can be called.
    Example:
        .. code-block:: python
            llm = Beam(model_name="gpt2",
                name="langchain-gpt2",
                cpu=8,
                memory="32Gi",
                gpu="A10G",
                python_version="python3.8",
                python_packages=[
                    "diffusers[torch]>=0.10",
                    "transformers",
                    "torch",
                    "pillow",
                    "accelerate",
                    "safetensors",
                    "xformers",],
                max_length=50)
            llm._deploy()
            call_result = llm._call(input)
    """
    model_name: str = ""
    name: str = ""
    cpu: str = ""
    memory: str = ""
    gpu: str = ""
    python_version: str = ""
    python_packages: List[str] = []
    max_length: str = ""
    url: str = ""
    """model endpoint to use"""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Holds any model parameters valid for `create` call not
    explicitly specified."""
    beam_client_id: str = ""
    beam_client_secret: str = ""
    app_id: Optional[str] = None
    class Config:
        """Configuration for this pydantic config."""
        extra = Extra.forbid
    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Build extra kwargs from additional params that were passed in."""
        all_required_field_names = {field.alias for field in cls.__fields__.values()}
        extra = values.get("model_kwargs", {})
        for field_name in list(values):
            if field_name not in all_required_field_names:
                if field_name in extra:
                    raise ValueError(f"Found {field_name} supplied twice.")
                logger.warning(
                    f"""{field_name} was transfered to model_kwargs.
                    Please confirm that {field_name} is what you intended."""
                )
                extra[field_name] = values.pop(field_name)
        values["model_kwargs"] = extra
        return values
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        beam_client_id = get_from_dict_or_env(
            values, "beam_client_id", "BEAM_CLIENT_ID"
        )
        beam_client_secret = get_from_dict_or_env(
            values, "beam_client_secret", "BEAM_CLIENT_SECRET"
        )
        values["beam_client_id"] = beam_client_id
        values["beam_client_secret"] = beam_client_secret
        return values
    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {
            "model_name": self.model_name,
            "name": self.name,
            "cpu": self.cpu,
            "memory": self.memory,
            "gpu": self.gpu,
            "python_version": self.python_version,
            "python_packages": self.python_packages,
            "max_length": self.max_length,
            "model_kwargs": self.model_kwargs,
        }
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "beam"
[docs]    def app_creation(self) -> None:
        """Creates a Python file which will contain your Beam app definition."""
        script = textwrap.dedent(
            """\
        import beam
        # The environment your code will run on
        app = beam.App(
            name="{name}",
            cpu={cpu},
            memory="{memory}",
            gpu="{gpu}",
            python_version="{python_version}",
            python_packages={python_packages},
        )
        app.Trigger.RestAPI(
            inputs={{"prompt": beam.Types.String(), "max_length": beam.Types.String()}},
            outputs={{"text": beam.Types.String()}},
            handler="run.py:beam_langchain",
        )
        """
        )
        script_name = "app.py"
        with open(script_name, "w") as file:
            file.write(
                script.format(
                    name=self.name,
                    cpu=self.cpu,
                    memory=self.memory,
                    gpu=self.gpu,
                    python_version=self.python_version,
                    python_packages=self.python_packages,
                )
            )
[docs]    def run_creation(self) -> None:
        """Creates a Python file which will be deployed on beam."""
        script = textwrap.dedent(
            """
        import os
        import transformers
        from transformers import GPT2LMHeadModel, GPT2Tokenizer
        model_name = "{model_name}"
        def beam_langchain(**inputs):
            prompt = inputs["prompt"]
            length = inputs["max_length"]
            tokenizer = GPT2Tokenizer.from_pretrained(model_name)
            model = GPT2LMHeadModel.from_pretrained(model_name)
            encodedPrompt = tokenizer.encode(prompt, return_tensors='pt')
            outputs = model.generate(encodedPrompt, max_length=int(length),
              do_sample=True, pad_token_id=tokenizer.eos_token_id)
            output = tokenizer.decode(outputs[0], skip_special_tokens=True)
            print(output)
            return {{"text": output}}
        """
        )
        script_name = "run.py"
        with open(script_name, "w") as file:
            file.write(script.format(model_name=self.model_name))
    def _deploy(self) -> str:
        """Call to Beam."""
        try:
            import beam  # type: ignore
            if beam.__path__ == "":
                raise ImportError
        except ImportError:
            raise ImportError(
                "Could not import beam python package. "
                "Please install it with `curl "
                "https://raw.githubusercontent.com/slai-labs"
                "/get-beam/main/get-beam.sh -sSfL | sh`."
            )
        self.app_creation()
        self.run_creation()
        process = subprocess.run(
            "beam deploy app.py", shell=True, capture_output=True, text=True
        )
        if process.returncode == 0:
            output = process.stdout
            logger.info(output)
            lines = output.split("\n")
            for line in lines:
                if line.startswith(" i  Send requests to: https://apps.beam.cloud/"):
                    self.app_id = line.split("/")[-1]
                    self.url = line.split(":")[1].strip()
                    return self.app_id
            raise ValueError(
                f"""Failed to retrieve the appID from the deployment output.
                Deployment output: {output}"""
            )
        else:
            raise ValueError(f"Deployment failed. Error: {process.stderr}")
    @property
    def authorization(self) -> str:
        if self.beam_client_id:
            credential_str = self.beam_client_id + ":" + self.beam_client_secret
        else:
            credential_str = self.beam_client_secret
        return base64.b64encode(credential_str.encode()).decode()
    def _call(
        self,
        prompt: str,
        stop: Optional[list] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """Call to Beam."""
        url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
        payload = {"prompt": prompt, "max_length": self.max_length}
        payload.update(kwargs)
        headers = {
            "Accept": "*/*",
            "Accept-Encoding": "gzip, deflate",
            "Authorization": "Basic " + self.authorization,
            "Connection": "keep-alive",
            "Content-Type": "application/json",
        }
        for _ in range(DEFAULT_NUM_TRIES):
            request = requests.post(url, headers=headers, data=json.dumps(payload))
            if request.status_code == 200:
                return request.json()["text"]
            time.sleep(DEFAULT_SLEEP_TIME)
        logger.warning("Unable to successfully call model.")
        return ""


Source code for langchain.callbacks.clearml_callback
import tempfile
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Sequence, Union
from langchain.callbacks.base import BaseCallbackHandler
from langchain.callbacks.utils import (
    BaseMetadataCallbackHandler,
    flatten_dict,
    hash_string,
    import_pandas,
    import_spacy,
    import_textstat,
    load_json,
)
from langchain.schema import AgentAction, AgentFinish, LLMResult
def import_clearml() -> Any:
    """Import the clearml python package and raise an error if it is not installed."""
    try:
        import clearml  # noqa: F401
    except ImportError:
        raise ImportError(
            "To use the clearml callback manager you need to have the `clearml` python "
            "package installed. Please install it with `pip install clearml`"
        )
    return clearml
[docs]class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
    """Callback Handler that logs to ClearML.
    Parameters:
        job_type (str): The type of clearml task such as "inference", "testing" or "qc"
        project_name (str): The clearml project name
        tags (list): Tags to add to the task
        task_name (str): Name of the clearml task
        visualize (bool): Whether to visualize the run.
        complexity_metrics (bool): Whether to log complexity metrics
        stream_logs (bool): Whether to stream callback actions to ClearML
    This handler will utilize the associated callback method and formats
    the input of each callback function with metadata regarding the state of LLM run,
    and adds the response to the list of records for both the {method}_records and
    action. It then logs the response to the ClearML console.
    """
    def __init__(
        self,
        task_type: Optional[str] = "inference",
        project_name: Optional[str] = "langchain_callback_demo",
        tags: Optional[Sequence] = None,
        task_name: Optional[str] = None,
        visualize: bool = False,
        complexity_metrics: bool = False,
        stream_logs: bool = False,
    ) -> None:
        """Initialize callback handler."""
        clearml = import_clearml()
        spacy = import_spacy()
        super().__init__()
        self.task_type = task_type
        self.project_name = project_name
        self.tags = tags
        self.task_name = task_name
        self.visualize = visualize
        self.complexity_metrics = complexity_metrics
        self.stream_logs = stream_logs
        self.temp_dir = tempfile.TemporaryDirectory()
        # Check if ClearML task already exists (e.g. in pipeline)
        if clearml.Task.current_task():
            self.task = clearml.Task.current_task()
        else:
            self.task = clearml.Task.init(  # type: ignore
                task_type=self.task_type,
                project_name=self.project_name,
                tags=self.tags,
                task_name=self.task_name,
                output_uri=True,
            )
        self.logger = self.task.get_logger()
        warning = (
            "The clearml callback is currently in beta and is subject to change "
            "based on updates to `langchain`. Please report any issues to "
            "https://github.com/allegroai/clearml/issues with the tag `langchain`."
        )
        self.logger.report_text(warning, level=30, print_console=True)
        self.callback_columns: list = []
        self.action_records: list = []
        self.complexity_metrics = complexity_metrics
        self.visualize = visualize
        self.nlp = spacy.load("en_core_web_sm")
    def _init_resp(self) -> Dict:
        return {k: None for k in self.callback_columns}
[docs]    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> None:
        """Run when LLM starts."""
        self.step += 1
        self.llm_starts += 1
        self.starts += 1
        resp = self._init_resp()
        resp.update({"action": "on_llm_start"})
        resp.update(flatten_dict(serialized))
        resp.update(self.get_custom_callback_meta())
        for prompt in prompts:
            prompt_resp = deepcopy(resp)
            prompt_resp["prompts"] = prompt
            self.on_llm_start_records.append(prompt_resp)
            self.action_records.append(prompt_resp)
            if self.stream_logs:
                self.logger.report_text(prompt_resp)
[docs]    def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
        """Run when LLM generates a new token."""
        self.step += 1
        self.llm_streams += 1
        resp = self._init_resp()
        resp.update({"action": "on_llm_new_token", "token": token})
        resp.update(self.get_custom_callback_meta())
        self.on_llm_token_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        """Run when LLM ends running."""
        self.step += 1
        self.llm_ends += 1
        self.ends += 1
        resp = self._init_resp()
        resp.update({"action": "on_llm_end"})
        resp.update(flatten_dict(response.llm_output or {}))
        resp.update(self.get_custom_callback_meta())
        for generations in response.generations:
            for generation in generations:
                generation_resp = deepcopy(resp)
                generation_resp.update(flatten_dict(generation.dict()))
                generation_resp.update(self.analyze_text(generation.text))
                self.on_llm_end_records.append(generation_resp)
                self.action_records.append(generation_resp)
                if self.stream_logs:
                    self.logger.report_text(generation_resp)
[docs]    def on_llm_error(
        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
    ) -> None:
        """Run when LLM errors."""
        self.step += 1
        self.errors += 1
[docs]    def on_chain_start(
        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
    ) -> None:
        """Run when chain starts running."""
        self.step += 1
        self.chain_starts += 1
        self.starts += 1
        resp = self._init_resp()
        resp.update({"action": "on_chain_start"})
        resp.update(flatten_dict(serialized))
        resp.update(self.get_custom_callback_meta())
        chain_input = inputs["input"]
        if isinstance(chain_input, str):
            input_resp = deepcopy(resp)
            input_resp["input"] = chain_input
            self.on_chain_start_records.append(input_resp)
            self.action_records.append(input_resp)
            if self.stream_logs:
                self.logger.report_text(input_resp)
        elif isinstance(chain_input, list):
            for inp in chain_input:
                input_resp = deepcopy(resp)
                input_resp.update(inp)
                self.on_chain_start_records.append(input_resp)
                self.action_records.append(input_resp)
                if self.stream_logs:
                    self.logger.report_text(input_resp)
        else:
            raise ValueError("Unexpected data format provided!")
[docs]    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
        """Run when chain ends running."""
        self.step += 1
        self.chain_ends += 1
        self.ends += 1
        resp = self._init_resp()
        resp.update({"action": "on_chain_end", "outputs": outputs["output"]})
        resp.update(self.get_custom_callback_meta())
        self.on_chain_end_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_chain_error(
        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
    ) -> None:
        """Run when chain errors."""
        self.step += 1
        self.errors += 1
[docs]    def on_tool_start(
        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
    ) -> None:
        """Run when tool starts running."""
        self.step += 1
        self.tool_starts += 1
        self.starts += 1
        resp = self._init_resp()
        resp.update({"action": "on_tool_start", "input_str": input_str})
        resp.update(flatten_dict(serialized))
        resp.update(self.get_custom_callback_meta())
        self.on_tool_start_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_tool_end(self, output: str, **kwargs: Any) -> None:
        """Run when tool ends running."""
        self.step += 1
        self.tool_ends += 1
        self.ends += 1
        resp = self._init_resp()
        resp.update({"action": "on_tool_end", "output": output})
        resp.update(self.get_custom_callback_meta())
        self.on_tool_end_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_tool_error(
        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
    ) -> None:
        """Run when tool errors."""
        self.step += 1
        self.errors += 1
[docs]    def on_text(self, text: str, **kwargs: Any) -> None:
        """
        Run when agent is ending.
        """
        self.step += 1
        self.text_ctr += 1
        resp = self._init_resp()
        resp.update({"action": "on_text", "text": text})
        resp.update(self.get_custom_callback_meta())
        self.on_text_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
        """Run when agent ends running."""
        self.step += 1
        self.agent_ends += 1
        self.ends += 1
        resp = self._init_resp()
        resp.update(
            {
                "action": "on_agent_finish",
                "output": finish.return_values["output"],
                "log": finish.log,
            }
        )
        resp.update(self.get_custom_callback_meta())
        self.on_agent_finish_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
        """Run on agent action."""
        self.step += 1
        self.tool_starts += 1
        self.starts += 1
        resp = self._init_resp()
        resp.update(
            {
                "action": "on_agent_action",
                "tool": action.tool,
                "tool_input": action.tool_input,
                "log": action.log,
            }
        )
        resp.update(self.get_custom_callback_meta())
        self.on_agent_action_records.append(resp)
        self.action_records.append(resp)
        if self.stream_logs:
            self.logger.report_text(resp)
[docs]    def analyze_text(self, text: str) -> dict:
        """Analyze text using textstat and spacy.
        Parameters:
            text (str): The text to analyze.
        Returns:
            (dict): A dictionary containing the complexity metrics.
        """
        resp = {}
        textstat = import_textstat()
        spacy = import_spacy()
        if self.complexity_metrics:
            text_complexity_metrics = {
                "flesch_reading_ease": textstat.flesch_reading_ease(text),
                "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
                "smog_index": textstat.smog_index(text),
                "coleman_liau_index": textstat.coleman_liau_index(text),
                "automated_readability_index": textstat.automated_readability_index(
                    text
                ),
                "dale_chall_readability_score": textstat.dale_chall_readability_score(
                    text
                ),
                "difficult_words": textstat.difficult_words(text),
                "linsear_write_formula": textstat.linsear_write_formula(text),
                "gunning_fog": textstat.gunning_fog(text),
                "text_standard": textstat.text_standard(text),
                "fernandez_huerta": textstat.fernandez_huerta(text),
                "szigriszt_pazos": textstat.szigriszt_pazos(text),
                "gutierrez_polini": textstat.gutierrez_polini(text),
                "crawford": textstat.crawford(text),
                "gulpease_index": textstat.gulpease_index(text),
                "osman": textstat.osman(text),
            }
            resp.update(text_complexity_metrics)
        if self.visualize and self.nlp and self.temp_dir.name is not None:
            doc = self.nlp(text)
            dep_out = spacy.displacy.render(  # type: ignore
                doc, style="dep", jupyter=False, page=True
            )
            dep_output_path = Path(
                self.temp_dir.name, hash_string(f"dep-{text}") + ".html"
            )
            dep_output_path.open("w", encoding="utf-8").write(dep_out)
            ent_out = spacy.displacy.render(  # type: ignore
                doc, style="ent", jupyter=False, page=True
            )
            ent_output_path = Path(
                self.temp_dir.name, hash_string(f"ent-{text}") + ".html"
            )
            ent_output_path.open("w", encoding="utf-8").write(ent_out)
            self.logger.report_media(
                "Dependencies Plot", text, local_path=dep_output_path
            )
            self.logger.report_media("Entities Plot", text, local_path=ent_output_path)
        return resp
    def _create_session_analysis_df(self) -> Any:
        """Create a dataframe with all the information from the session."""
        pd = import_pandas()
        on_llm_start_records_df = pd.DataFrame(self.on_llm_start_records)
        on_llm_end_records_df = pd.DataFrame(self.on_llm_end_records)
        llm_input_prompts_df = (
            on_llm_start_records_df[["step", "prompts", "name"]]
            .dropna(axis=1)
            .rename({"step": "prompt_step"}, axis=1)
        )
        complexity_metrics_columns = []
        visualizations_columns: List = []
        if self.complexity_metrics:
            complexity_metrics_columns = [
                "flesch_reading_ease",
                "flesch_kincaid_grade",
                "smog_index",
                "coleman_liau_index",
                "automated_readability_index",
                "dale_chall_readability_score",
                "difficult_words",
                "linsear_write_formula",
                "gunning_fog",
                "text_standard",
                "fernandez_huerta",
                "szigriszt_pazos",
                "gutierrez_polini",
                "crawford",
                "gulpease_index",
                "osman",
            ]
        llm_outputs_df = (
            on_llm_end_records_df[
                [
                    "step",
                    "text",
                    "token_usage_total_tokens",
                    "token_usage_prompt_tokens",
                    "token_usage_completion_tokens",
                ]
                + complexity_metrics_columns
                + visualizations_columns
            ]
            .dropna(axis=1)
            .rename({"step": "output_step", "text": "output"}, axis=1)
        )
        session_analysis_df = pd.concat([llm_input_prompts_df, llm_outputs_df], axis=1)
        # session_analysis_df["chat_html"] = session_analysis_df[
        #     ["prompts", "output"]
        # ].apply(
        #     lambda row: construct_html_from_prompt_and_generation(
        #         row["prompts"], row["output"]
        #     ),
        #     axis=1,
        # )
        return session_analysis_df
[docs]    def flush_tracker(
        self,
        name: Optional[str] = None,
        langchain_asset: Any = None,
        finish: bool = False,
    ) -> None:
        """Flush the tracker and setup the session.
        Everything after this will be a new table.
        Args:
            name: Name of the preformed session so far so it is identifyable
            langchain_asset: The langchain asset to save.
            finish: Whether to finish the run.
            Returns:
                None
        """
        pd = import_pandas()
        clearml = import_clearml()
        # Log the action records
        self.logger.report_table(
            "Action Records", name, table_plot=pd.DataFrame(self.action_records)
        )
        # Session analysis
        session_analysis_df = self._create_session_analysis_df()
        self.logger.report_table(
            "Session Analysis", name, table_plot=session_analysis_df
        )
        if self.stream_logs:
            self.logger.report_text(
                {
                    "action_records": pd.DataFrame(self.action_records),
                    "session_analysis": session_analysis_df,
                }
            )
        if langchain_asset:
            langchain_asset_path = Path(self.temp_dir.name, "model.json")
            try:
                langchain_asset.save(langchain_asset_path)
                # Create output model and connect it to the task
                output_model = clearml.OutputModel(
                    task=self.task, config_text=load_json(langchain_asset_path)
                )
                output_model.update_weights(
                    weights_filename=str(langchain_asset_path),
                    auto_delete_file=False,
                    target_filename=name,
                )
            except ValueError:
                langchain_asset.save_agent(langchain_asset_path)
                output_model = clearml.OutputModel(
                    task=self.task, config_text=load_json(langchain_asset_path)
                )
                output_model.update_weights(
                    weights_filename=str(langchain_asset_path),
                    auto_delete_file=False,
                    target_filename=name,
                )
            except NotImplementedError as e:
                print("Could not save model.")
                print(repr(e))
                pass
        # Cleanup after adding everything to ClearML
        self.task.flush(wait_for_uploads=True)
        self.temp_dir.cleanup()
        self.temp_dir = tempfile.TemporaryDirectory()
        self.reset_callback_meta()
        if finish:
            self.task.close()