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8287855dad66-3 | n_ctx = values["n_ctx"]
n_parts = values["n_parts"]
seed = values["seed"]
f16_kv = values["f16_kv"]
logits_all = values["logits_all"]
vocab_only = values["vocab_only"]
use_mlock = values["use_mlock"]
n_threads = values["n_threads"]
n_batch = values["n_batch"]
use_mmap = values["use_mmap"]
last_n_tokens_size = values["last_n_tokens_size"]
try:
from llama_cpp import Llama
values["client"] = Llama(
model_path=model_path,
lora_base=lora_base,
lora_path=lora_path,
n_ctx=n_ctx,
n_parts=n_parts,
seed=seed,
f16_kv=f16_kv,
logits_all=logits_all,
vocab_only=vocab_only,
use_mlock=use_mlock,
n_threads=n_threads,
n_batch=n_batch,
use_mmap=use_mmap,
last_n_tokens_size=last_n_tokens_size,
)
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: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
8287855dad66-4 | )
except Exception:
raise NameError(f"Could not load Llama model from path: {model_path}")
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 "llama.cpp"
def _get_parameters(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
"""
Performs sanity check, preparing paramaters in format needed by llama_cpp.
Args:
stop (Optional[List[str]]): List of stop sequences for llama_cpp.
Returns:
Dictionary containing the combined parameters.
""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
8287855dad66-5 | 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) -> 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):
combined_text_output += token["choices"][0]["text"]
return combined_text_output
else:
params = self._get_parameters(stop) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
8287855dad66-6 | else:
params = self._get_parameters(stop)
result = self.client(prompt=prompt, **params)
return result["choices"][0]["text"]
[docs] def stream(
self, prompt: str, stop: Optional[List[str]] = 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) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
8287855dad66-7 | """
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)
self.callback_manager.on_llm_new_token(
token=token, verbose=self.verbose, log_probs=log_probs
)
yield chunk
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
98b6ad15b4a4-0 | Source code for langchain.llms.aleph_alpha
"""Wrapper around Aleph Alpha APIs."""
from typing import Any, Dict, List, Optional, Sequence
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
[docs]class AlephAlpha(LLM):
"""Wrapper around Aleph Alpha large language models.
To use, you should have the ``aleph_alpha_client`` python package installed, and the
environment variable ``ALEPH_ALPHA_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Parameters are explained more in depth here:
https://github.com/Aleph-Alpha/aleph-alpha-client/blob/c14b7dd2b4325c7da0d6a119f6e76385800e097b/aleph_alpha_client/completion.py#L10
Example:
.. code-block:: python
from langchain.llms import AlephAlpha
alpeh_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")
"""
client: Any #: :meta private:
model: Optional[str] = "luminous-base"
"""Model name to use."""
maximum_tokens: int = 64
"""The maximum number of tokens to be generated."""
temperature: float = 0.0 | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
98b6ad15b4a4-1 | temperature: float = 0.0
"""A non-negative float that tunes the degree of randomness in generation."""
top_k: int = 0
"""Number of most likely tokens to consider at each step."""
top_p: float = 0.0
"""Total probability mass of tokens to consider at each step."""
presence_penalty: float = 0.0
"""Penalizes repeated tokens."""
frequency_penalty: float = 0.0
"""Penalizes repeated tokens according to frequency."""
repetition_penalties_include_prompt: Optional[bool] = False
"""Flag deciding whether presence penalty or frequency penalty are
updated from the prompt."""
use_multiplicative_presence_penalty: Optional[bool] = False
"""Flag deciding whether presence penalty is applied
multiplicatively (True) or additively (False)."""
penalty_bias: Optional[str] = None
"""Penalty bias for the completion."""
penalty_exceptions: Optional[List[str]] = None
"""List of strings that may be generated without penalty,
regardless of other penalty settings"""
penalty_exceptions_include_stop_sequences: Optional[bool] = None
"""Should stop_sequences be included in penalty_exceptions."""
best_of: Optional[int] = None
"""returns the one with the "best of" results
(highest log probability per token)
"""
n: int = 1 | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
98b6ad15b4a4-2 | (highest log probability per token)
"""
n: int = 1
"""How many completions to generate for each prompt."""
logit_bias: Optional[Dict[int, float]] = None
"""The logit bias allows to influence the likelihood of generating tokens."""
log_probs: Optional[int] = None
"""Number of top log probabilities to be returned for each generated token."""
tokens: Optional[bool] = False
"""return tokens of completion."""
disable_optimizations: Optional[bool] = False
minimum_tokens: Optional[int] = 0
"""Generate at least this number of tokens."""
echo: bool = False
"""Echo the prompt in the completion."""
use_multiplicative_frequency_penalty: bool = False
sequence_penalty: float = 0.0
sequence_penalty_min_length: int = 2
use_multiplicative_sequence_penalty: bool = False
completion_bias_inclusion: Optional[Sequence[str]] = None
completion_bias_inclusion_first_token_only: bool = False
completion_bias_exclusion: Optional[Sequence[str]] = None
completion_bias_exclusion_first_token_only: bool = False
"""Only consider the first token for the completion_bias_exclusion.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
98b6ad15b4a4-3 | contextual_control_threshold: Optional[float] = None
"""If set to None, attention control parameters only apply to those tokens that have
explicitly been set in the request.
If set to a non-None value, control parameters are also applied to similar tokens.
"""
control_log_additive: Optional[bool] = True
"""True: apply control by adding the log(control_factor) to attention scores.
False: (attention_scores - - attention_scores.min(-1)) * control_factor
"""
repetition_penalties_include_completion: bool = True
"""Flag deciding whether presence penalty or frequency penalty
are updated from the completion."""
raw_completion: bool = False
"""Force the raw completion of the model to be returned."""
aleph_alpha_api_key: Optional[str] = None
"""API key for Aleph Alpha API."""
stop_sequences: Optional[List[str]] = None
"""Stop sequences to use."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
aleph_alpha_api_key = get_from_dict_or_env(
values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY"
)
try:
import aleph_alpha_client | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
98b6ad15b4a4-4 | )
try:
import aleph_alpha_client
values["client"] = aleph_alpha_client.Client(token=aleph_alpha_api_key)
except ImportError:
raise ValueError(
"Could not import aleph_alpha_client python package. "
"Please install it with `pip install aleph_alpha_client`."
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling the Aleph Alpha API."""
return {
"maximum_tokens": self.maximum_tokens,
"temperature": self.temperature,
"top_k": self.top_k,
"top_p": self.top_p,
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"n": self.n,
"repetition_penalties_include_prompt": self.repetition_penalties_include_prompt, # noqa: E501
"use_multiplicative_presence_penalty": self.use_multiplicative_presence_penalty, # noqa: E501
"penalty_bias": self.penalty_bias,
"penalty_exceptions": self.penalty_exceptions,
"penalty_exceptions_include_stop_sequences": self.penalty_exceptions_include_stop_sequences, # noqa: E501 | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
98b6ad15b4a4-5 | "best_of": self.best_of,
"logit_bias": self.logit_bias,
"log_probs": self.log_probs,
"tokens": self.tokens,
"disable_optimizations": self.disable_optimizations,
"minimum_tokens": self.minimum_tokens,
"echo": self.echo,
"use_multiplicative_frequency_penalty": self.use_multiplicative_frequency_penalty, # noqa: E501
"sequence_penalty": self.sequence_penalty,
"sequence_penalty_min_length": self.sequence_penalty_min_length,
"use_multiplicative_sequence_penalty": self.use_multiplicative_sequence_penalty, # noqa: E501
"completion_bias_inclusion": self.completion_bias_inclusion,
"completion_bias_inclusion_first_token_only": self.completion_bias_inclusion_first_token_only, # noqa: E501
"completion_bias_exclusion": self.completion_bias_exclusion,
"completion_bias_exclusion_first_token_only": self.completion_bias_exclusion_first_token_only, # noqa: E501 | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
98b6ad15b4a4-6 | "contextual_control_threshold": self.contextual_control_threshold,
"control_log_additive": self.control_log_additive,
"repetition_penalties_include_completion": self.repetition_penalties_include_completion, # noqa: E501
"raw_completion": self.raw_completion,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "alpeh_alpha"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to Aleph Alpha's completion 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 = alpeh_alpha("Tell me a joke.")
"""
from aleph_alpha_client import CompletionRequest, Prompt
params = self._default_params
if self.stop_sequences is not None and stop is not None:
raise ValueError(
"stop sequences found in both the input and default params."
)
elif self.stop_sequences is not None: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
98b6ad15b4a4-7 | )
elif self.stop_sequences is not None:
params["stop_sequences"] = self.stop_sequences
else:
params["stop_sequences"] = stop
request = CompletionRequest(prompt=Prompt.from_text(prompt), **params)
response = self.client.complete(model=self.model, request=request)
text = response.completions[0].completion
# If stop tokens are provided, Aleph Alpha's endpoint returns them.
# In order to make this consistent with other endpoints, we strip them.
if stop is not None or self.stop_sequences is not None:
text = enforce_stop_tokens(text, params["stop_sequences"])
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/aleph_alpha.html |
f2df98df83b6-0 | Source code for langchain.llms.predictionguard
"""Wrapper around Prediction Guard APIs."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class PredictionGuard(LLM):
"""Wrapper around Prediction Guard large language models.
To use, you should have the ``predictionguard`` python package installed, and the
environment variable ``PREDICTIONGUARD_TOKEN`` set with your access token, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
pgllm = PredictionGuard(name="text-gen-proxy-name", token="my-access-token")
"""
client: Any #: :meta private:
name: Optional[str] = "default-text-gen"
"""Proxy name to use."""
max_tokens: int = 256
"""Denotes the number of tokens to predict per generation."""
temperature: float = 0.75
"""A non-negative float that tunes the degree of randomness in generation."""
token: Optional[str] = None
stop: Optional[List[str]] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator() | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
f2df98df83b6-1 | extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the access token and python package exists in environment."""
token = get_from_dict_or_env(values, "token", "PREDICTIONGUARD_TOKEN")
try:
import predictionguard as pg
values["client"] = pg.Client(token=token)
except ImportError:
raise ValueError(
"Could not import predictionguard python package. "
"Please install it with `pip install predictionguard`."
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Cohere API."""
return {
"max_tokens": self.max_tokens,
"temperature": self.temperature,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"name": self.name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "predictionguard"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to Prediction Guard's model proxy.
Args:
prompt: The prompt to pass into the model.
Returns:
The string generated by the model.
Example:
.. code-block:: python | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
f2df98df83b6-2 | The string generated by the model.
Example:
.. code-block:: python
response = pgllm("Tell me a joke.")
"""
params = self._default_params
if self.stop is not None and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
elif self.stop is not None:
params["stop_sequences"] = self.stop
else:
params["stop_sequences"] = stop
response = self.client.predict(
name=self.name,
data={
"prompt": prompt,
"max_tokens": params["max_tokens"],
"temperature": params["temperature"],
},
)
text = response["text"]
# If stop tokens are provided, Prediction Guard's endpoint returns them.
# In order to make this consistent with other endpoints, we strip them.
if stop is not None or self.stop is not None:
text = enforce_stop_tokens(text, params["stop_sequences"])
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
3eb2453c76c6-0 | 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.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")) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
3eb2453c76c6-1 | 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 | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
3eb2453c76c6-2 | 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. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
3eb2453c76c6-3 | 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: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
3eb2453c76c6-4 | """
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 ValueError(
"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.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
3eb2453c76c6-5 | """Return type of llm."""
return "sagemaker_endpoint"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> 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 {}
_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
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
b74196913ec8-0 | Source code for langchain.llms.huggingface_hub
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
DEFAULT_REPO_ID = "gpt2"
VALID_TASKS = ("text2text-generation", "text-generation")
[docs]class HuggingFaceHub(LLM):
"""Wrapper around HuggingFaceHub models.
To use, you should have the ``huggingface_hub`` python package installed, and the
environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Only supports `text-generation` and `text2text-generation` for now.
Example:
.. code-block:: python
from langchain.llms import HuggingFaceHub
hf = HuggingFaceHub(repo_id="gpt2", huggingfacehub_api_token="my-api-key")
"""
client: Any #: :meta private:
repo_id: str = DEFAULT_REPO_ID
"""Model name to use."""
task: Optional[str] = None
"""Task to call the model with. Should be a task that returns `generated_text`."""
model_kwargs: Optional[dict] = None | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
b74196913ec8-1 | model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
huggingfacehub_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
from huggingface_hub.inference_api import InferenceApi
repo_id = values["repo_id"]
client = InferenceApi(
repo_id=repo_id,
token=huggingfacehub_api_token,
task=values.get("task"),
)
if client.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {client.task}, "
f"currently only {VALID_TASKS} are supported"
)
values["client"] = client
except ImportError:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return { | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
b74196913ec8-2 | _model_kwargs = self.model_kwargs or {}
return {
**{"repo_id": self.repo_id, "task": self.task},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "huggingface_hub"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to HuggingFace Hub's 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 = hf("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
response = self.client(inputs=prompt, params=_model_kwargs)
if "error" in response:
raise ValueError(f"Error raised by inference API: {response['error']}")
if self.client.task == "text-generation":
# Text generation return includes the starter text.
text = response[0]["generated_text"][len(prompt) :]
elif self.client.task == "text2text-generation":
text = response[0]["generated_text"]
else:
raise ValueError(
f"Got invalid task {self.client.task}, " | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
b74196913ec8-3 | f"Got invalid task {self.client.task}, "
f"currently only {VALID_TASKS} are supported"
)
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 huggingface_hub.
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_hub.html |
a65ffb303310-0 | Source code for langchain.llms.writer
"""Wrapper around Writer APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
[docs]class Writer(LLM):
"""Wrapper around Writer large language models.
To use, you should have the environment variable ``WRITER_API_KEY``
set with your API key.
Example:
.. code-block:: python
from langchain import Writer
writer = Writer(model_id="palmyra-base")
"""
model_id: str = "palmyra-base"
"""Model name to use."""
tokens_to_generate: int = 24
"""Max number of tokens to generate."""
logprobs: bool = False
"""Whether to return log probabilities."""
temperature: float = 1.0
"""What sampling temperature to use."""
length: int = 256
"""The maximum number of tokens to generate in the completion."""
top_p: float = 1.0
"""Total probability mass of tokens to consider at each step."""
top_k: int = 1
"""The number of highest probability vocabulary tokens to
keep for top-k-filtering."""
repetition_penalty: float = 1.0
"""Penalizes repeated tokens according to frequency."""
random_seed: int = 0 | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
a65ffb303310-1 | random_seed: int = 0
"""The model generates random results.
Changing the random seed alone will produce a different response
with similar characteristics. It is possible to reproduce results
by fixing the random seed (assuming all other hyperparameters
are also fixed)"""
beam_search_diversity_rate: float = 1.0
"""Only applies to beam search, i.e. when the beam width is >1.
A higher value encourages beam search to return a more diverse
set of candidates"""
beam_width: Optional[int] = None
"""The number of concurrent candidates to keep track of during
beam search"""
length_pentaly: float = 1.0
"""Only applies to beam search, i.e. when the beam width is >1.
Larger values penalize long candidates more heavily, thus preferring
shorter candidates"""
writer_api_key: Optional[str] = None
stop: Optional[List[str]] = None
"""Sequences when completion generation will stop"""
base_url: Optional[str] = None
"""Base url to use, if None decides based on model name."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
writer_api_key = get_from_dict_or_env( | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
a65ffb303310-2 | writer_api_key = get_from_dict_or_env(
values, "writer_api_key", "WRITER_API_KEY"
)
values["writer_api_key"] = writer_api_key
return values
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling Writer API."""
return {
"tokens_to_generate": self.tokens_to_generate,
"stop": self.stop,
"logprobs": self.logprobs,
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
"repetition_penalty": self.repetition_penalty,
"random_seed": self.random_seed,
"beam_search_diversity_rate": self.beam_search_diversity_rate,
"beam_width": self.beam_width,
"length_pentaly": self.length_pentaly,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_id": self.model_id}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "writer"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
a65ffb303310-3 | """Call out to Writer'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.
Example:
.. code-block:: python
response = Writer("Tell me a joke.")
"""
if self.base_url is not None:
base_url = self.base_url
else:
base_url = (
"https://api.llm.writer.com/v1/models/{self.model_id}/completions"
)
response = requests.post(
url=base_url,
headers={
"Authorization": f"Bearer {self.writer_api_key}",
"Content-Type": "application/json",
"Accept": "application/json",
},
json={"prompt": prompt, **self._default_params},
)
text = response.text
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
f2e99c20b819-0 | Source code for langchain.llms.self_hosted
"""Run model inference on self-hosted remote hardware."""
import importlib.util
import logging
import pickle
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
logger = logging.getLogger(__name__)
def _generate_text(
pipeline: Any,
prompt: str,
*args: Any,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> str:
"""Inference function to send to the remote hardware.
Accepts a pipeline callable (or, more likely,
a key pointing to the model on the cluster's object store)
and returns text predictions for each document
in the batch.
"""
text = pipeline(prompt, *args, **kwargs)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
def _send_pipeline_to_device(pipeline: Any, device: int) -> Any:
"""Send a pipeline to a device on the cluster."""
if isinstance(pipeline, str):
with open(pipeline, "rb") as f:
pipeline = pickle.load(f)
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError( | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f2e99c20b819-1 | raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
pipeline.device = torch.device(device)
pipeline.model = pipeline.model.to(pipeline.device)
return pipeline
[docs]class SelfHostedPipeline(LLM):
"""Run model inference on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the ``runhouse`` python package installed.
Example for custom pipeline and inference functions:
.. code-block:: python
from langchain.llms import SelfHostedPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def load_pipeline():
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
return pipeline( | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f2e99c20b819-2 | return pipeline(
"text-generation", model=model, tokenizer=tokenizer,
max_new_tokens=10
)
def inference_fn(pipeline, prompt, stop = None):
return pipeline(prompt)[0]["generated_text"]
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
llm = SelfHostedPipeline(
model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):
.. code-block:: python
from langchain.llms import SelfHostedPipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
my_model = ...
llm = SelfHostedPipeline.from_pipeline(
pipeline=my_model,
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
Example passing model path for larger models:
.. code-block:: python
from langchain.llms import SelfHostedPipeline
import runhouse as rh
import pickle
from transformers import pipeline
generator = pipeline(model="gpt2")
rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"
).save().to(gpu, path="models")
llm = SelfHostedPipeline.from_pipeline(
pipeline="models/pipeline.pkl", | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f2e99c20b819-3 | pipeline="models/pipeline.pkl",
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
"""
pipeline_ref: Any #: :meta private:
client: Any #: :meta private:
inference_fn: Callable = _generate_text #: :meta private:
"""Inference function to send to the remote hardware."""
hardware: Any
"""Remote hardware to send the inference function to."""
model_load_fn: Callable
"""Function to load the model remotely on the server."""
load_fn_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model load function."""
model_reqs: List[str] = ["./", "torch"]
"""Requirements to install on hardware to inference the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any):
"""Init the pipeline with an auxiliary function.
The load function must be in global scope to be imported
and run on the server, i.e. in a module and not a REPL or closure.
Then, initialize the remote inference function.
"""
super().__init__(**kwargs)
try:
import runhouse as rh
except ImportError:
raise ValueError(
"Could not import runhouse python package. "
"Please install it with `pip install runhouse`."
)
remote_load_fn = rh.function(fn=self.model_load_fn).to( | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f2e99c20b819-4 | self.hardware, reqs=self.model_reqs
)
_load_fn_kwargs = self.load_fn_kwargs or {}
self.pipeline_ref = remote_load_fn.remote(**_load_fn_kwargs)
self.client = rh.function(fn=self.inference_fn).to(
self.hardware, reqs=self.model_reqs
)
[docs] @classmethod
def from_pipeline(
cls,
pipeline: Any,
hardware: Any,
model_reqs: Optional[List[str]] = None,
device: int = 0,
**kwargs: Any,
) -> LLM:
"""Init the SelfHostedPipeline from a pipeline object or string."""
if not isinstance(pipeline, str):
logger.warning(
"Serializing pipeline to send to remote hardware. "
"Note, it can be quite slow"
"to serialize and send large models with each execution. "
"Consider sending the pipeline"
"to the cluster and passing the path to the pipeline instead."
)
load_fn_kwargs = {"pipeline": pipeline, "device": device}
return cls(
load_fn_kwargs=load_fn_kwargs,
model_load_fn=_send_pipeline_to_device,
hardware=hardware,
model_reqs=["transformers", "torch"] + (model_reqs or []),
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
f2e99c20b819-5 | def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"hardware": self.hardware},
}
@property
def _llm_type(self) -> str:
return "self_hosted_llm"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
return self.client(pipeline=self.pipeline_ref, prompt=prompt, stop=stop)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
25ebb0dde0fd-0 | Source code for langchain.llms.ai21
"""Wrapper around AI21 APIs."""
from typing import Any, Dict, List, Optional
import requests
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
class AI21PenaltyData(BaseModel):
"""Parameters for AI21 penalty data."""
scale: int = 0
applyToWhitespaces: bool = True
applyToPunctuations: bool = True
applyToNumbers: bool = True
applyToStopwords: bool = True
applyToEmojis: bool = True
[docs]class AI21(LLM):
"""Wrapper around AI21 large language models.
To use, you should have the environment variable ``AI21_API_KEY``
set with your API key.
Example:
.. code-block:: python
from langchain.llms import AI21
ai21 = AI21(model="j2-jumbo-instruct")
"""
model: str = "j2-jumbo-instruct"
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
maxTokens: int = 256
"""The maximum number of tokens to generate in the completion."""
minTokens: int = 0
"""The minimum number of tokens to generate in the completion."""
topP: float = 1.0
"""Total probability mass of tokens to consider at each step."""
presencePenalty: AI21PenaltyData = AI21PenaltyData() | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
25ebb0dde0fd-1 | presencePenalty: AI21PenaltyData = AI21PenaltyData()
"""Penalizes repeated tokens."""
countPenalty: AI21PenaltyData = AI21PenaltyData()
"""Penalizes repeated tokens according to count."""
frequencyPenalty: AI21PenaltyData = AI21PenaltyData()
"""Penalizes repeated tokens according to frequency."""
numResults: int = 1
"""How many completions to generate for each prompt."""
logitBias: Optional[Dict[str, float]] = None
"""Adjust the probability of specific tokens being generated."""
ai21_api_key: Optional[str] = None
stop: Optional[List[str]] = None
base_url: Optional[str] = None
"""Base url to use, if None decides based on model name."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
ai21_api_key = get_from_dict_or_env(values, "ai21_api_key", "AI21_API_KEY")
values["ai21_api_key"] = ai21_api_key
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling AI21 API."""
return { | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
25ebb0dde0fd-2 | """Get the default parameters for calling AI21 API."""
return {
"temperature": self.temperature,
"maxTokens": self.maxTokens,
"minTokens": self.minTokens,
"topP": self.topP,
"presencePenalty": self.presencePenalty.dict(),
"countPenalty": self.countPenalty.dict(),
"frequencyPenalty": self.frequencyPenalty.dict(),
"numResults": self.numResults,
"logitBias": self.logitBias,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ai21"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to AI21'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.
Example:
.. code-block:: python
response = ai21("Tell me a joke.")
"""
if self.stop is not None and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
elif self.stop is not None:
stop = self.stop | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
25ebb0dde0fd-3 | elif self.stop is not None:
stop = self.stop
elif stop is None:
stop = []
if self.base_url is not None:
base_url = self.base_url
else:
if self.model in ("j1-grande-instruct",):
base_url = "https://api.ai21.com/studio/v1/experimental"
else:
base_url = "https://api.ai21.com/studio/v1"
response = requests.post(
url=f"{base_url}/{self.model}/complete",
headers={"Authorization": f"Bearer {self.ai21_api_key}"},
json={"prompt": prompt, "stopSequences": stop, **self._default_params},
)
if response.status_code != 200:
optional_detail = response.json().get("error")
raise ValueError(
f"AI21 /complete call failed with status code {response.status_code}."
f" Details: {optional_detail}"
)
response_json = response.json()
return response_json["completions"][0]["data"]["text"]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
772a0b3a4d28-0 | Source code for langchain.llms.replicate
"""Wrapper around Replicate API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class Replicate(LLM):
"""Wrapper around Replicate models.
To use, you should have the ``replicate`` python package installed,
and the environment variable ``REPLICATE_API_TOKEN`` set with your API token.
You can find your token here: https://replicate.com/account
The model param is required, but any other model parameters can also
be passed in with the format input={model_param: value, ...}
Example:
.. code-block:: python
from langchain.llms import Replicate
replicate = Replicate(model="stability-ai/stable-diffusion: \
27b93a2413e7f36cd83da926f365628\
0b2931564ff050bf9575f1fdf9bcd7478",
input={"image_dimensions": "512x512"})
"""
model: str
input: Dict[str, Any] = Field(default_factory=dict)
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
replicate_api_token: Optional[str] = None
class Config: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
772a0b3a4d28-1 | replicate_api_token: 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."""
replicate_api_token = get_from_dict_or_env(
values, "REPLICATE_API_TOKEN", "REPLICATE_API_TOKEN"
)
values["replicate_api_token"] = replicate_api_token
return values
@property | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
772a0b3a4d28-2 | return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of model."""
return "replicate"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call to replicate endpoint."""
try:
import replicate as replicate_python
except ImportError:
raise ValueError(
"Could not import replicate python package. "
"Please install it with `pip install replicate`."
)
# get the model and version
model_str, version_str = self.model.split(":")
model = replicate_python.models.get(model_str)
version = model.versions.get(version_str)
# sort through the openapi schema to get the name of the first input
input_properties = sorted(
version.openapi_schema["components"]["schemas"]["Input"][
"properties"
].items(),
key=lambda item: item[1].get("x-order", 0),
)
first_input_name = input_properties[0][0]
inputs = {first_input_name: prompt, **self.input}
outputs = replicate_python.run(self.model, input={**inputs})
return outputs[0]
By Harrison Chase
© Copyright 2023, Harrison Chase. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
772a0b3a4d28-3 | return outputs[0]
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
203eefee5f86-0 | Source code for langchain.llms.cohere
"""Wrapper around Cohere APIs."""
import logging
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class Cohere(LLM):
"""Wrapper around Cohere large language models.
To use, you should have the ``cohere`` python package installed, and the
environment variable ``COHERE_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.llms import Cohere
cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key")
"""
client: Any #: :meta private:
model: Optional[str] = None
"""Model name to use."""
max_tokens: int = 256
"""Denotes the number of tokens to predict per generation."""
temperature: float = 0.75
"""A non-negative float that tunes the degree of randomness in generation."""
k: int = 0
"""Number of most likely tokens to consider at each step."""
p: int = 1
"""Total probability mass of tokens to consider at each step."""
frequency_penalty: float = 0.0 | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
203eefee5f86-1 | frequency_penalty: float = 0.0
"""Penalizes repeated tokens according to frequency. Between 0 and 1."""
presence_penalty: float = 0.0
"""Penalizes repeated tokens. Between 0 and 1."""
truncate: Optional[str] = None
"""Specify how the client handles inputs longer than the maximum token
length: Truncate from START, END or NONE"""
cohere_api_key: Optional[str] = None
stop: Optional[List[str]] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
cohere_api_key = get_from_dict_or_env(
values, "cohere_api_key", "COHERE_API_KEY"
)
try:
import cohere
values["client"] = cohere.Client(cohere_api_key)
except ImportError:
raise ValueError(
"Could not import cohere python package. "
"Please install it with `pip install cohere`."
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Cohere API."""
return {
"max_tokens": self.max_tokens,
"temperature": self.temperature,
"k": self.k,
"p": self.p, | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
203eefee5f86-2 | "k": self.k,
"p": self.p,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"truncate": self.truncate,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model": self.model}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "cohere"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to Cohere's generate 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 = cohere("Tell me a joke.")
"""
params = self._default_params
if self.stop is not None and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
elif self.stop is not None:
params["stop_sequences"] = self.stop
else:
params["stop_sequences"] = stop
response = self.client.generate(model=self.model, prompt=prompt, **params)
text = response.generations[0].text | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
203eefee5f86-3 | text = response.generations[0].text
# If stop tokens are provided, Cohere's endpoint returns them.
# In order to make this consistent with other endpoints, we strip them.
if stop is not None or self.stop is not None:
text = enforce_stop_tokens(text, params["stop_sequences"])
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
db76613f6b67-0 | Source code for langchain.llms.rwkv
"""Wrapper for the RWKV model.
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
[docs]class RWKV(LLM, BaseModel):
r"""Wrapper around RWKV language models.
To use, you should have the ``rwkv`` python package installed, the
pre-trained model file, and the model's config information.
Example:
.. code-block:: python
from langchain.llms import RWKV
model = RWKV(model="./models/rwkv-3b-fp16.bin", strategy="cpu fp32")
# Simplest invocation
response = model("Once upon a time, ")
"""
model: str
"""Path to the pre-trained RWKV model file."""
tokens_path: str
"""Path to the RWKV tokens file."""
strategy: str = "cpu fp32"
"""Token context window."""
rwkv_verbose: bool = True
"""Print debug information."""
temperature: float = 1.0
"""The temperature to use for sampling.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
db76613f6b67-1 | temperature: float = 1.0
"""The temperature to use for sampling."""
top_p: float = 0.5
"""The top-p value to use for sampling."""
penalty_alpha_frequency: float = 0.4
"""Positive values penalize new tokens based on their existing frequency
in the text so far, decreasing the model's likelihood to repeat the same
line verbatim.."""
penalty_alpha_presence: float = 0.4
"""Positive values penalize new tokens based on whether they appear
in the text so far, increasing the model's likelihood to talk about
new topics.."""
CHUNK_LEN: int = 256
"""Batch size for prompt processing."""
max_tokens_per_generation: int = 256
"""Maximum number of tokens to generate."""
client: Any = None #: :meta private:
tokenizer: Any = None #: :meta private:
pipeline: Any = None #: :meta private:
model_tokens: Any = None #: :meta private:
model_state: Any = None #: :meta private:
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
"verbose": self.verbose,
"top_p": self.top_p,
"temperature": self.temperature, | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
db76613f6b67-2 | "temperature": self.temperature,
"penalty_alpha_frequency": self.penalty_alpha_frequency,
"penalty_alpha_presence": self.penalty_alpha_presence,
"CHUNK_LEN": self.CHUNK_LEN,
"max_tokens_per_generation": self.max_tokens_per_generation,
}
@staticmethod
def _rwkv_param_names() -> Set[str]:
"""Get the identifying parameters."""
return {
"verbose",
}
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ValueError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
try:
from rwkv.model import RWKV as RWKVMODEL
from rwkv.utils import PIPELINE
values["tokenizer"] = tokenizers.Tokenizer.from_file(values["tokens_path"])
rwkv_keys = cls._rwkv_param_names()
model_kwargs = {k: v for k, v in values.items() if k in rwkv_keys}
model_kwargs["verbose"] = values["rwkv_verbose"]
values["client"] = RWKVMODEL( | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
db76613f6b67-3 | values["client"] = RWKVMODEL(
values["model"], strategy=values["strategy"], **model_kwargs
)
values["pipeline"] = PIPELINE(values["client"], values["tokens_path"])
except ImportError:
raise ValueError(
"Could not import rwkv python package. "
"Please install it with `pip install rwkv`."
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
"model": self.model,
**self._default_params,
**{k: v for k, v in self.__dict__.items() if k in RWKV._rwkv_param_names()},
}
@property
def _llm_type(self) -> str:
"""Return the type of llm."""
return "rwkv-4"
def run_rnn(self, _tokens: List[str], newline_adj: int = 0) -> Any:
AVOID_REPEAT_TOKENS = []
AVOID_REPEAT = "，：？！"
for i in AVOID_REPEAT:
dd = self.pipeline.encode(i)
assert len(dd) == 1
AVOID_REPEAT_TOKENS += dd
tokens = [int(x) for x in _tokens]
self.model_tokens += tokens
out: Any = None
while len(tokens) > 0: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
db76613f6b67-4 | out: Any = None
while len(tokens) > 0:
out, self.model_state = self.client.forward(
tokens[: self.CHUNK_LEN], self.model_state
)
tokens = tokens[self.CHUNK_LEN :]
END_OF_LINE = 187
out[END_OF_LINE] += newline_adj # adjust \n probability
if self.model_tokens[-1] in AVOID_REPEAT_TOKENS:
out[self.model_tokens[-1]] = -999999999
return out
def rwkv_generate(self, prompt: str) -> str:
self.model_state = None
self.model_tokens = []
logits = self.run_rnn(self.tokenizer.encode(prompt).ids)
begin = len(self.model_tokens)
out_last = begin
occurrence: Dict = {}
decoded = ""
for i in range(self.max_tokens_per_generation):
for n in occurrence:
logits[n] -= (
self.penalty_alpha_presence
+ occurrence[n] * self.penalty_alpha_frequency
)
token = self.pipeline.sample_logits(
logits, temperature=self.temperature, top_p=self.top_p
)
END_OF_TEXT = 0
if token == END_OF_TEXT:
break
if token not in occurrence:
occurrence[token] = 1
else:
occurrence[token] += 1
logits = self.run_rnn([token]) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
db76613f6b67-5 | logits = self.run_rnn([token])
xxx = self.tokenizer.decode(self.model_tokens[out_last:])
if "\ufffd" not in xxx: # avoid utf-8 display issues
decoded += xxx
out_last = begin + i + 1
if i >= self.max_tokens_per_generation - 100:
break
return decoded
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
r"""RWKV generation
Args:
prompt: The prompt to pass into the model.
stop: A list of strings to stop generation when encountered.
Returns:
The string generated by the model.
Example:
.. code-block:: python
prompt = "Once upon a time, "
response = model(prompt, n_predict=55)
"""
text = self.rwkv_generate(prompt)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
74ae835e2027-0 | Source code for langchain.llms.openai
"""Wrapper around OpenAI APIs."""
from __future__ import annotations
import logging
import sys
import warnings
from typing import (
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,
Union,
)
from pydantic import Extra, Field, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.llms.base import BaseLLM
from langchain.schema import Generation, LLMResult
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def update_token_usage(
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
) -> None:
"""Update token usage."""
_keys_to_use = keys.intersection(response["usage"])
for _key in _keys_to_use:
if _key not in token_usage:
token_usage[_key] = response["usage"][_key]
else:
token_usage[_key] += response["usage"][_key]
def _update_response(response: Dict[str, Any], stream_response: Dict[str, Any]) -> None:
"""Update response from the stream response.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-1 | """Update response from the stream response."""
response["choices"][0]["text"] += stream_response["choices"][0]["text"]
response["choices"][0]["finish_reason"] = stream_response["choices"][0][
"finish_reason"
]
response["choices"][0]["logprobs"] = stream_response["choices"][0]["logprobs"]
def _streaming_response_template() -> Dict[str, Any]:
return {
"choices": [
{
"text": "",
"finish_reason": None,
"logprobs": None,
}
]
}
def _create_retry_decorator(llm: Union[BaseOpenAI, OpenAIChat]) -> Callable[[Any], Any]:
import openai
min_seconds = 4
max_seconds = 10
# Wait 2^x * 1 second between each retry starting with
# 4 seconds, then up to 10 seconds, then 10 seconds afterwards
return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_if_exception_type(openai.error.APIConnectionError) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-2 | | retry_if_exception_type(openai.error.RateLimitError)
| retry_if_exception_type(openai.error.ServiceUnavailableError)
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)
def completion_with_retry(llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return llm.client.create(**kwargs)
return _completion_with_retry(**kwargs)
async def acompletion_with_retry(
llm: Union[BaseOpenAI, OpenAIChat], **kwargs: Any
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
# Use OpenAI's async api https://github.com/openai/openai-python#async-api
return await llm.client.acreate(**kwargs)
return await _completion_with_retry(**kwargs)
class BaseOpenAI(BaseLLM): | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-3 | class BaseOpenAI(BaseLLM):
"""Wrapper around OpenAI large language models."""
client: Any #: :meta private:
model_name: str = "text-davinci-003"
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
max_tokens: int = 256
"""The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size."""
top_p: float = 1
"""Total probability mass of tokens to consider at each step."""
frequency_penalty: float = 0
"""Penalizes repeated tokens according to frequency."""
presence_penalty: float = 0
"""Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None
openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-4 | """Batch size to use when passing multiple documents to generate."""
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for requests to OpenAI completion API. Default is 600 seconds."""
logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
"""Adjust the probability of specific tokens being generated."""
max_retries: int = 6
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowed。"""
def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore
"""Initialize the OpenAI object."""
model_name = data.get("model_name", "")
if model_name.startswith("gpt-3.5-turbo") or model_name.startswith("gpt-4"):
warnings.warn(
"You are trying to use a chat model. This way of initializing it is "
"no longer supported. Instead, please use: "
"`from langchain.chat_models import ChatOpenAI`"
)
return OpenAIChat(**data) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-5 | )
return OpenAIChat(**data)
return super().__new__(cls)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.ignore
@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"""WARNING! {field_name} is not default parameter.
{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."""
openai_api_key = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
openai_api_base = get_from_dict_or_env(
values, | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-6 | openai_api_base = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
openai_organization = get_from_dict_or_env(
values,
"openai_organization",
"OPENAI_ORGANIZATION",
default="",
)
try:
import openai
openai.api_key = openai_api_key
if openai_api_base:
openai.api_base = openai_api_base
if openai_organization:
openai.organization = openai_organization
values["client"] = openai.Completion
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if values["streaming"] and values["n"] > 1:
raise ValueError("Cannot stream results when n > 1.")
if values["streaming"] and values["best_of"] > 1:
raise ValueError("Cannot stream results when best_of > 1.")
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
normal_params = {
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"top_p": self.top_p,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"n": self.n,
"best_of": self.best_of, | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-7 | "n": self.n,
"best_of": self.best_of,
"request_timeout": self.request_timeout,
"logit_bias": self.logit_bias,
}
return {**normal_params, **self.model_kwargs}
def _generate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Call out to OpenAI's endpoint with k unique prompts.
Args:
prompts: The prompts to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The full LLM output.
Example:
.. code-block:: python
response = openai.generate(["Tell me a joke."])
"""
# TODO: write a unit test for this
params = self._invocation_params
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
params["stream"] = True
response = _streaming_response_template()
for stream_resp in completion_with_retry( | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-8 | for stream_resp in completion_with_retry(
self, prompt=_prompts, **params
):
self.callback_manager.on_llm_new_token(
stream_resp["choices"][0]["text"],
verbose=self.verbose,
logprobs=stream_resp["choices"][0]["logprobs"],
)
_update_response(response, stream_resp)
choices.extend(response["choices"])
else:
response = completion_with_retry(self, prompt=_prompts, **params)
choices.extend(response["choices"])
if not self.streaming:
# Can't update token usage if streaming
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
async def _agenerate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Call out to OpenAI's endpoint async with k unique prompts."""
params = self._invocation_params
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-9 | # Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
params["stream"] = True
response = _streaming_response_template()
async for stream_resp in await acompletion_with_retry(
self, prompt=_prompts, **params
):
if self.callback_manager.is_async:
await self.callback_manager.on_llm_new_token(
stream_resp["choices"][0]["text"],
verbose=self.verbose,
logprobs=stream_resp["choices"][0]["logprobs"],
)
else:
self.callback_manager.on_llm_new_token(
stream_resp["choices"][0]["text"],
verbose=self.verbose,
logprobs=stream_resp["choices"][0]["logprobs"],
)
_update_response(response, stream_resp)
choices.extend(response["choices"])
else:
response = await acompletion_with_retry(self, prompt=_prompts, **params)
choices.extend(response["choices"])
if not self.streaming:
# Can't update token usage if streaming | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-10 | if not self.streaming:
# Can't update token usage if streaming
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(choices, prompts, token_usage)
def get_sub_prompts(
self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
if params["max_tokens"] == -1:
if len(prompts) != 1:
raise ValueError(
"max_tokens set to -1 not supported for multiple inputs."
)
params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
sub_prompts = [
prompts[i : i + self.batch_size]
for i in range(0, len(prompts), self.batch_size)
]
return sub_prompts
def create_llm_result(
self, choices: Any, prompts: List[str], token_usage: Dict[str, int]
) -> LLMResult:
"""Create the LLMResult from the choices and prompts."""
generations = []
for i, _ in enumerate(prompts): | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-11 | generations = []
for i, _ in enumerate(prompts):
sub_choices = choices[i * self.n : (i + 1) * self.n]
generations.append(
[
Generation(
text=choice["text"],
generation_info=dict(
finish_reason=choice.get("finish_reason"),
logprobs=choice.get("logprobs"),
),
)
for choice in sub_choices
]
)
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
return LLMResult(generations=generations, llm_output=llm_output)
def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator:
"""Call OpenAI with streaming flag and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.
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 from OpenAI.
Example:
.. code-block:: python
generator = openai.stream("Tell me a joke.")
for token in generator:
yield token
"""
params = self.prep_streaming_params(stop)
generator = self.client.create(prompt=prompt, **params)
return generator
def prep_streaming_params(self, stop: Optional[List[str]] = None) -> Dict[str, Any]: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-12 | """Prepare the params for streaming."""
params = self._invocation_params
if params["best_of"] != 1:
raise ValueError("OpenAI only supports best_of == 1 for streaming")
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
params["stream"] = True
return params
@property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
return self._default_params
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "openai"
def get_num_tokens(self, text: str) -> int:
"""Calculate num tokens with tiktoken package."""
# tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install tiktoken`."
) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-13 | "Please install it with `pip install tiktoken`."
)
enc = tiktoken.encoding_for_model(self.model_name)
tokenized_text = enc.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
# calculate the number of tokens in the encoded text
return len(tokenized_text)
def modelname_to_contextsize(self, modelname: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a model.
Args:
modelname: The modelname we want to know the context size for.
Returns:
The maximum context size
Example:
.. code-block:: python
max_tokens = openai.modelname_to_contextsize("text-davinci-003")
"""
model_token_mapping = {
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 2040,
"babbage": 2049,
"text-curie-001": 2049,
"curie": 2049,
"davinci": 2049,
"text-davinci-003": 4097,
"text-davinci-002": 4097,
"code-davinci-002": 8001,
"code-davinci-001": 8001, | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-14 | "code-davinci-001": 8001,
"code-cushman-002": 2048,
"code-cushman-001": 2048,
}
context_size = model_token_mapping.get(modelname, None)
if context_size is None:
raise ValueError(
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
"Known models are: " + ", ".join(model_token_mapping.keys())
)
return context_size
def max_tokens_for_prompt(self, prompt: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a prompt.
Args:
prompt: The prompt to pass into the model.
Returns:
The maximum number of tokens to generate for a prompt.
Example:
.. code-block:: python
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
"""
num_tokens = self.get_num_tokens(prompt)
# get max context size for model by name
max_size = self.modelname_to_contextsize(self.model_name)
return max_size - num_tokens
[docs]class OpenAI(BaseOpenAI):
"""Wrapper around OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-15 | Example:
.. code-block:: python
from langchain.llms import OpenAI
openai = OpenAI(model_name="text-davinci-003")
"""
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params}
[docs]class AzureOpenAI(BaseOpenAI):
"""Wrapper around Azure-specific OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import AzureOpenAI
openai = AzureOpenAI(model_name="text-davinci-003")
"""
deployment_name: str = ""
"""Deployment name to use."""
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {
**{"deployment_name": self.deployment_name},
**super()._identifying_params,
}
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"engine": self.deployment_name}, **super()._invocation_params}
[docs]class OpenAIChat(BaseLLM):
"""Wrapper around OpenAI Chat large language models. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-16 | """Wrapper around OpenAI Chat large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain.llms import OpenAIChat
openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
"""
client: Any #: :meta private:
model_name: str = "gpt-3.5-turbo"
"""Model name to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[str] = None
openai_api_base: Optional[str] = None
max_retries: int = 6
"""Maximum number of retries to make when generating."""
prefix_messages: List = Field(default_factory=list)
"""Series of messages for Chat input."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowed。""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-17 | """Set of special tokens that are not allowed。"""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.ignore
@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.")
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."""
openai_api_key = get_from_dict_or_env(
values, "openai_api_key", "OPENAI_API_KEY"
)
openai_api_base = get_from_dict_or_env(
values,
"openai_api_base",
"OPENAI_API_BASE",
default="",
)
openai_organization = get_from_dict_or_env(
values, "openai_organization", "OPENAI_ORGANIZATION", default=""
)
try: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-18 | )
try:
import openai
openai.api_key = openai_api_key
if openai_api_base:
openai.api_base = openai_api_base
if openai_organization:
openai.organization = openai_organization
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
try:
values["client"] = openai.ChatCompletion
except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
warnings.warn(
"You are trying to use a chat model. This way of initializing it is "
"no longer supported. Instead, please use: "
"`from langchain.chat_models import ChatOpenAI`"
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return self.model_kwargs
def _get_chat_params(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> Tuple:
if len(prompts) > 1:
raise ValueError(
f"OpenAIChat currently only supports single prompt, got {prompts}"
)
messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}] | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-19 | params: Dict[str, Any] = {**{"model": self.model_name}, **self._default_params}
if stop is not None:
if "stop" in params:
raise ValueError("`stop` found in both the input and default params.")
params["stop"] = stop
if params.get("max_tokens") == -1:
# for ChatGPT api, omitting max_tokens is equivalent to having no limit
del params["max_tokens"]
return messages, params
def _generate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
messages, params = self._get_chat_params(prompts, stop)
if self.streaming:
response = ""
params["stream"] = True
for stream_resp in completion_with_retry(self, messages=messages, **params):
token = stream_resp["choices"][0]["delta"].get("content", "")
response += token
self.callback_manager.on_llm_new_token(
token,
verbose=self.verbose,
)
return LLMResult(
generations=[[Generation(text=response)]],
)
else:
full_response = completion_with_retry(self, messages=messages, **params)
llm_output = {
"token_usage": full_response["usage"],
"model_name": self.model_name,
}
return LLMResult(
generations=[ | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-20 | }
return LLMResult(
generations=[
[Generation(text=full_response["choices"][0]["message"]["content"])]
],
llm_output=llm_output,
)
async def _agenerate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
messages, params = self._get_chat_params(prompts, stop)
if self.streaming:
response = ""
params["stream"] = True
async for stream_resp in await acompletion_with_retry(
self, messages=messages, **params
):
token = stream_resp["choices"][0]["delta"].get("content", "")
response += token
if self.callback_manager.is_async:
await self.callback_manager.on_llm_new_token(
token,
verbose=self.verbose,
)
else:
self.callback_manager.on_llm_new_token(
token,
verbose=self.verbose,
)
return LLMResult(
generations=[[Generation(text=response)]],
)
else:
full_response = await acompletion_with_retry(
self, messages=messages, **params
)
llm_output = {
"token_usage": full_response["usage"],
"model_name": self.model_name,
}
return LLMResult(
generations=[ | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-21 | }
return LLMResult(
generations=[
[Generation(text=full_response["choices"][0]["message"]["content"])]
],
llm_output=llm_output,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "openai-chat"
[docs] def get_num_tokens(self, text: str) -> int:
"""Calculate num tokens with tiktoken package."""
# tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
try:
import tiktoken
except ImportError:
raise ValueError(
"Could not import tiktoken python package. "
"This is needed in order to calculate get_num_tokens. "
"Please install it with `pip install tiktoken`."
)
# create a GPT-3.5-Turbo encoder instance
enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
# encode the text using the GPT-3.5-Turbo encoder
tokenized_text = enc.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
74ae835e2027-22 | disallowed_special=self.disallowed_special,
)
# calculate the number of tokens in the encoded text
return len(tokenized_text)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/openai.html |
82ae45670c26-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
VALID_TASKS = ("text2text-generation", "text-generation")
[docs]class HuggingFaceEndpoint(LLM):
"""Wrapper around HuggingFaceHub Inference Endpoints.
To use, you should have the ``huggingface_hub`` python package installed, and the
environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Only supports `text-generation` and `text2text-generation` for now.
Example:
.. code-block:: python
from langchain.llms import HuggingFaceEndpoint
endpoint_url = (
"https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
)
hf = HuggingFaceEndpoint(
endpoint_url=endpoint_url,
huggingfacehub_api_token="my-api-key"
)
"""
endpoint_url: str = ""
"""Endpoint URL to use."""
task: Optional[str] = None
"""Task to call the model with. Should be a task that returns `generated_text`.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
82ae45670c26-1 | model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
huggingfacehub_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = get_from_dict_or_env(
values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN"
)
try:
from huggingface_hub.hf_api import HfApi
try:
HfApi(
endpoint="https://huggingface.co", # Can be a Private Hub endpoint.
token=huggingfacehub_api_token,
).whoami()
except Exception as e:
raise ValueError(
"Could not authenticate with huggingface_hub. "
"Please check your API token."
) from e
except ImportError:
raise ValueError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_url": self.endpoint_url, "task": self.task}, | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
82ae45670c26-2 | **{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "huggingface_endpoint"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to HuggingFace Hub's 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 = hf("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
# payload samples
parameter_payload = {"inputs": prompt, "parameters": _model_kwargs}
# HTTP headers for authorization
headers = {
"Authorization": f"Bearer {self.huggingfacehub_api_token}",
"Content-Type": "application/json",
}
# send request
try:
response = requests.post(
self.endpoint_url, headers=headers, json=parameter_payload
)
except requests.exceptions.RequestException as e: # This is the correct syntax
raise ValueError(f"Error raised by inference endpoint: {e}")
generated_text = response.json()
if "error" in generated_text:
raise ValueError(
f"Error raised by inference API: {generated_text['error']}"
) | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
82ae45670c26-3 | )
if self.task == "text-generation":
# Text generation return includes the starter text.
text = generated_text[0]["generated_text"][len(prompt) :]
elif self.task == "text2text-generation":
text = generated_text[0]["generated_text"]
else:
raise ValueError(
f"Got invalid task {self.task}, "
f"currently only {VALID_TASKS} are supported"
)
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 huggingface_hub.
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
7d2947ec3dcd-0 | Source code for langchain.llms.deepinfra
"""Wrapper around DeepInfra APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
DEFAULT_MODEL_ID = "google/flan-t5-xl"
[docs]class DeepInfra(LLM):
"""Wrapper around DeepInfra deployed models.
To use, you should have the ``requests`` python package installed, and the
environment variable ``DEEPINFRA_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Only supports `text-generation` and `text2text-generation` for now.
Example:
.. code-block:: python
from langchain.llms import DeepInfra
di = DeepInfra(model_id="google/flan-t5-xl",
deepinfra_api_token="my-api-key")
"""
model_id: str = DEFAULT_MODEL_ID
model_kwargs: Optional[dict] = None
deepinfra_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
7d2947ec3dcd-1 | """Validate that api key and python package exists in environment."""
deepinfra_api_token = get_from_dict_or_env(
values, "deepinfra_api_token", "DEEPINFRA_API_TOKEN"
)
values["deepinfra_api_token"] = deepinfra_api_token
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "deepinfra"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to DeepInfra's inference API 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 = di("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
res = requests.post(
f"https://api.deepinfra.com/v1/inference/{self.model_id}",
headers={
"Authorization": f"bearer {self.deepinfra_api_token}",
"Content-Type": "application/json", | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
7d2947ec3dcd-2 | "Content-Type": "application/json",
},
json={"input": prompt, **_model_kwargs},
)
if res.status_code != 200:
raise ValueError("Error raised by inference API")
text = res.json()[0]["generated_text"]
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/deepinfra.html |
5f233327267b-0 | Source code for langchain.llms.self_hosted_hugging_face
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware."""
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from pydantic import Extra
from langchain.llms.self_hosted import SelfHostedPipeline
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = "gpt2"
DEFAULT_TASK = "text-generation"
VALID_TASKS = ("text2text-generation", "text-generation")
logger = logging.getLogger(__name__)
def _generate_text(
pipeline: Any,
prompt: str,
*args: Any,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> str:
"""Inference function to send to the remote hardware.
Accepts a Hugging Face pipeline (or more likely,
a key pointing to such a pipeline on the cluster's object store)
and returns generated text.
"""
response = pipeline(prompt, *args, **kwargs)
if pipeline.task == "text-generation":
# Text generation return includes the starter text.
text = response[0]["generated_text"][len(prompt) :]
elif pipeline.task == "text2text-generation":
text = response[0]["generated_text"]
else:
raise ValueError(
f"Got invalid task {pipeline.task}, " | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5f233327267b-1 | f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
if stop is not None:
text = enforce_stop_tokens(text, stop)
return text
def _load_transformer(
model_id: str = DEFAULT_MODEL_ID,
task: str = DEFAULT_TASK,
device: int = 0,
model_kwargs: Optional[dict] = None,
) -> Any:
"""Inference function to send to the remote hardware.
Accepts a huggingface model_id and returns a pipeline for the task.
"""
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import pipeline as hf_pipeline
_model_kwargs = model_kwargs or {}
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try:
if task == "text-generation":
model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
elif task == "text2text-generation":
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
) from e | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5f233327267b-2 | ) from e
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
pipeline = hf_pipeline(
task=task,
model=model,
tokenizer=tokenizer,
device=device,
model_kwargs=_model_kwargs,
)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
return pipeline
[docs]class SelfHostedHuggingFaceLLM(SelfHostedPipeline):
"""Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5f233327267b-3 | by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the ``runhouse`` python package installed.
Only supports `text-generation` and `text2text-generation` for now.
Example using from_model_id:
.. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-large", task="text2text-generation",
hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):
.. code-block:: python
from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline():
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer
)
return pipe
hf = SelfHostedHuggingFaceLLM(
model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
"""
model_id: str = DEFAULT_MODEL_ID
"""Hugging Face model_id to load the model.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5f233327267b-4 | """Hugging Face model_id to load the model."""
task: str = DEFAULT_TASK
"""Hugging Face task (either "text-generation" or "text2text-generation")."""
device: int = 0
"""Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc."""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
hardware: Any
"""Remote hardware to send the inference function to."""
model_reqs: List[str] = ["./", "transformers", "torch"]
"""Requirements to install on hardware to inference the model."""
model_load_fn: Callable = _load_transformer
"""Function to load the model remotely on the server."""
inference_fn: Callable = _generate_text #: :meta private:
"""Inference function to send to the remote hardware."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
def __init__(self, **kwargs: Any):
"""Construct the pipeline remotely using an auxiliary function.
The load function needs to be importable to be imported
and run on the server, i.e. in a module and not a REPL or closure.
Then, initialize the remote inference function.
"""
load_fn_kwargs = {
"model_id": kwargs.get("model_id", DEFAULT_MODEL_ID), | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
5f233327267b-5 | "task": kwargs.get("task", DEFAULT_TASK),
"device": kwargs.get("device", 0),
"model_kwargs": kwargs.get("model_kwargs", None),
}
super().__init__(load_fn_kwargs=load_fn_kwargs, **kwargs)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
return "selfhosted_huggingface_pipeline"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
return self.client(pipeline=self.pipeline_ref, prompt=prompt, stop=stop)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
6ed5c823f05f-0 | Source code for langchain.llms.forefrontai
"""Wrapper around ForefrontAI APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
[docs]class ForefrontAI(LLM):
"""Wrapper around ForefrontAI large language models.
To use, you should have the environment variable ``FOREFRONTAI_API_KEY``
set with your API key.
Example:
.. code-block:: python
from langchain.llms import ForefrontAI
forefrontai = ForefrontAI(endpoint_url="")
"""
endpoint_url: str = ""
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
length: int = 256
"""The maximum number of tokens to generate in the completion."""
top_p: float = 1.0
"""Total probability mass of tokens to consider at each step."""
top_k: int = 40
"""The number of highest probability vocabulary tokens to
keep for top-k-filtering."""
repetition_penalty: int = 1
"""Penalizes repeated tokens according to frequency."""
forefrontai_api_key: Optional[str] = None
base_url: Optional[str] = None
"""Base url to use, if None decides based on model name."""
class Config: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
6ed5c823f05f-1 | class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key exists in environment."""
forefrontai_api_key = get_from_dict_or_env(
values, "forefrontai_api_key", "FOREFRONTAI_API_KEY"
)
values["forefrontai_api_key"] = forefrontai_api_key
return values
@property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling ForefrontAI API."""
return {
"temperature": self.temperature,
"length": self.length,
"top_p": self.top_p,
"top_k": self.top_k,
"repetition_penalty": self.repetition_penalty,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"endpoint_url": self.endpoint_url}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "forefrontai"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Call out to ForefrontAI's complete endpoint.
Args:
prompt: The prompt to pass into the model. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
6ed5c823f05f-2 | 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 = ForefrontAI("Tell me a joke.")
"""
response = requests.post(
url=self.endpoint_url,
headers={
"Authorization": f"Bearer {self.forefrontai_api_key}",
"Content-Type": "application/json",
},
json={"text": prompt, **self._default_params},
)
response_json = response.json()
text = response_json["result"][0]["completion"]
if stop is not None:
# I believe this is required since the stop tokens
# are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
cc0243e010d3-0 | Source code for langchain.llms.huggingface_pipeline
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import Extra
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = "gpt2"
DEFAULT_TASK = "text-generation"
VALID_TASKS = ("text2text-generation", "text-generation")
logger = logging.getLogger(__name__)
[docs]class HuggingFacePipeline(LLM):
"""Wrapper around HuggingFace Pipeline API.
To use, you should have the ``transformers`` python package installed.
Only supports `text-generation` and `text2text-generation` for now.
Example using from_model_id:
.. code-block:: python
from langchain.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation"
)
Example passing pipeline in directly:
.. code-block:: python
from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline( | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
cc0243e010d3-1 | pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
"""
pipeline: Any #: :meta private:
model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
[docs] @classmethod
def from_model_id(
cls,
model_id: str,
task: str,
device: int = -1,
model_kwargs: Optional[dict] = None,
**kwargs: Any,
) -> LLM:
"""Construct the pipeline object from model_id and task."""
try:
from transformers import (
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers import pipeline as hf_pipeline
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"Please install it with `pip install transformers`."
)
_model_kwargs = model_kwargs or {}
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try:
if task == "text-generation":
model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
elif task == "text2text-generation": | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
cc0243e010d3-2 | elif task == "text2text-generation":
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
) from e
if importlib.util.find_spec("torch") is not None:
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 (default) for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
pipeline = hf_pipeline(
task=task,
model=model,
tokenizer=tokenizer,
device=device,
model_kwargs=_model_kwargs,
)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {pipeline.task}, " | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
cc0243e010d3-3 | f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_id": self.model_id},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str:
return "huggingface_pipeline"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
response = self.pipeline(prompt)
if self.pipeline.task == "text-generation":
# Text generation return includes the starter text.
text = response[0]["generated_text"][len(prompt) :]
elif self.pipeline.task == "text2text-generation":
text = response[0]["generated_text"]
else:
raise ValueError(
f"Got invalid task {self.pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
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 huggingface_hub.
text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
cc0243e010d3-4 | text = enforce_stop_tokens(text, stop)
return text
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 26, 2023. | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
68b59c6bd761-0 | Source code for langchain.llms.stochasticai
"""Wrapper around StochasticAI APIs."""
import logging
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
[docs]class StochasticAI(LLM):
"""Wrapper around StochasticAI large language models.
To use, you should have the environment variable ``STOCHASTICAI_API_KEY``
set with your API key.
Example:
.. code-block:: python
from langchain.llms import StochasticAI
stochasticai = StochasticAI(api_url="")
"""
api_url: str = ""
"""Model name to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not
explicitly specified."""
stochasticai_api_key: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
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.""" | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
68b59c6bd761-1 | """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 exists in environment."""
stochasticai_api_key = get_from_dict_or_env(
values, "stochasticai_api_key", "STOCHASTICAI_API_KEY"
)
values["stochasticai_api_key"] = stochasticai_api_key
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"endpoint_url": self.api_url},
**{"model_kwargs": self.model_kwargs},
}
@property
def _llm_type(self) -> str: | /content/https://python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
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