id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
e803b998817f-7 | )
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"]... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
c642d54e6b0f-0 | Source code for langchain.llms.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import Any, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms import OpenAI, OpenAIChat
from langchain.schema import LLMR... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-1 | parameters:
``pl_tags``: List of strings to tag the request with.
``return_pl_id``: If True, the PromptLayer request ID will be
returned in the ``generation_info`` field of the
``Generation`` object.
Example:
.. code-block:: python
from langchain.llms impo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-2 | ) -> LLMResult:
"""Call OpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(prompts, stop, run_manager)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-3 | params,
self.pl_tags,
resp,
request_start_time,
request_end_time,
get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info is None or not is... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-4 | generated_responses = await super()._agenerate(prompts, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": gene... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-5 | if self.return_pl_id:
if generation.generation_info is None or not isinstance(
generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-6 | ``return_pl_id``: If True, the PromptLayer request ID will be
returned in the ``generation_info`` field of the
``Generation`` object.
Example:
.. code-block:: python
from langchain.llms import PromptLayerOpenAIChat
openaichat = PromptLayerOpenAIChat(model_name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-7 | from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(prompts, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
for i in range(len(prompts)):
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-8 | get_api_key(),
return_pl_id=self.return_pl_id,
)
if self.return_pl_id:
if generation.generation_info is None or not isinstance(
generation.generation_info, dict
):
generation.generation_info = {}
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-9 | for i in range(len(prompts)):
prompt = prompts[i]
generation = generated_responses.generations[i][0]
resp = {
"text": generation.text,
"llm_output": generated_responses.llm_output,
}
params = {**self._identifying_params, **kwarg... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c642d54e6b0f-10 | generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
53999eb5b92f-0 | Source code for langchain.llms.vertexai
"""Wrapper around Google VertexAI models."""
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from pydantic import BaseModel, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils i... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
53999eb5b92f-1 | temperature: float = 0.0
"Sampling temperature, it controls the degree of randomness in token selection."
max_output_tokens: int = 128
"Token limit determines the maximum amount of text output from one prompt."
top_p: float = 0.95
"Tokens are selected from most probable to least until the sum of the... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
53999eb5b92f-2 | location: str = "us-central1"
"The default location to use when making API calls."
credentials: Any = None
"The default custom credentials (google.auth.credentials.Credentials) to use "
"when making API calls. If not provided, credentials will be ascertained from "
"the environment."
@property
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
53999eb5b92f-3 | "top_k": self.top_k,
"top_p": self.top_p,
}
def _predict(
self, prompt: str, stop: Optional[List[str]] = None, **kwargs: Any
) -> str:
params = {**self._default_params, **kwargs}
res = self.client.predict(prompt, **params)
return self._enforce_stop_wor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
53999eb5b92f-4 | @classmethod
def _try_init_vertexai(cls, values: Dict) -> None:
allowed_params = ["project", "location", "credentials"]
params = {k: v for k, v in values.items() if k in allowed_params}
init_vertexai(**params)
return None
[docs]class VertexAI(_VertexAICommon, LLM):
"""Wrapper aro... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
53999eb5b92f-5 | cls._try_init_vertexai(values)
tuned_model_name = values.get("tuned_model_name")
model_name = values["model_name"]
try:
if tuned_model_name or not is_codey_model(model_name):
from vertexai.preview.language_models import TextGenerationModel
if tuned_mod... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
53999eb5b92f-6 | run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call Vertex model to get predictions based on the prompt.
Args:
prompt: The prompt to pass into the model.
stop: A list of stop words (optional).
run_manager: A Callbackman... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
8487674cfd4c-0 | Source code for langchain.llms.mosaicml
"""Wrapper around MosaicML APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils impo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
8487674cfd4c-1 | instruction="{instruction}",
response_key=RESPONSE_KEY,
)
[docs]class MosaicML(LLM):
"""Wrapper around MosaicML's LLM inference service.
To use, you should have the
environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Example:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
8487674cfd4c-2 | endpoint_url: str = (
"https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict"
)
"""Endpoint URL to use."""
inject_instruction_format: bool = False
"""Whether to inject the instruction format into the prompt."""
model_kwargs: Optional[dict] = None
"""Key word arguments to p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
8487674cfd4c-3 | values, "mosaicml_api_token", "MOSAICML_API_TOKEN"
)
values["mosaicml_api_token"] = mosaicml_api_token
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
8487674cfd4c-4 | )
return prompt
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
is_retry: bool = False,
**kwargs: Any,
) -> str:
"""Call out to a MosaicML LLM inference endpoint.
Args:... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
8487674cfd4c-5 | payload.update(_model_kwargs)
payload.update(kwargs)
# HTTP headers for authorization
headers = {
"Authorization": f"{self.mosaicml_api_token}",
"Content-Type": "application/json",
}
# send request
try:
response = requests.post(self.end... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
8487674cfd4c-6 | raise ValueError(
f"Error raised by inference API: {parsed_response['error']}"
)
# The inference API has changed a couple of times, so we add some handling
# to be robust to multiple response formats.
if isinstance(parsed_response, dict):
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
8487674cfd4c-7 | text = first_item
elif isinstance(first_item, dict):
if "output" in parsed_response:
text = first_item["output"]
else:
raise ValueError(
f"No key data or output in response: {parsed_respon... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
c9884fe36a46-0 | Source code for langchain.llms.modal
"""Wrapper around Modal API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
c9884fe36a46-1 | endpoint_url: str = ""
"""model endpoint to use"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not
explicitly specified."""
class Config:
"""Configuration for this pydantic config."""
extra = Extra.forbid
@root_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
c9884fe36a46-2 | 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
@property
d... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
c9884fe36a46-3 | **kwargs: Any,
) -> str:
"""Call to Modal endpoint."""
params = self.model_kwargs or {}
params = {**params, **kwargs}
response = requests.post(
url=self.endpoint_url,
headers={
"Content-Type": "application/json",
},
json... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
217222ca0682-0 | 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.callbacks.manager import CallbackManagerFo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-1 | ) -> 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"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-2 | 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 h... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-3 | if task == "text-generation":
model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
elif task in ("text2text-generation", "summarization"):
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
else:
raise ValueError(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-4 | 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. de... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-5 | )
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 credent... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-6 | 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 SelfHosted... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-7 | model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
"""
model_id: str = DEFAULT_MODEL_ID
"""Hugging Face model_id to load the model."""
task: str = DEFAULT_TASK
"""Hugging Face task ("text-generation", "text2text-generation" or
"summarization")."""
device: int = 0
"""Device to use... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-8 | """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):... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
217222ca0682-9 | }
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
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
62b970dc7958-0 | Source code for langchain.llms.huggingface_text_gen_inference
"""Wrapper around Huggingface text generation inference API."""
from functools import partial
from typing import Any, Dict, List, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerFor... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-1 | - top_p: The cumulative probability threshold for generating text.
- typical_p: The typical probability threshold for generating text.
- temperature: The temperature to use when generating text.
- repetition_penalty: The repetition penalty to use when generating text.
- stop_sequences: A list of stop se... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-2 | - _acall: Async generates text based on a given prompt and stop sequences.
- _llm_type: Returns the type of LLM.
"""
"""
Example:
.. code-block:: python
# Basic Example (no streaming)
llm = HuggingFaceTextGenInference(
inference_server_url = "http://localh... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-3 | llm = HuggingFaceTextGenInference(
inference_server_url = "http://localhost:8010/",
max_new_tokens = 512,
top_k = 10,
top_p = 0.95,
typical_p = 0.95,
temperature = 0.01,
repetition_penalty = 1.03,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-4 | seed: Optional[int] = None
inference_server_url: str = ""
timeout: int = 120
server_kwargs: Dict[str, Any] = Field(default_factory=dict)
stream: bool = False
client: Any
async_client: Any
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@ro... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-5 | **values["server_kwargs"],
)
except ImportError:
raise ImportError(
"Could not import text_generation python package. "
"Please install it with `pip install text_generation`."
)
return values
@property
def _llm_type(self) -> str... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-6 | stop_sequences=stop,
max_new_tokens=self.max_new_tokens,
top_k=self.top_k,
top_p=self.top_p,
typical_p=self.typical_p,
temperature=self.temperature,
repetition_penalty=self.repetition_penalty,
seed=self.seed,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-7 | "top_k": self.top_k,
"top_p": self.top_p,
"typical_p": self.typical_p,
"temperature": self.temperature,
"repetition_penalty": self.repetition_penalty,
"seed": self.seed,
}
text = ""
for res in self.client... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-8 | run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if stop is None:
stop = self.stop_sequences
else:
stop += self.stop_sequences
if not self.stream:
res = await self.async_client.generate(
prompt,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-9 | : res.generated_text.index(stop_seq)
]
text: str = res.generated_text
else:
text_callback = None
if run_manager:
text_callback = partial(
run_manager.on_llm_new_token, verbose=self.verbose
)
p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
62b970dc7958-10 | is_stop = False
for stop_seq in stop:
if stop_seq in token.text:
is_stop = True
break
if is_stop:
break
if not token.special:
if text_callback:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_text_gen_inference.html |
c176425d0aad-0 | Source code for langchain.llms.manifest
"""Wrapper around HazyResearch's Manifest library."""
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
[docs]class ManifestWrapper(... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
c176425d0aad-1 | except ImportError:
raise ValueError(
"Could not import manifest python package. "
"Please install it with `pip install manifest-ml`."
)
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
kwargs = self.llm_kwargs or... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
c176425d0aad-2 | if stop is not None and len(stop) != 1:
raise NotImplementedError(
f"Manifest currently only supports a single stop token, got {stop}"
)
params = self.llm_kwargs or {}
params = {**params, **kwargs}
if stop is not None:
params["stop_token"] = st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
f1307184d796-0 | Source code for langchain.llms.pipelineai
"""Wrapper around Pipeline Cloud API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from l... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
f1307184d796-1 | .. code-block:: python
from langchain import PipelineAI
pipeline = PipelineAI(pipeline_key="")
"""
pipeline_key: str = ""
"""The id or tag of the target pipeline"""
pipeline_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any pipeline parameters valid for `creat... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
f1307184d796-2 | 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 pipeline_kwargs.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
f1307184d796-3 | return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"pipeline_key": self.pipeline_key},
**{"pipeline_kwargs": self.pipeline_kwargs},
}
@property
def _llm_type(self) -> str:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
f1307184d796-4 | "Please install it with `pip install pipeline-ai`."
)
client = PipelineCloud(token=self.pipeline_api_key)
params = self.pipeline_kwargs or {}
params = {**params, **kwargs}
run = client.run_pipeline(self.pipeline_key, [prompt, params])
try:
text = run.resul... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
c2e58a656166-0 | 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.callbacks.manager import CallbackManagerForLLMRun
f... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-1 | """
"""
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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-2 | @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.
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-3 | To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-4 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-5 | 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.
""... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-6 | 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
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-7 | import boto3
try:
if values["credentials_profile_name"] is not None:
session = boto3.Session(
profile_name=values["credentials_profile_name"]
)
else:
# use default credentials
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-8 | 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
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-9 | stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = se("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
_model_kwargs = {**_model_kwa... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
c2e58a656166-10 | )
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 m... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/sagemaker_endpoint.html |
bf1f7cccb064-0 | Source code for langchain.llms.llamacpp
"""Wrapper around llama.cpp."""
import logging
from typing import Any, Dict, Generator, List, Optional
from pydantic import Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
logger = logging.getLogger(__name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-1 | """
client: Any #: :meta private:
model_path: str
"""The path to the Llama model file."""
lora_base: Optional[str] = None
"""The path to the Llama LoRA base model."""
lora_path: Optional[str] = None
"""The path to the Llama LoRA. If None, no LoRa is loaded."""
n_ctx: int = Field(512, al... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-2 | """Use half-precision for key/value cache."""
logits_all: bool = Field(False, alias="logits_all")
"""Return logits for all tokens, not just the last token."""
vocab_only: bool = Field(False, alias="vocab_only")
"""Only load the vocabulary, no weights."""
use_mlock: bool = Field(False, alias="use_mlo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-3 | """Number of layers to be loaded into gpu memory. Default None."""
suffix: Optional[str] = Field(None)
"""A suffix to append to the generated text. If None, no suffix is appended."""
max_tokens: Optional[int] = 256
"""The maximum number of tokens to generate."""
temperature: Optional[float] = 0.8
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-4 | repeat_penalty: Optional[float] = 1.1
"""The penalty to apply to repeated tokens."""
top_k: Optional[int] = 40
"""The top-k value to use for sampling."""
last_n_tokens_size: Optional[int] = 64
"""The number of tokens to look back when applying the repeat_penalty."""
use_mmap: Optional[bool] = Tr... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-5 | "n_parts",
"seed",
"f16_kv",
"logits_all",
"vocab_only",
"use_mlock",
"n_threads",
"n_batch",
"use_mmap",
"last_n_tokens_size",
]
model_params = {k: values[k] for k in model_param_names}
#... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-6 | "use this embedding model: pip install llama-cpp-python"
)
except Exception as e:
raise ValueError(
f"Could not load Llama model from path: {model_path}. "
f"Received error {e}"
)
return values
@property
def _default_params(self... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-7 | }
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model_path": self.model_path}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "llamacpp"
def _get_parameters(... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-8 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-9 | 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, w... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-10 | return result["choices"][0]["text"]
[docs] def stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> Generator[Dict, None, None]:
"""Yields results objects as they are generated in real time.
BETA:... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-11 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
bf1f7cccb064-12 | log_probs = chunk["choices"][0].get("logprobs", None)
if run_manager:
run_manager.on_llm_new_token(
token=token, verbose=self.verbose, log_probs=log_probs
)
yield chunk
[docs] def get_num_tokens(self, text: str) -> int:
tokenized_tex... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
ef1aef0ca793-0 | Source code for langchain.llms.azureml_endpoint
"""Wrapper around AzureML Managed Online Endpoint API."""
import json
import urllib.request
from abc import abstractmethod
from typing import Any, Dict, List, Mapping, Optional
from pydantic import BaseModel, validator
from langchain.callbacks.manager import CallbackManag... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-1 | self.deployment_name = deployment_name
def call(self, body: bytes) -> bytes:
"""call."""
# The azureml-model-deployment header will force the request to go to a
# specific deployment. Remove this header to have the request observe the
# endpoint traffic rules.
headers = {
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-2 | .. code-block:: python
class ContentFormatter(ContentFormatterBase):
content_type = "application/json"
accepts = "application/json"
def format_request_payload(
self,
prompt: str,
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-3 | """The MIME type of the response data returned form the endpoint"""
@abstractmethod
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
"""Formats the request body according to the input schema of
the model. Returns bytes or seekable file like object in the
format... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-4 | input_str = json.dumps(
{"inputs": {"input_string": [prompt]}, "parameters": model_kwargs}
)
return str.encode(input_str)
def format_response_payload(self, output: bytes) -> str:
response_json = json.loads(output)
return response_json[0]["0"]
class HFContentFormatter(Cont... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-5 | class DollyContentFormatter(ContentFormatterBase):
"""Content handler for the Dolly-v2-12b model"""
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
input_str = json.dumps(
{"input_data": {"input_string": [prompt]}, "parameters": model_kwargs}
)
ret... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-6 | endpoint_api_key="my-api-key",
deployment_name="my-deployment-name",
content_formatter=content_formatter,
)
""" # noqa: E501
endpoint_url: str = ""
"""URL of pre-existing Endpoint. Should be passed to constructor or specified as
env var `AZUREML_ENDPOINT... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-7 | the endpoint"""
model_kwargs: Optional[dict] = None
"""Key word arguments to pass to the model."""
@validator("http_client", always=True, allow_reuse=True)
@classmethod
def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient:
"""Validate that api key and python pack... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-8 | return http_client
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"deployment_name": self.deployment_name},
**{"model_kwargs": _model_kwargs},
}
@p... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
ef1aef0ca793-9 | stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = azureml_model("Tell me a joke.")
"""
_model_kwargs = self.model_kwargs or {}
body = self.conten... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/azureml_endpoint.html |
a841f27d31a3-0 | Source code for langchain.llms.amazon_api_gateway
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
class ContentHandle... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
a841f27d31a3-1 | [docs]class AmazonAPIGateway(LLM):
"""Wrapper around custom Amazon API Gateway"""
api_url: str
"""API Gateway URL"""
model_kwargs: Optional[Dict] = None
"""Key word arguments to pass to the model."""
content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
"""The co... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
a841f27d31a3-2 | }
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "amazon_api_gateway"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
a841f27d31a3-3 | try:
response = requests.post(
self.api_url,
json=payload,
)
text = self.content_handler.transform_output(response)
except Exception as error:
raise ValueError(f"Error raised by the service: {error}")
if stop is not None:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/amazon_api_gateway.html |
705eaf3c22a2-0 | Source code for langchain.llms.beam
"""Wrapper around Beam API."""
import base64
import json
import logging
import subprocess
import textwrap
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import Callba... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
705eaf3c22a2-1 | to get these is available here: https://docs.beam.cloud/account/api-keys.
The wrapper can then be called as follows, where the name, cpu, memory, gpu,
python version, and python packages can be updated accordingly. Once deployed,
the instance can be called.
Example:
.. code-block:: python
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
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