id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
463f176f76a2-4 | return "vertexai"
@property
def is_codey_model(self) -> bool:
return is_codey_model(self.model_name)
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
463f176f76a2-5 | if stream or self.streaming:
params.pop("candidate_count")
return params
[docs]class VertexAI(_VertexAICommon, BaseLLM):
"""Google Vertex AI large language models."""
model_name: str = "text-bison"
"The name of the Vertex AI large language model."
tuned_model_name: Optional[str] = No... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
463f176f76a2-6 | [docs] def get_num_tokens(self, text: str) -> int:
"""Get the number of tokens present in the text.
Useful for checking if an input will fit in a model's context window.
Args:
text: The string input to tokenize.
Returns:
The integer number of tokens in the text... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
463f176f76a2-7 | [_response_to_generation(r) for r in res.candidates]
)
return LLMResult(generations=generations)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
463f176f76a2-8 | client: "PredictionServiceClient" = None #: :meta private:
async_client: "PredictionServiceAsyncClient" = None #: :meta private:
endpoint_id: str
"A name of an endpoint where the model has been deployed."
allowed_model_args: Optional[List[str]] = None
"""Allowed optional args to be passed to the m... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
463f176f76a2-9 | run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
try:
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
except ImportError:
ra... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
463f176f76a2-10 | from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
except ImportError:
raise ImportError(
"protobuf package not found, please install it with"
" `pip install protobuf`"
)
instances = []
for prom... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
c29bb7e5a646-0 | Source code for langchain.llms.google_palm
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
from langchain.callbacks.manager import... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
c29bb7e5a646-1 | """Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator()
@retry_decorator
def _generate_with_retry(**kwargs: Any) -> Any:
return llm.client.generate_text(**kwargs)
return _generate_with_retry(**kwargs)
def _strip_erroneous_leading_spaces(text: str) -> str:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
c29bb7e5a646-2 | """Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64."""
n: int = 1
"""Number of chat completions to generate for each prompt. Note that the API may
not return the full n completions if duplicates are generated."""
@property
def lc_... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
c29bb7e5a646-3 | raise ValueError("top_k must be positive")
if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0:
raise ValueError("max_output_tokens must be greater than zero")
return values
def _generate(
self,
prompts: List[str],
stop: Optional[List[s... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
9a65ddc07570-0 | Source code for langchain.llms.beam
import base64
import json
import logging
import subprocess
import textwrap
import time
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
9a65ddc07570-1 | max_length=50)
llm._deploy()
call_result = llm._call(input)
"""
model_name: str = ""
name: str = ""
cpu: str = ""
memory: str = ""
gpu: str = ""
python_version: str = ""
python_packages: List[str] = []
max_length: str = ""
url: str = ""
"""model endpoi... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
9a65ddc07570-2 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
beam_client_id = get_from_dict_or_env(
values, "beam_client_id", "BEAM_CLIENT_ID"
)
beam_client_secret = get_from_dict_or_env(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
9a65ddc07570-3 | python_packages={python_packages},
)
app.Trigger.RestAPI(
inputs={{"prompt": beam.Types.String(), "max_length": beam.Types.String()}},
outputs={{"text": beam.Types.String()}},
handler="run.py:beam_langchain",
)
"""
)
script_name = "app.... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
9a65ddc07570-4 | file.write(script.format(model_name=self.model_name))
def _deploy(self) -> str:
"""Call to Beam."""
try:
import beam # type: ignore
if beam.__path__ == "":
raise ImportError
except ImportError:
raise ImportError(
"Could not... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
9a65ddc07570-5 | self,
prompt: str,
stop: Optional[list] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call to Beam."""
url = "https://apps.beam.cloud/" + self.app_id if self.app_id else self.url
payload = {"prompt": prompt, "max_l... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
5c29157effa5-0 | Source code for langchain.llms.anthropic
import re
import warnings
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llm... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5c29157effa5-1 | """Whether to stream the results."""
default_request_timeout: Optional[float] = None
"""Timeout for requests to Anthropic Completion API. Default is 600 seconds."""
anthropic_api_url: Optional[str] = None
anthropic_api_key: Optional[SecretStr] = None
HUMAN_PROMPT: Optional[str] = None
AI_PROMPT:... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5c29157effa5-2 | api_key=values["anthropic_api_key"].get_secret_value(),
timeout=values["default_request_timeout"],
)
values["async_client"] = anthropic.AsyncAnthropic(
base_url=values["anthropic_api_url"],
api_key=values["anthropic_api_key"].get_secret_value(),
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5c29157effa5-3 | raise NameError("Please ensure the anthropic package is loaded")
if stop is None:
stop = []
# Never want model to invent new turns of Human / Assistant dialog.
stop.extend([self.HUMAN_PROMPT])
return stop
[docs]class Anthropic(LLM, _AnthropicCommon):
"""Anthropic large la... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5c29157effa5-4 | warnings.warn(
"This Anthropic LLM is deprecated. "
"Please use `from langchain.chat_models import ChatAnthropic` instead"
)
return values
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "anthropic-llm"
def _wrap_prompt(self, pro... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5c29157effa5-5 | prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
response = model(prompt)
"""
if self.streaming:
completion = ""
for chunk in self._stream(
prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
):
completion += chunk.... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5c29157effa5-6 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
r"""Call Anthropic completion_stream and return the resulting generator.
Args:
prompt: The prompt to pass into the... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
5c29157effa5-7 | Returns:
A generator representing the stream of tokens from Anthropic.
Example:
.. code-block:: python
prompt = "Write a poem about a stream."
prompt = f"\n\nHuman: {prompt}\n\nAssistant:"
generator = anthropic.stream(prompt)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
8774c8ff6f9d-0 | Source code for langchain.llms.ctransformers
from functools import partial
from typing import Any, Dict, List, Optional, Sequence
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.pydantic_v1 import root_valida... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
8774c8ff6f9d-1 | "model_type": self.model_type,
"model_file": self.model_file,
"config": self.config,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ctransformers"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
8774c8ff6f9d-2 | _run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()
for chunk in self.client(prompt, stop=stop, stream=True):
text.append(chunk)
_run_manager.on_llm_new_token(chunk, verbose=self.verbose)
return "".join(text)
async def _acall(
self,
promp... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
c9ff28d924c7-0 | Source code for langchain.llms.petals
import logging
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, Field, root_valida... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
c9ff28d924c7-1 | max_length: Optional[int] = None
"""The maximum length of the sequence to be generated."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call
not explicitly specified."""
huggingface_api_key: Optional[str] = None
class Config:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
c9ff28d924c7-2 | from transformers import AutoTokenizer
model_name = values["model_name"]
values["tokenizer"] = AutoTokenizer.from_pretrained(model_name)
values["client"] = AutoDistributedModelForCausalLM.from_pretrained(
model_name
)
values["huggingface_api_ke... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
c9ff28d924c7-3 | params = self._default_params
params = {**params, **kwargs}
inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"]
outputs = self.client.generate(inputs, **params)
text = self.tokenizer.decode(outputs[0])
if stop is not None:
# I believe this is required si... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/petals.html |
6dc18c4db5c3-0 | Source code for langchain.llms.tongyi
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from requests.exceptions import HTTPError
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
fr... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/tongyi.html |
6dc18c4db5c3-1 | elif resp.status_code in [400, 401]:
raise ValueError(
f"status_code: {resp.status_code} \n "
f"code: {resp.code} \n message: {resp.message}"
)
else:
raise HTTPError(
f"HTTP error occurred: status_code: {resp.status_code} \n "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/tongyi.html |
6dc18c4db5c3-2 | """Tongyi Qwen large language models.
To use, you should have the ``dashscope`` python package installed, and the
environment variable ``DASHSCOPE_API_KEY`` set with your API key, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain.llms ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/tongyi.html |
6dc18c4db5c3-3 | @root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
get_from_dict_or_env(values, "dashscope_api_key", "DASHSCOPE_API_KEY")
try:
import dashscope
except ImportError:
raise ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/tongyi.html |
6dc18c4db5c3-4 | **{"model": self.model_name},
**self._default_params,
**kwargs,
}
completion = generate_with_retry(
self,
prompt=prompt,
**params,
)
return completion["output"]["text"]
def _generate(
self,
prompts: List[str]... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/tongyi.html |
c71012282eac-0 | Source code for langchain.llms.fake
import asyncio
import time
from typing import Any, AsyncIterator, Iterator, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.schema.language_model im... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fake.html |
c71012282eac-1 | else:
self.i = 0
return response
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {"responses": self.responses}
[docs]class FakeStreamingListLLM(FakeListLLM):
"""Fake streaming list LLM for testing purposes."""
[docs] def stream(
self,
input... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fake.html |
7dd2e86421b2-0 | Source code for langchain.llms.clarifai
import logging
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
from langc... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
7dd2e86421b2-1 | """Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that we have all required info to access Clarifai
platform and python package exists in environment."""
values["pat"] = get_from_d... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
7dd2e86421b2-2 | @property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
**{
"user_id": self.user_id,
"app_id": self.app_id,
"model_id": self.model_id,
}
}
@property
def _llm_type... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
7dd2e86421b2-3 | user_app_id=self.userDataObject,
model_id=self.model_id,
version_id=self.model_version_id,
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(text=resources_pb2.Text(raw=prompt))
)
],
)
post_model_outp... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
7dd2e86421b2-4 | raise ImportError(
"Could not import clarifai python package. "
"Please install it with `pip install clarifai`."
)
# TODO: add caching here.
generations = []
batch_size = 32
for i in range(0, len(prompts), batch_size):
batch = promp... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/clarifai.html |
a8c3a00041c0-0 | Source code for langchain.llms.textgen
import json
import logging
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
import requests
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.pydantic... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-1 | (only the most likely token is used). Higher value = more randomness."""
top_p: Optional[float] = 0.1
"""If not set to 1, select tokens with probabilities adding up to less than this
number. Higher value = higher range of possible random results."""
typical_p: Optional[float] = 1
"""If not set to 1,... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-2 | """Penalty Alpha"""
length_penalty: Optional[float] = 1
"""Length Penalty"""
early_stopping: bool = Field(False, alias="early_stopping")
"""Early stopping"""
seed: int = Field(-1, alias="seed")
"""Seed (-1 for random)"""
add_bos_token: bool = Field(True, alias="add_bos_token")
"""Add the... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-3 | "repetition_penalty": self.repetition_penalty,
"top_k": self.top_k,
"min_length": self.min_length,
"no_repeat_ngram_size": self.no_repeat_ngram_size,
"num_beams": self.num_beams,
"penalty_alpha": self.penalty_alpha,
"length_penalty": self.length_pe... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-4 | if self.preset is None:
params = self._default_params
else:
params = {"preset": self.preset}
# then sets it as configured, or default to an empty list:
params["stopping_strings"] = self.stopping_strings or stop or []
return params
def _call(
self,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-5 | result = ""
return result
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call the textgen web API and return the output.
Args:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-6 | **kwargs: Any,
) -> Iterator[GenerationChunk]:
"""Yields results objects as they are generated in real time.
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 p... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-7 | text=result["text"],
generation_info=None,
)
yield chunk
elif result["event"] == "stream_end":
websocket_client.close()
return
if run_manager:
run_manager.on_llm_new_token(token=chunk.text)
as... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
a8c3a00041c0-8 | )
params = {**self._get_parameters(stop), **kwargs}
url = f"{self.model_url}/api/v1/stream"
request = params.copy()
request["prompt"] = prompt
websocket_client = websocket.WebSocket()
websocket_client.connect(url)
websocket_client.send(json.dumps(request))
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/textgen.html |
1bb0135bde89-0 | Source code for langchain.llms.huggingface_pipeline
from __future__ import annotations
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import BaseLLM
from langchain.llms.utils import enforce_st... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb0135bde89-1 | )
hf = HuggingFacePipeline(pipeline=pipe)
"""
pipeline: Any #: :meta private:
model_id: str = DEFAULT_MODEL_ID
"""Model name to use."""
model_kwargs: Optional[dict] = None
"""Keyword arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
"""Keyword argument... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb0135bde89-2 | elif task in ("text2text-generation", "summarization"):
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb0135bde89-3 | 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.",
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb0135bde89-4 | def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
# List to hold all results
text_generations: List[str] = []
for i in range(0, len(prompts)... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
1bb0135bde89-5 | )
if stop:
# Enforce stop tokens
text = enforce_stop_tokens(text, stop)
# Append the processed text to results
text_generations.append(text)
return LLMResult(
generations=[[Generation(text=text)] for text in text... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_pipeline.html |
9e987a642600-0 | Source code for langchain.llms.gigachat
from __future__ import annotations
import logging
from functools import cached_property
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchai... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gigachat.html |
9e987a642600-1 | streaming: bool = False
""" Whether to stream the results or not. """
temperature: Optional[float] = None
"""What sampling temperature to use."""
max_tokens: Optional[int] = None
""" Maximum number of tokens to generate """
@property
def _llm_type(self) -> str:
return "giga-chat-mode... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gigachat.html |
9e987a642600-2 | except ImportError:
raise ImportError(
"Could not import gigachat python package. "
"Please install it with `pip install gigachat`."
)
return values
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameter... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gigachat.html |
9e987a642600-3 | generations = []
for res in response.choices:
finish_reason = res.finish_reason
gen = Generation(
text=res.message.content,
generation_info={"finish_reason": finish_reason},
)
generations.append([gen])
if finish_reason !... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gigachat.html |
9e987a642600-4 | async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> LLMResult:
should_stream = stream if stream is not None else self.... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gigachat.html |
9e987a642600-5 | **kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
payload = self._build_payload([prompt])
async for chunk in self._client.astream(payload):
if chunk.choices:
content = chunk.choices[0].delta.content
yield GenerationChunk(text=content)
if ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gigachat.html |
d3adf7627ac8-0 | Source code for langchain.llms.titan_takeoff
from typing import Any, Iterator, List, Mapping, Optional
import requests
from requests.exceptions import ConnectionError
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
d3adf7627ac8-1 | @property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling Titan Takeoff Server."""
params = {
"generate_max_length": self.generate_max_length,
"sampling_topk": self.sampling_topk,
"sampling_topp": self.sampling_topp,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
d3adf7627ac8-2 | response = requests.post(url, json=params)
response.raise_for_status()
response.encoding = "utf-8"
text = ""
if "message" in response.json():
text = response.json()["message"]
else:
raise ValueError("Something went wrong.")
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
d3adf7627ac8-3 | if run_manager:
run_manager.on_llm_new_token(token=chunk.text)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"base_url": self.base_url, **{}, **self._default_params} | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/titan_takeoff.html |
363e03dcb9c2-0 | Source code for langchain.llms.promptlayer_openai
import datetime
from typing import Any, List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.openai import OpenAI, OpenAIChat
from langchain.schema import LLMResult
[docs]class Pr... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
363e03dcb9c2-1 | """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)
request_end_time = ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
363e03dcb9c2-2 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
363e03dcb9c2-3 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
363e03dcb9c2-4 | 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 isinstance(
generation.generation_in... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
363e03dcb9c2-5 | generation.generation_info, dict
):
generation.generation_info = {}
generation.generation_info["pl_request_id"] = pl_request_id
return generated_responses | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
c860d32942c2-0 | Source code for langchain.llms.manifest
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Extra, root_validator
[docs]class ManifestWrapper(LLM):
"""HazyResearch's Manifest libr... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
c860d32942c2-1 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/manifest.html |
02030726671a-0 | Source code for langchain.llms.ai21
from typing import Any, Dict, List, Optional, cast
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import BaseModel, Extra, SecretStr, root_validator
from langchain.utils import convert_to... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
02030726671a-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."""
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
02030726671a-2 | "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."""
r... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
02030726671a-3 | base_url = "https://api.ai21.com/studio/v1"
params = {**self._default_params, **kwargs}
self.ai21_api_key = cast(SecretStr, self.ai21_api_key)
response = requests.post(
url=f"{base_url}/{self.model}/complete",
headers={"Authorization": f"Bearer {self.ai21_api_key.get_secr... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ai21.html |
0e61e5288e31-0 | Source code for langchain.llms.mlflow_ai_gateway
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import BaseModel, Extra
# Ignoring type because below ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
0e61e5288e31-1 | try:
import mlflow.gateway
except ImportError as e:
raise ImportError(
"Could not import `mlflow.gateway` module. "
"Please install it with `pip install mlflow[gateway]`."
) from e
super().__init__(**kwargs)
if self.gateway_uri:... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
0e61e5288e31-2 | @property
def _llm_type(self) -> str:
return "mlflow-ai-gateway" | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
3f746b5cba90-0 | Source code for langchain.llms.mosaicml
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
3f746b5cba90-1 | )
"""Endpoint URL to use."""
inject_instruction_format: bool = False
"""Whether to inject the instruction format into the prompt."""
model_kwargs: Optional[dict] = None
"""Keyword arguments to pass to the model."""
retry_sleep: float = 1.0
"""How long to try sleeping for if a rate limit is e... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
3f746b5cba90-2 | 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:
prompt: The prompt to pass into the model.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
3f746b5cba90-3 | # to be robust to multiple response formats.
if isinstance(parsed_response, dict):
output_keys = ["data", "output", "outputs"]
for key in output_keys:
if key in parsed_response:
output_item = parsed_response[key]
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/mosaicml.html |
4d2648316a32-0 | Source code for langchain.llms.gradient_ai
import asyncio
import logging
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Dict, List, Mapping, Optional, Sequence, TypedDict
import aiohttp
import requests
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackMa... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-1 | gradient_access_token="gradientai-access_token",
)
"""
model_id: str = Field(alias="model", min_length=2)
"Underlying gradient.ai model id (base or fine-tuned)."
gradient_workspace_id: Optional[str] = None
"Underlying gradient.ai workspace_id."
gradient_access_token: Optional[str] = ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-2 | or len(values["gradient_access_token"]) < 10
):
raise ValueError("env variable `GRADIENT_ACCESS_TOKEN` must be set")
if (
values["gradient_workspace_id"] is None
or len(values["gradient_access_token"]) < 3
):
raise ValueError("env variable `GRADIEN... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-3 | _model_kwargs = self.model_kwargs or {}
return {
**{"gradient_api_url": self.gradient_api_url},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "gradient"
def _kwargs_post_fine_tune_request(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-4 | ),
),
)
def _kwargs_post_request(
self, prompt: str, kwargs: Mapping[str, Any]
) -> Mapping[str, Any]:
"""Build the kwargs for the Post request, used by sync
Args:
prompt (str): prompt used in query
kwargs (dict): model kwargs in payload
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-5 | Returns:
The string generated by the model.
"""
try:
response = requests.post(**self._kwargs_post_request(prompt, kwargs))
if response.status_code != 200:
raise Exception(
f"Gradient returned an unexpected response with status "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-6 | else:
async with self.aiosession.post(
**self._kwargs_post_request(prompt=prompt, kwargs=kwargs)
) as response:
if response.status != 200:
raise Exception(
f"Gradient returned an unexpected response with status "
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-7 | **kwargs: Any,
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
generations = []
for generation in asyncio.gather(
[self._acall(prompt, stop=stop, run_manager=run_manager, **kwargs)]
for prompt in prompts
):
generations.append([Gene... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
4d2648316a32-8 | f"{response.status}: {response.text}"
)
response_json = await response.json()
loss = (
response_json["sumLoss"]
/ response_json["numberOfTrainableTokens"]
)
else:
async... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/gradient_ai.html |
bb51a9ee8dd6-0 | Source code for langchain.llms.bedrock
import json
import warnings
from abc import ABC
from typing import Any, Dict, Iterator, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
bb51a9ee8dd6-1 | if input_text[: len("Human:")] == "Human:":
input_text = "\n\n" + input_text
input_text = _add_newlines_before_ha(input_text)
count = 0
# track alternation
for i in range(len(input_text)):
if input_text[i : i + len(HUMAN_PROMPT)] == HUMAN_PROMPT:
if count % 2 == 0:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
bb51a9ee8dd6-2 | input_body["prompt"] = _human_assistant_format(prompt)
elif provider == "ai21" or provider == "cohere":
input_body["prompt"] = prompt
elif provider == "amazon":
input_body = dict()
input_body["inputText"] = prompt
input_body["textGenerationConfig"] = {**mo... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/bedrock.html |
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