id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
3bad0e72466d-1 | use_memory: Optional[bool] = False
"""Whether to use the memory from the KoboldAI GUI when generating text."""
max_context_length: Optional[int] = 1600
"""Maximum number of tokens to send to the model.
minimum: 1
"""
max_length: Optional[int] = 80
"""Number of tokens to generate.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/koboldai.html |
3bad0e72466d-2 | """Typical sampling value.
maximum: 1
minimum: 0
"""
@property
def _llm_type(self) -> str:
return "koboldai"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/koboldai.html |
3bad0e72466d-3 | "typical": self.typical,
}
if stop is not None:
data["stop_sequence"] = stop
response = requests.post(
f"{clean_url(self.endpoint)}/api/v1/generate", json=data
)
response.raise_for_status()
json_response = response.json()
if (
"... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/koboldai.html |
3d7109fddf31-0 | Source code for langchain.llms.predictionguard
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
fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
3d7109fddf31-1 | 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 the access token and python package exists in environment."""
token = get_... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
3d7109fddf31-2 | The string generated by the model.
Example:
.. code-block:: python
response = pgllm("Tell me a joke.")
"""
import predictionguard as pg
params = self._default_params
if self.stop is not None and stop is not None:
raise ValueError("`stop` fo... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
ac751badc772-0 | Source code for langchain.llms.ctranslate2
from typing import Any, Dict, List, Optional, Union
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import BaseLLM
from langchain.pydantic_v1 import Field, root_validator
from langchain.schema.output import Generation, LLMResult
[docs]... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ctranslate2.html |
ac751badc772-1 | tokenizer: Any #: :meta private:
ctranslate2_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""
Holds any model parameters valid for `ctranslate2.Generator` call not
explicitly specified.
"""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate t... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ctranslate2.html |
ac751badc772-2 | prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
# build sampling parameters
params = {**self._default_params, **kwargs}
# call the model
encoded_prompts = self.tokeniz... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ctranslate2.html |
e798e15a2de7-0 | Source code for langchain.llms.self_hosted_hugging_face
import importlib.util
import logging
from typing import Any, Callable, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.self_hosted import SelfHostedPipeline
from langchain.llms.utils import enforce_stop_... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
e798e15a2de7-1 | 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.
"""
fro... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
e798e15a2de7-2 | 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 wi... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
e798e15a2de7-3 | 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 im... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
e798e15a2de7-4 | 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 auxiliar... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted_hugging_face.html |
e9f100163e72-0 | Source code for langchain.llms.replicate
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Extra, Field, root_valid... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e9f100163e72-1 | """Optionally pass in the model version object during initialization to avoid
having to make an extra API call to retrieve it during streaming. NOTE: not
serializable, is excluded from serialization.
"""
streaming: bool = False
"""Whether to stream the results."""
stop: List[str] = Fiel... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e9f100163e72-2 | 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_... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e9f100163e72-3 | stop_conditions = stop or self.stop
for s in stop_conditions:
if s in completion:
completion = completion[: completion.find(s)]
return completion
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[Callba... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
e9f100163e72-4 | model_str, version_str = self.model.split(":")
model = replicate_python.models.get(model_str)
self.version_obj = model.versions.get(version_str)
if self.prompt_key is None:
# sort through the openapi schema to get the name of the first input
input_properties = sor... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/replicate.html |
0b46a35545aa-0 | Source code for langchain.llms.octoai_endpoint
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, root_validator
from lang... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
0b46a35545aa-1 | octoai_api_token="octoai-api-key",
endpoint_url="https://llama-2-7b-chat-demo-kk0powt97tmb.octoai.run/v1/chat/completions",
model_kwargs={
"model": "llama-2-7b-chat",
"messages": [
{
"role": "syst... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
0b46a35545aa-2 | """Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_url": self.endpoint_url},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "octoai_endpoi... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
0b46a35545aa-3 | resp_json = octoai_client.infer(self.endpoint_url, parameter_payload)
text = resp_json["generated_text"]
except Exception as e:
# Handle any errors raised by the inference endpoint
raise ValueError(f"Error raised by the inference endpoint: {e}") from e
if stop is ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
b4044a8354f7-0 | Source code for langchain.llms.baidu_qianfan_endpoint
from __future__ import annotations
import logging
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from lan... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b4044a8354f7-1 | """Model name.
you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
preset models are mapping to an endpoint.
`model` will be ignored if `endpoint` is set
"""
endpoint: Optional[str] = None
"""Endpoint of the Qianfan LLM, required if custom model used."""
request_t... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b4044a8354f7-2 | except ImportError:
raise ImportError(
"qianfan package not found, please install it with "
"`pip install qianfan`"
)
return values
@property
def _identifying_params(self) -> Dict[str, Any]:
return {
**{"endpoint": self.endpoint... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b4044a8354f7-3 | **kwargs: Any,
) -> str:
"""Call out to an qianfan models endpoint for each generation with a prompt.
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.
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b4044a8354f7-4 | for res in self.client.do(**params):
if res:
chunk = GenerationChunk(text=res["result"])
yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text)
async def _astream(
self,
prompt: str,
stop: Optional[... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
ae2fa920f223-0 | Source code for langchain.llms.javelin_ai_gateway
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Ba... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
ae2fa920f223-1 | """The URI of the Javelin AI Gateway API."""
params: Optional[Params] = None
"""Parameters for the Javelin AI Gateway API."""
javelin_api_key: Optional[str] = None
"""The API key for the Javelin AI Gateway API."""
def __init__(self, **kwargs: Any):
try:
from javelin_sdk import (
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
ae2fa920f223-2 | run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call the Javelin AI Gateway API."""
data: Dict[str, Any] = {
"prompt": prompt,
**(self.params.dict() if self.params else {}),
}
if s := (stop or (self.params.stop if se... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
ae2fa920f223-3 | except KeyError:
return ""
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "javelin-ai-gateway" | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/javelin_ai_gateway.html |
06e439f79de9-0 | Source code for langchain.llms.self_hosted
import importlib.util
import logging
import pickle
from typing import Any, Callable, 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/self_hosted.html |
06e439f79de9-1 | 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 wi... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
06e439f79de9-2 | 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
i... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
06e439f79de9-3 | """Keyword 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):
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
06e439f79de9-4 | 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... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/self_hosted.html |
408339a0d5d8-0 | Source code for langchain.llms.nlpcloud
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
from langchain.utils import get_from_dict_or_env
[docs]class NLPCloud... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
408339a0d5d8-1 | top_p: int = 1
"""Total probability mass of tokens to consider at each step."""
top_k: int = 50
"""The number of highest probability tokens to keep for top-k filtering."""
repetition_penalty: float = 1.0
"""Penalizes repeated tokens. 1.0 means no penalty."""
num_beams: int = 1
"""Number of b... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
408339a0d5d8-2 | "length_no_input": self.length_no_input,
"remove_input": self.remove_input,
"remove_end_sequence": self.remove_end_sequence,
"bad_words": self.bad_words,
"top_p": self.top_p,
"top_k": self.top_k,
"repetition_penalty": self.repetition_penalty,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
408339a0d5d8-3 | "Pass in a list of length 1."
)
elif stop and len(stop) == 1:
end_sequence = stop[0]
else:
end_sequence = None
params = {**self._default_params, **kwargs}
response = self.client.generation(prompt, end_sequence=end_sequence, **params)
return res... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
a43f1f289360-0 | Source code for langchain.llms.anyscale
"""Wrapper around Anyscale Endpoint"""
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Mapping,
Optional,
Set,
Tuple,
cast,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerFor... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
a43f1f289360-1 | Generation(
text=choice["message"]["content"],
generation_info=dict(
finish_reason=choice.get("finish_reason"),
logprobs=choice.get("logprobs"),
),
)
]
)
llm_output = {"tok... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
a43f1f289360-2 | """Validate that api key and python package exists in environment."""
values["anyscale_api_base"] = get_from_dict_or_env(
values, "anyscale_api_base", "ANYSCALE_API_BASE"
)
values["anyscale_api_key"] = convert_to_secret_str(
get_from_dict_or_env(values, "anyscale_api_key"... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
a43f1f289360-3 | @property
def _llm_type(self) -> str:
"""Return type of llm."""
return "Anyscale LLM"
def _get_chat_messages(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> Tuple:
if len(prompts) > 1:
raise ValueError(
f"Anyscale currently only su... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
a43f1f289360-4 | run_manager.on_llm_new_token(token, chunk=chunk)
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
messages, params = self._get_... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
a43f1f289360-5 | "finish_reason": generation.generation_info.get("finish_reason")
if generation.generation_info
else None,
"logprobs": generation.generation_info.get("logprobs")
if generation.generation_info
else None... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
a43f1f289360-6 | if generation.generation_info
else None,
"logprobs": generation.generation_info.get("logprobs")
if generation.generation_info
else None,
}
)
else:
messages, par... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/anyscale.html |
f4f64394878b-0 | Source code for langchain.llms.symblai_nebula
import json
import logging
from typing import Any, Callable, Dict, List, Mapping, Optional
import requests
from requests import ConnectTimeout, ReadTimeout, RequestException
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
f4f64394878b-1 | """Optional"""
nebula_service_url: Optional[str] = None
nebula_service_path: Optional[str] = None
nebula_api_key: Optional[SecretStr] = None
model: Optional[str] = None
max_new_tokens: Optional[int] = 128
temperature: Optional[float] = 0.6
top_p: Optional[float] = 0.95
repetition_penalty... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
f4f64394878b-2 | nebula_service_path = "/" + nebula_service_path
values["nebula_service_url"] = nebula_service_url
values["nebula_service_path"] = nebula_service_path
values["nebula_api_key"] = nebula_api_key
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
f4f64394878b-3 | params["stop_sequences"] = stop_sequences
return {**params, **kwargs}
@staticmethod
def _process_response(response: Any, stop: Optional[List[str]]) -> str:
text = response["output"]["text"]
if stop:
text = enforce_stop_tokens(text, stop)
return text
def _call(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
f4f64394878b-4 | self: Nebula,
instruction: str,
conversation: str,
url: str = f"{DEFAULT_NEBULA_SERVICE_URL}{DEFAULT_NEBULA_SERVICE_PATH}",
params: Optional[Dict] = None,
) -> Any:
"""Generate text from the model."""
params = params or {}
api_key = None
if self.nebula_api_key is not None:
api_ke... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
f4f64394878b-5 | reraise=True,
stop=stop_after_attempt(max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type((RequestException, ConnectTimeout, ReadTimeout))
),
before_sleep=before_sleep_log(logger, logging.WARNING),
)... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/symblai_nebula.html |
9071be2a5a55-0 | Source code for langchain.llms.rwkv
"""RWKV models.
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 langchain.callbacks.manager import CallbackManagerFo... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
9071be2a5a55-1 | """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 ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
9071be2a5a55-2 | """Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ImportError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
9071be2a5a55-3 | 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(to... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
9071be2a5a55-4 | occurrence[token] += 1
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_ge... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
d2060c7ad336-0 | Source code for langchain.llms.cohere
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/cohere.html |
d2060c7ad336-1 | return llm.client.generate(**kwargs)
return _completion_with_retry(**kwargs)
[docs]def acompletion_with_retry(llm: Cohere, **kwargs: Any) -> Any:
"""Use tenacity to retry the completion call."""
retry_decorator = _create_retry_decorator(llm)
@retry_decorator
async def _completion_with_retry(**kwargs... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
d2060c7ad336-2 | )
client_name = values["user_agent"]
values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
values["async_client"] = cohere.AsyncClient(
cohere_api_key, client_name=client_name
)
return values
[docs]class Cohere(LLM, BaseCohere):... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
d2060c7ad336-3 | extra = Extra.forbid
@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,
"fre... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
d2060c7ad336-4 | if stop:
text = enforce_stop_tokens(text, stop)
return text
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to Cohere's generate endpoi... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
d2060c7ad336-5 | response = await acompletion_with_retry(
self, model=self.model, prompt=prompt, **params
)
_stop = params.get("stop_sequences")
return self._process_response(response, _stop) | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/cohere.html |
71e1fdf62f7e-0 | Source code for langchain.llms.human
from typing import Any, Callable, 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 Field
def _display_prompt(prompt: str... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/human.html |
71e1fdf62f7e-1 | """Returns the type of LLM."""
return "human-input"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""
Displays the prompt to the user and returns the... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/human.html |
aafb9f5590ae-0 | Source code for langchain.llms.fireworks
import asyncio
from concurrent.futures import ThreadPoolExecutor
from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Union
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llm... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
aafb9f5590ae-1 | return {"fireworks_api_key": "FIREWORKS_API_KEY"}
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
return True
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key in environment."""
try:
import fireworks.client
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
aafb9f5590ae-2 | response = completion_with_retry_batching(
self,
self.use_retry,
prompt=_prompts,
run_manager=run_manager,
stop=stop,
**params,
)
choices.extend(response)
return self.create_llm_result(choices... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
aafb9f5590ae-3 | """Create the LLMResult from the choices and prompts."""
generations = []
for i, _ in enumerate(prompts):
sub_choices = choices[i : (i + 1)]
generations.append(
[
Generation(
text=choice.__dict__["choices"][0].text,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
aafb9f5590ae-4 | "stream": True,
**self.model_kwargs,
}
async for stream_resp in await acompletion_with_retry_streaming(
self, self.use_retry, run_manager=run_manager, stop=stop, **params
):
chunk = _stream_response_to_generation_chunk(stream_resp)
yield chunk
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
aafb9f5590ae-5 | """Use tenacity to retry the completion call."""
import fireworks.client
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@conditional_decorator(use_retry, retry_decorator)
async def _completion_with_retry(**kwargs: Any) -> Any:
return await fireworks.client.Completion.acr... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
aafb9f5590ae-6 | retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@conditional_decorator(use_retry, retry_decorator)
async def _completion_with_retry(prompt: str) -> Any:
return await fireworks.client.Completion.acreate(**kwargs, prompt=prompt)
def run_coroutine_in_new_loop(
coroutine_... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
aafb9f5590ae-7 | llm: Fireworks,
*,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Callable[[Any], Any]:
"""Define retry mechanism."""
import fireworks.client
errors = [
fireworks.client.error.RateLimitError,
fireworks.client.error.Int... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
d6d7968870bb-0 | Source code for langchain.llms.huggingface_endpoint
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, roo... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
d6d7968870bb-1 | """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, "huggingfac... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
d6d7968870bb-2 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint.
Args:
prompt: The prompt to pass into the model.
stop: Optional list of... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
d6d7968870bb-3 | text = generated_text[0]["generated_text"]
elif self.task == "summarization":
text = generated_text[0]["summary_text"]
else:
raise ValueError(
f"Got invalid task {self.task}, "
f"currently only {VALID_TASKS} are supported"
)
if ... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
906298c27593-0 | Source code for langchain.llms.databricks
import os
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, List, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import (
BaseModel,
Extra... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
906298c27593-1 | values["api_url"] = api_url
return values
def post(self, request: Any) -> Any:
# See https://docs.databricks.com/machine-learning/model-serving/score-model-serving-endpoints.html
wrapped_request = {"dataframe_records": [request]}
response = self.post_raw(wrapped_request)["predictions... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
906298c27593-2 | )
[docs]def get_default_host() -> str:
"""Gets the default Databricks workspace hostname.
Raises an error if the hostname cannot be automatically determined.
"""
host = os.getenv("DATABRICKS_HOST")
if not host:
try:
host = get_repl_context().browserHostName
if not hos... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
906298c27593-3 | * **Serving endpoint** (recommended for both production and development).
We assume that an LLM was registered and deployed to a serving endpoint.
To wrap it as an LLM you must have "Can Query" permission to the endpoint.
Set ``endpoint_name`` accordingly and do not set ``cluster_id`` and
``clus... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
906298c27593-4 | If the endpoint model signature is different or you want to set extra params,
you can use `transform_input_fn` and `transform_output_fn` to apply necessary
transformations before and after the query.
"""
host: str = Field(default_factory=get_default_host)
"""Databricks workspace hostname.
If not... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
906298c27593-5 | You must not set both ``endpoint_name`` and ``cluster_id``.
"""
cluster_driver_port: Optional[str] = None
"""The port number used by the HTTP server running on the cluster driver node.
The server should listen on the driver IP address or simply ``0.0.0.0`` to connect.
We recommend the server using a... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
906298c27593-6 | raise ValueError(
"Neither endpoint_name nor cluster_id was set. "
"And the cluster_id cannot be automatically determined. Received"
f" error: {e}"
)
@validator("cluster_driver_port", always=True)
def set_cluster_driver_port(cls, v: Any... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
906298c27593-7 | cluster_driver_port=self.cluster_driver_port,
)
else:
raise ValueError(
"Must specify either endpoint_name or cluster_id/cluster_driver_port."
)
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "databricks"
def... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/databricks.html |
b466c108fddb-0 | Source code for langchain.llms.openlm
from typing import Any, Dict
from langchain.llms.openai import BaseOpenAI
from langchain.pydantic_v1 import root_validator
[docs]class OpenLM(BaseOpenAI):
"""OpenLM models."""
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.mod... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/openlm.html |
27c523d08f3a-0 | Source code for langchain.llms.pipelineai
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 BaseModel, Extra, Fie... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
27c523d08f3a-1 | 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 transferred to pipeline_kwargs.
Please confirm that {field_name}... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
27c523d08f3a-2 | )
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.result_preview[0][0]
except AttributeError:
raise... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
87313c360275-0 | Source code for langchain.llms.writer
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
fr... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
87313c360275-1 | logprobs: bool = False
"""Whether to return log probabilities."""
n: Optional[int] = None
"""How many completions to generate."""
writer_api_key: Optional[str] = None
"""Writer API key."""
base_url: Optional[str] = None
"""Base url to use, if None decides based on model name."""
class Co... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
87313c360275-2 | """Get the identifying parameters."""
return {
**{"model_id": self.model_id, "writer_org_id": self.writer_org_id},
**self._default_params,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "writer"
def _call(
self,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
87313c360275-3 | # are not enforced by the model parameters
text = enforce_stop_tokens(text, stop)
return text | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/writer.html |
7a120056efaa-0 | Source code for langchain.llms.ollama
import json
from typing import Any, Dict, Iterator, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import BaseLLM
from langchain.pydantic_v1 import Extra
from langchain.schema import LLMResult
from l... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
7a120056efaa-1 | of the output. A lower value will result in more focused and
coherent text. (Default: 5.0)"""
num_ctx: Optional[int] = None
"""Sets the size of the context window used to generate the
next token. (Default: 2048) """
num_gpu: Optional[int] = None
"""The number of GPUs to use. On macOS it defaults... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
7a120056efaa-2 | """Tail free sampling is used to reduce the impact of less probable
tokens from the output. A higher value (e.g., 2.0) will reduce the
impact more, while a value of 1.0 disables this setting. (default: 1)"""
top_k: Optional[int] = None
"""Reduces the probability of generating nonsense. A higher value (e... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
7a120056efaa-3 | "repeat_penalty": self.repeat_penalty,
"temperature": self.temperature,
"stop": self.stop,
"tfs_z": self.tfs_z,
"top_k": self.top_k,
"top_p": self.top_p,
},
"system": self.system,
"template": self.templat... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
7a120056efaa-4 | optional_detail = response.json().get("error")
raise ValueError(
f"Ollama call failed with status code {response.status_code}."
f" Details: {optional_detail}"
)
return response.iter_lines(decode_unicode=True)
def _stream_with_aggregation(
self,... | lang/api.python.langchain.com/en/latest/_modules/langchain/llms/ollama.html |
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