id stringlengths 14 15 | text stringlengths 44 2.47k | source stringlengths 61 181 |
|---|---|---|
7e73622bfa7f-2 | _model_kwargs = self.model_kwargs or {}
# Prepare the payload JSON
parameter_payload = {"inputs": prompt, "parameters": _model_kwargs}
try:
# Initialize the OctoAI client
from octoai import client
octoai_client = client.Client(token=self.octoai_api_token)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/octoai_endpoint.html |
18535cf8f2f5-0 | Source code for langchain.llms.minimax
"""Wrapper around Minimax APIs."""
from __future__ import annotations
import logging
from typing import (
Any,
Dict,
List,
Optional,
)
import requests
from langchain.callbacks.manager import (
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM
from... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/minimax.html |
18535cf8f2f5-1 | f"API {response.json()['base_resp']['status_code']}"
f" error: {response.json()['base_resp']['status_msg']}"
)
return response.json()["reply"]
[docs]class MinimaxCommon(BaseModel):
_client: _MinimaxEndpointClient
model: str = "abab5.5-chat"
"""Model name to use."""
ma... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/minimax.html |
18535cf8f2f5-2 | default="https://api.minimax.chat",
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return {
"model": self.model,
"tokens_to_generate": self.max_tokens,
"temperatur... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/minimax.html |
18535cf8f2f5-3 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
r"""Call out to Minimax's completion endpoint to chat
Args:
prompt: The prompt to pass into the model.
Returns:
The ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/minimax.html |
d3132f165513-0 | Source code for langchain.llms.bittensor
import http.client
import json
import ssl
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
[docs]class NIBittensorLLM(LLM):
"""
NIBittensorLLM is created by Neural Interne... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bittensor.html |
d3132f165513-1 | system_prompt(str): A system prompt defining how your model should respond.
top_responses(int): Total top miner responses to retrieve from Bittensor
protocol.
Return:
The generated response(s).
Example:
.. code-block:: python
from langc... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bittensor.html |
d3132f165513-2 | # Creating Header and getting top benchmark miner uids
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}",
"Endpoint-Version": "2023-05-19",
}
conn.request("GET", "/top_miner_uids", headers=headers)
miner_response = co... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bittensor.html |
d3132f165513-3 | "messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
],
}
)
conn.request("POST", "/chat", payload, headers)
response = conn.getresponse()
utf_st... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/bittensor.html |
3898108931ab-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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
3898108931ab-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:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
3898108931ab-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."""
@root_validator()
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
3898108931ab-3 | def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
generations = []
for prompt in prompts:
completion = generate_with_retry(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/google_palm.html |
00e0204beba9-0 | Source code for langchain.llms.edenai
"""Wrapper around EdenAI's Generation API."""
import logging
from typing import Any, Dict, List, Literal, Optional
from aiohttp import ClientSession
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
00e0204beba9-1 | model: Optional[str] = None
"""
model name for above provider (eg: 'text-davinci-003' for openai)
available models are shown on https://docs.edenai.co/ under 'available providers'
"""
# Optional parameters to add depending of chosen feature
# see api reference for more infos
temperature: Opt... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
00e0204beba9-2 | all_required_field_names = {field.alias for field in cls.__fields__.values()}
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name not in all_required_field_names:
if field_name in extra:
raise ValueError(f"Found {field_name... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
00e0204beba9-3 | "stop sequences found in both the input and default params."
)
elif self.stop_sequences is not None:
stops = self.stop_sequences
else:
stops = stop
url = f"{self.base_url}/{self.feature}/{self.subfeature}"
headers = {
"Authorization": f"Bea... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
00e0204beba9-4 | if provider_response.get("status") == "fail":
err_msg = provider_response.get("error", {}).get("message")
raise Exception(err_msg)
output = self._format_output(data)
if stops is not None:
output = enforce_stop_tokens(output, stops)
return output
async def ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
00e0204beba9-5 | "resolution": self.resolution,
**self.params,
**kwargs,
"num_images": 1, # always limit to 1 (ignored for text)
}
# filter `None` values to not pass them to the http payload as null
payload = {k: v for k, v in payload.items() if v is not None}
if self... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/edenai.html |
4e734d160234-0 | Source code for langchain.llms.fireworks
from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Union
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.llms.base import LLM, create_base_retry_decorator
from langchain.pyd... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
4e734d160234-1 | raise ImportError("") from e
fireworks_api_key = get_from_dict_or_env(
values, "fireworks_api_key", "FIREWORKS_API_KEY"
)
fireworks.client.api_key = fireworks_api_key
return values
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
4e734d160234-2 | prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = {
"model": self.model,
"prompt": prompt,
"stream": True,
**self.model_kwargs,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
4e734d160234-3 | if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
[docs] async def astream(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[List[str]... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
4e734d160234-4 | async def _completion_with_retry(**kwargs: Any) -> Any:
return await fireworks.client.Completion.acreate(
**kwargs,
)
return await _completion_with_retry(**kwargs)
[docs]async def acompletion_with_retry_streaming(
llm: Fireworks,
*,
run_manager: Optional[AsyncCallbackManagerF... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/fireworks.html |
fbbbadcbb4bf-0 | Source code for langchain.llms.loading
"""Base interface for loading large language model APIs."""
import json
from pathlib import Path
from typing import Union
import yaml
from langchain.llms import type_to_cls_dict
from langchain.llms.base import BaseLLM
[docs]def load_llm_from_config(config: dict) -> BaseLLM:
""... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/loading.html |
038ccda35398-0 | Source code for langchain.llms.vertexai
from __future__ import annotations
from concurrent.futures import Executor, ThreadPoolExecutor
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Dict,
Iterator,
List,
Optional,
Union,
)
from langchain.callbacks.manager import (
A... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-1 | Returns: True if the model name is a Codey model.
"""
return "code" in model_name
def _create_retry_decorator(
llm: VertexAI,
*,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Callable[[Any], Any]:
import google.api_core
error... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-2 | retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
def _completion_with_retry(*args: Any, **kwargs: Any) -> Any:
return llm.client.predict_streaming(*args, **kwargs)
return _completion_with_retry(*args, **kwargs)
[docs]async def acompletion_with_retry(
llm: ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-3 | "Underlying model name."
@classmethod
def _get_task_executor(cls, request_parallelism: int = 5) -> Executor:
if cls.task_executor is None:
cls.task_executor = ThreadPoolExecutor(max_workers=request_parallelism)
return cls.task_executor
class _VertexAICommon(_VertexAIBase):
client... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-4 | """Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _default_params(self) -> Dict[str, Any]:
if self.is_codey_model:
return {
"temperature": self.temperature,
"max_output_tokens": self.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-5 | cls._try_init_vertexai(values)
tuned_model_name = values.get("tuned_model_name")
model_name = values["model_name"]
try:
if not is_codey_model(model_name):
from vertexai.preview.language_models import TextGenerationModel
if tuned_model_name:
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-6 | )
generations.append([_response_to_generation(res)])
return LLMResult(generations=generations)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-7 | 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 model."""
prompt_arg: str = "prompt"
result_arg: str = "generated_text"
@root_validator()
def validate_environment(cls, valu... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-8 | from google.protobuf.struct_pb2 import Value
except ImportError:
raise ImportError(
"protobuf package not found, please install it with"
" `pip install protobuf`"
)
instances = []
for prompt in prompts:
if self.allowed_model_arg... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
038ccda35398-9 | instances = []
for prompt in prompts:
if self.allowed_model_args:
instance = {
k: v for k, v in kwargs.items() if k in self.allowed_model_args
}
else:
instance = {}
instance[self.prompt_arg] = prompt
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/vertexai.html |
b0316e61b778-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b0316e61b778-1 | """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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b0316e61b778-2 | except ImportError:
raise ValueError(
"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,... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b0316e61b778-3 | Args:
prompt: The prompt to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = qianfan_model("Tell me a joke.")
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
b0316e61b778-4 | yield chunk
if run_manager:
run_manager.on_llm_new_token(chunk.text)
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baidu_qianfan_endpoint.html |
349226420402-0 | Source code for langchain.llms.baseten
import logging
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 Field
logger = logging.getLogger(__name__)
[docs]class Baseten(LLM):
"""B... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
349226420402-1 | return "baseten"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call to Baseten deployed model endpoint."""
try:
import baseten
except ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/baseten.html |
795efb362e95-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
795efb362e95-1 | 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}... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
795efb362e95-2 | )
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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/pipelineai.html |
f07bd99b6f92-0 | Source code for langchain.llms.stochasticai
import logging
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.llms.utils import enforce_stop_tokens
from langchain.pydantic_v... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
f07bd99b6f92-1 | raise ValueError(f"Found {field_name} supplied twice.")
logger.warning(
f"""{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
f07bd99b6f92-2 | """
params = self.model_kwargs or {}
params = {**params, **kwargs}
response_post = requests.post(
url=self.api_url,
json={"prompt": prompt, "params": params},
headers={
"apiKey": f"{self.stochasticai_api_key}",
"Accept": "applic... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/stochasticai.html |
6b339f9e2d5e-0 | Source code for langchain.llms.forefrontai
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_validat... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
6b339f9e2d5e-1 | """Validate that api key exists in environment."""
forefrontai_api_key = get_from_dict_or_env(
values, "forefrontai_api_key", "FOREFRONTAI_API_KEY"
)
values["forefrontai_api_key"] = forefrontai_api_key
return values
@property
def _default_params(self) -> Mapping[str, ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
6b339f9e2d5e-2 | response = requests.post(
url=self.endpoint_url,
headers={
"Authorization": f"Bearer {self.forefrontai_api_key}",
"Content-Type": "application/json",
},
json={"text": prompt, **self._default_params, **kwargs},
)
response_jso... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/forefrontai.html |
61ebaf645591-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
61ebaf645591-1 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
61ebaf645591-2 | @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(
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
61ebaf645591-3 | 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.... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
61ebaf645591-4 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
61ebaf645591-5 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/beam.html |
05ef79867428-0 | 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]... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctranslate2.html |
05ef79867428-1 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctranslate2.html |
05ef79867428-2 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/ctranslate2.html |
89972e3a2a78-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
89972e3a2a78-1 | 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_... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
89972e3a2a78-2 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/predictionguard.html |
d1055512e2a4-0 | 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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
d1055512e2a4-1 | 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:... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
d1055512e2a4-2 | @property
def _llm_type(self) -> str:
return "mlflow-ai-gateway" | https://api.python.langchain.com/en/latest/_modules/langchain/llms/mlflow_ai_gateway.html |
1b6856aa3918-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
1b6856aa3918-1 | """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 ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
1b6856aa3918-2 | """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`."
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
1b6856aa3918-3 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
1b6856aa3918-4 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
eda025886041-0 | Source code for langchain.llms.chatglm
import logging
from typing import Any, 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
logger = logging.getLogger(__name__)
[docs]class... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/chatglm.html |
eda025886041-1 | return {
**{"endpoint_url": self.endpoint_url},
**{"model_kwargs": _model_kwargs},
}
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/chatglm.html |
eda025886041-2 | # Check if response content does exists
if isinstance(parsed_response, dict):
content_keys = "response"
if content_keys in parsed_response:
text = parsed_response[content_keys]
else:
raise ValueError(f"No content in resp... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/chatglm.html |
a1f30f06ee0e-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 import OpenAI, OpenAIChat
from langchain.schema import LLMResult
[docs]class PromptLay... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
a1f30f06ee0e-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 = ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
a1f30f06ee0e-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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
a1f30f06ee0e-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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
a1f30f06ee0e-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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/promptlayer_openai.html |
a1f30f06ee0e-5 | generation.generation_info, dict
):
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 |
6bb5fb71c765-0 | Source code for langchain.llms.modal
import logging
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, Fie... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
6bb5fb71c765-1 | logger.warning(
f"""{field_name} was transferred 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
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/modal.html |
896f9ce7c491-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/human.html |
896f9ce7c491-1 | """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... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/human.html |
a0c00e94e283-0 | Source code for langchain.llms.llamacpp
from __future__ import annotations
import logging
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Union
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0c00e94e283-1 | """Number of parts to split the model into.
If -1, the number of parts is automatically determined."""
seed: int = Field(-1, alias="seed")
"""Seed. If -1, a random seed is used."""
f16_kv: bool = Field(True, alias="f16_kv")
"""Use half-precision for key/value cache."""
logits_all: bool = Field(F... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0c00e94e283-2 | logprobs: Optional[int] = Field(None)
"""The number of logprobs to return. If None, no logprobs are returned."""
echo: Optional[bool] = False
"""Whether to echo the prompt."""
stop: Optional[List[str]] = []
"""A list of strings to stop generation when encountered."""
repeat_penalty: Optional[flo... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0c00e94e283-3 | grammar: formal grammar for constraining model outputs. For instance, the grammar
can be used to force the model to generate valid JSON or to speak exclusively in
emojis. At most one of grammar_path and grammar should be passed in.
"""
verbose: bool = True
"""Print verbose output to stderr."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0c00e94e283-4 | except Exception as e:
raise ValueError(
f"Could not load Llama model from path: {model_path}. "
f"Received error {e}"
)
if values["grammar"] and values["grammar_path"]:
grammar = values["grammar"]
grammar_path = values["grammar_pat... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0c00e94e283-5 | "repeat_penalty": self.repeat_penalty,
"top_k": self.top_k,
}
if self.grammar:
params["grammar"] = self.grammar
return params
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {**{"model_path": ... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0c00e94e283-6 | Args:
prompt: The prompt to use for generation.
stop: A list of strings to stop generation when encountered.
Returns:
The generated text.
Example:
.. code-block:: python
from langchain.llms import LlamaCpp
llm = LlamaCpp(mod... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
a0c00e94e283-7 | Returns:
A generator representing the stream of tokens being generated.
Yields:
A dictionary like objects containing a string token and metadata.
See llama-cpp-python docs and below for more.
Example:
.. code-block:: python
from langchain.l... | https://api.python.langchain.com/en/latest/_modules/langchain/llms/llamacpp.html |
f62ca232a4a5-0 | Source code for langchain.chat_loaders.utils
"""Utilities for chat loaders."""
from copy import deepcopy
from typing import Iterable, Iterator, List
from langchain.schema.chat import ChatSession
from langchain.schema.messages import AIMessage, BaseMessage
[docs]def merge_chat_runs_in_session(
chat_session: ChatSess... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/utils.html |
f62ca232a4a5-1 | """
for chat_session in chat_sessions:
yield merge_chat_runs_in_session(chat_session)
[docs]def map_ai_messages_in_session(chat_sessions: ChatSession, sender: str) -> ChatSession:
"""Convert messages from the specified 'sender' to AI messages.
This is useful for fine-tuning the AI to adapt to your v... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/utils.html |
f1a5c7315c50-0 | Source code for langchain.chat_loaders.whatsapp
import logging
import os
import re
import zipfile
from typing import Iterator, List, Union
from langchain.chat_loaders.base import BaseChatLoader
from langchain.schema import AIMessage, HumanMessage
from langchain.schema.chat import ChatSession
logger = logging.getLogger(... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/whatsapp.html |
f1a5c7315c50-1 | flags=re.IGNORECASE,
)
def _load_single_chat_session(self, file_path: str) -> ChatSession:
"""Load a single chat session from a file.
Args:
file_path (str): Path to the chat file.
Returns:
ChatSession: The loaded chat session.
"""
with open(fil... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/whatsapp.html |
f1a5c7315c50-2 | Args:
path (str): Path to the directory or zip file.
Yields:
str: The path to each file.
"""
if os.path.isfile(path):
yield path
elif os.path.isdir(path):
for root, _, files in os.walk(path):
for file in files:
... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/whatsapp.html |
73b01b0b5397-0 | Source code for langchain.chat_loaders.gmail
import base64
import re
from typing import Any, Iterator
from langchain.chat_loaders.base import BaseChatLoader
from langchain.schema.chat import ChatSession
from langchain.schema.messages import HumanMessage
def _extract_email_content(msg: Any) -> HumanMessage:
from_ema... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/gmail.html |
73b01b0b5397-1 | if in_reply_to is None:
raise ValueError
thread_id = msg["threadId"]
thread = service.users().threads().get(userId="me", id=thread_id).execute()
messages = thread["messages"]
response_email = None
for message in messages:
email_data = message["payload"]["headers"]
for values ... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/gmail.html |
73b01b0b5397-2 | super().__init__()
self.creds = creds
self.n = n
self.raise_error = raise_error
[docs] def lazy_load(self) -> Iterator[ChatSession]:
from googleapiclient.discovery import build
service = build("gmail", "v1", credentials=self.creds)
results = (
service.users... | https://api.python.langchain.com/en/latest/_modules/langchain/chat_loaders/gmail.html |
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