from __future__ import annotations
import os
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.load.serializable import Serializable
from langchain_core.outputs import GenerationChunk, LLMResult
from langchain_core.pydantic_v1 import Field, SecretStr
from langchain_core.utils import pre_init
from langchain_core.utils.env import get_from_dict_or_env
from langchain_core.utils.utils import convert_to_secret_str
def _stream_response_to_generation_chunk(stream_response: Any) -> GenerationChunk:
"""Convert a stream response to a generation chunk."""
if stream_response.event == "token_sampled":
return GenerationChunk(
text=stream_response.text,
generation_info={"token": str(stream_response.token)},
)
return GenerationChunk(text="")
[docs]class BaseFriendli(Serializable):
"""Base class of Friendli."""
# Friendli client.
client: Any = Field(default=None, exclude=True)
# Friendli Async client.
async_client: Any = Field(default=None, exclude=True)
# Model name to use.
model: str = "mixtral-8x7b-instruct-v0-1"
# Friendli personal access token to run as.
friendli_token: Optional[SecretStr] = None
# Friendli team ID to run as.
friendli_team: Optional[str] = None
# Whether to enable streaming mode.
streaming: bool = False
# Number between -2.0 and 2.0. Positive values penalizes tokens that have been
# sampled, taking into account their frequency in the preceding text. This
# penalization diminishes the model's tendency to reproduce identical lines
# verbatim.
frequency_penalty: Optional[float] = None
# Number between -2.0 and 2.0. Positive values penalizes tokens that have been
# sampled at least once in the existing text.
presence_penalty: Optional[float] = None
# The maximum number of tokens to generate. The length of your input tokens plus
# `max_tokens` should not exceed the model's maximum length (e.g., 2048 for OpenAI
# GPT-3)
max_tokens: Optional[int] = None
# When one of the stop phrases appears in the generation result, the API will stop
# generation. The phrase is included in the generated result. If you are using
# beam search, all of the active beams should contain the stop phrase to terminate
# generation. Before checking whether a stop phrase is included in the result, the
# phrase is converted into tokens.
stop: Optional[List[str]] = None
# Sampling temperature. Smaller temperature makes the generation result closer to
# greedy, argmax (i.e., `top_k = 1`) sampling. If it is `None`, then 1.0 is used.
temperature: Optional[float] = None
# Tokens comprising the top `top_p` probability mass are kept for sampling. Numbers
# between 0.0 (exclusive) and 1.0 (inclusive) are allowed. If it is `None`, then 1.0
# is used by default.
top_p: Optional[float] = None
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
"""Validate if personal access token is provided in environment."""
try:
import friendli
except ImportError as e:
raise ImportError(
"Could not import friendli-client python package. "
"Please install it with `pip install friendli-client`."
) from e
friendli_token = convert_to_secret_str(
get_from_dict_or_env(values, "friendli_token", "FRIENDLI_TOKEN")
)
values["friendli_token"] = friendli_token
friendli_token_str = friendli_token.get_secret_value()
friendli_team = values["friendli_team"] or os.getenv("FRIENDLI_TEAM")
values["friendli_team"] = friendli_team
values["client"] = values["client"] or friendli.Friendli(
token=friendli_token_str, team_id=friendli_team
)
values["async_client"] = values["async_client"] or friendli.AsyncFriendli(
token=friendli_token_str, team_id=friendli_team
)
return values
[docs]class Friendli(LLM, BaseFriendli):
"""Friendli LLM.
``friendli-client`` package should be installed with `pip install friendli-client`.
You must set ``FRIENDLI_TOKEN`` environment variable or provide the value of your
personal access token for the ``friendli_token`` argument.
Example:
.. code-block:: python
from langchain_community.llms import Friendli
friendli = Friendli(
model="mixtral-8x7b-instruct-v0-1", friendli_token="YOUR FRIENDLI TOKEN"
)
"""
@property
def lc_secrets(self) -> Dict[str, str]:
return {"friendli_token": "FRIENDLI_TOKEN"}
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Friendli completions API."""
return {
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"max_tokens": self.max_tokens,
"stop": self.stop,
"temperature": self.temperature,
"top_p": self.top_p,
}
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {"model": self.model, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "friendli"
def _get_invocation_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
params = self._default_params
if self.stop is not None and stop is not None:
raise ValueError("`stop` found in both the input and default params.")
elif self.stop is not None:
params["stop"] = self.stop
else:
params["stop"] = stop
return {**params, **kwargs}
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out Friendli's completions API.
Args:
prompt (str): The text prompt to generate completion for.
stop (Optional[List[str]], optional): When one of the stop phrases appears
in the generation result, the API will stop generation. The stop phrases
are excluded from the result. If beam search is enabled, all of the
active beams should contain the stop phrase to terminate generation.
Before checking whether a stop phrase is included in the result, the
phrase is converted into tokens. We recommend using stop_tokens because
it is clearer. For example, after tokenization, phrases "clear" and
" clear" can result in different token sequences due to the prepended
space character. Defaults to None.
Returns:
str: The generated text output.
Example:
.. code-block:: python
response = frienldi("Give me a recipe for the Old Fashioned cocktail.")
"""
params = self._get_invocation_params(stop=stop, **kwargs)
completion = self.client.completions.create(
model=self.model, prompt=prompt, stream=False, **params
)
return completion.choices[0].text
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out Friendli's completions API Asynchronously.
Args:
prompt (str): The text prompt to generate completion for.
stop (Optional[List[str]], optional): When one of the stop phrases appears
in the generation result, the API will stop generation. The stop phrases
are excluded from the result. If beam search is enabled, all of the
active beams should contain the stop phrase to terminate generation.
Before checking whether a stop phrase is included in the result, the
phrase is converted into tokens. We recommend using stop_tokens because
it is clearer. For example, after tokenization, phrases "clear" and
" clear" can result in different token sequences due to the prepended
space character. Defaults to None.
Returns:
str: The generated text output.
Example:
.. code-block:: python
response = await frienldi("Tell me a joke.")
"""
params = self._get_invocation_params(stop=stop, **kwargs)
completion = await self.async_client.completions.create(
model=self.model, prompt=prompt, stream=False, **params
)
return completion.choices[0].text
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = self._get_invocation_params(stop=stop, **kwargs)
stream = self.client.completions.create(
model=self.model, prompt=prompt, stream=True, **params
)
for line in stream:
chunk = _stream_response_to_generation_chunk(line)
yield chunk
if run_manager:
run_manager.on_llm_new_token(line.text, chunk=chunk)
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
params = self._get_invocation_params(stop=stop, **kwargs)
stream = await self.async_client.completions.create(
model=self.model, prompt=prompt, stream=True, **params
)
async for line in stream:
chunk = _stream_response_to_generation_chunk(line)
yield chunk
if run_manager:
await run_manager.on_llm_new_token(line.text, chunk=chunk)
def _generate(
self,
prompts: list[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out Friendli's completions API with k unique prompts.
Args:
prompt (str): The text prompt to generate completion for.
stop (Optional[List[str]], optional): When one of the stop phrases appears
in the generation result, the API will stop generation. The stop phrases
are excluded from the result. If beam search is enabled, all of the
active beams should contain the stop phrase to terminate generation.
Before checking whether a stop phrase is included in the result, the
phrase is converted into tokens. We recommend using stop_tokens because
it is clearer. For example, after tokenization, phrases "clear" and
" clear" can result in different token sequences due to the prepended
space character. Defaults to None.
Returns:
str: The generated text output.
Example:
.. code-block:: python
response = frienldi.generate(["Tell me a joke."])
"""
llm_output = {"model": self.model}
if self.streaming:
if len(prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = None
for chunk in self._stream(prompts[0], stop, run_manager, **kwargs):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
return LLMResult(generations=[[generation]], llm_output=llm_output)
llm_result = super()._generate(prompts, stop, run_manager, **kwargs)
llm_result.llm_output = llm_output
return llm_result
async def _agenerate(
self,
prompts: list[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out Friendli's completions API asynchronously with k unique prompts.
Args:
prompt (str): The text prompt to generate completion for.
stop (Optional[List[str]], optional): When one of the stop phrases appears
in the generation result, the API will stop generation. The stop phrases
are excluded from the result. If beam search is enabled, all of the
active beams should contain the stop phrase to terminate generation.
Before checking whether a stop phrase is included in the result, the
phrase is converted into tokens. We recommend using stop_tokens because
it is clearer. For example, after tokenization, phrases "clear" and
" clear" can result in different token sequences due to the prepended
space character. Defaults to None.
Returns:
str: The generated text output.
Example:
.. code-block:: python
response = await frienldi.agenerate(
["Give me a recipe for the Old Fashioned cocktail."]
)
"""
llm_output = {"model": self.model}
if self.streaming:
if len(prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation = None
async for chunk in self._astream(prompts[0], stop, run_manager, **kwargs):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
return LLMResult(generations=[[generation]], llm_output=llm_output)
llm_result = await super()._agenerate(prompts, stop, run_manager, **kwargs)
llm_result.llm_output = llm_output
return llm_result
