"""Wrapper around Anyscale Endpoint"""
from typing import (
Any,
Dict,
List,
Mapping,
Optional,
Set,
)
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from langchain_community.llms.openai import (
BaseOpenAI,
acompletion_with_retry,
completion_with_retry,
)
from langchain_community.utils.openai import is_openai_v1
DEFAULT_BASE_URL = "https://api.endpoints.anyscale.com/v1"
DEFAULT_MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"
[docs]def update_token_usage(
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
) -> None:
"""Update token usage."""
_keys_to_use = keys.intersection(response["usage"])
for _key in _keys_to_use:
if _key not in token_usage:
token_usage[_key] = response["usage"][_key]
else:
token_usage[_key] += response["usage"][_key]
[docs]def create_llm_result(
choices: Any, prompts: List[str], token_usage: Dict[str, int], model_name: str
) -> LLMResult:
"""Create the LLMResult from the choices and prompts."""
generations = []
for i, _ in enumerate(prompts):
choice = choices[i]
generations.append(
[
Generation(
text=choice["message"]["content"],
generation_info=dict(
finish_reason=choice.get("finish_reason"),
logprobs=choice.get("logprobs"),
),
)
]
)
llm_output = {"token_usage": token_usage, "model_name": model_name}
return LLMResult(generations=generations, llm_output=llm_output)
[docs]class Anyscale(BaseOpenAI):
"""Anyscale large language models.
To use, you should have the environment variable ``ANYSCALE_API_KEY``set with your
Anyscale Endpoint, or pass it as a named parameter to the constructor.
To use with Anyscale Private Endpoint, please also set ``ANYSCALE_BASE_URL``.
Example:
.. code-block:: python
from langchain.llms import Anyscale
anyscalellm = Anyscale(anyscale_api_key="ANYSCALE_API_KEY")
# To leverage Ray for parallel processing
@ray.remote(num_cpus=1)
def send_query(llm, text):
resp = llm(text)
return resp
futures = [send_query.remote(anyscalellm, text) for text in texts]
results = ray.get(futures)
"""
"""Key word arguments to pass to the model."""
anyscale_api_base: str = Field(default=DEFAULT_BASE_URL)
anyscale_api_key: SecretStr = Field(default=None)
model_name: str = Field(default=DEFAULT_MODEL)
prefix_messages: List = Field(default_factory=list)
[docs] @classmethod
def is_lc_serializable(cls) -> bool:
return False
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""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",
default=DEFAULT_BASE_URL,
)
values["anyscale_api_key"] = convert_to_secret_str(
get_from_dict_or_env(values, "anyscale_api_key", "ANYSCALE_API_KEY")
)
values["model_name"] = get_from_dict_or_env(
values,
"model_name",
"MODEL_NAME",
default=DEFAULT_MODEL,
)
try:
import openai
if is_openai_v1():
client_params = {
"api_key": values["anyscale_api_key"].get_secret_value(),
"base_url": values["anyscale_api_base"],
# To do: future support
# "organization": values["openai_organization"],
# "timeout": values["request_timeout"],
# "max_retries": values["max_retries"],
# "default_headers": values["default_headers"],
# "default_query": values["default_query"],
# "http_client": values["http_client"],
}
if not values.get("client"):
values["client"] = openai.OpenAI(**client_params).completions
if not values.get("async_client"):
values["async_client"] = openai.AsyncOpenAI(
**client_params
).completions
else:
values["openai_api_base"] = values["anyscale_api_base"]
values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
values["client"] = openai.Completion
except ImportError:
raise ImportError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
if values["streaming"] and values["n"] > 1:
raise ValueError("Cannot stream results when n > 1.")
if values["streaming"] and values["best_of"] > 1:
raise ValueError("Cannot stream results when best_of > 1.")
return values
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
**{"model_name": self.model_name},
**super()._identifying_params,
}
@property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
openai_creds: Dict[str, Any] = {
"model": self.model_name,
}
if not is_openai_v1():
openai_creds.update(
{
"api_key": self.anyscale_api_key.get_secret_value(),
"api_base": self.anyscale_api_base,
}
)
return {**openai_creds, **super()._invocation_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "Anyscale LLM"
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint with k unique prompts.
Args:
prompts: The prompts to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The full LLM output.
Example:
.. code-block:: python
response = openai.generate(["Tell me a joke."])
"""
# TODO: write a unit test for this
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
for _prompts in sub_prompts:
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
choices.append(
{
"text": generation.text,
"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,
}
)
else:
response = completion_with_retry(
## THis is the ONLY change from BaseOpenAI()._generate()
self,
prompt=_prompts[0],
run_manager=run_manager,
**params,
)
if not isinstance(response, dict):
# V1 client returns the response in an PyDantic object instead of
# dict. For the transition period, we deep convert it to dict.
response = response.dict()
choices.extend(response["choices"])
update_token_usage(_keys, response, token_usage)
if not system_fingerprint:
system_fingerprint = response.get("system_fingerprint")
return self.create_llm_result(
choices,
prompts,
params,
token_usage,
system_fingerprint=system_fingerprint,
)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint async with k unique prompts."""
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = 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
choices.append(
{
"text": generation.text,
"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,
}
)
else:
response = await acompletion_with_retry(
## THis is the ONLY change from BaseOpenAI()._agenerate()
self,
prompt=_prompts[0],
run_manager=run_manager,
**params,
)
if not isinstance(response, dict):
response = response.dict()
choices.extend(response["choices"])
update_token_usage(_keys, response, token_usage)
return self.create_llm_result(
choices,
prompts,
params,
token_usage,
system_fingerprint=system_fingerprint,
)