mirror of https://github.com/hwchase17/langchain
Modify Anyscale integration to work with Anyscale Endpoint (#11569)
**Description:** Modify Anyscale integration to work with [Anyscale Endpoint](https://docs.endpoints.anyscale.com/) and it supports invoke, async invoke, stream and async invoke features --------- Co-authored-by: Bagatur <baskaryan@gmail.com>pull/11718/head
kYLe
12 months ago
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GitHub
parent
51193309ea
commit
467b082c34
@ -1,126 +1,272 @@
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from typing import Any, Dict, List, Mapping, Optional
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"""Wrapper around Anyscale Endpoint"""
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from typing import (
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Any,
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AsyncIterator,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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Set,
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Tuple,
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)
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import requests
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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from langchain.llms.base import LLM
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from langchain.llms.utils import enforce_stop_tokens
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from langchain.pydantic_v1 import Extra, root_validator
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.llms.openai import (
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BaseOpenAI,
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acompletion_with_retry,
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completion_with_retry,
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)
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from langchain.pydantic_v1 import Field, root_validator
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from langchain.schema import Generation, LLMResult
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from langchain.schema.output import GenerationChunk
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from langchain.utils import get_from_dict_or_env
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class Anyscale(LLM):
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"""Anyscale Service models.
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def update_token_usage(
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keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
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) -> None:
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"""Update token usage."""
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_keys_to_use = keys.intersection(response["usage"])
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for _key in _keys_to_use:
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if _key not in token_usage:
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token_usage[_key] = response["usage"][_key]
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else:
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token_usage[_key] += response["usage"][_key]
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def create_llm_result(
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choices: Any, prompts: List[str], token_usage: Dict[str, int], model_name: str
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) -> LLMResult:
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"""Create the LLMResult from the choices and prompts."""
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generations = []
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for i, _ in enumerate(prompts):
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choice = choices[i]
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generations.append(
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[
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Generation(
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text=choice["message"]["content"],
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generation_info=dict(
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finish_reason=choice.get("finish_reason"),
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logprobs=choice.get("logprobs"),
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),
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)
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]
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)
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llm_output = {"token_usage": token_usage, "model_name": model_name}
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return LLMResult(generations=generations, llm_output=llm_output)
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To use, you should have the environment variable ``ANYSCALE_SERVICE_URL``,
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``ANYSCALE_SERVICE_ROUTE`` and ``ANYSCALE_SERVICE_TOKEN`` set with your Anyscale
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Service, or pass it as a named parameter to the constructor.
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class Anyscale(BaseOpenAI):
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"""Wrapper around Anyscale Endpoint.
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To use, you should have the environment variable ``ANYSCALE_API_BASE`` and
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``ANYSCALE_API_KEY``set with your Anyscale Endpoint, or pass it as a named
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parameter to the constructor.
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Example:
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.. code-block:: python
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from langchain.llms import Anyscale
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anyscale = Anyscale(anyscale_service_url="SERVICE_URL",
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anyscale_service_route="SERVICE_ROUTE",
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anyscale_service_token="SERVICE_TOKEN")
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# Use Ray for distributed processing
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import ray
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prompt_list=[]
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@ray.remote
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def send_query(llm, prompt):
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resp = llm(prompt)
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anyscalellm = Anyscale(anyscale_api_base="ANYSCALE_API_BASE",
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anyscale_api_key="ANYSCALE_API_KEY",
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model_name="meta-llama/Llama-2-7b-chat-hf")
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# To leverage Ray for parallel processing
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@ray.remote(num_cpus=1)
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def send_query(llm, text):
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resp = llm(text)
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return resp
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futures = [send_query.remote(anyscale, prompt) for prompt in prompt_list]
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futures = [send_query.remote(anyscalellm, text) for text in texts]
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results = ray.get(futures)
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"""
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model_kwargs: Optional[dict] = None
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"""Keyword arguments to pass to the model. Reserved for future use"""
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anyscale_service_url: Optional[str] = None
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anyscale_service_route: Optional[str] = None
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anyscale_service_token: Optional[str] = None
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"""Key word arguments to pass to the model."""
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anyscale_api_base: Optional[str] = None
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anyscale_api_key: Optional[str] = None
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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prefix_messages: List = Field(default_factory=list)
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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anyscale_service_url = get_from_dict_or_env(
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values, "anyscale_service_url", "ANYSCALE_SERVICE_URL"
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)
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anyscale_service_route = get_from_dict_or_env(
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values, "anyscale_service_route", "ANYSCALE_SERVICE_ROUTE"
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values["anyscale_api_base"] = get_from_dict_or_env(
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values, "anyscale_api_base", "ANYSCALE_API_BASE"
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)
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anyscale_service_token = get_from_dict_or_env(
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values, "anyscale_service_token", "ANYSCALE_SERVICE_TOKEN"
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values["anyscale_api_key"] = get_from_dict_or_env(
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values, "anyscale_api_key", "ANYSCALE_API_KEY"
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)
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if anyscale_service_url.endswith("/"):
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anyscale_service_url = anyscale_service_url[:-1]
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if not anyscale_service_route.startswith("/"):
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anyscale_service_route = "/" + anyscale_service_route
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try:
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anyscale_service_endpoint = f"{anyscale_service_url}/-/routes"
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headers = {"Authorization": f"Bearer {anyscale_service_token}"}
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requests.get(anyscale_service_endpoint, headers=headers)
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except requests.exceptions.RequestException as e:
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raise ValueError(e)
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values["anyscale_service_url"] = anyscale_service_url
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values["anyscale_service_route"] = anyscale_service_route
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values["anyscale_service_token"] = anyscale_service_token
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import openai
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## Always create ChatComplete client, replacing the legacy Complete client
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values["client"] = openai.ChatCompletion
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except ImportError:
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raise ImportError(
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"Could not import openai python package. "
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"Please install it with `pip install openai`."
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)
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if values["streaming"] and values["n"] > 1:
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raise ValueError("Cannot stream results when n > 1.")
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if values["streaming"] and values["best_of"] > 1:
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raise ValueError("Cannot stream results when best_of > 1.")
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {
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"anyscale_service_url": self.anyscale_service_url,
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"anyscale_service_route": self.anyscale_service_route,
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**{"model_name": self.model_name},
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**super()._identifying_params,
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}
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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"""Get the parameters used to invoke the model."""
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openai_creds: Dict[str, Any] = {
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"api_key": self.anyscale_api_key,
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"api_base": self.anyscale_api_base,
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}
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return {**openai_creds, **{"model": self.model_name}, **super()._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "anyscale"
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return "Anyscale LLM"
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def _get_chat_messages(
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self, prompts: List[str], stop: Optional[List[str]] = None
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) -> Tuple:
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if len(prompts) > 1:
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raise ValueError(
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f"Anyscale currently only supports single prompt, got {prompts}"
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)
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messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}]
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params: Dict[str, Any] = self._invocation_params
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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if params.get("max_tokens") == -1:
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# for Chat api, omitting max_tokens is equivalent to having no limit
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del params["max_tokens"]
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return messages, params
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def _call(
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def _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""Call out to Anyscale Service endpoint.
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Args:
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prompt: The prompt to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The string generated by the model.
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Example:
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.. code-block:: python
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response = anyscale("Tell me a joke.")
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"""
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anyscale_service_endpoint = (
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f"{self.anyscale_service_url}{self.anyscale_service_route}"
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)
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headers = {"Authorization": f"Bearer {self.anyscale_service_token}"}
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body = {"prompt": prompt}
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resp = requests.post(anyscale_service_endpoint, headers=headers, json=body)
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) -> Iterator[GenerationChunk]:
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messages, params = self._get_chat_messages([prompt], stop)
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params = {**params, **kwargs, "stream": True}
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for stream_resp in completion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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):
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token = stream_resp["choices"][0]["delta"].get("content", "")
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chunk = GenerationChunk(text=token)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(token, chunk=chunk)
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if resp.status_code != 200:
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raise ValueError(
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f"Error returned by service, status code {resp.status_code}"
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)
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text = resp.text
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async def _astream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[GenerationChunk]:
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messages, params = self._get_chat_messages([prompt], stop)
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params = {**params, **kwargs, "stream": True}
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async for stream_resp in await acompletion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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):
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token = stream_resp["choices"][0]["delta"].get("content", "")
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chunk = GenerationChunk(text=token)
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(token, chunk=chunk)
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if stop is not None:
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# This is a bit hacky, but I can't figure out a better way to enforce
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# stop tokens when making calls to huggingface_hub.
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text = enforce_stop_tokens(text, stop)
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return text
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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choices = []
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token_usage: Dict[str, int] = {}
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_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
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for prompt in prompts:
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if self.streaming:
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generation: Optional[GenerationChunk] = None
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for chunk in self._stream(prompt, stop, run_manager, **kwargs):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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choices.append(
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{
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"message": {"content": generation.text},
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"finish_reason": generation.generation_info.get("finish_reason")
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if generation.generation_info
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else None,
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"logprobs": generation.generation_info.get("logprobs")
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if generation.generation_info
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else None,
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}
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)
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else:
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messages, params = self._get_chat_messages([prompt], stop)
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params = {**params, **kwargs}
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response = completion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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)
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choices.extend(response["choices"])
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update_token_usage(_keys, response, token_usage)
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return create_llm_result(choices, prompts, token_usage, self.model_name)
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async def _agenerate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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choices = []
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token_usage: Dict[str, int] = {}
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_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
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for prompt in prompts:
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messages = self.prefix_messages + [{"role": "user", "content": prompt}]
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if self.streaming:
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generation: Optional[GenerationChunk] = None
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async for chunk in self._astream(prompt, stop, run_manager, **kwargs):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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choices.append(
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{
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"message": {"content": generation.text},
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"finish_reason": generation.generation_info.get("finish_reason")
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if generation.generation_info
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else None,
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"logprobs": generation.generation_info.get("logprobs")
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if generation.generation_info
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else None,
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}
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)
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else:
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messages, params = self._get_chat_messages([prompt], stop)
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params = {**params, **kwargs}
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response = await acompletion_with_retry(
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self, messages=messages, run_manager=run_manager, **params
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)
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choices.extend(response["choices"])
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update_token_usage(_keys, response, token_usage)
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return create_llm_result(choices, prompts, token_usage, self.model_name)
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