mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
a0c2281540
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
print(path)
with open(path, "rb") as f:
pyproject = tomllib.load(f)
try:
pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
"^1.10"
)
pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
"^0.5"
)
except KeyError:
continue
with open(path, "w") as f:
toml.dump(pyproject, f)
cwd = "/".join(path.split("/")[:-1])
completed = subprocess.run(
"poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
logs = completed.stdout.split("\n")
to_ignore = {}
for l in logs:
if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
path, line_no, error_type = re.match(
"^(.*)\:(\d+)\: error:.*\[(.*)\]", l
).groups()
if (path, line_no) in to_ignore:
to_ignore[(path, line_no)].append(error_type)
else:
to_ignore[(path, line_no)] = [error_type]
print(len(to_ignore))
for (error_path, line_no), error_types in to_ignore.items():
all_errors = ", ".join(error_types)
full_path = f"{cwd}/{error_path}"
try:
with open(full_path, "r") as f:
file_lines = f.readlines()
except FileNotFoundError:
continue
file_lines[int(line_no) - 1] = (
file_lines[int(line_no) - 1][:-1] + f" # type: ignore[{all_errors}]\n"
)
with open(full_path, "w") as f:
f.write("".join(file_lines))
subprocess.run(
"poetry run ruff format .; poetry run ruff --select I --fix .",
cwd=cwd,
shell=True,
capture_output=True,
text=True,
)
if __name__ == "__main__":
main()
```
320 lines
12 KiB
Python
320 lines
12 KiB
Python
"""Wrapper around Anyscale Endpoint"""
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from typing import (
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Any,
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Dict,
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List,
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Mapping,
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Optional,
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Set,
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)
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.outputs import Generation, GenerationChunk, LLMResult
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
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from langchain_community.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_community.utils.openai import is_openai_v1
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DEFAULT_BASE_URL = "https://api.endpoints.anyscale.com/v1"
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DEFAULT_MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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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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class Anyscale(BaseOpenAI):
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"""Anyscale large language models.
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To use, you should have the environment variable ``ANYSCALE_API_KEY``set with your
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Anyscale Endpoint, or pass it as a named parameter to the constructor.
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To use with Anyscale Private Endpoint, please also set ``ANYSCALE_BASE_URL``.
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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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anyscalellm = Anyscale(anyscale_api_key="ANYSCALE_API_KEY")
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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.invoke(text)
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return resp
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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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"""Key word arguments to pass to the model."""
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anyscale_api_base: str = Field(default=DEFAULT_BASE_URL)
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anyscale_api_key: SecretStr = Field(default=None)
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model_name: str = Field(default=DEFAULT_MODEL)
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prefix_messages: List = Field(default_factory=list)
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@classmethod
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def is_lc_serializable(cls) -> bool:
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return False
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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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values["anyscale_api_base"] = get_from_dict_or_env(
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values,
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"anyscale_api_base",
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"ANYSCALE_API_BASE",
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default=DEFAULT_BASE_URL,
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)
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values["anyscale_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "anyscale_api_key", "ANYSCALE_API_KEY")
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)
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values["model_name"] = get_from_dict_or_env(
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values,
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"model_name",
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"MODEL_NAME",
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default=DEFAULT_MODEL,
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)
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try:
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import openai
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if is_openai_v1():
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client_params = {
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"api_key": values["anyscale_api_key"].get_secret_value(),
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"base_url": values["anyscale_api_base"],
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# To do: future support
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# "organization": values["openai_organization"],
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# "timeout": values["request_timeout"],
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# "max_retries": values["max_retries"],
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# "default_headers": values["default_headers"],
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# "default_query": values["default_query"],
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# "http_client": values["http_client"],
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}
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if not values.get("client"):
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values["client"] = openai.OpenAI(**client_params).completions
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if not values.get("async_client"):
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values["async_client"] = openai.AsyncOpenAI(
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**client_params
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).completions
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else:
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values["openai_api_base"] = values["anyscale_api_base"]
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values["openai_api_key"] = values["anyscale_api_key"].get_secret_value()
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values["client"] = openai.Completion
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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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**{"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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"model": self.model_name,
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}
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if not is_openai_v1():
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openai_creds.update(
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{
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"api_key": self.anyscale_api_key.get_secret_value(),
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"api_base": self.anyscale_api_base,
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}
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)
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return {**openai_creds, **super()._invocation_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 LLM"
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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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"""Call out to OpenAI's endpoint with k unique prompts.
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Args:
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prompts: The prompts 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 full LLM output.
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Example:
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.. code-block:: python
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response = openai.generate(["Tell me a joke."])
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"""
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# TODO: write a unit test for this
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params = self._invocation_params
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params = {**params, **kwargs}
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sub_prompts = self.get_sub_prompts(params, prompts, stop)
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choices = []
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token_usage: Dict[str, int] = {}
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# Get the token usage from the response.
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# Includes prompt, completion, and total tokens used.
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_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
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system_fingerprint: Optional[str] = None
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for _prompts in sub_prompts:
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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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generation: Optional[GenerationChunk] = None
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for chunk in self._stream(_prompts[0], 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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"text": 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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response = completion_with_retry(
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## THis is the ONLY change from BaseOpenAI()._generate()
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self,
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prompt=_prompts[0],
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run_manager=run_manager,
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**params,
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)
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if not isinstance(response, dict):
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# V1 client returns the response in an PyDantic object instead of
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# dict. For the transition period, we deep convert it to dict.
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response = response.dict()
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choices.extend(response["choices"])
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update_token_usage(_keys, response, token_usage)
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if not system_fingerprint:
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system_fingerprint = response.get("system_fingerprint")
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return self.create_llm_result(
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choices,
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prompts,
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params,
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token_usage,
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system_fingerprint=system_fingerprint,
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)
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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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"""Call out to OpenAI's endpoint async with k unique prompts."""
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params = self._invocation_params
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params = {**params, **kwargs}
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sub_prompts = self.get_sub_prompts(params, prompts, stop)
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choices = []
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token_usage: Dict[str, int] = {}
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# Get the token usage from the response.
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# Includes prompt, completion, and total tokens used.
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_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
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system_fingerprint: Optional[str] = None
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for _prompts in sub_prompts:
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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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generation: Optional[GenerationChunk] = None
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async for chunk in self._astream(
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_prompts[0], stop, run_manager, **kwargs
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):
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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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"text": 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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response = await acompletion_with_retry(
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## THis is the ONLY change from BaseOpenAI()._agenerate()
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self,
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prompt=_prompts[0],
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run_manager=run_manager,
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**params,
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)
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if not isinstance(response, dict):
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response = response.dict()
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choices.extend(response["choices"])
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update_token_usage(_keys, response, token_usage)
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return self.create_llm_result(
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choices,
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prompts,
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params,
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token_usage,
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system_fingerprint=system_fingerprint,
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)
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