mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-27 11:41:51 +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()
```
267 lines
9.2 KiB
Python
267 lines
9.2 KiB
Python
"""Wrapper around EdenAI's Generation API."""
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import logging
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from typing import Any, Dict, List, Literal, Optional
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from aiohttp import ClientSession
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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.language_models.llms import LLM
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from langchain_core.pydantic_v1 import Extra, Field, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.llms.utils import enforce_stop_tokens
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from langchain_community.utilities.requests import Requests
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logger = logging.getLogger(__name__)
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class EdenAI(LLM):
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"""EdenAI models.
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To use, you should have
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the environment variable ``EDENAI_API_KEY`` set with your API token.
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You can find your token here: https://app.edenai.run/admin/account/settings
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`feature` and `subfeature` are required, but any other model parameters can also be
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passed in with the format params={model_param: value, ...}
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for api reference check edenai documentation: http://docs.edenai.co.
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"""
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base_url: str = "https://api.edenai.run/v2"
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edenai_api_key: Optional[str] = None
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feature: Literal["text", "image"] = "text"
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"""Which generative feature to use, use text by default"""
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subfeature: Literal["generation"] = "generation"
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"""Subfeature of above feature, use generation by default"""
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provider: str
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"""Generative provider to use (eg: openai,stabilityai,cohere,google etc.)"""
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model: Optional[str] = None
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"""
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model name for above provider (eg: 'gpt-3.5-turbo-instruct' for openai)
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available models are shown on https://docs.edenai.co/ under 'available providers'
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"""
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# Optional parameters to add depending of chosen feature
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# see api reference for more infos
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temperature: Optional[float] = Field(default=None, ge=0, le=1) # for text
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max_tokens: Optional[int] = Field(default=None, ge=0) # for text
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resolution: Optional[Literal["256x256", "512x512", "1024x1024"]] = None # for image
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params: Dict[str, Any] = Field(default_factory=dict)
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"""
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DEPRECATED: use temperature, max_tokens, resolution directly
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optional parameters to pass to api
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"""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""extra parameters"""
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stop_sequences: Optional[List[str]] = None
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"""Stop sequences to use."""
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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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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key exists in environment."""
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values["edenai_api_key"] = get_from_dict_or_env(
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values, "edenai_api_key", "EDENAI_API_KEY"
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)
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return values
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = {field.alias for field in cls.__fields__.values()}
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extra = values.get("model_kwargs", {})
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for field_name in list(values):
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if field_name not in all_required_field_names:
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if field_name in extra:
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raise ValueError(f"Found {field_name} supplied twice.")
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logger.warning(
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f"""{field_name} was transferred to model_kwargs.
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Please confirm that {field_name} is what you intended."""
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)
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extra[field_name] = values.pop(field_name)
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values["model_kwargs"] = extra
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return values
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@property
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def _llm_type(self) -> str:
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"""Return type of model."""
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return "edenai"
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def _format_output(self, output: dict) -> str:
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if self.feature == "text":
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return output[self.provider]["generated_text"]
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else:
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return output[self.provider]["items"][0]["image"]
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@staticmethod
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def get_user_agent() -> str:
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from langchain_community import __version__
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return f"langchain/{__version__}"
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def _call(
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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 EdenAI's text generation endpoint.
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Args:
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prompt: The prompt to pass into the model.
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Returns:
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json formatted str response.
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"""
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stops = None
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if self.stop_sequences is not None and stop is not None:
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raise ValueError(
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"stop sequences found in both the input and default params."
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)
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elif self.stop_sequences is not None:
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stops = self.stop_sequences
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else:
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stops = stop
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url = f"{self.base_url}/{self.feature}/{self.subfeature}"
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headers = {
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"Authorization": f"Bearer {self.edenai_api_key}",
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"User-Agent": self.get_user_agent(),
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}
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payload: Dict[str, Any] = {
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"providers": self.provider,
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"text": prompt,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"resolution": self.resolution,
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**self.params,
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**kwargs,
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"num_images": 1, # always limit to 1 (ignored for text)
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}
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# filter None values to not pass them to the http payload
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payload = {k: v for k, v in payload.items() if v is not None}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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request = Requests(headers=headers)
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response = request.post(url=url, data=payload)
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if response.status_code >= 500:
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raise Exception(f"EdenAI Server: Error {response.status_code}")
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elif response.status_code >= 400:
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raise ValueError(f"EdenAI received an invalid payload: {response.text}")
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elif response.status_code != 200:
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raise Exception(
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f"EdenAI returned an unexpected response with status "
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f"{response.status_code}: {response.text}"
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)
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data = response.json()
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provider_response = data[self.provider]
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if provider_response.get("status") == "fail":
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err_msg = provider_response.get("error", {}).get("message")
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raise Exception(err_msg)
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output = self._format_output(data)
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if stops is not None:
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output = enforce_stop_tokens(output, stops)
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return output
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async def _acall(
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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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) -> str:
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"""Call EdenAi model to get predictions based on the prompt.
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Args:
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prompt: The prompt to pass into the model.
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stop: A list of stop words (optional).
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run_manager: A callback manager for async interaction with LLMs.
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Returns:
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The string generated by the model.
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"""
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stops = None
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if self.stop_sequences is not None and stop is not None:
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raise ValueError(
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"stop sequences found in both the input and default params."
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)
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elif self.stop_sequences is not None:
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stops = self.stop_sequences
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else:
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stops = stop
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url = f"{self.base_url}/{self.feature}/{self.subfeature}"
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headers = {
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"Authorization": f"Bearer {self.edenai_api_key}",
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"User-Agent": self.get_user_agent(),
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}
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payload: Dict[str, Any] = {
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"providers": self.provider,
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"text": prompt,
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"max_tokens": self.max_tokens,
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"temperature": self.temperature,
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"resolution": self.resolution,
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**self.params,
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**kwargs,
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"num_images": 1, # always limit to 1 (ignored for text)
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}
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# filter `None` values to not pass them to the http payload as null
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payload = {k: v for k, v in payload.items() if v is not None}
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if self.model is not None:
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payload["settings"] = {self.provider: self.model}
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async with ClientSession() as session:
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async with session.post(url, json=payload, headers=headers) as response:
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if response.status >= 500:
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raise Exception(f"EdenAI Server: Error {response.status}")
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elif response.status >= 400:
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raise ValueError(
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f"EdenAI received an invalid payload: {response.text}"
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)
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elif response.status != 200:
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raise Exception(
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f"EdenAI returned an unexpected response with status "
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f"{response.status}: {response.text}"
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)
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response_json = await response.json()
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provider_response = response_json[self.provider]
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if provider_response.get("status") == "fail":
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err_msg = provider_response.get("error", {}).get("message")
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raise Exception(err_msg)
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output = self._format_output(response_json)
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if stops is not None:
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output = enforce_stop_tokens(output, stops)
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return output
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