Source code for langchain_community.llms.deepinfra

import json
from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional

import aiohttp
from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain_community.utilities.requests import Requests

DEFAULT_MODEL_ID = "google/flan-t5-xl"


[docs]class DeepInfra(LLM): """DeepInfra models. To use, you should have the environment variable ``DEEPINFRA_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Only supports `text-generation` and `text2text-generation` for now. Example: .. code-block:: python from langchain_community.llms import DeepInfra di = DeepInfra(model_id="google/flan-t5-xl", deepinfra_api_token="my-api-key") """ model_id: str = DEFAULT_MODEL_ID model_kwargs: Optional[Dict] = None deepinfra_api_token: Optional[str] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" deepinfra_api_token = get_from_dict_or_env( values, "deepinfra_api_token", "DEEPINFRA_API_TOKEN" ) values["deepinfra_api_token"] = deepinfra_api_token return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { **{"model_id": self.model_id}, **{"model_kwargs": self.model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "deepinfra" def _url(self) -> str: return f"https://api.deepinfra.com/v1/inference/{self.model_id}" def _headers(self) -> Dict: return { "Authorization": f"bearer {self.deepinfra_api_token}", "Content-Type": "application/json", } def _body(self, prompt: str, kwargs: Any) -> Dict: model_kwargs = self.model_kwargs or {} model_kwargs = {**model_kwargs, **kwargs} return { "input": prompt, **model_kwargs, } def _handle_status(self, code: int, text: Any) -> None: if code >= 500: raise Exception(f"DeepInfra Server: Error {code}") elif code >= 400: raise ValueError(f"DeepInfra received an invalid payload: {text}") elif code != 200: raise Exception( f"DeepInfra returned an unexpected response with status " f"{code}: {text}" ) def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to DeepInfra's inference API endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = di("Tell me a joke.") """ request = Requests(headers=self._headers()) response = request.post(url=self._url(), data=self._body(prompt, kwargs)) self._handle_status(response.status_code, response.text) data = response.json() return data["results"][0]["generated_text"] async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: request = Requests(headers=self._headers()) async with request.apost( url=self._url(), data=self._body(prompt, kwargs) ) as response: self._handle_status(response.status, response.text) data = await response.json() return data["results"][0]["generated_text"] def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: request = Requests(headers=self._headers()) response = request.post( url=self._url(), data=self._body(prompt, {**kwargs, "stream": True}) ) self._handle_status(response.status_code, response.text) for line in _parse_stream(response.iter_lines()): chunk = _handle_sse_line(line) if chunk: if run_manager: run_manager.on_llm_new_token(chunk.text) yield chunk async def _astream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: request = Requests(headers=self._headers()) async with request.apost( url=self._url(), data=self._body(prompt, {**kwargs, "stream": True}) ) as response: self._handle_status(response.status, response.text) async for line in _parse_stream_async(response.content): chunk = _handle_sse_line(line) if chunk: if run_manager: await run_manager.on_llm_new_token(chunk.text) yield chunk
def _parse_stream(rbody: Iterator[bytes]) -> Iterator[str]: for line in rbody: _line = _parse_stream_helper(line) if _line is not None: yield _line async def _parse_stream_async(rbody: aiohttp.StreamReader) -> AsyncIterator[str]: async for line in rbody: _line = _parse_stream_helper(line) if _line is not None: yield _line def _parse_stream_helper(line: bytes) -> Optional[str]: if line and line.startswith(b"data:"): if line.startswith(b"data: "): # SSE event may be valid when it contain whitespace line = line[len(b"data: ") :] else: line = line[len(b"data:") :] if line.strip() == b"[DONE]": # return here will cause GeneratorExit exception in urllib3 # and it will close http connection with TCP Reset return None else: return line.decode("utf-8") return None def _handle_sse_line(line: str) -> Optional[GenerationChunk]: try: obj = json.loads(line) return GenerationChunk( text=obj.get("token", {}).get("text"), ) except Exception: return None