mirror of
https://github.com/hwchase17/langchain.git
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community[major], core[patch], langchain[patch], experimental[patch]: Create langchain-community (#14463)
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
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219
libs/community/langchain_community/llms/deepinfra.py
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219
libs/community/langchain_community/llms/deepinfra.py
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import json
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from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional
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import aiohttp
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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.outputs import GenerationChunk
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from langchain_core.pydantic_v1 import Extra, root_validator
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from langchain_core.utils import get_from_dict_or_env
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from langchain_community.utilities.requests import Requests
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DEFAULT_MODEL_ID = "google/flan-t5-xl"
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class DeepInfra(LLM):
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"""DeepInfra models.
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To use, you should have the environment variable ``DEEPINFRA_API_TOKEN``
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set with your API token, or pass it as a named parameter to the
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constructor.
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Only supports `text-generation` and `text2text-generation` for now.
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Example:
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.. code-block:: python
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from langchain_community.llms import DeepInfra
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di = DeepInfra(model_id="google/flan-t5-xl",
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deepinfra_api_token="my-api-key")
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"""
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model_id: str = DEFAULT_MODEL_ID
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model_kwargs: Optional[Dict] = None
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deepinfra_api_token: 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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@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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deepinfra_api_token = get_from_dict_or_env(
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values, "deepinfra_api_token", "DEEPINFRA_API_TOKEN"
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)
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values["deepinfra_api_token"] = deepinfra_api_token
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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_id": self.model_id},
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**{"model_kwargs": self.model_kwargs},
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}
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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 "deepinfra"
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def _url(self) -> str:
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return f"https://api.deepinfra.com/v1/inference/{self.model_id}"
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def _headers(self) -> Dict:
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return {
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"Authorization": f"bearer {self.deepinfra_api_token}",
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"Content-Type": "application/json",
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}
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def _body(self, prompt: str, kwargs: Any) -> Dict:
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model_kwargs = self.model_kwargs or {}
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model_kwargs = {**model_kwargs, **kwargs}
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return {
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"input": prompt,
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**model_kwargs,
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}
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def _handle_status(self, code: int, text: Any) -> None:
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if code >= 500:
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raise Exception(f"DeepInfra Server: Error {code}")
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elif code >= 400:
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raise ValueError(f"DeepInfra received an invalid payload: {text}")
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elif code != 200:
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raise Exception(
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f"DeepInfra returned an unexpected response with status "
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f"{code}: {text}"
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)
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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 DeepInfra's inference API 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 = di("Tell me a joke.")
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"""
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request = Requests(headers=self._headers())
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response = request.post(url=self._url(), data=self._body(prompt, kwargs))
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self._handle_status(response.status_code, response.text)
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data = response.json()
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return data["results"][0]["generated_text"]
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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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request = Requests(headers=self._headers())
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async with request.apost(
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url=self._url(), data=self._body(prompt, kwargs)
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) as response:
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self._handle_status(response.status, response.text)
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data = await response.json()
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return data["results"][0]["generated_text"]
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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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) -> Iterator[GenerationChunk]:
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request = Requests(headers=self._headers())
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response = request.post(
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url=self._url(), data=self._body(prompt, {**kwargs, "stream": True})
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)
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self._handle_status(response.status_code, response.text)
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for line in _parse_stream(response.iter_lines()):
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chunk = _handle_sse_line(line)
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if chunk:
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(chunk.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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request = Requests(headers=self._headers())
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async with request.apost(
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url=self._url(), data=self._body(prompt, {**kwargs, "stream": True})
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) as response:
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self._handle_status(response.status, response.text)
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async for line in _parse_stream_async(response.content):
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chunk = _handle_sse_line(line)
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if chunk:
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(chunk.text)
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def _parse_stream(rbody: Iterator[bytes]) -> Iterator[str]:
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for line in rbody:
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_line = _parse_stream_helper(line)
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if _line is not None:
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yield _line
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async def _parse_stream_async(rbody: aiohttp.StreamReader) -> AsyncIterator[str]:
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async for line in rbody:
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_line = _parse_stream_helper(line)
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if _line is not None:
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yield _line
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def _parse_stream_helper(line: bytes) -> Optional[str]:
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if line and line.startswith(b"data:"):
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if line.startswith(b"data: "):
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# SSE event may be valid when it contain whitespace
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line = line[len(b"data: ") :]
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else:
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line = line[len(b"data:") :]
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if line.strip() == b"[DONE]":
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# return here will cause GeneratorExit exception in urllib3
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# and it will close http connection with TCP Reset
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return None
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else:
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return line.decode("utf-8")
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return None
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def _handle_sse_line(line: str) -> Optional[GenerationChunk]:
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try:
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obj = json.loads(line)
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return GenerationChunk(
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text=obj.get("token", {}).get("text"),
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)
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except Exception:
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return None
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