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b2fd41331e
Addded missed docstrings. Fixed inconsistency in docstrings. **Note** CC @efriis There were PR errors on `langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py` But, I didn't touch this file in this PR! Can it be some cache problems? I fixed this error.
418 lines
14 KiB
Python
418 lines
14 KiB
Python
import json
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import logging
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from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
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import requests
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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 Field
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logger = logging.getLogger(__name__)
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class TextGen(LLM):
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"""Text generation models from WebUI.
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To use, you should have the text-generation-webui installed, a model loaded,
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and --api added as a command-line option.
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Suggested installation, use one-click installer for your OS:
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https://github.com/oobabooga/text-generation-webui#one-click-installers
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Parameters below taken from text-generation-webui api example:
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https://github.com/oobabooga/text-generation-webui/blob/main/api-examples/api-example.py
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Example:
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.. code-block:: python
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from langchain_community.llms import TextGen
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llm = TextGen(model_url="http://localhost:8500")
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"""
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model_url: str
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"""The full URL to the textgen webui including http[s]://host:port """
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preset: Optional[str] = None
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"""The preset to use in the textgen webui """
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max_new_tokens: Optional[int] = 250
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"""The maximum number of tokens to generate."""
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do_sample: bool = Field(True, alias="do_sample")
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"""Do sample"""
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temperature: Optional[float] = 1.3
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"""Primary factor to control randomness of outputs. 0 = deterministic
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(only the most likely token is used). Higher value = more randomness."""
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top_p: Optional[float] = 0.1
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"""If not set to 1, select tokens with probabilities adding up to less than this
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number. Higher value = higher range of possible random results."""
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typical_p: Optional[float] = 1
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"""If not set to 1, select only tokens that are at least this much more likely to
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appear than random tokens, given the prior text."""
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epsilon_cutoff: Optional[float] = 0 # In units of 1e-4
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"""Epsilon cutoff"""
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eta_cutoff: Optional[float] = 0 # In units of 1e-4
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"""ETA cutoff"""
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repetition_penalty: Optional[float] = 1.18
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"""Exponential penalty factor for repeating prior tokens. 1 means no penalty,
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higher value = less repetition, lower value = more repetition."""
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top_k: Optional[float] = 40
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"""Similar to top_p, but select instead only the top_k most likely tokens.
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Higher value = higher range of possible random results."""
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min_length: Optional[int] = 0
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"""Minimum generation length in tokens."""
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no_repeat_ngram_size: Optional[int] = 0
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"""If not set to 0, specifies the length of token sets that are completely blocked
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from repeating at all. Higher values = blocks larger phrases,
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lower values = blocks words or letters from repeating.
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Only 0 or high values are a good idea in most cases."""
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num_beams: Optional[int] = 1
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"""Number of beams"""
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penalty_alpha: Optional[float] = 0
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"""Penalty Alpha"""
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length_penalty: Optional[float] = 1
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"""Length Penalty"""
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early_stopping: bool = Field(False, alias="early_stopping")
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"""Early stopping"""
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seed: int = Field(-1, alias="seed")
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"""Seed (-1 for random)"""
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add_bos_token: bool = Field(True, alias="add_bos_token")
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"""Add the bos_token to the beginning of prompts.
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Disabling this can make the replies more creative."""
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truncation_length: Optional[int] = 2048
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"""Truncate the prompt up to this length. The leftmost tokens are removed if
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the prompt exceeds this length. Most models require this to be at most 2048."""
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ban_eos_token: bool = Field(False, alias="ban_eos_token")
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"""Ban the eos_token. Forces the model to never end the generation prematurely."""
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skip_special_tokens: bool = Field(True, alias="skip_special_tokens")
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"""Skip special tokens. Some specific models need this unset."""
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stopping_strings: Optional[List[str]] = []
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"""A list of strings to stop generation when encountered."""
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streaming: bool = False
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"""Whether to stream the results, token by token."""
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling textgen."""
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return {
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"max_new_tokens": self.max_new_tokens,
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"do_sample": self.do_sample,
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"temperature": self.temperature,
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"top_p": self.top_p,
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"typical_p": self.typical_p,
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"epsilon_cutoff": self.epsilon_cutoff,
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"eta_cutoff": self.eta_cutoff,
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"repetition_penalty": self.repetition_penalty,
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"top_k": self.top_k,
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"min_length": self.min_length,
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"no_repeat_ngram_size": self.no_repeat_ngram_size,
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"num_beams": self.num_beams,
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"penalty_alpha": self.penalty_alpha,
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"length_penalty": self.length_penalty,
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"early_stopping": self.early_stopping,
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"seed": self.seed,
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"add_bos_token": self.add_bos_token,
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"truncation_length": self.truncation_length,
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"ban_eos_token": self.ban_eos_token,
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"skip_special_tokens": self.skip_special_tokens,
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"stopping_strings": self.stopping_strings,
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}
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model_url": self.model_url}, **self._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 "textgen"
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def _get_parameters(self, stop: Optional[List[str]] = None) -> Dict[str, Any]:
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"""
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Performs sanity check, preparing parameters in format needed by textgen.
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Args:
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stop (Optional[List[str]]): List of stop sequences for textgen.
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Returns:
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Dictionary containing the combined parameters.
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"""
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# Raise error if stop sequences are in both input and default params
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# if self.stop and stop is not None:
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if self.stopping_strings and stop is not None:
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raise ValueError("`stop` found in both the input and default params.")
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if self.preset is None:
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params = self._default_params
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else:
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params = {"preset": self.preset}
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# then sets it as configured, or default to an empty list:
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params["stopping_strings"] = self.stopping_strings or stop or []
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return params
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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 the textgen web API and return the output.
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Args:
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prompt: The prompt to use for generation.
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stop: A list of strings to stop generation when encountered.
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Returns:
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The generated text.
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Example:
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.. code-block:: python
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from langchain_community.llms import TextGen
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llm = TextGen(model_url="http://localhost:5000")
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llm("Write a story about llamas.")
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"""
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if self.streaming:
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combined_text_output = ""
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for chunk in self._stream(
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prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
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):
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combined_text_output += chunk.text
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result = combined_text_output
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else:
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url = f"{self.model_url}/api/v1/generate"
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params = self._get_parameters(stop)
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request = params.copy()
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request["prompt"] = prompt
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response = requests.post(url, json=request)
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if response.status_code == 200:
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result = response.json()["results"][0]["text"]
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else:
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print(f"ERROR: response: {response}")
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result = ""
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return result
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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 the textgen web API and return the output.
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Args:
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prompt: The prompt to use for generation.
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stop: A list of strings to stop generation when encountered.
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Returns:
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The generated text.
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Example:
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.. code-block:: python
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from langchain_community.llms import TextGen
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llm = TextGen(model_url="http://localhost:5000")
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llm("Write a story about llamas.")
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"""
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if self.streaming:
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combined_text_output = ""
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async for chunk in self._astream(
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prompt=prompt, stop=stop, run_manager=run_manager, **kwargs
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):
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combined_text_output += chunk.text
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result = combined_text_output
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else:
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url = f"{self.model_url}/api/v1/generate"
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params = self._get_parameters(stop)
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request = params.copy()
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request["prompt"] = prompt
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response = requests.post(url, json=request)
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if response.status_code == 200:
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result = response.json()["results"][0]["text"]
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else:
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print(f"ERROR: response: {response}")
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result = ""
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return result
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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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"""Yields results objects as they are generated in real time.
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It also calls the callback manager's on_llm_new_token event with
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similar parameters to the OpenAI LLM class method of the same name.
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Args:
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prompt: 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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A generator representing the stream of tokens being generated.
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Yields:
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A dictionary like objects containing a string token and metadata.
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See text-generation-webui docs and below for more.
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Example:
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.. code-block:: python
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from langchain_community.llms import TextGen
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llm = TextGen(
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model_url = "ws://localhost:5005"
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streaming=True
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)
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for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'",
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stop=["'","\n"]):
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print(chunk, end='', flush=True)
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"""
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try:
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import websocket
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except ImportError:
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raise ImportError(
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"The `websocket-client` package is required for streaming."
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)
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params = {**self._get_parameters(stop), **kwargs}
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url = f"{self.model_url}/api/v1/stream"
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request = params.copy()
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request["prompt"] = prompt
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websocket_client = websocket.WebSocket()
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websocket_client.connect(url)
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websocket_client.send(json.dumps(request))
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while True:
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result = websocket_client.recv()
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result = json.loads(result)
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if result["event"] == "text_stream":
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chunk = GenerationChunk(
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text=result["text"],
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generation_info=None,
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)
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yield chunk
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elif result["event"] == "stream_end":
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websocket_client.close()
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return
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if run_manager:
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run_manager.on_llm_new_token(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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"""Yields results objects as they are generated in real time.
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It also calls the callback manager's on_llm_new_token event with
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similar parameters to the OpenAI LLM class method of the same name.
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Args:
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prompt: 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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A generator representing the stream of tokens being generated.
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Yields:
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A dictionary like objects containing a string token and metadata.
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See text-generation-webui docs and below for more.
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Example:
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.. code-block:: python
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from langchain_community.llms import TextGen
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llm = TextGen(
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model_url = "ws://localhost:5005"
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streaming=True
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)
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for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'",
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stop=["'","\n"]):
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print(chunk, end='', flush=True)
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"""
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try:
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import websocket
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except ImportError:
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raise ImportError(
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"The `websocket-client` package is required for streaming."
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)
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params = {**self._get_parameters(stop), **kwargs}
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url = f"{self.model_url}/api/v1/stream"
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request = params.copy()
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request["prompt"] = prompt
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websocket_client = websocket.WebSocket()
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websocket_client.connect(url)
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websocket_client.send(json.dumps(request))
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while True:
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result = websocket_client.recv()
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result = json.loads(result)
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if result["event"] == "text_stream":
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chunk = GenerationChunk(
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text=result["text"],
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generation_info=None,
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)
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yield chunk
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elif result["event"] == "stream_end":
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websocket_client.close()
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return
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if run_manager:
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await run_manager.on_llm_new_token(token=chunk.text)
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