Source code for langchain_community.llms.textgen

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

import requests
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 Field

logger = logging.getLogger(__name__)


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