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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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176
libs/community/langchain_community/llms/vllm.py
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176
libs/community/langchain_community/llms/vllm.py
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from typing import Any, Dict, List, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import BaseLLM
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from langchain_core.outputs import Generation, LLMResult
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_community.llms.openai import BaseOpenAI
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from langchain_community.utils.openai import is_openai_v1
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class VLLM(BaseLLM):
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"""VLLM language model."""
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model: str = ""
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"""The name or path of a HuggingFace Transformers model."""
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tensor_parallel_size: Optional[int] = 1
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"""The number of GPUs to use for distributed execution with tensor parallelism."""
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trust_remote_code: Optional[bool] = False
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"""Trust remote code (e.g., from HuggingFace) when downloading the model
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and tokenizer."""
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n: int = 1
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"""Number of output sequences to return for the given prompt."""
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best_of: Optional[int] = None
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"""Number of output sequences that are generated from the prompt."""
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presence_penalty: float = 0.0
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"""Float that penalizes new tokens based on whether they appear in the
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generated text so far"""
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frequency_penalty: float = 0.0
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"""Float that penalizes new tokens based on their frequency in the
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generated text so far"""
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temperature: float = 1.0
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"""Float that controls the randomness of the sampling."""
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top_p: float = 1.0
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"""Float that controls the cumulative probability of the top tokens to consider."""
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top_k: int = -1
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"""Integer that controls the number of top tokens to consider."""
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use_beam_search: bool = False
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"""Whether to use beam search instead of sampling."""
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stop: Optional[List[str]] = None
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"""List of strings that stop the generation when they are generated."""
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ignore_eos: bool = False
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"""Whether to ignore the EOS token and continue generating tokens after
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the EOS token is generated."""
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max_new_tokens: int = 512
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"""Maximum number of tokens to generate per output sequence."""
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logprobs: Optional[int] = None
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"""Number of log probabilities to return per output token."""
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dtype: str = "auto"
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"""The data type for the model weights and activations."""
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download_dir: Optional[str] = None
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"""Directory to download and load the weights. (Default to the default
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cache dir of huggingface)"""
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vllm_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `vllm.LLM` call not explicitly specified."""
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client: Any #: :meta private:
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that python package exists in environment."""
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try:
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from vllm import LLM as VLLModel
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except ImportError:
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raise ImportError(
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"Could not import vllm python package. "
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"Please install it with `pip install vllm`."
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)
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values["client"] = VLLModel(
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model=values["model"],
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tensor_parallel_size=values["tensor_parallel_size"],
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trust_remote_code=values["trust_remote_code"],
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dtype=values["dtype"],
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download_dir=values["download_dir"],
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**values["vllm_kwargs"],
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)
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return values
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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 vllm."""
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return {
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"n": self.n,
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"best_of": self.best_of,
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"max_tokens": self.max_new_tokens,
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"top_k": self.top_k,
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"top_p": self.top_p,
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"temperature": self.temperature,
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"presence_penalty": self.presence_penalty,
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"frequency_penalty": self.frequency_penalty,
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"stop": self.stop,
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"ignore_eos": self.ignore_eos,
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"use_beam_search": self.use_beam_search,
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"logprobs": self.logprobs,
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}
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def _generate(
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self,
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prompts: List[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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) -> LLMResult:
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"""Run the LLM on the given prompt and input."""
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from vllm import SamplingParams
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# build sampling parameters
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params = {**self._default_params, **kwargs, "stop": stop}
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sampling_params = SamplingParams(**params)
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# call the model
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outputs = self.client.generate(prompts, sampling_params)
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generations = []
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for output in outputs:
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text = output.outputs[0].text
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generations.append([Generation(text=text)])
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return LLMResult(generations=generations)
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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 "vllm"
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class VLLMOpenAI(BaseOpenAI):
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"""vLLM OpenAI-compatible API client"""
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@classmethod
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def is_lc_serializable(cls) -> bool:
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return False
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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"""Get the parameters used to invoke the model."""
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params: Dict[str, Any] = {
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"model": self.model_name,
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**self._default_params,
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"logit_bias": None,
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}
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if not is_openai_v1():
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params.update(
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{
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"api_key": self.openai_api_key,
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"api_base": self.openai_api_base,
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}
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)
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return 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 "vllm-openai"
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