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import asyncio
from typing import Any, Dict, List, Optional, OrderedDict, Tuple, ValuesView
import gradio_client # type: ignore
from h2ogpt_client import _utils
from h2ogpt_client._h2ogpt_enums import (
DocumentChoices,
LangChainAction,
LangChainMode,
PromptType,
)
class Client:
"""h2oGPT Client."""
def __init__(self, src: str, huggingface_token: Optional[str] = None):
"""
Creates a GPT client.
:param src: either the full URL to the hosted h2oGPT
(e.g. "http://0.0.0.0:7860", "https://fc752f297207f01c32.gradio.live")
or name of the Hugging Face Space to load, (e.g. "h2oai/h2ogpt-chatbot")
:param huggingface_token: Hugging Face token to use to access private Spaces
"""
self._client = gradio_client.Client(
src=src, hf_token=huggingface_token, serialize=False, verbose=False
)
self._text_completion = TextCompletionCreator(self)
self._chat_completion = ChatCompletionCreator(self)
@property
def text_completion(self) -> "TextCompletionCreator":
"""Text completion."""
return self._text_completion
@property
def chat_completion(self) -> "ChatCompletionCreator":
"""Chat completion."""
return self._chat_completion
def _predict(self, *args, api_name: str) -> Any:
return self._client.submit(*args, api_name=api_name).result()
async def _predict_async(self, *args, api_name: str) -> Any:
return await asyncio.wrap_future(self._client.submit(*args, api_name=api_name))
class TextCompletionCreator:
"""Builder that can create text completions."""
def __init__(self, client: Client):
self._client = client
def create(
self,
prompt_type: PromptType = PromptType.plain,
input_context_for_instruction: str = "",
enable_sampler=False,
temperature: float = 1.0,
top_p: float = 1.0,
top_k: int = 40,
beams: float = 1.0,
early_stopping: bool = False,
min_output_length: int = 0,
max_output_length: int = 128,
max_time: int = 180,
repetition_penalty: float = 1.07,
number_returns: int = 1,
system_pre_context: str = "",
langchain_mode: LangChainMode = LangChainMode.DISABLED,
) -> "TextCompletion":
"""
Creates a new text completion.
:param prompt_type: type of the prompt
:param input_context_for_instruction: input context for instruction
:param enable_sampler: enable or disable the sampler, required for use of
temperature, top_p, top_k
:param temperature: What sampling temperature to use, between 0 and 3.
Lower values will make it more focused and deterministic, but may lead
to repeat. Higher values will make the output more creative, but may
lead to hallucinations.
:param top_p: cumulative probability of tokens to sample from
:param top_k: number of tokens to sample from
:param beams: Number of searches for optimal overall probability.
Higher values uses more GPU memory and compute.
:param early_stopping: whether to stop early or not in beam search
:param min_output_length: minimum output length
:param max_output_length: maximum output length
:param max_time: maximum time to search optimal output
:param repetition_penalty: penalty for repetition
:param number_returns:
:param system_pre_context: directly pre-appended without prompt processing
:param langchain_mode: LangChain mode
"""
params = _utils.to_h2ogpt_params(locals().copy())
params["instruction"] = "" # empty when chat_mode is False
params["iinput"] = "" # only chat_mode is True
params["stream_output"] = False
params["prompt_type"] = prompt_type.value # convert to serializable type
params["prompt_dict"] = "" # empty as prompt_type cannot be 'custom'
params["chat"] = False
params["instruction_nochat"] = None # future prompt
params["langchain_mode"] = langchain_mode.value # convert to serializable type
params["langchain_action"] = LangChainAction.QUERY.value
params["langchain_agents"] = []
params["top_k_docs"] = 4 # langchain: number of document chunks
params["chunk"] = True # langchain: whether to chunk documents
params["chunk_size"] = 512 # langchain: chunk size for document chunking
params["document_subset"] = DocumentChoices.Relevant.name
params["document_choice"] = []
return TextCompletion(self._client, params)
class TextCompletion:
"""Text completion."""
_API_NAME = "/submit_nochat"
def __init__(self, client: Client, parameters: OrderedDict[str, Any]):
self._client = client
self._parameters = parameters
def _get_parameters(self, prompt: str) -> ValuesView:
self._parameters["instruction_nochat"] = prompt
return self._parameters.values()
async def complete(self, prompt: str) -> str:
"""
Complete this text completion.
:param prompt: text prompt to generate completion for
:return: response from the model
"""
return await self._client._predict_async(
*self._get_parameters(prompt), api_name=self._API_NAME
)
def complete_sync(self, prompt: str) -> str:
"""
Complete this text completion synchronously.
:param prompt: text prompt to generate completion for
:return: response from the model
"""
return self._client._predict(
*self._get_parameters(prompt), api_name=self._API_NAME
)
class ChatCompletionCreator:
"""Chat completion."""
def __init__(self, client: Client):
self._client = client
def create(
self,
prompt_type: PromptType = PromptType.plain,
input_context_for_instruction: str = "",
enable_sampler=False,
temperature: float = 1.0,
top_p: float = 1.0,
top_k: int = 40,
beams: float = 1.0,
early_stopping: bool = False,
min_output_length: int = 0,
max_output_length: int = 128,
max_time: int = 180,
repetition_penalty: float = 1.07,
number_returns: int = 1,
system_pre_context: str = "",
langchain_mode: LangChainMode = LangChainMode.DISABLED,
) -> "ChatCompletion":
"""
Creates a new chat completion.
:param prompt_type: type of the prompt
:param input_context_for_instruction: input context for instruction
:param enable_sampler: enable or disable the sampler, required for use of
temperature, top_p, top_k
:param temperature: What sampling temperature to use, between 0 and 3.
Lower values will make it more focused and deterministic, but may lead
to repeat. Higher values will make the output more creative, but may
lead to hallucinations.
:param top_p: cumulative probability of tokens to sample from
:param top_k: number of tokens to sample from
:param beams: Number of searches for optimal overall probability.
Higher values uses more GPU memory and compute.
:param early_stopping: whether to stop early or not in beam search
:param min_output_length: minimum output length
:param max_output_length: maximum output length
:param max_time: maximum time to search optimal output
:param repetition_penalty: penalty for repetition
:param number_returns:
:param system_pre_context: directly pre-appended without prompt processing
:param langchain_mode: LangChain mode
"""
params = _utils.to_h2ogpt_params(locals().copy())
params["instruction"] = None # future prompts
params["iinput"] = "" # ??
params["stream_output"] = False
params["prompt_type"] = prompt_type.value # convert to serializable type
params["prompt_dict"] = "" # empty as prompt_type cannot be 'custom'
params["chat"] = True
params["instruction_nochat"] = "" # empty when chat_mode is True
params["langchain_mode"] = langchain_mode.value # convert to serializable type
params["langchain_action"] = LangChainAction.QUERY.value
params["langchain_agents"] = []
params["top_k_docs"] = 4 # langchain: number of document chunks
params["chunk"] = True # langchain: whether to chunk documents
params["chunk_size"] = 512 # langchain: chunk size for document chunking
params["document_subset"] = DocumentChoices.Relevant.name
params["document_choice"] = []
params["chatbot"] = [] # chat history
return ChatCompletion(self._client, params)
class ChatCompletion:
"""Chat completion."""
_API_NAME = "/instruction_bot"
def __init__(self, client: Client, parameters: OrderedDict[str, Any]):
self._client = client
self._parameters = parameters
def _get_parameters(self, prompt: str) -> ValuesView:
self._parameters["instruction"] = prompt
self._parameters["chatbot"] += [[prompt, None]]
return self._parameters.values()
def _get_reply(self, response: Tuple[List[List[str]]]) -> Dict[str, str]:
self._parameters["chatbot"][-1][1] = response[0][-1][1]
return {"user": response[0][-1][0], "gpt": response[0][-1][1]}
async def chat(self, prompt: str) -> Dict[str, str]:
"""
Complete this chat completion.
:param prompt: text prompt to generate completions for
:returns chat reply
"""
response = await self._client._predict_async(
*self._get_parameters(prompt), api_name=self._API_NAME
)
return self._get_reply(response)
def chat_sync(self, prompt: str) -> Dict[str, str]:
"""
Complete this chat completion.
:param prompt: text prompt to generate completions for
:returns chat reply
"""
response = self._client._predict(
*self._get_parameters(prompt), api_name=self._API_NAME
)
return self._get_reply(response)
def chat_history(self) -> List[Dict[str, str]]:
"""Returns the full chat history."""
return [{"user": i[0], "gpt": i[1]} for i in self._parameters["chatbot"]]
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