Kye
1 year ago
parent
b64bed4a6f
commit
33dec6c0ed
@ -1,10 +1,22 @@
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from swarms import GodMode
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from langchain.llms import GooglePalm, OpenAIChat
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from swarms.swarms.god_mode import Anthropic, GodMode
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claude = Anthropic(anthropic_api_key="")
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palm = GooglePalm(google_api_key="")
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gpt = OpenAIChat(
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openai_api_key=""
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)
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# Usage
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# Usage
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llms = [Anthropic(model="<model_name>", anthropic_api_key="my-api-key") for _ in range(5)]
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llms = [
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claude,
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palm,
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gpt
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]
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god_mode = GodMode(llms)
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god_mode = GodMode(llms)
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task = f"{anthropic.HUMAN_PROMPT} What are the biggest risks facing humanity?{anthropic.AI_PROMPT}"
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task = f"What are the biggest risks facing humanity?"
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god_mode.print_responses(task)
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god_mode.print_responses(task)
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@ -1,189 +1,163 @@
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# from __future__ import annotations
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from __future__ import annotations
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# import logging
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import logging
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# from swarms.utils.logger import logger
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from typing import Any, Callable, Dict, List, Optional
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# from typing import Any, Callable, Dict, List, Optional
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from langchain.callbacks.manager import CallbackManagerForLLMRun
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# from pydantic import BaseModel, model_validator
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from langchain.llms import BaseLLM
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# from tenacity import (
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from langchain.pydantic_v1 import BaseModel, root_validator
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# before_sleep_log,
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from langchain.schema import Generation, LLMResult
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# retry,
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from langchain.utils import get_from_dict_or_env
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# retry_if_exception_type,
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from tenacity import (
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# stop_after_attempt,
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before_sleep_log,
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# wait_exponential,
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retry,
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# )
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retry_if_exception_type,
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stop_after_attempt,
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# import google.generativeai as palm
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wait_exponential,
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)
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# class GooglePalmError(Exception):
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logger = logging.getLogger(__name__)
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# """Error raised when there is an issue with the Google PaLM API."""
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# def _truncate_at_stop_tokens(
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def _create_retry_decorator() -> Callable[[Any], Any]:
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# text: str,
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"""Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
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# stop: Optional[List[str]],
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try:
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# ) -> str:
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import google.api_core.exceptions
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# """Truncates text at the earliest stop token found."""
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except ImportError:
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# if stop is None:
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raise ImportError(
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# return text
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"Could not import google-api-core python package. "
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"Please install it with `pip install google-api-core`."
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# for stop_token in stop:
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)
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# stop_token_idx = text.find(stop_token)
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# if stop_token_idx != -1:
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multiplier = 2
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# text = text[:stop_token_idx]
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min_seconds = 1
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# return text
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max_seconds = 60
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max_retries = 10
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# def _response_to_result(response: palm.types.ChatResponse, stop: Optional[List[str]]) -> Dict[str, Any]:
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return retry(
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# """Convert a PaLM chat response to a result dictionary."""
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reraise=True,
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# result = {
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stop=stop_after_attempt(max_retries),
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# "id": response.id,
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wait=wait_exponential(multiplier=multiplier, min=min_seconds, max=max_seconds),
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# "created": response.created,
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retry=(
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# "model": response.model,
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retry_if_exception_type(google.api_core.exceptions.ResourceExhausted)
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# "usage": {
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| retry_if_exception_type(google.api_core.exceptions.ServiceUnavailable)
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# "prompt_tokens": response.usage.prompt_tokens,
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| retry_if_exception_type(google.api_core.exceptions.GoogleAPIError)
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# "completion_tokens": response.usage.completion_tokens,
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),
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# "total_tokens": response.usage.total_tokens,
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before_sleep=before_sleep_log(logger, logging.WARNING),
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# },
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)
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# "choices": [],
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# }
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# for choice in response.choices:
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def generate_with_retry(llm: GooglePalm, **kwargs: Any) -> Any:
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# result["choices"].append({
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"""Use tenacity to retry the completion call."""
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# "text": _truncate_at_stop_tokens(choice.text, stop),
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retry_decorator = _create_retry_decorator()
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# "index": choice.index,
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# "finish_reason": choice.finish_reason,
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@retry_decorator
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# })
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def _generate_with_retry(**kwargs: Any) -> Any:
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# return result
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return llm.client.generate_text(**kwargs)
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# def _messages_to_prompt_dict(messages: List[Dict[str, Any]]) -> Dict[str, Any]:
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return _generate_with_retry(**kwargs)
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# """Convert a list of message dictionaries to a prompt dictionary."""
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# prompt = {"messages": []}
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# for message in messages:
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def _strip_erroneous_leading_spaces(text: str) -> str:
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# prompt["messages"].append({
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"""Strip erroneous leading spaces from text.
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# "role": message["role"],
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# "content": message["content"],
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The PaLM API will sometimes erroneously return a single leading space in all
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# })
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lines > 1. This function strips that space.
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# return prompt
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"""
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has_leading_space = all(not line or line[0] == " " for line in text.split("\n")[1:])
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if has_leading_space:
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# def _create_retry_decorator() -> Callable[[Any], Any]:
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return text.replace("\n ", "\n")
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# """Create a retry decorator with exponential backoff."""
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else:
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# return retry(
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return text
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# retry=retry_if_exception_type(GooglePalmError),
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# stop=stop_after_attempt(5),
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# wait=wait_exponential(multiplier=1, min=2, max=30),
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class GooglePalm(BaseLLM, BaseModel):
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# before_sleep=before_sleep_log(logger, logging.DEBUG),
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"""Google PaLM models."""
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# reraise=True,
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# )
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client: Any #: :meta private:
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google_api_key: Optional[str]
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model_name: str = "models/text-bison-001"
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# ####################### => main class
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"""Model name to use."""
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# class GooglePalm(BaseModel):
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temperature: float = 0.7
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# """Wrapper around Google's PaLM Chat API."""
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"""Run inference with this temperature. Must by in the closed interval
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[0.0, 1.0]."""
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# client: Any #: :meta private:
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top_p: Optional[float] = None
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# model_name: str = "models/chat-bison-001"
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"""Decode using nucleus sampling: consider the smallest set of tokens whose
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# google_api_key: Optional[str] = None
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probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
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# temperature: Optional[float] = None
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top_k: Optional[int] = None
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# top_p: Optional[float] = None
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"""Decode using top-k sampling: consider the set of top_k most probable tokens.
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# top_k: Optional[int] = None
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Must be positive."""
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# n: int = 1
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max_output_tokens: Optional[int] = None
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"""Maximum number of tokens to include in a candidate. Must be greater than zero.
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# @model_validator(mode="pre")
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If unset, will default to 64."""
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# def validate_environment(cls, values: Dict) -> Dict:
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n: int = 1
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# # Same as before
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"""Number of chat completions to generate for each prompt. Note that the API may
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# pass
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not return the full n completions if duplicates are generated."""
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# def chat_with_retry(self, **kwargs: Any) -> Any:
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@root_validator()
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# """Use tenacity to retry the completion call."""
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def validate_environment(cls, values: Dict) -> Dict:
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# retry_decorator = _create_retry_decorator()
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"""Validate api key, python package exists."""
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google_api_key = get_from_dict_or_env(
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# @retry_decorator
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values, "google_api_key", "GOOGLE_API_KEY"
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# def _chat_with_retry(**kwargs: Any) -> Any:
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)
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# return self.client.chat(**kwargs)
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try:
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import google.generativeai as genai
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# return _chat_with_retry(**kwargs)
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genai.configure(api_key=google_api_key)
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# async def achat_with_retry(self, **kwargs: Any) -> Any:
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except ImportError:
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# """Use tenacity to retry the async completion call."""
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raise ImportError(
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# retry_decorator = _create_retry_decorator()
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"Could not import google-generativeai python package. "
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"Please install it with `pip install google-generativeai`."
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# @retry_decorator
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)
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# async def _achat_with_retry(**kwargs: Any) -> Any:
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# return await self.client.chat_async(**kwargs)
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values["client"] = genai
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# return await _achat_with_retry(**kwargs)
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if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
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raise ValueError("temperature must be in the range [0.0, 1.0]")
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# def __call__(
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# self,
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if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
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# messages: List[Dict[str, Any]],
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raise ValueError("top_p must be in the range [0.0, 1.0]")
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# stop: Optional[List[str]] = None,
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# **kwargs: Any,
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if values["top_k"] is not None and values["top_k"] <= 0:
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# ) -> Dict[str, Any]:
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raise ValueError("top_k must be positive")
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# prompt = _messages_to_prompt_dict(messages)
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if values["max_output_tokens"] is not None and values["max_output_tokens"] <= 0:
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# response: palm.types.ChatResponse = self.chat_with_retry(
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raise ValueError("max_output_tokens must be greater than zero")
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# model=self.model_name,
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# prompt=prompt,
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return values
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# temperature=self.temperature,
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# top_p=self.top_p,
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def _generate(
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# top_k=self.top_k,
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self,
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# candidate_count=self.n,
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prompts: List[str],
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# **kwargs,
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stop: Optional[List[str]] = None,
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# )
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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# return _response_to_result(response, stop)
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) -> LLMResult:
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generations = []
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# def generate(
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for prompt in prompts:
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# self,
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completion = generate_with_retry(
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# messages: List[Dict[str, Any]],
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self,
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# stop: Optional[List[str]] = None,
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model=self.model_name,
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# **kwargs: Any,
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prompt=prompt,
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# ) -> Dict[str, Any]:
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stop_sequences=stop,
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# prompt = _messages_to_prompt_dict(messages)
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temperature=self.temperature,
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top_p=self.top_p,
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# response: palm.types.ChatResponse = self.chat_with_retry(
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top_k=self.top_k,
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# model=self.model_name,
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max_output_tokens=self.max_output_tokens,
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# prompt=prompt,
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candidate_count=self.n,
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# temperature=self.temperature,
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**kwargs,
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# top_p=self.top_p,
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)
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# top_k=self.top_k,
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# candidate_count=self.n,
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prompt_generations = []
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# **kwargs,
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for candidate in completion.candidates:
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# )
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raw_text = candidate["output"]
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stripped_text = _strip_erroneous_leading_spaces(raw_text)
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# return _response_to_result(response, stop)
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prompt_generations.append(Generation(text=stripped_text))
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generations.append(prompt_generations)
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# async def _agenerate(
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# self,
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return LLMResult(generations=generations)
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# messages: List[Dict[str, Any]],
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# stop: Optional[List[str]] = None,
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@property
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# **kwargs: Any,
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def _llm_type(self) -> str:
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# ) -> Dict[str, Any]:
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"""Return type of llm."""
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# prompt = _messages_to_prompt_dict(messages)
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return "google_palm"
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# response: palm.types.ChatResponse = await self.achat_with_retry(
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# model=self.model_name,
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# prompt=prompt,
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# temperature=self.temperature,
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# top_p=self.top_p,
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# top_k=self.top_k,
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# candidate_count=self.n,
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# )
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# return _response_to_result(response, stop)
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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 {
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# "model_name": self.model_name,
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# "temperature": self.temperature,
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# "top_p": self.top_p,
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# "top_k": self.top_k,
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# "n": self.n,
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# }
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# @property
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# def _llm_type(self) -> str:
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# return "google-palm-chat"
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