Former-commit-id: 01e7e9b34c
group-chat
Kye
1 year ago
parent
9adf7b6084
commit
d0d51a5975
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[]
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from swarms import AutoBot, AutoScaler
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auto_scaler = AutoScaler()
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auto_scaler.start()
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for i in range(100):
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auto_scaler.add_task(f"Task {i}")
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@ -1,14 +1,18 @@
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#swarms
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#swarms
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from swarms.orchestrator.autoscaler import AutoScaler
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# worker
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# worker
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# from swarms.workers.worker_node import WorkerNode
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from swarms.workers.worker_node import WorkerNode
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from swarms.workers.autobot import AutoBot
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#boss
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#boss
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# from swarms.boss.boss_node import BossNode
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from swarms.boss.boss_node import BossNode
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#models
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#models
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from swarms.agents.models.anthropic import Anthropic
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from swarms.agents.models.anthropic import Anthropic
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from swarms.agents.models.huggingface import HuggingFaceLLM
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from swarms.agents.models.huggingface import HuggingFaceLLM
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from swarms.agents.models.palm import GooglePalm
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# from swarms.agents.models.palm import GooglePalm
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from swarms.agents.models.petals import Petals
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from swarms.agents.models.petals import Petals
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# from swarms.agents.models.openai import OpenAI
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from swarms.agents.models.openai import OpenAI
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@ -1,187 +1,189 @@
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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 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 pydantic import BaseModel, root_validator
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# from pydantic import BaseModel, model_validator
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from tenacity import (
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# from tenacity import (
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before_sleep_log,
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# before_sleep_log,
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retry,
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# retry,
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retry_if_exception_type,
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# retry_if_exception_type,
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stop_after_attempt,
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# stop_after_attempt,
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wait_exponential,
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# wait_exponential,
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)
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# )
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import google.generativeai as genai
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# import google.generativeai as palm
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class GooglePalmError(Exception):
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# class GooglePalmError(Exception):
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"""Error raised when there is an issue with the Google PaLM API."""
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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 _truncate_at_stop_tokens(
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text: str,
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# text: str,
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stop: Optional[List[str]],
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# stop: Optional[List[str]],
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) -> str:
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# ) -> str:
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"""Truncates text at the earliest stop token found."""
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# """Truncates text at the earliest stop token found."""
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if stop is None:
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# if stop is None:
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return text
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# return text
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for stop_token in stop:
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# for stop_token in stop:
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stop_token_idx = text.find(stop_token)
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# stop_token_idx = text.find(stop_token)
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if stop_token_idx != -1:
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# if stop_token_idx != -1:
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text = text[:stop_token_idx]
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# text = text[:stop_token_idx]
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return text
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# return text
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def _response_to_result(response: genai.types.ChatResponse, stop: Optional[List[str]]) -> Dict[str, Any]:
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"""Convert a PaLM chat response to a result dictionary."""
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# def _response_to_result(response: palm.types.ChatResponse, stop: Optional[List[str]]) -> Dict[str, Any]:
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result = {
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# """Convert a PaLM chat response to a result dictionary."""
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"id": response.id,
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# result = {
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"created": response.created,
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# "id": response.id,
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"model": response.model,
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# "created": response.created,
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"usage": {
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# "model": response.model,
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"prompt_tokens": response.usage.prompt_tokens,
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# "usage": {
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"completion_tokens": response.usage.completion_tokens,
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# "prompt_tokens": response.usage.prompt_tokens,
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"total_tokens": response.usage.total_tokens,
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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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"choices": [],
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# },
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}
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# "choices": [],
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for choice in response.choices:
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# }
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result["choices"].append({
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# for choice in response.choices:
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"text": _truncate_at_stop_tokens(choice.text, stop),
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# result["choices"].append({
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"index": choice.index,
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# "text": _truncate_at_stop_tokens(choice.text, stop),
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"finish_reason": choice.finish_reason,
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# "index": choice.index,
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})
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# "finish_reason": choice.finish_reason,
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return result
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# })
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# return result
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def _messages_to_prompt_dict(messages: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Convert a list of message dictionaries to a prompt dictionary."""
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# def _messages_to_prompt_dict(messages: List[Dict[str, Any]]) -> Dict[str, Any]:
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prompt = {"messages": []}
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# """Convert a list of message dictionaries to a prompt dictionary."""
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for message in messages:
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# prompt = {"messages": []}
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prompt["messages"].append({
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# for message in messages:
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"role": message["role"],
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# prompt["messages"].append({
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"content": message["content"],
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# "role": message["role"],
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})
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# "content": message["content"],
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return prompt
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# })
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# return prompt
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def _create_retry_decorator() -> Callable[[Any], Any]:
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"""Create a retry decorator with exponential backoff."""
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return retry(
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# def _create_retry_decorator() -> Callable[[Any], Any]:
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retry=retry_if_exception_type(GooglePalmError),
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# """Create a retry decorator with exponential backoff."""
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stop=stop_after_attempt(5),
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# return retry(
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wait=wait_exponential(multiplier=1, min=2, max=30),
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# retry=retry_if_exception_type(GooglePalmError),
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before_sleep=before_sleep_log(logger, logging.DEBUG),
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# stop=stop_after_attempt(5),
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reraise=True,
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# wait=wait_exponential(multiplier=1, min=2, max=30),
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)
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# before_sleep=before_sleep_log(logger, logging.DEBUG),
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# reraise=True,
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####################### => main class
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# )
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class GooglePalm(BaseModel):
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"""Wrapper around Google's PaLM Chat API."""
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# ####################### => main class
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client: Any #: :meta private:
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# class GooglePalm(BaseModel):
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model_name: str = "models/chat-bison-001"
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# """Wrapper around Google's PaLM Chat API."""
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google_api_key: Optional[str] = None
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temperature: Optional[float] = None
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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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top_k: Optional[int] = None
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# google_api_key: Optional[str] = None
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n: int = 1
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# temperature: Optional[float] = None
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# top_p: Optional[float] = None
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@root_validator()
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# top_k: Optional[int] = None
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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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pass
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# @model_validator(mode="pre")
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# def validate_environment(cls, values: Dict) -> Dict:
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def chat_with_retry(self, **kwargs: Any) -> Any:
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# # Same as before
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"""Use tenacity to retry the completion call."""
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# pass
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retry_decorator = _create_retry_decorator()
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# def chat_with_retry(self, **kwargs: Any) -> Any:
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@retry_decorator
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# """Use tenacity to retry the completion call."""
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def _chat_with_retry(**kwargs: Any) -> Any:
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# retry_decorator = _create_retry_decorator()
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return self.client.chat(**kwargs)
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# @retry_decorator
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return _chat_with_retry(**kwargs)
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# def _chat_with_retry(**kwargs: Any) -> Any:
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# return self.client.chat(**kwargs)
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async def achat_with_retry(self, **kwargs: Any) -> Any:
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"""Use tenacity to retry the async completion call."""
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# return _chat_with_retry(**kwargs)
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retry_decorator = _create_retry_decorator()
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# async def achat_with_retry(self, **kwargs: Any) -> Any:
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@retry_decorator
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# """Use tenacity to retry the async completion call."""
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async def _achat_with_retry(**kwargs: Any) -> Any:
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# retry_decorator = _create_retry_decorator()
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return await self.client.chat_async(**kwargs)
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# @retry_decorator
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return await _achat_with_retry(**kwargs)
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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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def __call__(
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self,
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# return await _achat_with_retry(**kwargs)
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messages: List[Dict[str, Any]],
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stop: Optional[List[str]] = None,
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# def __call__(
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**kwargs: Any,
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# self,
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) -> Dict[str, Any]:
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# messages: List[Dict[str, Any]],
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prompt = _messages_to_prompt_dict(messages)
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# stop: Optional[List[str]] = None,
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# **kwargs: Any,
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response: genai.types.ChatResponse = self.chat_with_retry(
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# ) -> Dict[str, Any]:
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model=self.model_name,
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# prompt = _messages_to_prompt_dict(messages)
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prompt=prompt,
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temperature=self.temperature,
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# response: palm.types.ChatResponse = self.chat_with_retry(
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top_p=self.top_p,
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# model=self.model_name,
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top_k=self.top_k,
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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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return _response_to_result(response, stop)
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# **kwargs,
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# )
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def generate(
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self,
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# return _response_to_result(response, stop)
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messages: List[Dict[str, Any]],
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stop: Optional[List[str]] = None,
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# def generate(
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**kwargs: Any,
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# self,
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) -> Dict[str, Any]:
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# messages: List[Dict[str, Any]],
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prompt = _messages_to_prompt_dict(messages)
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# stop: Optional[List[str]] = None,
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# **kwargs: Any,
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response: genai.types.ChatResponse = self.chat_with_retry(
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# ) -> Dict[str, Any]:
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model=self.model_name,
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# prompt = _messages_to_prompt_dict(messages)
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prompt=prompt,
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temperature=self.temperature,
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# response: palm.types.ChatResponse = self.chat_with_retry(
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top_p=self.top_p,
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# model=self.model_name,
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top_k=self.top_k,
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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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return _response_to_result(response, stop)
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# **kwargs,
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# )
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async def _agenerate(
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self,
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# return _response_to_result(response, stop)
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messages: List[Dict[str, Any]],
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stop: Optional[List[str]] = None,
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# async def _agenerate(
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**kwargs: Any,
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# self,
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) -> Dict[str, Any]:
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# messages: List[Dict[str, Any]],
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prompt = _messages_to_prompt_dict(messages)
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# stop: Optional[List[str]] = None,
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# **kwargs: Any,
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response: genai.types.ChatResponse = await self.achat_with_retry(
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# ) -> Dict[str, Any]:
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model=self.model_name,
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# prompt = _messages_to_prompt_dict(messages)
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prompt=prompt,
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temperature=self.temperature,
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# response: palm.types.ChatResponse = await self.achat_with_retry(
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top_p=self.top_p,
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# model=self.model_name,
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top_k=self.top_k,
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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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)
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# top_p=self.top_p,
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# top_k=self.top_k,
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return _response_to_result(response, stop)
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# candidate_count=self.n,
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# )
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@property
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# return _response_to_result(response, stop)
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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# @property
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return {
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# def _identifying_params(self) -> Dict[str, Any]:
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"model_name": self.model_name,
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# """Get the identifying parameters."""
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"temperature": self.temperature,
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# return {
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"top_p": self.top_p,
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# "model_name": self.model_name,
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"top_k": self.top_k,
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# "temperature": self.temperature,
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"n": self.n,
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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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# "n": self.n,
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@property
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# }
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def _llm_type(self) -> str:
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return "google-palm-chat"
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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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@ -0,0 +1,62 @@
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import threading
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import queue
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from time import sleep
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from swarms.workers.autobot import AutoBot
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# TODO Handle task assignment and task delegation
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# TODO: User task => decomposed into very small sub tasks => sub tasks assigned to workers => workers complete and update the swarm, can ask for help from other agents.
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# TODO: Missing, Task Assignment, Task delegation, Task completion, Swarm level communication with vector db
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class AutoScaler:
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def __init__(self,
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initial_agents=10,
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scale_up_factor=1,
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idle_threshold=0.2,
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busy_threshold=0.7
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):
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self.agents_pool = [AutoBot() for _ in range(initial_agents)]
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self.task_queue = queue.Queue()
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self.scale_up_factor = scale_up_factor
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self.idle_threshold = idle_threshold
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self.lock = threading.Lock()
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def add_task(self, task):
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self.tasks_queue.put(task)
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def scale_up(self):
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with self.lock:
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new_agents_counts = len(self.agents_pool) * self.scale_up_factor
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for _ in range(new_agents_counts):
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self.agents_pool.append(AutoBot())
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def scale_down(self):
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with self.lock:
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if len(self.agents_pool) > 10: #ensure minmum of 10 agents
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del self.agents_pool[-1] #remove last agent
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def monitor_and_scale(self):
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while True:
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sleep(60)#check minute
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pending_tasks = self.task_queue.qsize()
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active_agents = sum([1 for agent in self.agents_pool if agent.is_busy()])
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if pending_tasks / len(self.agents_pool) > self.busy_threshold:
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self.scale_up()
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elif active_agents / len(self.agents_pool) < self.idle_threshold:
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self.scale_down()
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def start(self):
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monitor_thread = threading.Thread(target=self.monitor_and_scale)
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monitor_thread.start()
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while True:
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task = self.task_queue.get()
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if task:
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available_agent = next((agent for agent in self.agents_pool))
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if available_agent:
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available_agent.run(task)
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@ -0,0 +1,75 @@
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import faiss
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from langchain.chat_models import ChatOpenAI
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from langchain.docstore import InMemoryDocstore
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.vectorstores import FAISS
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from langchain_experimental.autonomous_agents import AutoGPT
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from swarms.agents.tools.autogpt import (
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DuckDuckGoSearchRun,
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FileChatMessageHistory,
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ReadFileTool,
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WebpageQATool,
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WriteFileTool,
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load_qa_with_sources_chain,
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process_csv,
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# web_search,
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query_website_tool
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)
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ROOT_DIR = "./data/"
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class AutoBot:
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def __init__(self,
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model_name="gpt-4",
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openai_api_key=None,
|
||||||
|
ai_name="Autobot Swarm Worker",
|
||||||
|
ai_role="Worker in a swarm",
|
||||||
|
# embedding_size=None,
|
||||||
|
# k=None,
|
||||||
|
temperature=0.5):
|
||||||
|
self.openai_api_key = openai_api_key
|
||||||
|
self.temperature = temperature
|
||||||
|
self.llm = ChatOpenAI(model_name=model_name,
|
||||||
|
openai_api_key=self.openai_api_key,
|
||||||
|
temperature=self.temperature)
|
||||||
|
|
||||||
|
self.ai_name = ai_name
|
||||||
|
self.ai_role = ai_role
|
||||||
|
|
||||||
|
# self.embedding_size = embedding_size
|
||||||
|
# # self.k = k
|
||||||
|
|
||||||
|
self.setup_tools()
|
||||||
|
self.setup_memory()
|
||||||
|
self.setup_agent()
|
||||||
|
|
||||||
|
def setup_tools(self):
|
||||||
|
self.tools = [
|
||||||
|
WriteFileTool(root_dir=ROOT_DIR),
|
||||||
|
ReadFileTool(root_dir=ROOT_DIR),
|
||||||
|
process_csv,
|
||||||
|
query_website_tool,
|
||||||
|
]
|
||||||
|
|
||||||
|
def setup_memory(self):
|
||||||
|
embeddings_model = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
|
||||||
|
embedding_size = 1536
|
||||||
|
index = faiss.IndexFlatL2(embedding_size)
|
||||||
|
self.vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})
|
||||||
|
|
||||||
|
def setup_agent(self):
|
||||||
|
self.agent = AutoGPT.from_llm_and_tools(
|
||||||
|
ai_name=self.ai_name,
|
||||||
|
ai_role=self.ai_role,
|
||||||
|
tools=self.tools,
|
||||||
|
llm=self.llm,
|
||||||
|
memory=self.vectorstore.as_retriever(search_kwargs={"k": 8}),
|
||||||
|
)
|
||||||
|
|
||||||
|
def run(self, task):
|
||||||
|
result = self.agent.run([task])
|
||||||
|
return result
|
||||||
|
|
||||||
Loading…
Reference in new issue