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Manus 沒邀請碼怎麼辦?用 OpenManus 本地免費部署 Ollama 模型,三分鐘搞定

Manus 沒邀請碼怎麼辦?用 OpenManus 本地免費部署 Ollama 模型,三分鐘搞定

Views: 15

🚀 1. 本地端完美對接 Ollama AI 模型

OpenManus 最大的亮點在於能與目前最流行的 Ollama 本地端 AI 大模型平台進行完美整合。

  • Ollama 是一個輕量、高效的 AI 模型管理工具,讓你可以輕鬆在自己的電腦上運行各種強大的大模型(如 Llama3、Qwen、DeepSeek 系列模型等)。
  • OpenManus 透過 Ollama API 與這些模型無縫互動,你能輕易在本地體驗到媲美線上服務的智慧功能,並保護個人隱私。

💻 2. 跨平台支援 Windows、Mac、Linux

無論你使用哪個平台,OpenManus 都有完整的跨平台支援,讓你輕鬆安裝與運行:

  • Windows 用戶可透過 Conda 或 Docker 快速部署。
  • macOS 用戶可以使用 Homebrew 或直接透過終端機運行。
  • Linux 用戶則能自由選擇 Docker 或直接透過原生方式安裝。

🎯 3. 無需邀請碼,即裝即用!

不同於原始封閉的 Manus 需要透過邀請碼才能使用,OpenManus 堅持完全開源與自由的精神。
無須註冊、無須邀請碼,直接部署到自己的電腦,立即開始使用,毫無限制,這就是開源社群給予大家最棒的禮物。


如何快速部署 OpenManus?(以 Windows 為例)

只需幾個簡單步驟,即可享受本地端 AI 大模型:

建立 Conda 環境:

conda create -n openmanus python=3.12
conda activate openmanus

Git OpenManus 專案:

git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus

安裝所需依賴:

pip install -r requirements.txt

修改設定檔(config.toml):

cp config/config.example.toml config/config.toml

config.toml的內容如下,可以參考後修改

# Global LLM configuration
#[llm]
# model = "claude-3-7-sonnet-20250219"        # The LLM model to use
# base_url = "https://api.anthropic.com/v1/"  # API endpoint URL
# api_key = "YOUR_API_KEY"                    # Your API key
# max_tokens = 8192                           # Maximum number of tokens in the response
# temperature = 0.0                           # Controls randomness

# [llm] #AZURE OPENAI:
# api_type= 'azure'
# model = "YOUR_MODEL_NAME" #"gpt-4o-mini"
# base_url = "{YOUR_AZURE_ENDPOINT.rstrip('/')}/openai/deployments/{AZURE_DEPOLYMENT_ID}"
# api_key = "AZURE API KEY"
# max_tokens = 8096
# temperature = 0.0
# api_version="AZURE API VERSION" #"2024-08-01-preview"

[llm] #OLLAMA:
api_type = 'ollama'
model = "llama3.2"
base_url = "http://localhost:11434/v1"
api_key = "ollama"
max_tokens = 4096
temperature = 0.0

# Optional configuration for specific LLM models
#[llm.vision]
#model = "claude-3-7-sonnet-20250219"        # The vision model to use
#base_url = "https://api.anthropic.com/v1/"  # API endpoint URL for vision model
#api_key = "YOUR_API_KEY"                    # Your API key for vision model
#max_tokens = 8192                           # Maximum number of tokens in the response
#temperature = 0.0                           # Controls randomness for vision model

[llm.vision] #OLLAMA VISION:
api_type = 'ollama'
model = "llama3.2-vision"
base_url = "http://localhost:11434/v1"
api_key = "ollama"
max_tokens = 4096
temperature = 0.0

# Optional configuration for specific browser configuration
# [browser]
# Whether to run browser in headless mode (default: false)
#headless = false
# Disable browser security features (default: true)
#disable_security = true
# Extra arguments to pass to the browser
#extra_chromium_args = []
# Path to a Chrome instance to use to connect to your normal browser
# e.g. '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome'
#chrome_instance_path = ""
# Connect to a browser instance via WebSocket
#wss_url = ""
# Connect to a browser instance via CDP
#cdp_url = ""

# Optional configuration, Proxy settings for the browser
# [browser.proxy]
# server = "http://proxy-server:port"
# username = "proxy-username"
# password = "proxy-password"

# Optional configuration, Search settings.
# [search]
# Search engine for agent to use. Default is "Google", can be set to "Baidu" or "DuckDuckGo".
# engine = "Google"

啟動 OpenManus 服務:

python main.py

之後打開瀏覽器就可以了

測試 OpenManus :

可以輸入請他使用瀏覽器看某一個網站,並且執行SEO策略

打開 https://rain.tips/ 並且給予SEO的建議,並且把建議存放在桌面上.txt的文件

補充資料

Github

Aider Chat-邊聊邊寫程式

Aider Chat-邊聊邊寫程式

Views: 91

Aider 是一款突破性的 AI 程式設計助理,無論是終端操作還是透過瀏覽器,都可以享受與 Aider 的互動式編程體驗

多樣化運行模式

Aider 支援兩種運行模式:

  1. 本地模式:結合 Ollama 模型,支持用戶在本地執行各種大型語言模型(LLMs)。
  2. 瀏覽器模式:無需繁瑣的安裝配置,用戶只需透過瀏覽器即可啟動對話編程,輕鬆實現即時代碼編輯。

核心功能與亮點

Git 無縫整合

Aider 深度集成本地 Git 倉庫,讓程式碼管理變得簡單高效:

  • 代碼編輯:直接使用提示詞請求代碼修改、錯誤修復或改進功能,變更將即時應用至源代碼。
  • 自動提交:所有改動將自動生成具描述性的 Git 提交記錄,便於追蹤和審核。

直觀命令操作

用戶可通過多種命令與 Aider 互動,執行各類任務:

命令說明
/add將檔案新增到聊天中,讓 GPT 可以編輯或詳細檢查這些檔案。
/ask提出與程式碼庫相關的問題,無需編輯任何檔案。
/chat-mode切換到新的聊天模式。
/clear清除聊天記錄。
/clipboard將剪貼簿中的圖片或文字新增到聊天中(可選擇提供圖片名稱)。
/code請求對程式碼進行修改。
/commit提交在聊天外進行的編輯到程式庫(提交資訊為可選)。
/diff顯示自上次訊息以來的變更差異。
/drop從聊天會話中移除檔案以釋放上下文空間。
/exit離開應用程式。
/git執行一個 Git 命令。
/help提出與 Aider 相關的問題。
/lint對提供的檔案進行 Lint 檢查並修復;若未提供檔案,則修復聊天中的檔案。
/ls列出所有已知檔案,並指出哪些檔案包含在聊天會話中。
/map輸出當前程式庫的地圖。
/map-refresh強制刷新程式庫地圖。
/model切換到新的大型語言模型。
/models搜尋可用的模型列表。
/quit離開應用程式。
/read-only將檔案新增到聊天中,僅供參考,不能編輯。
/reset刪除所有檔案並清除聊天記錄。
/run執行一個 Shell 命令,並可選擇將輸出新增到聊天中(別名:!)。
/test執行一個 Shell 命令,若退出碼非零,則將輸出新增到聊天中。
/tokens報告當前聊天上下文使用的 Token 數量。
/undo如果上次 Git 提交是由 Aider 完成的,則撤銷該提交。
/voice記錄並轉錄語音輸入。
/web擷取網頁內容,轉換為 Markdown,並新增到聊天中。

完整命令列表涵蓋從文件管理到模型切換的全方位需求。

多模型支持

Aider 支援廣泛的 LLM,包括但不限於:

  • Ollama
  • OpenAI
  • Anthropic
  • DeepSeek
  • OpenRouter

安裝與使用

基礎安裝

透過 pip 安裝 Aider:

python3 -m pip install aider-chat

運行本地 Ollama 模型

export OLLAMA_API_BASE=http://127.0.0.1:11434
aider --model ollama/mistral

# Groq
export GROQ_API_KEY=sk-xx
aider --model groq/llama3-70b-8192

# OpenRouter
export OPENROUTER_API_KEY=sk-xx
# Or any other open router model
aider --model openrouter/<provider>/<model>
# List models available from OpenRouter
aider --models openrouter/

# Web
aider --browser

# GitHub 學習用
git clone https://github.com/mewmewdevart/SnakeGame
cd SnakeGame
aider

# 请解释这个项目的功能
# 这个项目是运用了哪些技术?
# 更改蛇的颜色为绿色,食物的颜色为红色

相關資料

Aider官網

Aider GitHub

Ollama 推出了支持 Llama Vision 的功能,可以讀懂圖片了

Ollama 推出了支持 Llama Vision 的功能,可以讀懂圖片了

Views: 472

Ollama 終於能支援 Llama 3.2 Vision 模型了,等了很久,並且都換去用 llava ,你只要升級到 Ollama 0.4版本,就可以直接使用 Vision 模型,這次一口氣支援了 llama3.2 的 11B 和 90B,不過應該很多人是沒法使用90B的吧:P

下載 llama 3.2 Vision

ollama run llama3.2-vision

如何使用 Ollama Vision

1.只要在ollama 的命令列下,直接提供圖片的路徑給他即可

說明 '圖片路徑'

2.要解釋圖表的話,可以下以下的 prompt

輸出 CSV 資料,並且用 Markdown 的格式: '圖片路徑'

3. 呼叫API

ollama docs api

Request,只要把圖片轉換成base64格式給他就可以了

curl http://localhost:11434/api/chat -d '{
  "model": "llava",
  "messages": [
    {
      "role": "user",
      "content": "what is in this image?",
      "images": ["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"]
    }
  ]
}'

Response

{
  "model": "llava",
  "created_at": "2023-12-13T22:42:50.203334Z",
  "message": {
    "role": "assistant",
    "content": " The image features a cute, little pig with an angry facial expression. It's wearing a heart on its shirt and is waving in the air. This scene appears to be part of a drawing or sketching project.",
    "images": null
  },
  "done": true,
  "total_duration": 1668506709,
  "load_duration": 1986209,
  "prompt_eval_count": 26,
  "prompt_eval_duration": 359682000,
  "eval_count": 83,
  "eval_duration": 1303285000
}

超強大的 90 B

可以辨識醫生的手寫字、也可以輕易地讀懂收據內的文字,更厲害的是圖表也沒問題

https://github.com/user-attachments/assets/82e25d0d-921c-4900-b78f-589c1bb86968

程式支援

為了讀取圖片,也支援了 Python Javascript 、 CURL

cURL 範例

curl http://localhost:11434/api/chat -d '{
  "model": "llama3.2-vision",
  "messages": [
    {
      "role": "user",
      "content": "what is in this image?",
      "images": ["<base64-encoded image data>"]
    }
  ]
}'

Meta Llama 3.2 官方資源

https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices

增強式 ChatTTS 跟 Ollama 的整合

Views: 110

可以中英文混合,笑聲,停頓的好用的語音生成模型

直接使用 ChatTTS

ChatTTS online DEMO https://chattts.com/#Demo

增強後好看又好用的 ChatTTS 外框 ChatTTS-Forge https://huggingface.co/spaces/lenML/ChatTTS-Forge

自行開發程式的重要資源

ChatTTS 官方說明 https://github.com/2noise/ChatTTS/blob/main/docs/cn/README.md

整合各種超強的 ChatTTS應用 https://github.com/libukai/Awesome-ChatTTS

ChatTTS 跟 Ollama 的整合 Demo https://github.com/melodylife/ollama-chat

延伸閱讀

GraphRAG 使用本地端的 Ollama

Views: 105

GraphRAG圖像檢索增強生成(Graph Retrieval-Augmented Generation,GraphRAG)超好用,但也超級貴,超級花錢,想要省錢的話,就要用本地端的服務如(Ollama),要用的話,可以按照下面的步驟處理,前提是你已經可以使用 OpenAI 版本的 GraphRAG 了,本篇是要把 OpenAI 改成 Ollama

講在前面

要先設定好 GraphRAG

下載以及安裝好 Ollama

安裝 ollama 的 Python 套件

pip install ollama

修改原先的 setting.yaml 檔案

把舊的 yaml 檔案改成 ollama 的設定檔,新的設定檔案如下

encoding_model: cl100k_base
skip_workflows: []
llm:
  api_key: ${GRAPHRAG_API_KEY}
  type: openai_chat # or azure_openai_chat
  # model: gpt-4o-mini
  model_supports_json: true # recommended if this is available for your model.
  # max_tokens: 4000
  # request_timeout: 180.0
  # api_base: https://<instance>.openai.azure.com
  # api_version: 2024-02-15-preview
  # organization: <organization_id>
  # deployment_name: <azure_model_deployment_name>
  # tokens_per_minute: 150_000 # set a leaky bucket throttle
  # requests_per_minute: 10_000 # set a leaky bucket throttle
  # max_retries: 10
  # max_retry_wait: 10.0
  # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
  # concurrent_requests: 25 # the number of parallel inflight requests that may be made

  # ollama api_base
  api_base: http://localhost:11434/v1
  model: llama3

parallelization:
  stagger: 0.3
  # num_threads: 50 # the number of threads to use for parallel processing

async_mode: threaded # or asyncio

embeddings:
  ## parallelization: override the global parallelization settings for embeddings
  async_mode: threaded # or asyncio
  llm:
    api_key: ${GRAPHRAG_API_KEY}
    type: openai_embedding # or azure_openai_embedding
    # model: text-embedding-3-small
    
    # ollama
    model: nomic-embed-text 
    api_base: http://localhost:11434/api

    # api_base: https://<instance>.openai.azure.com
    # api_version: 2024-02-15-preview
    # organization: <organization_id>
    # deployment_name: <azure_model_deployment_name>
    # tokens_per_minute: 150_000 # set a leaky bucket throttle
    # requests_per_minute: 10_000 # set a leaky bucket throttle
    # max_retries: 10
    # max_retry_wait: 10.0
    # sleep_on_rate_limit_recommendation: true # whether to sleep when azure suggests wait-times
    # concurrent_requests: 25 # the number of parallel inflight requests that may be made
    # batch_size: 16 # the number of documents to send in a single request
    # batch_max_tokens: 8191 # the maximum number of tokens to send in a single request
    # target: required # or optional
  
chunks:
  size: 300
  overlap: 100
  group_by_columns: [id] # by default, we don't allow chunks to cross documents
    
input:
  type: file # or blob
  file_type: text # or csv
  base_dir: "input"
  file_encoding: utf-8
  file_pattern: ".*\\.txt$"

cache:
  type: file # or blob
  base_dir: "cache"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

storage:
  type: file # or blob
  base_dir: "output/${timestamp}/artifacts"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

reporting:
  type: file # or console, blob
  base_dir: "output/${timestamp}/reports"
  # connection_string: <azure_blob_storage_connection_string>
  # container_name: <azure_blob_storage_container_name>

entity_extraction:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/entity_extraction.txt"
  entity_types: [organization,person,geo,event]
  max_gleanings: 0

summarize_descriptions:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/summarize_descriptions.txt"
  max_length: 500

claim_extraction:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  # enabled: true
  prompt: "prompts/claim_extraction.txt"
  description: "Any claims or facts that could be relevant to information discovery."
  max_gleanings: 0

community_report:
  ## llm: override the global llm settings for this task
  ## parallelization: override the global parallelization settings for this task
  ## async_mode: override the global async_mode settings for this task
  prompt: "prompts/community_report.txt"
  max_length: 2000
  max_input_length: 8000

cluster_graph:
  max_cluster_size: 10

embed_graph:
  enabled: true # if true, will generate node2vec embeddings for nodes
  # num_walks: 10
  # walk_length: 40
  # window_size: 2
  # iterations: 3
  # random_seed: 597832

umap:
  enabled: true # if true, will generate UMAP embeddings for nodes

snapshots:
  graphml: true
  raw_entities: true
  top_level_nodes: true

local_search:
  # text_unit_prop: 0.5
  # community_prop: 0.1
  # conversation_history_max_turns: 5
  # top_k_mapped_entities: 10
  # top_k_relationships: 10
  # max_tokens: 12000

global_search:
  # max_tokens: 12000
  # data_max_tokens: 12000
  # map_max_tokens: 1000
  # reduce_max_tokens: 2000
  # concurrency: 32

其中修改 llm 區塊

修改 model: llama3

加入 api_base: http://localhost:11434/v1

修改 embeddings 區塊

model: nomic-embed-text

api_base: http://localhost:11434/api

修改 GraphRAG 的程式碼

除了設定好 setting.yaml 以外,程式碼也要修改成可以支持 ollama 的程式碼,有兩處要改,可以用以下現成的程式碼

  1. C:\Users\xxx\anaconda3\envs\GraphRAG\Lib\site-packages\graphrag\llm\openai\openai_embeddings_llm.py

加入 ollama setting 區塊

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License

"""The EmbeddingsLLM class."""

from typing_extensions import Unpack

from graphrag.llm.base import BaseLLM
from graphrag.llm.types import (
    EmbeddingInput,
    EmbeddingOutput,
    LLMInput,
)
import ollama

from .openai_configuration import OpenAIConfiguration
from .types import OpenAIClientTypes


class OpenAIEmbeddingsLLM(BaseLLM[EmbeddingInput, EmbeddingOutput]):
    """A text-embedding generator LLM."""

    _client: OpenAIClientTypes
    _configuration: OpenAIConfiguration

    def __init__(self, client: OpenAIClientTypes, configuration: OpenAIConfiguration):
        self.client = client
        self.configuration = configuration

    async def _execute_llm(
        self, input: EmbeddingInput, **kwargs: Unpack[LLMInput]
    ) -> EmbeddingOutput | None:
        args = {
            "model": self.configuration.model,
            **(kwargs.get("model_parameters") or {}),
        }
        # openai setting
        # embedding = await self.client.embeddings.create(
        #    input=input,
        #    **args,
        #)
        #return [d.embedding for d in embedding.data]

        # ollama setting
        embedding_list=[]
        for inp in input:
            embedding = ollama.embeddings(model='qwen:7b', prompt=inp) #如果要改模型, 模型的名字要換掉
            embedding_list.append(embedding['embedding'])
        return embedding_list        
  1. C:\Users\xxx\anaconda3\envs\GraphRAG\Lib\site-packages\graphrag\query\llm\oai\embedding.py

加入 ollama setting 區塊,並且關閉 openai setting 區塊即可

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License

"""OpenAI Embedding model implementation."""

import asyncio
from collections.abc import Callable
from typing import Any

import numpy as np
import tiktoken
from tenacity import (
    AsyncRetrying,
    RetryError,
    Retrying,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential_jitter,
)

from graphrag.query.llm.base import BaseTextEmbedding
from graphrag.query.llm.oai.base import OpenAILLMImpl
from graphrag.query.llm.oai.typing import (
    OPENAI_RETRY_ERROR_TYPES,
    OpenaiApiType,
)
from graphrag.query.llm.text_utils import chunk_text
from graphrag.query.progress import StatusReporter
import ollama

class OpenAIEmbedding(BaseTextEmbedding, OpenAILLMImpl):
    """Wrapper for OpenAI Embedding models."""

    def __init__(
        self,
        api_key: str | None = None,
        azure_ad_token_provider: Callable | None = None,
        model: str = "text-embedding-3-small",
        deployment_name: str | None = None,
        api_base: str | None = None,
        api_version: str | None = None,
        api_type: OpenaiApiType = OpenaiApiType.OpenAI,
        organization: str | None = None,
        encoding_name: str = "cl100k_base",
        max_tokens: int = 8191,
        max_retries: int = 10,
        request_timeout: float = 180.0,
        retry_error_types: tuple[type[BaseException]] = OPENAI_RETRY_ERROR_TYPES,  # type: ignore
        reporter: StatusReporter | None = None,
    ):
        OpenAILLMImpl.__init__(
            self=self,
            api_key=api_key,
            azure_ad_token_provider=azure_ad_token_provider,
            deployment_name=deployment_name,
            api_base=api_base,
            api_version=api_version,
            api_type=api_type,  # type: ignore
            organization=organization,
            max_retries=max_retries,
            request_timeout=request_timeout,
            reporter=reporter,
        )

        self.model = model
        self.encoding_name = encoding_name
        self.max_tokens = max_tokens
        self.token_encoder = tiktoken.get_encoding(self.encoding_name)
        self.retry_error_types = retry_error_types

    def embed(self, text: str, **kwargs: Any) -> list[float]:
        """
        Embed text using OpenAI Embedding's sync function.

        For text longer than max_tokens, chunk texts into max_tokens, embed each chunk, then combine using weighted average.
        Please refer to: https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
        """
        token_chunks = chunk_text(
            text=text, token_encoder=self.token_encoder, max_tokens=self.max_tokens
        )
        chunk_embeddings = []
        chunk_lens = []
        for chunk in token_chunks:
            try:
                # openai setting
                #embedding, chunk_len = self._embed_with_retry(chunk, **kwargs)
                #chunk_embeddings.append(embedding)
                #chunk_lens.append(chunk_len)

                # ollama setting
                embedding = ollama.embeddings(model="nomic-embed-text", prompt=chunk)['embedding'] #如果要替換嵌入模型, 請修改此處的模型名稱
                chunk_lens.append(chunk)
                chunk_embeddings.append(embedding)
                chunk_lens.append(chunk_lens)                
            # TODO: catch a more specific exception
            except Exception as e:  # noqa BLE001
                self._reporter.error(
                    message="Error embedding chunk",
                    details={self.__class__.__name__: str(e)},
                )

                continue
        #chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens)
        #chunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings)
        return chunk_embeddings.tolist()

    async def aembed(self, text: str, **kwargs: Any) -> list[float]:
        """
        Embed text using OpenAI Embedding's async function.

        For text longer than max_tokens, chunk texts into max_tokens, embed each chunk, then combine using weighted average.
        """
        token_chunks = chunk_text(
            text=text, token_encoder=self.token_encoder, max_tokens=self.max_tokens
        )
        chunk_embeddings = []
        chunk_lens = []
        embedding_results = await asyncio.gather(*[
            self._aembed_with_retry(chunk, **kwargs) for chunk in token_chunks
        ])
        embedding_results = [result for result in embedding_results if result[0]]
        chunk_embeddings = [result[0] for result in embedding_results]
        chunk_lens = [result[1] for result in embedding_results]
        chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens)  # type: ignore
        chunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings)
        return chunk_embeddings.tolist()

    def _embed_with_retry(
        self, text: str | tuple, **kwargs: Any
    ) -> tuple[list[float], int]:
        try:
            retryer = Retrying(
                stop=stop_after_attempt(self.max_retries),
                wait=wait_exponential_jitter(max=10),
                reraise=True,
                retry=retry_if_exception_type(self.retry_error_types),
            )
            for attempt in retryer:
                with attempt:
                    embedding = (
                        self.sync_client.embeddings.create(  # type: ignore
                            input=text,
                            model=self.model,
                            **kwargs,  # type: ignore
                        )
                        .data[0]
                        .embedding
                        or []
                    )
                    return (embedding, len(text))
        except RetryError as e:
            self._reporter.error(
                message="Error at embed_with_retry()",
                details={self.__class__.__name__: str(e)},
            )
            return ([], 0)
        else:
            # TODO: why not just throw in this case?
            return ([], 0)

    async def _aembed_with_retry(
        self, text: str | tuple, **kwargs: Any
    ) -> tuple[list[float], int]:
        try:
            retryer = AsyncRetrying(
                stop=stop_after_attempt(self.max_retries),
                wait=wait_exponential_jitter(max=10),
                reraise=True,
                retry=retry_if_exception_type(self.retry_error_types),
            )
            async for attempt in retryer:
                with attempt:
                    embedding = (
                        await self.async_client.embeddings.create(  # type: ignore
                            input=text,
                            model=self.model,
                            **kwargs,  # type: ignore
                        )
                    ).data[0].embedding or []
                    return (embedding, len(text))
        except RetryError as e:
            self._reporter.error(
                message="Error at embed_with_retry()",
                details={self.__class__.__name__: str(e)},
            )
            return ([], 0)
        else:
            # TODO: why not just throw in this case?
            return ([], 0)

參考資料