行銷人真的有越來越酷的各種AI工具，尤其在影片製作的方面，是百花齊放，各式各樣的應用一直冒出來，這個 VOZO AI 工具很適合做一些社交膜體上詪片
只要選定影片，就可以重新改變該影片的對白
可以快速叫出影片中的字幕，更改成自己的劇本，讓選定的影片按照你的劇本再上一次字幕，並且有翻譯功能
可以快速幫你把自己的影片或是youtube中的影片，快速剪輯一個短影音版本，可惜現在還是要以英文為主
影片中有多人的話，可以支援多人的口型同步，這功能很新也很實用
內建翻譯多國語言功能
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
要先設定好 GraphRAG
下載以及安裝好 Ollama
pip install ollama
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
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
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
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)
隨著AI技術的進步，影片製作變得前所未有的簡單，工具也是超級的多，讓人無從選擇，但大多數是西方的畫風，少有東方的模型，而Vidu AI 是一款大陸清華大學設計的 AIGV 工具，讓使用者能夠免費生成 4 秒鐘(免費)或是 8 秒鐘的高品質影片，且不需要任何專業知識即可完成，以下是它的幾個主要特色：
Mapify 是一個心智圖的 AI 在線工具，專門為了快速且有效地整理和視覺化資訊而設計，無論是學生、教育者、專業人士或任何需要整理大量資訊的人士，都會發現這個工具非常有用。Mapify.so 的核心功能是將文檔（如 Word 或 PDF 文件）轉換成結構化的心智圖，這種轉換不僅迅速，而且保留了原始資訊的豐富性和複雜性。
Tensor.art 是一個 AI 的免費線上圖像生成平台，用穩定擴散模型（SD, Stable Diffusion）來生成圖片，用戶可以透過選擇不同的檢查點（模型）和細節調整元素（LURAs）來生成圖像，這些工具允許用戶調整生成圖像的細節和風格，Tensor.art 每天都提供用戶一定數量的免費信用額，一般使用者已經很夠用
Tensor.art 提供多種圖像生成選項，包括基於文本的圖像生成和圖像到圖像的轉換。用戶可以通過上傳參考圖像和相應的文本提示來精確控制生成的圖像類型和風格。此外，平台還支持使用控制網格（Control Net）進一步細調圖像的外觀，以及高解析度修正工具來解決非標準長寬比圖像生成時可能出現的問題。
Tensor.art 的界面友善，適合各種經驗水平的用戶。新手可以利用預設的模型和設置快速開始，而經驗豐富的創作者則可以探索更多高級功能，如自定義種子、詳細程度調整和不同的取樣方法，這些功能提供了豐富的視覺特性和個性化選項。
Meshy AI 是一個 AI 3D 建模的工具平台，利用人工智能技術來簡化3D內容的創建，這個工具的設計宗旨是將文字或圖像轉換成詳細的3D模型和紋理，大大加快了傳統的3D建模過程。
Meshy的功能還擴展到與 Unity 和 Blender 等流行軟件的整合，通過允許在這些平台內直接應用紋理和生成模型來增強工作流程。此外，Meshy還為開發者提供了API，使他們能夠在更大項目中自定義和自動化3D資產的創建，另外Meshy的紋理生成僅需約三分鐘，完整的3D模型從文字或圖像生成在15分鐘以內完成。這種快速的生產能力可以顯著縮短遊戲開發、數位藝術和虛擬實境等領域的項目時間。
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