vertai 0.1.3

VertAI - A vertical-domain AI agent development SDK with local-first design

VertAI | 垂直AI智能体SDK

垂直领域 AI 智能体开发 SDK，支持完全离线运行。

A vertical-domain AI agent development SDK designed for fully offline operation.

设计理念 | Design Philosophy

模块化架构：核心功能轻量安装，按需扩展语义能力。

Modular Architecture: Lightweight core installation with optional semantic capabilities.

vertai (核心 ~5MB | Core ~5MB)
├── Workflow        # 工作流编排 | Workflow orchestration
├── Dashboard       # 数据可视化 | Data visualization
├── DocGen          # 文档生成 | Document generation
├── DocParser       # 文档解析 (Markdown) | Document parsing (Markdown)
├── SessionMemory   # 会话管理 | Session management
└── VectorEngine    # 向量存储 (需嵌入模型提供语义能力) | Vector storage (requires embedding model for semantic capabilities)

可选扩展 | Optional Extensions
├── [embeddings]    # 离线语义搜索 | Offline semantic search
├── [doc-parser]    # 文档解析 (PDF/Word/Excel) | Document parsing (PDF/Word/Excel)
└── [production]    # 生产环境完整配置 | Complete production configuration

安装 | Installation

核心安装 | Core Installation

pip install vertai

扩展安装 | Optional Extensions

# 离线语义搜索支持
# Offline semantic search support
pip install vertai[embeddings]

# 文档解析支持 (PDF/Word/Excel/PPT)
# Document parsing support (PDF/Word/Excel/PPT)
pip install vertai[doc-parser]

# 完整生产配置
# Complete production configuration
pip install vertai[production]

安装选项	体积	功能
核心	~5MB	Workflow, Dashboard, DocGen, Markdown解析
[embeddings]	~500MB	离线语义向量搜索
[doc-parser]	~50MB	PDF/Word/Excel/PPT解析
[production]	~600MB	完整生产配置

Installation Option	Size	Features
Core	~5MB	Workflow, Dashboard, DocGen, Markdown parsing
[embeddings]	~500MB	Offline semantic vector search
[doc-parser]	~50MB	PDF/Word/Excel/PPT parsing
[production]	~600MB	Complete production configuration

快速开始 | Quick Start

工作流编排（完全离线）| Workflow Orchestration (Fully Offline)

from vertai import Workflow

wf = Workflow()
wf.step("load", lambda ctx: ctx.set("data", [1, 2, 3, 4, 5]))
wf.step("process", lambda ctx: ctx.set("sum", sum(ctx.get("data"))))
wf.step("output", lambda ctx: print(f"总和 | Sum: {ctx.get('sum')}"))
wf.run()

语义向量搜索（需安装 embeddings）| Semantic Vector Search (requires embeddings)

from vertai import VectorEngine, Document
from sentence_transformers import SentenceTransformer

# 加载嵌入模型（首次下载约100MB，之后离线可用）
# Load embedding model (~100MB first download, then works offline)
model = SentenceTransformer('bge-small-zh-v1.5')

def embedding_fn(text):
    return model.encode(text).tolist()

# 创建向量引擎
# Create vector engine
engine = VectorEngine(store_type="memory", embedding_fn=embedding_fn)

# 索引文档
# Index documents
engine.index_documents([
    Document(content="Python是一种编程语言，由Guido van Rossum创建 | Python is a programming language created by Guido van Rossum"),
    Document(content="机器学习是人工智能的子领域 | Machine learning is a subfield of artificial intelligence"),
    Document(content="深度学习使用多层神经网络 | Deep learning uses multi-layer neural networks"),
])

# 语义搜索
# Semantic search
results = engine.search("编程语言 | programming language")
# 返回：Python是一种编程语言...（语义匹配，非关键词匹配）
# Returns: Python是一种编程语言... (semantic match, not keyword match)

LLM 对话（需配置 API）| LLM Chat (requires API configuration)

from vertai import LLMEngine, LLMConfig, ModelProvider

config = LLMConfig(
    provider=ModelProvider.DEEPSEEK,
    base_url="https://api.deepseek.com/anthropic",
    api_key="sk-xxx",  # 或设置环境变量 VERTAI_API_KEY | Or set environment variable VERTAI_API_KEY
    model="deepseek-v4-flash",
)

llm = LLMEngine(config)

# 单次生成
# Single generation
result = llm.generate("你好 | Hello")

# 流式输出
# Streaming output
for chunk in llm.stream("讲个故事 | Tell a story"):
    print(chunk, end="", flush=True)

# 多轮对话
# Multi-turn conversation
messages = [
    {"role": "user", "content": "我叫小明 | My name is Xiao Ming"},
    {"role": "assistant", "content": "你好小明！| Hello Xiao Ming!"},
    {"role": "user", "content": "我叫什么名字？| What's my name?"},
]
result = llm.chat(messages)

结构化数据提取 | Structured Data Extraction

from vertai import StructuredOutput

schema = {"name": "string", "amount": "number"}

# 正则模式（完全离线，简单模式）
# Regex mode (fully offline, simple patterns)
output = StructuredOutput(schema)
result = output.extract("张三报销500元 | Zhang San expense 500 yuan")
# {'name': '张三', 'amount': 500.0} | Result shows extracted name and amount

# LLM模式（需配置API，语义理解）
# LLM mode (requires API, semantic understanding)
from vertai import LLMEngine, LLMConfig, ModelProvider
llm = LLMEngine(LLMConfig(
    provider=ModelProvider.DEEPSEEK,
    base_url="https://api.deepseek.com/anthropic",
    api_key="sk-xxx",
))
output = StructuredOutput(schema, llm=llm)
result = output.extract("李四消费了三百块 | Li Si spent three hundred yuan")
# {'name': '李四', 'amount': 300.0}（语义理解中文数字）
# {'name': 'Li Si', 'amount': 300.0} - semantic understanding of Chinese numbers

功能模块 | Feature Modules

模块	离线可用	依赖
Workflow	✅	无
Dashboard	✅	无
DocGen (Markdown/HTML)	✅	无
DocParser (Markdown)	✅	无
SessionMemory	✅	无
VectorEngine (存储)	✅	无
VectorEngine (语义搜索)	✅	sentence-transformers
StructuredOutput (正则)	✅	无
StructuredOutput (语义)	❌	LLM API
LLMEngine	❌	LLM API 或 Ollama
KnowledgeQA	✅	向量搜索离线，生成需LLM
LocalModelManager	✅	模型文件本地存储

Module	Offline	Dependencies
Workflow	✅	None
Dashboard	✅	None
DocGen (Markdown/HTML)	✅	None
DocParser (Markdown)	✅	None
SessionMemory	✅	None
VectorEngine (Storage)	✅	None
VectorEngine (Semantic Search)	✅	sentence-transformers
StructuredOutput (Regex)	✅	None
StructuredOutput (Semantic)	❌	LLM API
LLMEngine	❌	LLM API or Ollama
KnowledgeQA	✅	Vector search offline, generation needs LLM
LocalModelManager	✅	Local model file storage

本地模型 | Local Models

嵌入模型 | Embedding Models

模型	体积	语言	离线
bge-small-zh-v1.5	100MB	中文	✅
bge-large-zh-v1.5	650MB	中文	✅
all-MiniLM-L6-v2	80MB	英文	✅

Model	Size	Language	Offline
bge-small-zh-v1.5	100MB	Chinese	✅
bge-large-zh-v1.5	650MB	Chinese	✅
all-MiniLM-L6-v2	80MB	English	✅

语音模型 | Speech Models

模型	体积	最低配置	离线
whisper-tiny	75MB	1GB RAM	✅
whisper-small	466MB	2GB RAM	✅
whisper-large-v3	2.9GB	10GB RAM	✅

Model	Size	Min Requirements	Offline
whisper-tiny	75MB	1GB RAM	✅
whisper-small	466MB	2GB RAM	✅
whisper-large-v3	2.9GB	10GB RAM	✅

from vertai import LocalModelManager

manager = LocalModelManager()
manager.download("bge-small-zh-v1.5")  # 首次下载 | First download
model = manager.load("bge-small-zh-v1.5")  # 之后离线加载 | Then load offline

测试 | Testing

pip install vertai[dev]
python -m pytest tests/ -v --cov=vertai

# 642 passed, 20 skipped, 94% coverage | 642 通过, 20 跳过, 94% 覆盖率

许可证 | License

MIT License - Copyright (c) 2026 VertAI Team

详见 LICENSE 文件。