semvec/pypi-packages huggingface dataset:
programgarden-community 1.7.0
ProgramGarden Community - 전략 플러그인 모음
Requires Python: >=3.12

# ProgramGarden Community

커뮤니티 전략 플러그인 모음입니다. ConditionNode에 연결하여 다양한 기술적 분석 및 포지션 관리 전략을 적용할 수 있습니다.

## 설치

```bash
pip install programgarden-community

# Poetry 사용 시 (개발 환경)
poetry add programgarden-community
```

요구 사항: Python 3.12+

## 포함 플러그인 (14개)

### Technical (기술적 분석) - 11개

| 플러그인 | 설명 |
|----------|------|
| RSI | RSI 과매수/과매도 조건 |
| MACD | MACD 크로스오버 조건 |
| BollingerBands | 볼린저밴드 이탈/복귀 조건 |
| VolumeSpike | 거래량 급증 감지 |
| MovingAverageCross | 이동평균 골든/데드 크로스 |
| DualMomentum | 듀얼 모멘텀 (절대 + 상대) |
| Stochastic | 스토캐스틱 오실레이터 (%K, %D) |
| ATR | ATR 변동성 측정 |
| PriceChannel | 가격 채널 / 돈치안 채널 |
| ADX | ADX 추세 강도 측정 |
| OBV | OBV 거래량 기반 모멘텀 |

### Position (포지션 관리) - 3개

| 플러그인 | 설명 |
|----------|------|
| StopLoss | 손절 (손실 한도 도달 시 매도) |
| ProfitTarget | 익절 (수익 목표 도달 시 매도) |
| TrailingStop | 트레일링 스탑 (HWM 기반 drawdown 관리) |

## 사용법

```python
from programgarden_community.plugins import register_all_plugins, get_plugin, list_plugins

# 모든 플러그인 등록
register_all_plugins()

# 특정 플러그인 스키마 조회
schema = get_plugin("RSI")

# 카테고리별 플러그인 목록
plugins = list_plugins(category="technical")
```

## 기여하기

새로운 전략 플러그인 PR을 환영합니다. `plugins/` 디렉토리에 새 폴더를 만들고 `*_SCHEMA`와 `*_condition` 함수를 구현하면 됩니다.

## 변경 로그

자세한 변경 사항은 `CHANGELOG.md`를 참고하세요.
sqliteai-vector 0.9.91
Python prebuilt binaries for SQLite Vector extension for all supported platforms and architectures.
Requires Python: >=3

## SQLite Vector Python package

This package provides the sqlite-vector extension prebuilt binaries for multiple platforms and architectures.

### SQLite Vector

SQLite Vector is a cross-platform, ultra-efficient SQLite extension that brings vector search capabilities to your embedded database. It works seamlessly on iOS, Android, Windows, Linux, and macOS, using just 30MB of memory by default. With support for Float32, Float16, BFloat16, Int8, and UInt8, and highly optimized distance functions, it's the ideal solution for Edge AI applications.

More details on the official repository [sqliteai/sqlite-vector](https://github.com/sqliteai/sqlite-vector).

### Documentation

For detailed information on all available functions, their parameters, and examples, refer to the [comprehensive API Reference](https://github.com/sqliteai/sqlite-vector/blob/main/API.md).

### Supported Platforms and Architectures

| Platform      | Arch         | Subpackage name          | Binary name  |
| ------------- | ------------ | ------------------------ | ------------ |
| Linux (CPU)   | x86_64/arm64 | sqlite_vector.binaries   | vector.so    |
| Windows (CPU) | x86_64       | sqlite_vector.binaries   | vector.dll   |
| macOS (CPU)   | x86_64/arm64 | sqlite_vector.binaries   | vector.dylib |

## Usage

> **Note:** Some SQLite installations on certain operating systems may have extension loading disabled by default.   
If you encounter issues loading the extension, refer to the [sqlite-extensions-guide](https://github.com/sqliteai/sqlite-extensions-guide/) for platform-specific instructions on enabling and using SQLite extensions.

```python
import importlib.resources
import sqlite3

# Connect to your SQLite database
conn = sqlite3.connect("example.db")

# Load the sqlite-vector extension
# pip will install the correct binary package for your platform and architecture
ext_path = importlib.resources.files("sqlite_vector.binaries") / "vector"

conn.enable_load_extension(True)
conn.load_extension(str(ext_path))
conn.enable_load_extension(False)


# Now you can use sqlite-vector features in your SQL queries
print(conn.execute("SELECT vector_version();").fetchone())
```
semanticapi-langchain 0.1.1
LangChain integration for Semantic API — find the right API for any task using natural language
Requires Python: >=3.9
Keywords: api-discovery, langchain, llm, semantic-api, tools

# semanticapi-langchain

LangChain integration for [Semantic API](https://semanticapi.dev) — find the right API for any task using natural language.

[![PyPI](https://img.shields.io/pypi/v/semanticapi-langchain)](https://pypi.org/project/semanticapi-langchain/)
[![Python](https://img.shields.io/pypi/pyversions/semanticapi-langchain)](https://pypi.org/project/semanticapi-langchain/)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## What is Semantic API?

Semantic API matches natural language queries to real API endpoints. Ask "send an SMS" and get back the best provider, endpoint, parameters, and auth docs — instantly.

## Installation

```bash
pip install semanticapi-langchain
```

## Quick Start

```python
from semanticapi_langchain import SemanticAPIToolkit

# Create toolkit (uses SEMANTICAPI_API_KEY env var)
toolkit = SemanticAPIToolkit(api_key="sapi_your_key")
tools = toolkit.get_tools()

# Use the query tool directly
query_tool = tools[0]
result = query_tool.run("send an SMS")
print(result)
```

## Tools

| Tool | Description |
|------|-------------|
| `semanticapi_query` | Find the best API for a task described in plain English |
| `semanticapi_search` | Search and discover available API capabilities |

## With a LangChain Agent

```python
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate
from semanticapi_langchain import SemanticAPIToolkit

toolkit = SemanticAPIToolkit()
tools = toolkit.get_tools()
llm = ChatOpenAI(model="gpt-4o-mini")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You find APIs for tasks. Use semanticapi tools to discover them."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

response = executor.invoke({"input": "How do I send an email programmatically?"})
print(response["output"])
```

## Using the Wrapper Directly

```python
from semanticapi_langchain import SemanticAPIWrapper

api = SemanticAPIWrapper(api_key="sapi_your_key")

# Query for an API
result = api.query("send an SMS")

# Search capabilities
results = api.search("weather")

# List providers
providers = api.list_providers()

# Async support
result = await api.aquery("send an SMS")
```

## Configuration

| Parameter | Env Var | Description |
|-----------|---------|-------------|
| `api_key` | `SEMANTICAPI_API_KEY` | Your Semantic API key |
| `base_url` | — | Override API base URL (default: `https://semanticapi.dev/api`) |
| `timeout` | — | Request timeout in seconds (default: 30) |

## Development

```bash
git clone https://github.com/semanticapi/semanticapi-langchain.git
cd semanticapi-langchain
pip install -e ".[dev]"
pytest
```

## License

MIT