semvec/pypi-packages huggingface dataset:
flexinfer-vector-utils 0.2.1
Unified vector storage and embedding utilities for FlexInfer services
License: MIT
Requires Python: >=3.11

![Banner](assets/banner.png)
# flexinfer-vector-utils

![Header](assets/header.svg)

[![pipeline status](https://gitlab.flexinfer.ai/libs/py-vector-utils/badges/main/pipeline.svg)](https://gitlab.flexinfer.ai/libs/py-vector-utils/-/commits/main)
[![coverage report](https://gitlab.flexinfer.ai/libs/py-vector-utils/badges/main/coverage.svg)](https://gitlab.flexinfer.ai/libs/py-vector-utils/-/commits/main)

Unified vector storage and embedding utilities for FlexInfer services.

## Features

- **Hybrid Embedder**: Automatic fallback from OpenAI/vLLM to local `sentence-transformers` models.
- **Qdrant Storage**: Generalized async client for Qdrant collection management and search.
- **Batching**: Optimized batch processing for high-throughput embedding generation.

## Usage

### Embeddings

```python
from vector_utils.embeddings import HybridEmbedder

# Tries OpenAI -> vLLM -> Local fallback
embedder = HybridEmbedder(
    openai_api_key="...",
    enable_local_fallback=True
)

embeddings = await embedder.embed_batch(["text1", "text2"])
```

### Vector Store

```python
from vector_utils.store import QdrantStore, VectorItem

store = QdrantStore(url="http://qdrant:6333", collection_name="docs")
await store.initialize()

await store.upsert([
    VectorItem(id="1", vector=[0.1, ...], payload={"type": "doc"})
])
```

## Installation

```bash
pip install flexinfer-vector-utils
# With local fallback support
pip install "flexinfer-vector-utils[local]"
```
flexinfer-browser-kit 0.2.1
Production-ready Playwright wrapper with stealth and session management
License: MIT
Requires Python: >=3.11

![Banner](assets/banner.png)
# flexinfer-browser-kit

![Header](assets/header.svg)

[![pipeline status](https://gitlab.flexinfer.ai/libs/py-browser-kit/badges/main/pipeline.svg)](https://gitlab.flexinfer.ai/libs/py-browser-kit/-/commits/main)
[![coverage report](https://gitlab.flexinfer.ai/libs/py-browser-kit/badges/main/coverage.svg)](https://gitlab.flexinfer.ai/libs/py-browser-kit/-/commits/main)

Production-ready browser automation library for Python, built on Playwright. Features advanced stealth evasion techniques and robust session management.

## Features

- **Stealth Mode**: Pre-configured evasion scripts to bypass bot detection (custom args, JS injection).
- **Session Management**: persistent context storage with file locking for safe concurrency.
- **Resilience**: Configurable timeouts, proxy support, and automatic retries.

## Installation

```bash
pip install flexinfer-browser-kit
# For stealth features
pip install "flexinfer-browser-kit[stealth]"
```

## Usage

### Basic Scraping

```python
from browser_kit import BrowserManager

manager = BrowserManager(headless=True)

with manager.new_context() as context:
    page = context.new_page()
    page.goto("https://network-tools.com/request")
    print(page.title())
```

### Stealth Mode

```python
from browser_kit import BrowserManager

# Uses custom args and evasion scripts by default
manager = BrowserManager(
    headless=True,
    stealth=True,  # Enable evasion
    user_agent="Mozilla/5.0..."
)

with manager.new_context() as context:
    page = context.new_page()
    page.goto("https://bot-detection-check.com")
```

### Persistent Sessions

```python
manager = BrowserManager(storage_dir="./sessions")

# Automatically loads/saves cookies to ./sessions/my-session.json
context = manager.new_context(session_id="my-session")
```