semvec/pypi-packages huggingface dataset:
parsec-cloud 3.8.0a1
Secure file sharing in the cloud
Requires Python: ~=3.12.0
Keywords: parsec, cryptography, sharing, encryption

[Parsec](https://docs.parsec.cloud/en/latest/userguide/first_steps.html) is a cloud-based application for simple and cryptographically secure file sharing.

- 🔐 Client-side E2EE to ensure your data is only accessed by you and the people you share it with.
- ✒️ Cryptographic signature to identify the author of each change.
- 🤝 Securely invite users to join your organization via link & token code.
- 📂 Virtual drive so you can access your data locally with your preferred software.
- 📆 Browse data history and recover files from any point in time.
- 🧑‍💻 Easy to self-host

This Pypi project contains the server application packaged, you can run it with `python -m parsec run --dev`.

For more in-depth examples, take a look at the [hosting documentation](https://docs.parsec.cloud/en/latest/hosting/introduction.html).
phonexia-deepfake-detection-client 4.0.1
Deepfake Detection Client
Requires Python: >=3.9
Keywords: grpc, voice, voice-biometry, speech, referential, deepfake, detection

![](https://www.phonexia.com/wp-content/uploads/phonexia-logo-transparent-500px.png)

# Phonexia deepfake detection client

This module contains client for communication with [Deepfake Detection microservice](https://hub.docker.com/repository/docker/phonexia/deepfake-detection/general) developed by [Phonexia](https://phonexia.com).

To use this client you will first need a running instance of any *Phonexia Deepfake Detection microservice*. If you don't yet have any running instance, don't hesitate to [contact our sales department](mailto:info@phonexia.com).

You can learn more about the speaker identification technology [here](https://docs.phonexia.com/technologies/speaker-identification/).

On [this page](https://docs.phonexia.com/products/speech-platform-4/grpc/api/phonexia/grpc/technologies/deepfake_detection/v1/deepfake_detection.proto), you will find a *gRPC API* reference for *Deepfake Detection microservice*.
phonexia-audio-quality-estimation-client 2.0.2
Audio Quality Estimation Client
Requires Python: >=3.9
Keywords: grpc, voice, voice-biometry, speech, age, estimation, age estimation

![](https://www.phonexia.com/wp-content/uploads/phonexia-logo-transparent-500px.png)

# Phonexia audio quality estimation client

This module contains client for communication with [audio quality estimation](https://hub.docker.com/repository/docker/phonexia/audio-quality-estimation/general) developed by [Phonexia](https://phonexia.com).

To use this client you will first need a running instance of any *Phonexia audio quality estimation microservice*. If you don't yet have any running instance, don't hesitate to [contact our sales department](mailto:info@phonexia.com).

You can learn more about the audio quality estimation technology [here](https://docs.phonexia.com/products/speech-platform-4/technologies/audio-quality-estimation/).

On [this page](https://docs.phonexia.com/products/speech-platform-4/grpc/api/phonexia/grpc/technologies/audio_quality_estimation/v1/audio_quality_estimation.proto#audioqualityestimation), you will find a *gRPC API* reference for *audio quality estimation microservice*.
crm-automator 1.11.5
AI-powered email processing and CRM automation
Requires Python: >=3.10

# CRM Automator: Modular Agentic CRM Toolkit

**Author**: Trung Le  
**Team**: RealTimeX.ai  
**Repository**: [https://github.com/therealtimex/crm-automator](https://github.com/therealtimex/crm-automator)

---

## Overview

The **CRM Automator** is a modular, agentic toolkit designed to transform unstructured data (emails, transcripts, documents) into structured CRM records. Unlike monolithic automation scripts, this toolkit is built as a collection of reusable **Tools** that can be orchestrated by AI agents or run as standalone pipelines.

**Current Status**: Production-Ready (v2.0)

## Key Features

-   **🧩 Modular Architecture**: Decoupled *Ingestion* (EML/Text), *Intelligence* (LLM Extraction), and *Integration* (CRM Sync).
-   **🤖 Agent-Ready**: Components like `IntelligenceLayer` and `RealTimeXClient` are designed to be imported as tools for frameworks like LangChain or Autogen.
-   **🛡️ Idempotent & Safe**: Implements "Search-before-Update" patterns and SQLite-based state tracking to prevent duplicate records.
-   **🧹 Smart Cleaning Pipeline**: Automatically converts bloaty HTML into clean Markdown, strips noise, and **resolves tracking links** (e.g., unwraps Proofpoint/Safelinks and resolves HubSpot redirects) to ensure accurate URL extraction.
-   **🧠 OpenAI-Compatible**: Works independently with OpenAI, Anthropic, or local LLMs (via LM Studio/Ollama) using `instructor`.

## Installation

1.  Clone the repository:
    ```bash
    git clone https://github.com/therealtimex/crm-automator.git
    cd crm-automator
    ```

2.  Install dependencies:
    ```bash
    # Option A: Standard pip
    pip install -r requirements.txt

    # Option B: Modern uv (Recommended)
    uv sync
    ```

3.  Configure environment:
    ```bash
    cp .env.example .env
    # Edit .env with your CRM and LLM credentials
    ```

## Usage

### 1. Process Emails (`.eml`)
Run the automator on a single EML file or an entire directory recursively.

**Using uvx (Fastest, zero-install):**
```bash
uvx eml/eml_automator.py "path/to/email.eml" --env-file ".env"
```

**Using a directory:**
```bash
uv run python eml/eml_automator.py "path/to/directory" --env-file ".env"
```

**Options:**
-   `--force`, `-f`: Force re-processing of an email even if it was already synced.
-   `--verbose`, `-v`: Enable debug logs to see HTTP requests and LLM thought process.

### 2. Generic Agent Demo
See how the toolkit handles raw text (e.g., meeting transcripts):

```bash
python3 eml/agent_demo.py --api-key "your_key"
```

## Architecture

```mermaid
graph TD
    Ingest[Ingestion Layer] --> Intelligence[Intelligence Layer]
    Intelligence <--> Search[Web Search Tool]
    Intelligence --> Client[CRM Client]
    Client --> CRM[(RealTimeX CRM)]
```

-   **`crm_client.py`**: The "hands" – handles all API calls with retries and 10s timeouts.
-   **`intelligence.py`**: The "brain" – uses Pydantic models to extract structured data from text.
-   **`persistence.py`**: The "memory" – prevents processing the same resource twice.

---
*Built with ❤️ by the RealTimeX.ai Team*
phonexia-denoiser-client 2.0.1
Client for communicating with Phonexia denoiser microservice
Requires Python: >=3.9
Keywords: grpc, voice, speech, noise, denoise, denoising, noise-reduction, noise-removal, voice-isolation, noise-cancellation

![](https://www.phonexia.com/wp-content/uploads/phonexia-logo-transparent-500px.png)

# Phonexia denoiser client

This module contains client for communication with [denoiser](https://hub.docker.com/repository/docker/phonexia/denoiser/general) developed by [Phonexia](https://phonexia.com).

To use this client you will first need a running instance of any *Phonexia denoiser*. If you don't yet have any running instance, don't hesitate to [contact our sales department](mailto:info@phonexia.com).

You can learn more about the denoiser technology [here](https://docs.phonexia.com/products/speech-platform-4/technologies/denoiser/).

On [this page](https://docs.phonexia.com/products/speech-platform-4/grpc/api/phonexia/grpc/technologies/denoiser/v1/denoiser.proto), you will find a *gRPC API* reference for *denoiser microservice*.