semvec/pypi-packages huggingface dataset:
fivetran-connector-sdk 2.6.1
Build custom connectors on Fivetran platform
Requires Python: >=3.10

# **fivetran-connector-sdk**
[![Downloads](https://static.pepy.tech/badge/fivetran-connector-sdk)](https://pepy.tech/project/fivetran-connector-sdk)

The *fivetran-connector-sdk* allows users to execute custom, self-written Python code within [Fivetran's](https://www.fivetran.com/) secure cloud environment. Fivetran automatically manages running the connectors on your scheduled frequency and manages the required compute resources.

The Connector SDK service is the best fit for the following use cases:
- Fivetran doesn't have a connector for your source and is unlikely to support it soon.
- You are using private APIs, custom applications, unsupported file formats, or those that require pre-processing.
- You have sensitive data that needs filtering or anonymizing before entering the destination.
- You don't want to introduce a 3rd party into your pipeline (e.g., to host a custom function).

To learn more, see our [Connector SDK documentation](https://fivetran.com/docs/connectors/connector-sdk)

## **Install**

    pip install fivetran-connector-sdk

## **Requirements**
- Python version ≥3.10 and ≤3.14
- Operating system:
  - Windows: 10 or later (64-bit only)
  - macOS: 13 (Ventura) or later (Apple Silicon [arm64] or Intel [x86_64])
  - Linux: Distributions such as Ubuntu 20.04 or later, Debian 10 or later, or Amazon Linux 2 or later (arm64 or x86_64)

## **Getting started**
See [Quickstart guide](https://fivetran.com/docs/connectors/connector-sdk/quickstart-guide) to get started.

## **Usage**
Import required classes from fivetran_connector_sdk to begin writing your custom data connector

```python
  from fivetran_connector_sdk import Connector
  from fivetran_connector_sdk import Logging as log
  from fivetran_connector_sdk import Operations as op
```
See our [Technical Reference](https://fivetran.com/docs/connectors/connector-sdk/technical-reference#update) and [Best Practices](https://fivetran.com/docs/connectors/connector-sdk/best-practices) documentation for more details.

You can also refer to our [existing examples](https://github.com/fivetran/fivetran_connector_sdk) to better understand Fivetran's Connector SDK and start creating custom data connectors.

## **Maintenance**
This package is actively maintained by Fivetran Developers. Please reach out to our [Support team](https://support.fivetran.com/hc/en-us) for any inquiries.
gllm-datastore-binary 0.5.62
A library containing data store components for Gen AI applications.
Requires Python: <3.13,>=3.11

# GLLM Datastore

## Description

A library for managing data storage and retrieval operations in Generative AI applications.

---

## Installation

### Prerequisites

Mandatory:

1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)
4. gcloud CLI (for authentication) — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:

   ```bash
   gcloud auth login
   ```

---

### Install from Artifact Registry

This requires authentication via the `gcloud` CLI.

```bash
uv pip install \
  --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" \
  gllm-datastore
```

---

## Local Development Setup

### Prerequisites

1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)
4. gcloud CLI — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:

   ```bash
   gcloud auth login
   ```

5. Git — [Install here](https://git-scm.com/downloads)
6. Access to the [GDP Labs SDK GitHub repository](https://github.com/GDP-ADMIN/gl-sdk)

---

### 1. Clone Repository

```bash
git clone git@github.com:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-datastore
```

---

### 2. Setup Authentication

Set the following environment variables to authenticate with internal package indexes:

```bash
export UV_INDEX_GEN_AI_INTERNAL_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_INTERNAL_PASSWORD="$(gcloud auth print-access-token)"
export UV_INDEX_GEN_AI_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_PASSWORD="$(gcloud auth print-access-token)"
```

---

### 3. Quick Setup

Run:

```bash
make setup
```

---

### 4. Activate Virtual Environment

```bash
source .venv/bin/activate
```

---

## Local Development Utilities

The following Makefile commands are available for quick operations:

### Install uv

```bash
make install-uv
```

### Install Pre-Commit

```bash
make install-pre-commit
```

### Install Dependencies

```bash
make install
```

### Update Dependencies

```bash
make update
```

### Run Tests

```bash
make test
```

---

## Contributing

Please refer to the [Python Style Guide](https://docs.google.com/document/d/1uRggCrHnVfDPBnG641FyQBwUwLoFw0kTzNqRm92vUwM/edit?usp=sharing)
for information about code style, documentation standards, and SCA requirements.