semvec/pypi-packages huggingface dataset:
octodns-easydns 1.0.0
easyDns API v3 provider for octoDNS
License: MIT
Requires Python: >=3.9

## easyDns API v3 provider for octoDNS

An [octoDNS](https://github.com/octodns/octodns/) provider that targets [easyDNS](https://easydns.com/).

### Installation

#### Command line

```
pip install octodns-easydns
```

#### requirements.txt/setup.py

Pinning specific versions or SHAs is recommended to avoid unplanned upgrades.

##### Versions

```
# Start with the latest versions and don't just copy what's here
octodns==0.9.14
octodns-easydns==0.0.1
```

##### SHAs

```
# Start with the latest/specific versions and don't just copy what's here
-e git+https://git@github.com/octodns/octodns.git@9da19749e28f68407a1c246dfdf65663cdc1c422#egg=octodns
-e git+https://git@github.com/octodns/octodns-easydns.git@ec9661f8b335241ae4746eea467a8509205e6a30#egg=octodns_easydns
```

### Configuration

```yaml
providers:
  easydns:
    class: octodns_easydns.EasyDnsProvider
    # Your EasyDNS API token (required)
    token: env/EASYDNS_TOKEN
    # Your EasyDNS API Key (required)
    api_key: env/EASYDNS_API_KEY
    # Use SandBox or Live environment, optional, defaults to live
    #sandbox: False
    # Currency to use for creating domains, default CAD
    #default_currency: CAD
    # Domain Portfolio under which to create domains
    portfolio: env/EASYDNS_PORTFOLIO
```

### Support Information

#### Records

EasyDnsProvider supports A, AAAA, CAA, CNAME, DS, MX, NAPTR, NS, SRV, and TXT.

#### Root NS Records

EasyDnsProvider supports full root NS record management.

#### Dynamic

EasyDnsProvider does not support dynamic records.

### Development

See the [/script/](/script/) directory for some tools to help with the development process. They generally follow the [Script to rule them all](https://github.com/github/scripts-to-rule-them-all) pattern. Most useful is `./script/bootstrap` which will create a venv and install both the runtime and development related requirements. It will also hook up a pre-commit hook that covers most of what's run by CI.
langchain-google-vertexai_yd 2.0.21rc1
An integration package connecting Google VertexAI and LangChain
License: MIT
Requires Python: <4.0,>=3.9

# langchain-google-vertexai

This package contains the LangChain integrations for Google Cloud generative models.

## Installation

```bash
pip install -U langchain-google-vertexai
```

## Chat Models

`ChatVertexAI` class exposes models such as `gemini-pro` and `chat-bison`.

To use, you should have Google Cloud project with APIs enabled, and configured credentials. Initialize the model as:

```python
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model_name="gemini-pro")
llm.invoke("Sing a ballad of LangChain.")
```

You can use other models, e.g. `chat-bison`:

```python
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model_name="chat-bison", temperature=0.3)
llm.invoke("Sing a ballad of LangChain.")
```

#### Multimodal inputs

Gemini vision model supports image inputs when providing a single chat message. Example:

```python
from langchain_core.messages import HumanMessage
from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model_name="gemini-pro-vision")
# example
message = HumanMessage(
    content=[
        {
            "type": "text",
            "text": "What's in this image?",
        },  # You can optionally provide text parts
        {"type": "image_url", "image_url": {"url": "https://picsum.photos/seed/picsum/200/300"}},
    ]
)
llm.invoke([message])
```

The value of `image_url` can be any of the following:

- A public image URL
- An accessible gcs file (e.g., "gcs://path/to/file.png")
- A base64 encoded image (e.g., `data:image/png;base64,abcd124`)

## Embeddings

You can use Google Cloud's embeddings models as:

```python
from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings()
embeddings.embed_query("hello, world!")
```

## LLMs

You can use Google Cloud's generative AI models as Langchain LLMs:

```python
from langchain_core.prompts import PromptTemplate
from langchain_google_vertexai import ChatVertexAI

template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)

llm = ChatVertexAI(model_name="gemini-pro")
chain = prompt | llm

question = "Who was the president of the USA in 1994?"
print(chain.invoke({"question": question}))
```

You can use Gemini and Palm models, including code-generations ones:

```python
from langchain_google_vertexai import VertexAI

llm = VertexAI(model_name="code-bison", max_output_tokens=1000, temperature=0.3)

question = "Write a python function that checks if a string is a valid email address"

output = llm(question)
```