semvec/pypi-packages huggingface dataset:
langchain-dappier 0.2.4
An integration package connecting Dappier and LangChain
License: MIT
Requires Python: <4.0,>=3.9

# langchain-dappier

This package contains the LangChain integration with Dappier

## Installation

```bash
pip install -U langchain-dappier
```

And you should configure credentials by setting the following environment variables:

```bash
export DAPPIER_API_KEY="your-api-key-here"
```

## Retrievers

`DappierRetriever` class exposes chat models from Dappier.

**Parameters:**

- `data_model_id` (str): Data model ID, starting with `dm_`. You can find the available data model IDs at [Dappier marketplace](https://platform.dappier.com/marketplace).
- `k` (int): Number of documents to return.
- `ref` (Optional[str]): Site domain where AI recommendations are displayed.
- `num_articles_ref` (int): Minimum number of articles from the specified `ref` domain. The rest will come from other sites within the RAG model.
- `search_algorithm` (Literal["most_recent", "most_recent_semantic", "semantic", "trending"]): Search algorithm for retrieving articles.
- `api_key` (Optional[str]): The API key used to interact with the Dappier APIs.

```python
from langchain_dappier import DappierRetriever

retriever = DappierRetriever(
    data_model_id="dm_01jagy9nqaeer9hxx8z1sk1jx6"
)

query = "latest tech news"

retriever.invoke(query)
```
dki 0.1.2
docker image build and pull tools
License: MIT
Requires Python: >=3.9

dki
==========

Docker Image.

快速构建和拉取不同位置的镜像，以及构建缺失的镜像。

Install
-------

``pipx install dki``

Usage
-----

dki build --template python_base --name svtter/python_base --tag latest
v2v-toolkit 1.1.1
Ver2Vision-Toolkit: Unified interface for streamlining 'Verbal Data to Vision Synthesis with Latent Diffusion Models' project. Provides tools for managing low-level downstream tasks.
License: MIT
Requires Python: >=3.10

**Ver2Vision Toolkit** library aims to deliver a unified interface and tools to facilitate the development and execution of *Verbal Data to Vision Synthesis with Latent Diffusion Models* project. 
Provides tools for managing downstream tasks like execution graphs, scheduling, low-level parallelism, and handling dependencies resolution, allowing developers to focus on extending core modules and defining concise configurations. 
Built-in support for fault tolerance, system and processes monitoring, custom configuration and scalable execution, `v2v` library streamlines the process of running experiments in `Ver2Vision` project.