semvec/pypi-packages huggingface dataset:
elasticsearch-serverless 0.8.0.20231031
Python client for Elasticsearch Serverless
Requires Python: >=3.9
Keywords: REST, client, elastic, elasticsearch, index, kibana, mapping, search

# Elasticsearch Serverless Python Client

<p align="center">
  <a href="https://pypi.org/project/elasticsearch-serverless/"><img alt="PyPI Version" src="https://img.shields.io/pypi/v/elasticsearch-serverless" /></a>
  <a href="https://pypi.org/project/elasticsearch-serverless/"><img alt="Python Versions" src="https://img.shields.io/pypi/pyversions/elasticsearch-serverless" /></a>
  <a href="https://pepy.tech/project/elasticsearch-serverless?versions=*"><img alt="Downloads" src="https://static.pepy.tech/badge/elasticsearch-serverless" /></a>
  <a href="https://elasticsearch-serverless-python.readthedocs.io/"><img alt="Documentation Status" src="https://readthedocs.org/projects/elasticsearch-serverless-python/badge/?version=latest" /></a>
</p>

> [!WARNING]
> Starting with the release of the Elastic Stack 9.0.0, this client will be discontinued. Instead, you can use the latest version of the [Elasticsearch Python Client](https://github.com/elastic/elasticsearch-py) to build your Elasticsearch Serverless Python applications.

## Features

* Translating basic Python data types to and from JSON
* Configurable automatic discovery of cluster nodes
* Persistent connections
* Load balancing (with pluggable selection strategy) across available nodes
* Failed connection penalization (time based - failed connections won't be
  retried until a timeout is reached)
* Support for TLS and HTTP authentication
* Thread safety across requests
* Pluggable architecture
* Helper functions for idiomatically using APIs together

## Quick Start


```python
# Import the client from the 'elasticsearch' module
# Import the client from the 'elasticsearch' module
>>> from elasticsearch_serverless import Elasticsearch

# Instantiate a client instance
>>> client = Elasticsearch("http://localhost:9200")

# Call an API, in this example `info()`
>>> resp = client.info()

# View the result
>>> resp
{
"name" : "instance-name",
"cluster_name" : "cluster-name",
"cluster_uuid" : "cluster-uuid",
"version" : {
   "number" : "7.14.0",
   ...
},
"tagline" : "You know, for Search"
}
```

## License

This software is licensed under the [Apache License 2.0](./LICENSE). See [NOTICE](./NOTICE).
composio-togetherai 0.7.6
Use Composio to get an array of tools with your Together AI Function Call.
Requires Python: <4,>=3.9

## 🚀🔗 Leveraging Together AI with Composio

Facilitate the integration of Together AI with Composio to empower open source models to directly interact with external applications, broadening their capabilities and application scope.

### Objective

- **Automate starring a GitHub repository** using conversational instructions via Together AI Function Calls.

### Installation and Setup

Ensure you have the necessary packages installed and connect your GitHub account to allow your agents to utilize GitHub functionalities.

```bash
# Install Composio LangChain package
pip install composio-togetherai

# Connect your GitHub account
composio add github

# View available applications you can connect with
composio apps

### Usage Steps

#### 1. Import Base Packages

Prepare your environment by initializing necessary imports from OpenAI and setting up your client.

```python
from together import Together

# Initialize OpenAI client
client = Together(api_key="<together-ai-api-key>")
```

### Step 2: Integrating GitHub Tools with Composio

This step involves fetching and integrating GitHub tools provided by Composio, enabling enhanced functionality for LangChain operations.
```python
from composio_togetherai import Action, ComposioToolSet, App


toolset = ComposioToolSet()
actions = toolset.get_tools(apps=[App.GITHUB])
```

### Step 3: Agent Execution

This step involves configuring and executing the agent to carry out actions, such as starring a GitHub repository.

```python
my_task = "Star a repo composiohq/composio on GitHub"

# Create a chat completion request to decide on the action
response = client.chat.completions.create(model="gpt-4o",
    tools=actions, # Passing actions we fetched earlier.
    messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": my_task}
        ]
    )

print(response)
```

### Step 4: Validate Execution Response

Execute the following code to validate the response, ensuring that the intended task has been successfully completed.

```python
result = toolset.handle_tool_calls(response)
pprint(result)
```
academicagent 0.1.2
A package to download arXiv papers and interact with PDFs using Ollama LLM
Requires Python: >=3.6

# Academicagent

Academicagent is a Python package that integrates downloading papers from arXiv and evaluating them using a local large model (Ollama).

<p align="center">
  <a href="./README.md">English</a> |
  <a href="./README_zh.md">简体中文</a> 
</p>

---

## Features

- **Download arXiv Papers**  
  Search for and download a specified number of PDF papers from arXiv based on the keywords provided by the user.

- **Large Model Q&A**  
  For each downloaded paper, extract the first page from the PDF and use the local large model to generate a Chinese summary along with an evaluation score for the paper.

---

## Installation

1. **Download Ollama and Start the Local Large Model Service**

   Download Ollama from: [https://ollama.com/](https://ollama.com/)

   For example, to download the `deepseek-r1:1.5b` model, run:
   ```bash
   ollama pull deepseek-r1:1.5b
   ```

2. **Install paperagent**

Install using pip:
```bash
pip install paperagent
```

## Usage Example

```python
from academicagent.agent import run_agent

run_agent(paper_keyword="object detection", total_count=1, save_path="papers", model_name="deepseek-r1:1.5b")
```

### Input Parameters
- **paper_keyword** (string):
    The keyword used to search for papers on arXiv.  
    Example: "object detection"

- **total_count** (integer):
The total number of papers to download.  
Example: 5

- **save_path** (string, default "papers"):
The folder path where the downloaded PDFs will be saved. If not provided, it defaults to "papers".  
Example: "papers"

- **question** (string, optional):
The question to provide to the large model. If not specified, the default question is:  
"Please generate a Chinese summary of this paper and evaluate its value based on originality, effectiveness, and scope, on a scale of 0 to 10, then provide your score after the summary is generated."

- **model_name** (string, default "deepseek-r1:1.5b"):  
The name of the local large model to be used for invoking Ollama.

### Output
- **PDF Download**:
The PDFs of the papers are downloaded from arXiv into the specified save_path folder based on the provided keyword and count.

- **Evaluation File**:
The title of each paper and the large model's response are written into a Markdown file.
---

## Version History

- **v0.1.0**  
Initial release, implementing arXiv paper downloading and large model Q&A functionality.


## Contact
If you have any questions or suggestions, please feel free to submit an issue or pull request.