semvec/pypi-packages huggingface dataset:
llemacs-python 0.1.0
Python Utilities for LLEmacs
License: MIT
Requires Python: >=3.8
Keywords: llemacs, llm, agent

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!-- Timestamp: 2025-03-11 08:51:01
!-- Author: ywatanabe
!-- File: /home/ywatanabe/proj/llemacs/llemacs-py/README.md
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# LLEmacs-Python

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tatry 1.1.2
Python client for the Tatry Content Retrieval API
License: MIT
Requires Python: >=3.8

# Tatry

Python client for the Tatry Content Retrieval API

## Installation

Install from PyPI:

```bash
pip install tatry
```

To include LangChain integration:

```bash
pip install tatry[langchain]
```

## Basic Usage

```python
from tatry import TatryRetriever

# Initialize the client
retriever = TatryRetriever(api_key="your-api-key")

# Search for documents
results = retriever.retrieve(
    query="example query",
    max_results=5,
    sources=["source1", "source2"],
    min_score=0.7
)

# Access the results
for doc in results.documents:
    print(f"Document ID: {doc.id}")
    print(f"Content: {doc.content}")
    print(f"Relevance: {doc.relevance_score}")
    print(f"Source: {doc.metadata.source}")
    print(f"Published Date: {doc.metadata.published_date}")
    print("---")
```

## Features

- Simple and intuitive interface for retrieving relevant content
- Support for multiple sources in a single query
- Batch retrieval for efficiently processing multiple queries
- Comprehensive error handling
- Rate limiting and retry capabilities
- Detailed document metadata

## LangChain Integration

Tatry can be seamlessly integrated with LangChain:

```python
from tatry.integrations.langchain import TatryRetriever

# Initialize the retriever
retriever = TatryRetriever(
    api_key="your-api-key",
    max_results=5,
    sources=["source1", "source2"],
    min_score=0.7
)

# Use it in any LangChain pipeline
documents = retriever.get_relevant_documents("your query")

# Example with a RetrievalQA chain
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=retriever
)

result = qa.run("What is the capital of France?")
```

## API Endpoints

The client supports the following API endpoints:

### Document Retrieval

```python
# Simple retrieve
response = retriever.retrieve(query="example", max_results=10)

# Batch retrieve
queries = [
    {"query": "example 1", "max_results": 5},
    {"query": "example 2", "max_results": 10, "sources": ["source1"]}
]
batch_response = retriever.batch_retrieve(queries)
```



### Authentication

```python
# Validate your API key
validation = retriever.validate_api_key()
```

## Error Handling

The client includes various exception types to help you handle errors:

```python
from tatry import (
    RetrieverError,
    RetrieverAPIError,
    RetrieverAuthError,
    RetrieverConfigError,
    RetrieverTimeoutError,
    RetrieverConnectionError,
)

try:
    retriever.retrieve("example")
except RetrieverAuthError:
    print("Authentication failed")
except RetrieverTimeoutError:
    print("Request timed out")
except RetrieverConnectionError:
    print("Connection error")
except RetrieverAPIError as e:
    print(f"API error: {e.status_code}")
    print(f"Details: {e.details}")
except RetrieverError:
    print("Generic error")
```

## Development

To set up the development environment:

```bash
pip install -e ".[dev]"
```

Run tests:

```bash
pytest
```
robotframework-async-keyword 1.1.15
Generic Robot Framework library for asynchronous keyword execution
License: MIT
Requires Python: >=3.7
Keywords: async, robotframework

Robot Framework AsyncLibrary
============================
Generic Robot Framework library for asynchronous keyword execution

Installation
============
Install the latest release via PyPi using pip:

::

    pip install robotframework-async-keyword

Or add to your ``conda.yaml`` file:

::

    - pip:
        - robotframework-async-keyword


In oder to help with development you can directly install from GitHub via:

::

    pip install git+https://github.com/rlehfeld/robotframework-async-keyword.git

Or add to your ``conda.yaml`` file:

::

    - pip:
        - git+https://github.com/rlehfeld/robotframework-async-keyword.git


Usage
=====

#) Import into a test suite with:

   .. code:: robotframework

      Library AsyncLibrary

#) To run a keyword asynchronously:

   .. code:: robotframework

      ${handle}    Async Run    some keyword    first argument    second argument

#) To retrieve the return value, a blocking call to ``Async Get`` is called with the handle:

   .. code:: robotframework

      ${return_value}    Async Get    ${handle}

#) To wait with a timeout use

   .. code:: robotframework

      ${return_value}    Async Get    ${handle}    timeout=5 sec
haiku-ollama 0.1.0
A command-line interface for interacting with Ollama AI models
License: MIT
Requires Python: >=3.8


# Haiku 
A clean, elegant command-line interface for chatting with local AI models via Ollama, featuring real-time Markdown rendering. 
 
## Features 
 
- 💬 Interactive chat interface with Ollama models 
- 📝 Real-time Markdown rendering (code blocks, tables, lists, etc.) 
- 🔄 Full conversation context preservation 
- 🎯 Customizable system prompts 
- 🌡️ Adjustable temperature settings 
- 💾 Conversation saving to files 
 
## Installation 
 
```bash 
pip install haiku-ollama 
```
 
Make sure you have Ollama installed and running before using Haiku. 
 
## Usage 
 
Start a conversation with the default model (llama3.1:8b): 
 
```bash 
haiku 
```
 
### Command Line Options 
 
| Option | Description | 
|--------|-------------| 
| --model | Specify which Ollama model to use (default: llama3.1:8b) | 
| --keep-context, -k | Maintain full conversation history between prompts | 
| --system, -s | Set a custom system prompt to guide the model's behavior | 
| --temperature, -t | Set temperature (0.0-1.0) - lower values are more deterministic | 
| --save | Save the conversation to a specified file | 
 
### Examples 
 
Using a specific model: 
 
```bash 
haiku --model mistral:7b 
```
 
Preserving conversation context: 
 
```bash 
haiku --keep-context 
````
 
Setting a system prompt: 
 
```bash 
haiku --system "You are an expert programmer who explains code concisely" 
```
 
Adjusting temperature: 
 
```bash 
haiku --temperature 0.2 
```
 
Saving your conversation: 
 
```bash 
haiku --save conversation.md 
```
 
Combining multiple options: 
 
```bash 
haiku --model codellama --keep-context --system "You write Python code" --temperature 0.3 --save coding_session.md 
 ```
 
## Exiting 
 
To exit the program, simply type exit or bye, or press Ctrl+C. 
 
## Requirements 
 
- Python 3.8+ 
- Ollama installed and running 
- Python packages: ollama, rich 
 
## Contributing 
 
Contributions are welcome! Feel free to submit issues or pull requests. 
 
## License 
 
MIT License