semvec/pypi-packages huggingface dataset:
trip-travel-planner 0.0.2
This is a Travel Planner App built with Streamlit that generates personalized travel itineraries based on user input
Requires Python: >=3.0

# trip Travelplanner app

This is a Travel Planner App built with Streamlit that generates personalized travel itineraries based on user input. The app takes a user's travel request (destination and duration) and provides detailed information, including weather data, itinerary recommendations, accommodations, local attractions, and more.

<br>
Example Usage
Here’s an example of how you can use the Travel Planner agent in your own code:
<br>

```
from trip_travel_planner import TravelPlannerAgent

openai_api_key = "your_openai_api_key"
weather_api_key = "your_weather_api_key"
geolocation_api_key = "your_geolocation_api_key"

planner = TravelPlannerAgent(weather_api_key, geolocation_api_key, openai_api_key)
prompt = "travel plan to Paris for 5 days"
travel_plan = planner.generate_travel_plan(prompt)

print(travel_plan)

```
lzkit 0.0.2.dev11
Lazy kit for all the things.
Requires Python: <4.0,>=3.11
Keywords: python

# lzkit

[![Release](https://img.shields.io/github/v/release/lzkit/lzkit)](https://img.shields.io/github/v/release/lzkit/lzkit)
[![Build status](https://img.shields.io/github/actions/workflow/status/lzkit/lzkit/main.yml?branch=main)](https://github.com/lzkit/lzkit/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/lzkit/lzkit/branch/main/graph/badge.svg)](https://codecov.io/gh/lzkit/lzkit)
[![Commit activity](https://img.shields.io/github/commit-activity/m/lzkit/lzkit)](https://img.shields.io/github/commit-activity/m/lzkit/lzkit)
[![License](https://img.shields.io/github/license/lzkit/lzkit)](https://img.shields.io/github/license/lzkit/lzkit)

Lazy kit for all the things.

- **Github repository**: <https://github.com/lzkit/lzkit/>
- **Documentation** <https://lzkit.com/lzkit/>
parquet2lance 0.5.0
The Python wrapper for the Rust parquet2lance
Requires Python: >=3.12
Keywords: arrow, lance, parquet


- Convert parquet files to [lance](https://github.com/lancedb/lance)

```py
from parquet2lance import p2l

p2l("gs://cloud-samples-data/bigquery/us-states/", "test.lance")
```
document-clustering 0.1.1
Clustering algorithms for text collections
Requires Python: <4.0,>=3.10

# Document Clustering for Selective Search

This repository contains an open source implementation of the SB² K-means clustering algorithm for document collections. It supports a standard KLD-based distance metric, but also query-biased distance metric QKLD and query-biased centroid initialization QInit. Tokenization and vectorization is performed using scikit-learn, and we implemented the clustering algorithm efficiently (and parallelized wherever possible) using custom Cython code.

## Installation

Simply install the dependencies and `doc_clustering` module using Poetry:

    poetry install

We plan to also publish this as a PyPI package soon, stay tuned!

## Usage

The clustering API closely follows the scikit-learn API:

```python
from doc_clustering import Clustering

num_clusters = 5
clustering = Clustering(num_clusters).fit(['document A', 'document B'])

mapping = clustering.transform(['document C'])
```

To use the QKLD distance metric (instead of the default KLD), supply the `metric` parameter and supply queries in the call to `fit`.

```python
clustering = Clustering(num_clusters, metric='qkld')
clustering.fit(['document A', 'document B'], ['query 1', 'query 2'])

mapping = clustering.transform(['document C'])
```

To use the QInit centroid initialization, supply the `centroid_init` and `glove_vectors` parameters, and again supply queries in the `fit` call:

```python
clustering = Clustering(num_clusters, centroid_init='qinit', glove_vectors='/path/to/glove.6B.100d.txt')
clustering.fit(['document A', 'document B'], ['query 1', 'query 2'])

mapping = clustering.transform(['document C'])
```

Finally, to perform size-bounded clustering, you can use the `split_large_shards` and `merge_small_shards` parameters.

```python
clustering = Clustering(num_clusters, split_large_shards=True, merge_small_shards=True)
clustering.fit(['document A', 'document B'])

mapping = clustering.transform(['document C'])
```

For a full example, check out [the script we used to cluster the TREC CAsT corpus](cluster_cast.py).

## References

1. Kulkarni, A. 2013. Efficient and Effective Large-scale Search. Carnegie Mellon University.
2. Dai, Z. et al. 2016. Query-Biased Partitioning for Selective Search. CIKM 2016.
travelplanner 0.0.2
collection of biblical humans and their family relations
Requires Python: >=3.0

# Travelplanner app

This is a Travel Planner App built with Streamlit that generates personalized travel itineraries based on user input. The app takes a user's travel request (destination and duration) and provides detailed information, including weather data, itinerary recommendations, accommodations, local attractions, and more.

<br>
Example Usage
Here’s an example of how you can use the Travel Planner agent in your own code:
<br>

```
from travel_planner import TravelPlannerAgent

openai_api_key = "your_openai_api_key"
weather_api_key = "your_weather_api_key"
geolocation_api_key = "your_geolocation_api_key"

planner = TravelPlannerAgent(weather_api_key, geolocation_api_key, openai_api_key)
prompt = "travel plan to Paris for 5 days"
travel_plan = planner.generate_travel_plan(prompt)

print(travel_plan)

```