semvec/pypi-packages huggingface dataset:
langchain-galaxia-retriever 0.1.2
langchain galaxia integration
License: MIT
Requires Python: <4.0,>=3.10

# Langchain integration for Galaxia retriever


## Galaxia Knowledge Base

> Galaxia Knowledge Base is an integrated knowledge base and retrieval mechanism for RAG. In contrast to standard solution, it is based on Knowledge Graphs built using symbolic NLP and Knowledge Representation solutions. Provided texts are analysed and transformed into Graphs containing text, language and semantic information. This rich structure allows for retrieval that is based on semantic information, not on vector similarity/distance.

Implementing RAG using Galaxia involves first uploading your files to [Galaxia](https://beta.cloud.smabbler.com/home), analyzing them there and then building a model (knowledge graph). When the model is built, you can use `GalaxiaRetriever` to connect to the API and start retrieving.


## Installation
```
pip install langchain-galaxia-retriever
```

## Usage

```
from langchain_galaxia_retriever.retriever import GalaxiaRetriever

gr = GalaxiaRetriever(
    api_url="beta.api.smabbler.com",
    api_key="<key>",
    knowledge_base_id="<knowledge_base_id>",
    n_retries=10,
    wait_time=5,
)

result = gr.invoke('<test question>')
print(result)
```
ansys-api-platform-instancemanagement 1.1.3
Autogenerated python gRPC interface package for ansys-api-platform-instancemanagement, built on 11:17:24 on 11 April 2025
License: MIT
Requires Python: >=3.8

### ansys-api-platform-instancemanagement gRPC Interface Package

![Ansys logo](./doc/source/_static/ansys-logo.png)

This Python package contains the auto-generated gRPC Python interface files for
the Product Instance Management (PIM) API.


#### Installation

Provided that these wheels have been published to public PyPI, they can be
installed with this command:

```
pip install ansys-api-platform-instancemanagement
```

#### Build

To build the gRPC packages, run this command:

```
python -m build
```

The preceding command creates both the source distribution containing only the PROTO files
and the wheel containing the PROTO files and build Python interface files.

Note that the interface files are identical regardless of the version of Python
used to generate them, but the last pre-built wheel for ``grpcio~=1.17`` was
Python 3.7. To improve your build time, use Python 3.7 when building the
wheel.


#### Manual Deployment

After building the packages, manually deploy them with these commands:

```
pip install twine
twine upload dist/*
```

Note that this is automatically done through CI/CD.


#### Automatic Deployment

This repository contains GitHub CI/CD that enables the automatic building of
source and wheel packages for these gRPC Python interface files. By default,
these are built on PRs, the main branch, and on tags when pushing. Artifacts
are uploaded for each PR.

To publicly release wheels to PyPI, ensure your branch is up-to-date and then
push tags. These commands provides an example for the version ``v0.5.0``.

```bash
git tag v0.5.0
git push --tags
```
sensitivities 1.2.0
Linear and stochastic sensitivity analysis and uncertainty propagation
License: MIT
Requires Python: >=3.12

[![Tests](https://github.com/himbeles/sensitivities/actions/workflows/tests.yml/badge.svg)](https://github.com/himbeles/sensitivities/actions/workflows/tests.yml)

# sensitivities

(L. Riegger, 2025)

Methods for linear and stochastic sensitivities analysis and uncertainty propagation, following the BIPM GUM (JCGM 102:2011).

## Install

You can install the `sensitivities` module using pip:

```shell
pip install sensitivities
```


## Usage

### `sensitivities.stochastic`

The `sensitivities.stochastic` module provides functionality for stochastic sensitivity analysis. It allows you to stochastically sample input parameters from various, potentially correlated distributions and evaluate the sensitivities of a function to those inputs. Available distributions are 
- Gaussian (given mean and standard deviation)
- Uniform (between lower and upper bound)
- Discrete (multiple discrete options)

Example for stochastic sampling:

```python
from sensitivities.stochastic import sample, Gaussian, Uniform, Discrete
import matplotlib.pyplot as plt

def my_function(a, b, c=0, d=0):
    return a + b + c + d


samples = sample(
    my_function,
    [
        Gaussian(10, 0.05),
        Discrete([1, 2]),
    ],
    {"c": Uniform(-0.4, 0.4), "d": -100},
    corr=[("a", "c", 0.5)],
    n=100000,
)

plt.hist(np.array(samples), 100)
plt.show()
```

![](doc/images/hist_bimodal.png)


### `sensitivities.linear`

This module contains the `propagate_uncertainties` function for propagation of uncertainties for a given function based on the principle of linear differentials. It makes use of the scipy function `approx_fprime` to calculate the partial derivatives of the given function. 

Here is a simple usage example:

```python
from sensitivities.linear import propagate_uncertainties

def my_function(x, y):
    return [x ** 2, x, y]

print(propagate_uncertainties(my_function, uncertainties=[0.1, 0.2], x0=[1, 1]))
```

This will output the propagated standard uncertainties and correlation matrix between the function outputs:

```python
(
    np.array([0.2, 0.1, 0.2]),
    np.array([[1.0, 1.0, 0.0], [1.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
)
```


## Development

Setup development environment

```shell
uv sync --extra dev
```