semvec/pypi-packages huggingface dataset:
itsai-data-engine 0.2.0
Conversational AI for Structured Data
Requires Python: >=3.12

# Data Engine

## Core Engine Scope

-   Connect to arbitrary datastore (database, lakehouse, file upload)
-   generate SQL from text, following schema, metadata, and business rules
    -   record past interactions
-   create visualization directives

## UI/UX

-   Split screen view (horizontal or vertical)
-   Emphasis on data and visuals
-   Only a single text input box (not a scrolling chat window)

## Branch Workflow

-   Every action updates the state in the same page
-   State is represented as a DAG, similar to git commits/branches
-   Advantage over linear tracking and time travel is allows for exploration and experimentation of intermediate data views
-   Easiest implementation would be a forward-only flow, where instructions would operate on the current view, and create a new view
-   A more advanced implementation will backtrack and do a tree search to resolve the query with the best existing view
    (in case fields have been dropped or transformed, and we need earlier views).
    This will also move us to that state, without overwriting the “future” commits

## High Level Architecture

-   Zero shot, as well as a learning/trainable system.
    -   Zero shot for file upload functionality
-   Metadata generation
-   Table/column descriptions, cardinality (incl. unique values), data types,

## Phases

-   Data Extraction
-   Data Transformation
-   Visualization
    -   Operates using a single view/table
    -   Further transform the table if too granular (aggregations, etc.)
    -   Identify the relevant column(s) for visualization, and "role"

## Roadmap

-   Create Agents
    -   SQL Extractor
        -   Agentic RAG: Currently doing naive RAG, support table level (and column subset for larger table) tool-based querying.
    -   SQL Transformer (for viz/summarization)
    -   Visualization
-   query based ML
    -   use past questions and generated SQL to "recommend" related SQL internally. This can be verified via LLM
-   construct views for each question, in a lineage graph. metadata need to propogate from base tables, taking into account sql transforms.
    views can be shared between users, and visualizations created for any specific view can also be recommended
rr-echelon-f-48 0.0.1
A small package for solving linear equations system and converting into reduced row echelon form
Requires Python: >=3.4
Keywords: reduced_row_echelon_form, Gauss_jordan_elimination

# RREF_Package

The **RREF_Package** provides an efficient implementation for computing the **Reduced Row Echelon Form (RREF)** of a given matrix. It simplifies matrices to a canonical form, making it useful for solving linear systems, rank determination, and inverse calculations.

Developed by [Your Name] (c) 2024

## Installation

You can install the package using pip:
```sh
pip install rref_package
```

## Features
- Compute the **Reduced Row Echelon Form (RREF)** of any matrix.
- Supports integer, floating-point matrices.
- Handles both square and rectangular matrices.
- Provides step-by-step transformation details (optional).

## Example Usage

```python
from rref_package import RREF

# Define a matrix
matrix = [
    [1, 2, -1, 3],
    [2, 3, 4, 1],
    [1, -1, 1, 2]
]

# Compute RREF
rref_matrix = rref.full_solve(list of equations)

# Print result
for row in rref_matrix:
    print(row)
```

## Additional Features

```python
# Optional: Show step-by-step row operations
rref_matrix, steps = RREF.compute_with_steps(matrix)

# Display transformation steps
for step in steps:
    print(step)
```

## API Reference

### `RREF.compute(matrix: list) -> list`
Computes the reduced row echelon form of the given matrix.

### `RREF.compute_with_steps(matrix: list) -> tuple`
Returns the RREF along with a list of row operations performed.

## License
This project is licensed under the MIT License.

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Feel free to contribute or report issues on GitHub!