semvec/pypi-packages huggingface dataset:
qualtop-conversational-analysis 0.33
Experimental libraries for conversational analysis with llms.

# Conversational analysis based on communication with the OpenAI API (regardless of the API Base)

Implementation of several methods for communication with the OpenAI API, embedding-based retrieval and data preparation

## Getting Started

Can be installed directly with pip (a `setup.py` file is provided, if needed).

Notably the module contains the `ChatGPTMemory` class, which enables communication with the OpenAI API (through the ChatCompletion endpoint) while managing conversation memory.

### Data preparation modules

This project was not originally intended for production. A lot of the scripts contained here are mainly for data preparation (to demonstrate different use cases). However, some functions are being used by both the [qualtop-llmapi](https://github.com/QOPA-LLM/qualtop-llmapi) and the QOPA-LLM [demo](https://github.com/QOPA-LLM/qualtop_llm_frontend) sides of the project.

In the future, this module should be replaced by a communication module. Also, any existent calculation functions should be passed to the server.
saic-ismart-client 1.7.2
SAIC client library (MG iSMART)
Requires Python: >=3.9

# SAIC Python client library

## About

A Python package for interacting with the MG iSMART API.

MG iSMART is the connectivity system in your MG car (MG5 MG4, ZS...).

The implementation is based on the findings from the [SAIC-iSmart-API Documentation](https://github.com/SAIC-iSmart-API/documentation) project.

Supported functionality

* login
* set alarm switches
* get message list
* get vehicle status
* get charging status
* lock vehicle
* unlock vehicle
* start rear window heating
* stop rear window heating

## Prerequisites

You have an iSMART account (can be created in the iSMART app)
qualtop-llmapi 0.47
Experimental libraries for communication with LLMs.

# Implementation of the OpenAI API for local LLM use

Implementation of the OpenAI API. Currently supports two end points (ChatCompletion and Embeddings) with the following models:

ChatCompletion models:

* 'mistral-7b': 7B Instruct model trained by MistralAI.
* 'llama-13b': 13B Llama 2 model trained by Meta.
* 'llama-7b': 7B Instruct Llama 2 model trained by Together AI (32K context).
* 'codellama-13b': 13B CodeLlama model trained by Meta.

Embeddings models:

* 'mistral-7b' : Encodes documents into 4096-dimension vectors.
* 'bert' : Encodes documents into 386-dimension vectors.

## Getting Started

Can be installed directly with pip (a `setup.py` file is provided, if needed).

Once installed, the FastAPI server (Uvicorn) can be started with:

```
run_server
```

### Prompts

Currently, two kinds of prompts are supported.

* Open ended questions to engage in conversation with the model.
* Instruct prompts for vector-based retrieval over some test collections.

### Some considerations

This project is by no means production ready. The ChatCompletion endpoint in particular has been tailored to communicate with the QOPA-LLM [demo](https://github.com/QOPA-LLM/qualtop_llm_frontend). A lot of work is needed security-wise to avoid data leaking.
django-jsonapi-framework 0.0.5
Django JSON:API Framework makes it easier to build Django apps with REST API's that follow the JSON:API specification.
Requires Python: >=3.7

# Django JSON:API Framework

Django JSON:API Framework makes it easier to build Django apps with REST API's
that follow the JSON:API specification.

## Resources

- Django: https://www.djangoproject.com/
- JSON:API: https://jsonapi.org/