SpeechRecognition VS LLMStack

Start and Stop Listening Example

There is a great library that has support not only with OpenAIs whisper but many others that also work offline. https://github.com/Uberi/speech_recognition

This is the library: https://github.com/Uberi/speech_recognition

It is python remote plugin that is a tin wrapper around speech_recognition package.

I am less familiar with speech recognition myself. I have implemented something similar many years ago, back when Google had a REST API that allowed you to upload audio and they would respond with the recognized words/sentence. I think they still have the same API available, though. They limited how much you could send, but for voice commands it was pretty solid. However, SpeechRecognition looks like a library worth trying out for this, as that seems like it could do offline processing depending on the underlying library. They also have some examples to look at.

SpeechRecognition

I found this one here

https://github.com/Uberi/speech_recognition/blob/master/reference/library-reference.rst this might help. I guess your best bet is to rtfm.

We have recently added support to query data from SingleStore to our agent framework, LLMStack (https://github.com/trypromptly/LLMStack). Out of the box performance performance when prompting with just the table schemas is pretty good with GPT-4.

The more domain specific knowledge needed for queries, the harder it has gotten in general. We've had good success `teaching` the model different concepts in relation to the dataset and giving it example questions and queries greatly improved performance.

This will hopefully improve the startup times for FFmpeg when streaming from virtual display buffers. We use FFmpeg in LLMStack (low-code framework to build and run LLM agents) to stream browser video. We use playwright to automate browser interactions and provide that as tool to the LLM. When this tool is invoked, we stream the video of these browser interactions with FFmpeg by streaming the virtual display buffer the browser is using.

There is a noticeable delay booting up this pipeline for each tool invoke right now. We are working on putting in some optimizations but improvements in FFmpeg will definitely help. https://github.com/trypromptly/LLMStack is the project repo for the curious.

We built https://github.com/trypromptly/LLMStack to serve exactly this persona. A low-code platform to quickly build RAG pipelines and other LLM applications.

Kudos to the team for a very detailed notebook going into things like pipeline evaluation wrt performance and costs etc. Even if we ignore the framework specific bits, it is a great guide to follow when building RAG systems in production.

We have been building RAG systems in production for a few months and have been tinkering with different strategies to get the most performance out of these pipelines. As others have pointed out, vector database may not be the right strategy for every problem. Similarly there are things like lost in the middle problems (https://arxiv.org/abs/2307.03172) that one may have to deal with. We put together our learnings building and optimizing these pipelines in a post at https://llmstack.ai/blog/retrieval-augmented-generation.

https://github.com/trypromptly/LLMStack is a low-code platform we open-sourced recently that ships these RAG pipelines out of the box with some app templates if anyone wants to try them out.

Django has been my go to framework for any new web project I start for more than a decade. Its batteries-included approach meant that one could go pretty far with just Django alone. Included admin interface and the views/templating setup was what first drew me to the project.

Django project itself has kept pace with recent developments in web development. I still remember migrations being an external project, getting merged in and the transition that followed. Ecosystem is pretty powerful too with projects like drf, channels, social-auth etc., covering most things we need to run in production.

https://github.com/trypromptly/LLMStack is a recent project I built entirely with Django. It uses django channels for websockets, drf for API and reactjs for the frontend.

We recently opensourced a similar platform for building workflows by chaining LLMs visually along with LocalAI support.

Check it out at https://github.com/trypromptly/LLMStack. Like you said, it was fairly easy to integrate LocalAI and is a great project.

Would you mind expanding why it was tough to get started with Retool?

We are building https://github.com/trypromptly/LLMStack, a low-code platform to build LLM apps with a goal of making it easy for non-tech people to leverage LLMs in their workflows. Would love to learn about your experience with retool and incorporate some of that feedback into LLMStack.

We built LLMStack for our internal purposes and pulled it out into its own repo and open sourced it at https://github.com/trypromptly/LLMStack.

LLMStack (https://github.com/trypromptly/LLMStack) is a no-code platform to build LLM apps that we have been working on for a few months and open-sourced recently. It comes with everything out of the box that one needs to build LLM apps locally or in an enterprise setting.