updated sponsors in readme

README.md

@@ -49,15 +49,11 @@ response = client.chat.completions.create(
 ```
 The Pipe's output is a list of sensible "chunks", and thus can be used either for storage in a vector database or for direct use as a prompt. Extra features such as data table extraction, bar chart extraction, custom web authentications and more are available in the [API documentation](https://thepi.pe/docs). [LiteLLM](https://github.com/BerriAI/litellm) can be used to easily integrate The Pipe with any LLM provider.
 
-You can also use The Pipe from the command line. Here's how to recursively extract from a directory:
+You can also use The Pipe from the command line. Here's how to recursively extract from a directory, matching only a specific file type:
 ```
-thepipe path/to/folder
+thepipe path/to/folder --match *jsx
 ```
 
-##  How it works 🛠️
-
-The pipe is accessible from the command line or from [Python](https://www.python.org/downloads/). The input source is either a file path, a URL, or a directory. The pipe will extract information from the source and process it for downstream use with [language models](https://en.wikipedia.org/wiki/Large_language_model), [vision transformers](https://en.wikipedia.org/wiki/Vision_transformer), or [vision-language models](https://arxiv.org/abs/2304.00685). The output from the pipe is a sensible text-based (or multimodal) representation of the extracted information, carefully crafted to fit within context windows for any models from [gemma-7b](https://huggingface.co/google/gemma-7b) to [GPT-4](https://openai.com/gpt-4). It uses a variety of heuristics for optimal performance with vision-language models, including AI filetype detection with [filetype detection](https://opensource.googleblog.com/2024/02/magika-ai-powered-fast-and-efficient-file-type-identification.html), AI [PDF extraction](thepi.pe/docs), efficient [token compression](https://arxiv.org/abs/2403.12968), automatic [image encoding](https://en.wikipedia.org/wiki/Base64), [reranking](https://arxiv.org/abs/2310.06839) for [lost-in-the-middle](https://arxiv.org/abs/2307.03172) effects, and more, all pre-built to work out-of-the-box.
-
 ## Supported File Types 📚
 
 | Source Type                           | Input types        | Token Compression 🗜️ | Image Extraction 👁️ | Notes 📌                                                  |
@@ -75,6 +71,11 @@ The pipe is accessible from the command line or from [Python](https://www.python
 | GitHub Repository                     | GitHub repo URLs                         | ✔️               | ✔️                | Extracts from GitHub repositories; supports branch specification         |
 | ZIP File                              | `.zip`                                  | ✔️               | ✔️                | Extracts contents of ZIP files; supports nested directory extraction     |
 
+##  How it works 🛠️
+
+The pipe is accessible from the command line or from [Python](https://www.python.org/downloads/). The input source is either a file path, a URL, or a directory. The pipe will extract information from the source and process it for downstream use with [language models](https://en.wikipedia.org/wiki/Large_language_model), [vision transformers](https://en.wikipedia.org/wiki/Vision_transformer), or [vision-language models](https://arxiv.org/abs/2304.00685). The output from the pipe is a sensible text-based (or multimodal) representation of the extracted information, carefully crafted to fit within context windows for any models from [gemma-7b](https://huggingface.co/google/gemma-7b) to [GPT-4](https://openai.com/gpt-4). It uses a variety of heuristics for optimal performance with vision-language models, including AI filetype detection with [filetype detection](https://opensource.googleblog.com/2024/02/magika-ai-powered-fast-and-efficient-file-type-identification.html), AI [PDF extraction](thepi.pe/docs), efficient [token compression](https://arxiv.org/abs/2403.12968), automatic [image encoding](https://en.wikipedia.org/wiki/Base64), [reranking](https://arxiv.org/abs/2310.06839) for [lost-in-the-middle](https://arxiv.org/abs/2307.03172) effects, and more, all pre-built to work out-of-the-box.
+
+
 ## Local Installation 🛠️
 
 To use The Pipe locally, you will need [playwright](https://github.com/microsoft/playwright), [ctags](https://github.com/universal-ctags/), [pytesseract](https://github.com/h/pytesseract), and the local python requirements, which differ from the more lightweight API requirements. You will also need to use the local version of the requirements file:
@@ -106,4 +107,8 @@ Arguments are:
 - `ai_extraction` (optional): Extract tables, figures, and math from PDFs using our extractor. Incurs extra costs.
 - `text_only` (optional): Do not extract images from documents or websites. Additionally, image files will be represented with OCR instead of as images.
 
-<a href="https://cal.com/emmett-mcf/30min"><img alt="Book us with Cal.com" src="https://cal.com/book-with-cal-dark.svg" /></a>
+<a href="https://cal.com/emmett-mcf/30min"><img alt="Book us with Cal.com" src="https://cal.com/book-with-cal-dark.svg" /></a>
+
+# Sponsors
+
+Thank you to [Cal.com](https://cal.com/) for sponsoring this project.