From cc3d8fe5646741001aa1c227309f64c2af908915 Mon Sep 17 00:00:00 2001 From: Emmett McFaralne Date: Thu, 18 Apr 2024 12:13:11 -0400 Subject: [PATCH] added image to getting started --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 317f694..da9c848 100644 --- a/README.md +++ b/README.md @@ -50,6 +50,8 @@ response = client.chat.completions.create( ) ``` +![Just call OpenAI](https://rpnutzemutbrumczwvue.supabase.co/storage/v1/object/public/assets/IMG_0180.jpg) + 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: ```bash thepipe path/to/folder --match *jsx @@ -89,7 +91,7 @@ The input source is either a file path, a URL, or a directory. The pipe will ext ... ] ``` -The text and images from these messages may also be prepared for a vector database with `thepipe.core.create_chunks_from_messages` or for downstream use with RAG frameworks such as Llamaindex or Langchain. [LiteLLM](https://github.com/BerriAI/litellm) can be used to easily integrate The Pipe with any LLM provider. +The text and images from these messages may also be prepared for a vector database with `thepipe.core.create_chunks_from_messages` or for downstream use with RAG frameworks. [LiteLLM](https://github.com/BerriAI/litellm) can be used to easily integrate The Pipe with any LLM provider. 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), opt-in 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.