From 28c40e668f1d554a5926ba3f008ac58fb64f7d89 Mon Sep 17 00:00:00 2001
From: Jeff Emanuel <35050222+Dicklesworthstone@users.noreply.github.com>
Date: Thu, 14 Mar 2024 04:15:34 -0400
Subject: [PATCH] Update README.md

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 1 file changed, 1 insertion(+), 1 deletion(-)

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 ## Introduction
 
-The Swiss Army Llama is designed to facilitate and optimize the process of working with local LLMs by using FastAPI to expose convenient REST endpoints for various tasks, including obtaining text embeddings and completions using different LLMs via llama_cpp, as well as automating the process of obtaining all the embeddings for most common document types, including PDFs (even ones that require OCR), Word file, etc; it even allows you to submit an audio file and automatically transcribes it with the Whisper model, cleans up the resulting text, and then computes the embeddings for it. To avoid wasting computation, these embeddings are cached in SQlite and retrieved if they have already been computed before. To speed up the process of loading multiple LLMs, optional RAM Disks can be used, and the process for creating and managing them is handled automatically for you. With a quick and easy setup process, you will immediately get access to a veritable "Swiss Army Knife" of LLM related tools, all accessible via a convenient Swagger UI and ready to be integrated into your own applications with minimal fuss or configuration required.
+The Swiss Army Llama is designed to facilitate and optimize the process of working with local LLMs by using FastAPI to expose convenient REST endpoints for various tasks, including obtaining text embeddings and completions using different LLMs via llama_cpp, as well as automating the process of obtaining all the embeddings for most common document types, including PDFs (even ones that require OCR), Word files, etc; it even allows you to submit an audio file and automatically transcribes it with the Whisper model, cleans up the resulting text, and then computes the embeddings for it. To avoid wasting computation, these embeddings are cached in SQlite and retrieved if they have already been computed before. To speed up the process of loading multiple LLMs, optional RAM Disks can be used, and the process for creating and managing them is handled automatically for you. With a quick and easy setup process, you will immediately get access to a veritable "Swiss Army Knife" of LLM related tools, all accessible via a convenient Swagger UI and ready to be integrated into your own applications with minimal fuss or configuration required.
 
 Some additional useful endpoints are provided, such as computing semantic similarity between submitted text strings. The service leverages a high-performance Rust-based library, `fast_vector_similarity`, to offer a range of similarity measures including `spearman_rho`, `kendall_tau`, `approximate_distance_correlation`, `jensen_shannon_similarity`, and [`hoeffding_d`](https://blogs.sas.com/content/iml/2021/05/03/examples-hoeffding-d.html). Additionally, semantic search across all your cached embeddings is supported using FAISS vector searching. You can either use the built in cosine similarity from FAISS, or supplement this with a second pass that computes the more sophisticated similarity measures for the most relevant subset of the stored vectors found using cosine similarity (see the advanced semantic search endpoint for this functionality).