From 535db72607c4ae308566ede4af65295967bb33a8 Mon Sep 17 00:00:00 2001 From: Lance Martin <122662504+rlancemartin@users.noreply.github.com> Date: Wed, 20 Dec 2023 20:07:38 -0800 Subject: [PATCH] Update Ollama multi-modal multi-vector template README.md (#14995) --- templates/rag-multi-modal-mv-local/README.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/templates/rag-multi-modal-mv-local/README.md b/templates/rag-multi-modal-mv-local/README.md index 9a037b4acd..cdc1ab3390 100644 --- a/templates/rag-multi-modal-mv-local/README.md +++ b/templates/rag-multi-modal-mv-local/README.md @@ -1,13 +1,17 @@ # rag-multi-modal-mv-local -Visual search is a famililar application to many with iPhones or Android devices: use natural language to search across your photo collection. +Visual search is a famililar application to many with iPhones or Android devices. It allows user to serch photos using natural language. -With the release of open source, multi-modal LLMs it's possible to build this kind of application for yourself and have it run on your personal laptop. +With the release of open source, multi-modal LLMs it's possible to build this kind of application for yourself for your own private photo collection. -This template demonstrates how to perform visual search and question-answering over a collection of photos. +This template demonstrates how to perform private visual search and question-answering over a collection of your photos. + +It uses an open source multi-modal LLM of your choice to create image summaries for each photos, embeds the summaries, and stores them in Chroma. -Given a set of photos, it will produce image summaries and index them, retrieve photos relevant to user question using the summaries, and use Ollama to run a local, open-source multi-modal LLM to answer questions about the retrieved photos. +Given a question, relevat photos are retrieved and passed to the multi-modal LLM for answer synthesis. + +![mm-caption-local](https://github.com/langchain-ai/langchain/assets/122662504/cd9b3d82-9b06-4a39-8490-7482466baf43) ## Input @@ -115,4 +119,4 @@ We can access the template from code with: from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-multi-modal-mv-local") -``` \ No newline at end of file +```