From 94586ec242fe2da452e10b720454941b959eed8b Mon Sep 17 00:00:00 2001 From: Lance Martin <122662504+rlancemartin@users.noreply.github.com> Date: Wed, 20 Dec 2023 20:07:27 -0800 Subject: [PATCH] Update Ollama multi-modal template README.md (#14994) --- templates/rag-multi-modal-local/README.md | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/templates/rag-multi-modal-local/README.md b/templates/rag-multi-modal-local/README.md index 38d8bcefcd..6ad78faf5a 100644 --- a/templates/rag-multi-modal-local/README.md +++ b/templates/rag-multi-modal-local/README.md @@ -1,13 +1,17 @@ # rag-multi-modal-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 OpenCLIP embeddings to embed all of the photos and stores them in Chroma. + +Given a question, relevat photos are retrieved and passed to an open source multi-modal LLM of your choice for answer synthesis. -Given a set of photos, it will use OpenCLIP embeddings to index them, retrieve photos relevant to user question, and use Ollama to run a local, open-source multi-modal LLM to answer questions about the retrieved photos. +![mm-local](https://github.com/langchain-ai/langchain/assets/122662504/da543b21-052c-4c43-939e-d4f882a45d75) ## Input @@ -119,4 +123,4 @@ We can access the template from code with: from langserve.client import RemoteRunnable runnable = RemoteRunnable("http://localhost:8000/rag-chroma-multi-modal") -``` \ No newline at end of file +```