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Update Ollama multi-modal multi-vector template README.md (#14995)

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templates/rag-multi-modal-mv-local/README.md
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# rag-multi-modal-mv-local # 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 ## Input
@ -115,4 +119,4 @@ We can access the template from code with:
from langserve.client import RemoteRunnable from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/rag-multi-modal-mv-local") runnable = RemoteRunnable("http://localhost:8000/rag-multi-modal-mv-local")
``` ```