mirror of
https://github.com/hwchase17/langchain
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127 lines
4.0 KiB
Markdown
127 lines
4.0 KiB
Markdown
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# rag-multi-modal-local
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Visual search is a famililar application to many with iPhones or Android devices. It allows user to serch photos using natural language.
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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.
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This template demonstrates how to perform private visual search and question-answering over a collection of your photos.
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It uses OpenCLIP embeddings to embed all of the photos and stores them in Chroma.
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Given a question, relevat photos are retrieved and passed to an open source multi-modal LLM of your choice for answer synthesis.
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## Input
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Supply a set of photos in the `/docs` directory.
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By default, this template has a toy collection of 3 food pictures.
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Example questions to ask can be:
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```
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What kind of soft serve did I have?
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```
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In practice, a larger corpus of images can be tested.
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To create an index of the images, run:
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```
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poetry install
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python ingest.py
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```
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## Storage
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This template will use [OpenCLIP](https://github.com/mlfoundations/open_clip) multi-modal embeddings to embed the images.
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You can select different embedding model options (see results [here](https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_results.csv)).
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The first time you run the app, it will automatically download the multimodal embedding model.
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By default, LangChain will use an embedding model with moderate performance but lower memory requirments, `ViT-H-14`.
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You can choose alternative `OpenCLIPEmbeddings` models in `rag_chroma_multi_modal/ingest.py`:
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```
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vectorstore_mmembd = Chroma(
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collection_name="multi-modal-rag",
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persist_directory=str(re_vectorstore_path),
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embedding_function=OpenCLIPEmbeddings(
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model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
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),
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)
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```
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## LLM
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This template will use [Ollama](https://python.langchain.com/docs/integrations/chat/ollama#multi-modal).
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Download the latest version of Ollama: https://ollama.ai/
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Pull the an open source multi-modal LLM: e.g., https://ollama.ai/library/bakllava
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```
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ollama pull bakllava
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```
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The app is by default configured for `bakllava`. But you can change this in `chain.py` and `ingest.py` for different downloaded models.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-chroma-multi-modal
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-chroma-multi-modal
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```
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And add the following code to your `server.py` file:
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```python
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from rag_chroma_multi_modal import chain as rag_chroma_multi_modal_chain
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add_routes(app, rag_chroma_multi_modal_chain, path="/rag-chroma-multi-modal")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/rag-chroma-multi-modal/playground](http://127.0.0.1:8000/rag-chroma-multi-modal/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-chroma-multi-modal")
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```
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