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Thank you for contributing to LangChain! **Description:** Adds Langchain support for Nomic Embed Vision **Twitter handle:** nomic_ai,zach_nussbaum - [x] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17. --------- Co-authored-by: Lance Martin <122662504+rlancemartin@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>
128 lines
4.1 KiB
Markdown
128 lines
4.1 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 search 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 [`nomic-embed-vision-v1`](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) multi-modal embeddings to embed the images and `Ollama` for question-answering.
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Given a question, relevant 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 [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) multi-modal embeddings to embed the images.
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The first time you run the app, it will automatically download the multimodal embedding model.
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You can choose alternative models in `rag_chroma_multi_modal/ingest.py`, such as `OpenCLIPEmbeddings`.
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```
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langchain_experimental.open_clip import OpenCLIPEmbeddings
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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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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=embedding_function
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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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You can sign up for LangSmith [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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