embeddings: nomic embed vision (#22482)

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>

templates/rag-multi-modal-local/rag_multi_modal_local/chain.py

@@ -9,7 +9,7 @@ from langchain_core.messages import HumanMessage
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.pydantic_v1 import BaseModel
 from langchain_core.runnables import RunnableLambda, RunnablePassthrough
-from langchain_experimental.open_clip import OpenCLIPEmbeddings
+from langchain_nomic import NomicMultimodalEmbeddings
 from PIL import Image
 
 
@@ -102,8 +102,8 @@ def multi_modal_rag_chain(retriever):
 vectorstore_mmembd = Chroma(
     collection_name="multi-modal-rag",
     persist_directory=str(Path(__file__).parent.parent / "chroma_db_multi_modal"),
-    embedding_function=OpenCLIPEmbeddings(
-        model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
+    embedding_function=NomicMultimodalEmbeddings(
+        vision_model="nomic-embed-vision-v1", text_model="nomic-embed-text-v1"
     ),
 )