langchain[patch]: support ollama in init_embeddings (#29349)

Why not Ollama ?

Thank you for contributing to LangChain!

- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
  - Example: "community: add foobar LLM"


- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!


- [ ] **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: Chester Curme <chester.curme@gmail.com>

libs/langchain/langchain/embeddings/base.py

@@ -13,6 +13,7 @@ _SUPPORTED_PROVIDERS = {
     "google_vertexai": "langchain_google_vertexai",
     "huggingface": "langchain_huggingface",
     "mistralai": "langchain_mistralai",
+    "ollama": "langchain_ollama",
     "openai": "langchain_openai",
 }
 
@@ -209,6 +210,10 @@ def init_embeddings(
         from langchain_huggingface import HuggingFaceEmbeddings
 
         return HuggingFaceEmbeddings(model_name=model_name, **kwargs)
+    elif provider == "ollama":
+        from langchain_ollama import OllamaEmbeddings
+
+        return OllamaEmbeddings(model=model_name, **kwargs)
     else:
         raise ValueError(
             f"Provider '{provider}' is not supported.\n"