mongodb[minor]: Add MongoDB LLM Cache (#17470)

# Description

- **Description:** Adding MongoDB LLM Caching Layer abstraction
- **Issue:** N/A
- **Dependencies:** None
- **Twitter handle:** @mongodb

Checklist:

- [x] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
  - Example: "community: add foobar LLM"
- [x] PR Message (above)
- [x] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [ ] 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.

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, @hwchase17.

---------

Co-authored-by: Jib <jib@byblack.us>

docs/docs/integrations/providers/mongodb_atlas.mdx

@@ -22,3 +22,61 @@ See a [usage example](/docs/integrations/vectorstores/mongodb_atlas).
 from langchain_mongodb import MongoDBAtlasVectorSearch
 ```
 
+
+## LLM Caches
+
+### MongoDBCache
+An abstraction to store a simple cache in MongoDB. This does not use Semantic Caching, nor does it require an index to be made on the collection before generation.
+
+To import this cache:
+```python
+from langchain_mongodb.cache import MongoDBCache
+```
+
+To use this cache with your LLMs:
+```python
+from langchain_core.globals import set_llm_cache
+
+# use any embedding provider...
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+mongodb_atlas_uri = "<YOUR_CONNECTION_STRING>"
+COLLECTION_NAME="<YOUR_CACHE_COLLECTION_NAME>"
+DATABASE_NAME="<YOUR_DATABASE_NAME>"
+
+set_llm_cache(MongoDBCache(
+    connection_string=mongodb_atlas_uri,
+    collection_name=COLLECTION_NAME,
+    database_name=DATABASE_NAME,
+))
+```
+
+
+### MongoDBAtlasSemanticCache
+Semantic caching allows users to retrieve cached prompts based on semantic similarity between the user input and previously cached results. Under the hood it blends MongoDBAtlas as both a cache and a vectorstore.
+The MongoDBAtlasSemanticCache inherits from `MongoDBAtlasVectorSearch` and needs an Atlas Vector Search Index defined to work. Please look at the [usage example](/docs/integrations/vectorstores/mongodb_atlas) on how to set up the index.
+
+To import this cache:
+```python
+from langchain_mongodb.cache import MongoDBAtlasSemanticCache
+```
+
+To use this cache with your LLMs:
+```python
+from langchain_core.globals import set_llm_cache
+
+# use any embedding provider...
+from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
+
+mongodb_atlas_uri = "<YOUR_CONNECTION_STRING>"
+COLLECTION_NAME="<YOUR_CACHE_COLLECTION_NAME>"
+DATABASE_NAME="<YOUR_DATABASE_NAME>"
+
+set_llm_cache(MongoDBAtlasSemanticCache(
+    embedding=FakeEmbeddings(),
+    connection_string=mongodb_atlas_uri,
+    collection_name=COLLECTION_NAME,
+    database_name=DATABASE_NAME,
+))
+```
+``