mongodb[minor]: Add MongoDB LLM Cache (langchain-ai#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,
+))
+```
+``

libs/partners/mongodb/langchain_mongodb/cache.py

@@ -0,0 +1,312 @@
+"""
+LangChain MongoDB Caches
+
+Functions "_loads_generations" and "_dumps_generations"
+are duplicated in this utility from modules:
+    - "libs/community/langchain_community/cache.py"
+"""
+
+import json
+import logging
+import time
+from importlib.metadata import version
+from typing import Any, Callable, Dict, Optional, Union
+
+from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
+from langchain_core.embeddings import Embeddings
+from langchain_core.load.dump import dumps
+from langchain_core.load.load import loads
+from langchain_core.outputs import Generation
+from pymongo import MongoClient
+from pymongo.collection import Collection
+from pymongo.database import Database
+from pymongo.driver_info import DriverInfo
+
+from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
+
+logger = logging.getLogger(__file__)
+
+
+def _generate_mongo_client(connection_string: str) -> MongoClient:
+    return MongoClient(
+        connection_string,
+        driver=DriverInfo(name="Langchain", version=version("langchain-mongodb")),
+    )
+
+
+def _dumps_generations(generations: RETURN_VAL_TYPE) -> str:
+    """
+    Serialization for generic RETURN_VAL_TYPE, i.e. sequence of `Generation`
+
+    Args:
+        generations (RETURN_VAL_TYPE): A list of language model generations.
+
+    Returns:
+        str: a single string representing a list of generations.
+
+    This function (+ its counterpart `_loads_generations`) rely on
+    the dumps/loads pair with Reviver, so are able to deal
+    with all subclasses of Generation.
+
+    Each item in the list can be `dumps`ed to a string,
+    then we make the whole list of strings into a json-dumped.
+    """
+    return json.dumps([dumps(_item) for _item in generations])
+
+
+def _loads_generations(generations_str: str) -> Union[RETURN_VAL_TYPE, None]:
+    """
+    Deserialization of a string into a generic RETURN_VAL_TYPE
+    (i.e. a sequence of `Generation`).
+
+    See `_dumps_generations`, the inverse of this function.
+
+    Args:
+        generations_str (str): A string representing a list of generations.
+
+    Compatible with the legacy cache-blob format
+    Does not raise exceptions for malformed entries, just logs a warning
+    and returns none: the caller should be prepared for such a cache miss.
+
+    Returns:
+        RETURN_VAL_TYPE: A list of generations.
+    """
+    try:
+        generations = [loads(_item_str) for _item_str in json.loads(generations_str)]
+        return generations
+    except (json.JSONDecodeError, TypeError):
+        # deferring the (soft) handling to after the legacy-format attempt
+        pass
+
+    try:
+        gen_dicts = json.loads(generations_str)
+        # not relying on `_load_generations_from_json` (which could disappear):
+        generations = [Generation(**generation_dict) for generation_dict in gen_dicts]
+        logger.warning(
+            f"Legacy 'Generation' cached blob encountered: '{generations_str}'"
+        )
+        return generations
+    except (json.JSONDecodeError, TypeError):
+        logger.warning(
+            f"Malformed/unparsable cached blob encountered: '{generations_str}'"
+        )
+        return None
+
+
+def _wait_until(
+    predicate: Callable, success_description: Any, timeout: float = 10.0
+) -> None:
+    """Wait up to 10 seconds (by default) for predicate to be true.
+
+    E.g.:
+
+        wait_until(lambda: client.primary == ('a', 1),
+                'connect to the primary')
+
+    If the lambda-expression isn't true after 10 seconds, we raise
+    AssertionError("Didn't ever connect to the primary").
+
+    Returns the predicate's first true value.
+    """
+    start = time.time()
+    interval = min(float(timeout) / 100, 0.1)
+    while True:
+        retval = predicate()
+        if retval:
+            return retval
+
+        if time.time() - start > timeout:
+            raise TimeoutError("Didn't ever %s" % success_description)
+
+        time.sleep(interval)
+
+
+class MongoDBCache(BaseCache):
+    """MongoDB Atlas cache
+
+    A cache that uses MongoDB Atlas as a backend
+    """
+
+    PROMPT = "prompt"
+    LLM = "llm"
+    RETURN_VAL = "return_val"
+    _local_cache: Dict[str, Any]
+
+    def __init__(
+        self,
+        connection_string: str,
+        collection_name: str = "default",
+        database_name: str = "default",
+        **kwargs: Dict[str, Any],
+    ) -> None:
+        """
+        Initialize Atlas Cache. Creates collection on instantiation
+
+        Args:
+            collection_name (str): Name of collection for cache to live.
+                Defaults to "default".
+            connection_string (str): Connection URI to MongoDB Atlas.
+                Defaults to "default".
+            database_name (str): Name of database for cache to live.
+                Defaults to "default".
+        """
+        self.client = _generate_mongo_client(connection_string)
+        self.__database_name = database_name
+        self.__collection_name = collection_name
+        self._local_cache = {}
+
+        if self.__collection_name not in self.database.list_collection_names():
+            self.database.create_collection(self.__collection_name)
+            # Create an index on key and llm_string
+            self.collection.create_index([self.PROMPT, self.LLM])
+
+    @property
+    def database(self) -> Database:
+        """Returns the database used to store cache values."""
+        return self.client[self.__database_name]
+
+    @property
+    def collection(self) -> Collection:
+        """Returns the collection used to store cache values."""
+        return self.database[self.__collection_name]
+
+    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        cache_key = self._generate_local_key(prompt, llm_string)
+        if cache_key in self._local_cache:
+            return self._local_cache[cache_key]
+
+        return_doc = (
+            self.collection.find_one(self._generate_keys(prompt, llm_string)) or {}
+        )
+        return_val = return_doc.get(self.RETURN_VAL)
+        return _loads_generations(return_val) if return_val else None  # type: ignore
+
+    def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None:
+        """Update cache based on prompt and llm_string."""
+        cache_key = self._generate_local_key(prompt, llm_string)
+        self._local_cache[cache_key] = return_val
+
+        self.collection.update_one(
+            {**self._generate_keys(prompt, llm_string)},
+            {"$set": {self.RETURN_VAL: _dumps_generations(return_val)}},
+            upsert=True,
+        )
+
+    def _generate_keys(self, prompt: str, llm_string: str) -> Dict[str, str]:
+        """Create keyed fields for caching layer"""
+        return {self.PROMPT: prompt, self.LLM: llm_string}
+
+    def _generate_local_key(self, prompt: str, llm_string: str) -> str:
+        """Create keyed fields for local caching layer"""
+        return f"{prompt}#{llm_string}"
+
+    def clear(self, **kwargs: Any) -> None:
+        """Clear cache that can take additional keyword arguments.
+        Any additional arguments will propagate as filtration criteria for
+        what gets deleted.
+
+        E.g.
+            # Delete only entries that have llm_string as "fake-model"
+            self.clear(llm_string="fake-model")
+        """
+        self.collection.delete_many({**kwargs})
+
+
+class MongoDBAtlasSemanticCache(BaseCache, MongoDBAtlasVectorSearch):
+    """MongoDB Atlas Semantic cache.
+
+    A Cache backed by a MongoDB Atlas server with vector-store support
+    """
+
+    LLM = "llm_string"
+    RETURN_VAL = "return_val"
+    _local_cache: Dict[str, Any]
+
+    def __init__(
+        self,
+        connection_string: str,
+        embedding: Embeddings,
+        collection_name: str = "default",
+        database_name: str = "default",
+        wait_until_ready: bool = False,
+        **kwargs: Dict[str, Any],
+    ):
+        """
+        Initialize Atlas VectorSearch Cache.
+        Assumes collection exists before instantiation
+
+        Args:
+            connection_string (str): MongoDB URI to connect to MongoDB Atlas cluster.
+            embedding (Embeddings): Text embedding model to use.
+            collection_name (str): MongoDB Collection to add the texts to.
+                Defaults to "default".
+            database_name (str): MongoDB Database where to store texts.
+                Defaults to "default".
+            wait_until_ready (bool): Block until MongoDB Atlas finishes indexing
+                the stored text. Hard timeout of 10 seconds. Defaults to False.
+        """
+        client = _generate_mongo_client(connection_string)
+        self.collection = client[database_name][collection_name]
+        self._wait_until_ready = wait_until_ready
+        super().__init__(self.collection, embedding, **kwargs)  # type: ignore
+        self._local_cache = dict()
+
+    def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]:
+        """Look up based on prompt and llm_string."""
+        cache_key = self._generate_local_key(prompt, llm_string)
+        if cache_key in self._local_cache:
+            return self._local_cache[cache_key]
+        search_response = self.similarity_search_with_score(
+            prompt, 1, pre_filter={self.LLM: {"$eq": llm_string}}
+        )
+        if search_response:
+            return_val = search_response[0][0].metadata.get(self.RETURN_VAL)
+            response = _loads_generations(return_val) or return_val  # type: ignore
+            self._local_cache[cache_key] = response
+            return response
+        return None
+
+    def update(
+        self,
+        prompt: str,
+        llm_string: str,
+        return_val: RETURN_VAL_TYPE,
+        wait_until_ready: Optional[bool] = None,
+    ) -> None:
+        """Update cache based on prompt and llm_string."""
+        cache_key = self._generate_local_key(prompt, llm_string)
+        self._local_cache[cache_key] = return_val
+
+        self.add_texts(
+            [prompt],
+            [
+                {
+                    self.LLM: llm_string,
+                    self.RETURN_VAL: _dumps_generations(return_val),
+                }
+            ],
+        )
+        wait = self._wait_until_ready if wait_until_ready is None else wait_until_ready
+
+        def is_indexed() -> bool:
+            return self.lookup(prompt, llm_string) == return_val
+
+        if wait:
+            _wait_until(is_indexed, return_val)
+
+    def _generate_local_key(self, prompt: str, llm_string: str) -> str:
+        """Create keyed fields for local caching layer"""
+        return f"{prompt}#{llm_string}"
+
+    def clear(self, **kwargs: Any) -> None:
+        """Clear cache that can take additional keyword arguments.
+        Any additional arguments will propagate as filtration criteria for
+        what gets deleted. It will delete any locally cached content regardless
+
+        E.g.
+            # Delete only entries that have llm_string as "fake-model"
+            self.clear(llm_string="fake-model")
+        """
+        self.collection.delete_many({**kwargs})
+        self._local_cache.clear()