MongoDB Atlas
MongoDB Atlas is a fully-managed cloud database available in AWS, Azure, and GCP. It now has support for native Vector Search on the MongoDB document data.
Installation and Setupβ
See detail configuration instructions.
We need to install langchain-mongodb
python package.
pip install langchain-mongodb
Vector Storeβ
See a usage example.
from langchain_mongodb import MongoDBAtlasVectorSearch
API Reference:
LLM Cachesβ
MongoDBCacheβ
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:
from langchain_mongodb.cache import MongoDBCache
API Reference:
To use this cache with your LLMs:
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,
))
API Reference:
MongoDBAtlasSemanticCacheβ
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 on how to set up the index.
To import this cache:
from langchain_mongodb.cache import MongoDBAtlasSemanticCache
API Reference:
To use this cache with your LLMs:
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,
))
API Reference:
``