langchain/libs/partners/mongodb
2024-03-06 19:44:14 +00:00
..
langchain_mongodb mongodb[patch]: include LLM caches in toplevel library import (#18601) 2024-03-05 16:35:13 -08:00
scripts
tests mongodb[patch]: include LLM caches in toplevel library import (#18601) 2024-03-05 16:35:13 -08:00
.gitignore
LICENSE
Makefile
poetry.lock mongodb[patch]: release 0.1.1 (#18692) 2024-03-06 19:44:14 +00:00
pyproject.toml mongodb[patch]: release 0.1.1 (#18692) 2024-03-06 19:44:14 +00:00
README.md

langchain-mongodb

Installation

pip install -U langchain-mongodb

Usage

Using MongoDBAtlasVectorSearch

from langchain_mongodb import MongoDBAtlasVectorSearch

# Pull MongoDB Atlas URI from environment variables
MONGODB_ATLAS_CLUSTER_URI = os.environ.get("MONGODB_ATLAS_CLUSTER_URI")

DB_NAME = "langchain_db"
COLLECTION_NAME = "test"
ATLAS_VECTOR_SEARCH_INDEX_NAME = "index_name"
MONGODB_COLLECTION = client[DB_NAME][COLLECITON_NAME]

# Create the vector search via `from_connection_string`
vector_search = MongoDBAtlasVectorSearch.from_connection_string(
    MONGODB_ATLAS_CLUSTER_URI,
    DB_NAME + "." + COLLECTION_NAME,
    OpenAIEmbeddings(disallowed_special=()),
    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)

# Initialize MongoDB python client
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
# Create the vector search via instantiation
vector_search_2 = MongoDBAtlasVectorSearch(
    collection=MONGODB_COLLECTION,
    embeddings=OpenAIEmbeddings(disallowed_special=()),
    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)