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Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Dan O'Donovan <dan.odonovan@gmail.com> Co-authored-by: Tom Daniel Grande <tomdgrande@gmail.com> Co-authored-by: Grande <Tom.Daniel.Grande@statsbygg.no> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: ccurme <chester.curme@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Tomaz Bratanic <bratanic.tomaz@gmail.com> Co-authored-by: ZhangShenao <15201440436@163.com> Co-authored-by: Friso H. Kingma <fhkingma@gmail.com> Co-authored-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Nuno Campos <nuno@langchain.dev> Co-authored-by: Morgante Pell <morgantep@google.com>
langchain-mongodb
Installation
pip install -U langchain-mongodb
Usage
- See Getting Started with the LangChain Integration for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
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][COLLECTION_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,
)