langchain-mongodb
Installation
pip install -U langchain-mongodb
Usage
For more detailed usage examples and documentation, please refer to the MongoDB LangChain documentation.
- Integrate Atlas Vector Search with LangChain for a walkthrough on using your first LangChain implementation with MongoDB Atlas.
Using MongoDBAtlasVectorSearch
import os
from langchain_mongodb import MongoDBAtlasVectorSearch
from langchain_openai import OpenAIEmbeddings
# Pull MongoDB Atlas URI from environment variables
MONGODB_ATLAS_CONNECTION_STRING = os.environ["MONGODB_CONNECTION_STRING"]
DB_NAME = "langchain_db"
COLLECTION_NAME = "test"
VECTOR_SEARCH_INDEX_NAME = "index_name"
MODEL_NAME = "text-embedding-3-large"
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
vectorstore = MongoDBAtlasVectorSearch.from_connection_string(
connection_string=MONGODB_ATLAS_CONNECTION_STRING,
namespace=DB_NAME + "." + COLLECTION_NAME,
embedding=OpenAIEmbeddings(model=MODEL_NAME),
index_name=VECTOR_SEARCH_INDEX_NAME,
)
retrieved_docs = vectorstore.similarity_search(
"How do I deploy MongoDBAtlasVectorSearch in our production environment?")
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