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## Description This pull-request improves the treatment of document IDs in `MongoDBAtlasVectorSearch`. Class method signatures of add_documents, add_texts, delete, and from_texts now include an `ids:Optional[List[str]]` keyword argument permitting the user greater control. Note that, as before, IDs may also be inferred from `Document.metadata['_id']` if present, but this is no longer required, IDs can also optionally be returned from searches. This PR closes the following JIRA issues. * [PYTHON-4446](https://jira.mongodb.org/browse/PYTHON-4446) MongoDBVectorSearch delete / add_texts function rework * [PYTHON-4435](https://jira.mongodb.org/browse/PYTHON-4435) Add support for "Indexing" * [PYTHON-4534](https://jira.mongodb.org/browse/PYTHON-4534) Ensure datetimes are json-serializable --------- Co-authored-by: Erick Friis <erick@langchain.dev>
langchain-mongodb
Installation
pip install -U langchain-mongodb
Usage
- See integrations doc for more in-depth usage instructions.
- 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][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,
)