MongoDB Atlas#
If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
!pip install llama-index
# Provide URI to constructor, or use environment variable
import pymongo
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from llama_index.indices.vector_store.base import VectorStoreIndex
from llama_index.storage.storage_context import StorageContext
from llama_index.readers.file.base import SimpleDirectoryReader
Download Data
!mkdir -p 'data/10k/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'
# mongo_uri = os.environ["MONGO_URI"]
mongo_uri = (
"mongodb+srv://<username>:<password>@<host>?retryWrites=true&w=majority"
)
mongodb_client = pymongo.MongoClient(mongo_uri)
store = MongoDBAtlasVectorSearch(mongodb_client)
storage_context = StorageContext.from_defaults(vector_store=store)
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
index = VectorStoreIndex.from_documents(
uber_docs, storage_context=storage_context
)
response = index.as_query_engine().query("What was Uber's revenue?")
display(Markdown(f"<b>{response}</b>"))
Uber's revenue for 2021 was $17,455 million.
from llama_index.response.schema import Response
# Initial size
print(store._collection.count_documents({}))
# Get a ref_doc_id
typed_response = (
response if isinstance(response, Response) else response.get_response()
)
ref_doc_id = typed_response.source_nodes[0].node.ref_doc_id
print(store._collection.count_documents({"metadata.ref_doc_id": ref_doc_id}))
# Test store delete
if ref_doc_id:
store.delete(ref_doc_id)
print(store._collection.count_documents({}))
4454
1
4453
Note: For MongoDB Atlas, you have to additionally create an Atlas Search Index.