from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
from llama_index.ingestion import IngestionPipeline, DocstoreStrategy
from llama_index.storage.docstore import MongoDocumentStore
from llama_index.storage.index_store import MongoIndexStore

mongo_vector_store = MongoDBAtlasVectorSearch(
    mongodb_client=mongodb_client,
    db_name=f"{collection_name}_db",
    collection_name=collection_name,
    index_name="llm_experiments_index"
)
mongo_doc_store = MongoDocumentStore.from_uri(
    uri=mongo_uri,
    db_name=f"{collection_name}_db"
)

ingestion_pipeline = IngestionPipeline(
            transformations=[embed_model],
            docstore=mongo_doc_store,
            docstore_strategy=DocstoreStrategy.UPSERTS,
            vector_store=mongo_vector_store, <--- "Intellisense below error"
        )

ValidationError: 1 validation error for IngestionPipeline
vector_store
  value is not a valid dict (type=type_error.dict)

Expected type 'Optional[BasePydanticVectorStore]', got 'MongoDBAtlasVectorSearch' instead

CREATE INDEX ON embeddings USING hnsw (embedding vector_cosine_ops) WITH (m = 10, ef_construction = 64);