Amazon Neptune - Neptune Analytics vector store
If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.
%pip install llama-index-vector-stores-neptuneInitiate Neptune Analytics vector wrapper
Section titled “Initiate Neptune Analytics vector wrapper”from llama_index.vector_stores.neptune import NeptuneAnalyticsVectorStore
graph_identifier = ""embed_dim = 1536
neptune_vector_store = NeptuneAnalyticsVectorStore( graph_identifier=graph_identifier, embedding_dimension=1536)Load documents, build the VectorStoreIndex
Section titled “Load documents, build the VectorStoreIndex”from llama_index.core import VectorStoreIndex, SimpleDirectoryReaderfrom IPython.display import Markdown, displayDownload Data
!mkdir -p 'data/paul_graham/'!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'# load documentsdocuments = SimpleDirectoryReader("./data/paul_graham").load_data()from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults( vector_store=neptune_vector_store)index = VectorStoreIndex.from_documents( documents, storage_context=storage_context)query_engine = index.as_query_engine()response = query_engine.query("What happened at interleaf?")display(Markdown(f"<b>{response}</b>"))Note for AI agents: this documentation is built for programmatic access.
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- A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/