llamaindex_milvus_vector_store_index_quickstart_query.py

1import logging
2import sys
3
4# Optional: set up logging to see what's happening under the hood
5logging.basicConfig(stream=sys.stdout, level=logging.INFO)
6logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
7
8from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
9from llama_index.vector_stores.milvus import MilvusVectorStore
10
11# 1. Load documents (Assumes you have a 'data' directory with text files)
12# For this example, we'll use the SimpleDirectoryReader
13documents = SimpleDirectoryReader("./data").load_data()
14
15# 2. Initialize the Milvus Vector Store
16# By default, MilvusVectorStore will create a local file 'milvus_lite.db' 
17# if no URI is provided, or you can connect to a running Milvus/Zilliz instance.
18vector_store = MilvusVectorStore(
19    uri="./milvus_lite.db", 
20    collection_name="quickstart_collection",
21    dim=1536,  # Match the dimension of your embeddings (e.g., 1536 for OpenAI)
22    overwrite=True
23)
24
25# 3. Set up the Storage Context
26storage_context = StorageContext.from_defaults(vector_store=vector_store)
27
28# 4. Create the index from documents
29index = VectorStoreIndex.from_documents(
30    documents, storage_context=storage_context
31)
32
33# 5. Query the index
34query_engine = index.as_query_engine()
35response = query_engine.query("What is the main topic of the documents?")
36
37print(response)