langchain_milvus_vector_store_openai_embeddings_similarity_search.py

1from langchain_milvus import Milvus
2from langchain_openai import OpenAIEmbeddings
3from langchain_core.documents import Document
4
5# 1. Initialize embeddings model
6# Note: Ensure OPENAI_API_KEY is set in your environment variables
7embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
8
9# 2. Prepare sample documents
10docs = [
11    Document(page_content="Milvus is a high-performance vector database.", metadata={"id": 1}),
12    Document(page_content="LangChain is a framework for developing LLM applications.", metadata={"id": 2}),
13    Document(page_content="The integration of Milvus and LangChain enables efficient RAG workflows.", metadata={"id": 3}),
14]
15
16# 3. Initialize Milvus vector store and add documents
17# This connects to a local Milvus instance or a Milvus Lite file (milvus_demo.db)
18vector_db = Milvus.from_documents(
19    docs,
20    embeddings,
21    connection_args={"uri": "./milvus_demo.db"},
22    drop_old=True,  # Drop existing collection if it exists
23)
24
25# 4. Perform a similarity search
26query = "What is Milvus?"
27results = vector_db.similarity_search(query, k=1)
28
29# 5. Print results
30for res in results:
31    print(f"Content: {res.page_content}")
32    print(f"Metadata: {res.metadata}")