langchain-milvus

import os from langchain_milvus import Milvus from langchain_openai import OpenAIEmbeddings from langchain_core.documents import Document # Ensure OPENAI_API_KEY is set in your environment # Replace 'YOUR_OPENAI_API_KEY' with your actual key if not using env vars. os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "sk-...") # Initialize embeddings embeddings = OpenAIEmbeddings() # Connect to Milvus Lite (local file) or a Milvus server. # For Milvus Lite, use a local file path as the URI. E.g., "./milvus_example.db" # For a Milvus server, use its URI, e.g., "http://localhost:19530" MILVUS_URI = "./milvus_example.db" # Create a Milvus vector store vector_store = Milvus( embedding_function=embeddings, collection_name="my_langchain_documents", connection_args={"uri": MILVUS_URI}, auto_id=True # Milvus 2.2.0 or later supports auto-generated IDs ) # Add documents documents = [ Document(page_content="The quick brown fox jumps over the lazy dog.", metadata={"source": "lorem"}), Document(page_content="Milvus is an open-source vector database designed for AI applications.", metadata={"source": "milvus_docs"}), Document(page_content="LangChain is a framework for developing applications with LLMs.", metadata={"source": "langchain_docs"}), ] vector_store.add_documents(documents) # Perform a similarity search query = "What is LangChain used for?" results = vector_store.similarity_search(query, k=1) print("Similarity search results:") for doc in results: print(f"- Content: {doc.page_content[:60]}... Metadata: {doc.metadata}")