llama-index-vector-stores-qdrant

import os from llama_index.core import VectorStoreIndex, Document from llama_index.vector_stores.qdrant import QdrantVectorStore from qdrant_client import QdrantClient from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.core import Settings # Ensure you have your OpenAI API key set as an environment variable # os.environ["OPENAI_API_KEY"] = "sk-..." # Fallback for API key or local Qdrant for quick demo without remote setup if os.environ.get("OPENAI_API_KEY") is None or os.environ.get("OPENAI_API_KEY") == "": print("Warning: OPENAI_API_KEY not set. Using a dummy key for example purposes. This will fail if you try to use OpenAI models.") os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "sk-dummy-key") # Initialize Qdrant client (local in-memory for quick start, or connect to a server) # For persistent storage, use QdrantClient(path="./qdrant_data") or connect to a running Qdrant instance client = QdrantClient(location=":memory:") # In-memory Qdrant instance # Create a QdrantVectorStore instance vector_store = QdrantVectorStore(client=client, collection_name="my_documents") # Configure LlamaIndex settings (important for v0.10+) Settings.embed_model = OpenAIEmbedding() # Create a dummy document documents = [ Document(text="LlamaIndex is a data framework for LLM applications."), Document(text="Qdrant is a vector similarity search engine."), Document(text="Combining LlamaIndex with Qdrant enables powerful RAG systems."), ] # Build the VectorStoreIndex index = VectorStoreIndex.from_documents(documents, vector_store=vector_store) # Query the index query_engine = index.as_query_engine() response = query_engine.query("What is LlamaIndex?") print(f"Response: {response}") response = query_engine.query("What is Qdrant used for?") print(f"Response: {response}")