diff --git a/README.md b/README.md index 4700f24b..d4c88261 100644 --- a/README.md +++ b/README.md @@ -246,36 +246,36 @@ pip install qdrant-client[fastembed-gpu] You might have to use quotes ```pip install 'qdrant-client[fastembed]'``` on zsh. ```python -from qdrant_client import QdrantClient +from qdrant_client import QdrantClient, models # Initialize the client client = QdrantClient("localhost", port=6333) # For production -# client = QdrantClient(":memory:") # For small experiments +# client = QdrantClient(":memory:") # For experimentation -# Prepare your documents, metadata, and IDs -docs = ["Qdrant has Langchain integrations", "Qdrant also has Llama Index integrations"] -metadata = [ - {"source": "Langchain-docs"}, - {"source": "Llama-index-docs"}, +model_name = "sentence-transformers/all-MiniLM-L6-v2" +payload = [ + {"document": "Qdrant has Langchain integrations", "source": "Langchain-docs", }, + {"document": "Qdrant also has Llama Index integrations", "source": "LlamaIndex-docs"}, ] +docs = [models.Document(text=data["document"], model=model_name) for data in payload] ids = [42, 2] -# If you want to change the model: -# client.set_model("sentence-transformers/all-MiniLM-L6-v2") -# List of supported models: https://qdrant.github.io/fastembed/examples/Supported_Models +client.create_collection( + "demo_collection", + vectors_config=models.VectorParams( + size=client.get_embedding_size(model_name), distance=models.Distance.COSINE) +) -# Use the new add() instead of upsert() -# This internally calls embed() of the configured embedding model -client.add( +client.upload_collection( collection_name="demo_collection", - documents=docs, - metadata=metadata, - ids=ids + vectors=docs, + ids=ids, + payload=payload, ) -search_result = client.query( +search_result = client.query_points( collection_name="demo_collection", - query_text="This is a query document" -) + query=models.Document(text="This is a query document", model=model_name) +).points print(search_result) ```