Weaviate Vector Store - Hybrid Search
Source: llama_index

<a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/docs/examples/vector_stores/WeaviateIndexDemo-Hybrid.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a># Weaviate Vector Store - Hybrid SearchIf you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.```python%pip install llama-index-vector-stores-weaviate``````python!pip install llama-index``````pythonimport loggingimport syslogging.basicConfig(stream=sys.stdout, level=logging.INFO)logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))```## Creating a Weaviate Client```pythonimport osimport openaios.environ["OPENAI_API_KEY"] = ""openai.api_key = os.environ["OPENAI_API_KEY"]``````pythonimport weaviate``````python# Connect to cloud instancecluster_url = ""api_key = ""client = weaviate.connect_to_wcs(    cluster_url=cluster_url,    auth_credentials=weaviate.auth.AuthApiKey(api_key),)# Connect to local instance# client = weaviate.connect_to_local()``````pythonfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReaderfrom llama_index.vector_stores.weaviate import WeaviateVectorStorefrom llama_index.core.response.notebook_utils import display_response```## Download Data```python!mkdir -p 'data/paul_graham/'!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'```## Load documents```python# load documentsdocuments = SimpleDirectoryReader("./data/paul_graham/").load_data()```## Build the VectorStoreIndex with WeaviateVectorStore```pythonfrom llama_index.core import StorageContextvector_store = WeaviateVectorStore(weaviate_client=client)storage_context = StorageContext.from_defaults(vector_store=vector_store)index = VectorStoreIndex.from_documents(    documents, storage_context=storage_context)# NOTE: you may also choose to define a index_name manually.# index_name = "test_prefix"# vector_store = WeaviateVectorStore(weaviate_client=client, index_name=index_name)```## Query Index with Default Vector Search```python# set Logging to DEBUG for more detailed outputsquery_engine = index.as_query_engine(similarity_top_k=2)response = query_engine.query("What did the author do growing up?")``````pythondisplay_response(response)```## Query Index with Hybrid SearchUse hybrid search with bm25 and vector.  `alpha` parameter determines weighting (alpha = 0 -> bm25, alpha=1 -> vector search).  ### By default, `alpha=0.75` is used (very similar to vector search)  ```python# set Logging to DEBUG for more detailed outputsquery_engine = index.as_query_engine(    vector_store_query_mode="hybrid", similarity_top_k=2)response = query_engine.query(    "What did the author do growing up?",)``````pythondisplay_response(response)```### Set `alpha=0.` to favor bm25```python# set Logging to DEBUG for more detailed outputsquery_engine = index.as_query_engine(    vector_store_query_mode="hybrid", similarity_top_k=2, alpha=0.0)response = query_engine.query(    "What did the author do growing up?",)``````pythondisplay_response(response)```