Weaviate Vector Store - Hybrid Search

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

%pip install llama-index-vector-stores-weaviate

!pip install llama-index

import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

Creating a Weaviate Client

import os
import openai

os.environ["OPENAI_API_KEY"] = ""
openai.api_key = os.environ["OPENAI_API_KEY"]

import weaviate

# Connect to cloud instance
cluster_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()

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core.response.notebook_utils import display_response

Download Data

!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

# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()

Build the VectorStoreIndex with WeaviateVectorStore

from llama_index.core import StorageContext


vector_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

# set Logging to DEBUG for more detailed outputs
query_engine = index.as_query_engine(similarity_top_k=2)
response = query_engine.query("What did the author do growing up?")

display_response(response)

Query Index with Hybrid Search

Use 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)

# set Logging to DEBUG for more detailed outputs
query_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?",
)

display_response(response)

Set alpha=0. to favor bm25

# set Logging to DEBUG for more detailed outputs
query_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?",
)

display_response(response)