Weaviate Vector Store¶

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

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

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

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!pip install llama-index

!pip install llama-index

Creating a Weaviate Client¶

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import os
import openai

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

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

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import logging
import sys

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

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

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import weaviate

import weaviate

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# cloud
cluster_url = ""
api_key = ""

client = weaviate.connect_to_wcs(
    cluster_url=cluster_url,
    auth_credentials=weaviate.auth.AuthApiKey(api_key),
)

# local
# client = connect_to_local()

# cloud cluster_url = "" api_key = "" client = weaviate.connect_to_wcs( cluster_url=cluster_url, auth_credentials=weaviate.auth.AuthApiKey(api_key), ) # local # client = connect_to_local()

Load documents, build the VectorStoreIndex¶

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from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from IPython.display import Markdown, display

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader from llama_index.vector_stores.weaviate import WeaviateVectorStore from IPython.display import Markdown, display

Download Data

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!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'

!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'

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

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

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from llama_index.core import StorageContext

# If you want to load the index later, be sure to give it a name!
vector_store = WeaviateVectorStore(
    weaviate_client=client, index_name="LlamaIndex"
)
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)

from llama_index.core import StorageContext # If you want to load the index later, be sure to give it a name! vector_store = WeaviateVectorStore( weaviate_client=client, index_name="LlamaIndex" ) 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)

Using a custom batch configuration¶

Llamaindex defaults to Weaviate's dynamic batching, optimized for most common scenarios. However, in low-latency setups, this can overload the server or max out any GRPC Message limits in place. For more control and a better ingestion process, consider adjusting batch size by using the fixed size batch.

Here is how you can fine tune WeaviateVectorStore and define a custom batch:

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from weaviate.classes.config import ConsistencyLevel

custom_batch = client.batch.fixed_size(
    batch_size=123,
    concurrent_requests=3,
    consistency_level=ConsistencyLevel.ALL,
)
vector_store_fixed = WeaviateVectorStore(
    weaviate_client=client,
    index_name="LlamaIndex",
    # we pass our custom batch as a client_kwargs
    client_kwargs={"custom_batch": custom_batch},
)

from weaviate.classes.config import ConsistencyLevel custom_batch = client.batch.fixed_size( batch_size=123, concurrent_requests=3, consistency_level=ConsistencyLevel.ALL, ) vector_store_fixed = WeaviateVectorStore( weaviate_client=client, index_name="LlamaIndex", # we pass our custom batch as a client_kwargs client_kwargs={"custom_batch": custom_batch}, )

Query Index¶

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

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

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display(Markdown(f"<b>{response}</b>"))

display(Markdown(f"{response}"))

Loading the index¶

Here, we use the same index name as when we created the initial index. This stops it from being auto-generated and allows us to easily connect back to it.

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cluster_url = ""
api_key = ""

client = weaviate.connect_to_wcs(
    cluster_url=cluster_url,
    auth_credentials=weaviate.auth.AuthApiKey(api_key),
)

# local
# client = weaviate.connect_to_local()

cluster_url = "" api_key = "" client = weaviate.connect_to_wcs( cluster_url=cluster_url, auth_credentials=weaviate.auth.AuthApiKey(api_key), ) # local # client = weaviate.connect_to_local()

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vector_store = WeaviateVectorStore(
    weaviate_client=client, index_name="LlamaIndex"
)

loaded_index = VectorStoreIndex.from_vector_store(vector_store)

vector_store = WeaviateVectorStore( weaviate_client=client, index_name="LlamaIndex" ) loaded_index = VectorStoreIndex.from_vector_store(vector_store)

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# set Logging to DEBUG for more detailed outputs
query_engine = loaded_index.as_query_engine()
response = query_engine.query("What happened at interleaf?")
display(Markdown(f"<b>{response}</b>"))

# set Logging to DEBUG for more detailed outputs query_engine = loaded_index.as_query_engine() response = query_engine.query("What happened at interleaf?") display(Markdown(f"{response}"))

Metadata Filtering¶

Let's insert a dummy document, and try to filter so that only that document is returned.

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from llama_index.core import Document

doc = Document.example()
print(doc.metadata)
print("-----")
print(doc.text[:100])

from llama_index.core import Document doc = Document.example() print(doc.metadata) print("-----") print(doc.text[:100])

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loaded_index.insert(doc)

loaded_index.insert(doc)

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from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="filename", value="README.md")]
)
query_engine = loaded_index.as_query_engine(filters=filters)
response = query_engine.query("What is the name of the file?")
display(Markdown(f"<b>{response}</b>"))

from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters filters = MetadataFilters( filters=[ExactMatchFilter(key="filename", value="README.md")] ) query_engine = loaded_index.as_query_engine(filters=filters) response = query_engine.query("What is the name of the file?") display(Markdown(f"{response}"))

Deleting the index completely¶

You can delete the index created by the vector store using the delete_index function

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vector_store.delete_index()

vector_store.delete_index()

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vector_store.delete_index()  # calling the function again does nothing

vector_store.delete_index() # calling the function again does nothing

Connection Termination¶

You must ensure your client connections are closed:

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client.close()

client.close()