---
title: Weaviate Vector Store
 | LlamaIndex OSS Documentation
---

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
```

#### Creating a Weaviate Client

```
import os
import openai


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

```
import logging
import sys


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

```
import weaviate
```

```
# 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

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

Download Data

```
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
```

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

```
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:

```
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

```
# 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?")
```

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

## 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.

```
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()
```

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


loaded_index = VectorStoreIndex.from_vector_store(vector_store)
```

```
# 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>"))
```

## Metadata Filtering

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

```
from llama_index.core import Document


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

```
loaded_index.insert(doc)
```

```
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>"))
```

# Deleting the index completely

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

```
vector_store.delete_index()
```

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

# Connection Termination

You must ensure your client connections are closed:

```
client.close()
```