Registry / llm-agents / llama-index-vector-stores-weaviate

llama-index-vector-stores-weaviate

library1.6.1pypypi✓ verified 84d ago

LlamaIndex integration for Weaviate vector database. Supports hybrid search, vector indexing, filtering, and metadata storage. Version 1.6.0 requires Python >=3.10, <4.0. Active development.

llm-agents vector-search database

pip install llama-index-vector-stores-weaviate

package ↗homepage ↗

INSTALL

IMPORT

SIG · LLAMA-INDEX-VECTOR

llama-index-vector-stores-weaviate

llm-agentspythonv1.6.1

Install

21.6s avg

Import

8875ms

Disk

297MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.6.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 7.760s · 286.6MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 21.6s · import 6.440s · 280MB

297MB installed

● package 297MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

WeaviateVectorStore

✓ from llama_index.vector_stores.weaviate import WeaviateVectorStore

Standard import path after version 0.9+; earlier used 'llama_index.vector_stores.weaviate' (no 'vector_stores' package) but was deprecated in 0.9.

WeaviateProperty

✓ from llama_index.vector_stores.weaviate import WeaviateProperty

Used for defining additional metadata properties.

Basic usage: connect to Weaviate, store documents, and query.

import weaviate from llama_index.core import VectorStoreIndex, StorageContext from llama_index.vector_stores.weaviate import WeaviateVectorStore # Connect to Weaviate (e.g., local Docker) client = weaviate.Client("http://localhost:8080") # Create vector store vector_store = WeaviateVectorStore(weaviate_client=client, index_name="LlamaIndex") # Create index from documents documents = [Document(text="llama-index is awesome")] storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents(documents, storage_context=storage_context) # Query query_engine = index.as_query_engine() response = query_engine.query("What is llama-index?") print(response)

Debug

Known issues

breakingIn version 0.9+, the import path changed from 'llama_index.vector_stores.weaviate' to 'llama_index.vector_stores.weaviate' (the package was restructured). Old code using 'from llama_index.vector_stores.weaviate import WeaviateVectorStore' will break unless you downgrade.

fix

Update import to 'from llama_index.vector_stores.weaviate import WeaviateVectorStore'.

affects: >=0.9.0

deprecatedThe 'from llama_index import VectorStoreIndex' pattern is deprecated in favor of 'from llama_index.core import VectorStoreIndex' since version 0.10.

fix

Use 'from llama_index.core import VectorStoreIndex'.

affects: >=0.10.0

gotchaWeaviateVectorStore expects a 'weaviate_client' parameter (not 'client'). Passing 'client' will raise a TypeError.

fix

Use keyword argument 'weaviate_client=client'.

affects: All

gotchaThe 'index_name' parameter must match an existing class in Weaviate, or Weaviate will create one automatically. Ensure it is a valid CamelCase name.

fix

Use proper CamelCase (e.g., 'LlamaIndex' not 'llama_index').

affects: All

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'llama_index.vector_stores.weaviate'

Package not installed or wrong import path for older versions.

fix

Install with 'pip install llama-index-vector-stores-weaviate' and use correct import: 'from llama_index.vector_stores.weaviate import WeaviateVectorStore'.

TypeError: WeaviateVectorStore.__init__() got an unexpected keyword argument 'client'

Parameter name is 'weaviate_client', not 'client'.

fix

Use 'WeaviateVectorStore(weaviate_client=client, ...)'.

weaviate.exceptions.WeaviateClosedError: Weaviate is closed or not responding

Weaviate server not running or incorrect URL.

fix

Ensure Weaviate is running (e.g., 'docker run -p 8080:8080 semitechnologies/weaviate:latest') and use correct URL like 'http://localhost:8080'.

ValueError: Could not find class 'LlamaIndex' in Weaviate schema

Class name does not exist; Weaviate supports automatic schema creation but may require explicit creation if settings disallow auto-schema.

fix

Ensure 'auto_schema.enabled' is true in Weaviate configuration, or manually create the class with 'client.schema.create_class(...)'.

Upgrade

Version history

1.6.1latest on PyPI · released May 20, 2026

Audit

Dependencies

llama-index-corerequiredCore dependency for LlamaIndex abstractions

weaviate-clientrequiredOfficial Weaviate Python client

Agent activity

44 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/llama-index-vector-stores-weaviate/ ↗

homepagehttps://www.weaviate.io ↗