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

llama-index-vector-stores-azureaisearch

library0.5.0pypypi✓ verified 85d ago

Integration between LlamaIndex and Azure AI Search (formerly Azure Cognitive Search) for vector storage and hybrid search. Current version: 0.5.0. Released as part of the LlamaIndex plugin ecosystem; updates generally follow the LlamaIndex core release cadence.

llm-agents vector-search azure ai-ml

pip install llama-index-vector-stores-azureaisearch

package ↗homepage ↗

INSTALL

IMPORT

SIG · LLAMA-INDEX-VECTOR

llama-index-vector-stores-azureaisearch

llm-agentspythonv0.5.0

Install

18.4s avg

Import

6167ms

Disk

253MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.5.0 · 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 5.118s · 242.6MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 18.4s · import 4.750s · 239MB

253MB installed

● package 253MB

Code

Verified usage

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

AzureAISearchVectorStore

✓ from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore

✗ from llama_index.vector_stores import AzureAISearchVectorStore

The vector store class is nested under the azureaisearch submodule; direct import from vector_stores package fails.

AzureAISearchIndex

✓ from llama_index.vector_stores.azureaisearch import AzureAISearchIndex

Correct import path; no common wrong pattern known.

Minimal setup to create an AzureAISearchVectorStore using DefaultAzureCredential.

import os from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore from azure.identity import DefaultAzureCredential # Use environment variables for credentials endpoint = os.environ.get('AZURE_SEARCH_ENDPOINT', 'https://mysearch.search.windows.net') credential = DefaultAzureCredential() vector_store = AzureAISearchVectorStore( search_or_index_endpoint=endpoint, credential=credential, index_name='my-index', embedding_dimensionality=1536 ) # Then integrate with LlamaIndex StorageContext

Debug

Known issues

breakingBreaking change in 0.4.0: `index_name` is no longer inferred from other parameters; must be explicitly provided.

fix

Always pass `index_name` when constructing AzureAISearchVectorStore.

affects: >=0.4.0

deprecatedThe `search_or_index_endpoint` parameter is deprecated in favor of `search_service_endpoint` and `index_name` combination (some versions). Check current docs.

fix

Use `search_service_endpoint` and `index_name` individually if required.

affects: 0.5.0

gotchaDefaultAzureCredential fails in non-Azure environments (local dev) unless environment variables AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET are set.

fix

Use AzureCliCredential or EnvironmentCredential with explicit environment variables for local testing.

affects: all

gotchaEmbedding dimensionality must match the Azure AI Search index field definition. Mismatch causes silent query errors.

fix

Ensure `embedding_dimensionality` matches the index schema (e.g., 1536 for text-embedding-ada-002).

affects: all

Errors

Common errors & fixes

ImportError: cannot import name 'AzureAISearchVectorStore' from 'llama_index.vector_stores'

Wrong import path: the class is in a submodule, not directly in vector_stores.

fix

Use 'from llama_index.vector_stores.azureaisearch import AzureAISearchVectorStore'.

azure.core.exceptions.ServiceRequestError: ... The request failed due to connection failure

Azure AI Search endpoint is unreachable or credentials are missing/incorrect.

fix

Verify AZURE_SEARCH_ENDPOINT environment variable and that DefaultAzureCredential is configured.

azure.core.exceptions.HttpResponseError: (InvalidRequest) ... The request is invalid.

Index schema mismatch (e.g., missing vector configuration or field type).

fix

Check that the Azure AI Search index has a vector field with correct dimensionality and HNSW configuration.

Upgrade

Version history

0.5.0latest on PyPI · released Mar 12, 2026

Audit

Dependencies

llama-index-corerequiredCore LlamaIndex library for index and query engine abstractions

azure-search-documentsrequiredAzure SDK for interacting with Azure AI Search (cognitive search)

azure-identityoptionalAzure authentication (DefaultAzureCredential, etc.)

Agent activity

39 hits · last 30 days

node

OpenAI (training)

Resources

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

homepagehttps://www.llamaindex.ai ↗