Azure Search Documents Py
Azure Search Documents Py is an code AI skill with a core value of |. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
|
Quick Facts
mkdir -p ./skills/azure-search-documents-py && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-search-documents-py/SKILL.md -o ./skills/azure-search-documents-py/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Azure AI Search SDK for Python
Full-text, vector, and hybrid search with AI enrichment capabilities.
Installation
pip install azure-search-documentsEnvironment Variables
AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net
AZURE_SEARCH_API_KEY=<your-api-key>
AZURE_SEARCH_INDEX_NAME=<your-index-name>Authentication
API Key
from azure.search.documents import SearchClient
from azure.core.credentials import AzureKeyCredential
client = SearchClient(
endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
credential=AzureKeyCredential(os.environ["AZURE_SEARCH_API_KEY"])
)Entra ID (Recommended)
from azure.search.documents import SearchClient
from azure.identity import DefaultAzureCredential
client = SearchClient(
endpoint=os.environ["AZURE_SEARCH_ENDPOINT"],
index_name=os.environ["AZURE_SEARCH_INDEX_NAME"],
credential=DefaultAzureCredential()
)Client Types
| Client | Purpose |
|--------|---------|
| `SearchClient` | Search and document operations |
| `SearchIndexClient` | Index management, synonym maps |
| `SearchIndexerClient` | Indexers, data sources, skillsets |
Create Index with Vector Field
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
SearchIndex,
SearchField,
SearchFieldDataType,
VectorSearch,
HnswAlgorithmConfiguration,
VectorSearchProfile,
SearchableField,
SimpleField
)
index_client = SearchIndexClient(endpoint, AzureKeyCredential(key))
fields = [
SimpleField(name="id", type=SearchFieldDataType.String, key=True),
SearchableField(name="title", type=SearchFieldDataType.String),
SearchableField(name="content", type=SearchFieldDataType.String),
SearchField(
name="content_vector",
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=1536,
vector_search_profile_name="my-vector-profile"
)
]
vector_search = VectorSearch(
algorithms=[
HnswAlgorithmConfiguration(name="my-hnsw")
],
profiles=[
VectorSearchProfile(
name="my-vector-profile",
algorithm_configuration_name="my-hnsw"
)
]
)
index = SearchIndex(
name="my-index",
fields=fields,
vector_search=vector_search
)
index_client.create_or_update_index(index)Upload Documents
from azure.search.documents import SearchClient
client = SearchClient(endpoint, "my-index", AzureKeyCredential(key))
documents = [
{
"id": "1",
"title": "Azure AI Search",
"content": "Full-text and vector search service",
"content_vector": [0.1, 0.2, ...] # 1536 dimensions
}
]
result = client.upload_documents(documents)
print(f"Uploaded {len(result)} documents")Keyword Search
results = client.search(
search_text="azure search",
select=["id", "title", "content"],
top=10
)
for result in results:
print(f"{result['title']}: {result['@search.score']}")Vector Search
from azure.search.documents.models import VectorizedQuery
# Your query embedding (1536 dimensions)
query_vector = get_embedding("semantic search capabilities")
vector_query = VectorizedQuery(
vector=query_vector,
k_nearest_neighbors=10,
fields="content_vector"
)
results = client.search(
vector_queries=[vector_query],
select=["id", "title", "content"]
)
for result in results:
print(f"{result['title']}: {result['@search.score']}")Hybrid Search (Vector + Keyword)
from azure.search.documents.models import VectorizedQuery
vector_query = VectorizedQuery(
vector=query_vector,
k_nearest_neighbors=10,
fields="content_vector"
)
results = client.search(
search_text="azure search",
vector_queries=[vector_query],
sele🎯 Best For
- Technical writers
- API documentation teams
- Claude users
- Software engineers
- Development teams
💡 Use Cases
- Generating JSDoc/TSDoc comments
- Writing README files for new projects
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azure Search Documents Py to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Does it follow my documentation style?
Most documentation skills respect existing style. Provide a style guide or example in your prompt.
Is Azure Search Documents Py compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Azure Search Documents Py?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Azure Search Documents Py?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-search-documents-py/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Auto-generating without reviewing
AI documentation can contain inaccuracies. Always verify technical accuracy.
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.