Azure Search Documents Py

Azure AI Search SDK for Python

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/azure-search-documents-py from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses metadata.json plus ORIGIN.md as the provenance anchor for review.

Azure AI Search SDK for Python Full-text, vector, and hybrid search with AI enrichment capabilities.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Authentication, Client Types, Upload Documents, Keyword Search, Vector Search.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. bash pip install azure-search-documents bash pip install azure-search-documents azure-identity
2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: Installation

pip install azure-search-documents

Imported: Installation

pip install azure-search-documents azure-identity

Imported: Environment Variables

AZURE_SEARCH_ENDPOINT=https://<service-name>.search.windows.net
AZURE_SEARCH_API_KEY=<your-api-key>
AZURE_SEARCH_INDEX_NAME=<your-index-name>

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-search-documents-py to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @azure-search-documents-py against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @azure-search-documents-py for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @azure-search-documents-py using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Use hybrid search for best relevance combining vector and keyword
• Enable semantic ranking for natural language queries
• Index in batches of 100-1000 documents for efficiency
• Use filters to narrow results before ranking
• Configure vector dimensions to match your embedding model
• Use HNSW algorithm for large-scale vector search
• Create suggesters at index creation time (cannot add later)

Imported Operating Notes

Imported: Best Practices

1. Use hybrid search for best relevance combining vector and keyword
2. Enable semantic ranking for natural language queries
3. Index in batches of 100-1000 documents for efficiency
4. Use filters to narrow results before ranking
5. Configure vector dimensions to match your embedding model
6. Use HNSW algorithm for large-scale vector search
7. Create suggesters at index creation time (cannot add later)

Imported: Best Practices

1. Use environment variables for endpoints, keys, and deployment names
2. Prefer DefaultAzureCredential over API keys for production
3. Use SearchIndexingBufferedSender for batch uploads (handles batching/retries)
4. Always define semantic configuration for agentic retrieval indexes
5. Use create_or_update_index for idempotent index creation
6. Close clients with context managers or explicit close()

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/azure-search-documents-py, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Load only the files that materially change the answer, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @20-andruia-niche-intelligence-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @2d-games - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 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)

Imported: Indexer with Skillset

from azure.search.documents.indexes import SearchIndexerClient
from azure.search.documents.indexes.models import (
    SearchIndexer,
    SearchIndexerDataSourceConnection,
    SearchIndexerSkillset,
    EntityRecognitionSkill,
    InputFieldMappingEntry,
    OutputFieldMappingEntry
)

indexer_client = SearchIndexerClient(endpoint, AzureKeyCredential(key))

# Create data source
data_source = SearchIndexerDataSourceConnection(
    name="my-datasource",
    type="azureblob",
    connection_string=connection_string,
    container={"name": "documents"}
)
indexer_client.create_or_update_data_source_connection(data_source)

# Create skillset
skillset = SearchIndexerSkillset(
    name="my-skillset",
    skills=[
        EntityRecognitionSkill(
            inputs=[InputFieldMappingEntry(name="text", source="/document/content")],
            outputs=[OutputFieldMappingEntry(name="organizations", target_name="organizations")]
        )
    ]
)
indexer_client.create_or_update_skillset(skillset)

# Create indexer
indexer = SearchIndexer(
    name="my-indexer",
    data_source_name="my-datasource",
    target_index_name="my-index",
    skillset_name="my-skillset"
)
indexer_client.create_or_update_indexer(indexer)

Imported: Reference Files

Imported: Index Creation Pattern

from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    SearchIndex, SearchField, VectorSearch, VectorSearchProfile,
    HnswAlgorithmConfiguration, AzureOpenAIVectorizer,
    AzureOpenAIVectorizerParameters, SemanticSearch,
    SemanticConfiguration, SemanticPrioritizedFields, SemanticField
)

index = SearchIndex(
    name=index_name,
    fields=[
        SearchField(name="id", type="Edm.String", key=True),
        SearchField(name="content", type="Edm.String", searchable=True),
        SearchField(name="embedding", type="Collection(Edm.Single)",
                   vector_search_dimensions=3072,
                   vector_search_profile_name="vector-profile"),
    ],
    vector_search=VectorSearch(
        profiles=[VectorSearchProfile(
            name="vector-profile",
            algorithm_configuration_name="hnsw-algo",
            vectorizer_name="openai-vectorizer"
        )],
        algorithms=[HnswAlgorithmConfiguration(name="hnsw-algo")],
        vectorizers=[AzureOpenAIVectorizer(
            vectorizer_name="openai-vectorizer",
            parameters=AzureOpenAIVectorizerParameters(
                resource_url=aoai_endpoint,
                deployment_name=embedding_deployment,
                model_name=embedding_model
            )
        )]
    ),
    semantic_search=SemanticSearch(
        default_configuration_name="semantic-config",
        configurations=[SemanticConfiguration(
            name="semantic-config",
            prioritized_fields=SemanticPrioritizedFields(
                content_fields=[SemanticField(field_name="content")]
            )
        )]
    )
)

index_client = SearchIndexClient(endpoint, credential)
index_client.create_or_update_index(index)

Imported: Field Types Reference

Imported: 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()
)

Imported: Client Types

Imported: 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")

Imported: 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']}")

Imported: 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']}")

Imported: 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],
    select=["id", "title", "content"],
    top=10
)

Imported: Semantic Ranking

from azure.search.documents.models import QueryType

results = client.search(
    search_text="what is azure search",
    query_type=QueryType.SEMANTIC,
    semantic_configuration_name="my-semantic-config",
    select=["id", "title", "content"],
    top=10
)

for result in results:
    print(f"{result['title']}")
    if result.get("@search.captions"):
        print(f"  Caption: {result['@search.captions'][0].text}")

Imported: Filters

results = client.search(
    search_text="*",
    filter="category eq 'Technology' and rating gt 4",
    order_by=["rating desc"],
    select=["id", "title", "category", "rating"]
)

Imported: Facets

results = client.search(
    search_text="*",
    facets=["category,count:10", "rating"],
    top=0  # Only get facets, no documents
)

for facet_name, facet_values in results.get_facets().items():
    print(f"{facet_name}:")
    for facet in facet_values:
        print(f"  {facet['value']}: {facet['count']}")

Imported: Autocomplete & Suggest

# Autocomplete
results = client.autocomplete(
    search_text="sea",
    suggester_name="my-suggester",
    mode="twoTerms"
)

# Suggest
results = client.suggest(
    search_text="sea",
    suggester_name="my-suggester",
    select=["title"]
)

Imported: Additional Azure AI Search Patterns

Azure AI Search Python SDK

Write clean, idiomatic Python code for Azure AI Search using azure-search-documents.

Imported: Environment Variables

AZURE_SEARCH_ENDPOINT=https://<search-service>.search.windows.net
AZURE_SEARCH_INDEX_NAME=<index-name>
# For API key auth (not recommended for production)
AZURE_SEARCH_API_KEY=<api-key>

Imported: Authentication

DefaultAzureCredential (preferred):

from azure.identity import DefaultAzureCredential
from azure.search.documents import SearchClient

credential = DefaultAzureCredential()
client = SearchClient(endpoint, index_name, credential)

API Key:

from azure.core.credentials import AzureKeyCredential
from azure.search.documents import SearchClient

client = SearchClient(endpoint, index_name, AzureKeyCredential(api_key))

Imported: Client Selection

Imported: Document Operations

from azure.search.documents import SearchIndexingBufferedSender

# Batch upload with automatic batching
with SearchIndexingBufferedSender(endpoint, index_name, credential) as sender:
    sender.upload_documents(documents)

# Direct operations via SearchClient
search_client = SearchClient(endpoint, index_name, credential)
search_client.upload_documents(documents)      # Add new
search_client.merge_documents(documents)       # Update existing
search_client.merge_or_upload_documents(documents)  # Upsert
search_client.delete_documents(documents)      # Remove

Imported: Search Patterns

# Basic search
results = search_client.search(search_text="query")

# Vector search
from azure.search.documents.models import VectorizedQuery

results = search_client.search(
    search_text=None,
    vector_queries=[VectorizedQuery(
        vector=embedding,
        k_nearest_neighbors=5,
        fields="embedding"
    )]
)

# Hybrid search (vector + keyword)
results = search_client.search(
    search_text="query",
    vector_queries=[VectorizedQuery(vector=embedding, k_nearest_neighbors=5, fields="embedding")],
    query_type="semantic",
    semantic_configuration_name="semantic-config"
)

# With filters
results = search_client.search(
    search_text="query",
    filter="category eq 'technology'",
    select=["id", "title", "content"],
    top=10
)

Imported: Agentic Retrieval (Knowledge Bases)

For LLM-powered Q&A with answer synthesis, see references/agentic-retrieval.md.

Key concepts:

• Knowledge Source: Points to a search index
• Knowledge Base: Wraps knowledge sources + LLM for query planning and synthesis
• Output modes: EXTRACTIVE_DATA (raw chunks) or ANSWER_SYNTHESIS (LLM-generated answers)

Imported: Async Pattern

from azure.search.documents.aio import SearchClient

async with SearchClient(endpoint, index_name, credential) as client:
    results = await client.search(search_text="query")
    async for result in results:
        print(result["title"])

Imported: Error Handling

from azure.core.exceptions import (
    HttpResponseError,
    ResourceNotFoundError,
    ResourceExistsError
)

try:
    result = search_client.get_document(key="123")
except ResourceNotFoundError:
    print("Document not found")
except HttpResponseError as e:
    print(f"Search error: {e.message}")

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.