Semantic Search

Overview

Moorcheh’s Search API allows you to perform advanced semantic searches across one or multiple namespaces using text queries or vector embeddings. The API uses ITS (Information Theoretic Similarity) scoring to provide highly accurate relevance rankings.

The Search API supports both text and vector namespaces, with automatic embedding generation for text queries and advanced binarization techniques for optimal performance.

Search Types

Text Search
Vector Search

Text Search

Search text namespaces using natural language queries. Moorcheh automatically generates embeddings for your query.

# Search with text query
results = client.search(
    namespaces=["my-documents"],
    query="What is Moorcheh?",
    query_type="text",
    top_k=5
)

Best for:

Document search
Q&A systems
Knowledge base queries

Vector Search

Search vector namespaces using pre-computed embeddings for maximum control.

# Search with vector query
query_vector = [0.1, 0.2, 0.3, ...]  # Your embedding

results = client.search(
    namespaces=["my-vectors"],
    query=query_vector,
    query_type="vector",
    top_k=5
)

Best for:

Custom embedding models
Advanced similarity search
Cross-modal search

Basic Search

from moorcheh_sdk import MoorchehClient

client = MoorchehClient(api_key="your-api-key")

# Perform search
results = client.search(
    namespaces=["my-documents"],
    query="What is Moorcheh?",
    top_k=5
)

# Display results
for match in results["matches"]:
    print(f"ID: {match['id']}")
    print(f"Score: {match['score']}")
    print(f"Text: {match.get('text', 'N/A')}")

curl -X POST "https://api.moorcheh.ai/v1/search" \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-api-key" \
  -d '{
    "namespaces": ["my-documents"],
    "query": "What is Moorcheh?",
    "top_k": 5
  }'

Search Parameters

Parameter	Type	Required	Description
`namespaces`	array	Yes	Array of namespace names to search
`query`	string or array	Yes	Text query or vector array
`top_k`	number	No	Number of results (default: 10)
`threshold`	number	No	Minimum score threshold (0-1)
`kiosk_mode`	boolean	No	Enable strict filtering

Metadata Filtering

Filter search results using metadata fields for highly targeted results.

Syntax

Metadata filters: #key:value
Keyword filters: #keyword

Examples

# Search with metadata filter
results = client.search(
    namespaces=["my-documents"],
    query="technical documentation #category:api",
    top_k=5
)

# Multiple filters
results = client.search(
    namespaces=["my-documents"],
    query="payment processing #category:financial #priority:high",
    top_k=5
)

# Keyword filters
results = client.search(
    namespaces=["my-documents"],
    query="deployment guide #important #urgent",
    top_k=5
)

Filters only apply to text search and metadata must be included when uploading documents.

Multi-Namespace Search

Search across multiple namespaces simultaneously:

# Search multiple namespaces
results = client.search(
    namespaces=["docs", "blog-posts", "support-tickets"],
    query="How to integrate the API?",
    top_k=10
)

Kiosk Mode

Enable kiosk mode for production environments with strict filtering:

# Search with kiosk mode
results = client.search(
    namespaces=["my-documents"],
    query="product features",
    kiosk_mode=True,
    threshold=0.7,  # Required when kiosk_mode is True
    top_k=5
)

Kiosk Mode Benefits:

Filters results below threshold
More controlled results
Better for production environments

Response Format

{
  "matches": [
    {
      "id": "doc-123",
      "score": 0.95,
      "text": "Document content...",
      "metadata": {
        "category": "api",
        "source": "documentation"
      }
    }
  ],
  "total": 1
}

Best Practices

Optimize top_k

Use lower values (3-5) for focused results, higher values (10-20) for broader exploration

Use Metadata Filters

Combine semantic search with metadata for precise targeting

Set Thresholds

Use threshold parameter to filter low-relevance results

Multi-Namespace Search

Search across related namespaces for comprehensive results

Next Steps

AI Generation

Generate answers from search results

Upload Data

Learn how to upload documents

API Reference

Complete search API documentation

Examples

See real-world search examples

Getting Started

Core Concepts

Advanced

Legal

Overview

Search Types

Text Search

Vector Search

Basic Search

Search Parameters

Metadata Filtering

Syntax

Examples

Multi-Namespace Search

Kiosk Mode

Response Format

Best Practices

Optimize top_k

Use Metadata Filters

Set Thresholds

Multi-Namespace Search

Next Steps

AI Generation

Upload Data

API Reference

Examples

​Overview

​Search Types

​Text Search

​Vector Search

​Basic Search

​Search Parameters

​Metadata Filtering

​Syntax

​Examples

​Multi-Namespace Search

​Kiosk Mode

​Response Format

​Best Practices

Optimize top_k

Use Metadata Filters

Set Thresholds

Multi-Namespace Search

​Next Steps

AI Generation

Upload Data

API Reference

Examples

Overview

Search Types

Text Search

Vector Search

Basic Search

Search Parameters

Metadata Filtering

Syntax

Examples

Multi-Namespace Search

Kiosk Mode

Response Format

Best Practices

Next Steps