Content Indexing Overview
Vertesia provides powerful content indexing and search capabilities that enable AI agents and applications to efficiently find and retrieve relevant documents from your knowledge base.
What is Content Indexing?
Content indexing is the process of analyzing, embedding, and organizing your documents to make them searchable. Vertesia automatically indexes content when documents are created or updated, maintaining search indexes that support multiple search strategies.
When you upload documents to Vertesia, the platform:
- Extracts content from various file formats (PDF, Word, images, etc.)
- Generates embeddings using AI models to capture semantic meaning
- Indexes metadata including document properties, types, and relationships
- Maintains search indexes for fast retrieval
Each step of this pipeline can be tuned per content type — markdown conversion, IDP extraction and review, rendered text, and embeddings — using Intake Configuration.
Search Infrastructure
Vertesia search runs on Elasticsearch, which backs every search capability:
- Vector search using document embeddings for semantic similarity
- Full-text search with advanced text analysis, stemming, and fuzzy matching
- Hybrid search combining full-text and vector search with configurable weights
- Complex aggregations for analytics and faceted navigation
- DSL queries for complete control over search behavior
- High-volume indexing with zero-downtime reindexing
Elasticsearch must be enabled for your project before documents can be indexed and searched. See Search Configuration for prerequisites and setup.
Search Types
Vertesia supports multiple search strategies that can be used individually or combined:
Semantic Search (Vector)
Semantic search uses embeddings to find documents based on meaning rather than exact keywords. When you search for "quarterly financial report," semantic search can find documents about "Q3 earnings summary" even without matching words.
Best for:
- Finding conceptually similar documents
- Natural language queries
- Cross-language search (when embeddings support it)
Full-Text Search
Full-text search matches documents based on keywords with support for:
- Stemming: Matching "running" with "run," "runs," "ran"
- Fuzzy matching: Finding documents despite typos
- Phrase matching: Exact phrase requirements
- Field-specific search: Targeting specific metadata fields
Best for:
- Known keyword searches
- Exact phrase matching
- Technical terminology
Hybrid Search
Hybrid search combines semantic and full-text search for the best of both approaches. Vertesia supports multiple score aggregation methods:
| Method | Description |
|---|---|
| RRF (Reciprocal Rank Fusion) | Combines result rankings, good when relevance scores aren't comparable |
| RSF (Relevance Score Fusion) | Combines normalized relevance scores for direct score comparison |
| Smart | Automatically selects the best method based on available search types |
You can also configure weights to prioritize one search type over another:
{
"full_text": "quarterly report",
"vector": { "text": "financial analysis" },
"weights": { "full_text": 2, "vector": 3 }
}
Getting Started
- Enable Elasticsearch for the project. See Search Configuration.
- Configure embeddings to enable semantic search. See Embeddings Configuration.
- Use the search_documents tool to search from agents. See Built-in Tools.
Next Steps
- Embeddings Configuration - Configure text, image, and properties embeddings
- Search Configuration - Enable and manage the Elasticsearch backend
- Built-in Tools - Learn about the search_documents tool