Visual interface
Drag-and-drop query building with no coding required. Build complex queries visually with an intuitive interface.
Query
Visual Firestore query builder
Build complex Firestore queries visually with advanced filtering, vector search, JSON editing, query sharing, and AI-powered embeddings. No coding required.
Capabilities
Query builder features
JSON editor
Edit queries directly as JSON with syntax highlighting. Copy and paste queries between projects for quick reuse and modification.
Query sharing
Share queries with team members via shareable links. Export as JSON or code snippets for collaboration and documentation.
Save & organize
Save frequently used queries, create favorites, and use them as global search filters. Organize queries into collections for easy access.
Query analysis
Analyze query performance with detailed metrics. Get optimization suggestions, index recommendations, and execution time insights.
Real-time preview
See query results instantly as you build with real-time data preview, result count, and validation.
Query types
Supported query types
Simple filters
Basic equality, comparison, and range queries on document fields with operators like ==, !=, <, <=, >, >=.
StartsWith queries
Quick shortcut for prefix matching queries. Search for documents where a field starts with a specific string value.
Between queries
Convenient shortcut for range queries. Find documents where a field value falls between two values with a single operation.
Vector search (findNearest)
Semantic search with vector embeddings. Generate vectors using OpenAI, Google Gemini, Cohere, or custom embedding models for similarity search.
Array queries
Array-contains, array-contains-any, in, and not-in operations for working with array and list fields.
Compound queries
Multiple conditions with AND/OR logic for complex filtering requirements across different fields.
Sorting & limits
Order results by multiple fields with ascending/descending options, pagination, and result limits.
Aggregation
Count queries, sum, average, and other aggregation operations for data analysis and reporting.
Subcollections
Query across subcollections and nested data structures efficiently with collection group queries.
Manage & share
Advanced query management
JSON editor & copy/paste
Edit queries directly as JSON for advanced customization:
- Switch between visual and JSON editing modes
- Copy query JSON to clipboard with one click
- Paste queries from other projects or documentation
- Syntax highlighting and validation for JSON
- Import/export queries as JSON files
Query sharing & collaboration
Share queries with your team for better collaboration:
- Generate shareable links for queries
- Export as code snippets for team documentation
- Share queries across multiple projects
- Version control and query history tracking
- Collaborate on complex query building
Organize
Save, organize & analyze queries
Save & favorites
Organize your queries for quick access:
- Save frequently used queries
- Create favorites for instant access
- Organize queries into collections
- Tag queries for easy filtering
- Use saved queries as global search filters
- Quick search through saved queries
Global search integration
Use queries as powerful search filters:
- Apply saved queries to global search
- Combine multiple query filters
- Search across all collections
- Save search results as new queries
- Filter by query complexity
- Export search results in bulk
Query analysis
Optimize performance with insights:
- Execution time metrics
- Index usage analysis
- Missing index recommendations
- Query cost estimation
- Performance comparison
- Optimization suggestions
AI & search
Vector search & AI embeddings
Semantic search with findNearest
Perform similarity searches using vector embeddings powered by leading AI models. Find semantically similar documents based on meaning, not just exact matches.
OpenAI embeddings
Use OpenAI's text-embedding-3-small, text-embedding-3-large, and ada-002 models for high-quality embeddings.
Google Gemini
Generate embeddings with Google's Gemini models for multilingual and multimodal search capabilities.
Cohere embeddings
Leverage Cohere's embed models optimized for semantic search and classification tasks.
Custom models
Integrate your own embedding models or use custom API endpoints for specialized use cases.
Vector search features
- Generate embeddings from text input directly in the UI
- Configure distance metrics (cosine, euclidean, dot product)
- Set similarity thresholds and result limits
- Combine vector search with traditional filters
- Preview similar documents in real-time
Use cases
- Semantic document search and discovery
- Content recommendation systems
- Duplicate detection and deduplication
- Question-answering applications
- Product catalog similarity matching
FAQ
Frequently asked questions
Can I build complex queries without coding?
Yes, Fuego's visual query builder allows you to create complex Firestore queries using a drag-and-drop interface with conditions, sorting, and aggregations without writing any code.
Does the query builder support vector search?
Yes, Fuego supports Firestore vector search with findNearest queries. You can generate embeddings using popular AI models like OpenAI, Gemini, and Cohere directly in the interface.
Can I share and export queries?
Yes, you can edit queries as JSON, share them with team members, save them as favorites, and export them as code snippets for multiple programming languages.
Get started
Build powerful Firestore queries
Create complex Firestore queries with vector search, AI embeddings, and advanced analysis tools using Fuego's intuitive visual query builder.