> ## Documentation Index > Fetch the complete documentation index at: https://docs.visual-layer.com/llms.txt > Use this file to discover all available pages before exploring further. # VL Chat > Explore and search your visual datasets using natural language queries with VL Chat's conversational interface. **VL Chat** enables you to explore visual datasets through natural language. Instead of navigating filters and dropdown menus, ask questions directly — find images by quality issue, label, tag, custom metadata, or cluster. The system interprets your intent and returns results with explanations of what it found. ## Common Use Cases **VL Chat** supports a range of workflows across dataset exploration and curation:
| Use Case | Description | Examples | | ------------------------- | ------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Quality Assurance** | Quickly identify and review quality issues. | "Show me all blur issues from today's production run"
"Find outlier images in batch 12345"
"Display mislabeled images above 90% confidence" | | **Dataset Curation** | Organize and filter datasets for training. | "Show me the most unique images from each cluster"
"Find images labeled as cats that don't have the reviewed tag"
"Display images with low uniqueness scores" | | **Research and Analysis** | Explore dataset composition and patterns. | "Show me the largest clusters"
"Find images with the most labels"
"Display images that appear in multiple clusters" |
## How to Use VL Chat To access **VL Chat**, open any dataset in Visual Layer and click VL Chat button in the top navigation bar. The chat interface appears as a panel on the right side of your screen, allowing you to explore while viewing your dataset. Each dataset maintains its own conversation thread, which persists across sessions. To start a fresh conversation, click **New Thread** in the chat panel. ### Asking Questions Type your question in natural language into the chat input field and press Enter. **VL Chat** processes your query and returns results along with an explanation of what it understood and what it found. Query capabilities depend on the dataset configuration. **VL Chat** can only search fields that exist in your data: datasets without annotations cannot filter by labels, and cluster-based queries require **Similarity Clusters** to be generated first. If you reference a field that isn't configured, the system explains this and applies the filters it can. **Example queries:** * "Show me images with blur issues" * "Find all images tagged as defective" * "Display images from cluster 5" * "Show me images with high uniqueness scores" * "Find images labeled as cats" VL Chat finding cats and dogs with unusual expressions #### Types of Queries You Can Ask **VL Chat** understands queries about different aspects of your dataset:
| Type | Description | Examples | | ----------------------------- | ------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Filter by Issues** | Find images with specific quality issues detected by Visual Layer. | "Show me blurry images"
"Find all images with duplicates"
"Display images that have outlier issues"
"Show me images with mislabel issues above 80% confidence" | | **Filter by Labels** | Search for specific labels or annotations in your dataset. | "Show me images labeled as cats"
"Find all images with car labels"
"Display images labeled as defective" | | **Filter by Tags** | Query images based on user-assigned tags. | "Show me images tagged as urgent"
"Find all images with the reviewed tag"
"Display images tagged for training" | | **Filter by Custom Metadata** | Query **Custom Metadata** fields directly if your dataset includes them. | "Show me images with temperature above 30"
"Find images from Station A"
"Display images where batch number is 12345" | | **Navigate Clusters** | Explore **Similarity Clusters** in your dataset. | "Show me cluster 5"
"Display the largest cluster"
"Find clusters with more than 100 images" | | **Combine Multiple Criteria** | Build complex queries by combining different filter types. | "Show me blurry images from cluster 3"
"Find images labeled as cats with high uniqueness scores"
"Display images tagged as urgent that also have blur issues" |
#### Tips for Effective Queries Use these guidelines to get accurate, relevant results from **VL Chat**: | Rule | Explanation | Good | Less Clear | | ------------------------------------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------- | | **Be Specific About Filter Types** | When referencing labels, tags, or custom fields, use clear terminology. | "Show me images labeled as defective" | "Show me defective images" (could refer to labels, tags, or quality issues) | | **Use Exact Field Names** | For custom metadata, use the exact field name as it appears in your dataset. | "Show me images where Station equals A" | "Show me images from station A" (if the field is named "StationID") | | **Specify Thresholds Explicitly** | When filtering by numeric values or confidence scores, include specific thresholds. | "Show me blur issues above 85% confidence" | "Show me images with high blur" (what threshold defines "high"?) | | **Build Complex Queries Iteratively** | Start with a simple query and refine it through follow-up questions. | "Show me images with blur"
"Now filter to cluster 5"
"Show only the ones tagged as urgent" | "Show me blurry images from cluster 5 tagged as urgent" | ### Understanding Responses When you ask a question, **VL Chat** provides: * **Interpretation summary**: A clear statement of what the system understood from your query. * **Validation feedback**: Information about which parts of your query were applied successfully and which weren't available. * **Visual results**: The actual images or objects matching your criteria. * **Alternative interpretations**: Suggestions if your query was ambiguous or if certain fields aren't available. Each response includes a confidence score. Lower scores (below 0.5) indicate ambiguity. If results don't match your expectations, review the alternative interpretations provided — these often reveal where the system interpretation differed from your intent. For complex criteria, break queries into multiple steps rather than a single nested question. **Example response:** ``` Understood query with confidence 0.85. Applied blur filter successfully and filtered to cluster 5. Showing 47 images that match your criteria. ``` If part of your query can't be processed, the system explains why: ``` I found 23 blurry images in your dataset. Note: This dataset doesn't have a 'Temperature' custom metadata field configured, so I could only search for blur. ``` ### Multi-Turn Conversations **VL Chat** maintains context across multiple messages, allowing you to refine your queries progressively: **You:** "Show me images with blur" **VL Chat:** *Returns 156 blurry images* **You:** "Now show only the ones from last week" **VL Chat:** *Filters the previous results to show 23 images from last week* **You:** "Which cluster has the most of these?" **VL Chat:** *Analyzes the filtered results and highlights cluster 12* Each follow-up question builds on the previous context, making exploration feel natural and conversational. VL Chat multi-turn conversation finding real people in a dataset ## Related Resources Learn about all dataset exploration features Manual filtering and search options How similarity clustering works Adding custom metadata fields to your datasets