# Chunk Relevancy Sort

2 min read

**The service is integrated into the following spaces and modules:**

- [Unique AI Space](https://docs.unique.ai/administrators/space-management/create-spaces/unique-ai-space)
- [Document Search V2](https://docs.unique.ai/administrators/space-management/create-spaces/unique-custom-space/add-ai-assistant-and-modules/document-search-v2)
- [Question Answerer](https://docs.unique.ai/administrators/space-management/create-spaces/unique-custom-space/add-ai-assistant-and-modules/question-answerer)
- [Web Search Module](https://docs.unique.ai/administrators/space-management/create-spaces/unique-custom-space/add-ai-assistant-and-modules/web-search-module)

# Functionality

The **Chunk Relevancy Service** is an optional post-processing service that improves the quality of search results by **re-ranking text chunks** retrieved from **semantic vector search** and/or **combined search**. Instead of relying solely on vector similarity and/or full-text search, it evaluates the actual relevance of each chunk to the user’s query using a language model.

## **Purpose**

While vector search and fast-text search are fast and effective, they may overlook subtle context or nuanced details. The Chunk Relevancy Service enhances precision by analyzing each chunk in-depth, ensuring the most contextually relevant information is prioritized in the final response.

## **How It Works**

1. **Initial Chunk Retrieval**  
   A search query returns a list of top-ranked chunks from the vector search or a combined full-text and vector search.

2. **Per-Chunk Relevance Evaluation**  
   Each chunk is then passed, alongside the search query, into a dedicated language model call. The model evaluates the chunk’s relevance in context and classifies it as:
   - High
   - Medium
   - Low

3. **Final Re-ranking**  
   Based on the model-assigned relevance levels, the service reorders the chunks to ensure that the most important content appears first.

## **Why Use Chunk Relevancy Sorting?**

- ✅ **Increased Accuracy:** Goes beyond token similarity and key-word search by evaluating actual semantic relevance.
- ✅ **Detail-Aware:** Captures subtle context and phrasing missed by embeddings alone.

## **Trade-offs and Performance Impact**

- **LLM Call per Chunk:** Each chunk requires its own LLM call.  
   For example, re-ranking the top 100 chunks results in **100 individual LLM calls**.
- **Latency:** Additional processing time is introduced due to the sequential evaluation of chunks.
- **Cost:** LLM usage increases significantly with the number of chunks being evaluated.

# Configuration

The `ChunkRelevancySortConfig` schema defines the settings for sorting data chunks based on relevancy.

## Default Configuration

```json
{
  "enabled": false,
  "relevancyLevelsToConsider": ["high","medium","low"],
  "relevancyLevelOrder": {
    "high": 0,
    "medium": 1,
    "low": 2
  },
  "languageModel": "AZURE_GPT_35_TURBO_0125",
  "fallbackLanguageModel": "AZURE_GPT_35_TURBO_0125",
  "additionalLlmOptions": {},
  "maxTasks": null
}
```

## Fields Documentation

| **Field Name** | **Description** | **Type** | **Default Value** |
| `enabled` | Whether to enable the chunk relevancy sort. | boolean | `false` |
| `relevancyLevelsToConsider` | The relevancy levels to consider. | array | `['high', 'medium', 'low']` |
| `relevancyLevelOrder` | The relevancy level order. | object | `{ 'high': 0, 'medium': 1, 'low': 2 }` |
| `languageModel` | The language model to use for the chunk relevancy sort. | string | `AZURE_GPT_35_TURBO_0125` |
| `fallbackLanguageModel` | The fallback language model to use for the chunk relevancy sort. | string | `AZURE_GPT_35_TURBO_0125` |
| `additionalLlmOptions` | Additional parameters given to the LLM | dict | {} |
| `maxTasks` | The maximum number of parallel tasks to use for the chunk relevancy sort. | integer | `null` |

### Dependencies

This table describes conditions where fields depend on other fields.

| Field | Depends On | Condition |
| --- | --- | --- |
| languageModel | fallbackLanguageModel | If languageModel fails, fallbackLanguageModel is used.