---
schema_version: "1"
id: 4a1d4b6fd3dbcc3f09d4fadf2e95ef55395802981bd4d4a76bd59ce070ace8ea
title: Hybrid Search Best Practices
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_hybridsearch_best_practices.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_hybridsearch_best_practices.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: 6967a74a0fd98b97ec3768d6c98a730bda36551aaaf2a15ed7d23f01810b7131
description: Enhance your hybrid search retrieval and search relevance by chunking the correct fields, selecting pre-filter fields, and adding ranking factors.
products:
  - Data 360
release: "264"
---
# Hybrid Search Best Practices

Enhance your hybrid search retrieval and search relevance by chunking the correct fields, selecting pre-filter fields, and adding ranking factors.

Use chunking fields that provide context and semantic meaning. You can select multiple fields for chunking your data. Consider how you expect the retrieved results to be used in your gen AI application. For example, select fields such as title, body, or description of your records. Fields that have a small set of values, such as Open or Closed, aren’t ideal for chunking.

Increase the precision of search results by adding filter fields to narrow the results. Use filters that make sense for your application. For example, pre-filtering on the case status field can improve the relevance of search results on case conversation transcripts.

Refine result relevance with ranking models by sending relevance-specific parameters. Use advanced formulas from the Reciprocal Rank Fusion (RRF) and Linear Fusion Ranker (LFR) models to tweak parameter values and achieve relevant results.

Enhance relevance by adding ranking factors to your search index configuration.

- Add the popularity of records as a factor, such as selecting fields that track ViewCount. For instance, when you index a DMO created from CRM knowledge articles, import the entity KnowledgeArticleViewStat into Data 360, and use KnowledgeArticleViewStat.ViewCount as the popularity factor.
- Add recency of records as a factor. For example, when you index a DMO created from CRM knowledge articles, use LastModifiedDate\_\_C field as the recency factor.

![Example](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-data-data_cloud-264-0-0-production-enus/f3569962-3c9b-4f83-9d0c-b6a574e785da/images/icon_example.png)

Example This POST request creates a hybrid search index configuration on the Knowledge\_kav\_Home object. The configuration uses the Content field to chunk the data and the LastModifiedDate\_\_c field to track recency.

```
{
"label": "record9357",
"developerName": "record9357",
"description": "A sample description",
"sourceDmoDeveloperName": "Knowledge_kav_Home__dlm",
"chunkDmoName": "chunk9357",
"vectorDmoName": "vector9357",
"chunkDmoDeveloperName": "chunk9357",
"vectorDmoDeveloperName": "vector9357",
"searchType": "HYBRID"
"vectorEmbedding": {
"vectorEmbeddingRelatedFields": [
{
"relatedDmoDeveloperName": "Knowledge_kav_Home__dlm",
"relatedDmoFieldDeveloperName": "ssot__Name__c",
"relationships": [
{
"sourceDmoDeveloperName": "Knowledge_kav_Home__dlm",
"sourceDmoFieldDeveloperName": "Content_c__c",
"targetDmoDeveloperName": "Knowledge_kav_Home__dlm",
"targetDmoFieldDeveloperName": "Content_c__c"
}
]
}
]
},
"rankingConfigurations": [
{
"relatedDmoDeveloperName": "Knowledge_kav_Home__dlm",
"relatedDmoFieldDeveloperName": "LastModifiedDate__c",
"rankType": "RECENCY",
"developerName": "rank_time_freshness",
"relationships": [
{
"sourceDmoDeveloperName": "Knowledge_kav_Home__dlm",
"sourceDmoFieldDeveloperName": "Content_c__c",
"targetDmoDeveloperName": "Knowledge_kav_Home__dlm",
"targetDmoFieldDeveloperName": "Content_c__c"
}
]
}
],
"chunkingConfiguration": {
"fieldLevelConfigurations": [
{
"sourceDmoDeveloperName": "Knowledge_kav_Home__dlm",
"sourceDmoFieldDeveloperName": "Content_c__c",
"config": {
"id": "passage_extraction",
"userValues": [
{
"id": "max_tokens",
"value": "512"
},
{
"id": "strip_html",
"value": "true"
}
]
}
}
]
},
"vectorEmbeddingConfiguration": {
"embeddingModel": {
"id": "e5_large_v2",
"userValues": [
{
"id": "dimension",
"value": "1024"
},
{
"id": "max_token_limit",
"value": "512"
}
]
},
"index": {
"id": "HNSW",
"userValues": [
{
"id": "hnswEfConstruction",
"value": "8"
},
{
"id": "M",
"value": "4"
}
```

#### See Also

- [_Salesforce Help_: Hybrid Search Fusion Ranking](https://help.salesforce.com/s/articleView?id=data.c360_a_hybridsearch_fusion_ranking.htm&release=264.0.0&type=5 "Ranking reorders the search results so that the most relevant results to a user query are at the top. Data 360 Hybrid search retrieves results from two indexes—keyword and vector— based on the similarity with the query and then merges (fuses) the results. The ranking model then reranks the merged results.")
- [_Salesforce Help_: Create Search Index](https://help.salesforce.com/s/articleView?id=data.c360_a_hybridsearch_index_create.htm&release=264.0.0&type=5 "Configure a hybrid search index for a data model object (DMO) or an unstructured data model object (UDMO) to provide relevant information to generative AI applications. Data 360 breaks up the referenced data into semantically related chunks and generates searchable vectors, and then generates a vector index and a keyword index.")
- [_Salesforce Help_: Hybrid Search Autodrop](https://help.salesforce.com/s/articleView?id=data.c360_a_hybridsearch_relevance_configuration.htm&release=264.0.0&type=5 "Increase the accuracy of search index query results and improve the quality of RAG applications by sending relevance-specific parameters. Using SQL, you can send relevance configuration settings for supported fusion ranker models and improve the result relevance for your specific use case.")