AI Search Enhancement

Feature Description

The ai-search plugin enhances the accuracy and timeliness of AI model responses by integrating real-time results from search engines (Google/Bing/Arxiv/Elasticsearch etc.). The plugin automatically injects search results into the prompt template and determines whether to add reference sources in the final response based on configuration.

Runtime Properties

Plugin execution stage: Default stage Plugin execution priority: 440

Configuration Fields

Name	Data Type	Requirement	Default Value	Description
defaultEnable	bool	Optional	true	Whether the plugin functionality is enabled by default. When set to false, the plugin will only be activated when the request contains a web_search_options field
needReference	bool	Optional	false	Whether to add reference sources in the response
referenceFormat	string	Optional	"**References:**\n%s"	Reference content format, must include %s placeholder
referenceLocation	string	Optional	”head”	Reference position: “head” at the beginning of the response, “tail” at the end of the response
defaultLang	string	Optional	-	Default search language code (e.g. zh-CN/en-US)
promptTemplate	string	Optional	Built-in template	Prompt template, must include {search_results} and {question} placeholders
searchFrom	array of object	Required	-	Refer to search engine configuration below, at least one engine must be configured
searchRewrite	object	Optional	-	Search rewrite configuration, used to optimize search queries using an LLM service

Search Rewrite Description

The search rewrite feature uses an LLM service to analyze and optimize the user’s original query, which can:

1. Determine whether the user’s question requires a search engine query. If it does not, the search-related logic will not be executed
2. Convert natural language queries into keyword combinations better suited for search engines
3. For Arxiv paper searches, automatically identify relevant paper categories and add category constraints
4. For private knowledge base searches, break down long queries into multiple precise keyword combinations

It is strongly recommended to enable this feature when using Arxiv or Elasticsearch engines. For Arxiv searches, it can accurately identify paper domains and optimize English keywords; for private knowledge base searches, it can provide more precise keyword matching, significantly improving search effectiveness.

Search Rewrite Configuration

Name	Data Type	Requirement	Default Value	Description
llmServiceName	string	Required	-	LLM service name
llmServicePort	number	Required	-	LLM service port
llmApiKey	string	Optional	-	LLM service API key
llmUrl	string	Required	-	LLM service API URL
llmModelName	string	Required	-	LLM model name
timeoutMillisecond	number	Optional	30000	API call timeout (milliseconds)
maxCount	number	Optional	3	Maximum number of search queries generated by the search rewrite

Search Engine Common Configuration

Name	Data Type	Requirement	Default Value	Description
type	string	Required	-	Engine type (google/bing/arxiv/elasticsearch/quark)
apiKey	string	Required	-	Search engine API key/Aliyun AccessKey
serviceName	string	Required	-	Backend service name
servicePort	number	Required	-	Backend service port
count	number	Optional	10	Number of results returned per search
start	number	Optional	0	Search result offset (start returning from the start+1 result)
timeoutMillisecond	number	Optional	5000	API call timeout (milliseconds)
optionArgs	map	Optional	-	Search engine specific parameters (key-value format)

Google Specific Configuration

Name	Data Type	Requirement	Default Value	Description
cx	string	Required	-	Google Custom Search Engine ID, used to specify search scope

Arxiv Specific Configuration

Name	Data Type	Requirement	Default Value	Description
arxivCategory	string	Optional	-	Search paper category (e.g. cs.AI, cs.CL etc.)

Elasticsearch Specific Configuration

Name	Data Type	Requirement	Default Value	Description
index	string	Required	-	Elasticsearch index name to search
contentField	string	Required	-	Content field name to query
semanticTextField	string	Required	-	Embedding field name to query
linkField	string	Optional	-	Result link field name, needed when needReference is configured
titleField	string	Optional	-	Result title field name, needed when needReference is configured
username	string	Optional	-	Elasticsearch username
password	string	Optional	-	Elasticsearch password

The Reciprocal Rank Fusion (RRF) query used in hybrid search requires Elasticsearch version 8.8 or higher.

Currently, document vectorization relies on Elasticsearch’s embedding model, which requires an Elasticsearch Enterprise license or a 30-day Trial license. To install the built-in embedding model in Elasticsearch, please refer to this documentation. If you want to install a third-party embedding model, please refer to this guide.

For a complete tutorial on integrating the ai-search plugin with Elasticsearch, please refer to: Building a RAG Application with LangChain + Higress + Elasticsearch.

Quark Specific Configuration

Name	Data Type	Requirement	Default Value	Description
contentMode	string	Optional	”summary”	Content mode: “summary” uses snippet, “full” uses full text (markdownText first, then mainText if empty)

Configuration Examples

Basic Configuration (Single Search Engine)

needReference: true
searchFrom:
- type: google
  apiKey: "your-google-api-key"
  cx: "search-engine-id"
  serviceName: "google-svc.dns"
  servicePort: 443
  count: 5
  optionArgs:
    fileType: "pdf"

Arxiv Search Configuration

searchFrom:
- type: arxiv
  serviceName: "arxiv-svc.dns"
  servicePort: 443
  arxivCategory: "cs.AI"
  count: 10

Quark Search Configuration

searchFrom:
- type: quark
  serviceName: "quark-svc.dns"
  servicePort: 443
  apiKey: "quark api key"
  contentMode: "full"  # Optional values: "summary"(default) or "full"

Multiple Search Engines Configuration

defaultLang: "en-US"
promptTemplate: |
  # Search Results:
  {search_results}

  # Please answer this question:
  {question}
searchFrom:
- type: google
  apiKey: "google-key"
  cx: "github-search-id"  # Search engine ID specifically for GitHub content
  serviceName: "google-svc.dns"
  servicePort: 443
- type: google
  apiKey: "google-key"
  cx: "news-search-id"    # Search engine ID specifically for Google News content
  serviceName: "google-svc.dns"
  servicePort: 443
- type: bing
  apiKey: "bing-key"
  serviceName: "bing-svc.dns"
  servicePort: 443
  optionArgs:
    answerCount: "5"

Concurrent Query Configuration

Since search engines limit the number of results per query (e.g. Google limits to 100 results per query), you can get more results by:

1. Setting a smaller count value (e.g. 10)
2. Specifying result offset with start parameter
3. Concurrently initiating multiple query requests, with each request’s start value incrementing by count

For example, to get 30 results, configure count=10 and concurrently initiate 3 queries with start values 0,10,20 respectively:

searchFrom:
- type: google
  apiKey: "your-google-api-key"
  cx: "search-engine-id"
  serviceName: "google-svc.dns"
  servicePort: 443
  start: 0
  count: 10
- type: google
  apiKey: "your-google-api-key"
  cx: "search-engine-id"
  serviceName: "google-svc.dns"
  servicePort: 443
  start: 10
  count: 10
- type: google
  apiKey: "your-google-api-key"
  cx: "search-engine-id"
  serviceName: "google-svc.dns"
  servicePort: 443
  start: 20
  count: 10

Note that excessive concurrency may lead to rate limiting, adjust according to actual situation.

Elasticsearch Configuration (For Private Knowledge Base Integration)

searchFrom:
- type: elasticsearch
  serviceName: "es-svc.static"
  index: "knowledge_base"
  contentField: "content"
  semanticTextField: "semantic_text"
  # username: "elastic"
  # password: "password"

Custom Reference Format

needReference: true
referenceFormat: "### Data Sources\n%s"
searchFrom:
- type: bing
  apiKey: "your-bing-key"
  serviceName: "search-service.dns"
  servicePort: 8080

Custom Reference Location

needReference: true
referenceLocation: "tail"  # Add references at the end of the response instead of the beginning
searchFrom:
- type: bing
  apiKey: "your-bing-key"
  serviceName: "search-service.dns"
  servicePort: 8080

Search Rewrite Configuration

searchFrom:
- type: google
  apiKey: "your-google-api-key"
  cx: "search-engine-id"
  serviceName: "google-svc.dns"
  servicePort: 443
searchRewrite:
  llmServiceName: "llm-svc.dns"
  llmServicePort: 443
  llmApiKey: "your-llm-api-key"
  llmUrl: "https://api.example.com/v1/chat/completions"
  llmModelName: "gpt-3.5-turbo"
  timeoutMillisecond: 15000

On-Demand Plugin Activation Configuration

Configure the plugin to only be enabled when the request contains a web_search_options field:

defaultEnable: false
searchFrom:
- type: google
  apiKey: "your-google-api-key"
  cx: "search-engine-id"
  serviceName: "google-svc.dns"
  servicePort: 443

When the request contains a web_search_options field, even if it’s an empty object ("web_search_options": {}), the plugin will be activated.

Search Context Size Configuration

You can dynamically adjust the number of search queries by adding a search_context_size parameter in the web_search_options field of the request:

{
  "web_search_options": {
    "search_context_size": "medium"
  }
}

The search_context_size supports three levels:

• low: Generates 1 search query (suitable for simple questions)
• medium: Generates 3 search queries (default)
• high: Generates 5 search queries (suitable for complex questions)

This setting overrides the maxCount value in the configuration, allowing clients to dynamically adjust search depth based on question complexity.

Notes

1. The prompt template must include {search_results} and {question} placeholders, optionally use {cur_date} to insert current date (format: January 2, 2006)
2. The default template includes search results processing instructions and response specifications, you can use the default template unless there are special needs
3. Multiple search engines query in parallel, total timeout = maximum timeoutMillisecond value among all search engine configurations + processing time
4. Arxiv search doesn’t require API key, but you can specify paper category (arxivCategory) to narrow search scope