Guides/Search

Search Integration

Glean's Search API provides powerful enterprise search capabilities that you can integrate into your applications. Whether you're building a search interface, adding search functionality to existing tools, or creating intelligent agents, Glean's search capabilities can help users find the information they need.

Key Features

• Enterprise Search: Search across all your organization's connected data sources
• Intelligent Ranking: AI-powered relevance scoring and result ranking
• Faceted Filtering: Filter results by source, type, date, and custom attributes
• Real-time Results: Fast search with sub-second response times
• Permission Awareness: Results respect user permissions and data access controls

Getting Started

Search API Overview

Learn about the Search API endpoints and capabilities

Filtering Results

Advanced filtering techniques and examples

Datasource Filters

Filter search results by specific data sources

Faceted Filtering

Use facets to create dynamic search interfaces

Common Use Cases

Knowledge Base Search

Build a search interface for your internal documentation, wikis, and knowledge repositories.

Code Search

Search through your organization's source code, documentation, and development resources.

Document Discovery

Help users find relevant documents, presentations, and files across all connected systems.

Expert Finding

Locate subject matter experts and colleagues with specific knowledge or experience.

Search Patterns

Basic Search

Simple text-based search across all available content:

import requests
import os

api_token = os.getenv("GLEAN_API_TOKEN")
server_url = os.getenv("GLEAN_SERVER_URL")
base_url = f"{server_url}/rest/api/v1"

headers = {
    "Authorization": f"Bearer {api_token}",
    "Content-Type": "application/json"
}

response = requests.post(
    f"{base_url}/search",
    headers=headers,
    json={
        "query": "vacation policy",
        "pageSize": 10
    }
)
response.raise_for_status()

results = response.json().get("results", [])
for result in results:
    print(f"Title: {result.get('title', 'No title')}")
    print(f"URL: {result.get('url', 'No URL')}")
    snippets = result.get('snippets', [])
    if snippets:
        print(f"Snippet: {snippets[0].get('snippet', '')}")

Filtered Search

Search with specific filters to narrow results:

from glean.api_client import Glean, models

with Glean(api_token=api_token, server_url=server_url) as glean:
    response = glean.client.search.query(
        query="quarterly results",
        page_size=10,
        request_options=models.SearchRequestOptions(
            facet_bucket_size=10,
            facet_filters=[
                models.FacetFilter(
                    field_name="app",
                    values=[
                        models.FacetFilterValue(
                            value="confluence",
                            relation_type=models.RelationType.EQUALS,
                        ),
                        models.FacetFilterValue(
                            value="sharepoint",
                            relation_type=models.RelationType.EQUALS,
                        ),
                    ],
                ),
                models.FacetFilter(
                    field_name="type",
                    values=[
                        models.FacetFilterValue(
                            value="document",
                            relation_type=models.RelationType.EQUALS,
                        ),
                    ],
                ),
            ],
        ),
    )

Search with Date Filters

Find recent or time-specific content:

from datetime import datetime, timedelta
from glean.api_client import Glean, models

last_month = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")

with Glean(api_token=api_token, server_url=server_url) as glean:
    response = glean.client.search.query(
        query="product updates",
        request_options=models.SearchRequestOptions(
            facet_bucket_size=10,
            facet_filters=[
                models.FacetFilter(
                    field_name="last_updated_at",
                    values=[
                        models.FacetFilterValue(
                            value=last_month,
                            relation_type=models.RelationType.GT,
                        ),
                    ],
                ),
            ],
        ),
    )

See Filtering Results for the full set of last_updated_at values, including special ranges like today and past_week.

Advanced Features

Autocomplete Search

Implement search suggestions and autocomplete:

with Glean(api_token=api_token, server_url=server_url) as glean:
    autocomplete_response = glean.client.search.autocomplete(
        query="vacat",
        result_size=5,
    )

    for r in autocomplete_response.results or []:
        print(f"Suggestion: {r.result}")

Search Analytics

Track search performance and user behavior:

from glean.api_client import Glean, models

with Glean(api_token=api_token, server_url=server_url) as glean:
    response = glean.client.search.query(query="benefits")

    # Each response and each result carries a trackingToken; report result
    # events back via /feedback to improve ranking quality.
    clicked_result = response.results[0]
    glean.client.activity.feedback(feedback1={
        "tracking_tokens": [clicked_result.tracking_token],
        "event": models.FeedbackEvent.CLICK,
    })

Federated Search

Search across multiple instances or systems:

def federated_search(query: str):
    # Search primary instance
    with Glean(api_token=primary_token, server_url=primary_url) as glean:
        primary_results = glean.client.search.query(query=query)

    # Search secondary instance
    with Glean(api_token=secondary_token, server_url=secondary_url) as glean:
        secondary_results = glean.client.search.query(query=query)

    # Combine result lists; each list arrives ranked by Glean already
    return (primary_results.results or []) + (secondary_results.results or [])

Building Search Interfaces

React Search Component

import { useState, useEffect } from 'react';
import { Glean } from '@gleanwork/api-client';

const SearchComponent = () => {
  const [query, setQuery] = useState('');
  const [results, setResults] = useState([]);
  const [loading, setLoading] = useState(false);
  
  const client = new Glean({
    apiToken: process.env.REACT_APP_GLEAN_TOKEN,
    serverURL: process.env.REACT_APP_GLEAN_SERVER_URL
  });
  
  const handleSearch = async (searchQuery: string) => {
    if (!searchQuery.trim()) return;
    
    setLoading(true);
    try {
      const response = await client.client.search.query({
        query: searchQuery,
        pageSize: 20,
      });
      setResults(response.results || []);
    } catch (error) {
      console.error('Search error:', error);
    } finally {
      setLoading(false);
    }
  };
  
  return (
    <div>
      <input
        type="text"
        value={query}
        onChange={(e) => setQuery(e.target.value)}
        onKeyPress={(e) => e.key === 'Enter' && handleSearch(query)}
        placeholder="Search your organization's knowledge..."
      />
      
      {loading && <div>Searching...</div>}
      
      <div>
        {results.map((result, index) => (
          <div key={index} className="search-result">
            <h3><a href={result.url}>{result.title}</a></h3>
            <p>{result.snippets?.[0]?.snippet}</p>
          </div>
        ))}
      </div>
    </div>
  );
};

Search with Filters UI

const FilteredSearchComponent = () => {
  const [filters, setFilters] = useState({
    datasources: [],
    dateRange: null,
    objectTypes: []
  });
  
  const applyFilters = async (query: string) => {
    const facetFilters = [];
    
    if (filters.datasources.length > 0) {
      facetFilters.push({
        fieldName: "app",
        values: filters.datasources.map((d) => ({
          value: d,
          relationType: "EQUALS",
        })),
      });
    }

    if (filters.objectTypes.length > 0) {
      facetFilters.push({
        fieldName: "type",
        values: filters.objectTypes.map((t) => ({
          value: t,
          relationType: "EQUALS",
        })),
      });
    }

    const response = await client.client.search.query({
      query,
      requestOptions: { facetBucketSize: 10, facetFilters },
    });
    
    return response.results;
  };
  
  // UI implementation...
};

Performance Optimization

Caching Search Results

import hashlib
import time

class CachedSearchClient:
    def __init__(self, client: Glean):
        self.client = client
        self._cache = {}
    
    def _cache_key(self, query: str) -> str:
        return hashlib.md5(query.encode()).hexdigest()
    
    def search(self, query: str, cache_ttl: int = 300):
        cache_key = self._cache_key(query)

        if cache_key in self._cache:
            cached_result, timestamp = self._cache[cache_key]
            if time.time() - timestamp < cache_ttl:
                return cached_result

        result = self.client.client.search.query(query=query)

        self._cache[cache_key] = (result, time.time())
        return result

Pagination and Infinite Scroll

def paginated_search(glean, query: str, page_size: int = 20):
    cursor = None
    all_results = []

    while True:
        response = glean.client.search.query(
            query=query,
            page_size=page_size,
            cursor=cursor,
        )
        all_results.extend(response.results or [])

        if not response.has_more_results:
            break

        cursor = response.cursor

    return all_results

Error Handling

import time

from glean.api_client import Glean, errors

def robust_search(glean, query: str, max_retries: int = 3):
    for attempt in range(max_retries):
        try:
            return glean.client.search.query(query=query)
        except errors.GleanError as e:
            if e.status_code == 429:  # Rate limited
                time.sleep(2 ** attempt)
                continue
            elif e.status_code >= 500:  # Server error
                if attempt < max_retries - 1:
                    time.sleep(1)
                    continue
            raise e

    raise Exception(f"Search failed after {max_retries} attempts")

Next Steps

1. Explore Examples: Check out specific filtering and search examples
2. Try the API: Test search queries in your environment
3. Build Interfaces: Create search UIs for your applications
4. Optimize: Implement caching and performance improvements

Related Resources

• Search API Reference
• Authentication Guide
• Chat Integration - Combine search with conversational AI
• Agent Examples - Use search in intelligent agents