Alfresco Collection

Overview

SearchBlox provides an Alfresco Collection to index documents stored in Alfresco Content Services. It connects directly to your Alfresco repository, automatically crawls the stored content, and indexes it for search — making large document repositories easily searchable without manually uploading files into SearchBlox.

Note: Alfresco Collection supports RAG for AI-powered search, Knowledge Graph for entity extraction, private access control, content encryption, and configurable language settings.

Prerequisites for Alfresco Collection

Before creating an Alfresco Collection in SearchBlox, ensure the following are in place:

Alfresco Repository URL — The base URL of your Alfresco repository, starting with http:// or https:// (e.g., https://host/alfresco), reachable from the SearchBlox server.
Valid user account — A username and password for an Alfresco repository account with sufficient permissions to read/crawl the target sites or content.
Read access to target content — The account used must have at least read (Consumer) permission on the sites/folders to be indexed.
Network/firewall access — Port and firewall rules must allow SearchBlox to reach the Alfresco repository URL.
SSL certificate (if applicable) — If the repository is served over HTTPS with a self-signed or internal CA certificate, it must be trusted by the SearchBlox server/JVM truststore.

Creating an Alfresco Collection

Log in to the Admin Console.
Navigate to the Collections tab.
Click the Create button or the + icon.
Select Alfresco Collection as the collection type.
Enter a Collection Name. The name must be unique and contain 3–36 alphanumeric characters. Only underscores (_) are allowed as special characters.
Configure Enable RAG by turning it ON to allow the collection to be used for Retrieval Augmented Generation, or turn it OFF if AI-based retrieval is not required.
Configure Enable Knowledge Graph by turning it ON to extract entities and relationships from documents, or turn it OFF if this feature is not needed.
Configure Private Collection Access by enabling it to restrict access to authenticated users only, or disabling it to allow public (unauthenticated) access.
Configure Collection Encryption if required to protect document content or metadata fields. Metadata fields can be encrypted using the deid_ prefix.
Select the Collection Language based on the primary language used in the repository content. The default language is English.
Click Create to create the Alfresco Collection.

Once the collection is created, you will be taken to the Alfresco Settings tab to configure the connection details for crawling your Alfresco repository.

Configuring Alfresco Settings

To configure the connection for your Alfresco Collection, follow these steps:

Authentication

Go to the Settings tab within the collection.
Enter the User name.
The username for your Alfresco repository account (e.g., admin).
Enter the Alfresco Repository URL.
Specify your Alfresco repository base URL. The URL must start with http:// or https:// (e.g., https://host/alfresco).
Enter the Password.
The password for your Alfresco repository account.

Generate Using LLM

Enable Title to automatically generate concise and relevant titles for the documents using LLM while indexing.
Enable Description to generate relevant descriptions for the documents using LLM while indexing.
Enable Topics to generate relevant topics for the documents using LLM while indexing.

Relevance

Auto Relevance — Enable to use Hybrid Search for automatic relevance ranking.

Click Save to store the configuration, or Cancel to discard changes.

Sites

Synonyms

Synonyms help the search show relevant documents even when the exact search word is not used.
For example, if someone searches for “global,” the results can also include documents that use “world” or “international.”
We have an option to load Synonyms from the existing documents.

Stopwords

Stopwords are common, high-frequency words that carry minimal semantic value and are typically excluded during text processing, indexing, or search operations. Examples include articles (a, an, the), conjunctions (and, but, or), prepositions (in, on, about), and auxiliary verbs (is, was, would, had).

Purpose:

Reduce noise in search indexing and text analysis
Improve processing efficiency by excluding low-value tokens
Enhance search relevance by prioritizing meaningful keywords

Schedule and Index

Sets the frequency and the start date/time for indexing a collection. Schedule Frequency supported in SearchBlox is as follows:

Once
Hourly
Daily
Every 48 Hours
Every 96 Hours
Weekly
Monthly

The following operation can be performed in Azure blob collections

Activity	Description
Enable Scheduler for Indexing	Once enabled, you can set the Start Date and Frequency
Schedule	For each collection, indexing can be scheduled based on the above options.
View all Schedules	Redirects to the Schedules section, where all the Collection Schedules are listed.

Manage Documents Tab

Using Manage Documents tab we can do the following operations:
1. Filter
2. View content
3. View metadata
4. Refresh
5. Delete
To delete a file from your collection, enter the file path and click "Delete".
To see the status of an indexed file, click "View Metadata".

Data Fields Tab

Using the Data Fields tab, you can create custom fields for search and view the default and configured fields for the collection.

Toggle Show Defaults ON to display the collection's default system fields, in addition to any custom fields.
Use the + icon to add a new custom Data Field.
Use the info icon to view details about field configuration.
Use the refresh icon to reload the fields list.

Each field is listed with the following columns:

Column	Description
Name	The name of the data field (e.g., `col_id`, `content_suggest`, `topics`).
Type	The data type assigned to the field.
Analyzer	The text analyzer applied to the field, if any (e.g., `comma_analyzer`). Shown as `—` when no analyzer is applied.

SearchBlox supports the following Data Field types:

Type	Description
Keyword	Used for alphanumeric values such as IDs, tags, codes, or other exact-match fields (e.g., `col_id`, `faq_content`, `image_path`).
Text	Used for full-text search within custom field content (e.g., `content_suggest`, `topics`).
KNN_Vector	Used to store vector embeddings for semantic/similarity search (e.g., `page_dna_vector`).
Binary	Used to store binary data such as images or files (e.g., `imagedata`).
Boolean	Used for true/false values (e.g., `needsReview`, `approved`).
Number	Used for numeric values such as prices, quantities, ratings, or counts.
Date	Used for date values that can be searched, sorted, and filtered.

Note: Once Data Fields are configured, the collection must be cleared and re-indexed for the changes to take effect.

Prompts

When LLM/RAG is enabled, you can edit AI-based prompts for Title, Description, Topic, Image Description, and Smart FAQs.
You can customize these prompts anytime, and use Restore Default to reset them back to the original SearchBlox settings.

Models

The Models section lets you override the global embedding, reranking, and LLM settings for this specific collection. Changes made here apply only to the current collection and do not affect other collections.

Embedding

Provider specifies the embedding provider used to generate vector representations of documents
Model defines the embedding model used to convert document content into vectors for semantic search

Reranker

Provider specifies the reranker provider used for improving search result relevance
Model defines the reranker model used to re-score and reorder search results based on relevance

LLM

Provider specifies the Large Language Model provider used for AI-powered features
Model defines the LLM used for tasks such as document enrichment, summaries, and SmartFAQs

Knowledge Graph

Enable Knowledge Graph — Turn ON to extract entities and relationships from this collection into a Knowledge Graph. This setting applies at the next index — re-index the collection to build or rebuild the graph.