Table of Contents

• Configuring SWIRL AI Search, Enterprise Edition
  • Licensing
  • Database
  • PostgreSQL Configuration
  • Connecting to M365
  • Connecting to Other Authentication Systems
    • Managing Authenticators
• Connecting to Generative AI (GAI) and Large Language Models (LLMs)
  • Roles for Generative AI / Large Language Models
  • Managing AI Providers
  • Supported Generative AI (GAI) and LLMs
  • Editing AI Providers
  • Activating AI Providers
  • Switching AI Provider Defaults
  • Adding AI Providers
    • Via the Admin Console
    • Via Copy/Paste
  • Using the Bearer Token Service to Update AI Providers
• Managing Prompts
  • Modifying the Standard Prompts
  • Customizing the AI Search RAG Prompt
  • Restoring Standard Prompts
    • Restoring All Prompts to Default
  • Specifying a Saved Prompt when Generating AI Insights
  • Using a Prompt in a Query Processor or Connector
• Optimizing RAG
  • Using Summaries
  • Distribution Strategy
  • Model Maximum Pages and Tokens
  • Notes
• Configuring the Authenticating Page Fetcher for RAG with Enterprise Content
  • Google PSE SearchProviders
  • Notes
• M365 Configurations
  • Microsoft Outlook Messages
  • Microsoft Calendar
  • Microsoft OneDrive
  • Microsoft SharePoint
    • Customizing
    • query_processors
    • post_query_template
    • Quote Handling
• Extracting Enterprise Content with Apache Tika
  • Running Apache Tika
  • SearchProvider Configuration
  • Configuring Passage Detection with Reader LLM
  • Configuration Options
  • Example .env Configuration
• Text Summarization
• Text Truncation

AI Search Guide
Enterprise Edition

Please contact SWIRL for access to SWIRL Enterprise.

Please note: we've renamed our products! SWIRL AI Connect is now SWIRL AI Search 🔎 and SWIRL AI Co-Pilot is now SWIRL AI Search Assistant 🤖

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Configuring SWIRL AI Search, Enterprise Edition

Licensing

Add the license provided by SWIRL to the installation’s .env file in the following format:

SWIRL_LICENSE={"owner": "<owner-name>", "expiration": "<expiration-date>", "key": "<public-key>"}

If the license is invalid, a message will appear in logs/django.log. Please contact support for assistance.

Database

For Proof of Value (POV) testing, SWIRL AI Search, Enterprise Edition can use SQLite3. Contact support for assistance with this configuration.

For production environments, SWIRL recommends PostgreSQL.

PostgreSQL Configuration

Modify the database settings in swirl_server/settings.py:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql_psycopg2',
        'NAME': '<database-name>',
        'USER': '<database-username>',
        'PASSWORD': '<database-password>',
        'HOST': '<database-hostname>',
        'PORT': '<database-port>',
    }
}

Connecting to M365

To connect SWIRL to your Microsoft 365 (M365) tenant, follow the instructions in the Microsoft 365 Guide.

Connecting to Other Authentication Systems

To integrate SWIRL with an Identity Provider (IDP) or Single Sign-On (SSO) system, you need to configure an Authenticator object.

Managing Authenticators

Use the Admin Console to view, edit, add or delete authenticators, here: http://localhost:8000/admin/swirl

Click on "Authenticators" as shown to view the list of available ones, or add a new one:

Click any of the Authenticators shown in the list to view or edit it. For example, here's the default Microsoft authenticator:

Click the "SAVE" button at the bottom of the page to commit changes.

Authenticator Fields

Field	Description
idp	Name of the authenticator object (used as the URL)
name	Display name of the authenticator
active	Boolean; if false, the authenticator is disabled, and no authentication switch appears in the UI
callback_path	URL path where the IDP should redirect with user tokens
client_id	Client ID for authentication
client_secret	Shared secret for authentication
app_uri	Location of the SWIRL application
auth_uri	URL of the authentication system
token_uri	URL for retrieving authentication tokens
user_data_url	URL for retrieving user profile data
user_data_params	Parameters needed from the user profile
user_data_headers	Headers required for requesting tokens (e.g., "Authorization")
user_data_method	HTTP method used to request user profile data
initiate_auth_close_flow_params	Parameters for CAS2 and other custom authentication flows
exchange_code_params	Parameters for exchanging authorization codes in custom flows
is_code_challenge	Boolean; determines if exchange code parameters are required (default: True)
scopes	List of authorization scopes
should_expire	Boolean; determines if tokens need refreshing (default: True)
use_basic_auth	Boolean; enables basic authentication instead of SSO

For authentication with Elastic, OpenSearch, CAS2, Salesforce, ServiceNow, Okta, Auth0, Ping Federate, and other systems, please contact support.

Connecting to Generative AI (GAI) and Large Language Models (LLMs)

Roles for Generative AI / Large Language Models

GAI/LLMs in SWIRL serve four distinct roles:

Role	Description
reader	Generates embeddings for SWIRL’s Reader LLM to enhance search result re-ranking
query	Provides query completions for transformations
connector	Answers direct questions without RAG
rag	Generates Retrieval-Augmented Generation (RAG) responses using retrieved data

Managing AI Providers

Use the Admin Console to view, add, edit or delete AI Providers, here: http://localhost:8000/admin/swirl

Click "AIProviders" as shown to access the full list:

Supported Generative AI (GAI) and LLMs

SWIRL, via LiteLLM and direct connections, supports major GAI/LLMs, including:

• OpenAI (ChatGPT API)
• OpenAI deployed in Azure
• AWS Bedrock
• Google Gemini
• Anthropic Claude
• Cohere
• Meta Llama
• Hugging Face
• Locally fine-tuned models

The full list of supported embeddings and LLMs are here:

• Full list of Supported Embeddings
• Full list of Supported GAI/LLMs

For assistance with any of these or additional models, please contact support.

Editing AI Providers

From the Admin Console as shown above, click "AIProviders" to view the list of all available providers. Then, click on a specific AIProvider to edit it:

This will bring up an edit form:

Click the "SAVE" button at the bottom of the page to commit changes you make.

Click "Delete" to delete the provider entirely. Note: there is no "undo" option, deleted providers are lost forever. To save an AIProvider without having it used, set the active property to false (unchecked).

Activating AI Providers

To activate a preloaded AI provider:

• Make sure the active property is checked
• Add a valid api_key to the credentials field
• Change the model and any required config items
• Ensure the provider has the correct tags and default settings

Switching AI Provider Defaults

To switch the provider for a given role, use the active property. Only one AIProvider should be active for each system defined role.

Adding AI Providers

Via the Admin Console

From the Admin Console, click on AIProviders as shown above. Then, click "Add AIProvider":

This will bring up a blank form for a new AIProvider:

Fill out the form and click the "SAVE" button at the bottom.

Via Copy/Paste

To manually install an AI provider using JSON:

1. Make sure you are logged in as the admin user
2. Open the AI Providers API endpoint: http://localhost:8000/swirl/aiproviders/
3. Click the Raw data tab at the bottom of the page
4. Paste the AI provider’s JSON
5. Press the POST button

SWIRL will respond with the registered AI provider.

Bulk Loading AI Providers

Use the swirl_load.py script to load multiple AI providers.

Using the Bearer Token Service to Update AI Providers

SWIRL AI Search, Enterprise Edition, includes a Bearer Token Service that refreshes tokens automatically.

How It Works

1. Sends a POST request to an Identity Provider (IDP) URL with user credentials.
2. Extracts a bearer_token from the response.
3. Updates the api_key of the configured AI provider.

Configuration

# 1. Add the following settings to .env:

BT_IDP_URL=''
BT_IDP_CLIENT_ID=''
BT_IDP_CLIENT_SECRET=''

# 2. Specify the AI Provider IDs to update:

BT_AIP=9

For multiple providers, use a comma-separated list:

BT_AIP='9,10'

# 3. Schedule Token Refresh in swirl_server/settings.py:

By default, the service runs every 20 minutes. Adjust the schedule in CELERY_BEAT_SCHEDULE:

CELERY_BEAT_SCHEDULE = {
    # Bearer Token Service (default: every 20 minutes)
    'bt_service': {
         'task': 'bt_service',
         'schedule': crontab(minute='*/20'),
    },
}

# 4. Start the celery-beats service:

python swirl.py start celery-beats

# 5. Restart the logs:

If python swirl.py logs is running, restart it to view celery-beats messages.

Most Bearer Token service logs appear in logs/celery-worker.log.

This ensures automatic updates for AI provider credentials, reducing manual intervention.

Managing Prompts

SWIRL Enterprise includes a set of pre-loaded standard prompts used when Generating AI Insights via RAG.

Each consiss of three key components:

Field	Description
prompt	The main body of the prompt. Use {query} to represent the SWIRL query.
note	Text appended to search result data sent to the LLM for insight generation.
footer	Additional instructions appended after the prompt and RAG data. This is ideal for formatting guidance.

The name of the prompt has no importance. SWIRL uses the tags field to determine which prompt is used for a given function.

The following table presents the tags options:

Tag	LLM Role
search-rag	Used by AI Search, Generate AI Insight (RAG) switch, somewhat technical
chat	Used by AI Search Assistant for chat conversations, including company background; not technical
chat-rag	Used by AI Search Assistant to answer questions and summarize data via RAG; somewhat technical

Note that there must be at least one active prompt for each of these tags for the relevant SWIRL features to work.

Modifying the Standard Prompts

Warning: never modify the standard prompts! All changes will be discarded when SWIRL updates.

Use the Customizing Prompts procedure, below, instead.

Customizing the AI Search RAG Prompt

Warning: Removing important sections of any prompt such as variables like {header} and {query} may cause AI Insight generation to fail or not contain important features such as follow-up questions or citations.

The following procedure below below to copy the standard prompts, modify them, then make them active. New prompts won't be disturbed when SWIRL upgrades.

1. Open the Admin Console here: http://localhost:8000/admin/swirl

2. click Prompts near the bottom of the page:

3. Click the search_rag_standard or, if using Deep Linking search_rag_deeplink prompt:

4. Using the form, uncheck active. Click the "SAVE" button at the bottom of the page.

5. Change the name of the prompt to something appropriate like my_custom_prompt. Click the "Save as new" button at the bottom of the page.

6. Set the prompt to active. Click the "SAVE" button at the bottom of the page to save the new prompt.

7. If you don't wish to share this prompt with other users, set shared to false.

8. Modify the prompt, note and/or footer as needed, while retaining all critical instructions. For example, to instruct the LLM to use pirate-speak:

9. Click "SAVE" at the bottom of the page to save changes.

10. Try the new prompt from the Galaxy Search form!

Restoring Standard Prompts

To go back to a standard prompt after creating a new one:

• Open the Admin Console and select Prompts
• Edit the new prompt, uncheck the active property, and click the "SAVE" button.
• Edit the system prompt, check the active property, and click the "SAVE" button.

Restoring All Prompts to Default

To restore all prompts to the default, refer to the Admin Guide on Resetting Prompts.

Specifying a Saved Prompt when Generating AI Insights

There are two ways to select a saved prompt:

1. Use the prompt selector on the AI Search form:

2. Use the prompt operator in your query. For example:

Swirl AI Search prompt:pirate

In either case, the response is generated in pirate-speak as the prompt instructs:

Using a Prompt in a Query Processor or Connector

To specify a prompt when using Generative AI (GAI) for query rewriting or direct question answering:

1. Follow the steps in Connecting to Enterprise AI.
2. Refer to the Developer Guide section on Using an LLM to Rewrite Queries.

This ensures prompts are correctly applied when interacting with AI-based query transformations.

Optimizing RAG

Using Summaries

Set SWIRL_ALWAYS_FALL_BACK_TO_SUMMARY to True to enable SWIRL to use result summaries for RAG. This is the best option for sources where full-page fetching is restricted due to authentication limitations.

Distribution Strategy

The distribution strategy determines how SWIRL selects pages from search results per source. Configure this by setting SWIRL_RAG_DISTRIBUTION_STRATEGY to one of the following:

• Distributed – Maintains the original sort order and evenly selects pages from each source.
  • Example: If two sources return results, SWIRL selects five pages from each, adding them to the prompt until token limits are reached.
  • The sort order remains unchanged, and swirl_score is not used.
• RoundRobin – Selects pages in a round-robin fashion across sources.
  • Respects the source’s internal sort order but ignores swirl_score.
• Sorted (default) – Selects pages based on swirl_score, ensuring only results with a swirl_score greater than 50 are used.

Model Maximum Pages and Tokens

• Set the RAG model: Use SWIRL_RAG_MODEL to specify the generative AI model for RAG.
• Limit the number of pages considered: Configure SWIRL_RAG_MAX_TO_CONSIDER to control how many pages are used.
• Control token usage: Use SWIRL_RAG_TOK_MAX to set the maximum number of tokens in the prompt sent to ChatGPT.

Notes

• When adjusting SWIRL_RAG_MODEL or SWIRL_RAG_TOK_MAX, ensure values remain within the model’s token limit.
• SWIRL uses model-specific encodings to count tokens but also respects user-defined limits.
• The default SWIRL_RAG_TOK_MAX is set below the model’s maximum to prevent excessive latency in responses.

Configuring the Authenticating Page Fetcher for RAG with Enterprise Content

SWIRL AI Search, Enterprise Edition, includes a Page Fetcher that retrieves results from sources requiring authentication.

The Page Fetcher authenticates using the user’s token or a configured authentication method for each source.

The following sections explain how to configure Page Fetching for specific SearchProviders.

Google PSE SearchProviders

For public source data via Google PSE SearchProviders, the recommended configuration uses Diffbot—a page fetching and content cleaning service.

Configuration with Diffbot

"page_fetch_config_json": {
    "cache": "false",
    "fallback": "diffbot",
    "diffbot": {
        "token": "<Diffbot-API-Token-Here>",
        "scholar.google.com": {
            "extract_entity": "article"
        }
    },
    "headers": {
        "User-Agent": "Swirlbot/1.0 (+http://swirl.today)"
    },
    "www.businesswire.com": {
        "timeout": 60
    },
    "www.linkedin.com": {
        "timeout": 5
    },
    "rs.linkedin.com": {
        "timeout": 5
    },
    "uk.linkedin.com": {
        "timeout": 5
    },
    "au.linkedin.com": {
        "timeout": 5
    },
    "timeout": 30
}

To obtain a Diffbot token, sign up at:
https://www.diffbot.com/

Configuration Without Diffbot

If you prefer not to use Diffbot, use the following configuration:

"page_fetch_config_json": {
    "cache": "false",
    "headers": {
        "User-Agent": "Swirlbot/1.0 (+http://swirl.today)"
    },
    "www.businesswire.com": {
        "timeout": 60
    },
    "www.linkedin.com": {
        "timeout": 5
    },
    "rs.linkedin.com": {
        "timeout": 5
    },
    "uk.linkedin.com": {
        "timeout": 5
    },
    "au.linkedin.com": {
        "timeout": 5
    },
    "timeout": 30
}

Note: For more details on configuring Google PSE SearchProviders, refer to the SearchProvider Guide.

Notes

• cache is set to false by default as of Release 3.0.
• fallback: "diffbot" enables automatic failover—SWIRL attempts normal fetching first, using Diffbot only if the initial fetch fails.
  • This improves speed, as Diffbot requests are slower and should only be used when necessary.
• headers define request headers sent with each page request.
• Domain-specific timeout values serve two purposes:
  • Allowing slow but valuable sources (e.g., www.businesswire.com) to return data.
  • Enforcing quick failures for unsupported sites (e.g., www.linkedin.com), so Diffbot can be used instead.
• Diffbot requires a paid account with an associated API token.

This configuration ensures efficient, authenticated page fetching while minimizing request costs.

M365 Configurations

Warning: Diffbot should not be used with Microsoft sources.

Note: The content_url field is a template URL that dynamically constructs a URL using search result data. SWIRL uses this URL to fetch actual content.

Microsoft Outlook Messages

Add the following configuration to the Microsoft Outlook Messages SearchProvider:

"page_fetch_config_json": {
    "cache": "false",
    "headers": {
        "User-Agent": "Swirlbot/1.0 (+http://swirl.today)"
    },
    "timeout": 10
}

Microsoft Calendar

Add the following configuration to the Microsoft Calendar SearchProvider:

"page_fetch_config_json": {
    "cache": "false",
    "content_url": "https://graph.microsoft.com/v1.0/me/events/'{hitId}'",
    "headers": {
        "User-Agent": "Swirlbot/1.0 (+http://swirl.today)"
    },
    "timeout": 30
}

Microsoft OneDrive

Configuration Options

Field	Description
content_url	The URL to fetch the page content if different from the URL mapped to SWIRL’s url field.
mimetype_url	The URL to fetch the mimetype of the content.
mimetype_path	JSON path to extract the mimetype from the fetched object.
mimetype_whitelist	List of mimetypes allowed for content fetching.

OneDrive Configuration

• The configuration below enables fetching HTML, PDFs, and Microsoft Office documents.
• Binary content (PDF, DOCX, PPTX, etc.) requires a configured text extractor for RAG.

"page_fetch_config_json": {
    "cache": "false",
    "content_url": "https://graph.microsoft.com/v1.0/drives/'{resource.parentReference.driveId}'/items/'{resource.id}'/content",
    "mimetype_url": "https://graph.microsoft.com/v1.0/drives/'{resource.parentReference.driveId}'/items/'{resource.id}'",
    "mimetype_path": "'{file.mimeType}'",
    "mimetype_whitelist": [
        "application/pdf",
        "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
        "application/vnd.openxmlformats-officedocument.presentationml.presentation",
        "image/png",
        "text/html"
    ],
    "headers": {
        "User-Agent": "Swirlbot/1.0 (+http://swirl.today)"
    },
    "timeout": 30
}

Microsoft SharePoint

To fetch Sharepoint objects, add the following configuration to the Microsoft SharePoint SearchProvider:

"page_fetch_config_json": {
    "cache": "false",
    "content_url": "https://graph.microsoft.com/beta/sites/'{hitId}'/drives",
    "headers": {
        "User-Agent": "Swirlbot/1.0 (+http://swirl.today)"
    },
    "timeout": 10
}

This ensures SWIRL correctly fetches authenticated content from Microsoft sources while maintaining security and efficiency.

Customizing

SWIRL recommends using the RequestsPost connector to perform advanced querying. This section describes how to configure it.

Here is a sample SearchProvider:

 {
        "name": "Sharepoint Advanced Query",
        "description": "Searches a set of documents. Supports most languages.",
        "owner": "admin",
        "shared": true,
        "active": true,
        "default": false,
        "authenticator": "Microsoft",
        "connector": "RequestsPost",
        "url": "https://graph.microsoft.com/beta/search/microsoft.graph.query",
        "query_template": "{url}",
        "query_template_json": {},
        "post_query_template": {
            "requests": [
                {
                    "from": 0,
                    "size": 10,
                    "query": {
                        "queryString": "( {query_string} )  AND  site:\"https://<m365-tenant-name>.sharepoint.com/sites/<site-name>*\" AND (ContentTypeId:0x0101009D1CB255DA76424F860D91F20E6C411800FE9B4A881A37C14EB317C8BB00D7678E OR ContentTypeId:0x0101005F15B24C83D3604385BF439DD37F6A1D)"
                    },
                    "fields": [
                        "ContentTypeId",
                        "title",
                        "webUrl",
                        "lastModifiedDateTime",
                        "description"
                    ],
                    "entityTypes": [
                        "driveItem",
                        "listItem"
                    ]
                }
            ]
        },
        "http_request_headers": {
            "Content-Type": "application/json"
        },
        "page_fetch_config_json": {},
        "query_processors": [
            "NoModQueryProcessor"
        ],
        "query_mappings": "NO_URL_ENCODE",
        "result_grouping_field": "",
        "result_processors": [
            "MappingResultProcessor",
            "CosineRelevancyResultProcessor"
        ],
        "response_mappings": "FOUND=value[0].hitsContainers[0].total,RESULTS=value[0].hitsContainers[0].hits",
        "result_mappings": "url=resource.webUrl,title='{$[*].resource..fields.title}',body='{summary} - {resource.fields.description}',date_published=resource.createdDateTime,author=resource.createdBy.user.displayName",
        "results_per_query": 10,
        "credentials": "",
        "eval_credentials": "",
        "tags": [
            "sp1"
        ],
        "ephemeral_store_config_json": {
            "ephemeral": false
        },
        "query_language": "Generic_Keyword",
        "config": {}
    }

Warning: The SP above must be edited before using!!

Warning: The SP above will not work without the quote handling transformer installed. Follow the procedure under Quote Handling to install it.

query_processors

The NoModQueryProcessor should be specified in the query_processors list, instead of the AdaptiveQueryProcessor. This will ensure no modification of the query in the SearchProvider.

post_query_template

The post_query_template field contains JSON that wraps additional query parameters.

• Change and to the appropriate values for your tenant.

• The query_string will be filled-in at run time by SWIRL. The value is in parenthesis because it may contain keywords like AND, OR or NOT, and also nested parens.

• The ContentTypeId specifies which types of documents to focus on. For Sharepoint and OneDrive, the main types are driveItem and listItem.

Quote Handling

SWIRL Enterprise 4.2 and earlier requires a query transformer to handle quoted searches in Sharepoint correctly.

To install this transformer:

• Download the file

• Login to the SWIRL admin, query transform CSV page: http://localhost/api/swirl/query_transform_form/

• Enter name escape_quotes

• Leave the type set to Rewrite

• Click "Choose file", select the downloaded CSV file, and then click "Upload" (highlighted in green above).

The file should upload almost instantly, and redirect you back to the homepage.

Please, contact support if you receive an error message.

Extracting Enterprise Content with Apache Tika

SWIRL integrates Apache Tika to extract text from various file types. The following sections explain how to deploy and configure it.

Running Apache Tika

For local installations, start Tika using Docker:

docker run -d -p 9998:9998 apache/tika

To use a remote Tika instance, set TIKA_SERVER_ENDPOINT in SWIRL’s .env file:

TIKA_SERVER_ENDPOINT='http://<your-tika-server>:9998/'

Restart SWIRL after making changes.

SearchProvider Configuration

Refer to the Microsoft OneDrive section for a Page Fetcher configuration that integrates Tika for extracting text from PDFs, Microsoft Office documents, and other file formats retrieved via the Microsoft Graph API.

To support additional file types, expand the whitelist to include any document format that Tika supports.

Configuring Passage Detection with Reader LLM

SWIRL AI Search, Enterprise Edition, includes passage detection in the Reader LLM, which enhances RAG accuracy by identifying relevant sections of text.

Running Passage Detection Locally

Start the passage detection service using Docker:

docker run -p 7029:7029 -e SWIRL_TEXT_SERVICE_PORT=7029 swirlai/swirl-integrations:topic-text-matcher

Configuration Options

The following environment variables allow customization of Reader LLM and RAG settings:

Variable	Description	Example
SWIRL_TEXT_SUMMARIZATION_URL	URL where the passage detection service is running	http://localhost:7029/
SWIRL_TEXT_SUMMARIZATION_TIMEOUT	Maximum response wait time for RAG	60s
SWIRL_TEXT_SUMMARIZATION_MAX_SIZE	Maximum text block size sent for summarization	4K
SWIRL_TEXT_SUMMARIZATION_TRUNCATION	If true, only text containing summarization tags is included in the RAG prompt	true
SWIRL_RAG_MODEL	ChatGPT model identifier used for RAG	"gpt-4"
SWIRL_RAG_TOK_MAX	Maximum number of tokens sent to ChatGPT	4000
SWIRL_RAG_MAX_TO_CONSIDER	Maximum search results considered for RAG	10
SWIRL_RAG_DISTRIBUTION_STRATEGY	Defines how search results are selected for RAG: Distributed, RoundRobin, or Sorted	RoundRobin

About SWIRL_RAG_DISTRIBUTION_STRATEGY

If set to Distributed, and the number of documents is less than SWIRL_RAG_MAX_TO_CONSIDER, SWIRL backfills results by iterating through the next available results.

Example .env Configuration

SWIRL_TEXT_SUMMARIZATION_URL='http://localhost:7029/'
SWIRL_TEXT_SUMMARIZATION_TRUNCATION=True
SWIRL_RAG_DISTRIBUTION_STRATEGY='RoundRobin'
TIKA_SERVER_ENDPOINT='http://localhost:9998/'

This configuration ensures Apache Tika and Reader LLM passage detection are correctly integrated into SWIRL AI Search.

Text Summarization

When SWIRL_TEXT_SUMMARIZATION_URL is set to a Text Analyzer URL, SWIRL sends text to the Text Analyzer before further RAG processing.
This allows SWIRL to tag important parts of the text that are most relevant to the query before they are included in the ChatGPT prompt.

Example: Tagged Text in a Prompt

--- Content Details ---
Type: Web Page
Domain: swirl.today
Query Terms: 'Swirl'
Important: Text between <SW-IMPORTANT> and </SW-IMPORTANT> is most pertinent to the query.

--- Content ---
<SW-IMPORTANT>WHO IS SWIRL? </SW-IMPORTANT>
<SW-IMPORTANT>Getting to know Swirl Swirl is a powerful solution for identifying and using information. </SW-IMPORTANT>
<SW-IMPORTANT>Swirl was launched in 2022 and operates under the Apache 2.0 license. </SW-IMPORTANT>
<SW-IMPORTANT>At Swirl we follow an iterative approach to software development adhering to the principles of agile methodology. </SW-IMPORTANT>
We believe in delivering high-quality releases through each stage of our development lifecycle.

Text Truncation

When text truncation is enabled, only text that contains at least one important tagged section (as shown above) is included in the ChatGPT prompt.

Enabling Text Truncation

To activate this feature, ensure both conditions are met:

1. SWIRL_TEXT_SUMMARIZATION_URL is set to a valid Text Analyzer URL.
2. SWIRL_TEXT_SUMMARIZATION_TRUNCATION is set to true.

Log Entries for Truncated Content

When a text chunk is excluded due to summarization truncation, logs will show entries like this:

2023-10-19 09:34:01,828 INFO     RAG: url:https://www.wendoverart.com/wtfh0301 problem:RAG Chunk not added for 'Swirl' : SUMMARIZATION TRUNCATION

This configuration ensures that only the most relevant content is included in RAG, improving accuracy and efficiency.