To report a bug, please post an issue in our GitHub detailing your logs with:

• Your Operating System (Mac, Windows or Linux)

• The version of your Operating System

• The version of Datashare

• Screenshots of your issue

• A description of your issue

If, for confidentiality reasons, you don't want to open an issue on Github, please write to .

In local mode, Datashare provides a self-contained software application that users can install and run on their own local machines.

The software allows users to search into their documents within their own local environment, without relying on external servers or cloud infrastructure.

This mode offers enhanced data privacy and control, as the datasets and analysis remain entirely within the user's own infrastructure.

Web modes

There are two modes:

In local mode and embedded mode, Datashare provides a self-contained software application that users can install and run on their own local machines. The software allows users to search into their documents within their own local environments, without relying on external servers or cloud infrastructure. This mode offers enhanced data privacy and control, as the datasets and analysis remain entirely within the user's own infrastructure.

In server mode, Datashare operates as a centralized server-based system. Users can access to the platform through a web interface, and the documents are stored and processed on Datashare's servers. This mode offers the advantage of easy accessibility from anywhere with an internet connection, as users can log in to the platform remotely. It also facilitate seamless collaboration among users, as all the documents and analysis are centralized.

Comparaison between modes

The running modes offer advantages and limitations. This matrix summarizes the differences:

When running Datashare in local mode, users can choose to use embedded services (like ElasticSearch, SQLITE, in-memory key/value store) on the same JVM than Datashare. This variant of the local mode is called "embedded mode" and allows user to run Datashare without having to setup any additional software. The embedded mode is used by default.

CLI mode

In cli mode, Datashare starts without a web server and allows user to perform tasks over their documents. This mode can be used in conjunction with both local and server modes, while allowing users to distribute heavy tasks between several servers.

If you want to learn more about which tasks you can execute in this mode, checkout the .

Daemon modes

Those modes are intended to be used for action that requires to "wait" for pendings tasks.

In batch download mode, the daemon waits for a user to request a batch download of documents. When a request is received, the daemon starts a task to download the document matching the user search, and bundle them into a zip file.

In batch search mode, the daemon waits for a user to request a batch search of documents. To create a batch search, users must go through the dedicated form on Datashare where they can upload a list of search terms (in CSV format). The daemon will then start a task to search all matching documents and store every occurrences in the database.

How to change modes

Datashare is shipped as a single executable, with all modes available. As previously mentioned, the default mode is the embedded mode. Yet when starting Datashare in command line, you can explicitly specify the running mode. For instance on Ubuntu/Debian:

Once Datashare is , go to 'Finder' > 'Applications', and double-click on 'Datashare':

A Terminal window called 'Datashare.command' opens and describes the technical operations going on during the opening:

⇒ Important: Keep this Terminal window open as long as you use Datashare.

Once the process is done, Datashare should now automatically open in your default internet browser. If it doesn’t, type '

The CLI stages are primarily intented to be run for an instance of Datashare that uses non-embedded resources (ElasticSearch, database, key/value memory store). This allows you to distribute heavy tasks between servers.

1. SCAN

This is the first step to add documents to Datashare from the command-line. The SCAN stage allows you to queue all the files that need to be indexed (next step). Once this task is done, you can move to the next step. This stage cannot be distributed.

datashare --mode CLI \  
  # Select the SCAN stage
  --stage SCAN \
  # Where the document are located
  --dataDir /path/to/documents \
  # Store the queued files in Redis
  --dataBusType REDIS \
  # URI of Redis 
  --redisAddress redis://redis:6379

2. INDEX

The INDEX stage is probably the most important (and heavy!) one. It pulls documents to index from the queue created in the previous step, then use a combination of and to extract text, metadata and OCR images. The result documents are stored in ElasticSearch. The queue used to store documents to index is a "blocking list", meaning that only one client can pull a concurrent value at the time. This allows users to distribute this command on several servers.

3. NLP

Once a document is available for search (stored in ElasticSearch), you can use the NLP stage to extract named entities from the text. This process will not only create named entity mentions in ElasticSearch, it will also mark every analyzed document with the corresponding NLP pipeline (CORENLP by default). In other words, the process is idempotent and can be parallelized as well on several servers.

You can now .

What is Datashare?

Welcome to Datashare - a self-hosted documents search software. It is a free and open-source software developed by the International Consortium of Investigative Journalists (ICIJ). Initially created to combine multiple named-entity recognition pipelines, this tool is now a fully-featured search interface to dig into your documents.

With the help of several open-source tools (Extract, , , , , , and more), Datashare can be used on one single personal computer, as well as on 100 interconnected servers.

Who uses it?

Datashare is developed by the ICIJ, a collective of investigative journalists. Datashare is built at the top of technologies and methods already tested with investigations like the or the .

Seeing the growing interest for ICIJ's technology, we decided to open source this key component of our investigations so a single journalist as well as big media organizations could use it for their own documents.

Datashare is free so anyone can use it and find is useful.

Curious to know more about how we use Datashare?

Where can I see Datashare in action?

We setup a with a small set of documents from the investigation (2014). When using this instance, you will be assigned a temporary user which can star, tag, recommend and explore documents.

Can I run Datashare on my server?

Datashare was also built to run on a server. This is how we use it for our collaborative projects. Please refer to to know how it works.

Can I customize Datashare?

When building Datashare, one of our first decisions was to use to create an index of documents. It would be fair to describe Datashare as a nice looking web interface for Elasticsearch. We want our search platform to be user-friendly while keeping all the powerful Elasticsearch features available for advanced users. This way we ensure that Datashare is usable by non tech-savvy reporters, but still robust enough to satisfy data analysts and developers who want to query the index directly .

We implemented the possibility to create plugins, to make this process more accessible. Instead of modifying Datashare directly, you could isolate your code with a specific set of features and then configure Datashare to use it. Each Datashare user can pick the plugins they need or want, and have a fully customized installation of our search platform. Please have a look at the .

In which languages is Datashare available?

This project is currently available in English, French and Spanish. You can help improve and complete translations on .

The installer will take care of checking that your system have all the dependencies to run Datashare. Because this software use (to perform Optical Character Recognition, OCR) and Mac doesn't support them out-of-box, heavy dependencies must be downloaded. If your system have none of those dependencies, the first installation of Datashare can take up to 30 minutes.

The installer will set up:

• Xcode Command Line Tools (if neither XCode or are installed)

• Homebrew

You can now .

Currently, only a .deb package for Debian/Ubuntu is provided.

If you want to run it with another Linux distribution, you can download the latest version of the Datashare jar here: https://github.com/ICIJ/datashare/releases/latest

And adapt the following launch script to your environment: https://github.com/ICIJ/datashare/blob/master/datashare-dist/src/main/deb/bin/datashare.

You can now .

Open the Windows main menu at the left of the bar at the bottom of your computer screen and click on 'Datashare'. (The numbers after 'Datashare' just indicate which version of Datashare you installed.)

A window called 'Terminal' will have opened, showing the progress of opening Datashare. Do not close this black window as long as you use Datashare.

Keep this Terminal window open as long as you use Datashare.

Datashare should now automatically open in your default internet browser. If it doesn’t, type 'localhost:8080' in your browser.

Datashare must be accessed from your internet browser (Firefox, Chome, etc), even though it works offline without Internet connection (see FAQ: ).

You can now .

You can now .

You can now .

Start Datashare by launching it from the command-line:

Datashare should now automatically open in your default internet browser. If it doesn’t, type '' in your browser.

Datashare must be accessed from your internet browser (Firefox, Chome, etc), even though it works offline without Internet connection (see: ).

It's now time to .

Plugins are front-end modules to add new features in Datashare's user interface.

Extensions are back-end modules to add new features to store and manipulate data with Datashare.

Add plugins to Datashare's front-end

Prerequisites

Datashare platform is designed to function effectively by utilizing a combination of various services. To streamline the development and deployment workflows, Datashare relies on the use of Docker containers. Docker provides a lightweight and efficient way to package and distribute software applications, making it easier to manage dependencies and ensure consistency across different environments.

To be able to perform OCR, Datashare uses an open source technology called Apache Tesseract. When Tesseract extracts text from images, it uses 'language packages' especially trained for each specific languages. Unfortunately, those packages can be heavy and to ensure a lightweight installation of Datashare, the installer doesn't use them all by default. In the case Datashare informs you of a missing package, this guide explains you how to manually install it on your system.

Install packages on Linux

To add OCR languages on Linux, simply use the following command:

Install the Neo4j plugin

Install the Neo4j plugin following these instructions.

Configure the Neo4j plugin

1. At the bottom of the menu, click on the 'Settings' icon:

2. Make sure the following settings are properly set:

• Neo4j Host should be localhost or the address where your Neo4j instance is running

• Neo4j Port should be the port where your Neo4j instance is running (7687 by default)

3. When running Neo4j Community Edition, set the Neo4j Single Project value. In community edition, the Neo4j DBMS is restricted to a single database. Since Datashare supports multiple projects, you must set the Neo4j Single Project with the name of the project which will use Neo4j plugin. Other projects won't be able to use the Neo4j plugin.

4. Restart Datashare to apply the changes. Check how for , or .

5. Go to 'Projects' > your project's page > the Graph tab. You should see the Neo4j widget. After a little while, its status should be RUNNING:

You can now .

In server mode, Datashare operates as a centralized server-based system. Users can access the platform through a web interface, and the documents are stored and processed on Datashare's servers.

This mode offers the advantage of easy accessibility from anywhere with an internet connection, as users can log in to the platform remotely. It also facilitate seamless collaboration among users, as all the documents and analysis are centralized.

Launch configuration

Datashare is launched with --mode SERVER and you have to provide:

• The external elasticsearch index address elasticsearchAddress

• A Redis store address redisAddress

• A Redis data bus address messageBusAddress

Example:

This document assumes that you have installed Datashare in server mode within Docker.

In server mode, it's important to understand that Datashare does not provide an interface to add documents. As there is no build-in roles and permission in Datashare's data model, we have no way to differentiate users to offer admin additional tools.

This is likely to be changed in the near future, but in the meantime, you can still add documents to Datashare using the command-line interface.

Here is a simple command to scan a directory and index its files:

What's happening here:

• Datashare starts in "CLI"

• We ask to process both SCAN and INDEX at the same time

• The SCAN stage feeds a queue in memory with file to add

• The INDEX stage pulls files from the queue to add them to ElasticSearch

• We tell Datashare to use the elasticsearch service

• Files to add are located in /home/datashare/Datashare/ which is a directory mounted from the host machine

Alternatively, you can do this in two separated phases, as long as you tell Datashare to store the queue in a shared resource. Here, we use the Redis:

Once the operation is done, we can easily check the content of queue created by Datashare in Redis. In this example we only display the 20 first files in the datashare:queue:

The INDEX can now be executed in the same container:

Once the indexing is done, Datashare will exit gracefully and your document will already be visible on Datashare.

Sometimes you will face the case where you have an existing index, and you want to index additional documents inside your working directory without processing every document again. It can be done in two steps :

• Scan the existing ElasticSearch index and gather document paths to store it inside a report queue

• Scan and index (with OCR) the documents in the directory, thanks to the previous report queue, it will skip the paths inside of it

Prerequisites

Get Neo4j up and running

Follow the instructions of the to get Neo4j up and running.

We recommend using a recent release of Datashare (>= 14.0.0) to use this feature, click on the 'Other platforms and versions' button when downloading to access versions if necessary.

Add entities

If it's not done yet and extract names of people, organizations and locations as well as email addresses.

If your project contains emails, make sure to also extract email addresses.

Next step

You can now .

This document assumes that you have installed Datashare in server mode within Docker and already added documents to Datashare.

In server mode, it's important to understand that Datashare does not provide an interface to add documents. As there is no build-in roles and permission in Datashare's data model, we have no way to differentiate users to offer admin additional tools.

This is likely to be changed in the near future, but in the meantime, you can extract named entities using the command-line interface.

Datashare has the ability to detect email addresses, name of people, organizations and locations. This process use a Natural Language Processing (NLP) pipeline called CORENLP. Once your documents have been indexed in Datashare, you can perform the named entities extraction in the same fashion as the previous CLI's stages:

What's happening here:

• Datashare starts in "CLI"

• We ask to process the NLP

• We tell Datashare to use the elasticsearch service

Datashare will use the output queue from the previous INDEX stage (by default extract:queue:nlp in Redis) that contains all the document ids to be analyzed.

The first time you run this command you will have to wait a little bit because Datashare need to download CORENLP's models which can be big.

You can also use chain the 3 stages altogether:

As for the previous you may want to restore the output queue from the INDEX stage. You can do:

The added ENQUEUEIDX stage will read Elasticsearch index, find all documents that have not already been analyzed by the CORENLP NER pipeline, and put the IDs of those documents into the extract:queue:nlp queue.

Prerequisites

Datashare platform is designed to function effectively by utilizing a combination of various services. To streamline the development and deployment workflows, Datashare relies on the use of Docker containers. Docker provides a lightweight and efficient way to package and distribute software applications, making it easier to manage dependencies and ensure consistency across different environments.

Read more about how to install Docker on your system.

Starting Datashare with multiple containers

Within Datashare, Docker Compose can play a significant role in enabling the setup of separated and persistent services for essential components. By utilizing Docker Compose, you can define and manage multiple containers as part of a unified service. This allows for seamless orchestration and deployment of interconnected services, each serving a specific purpose within the Datashare ecosystem.

Specifically, Docker Compose allows you to configure and launch separate containers for PostgreSQL, Elasticsearch, and Redis. These containers can be set up in a way that ensures their data is persistently stored, meaning that any information or changes made to the database, search index, or key-value store will be retained even if the containers are restarted or redeployed.

This separation of services using Docker Compose provides several advantages. It enhances modularity, scalability, and maintainability within the Datashare platform. It allows for independent management and scaling of each service, facilitating efficient resource utilization and enabling seamless upgrades or replacements of individual components as needed.

To start Datashare in server mode with , you can use the following docker-compose.yml file for version 20.1.4 (check latest version on ):

Open a terminal or command prompt and navigate to the directory where you saved the docker-compose.yml file. Then run the following command to start the Datashare service:

The -d flag runs the containers in detached mode, allowing them to run in the background.

Docker Compose will pull the necessary Docker images (if not already present) and start the containers defined in the YAML file. Datashare will take a few seconds to start. You can check the progression of this opperation with:

Once the containers are up and running, you can access the Datashare service by opening a web browser and entering http://localhost:8080. This assumes that the default port mapping of 8080:8080 is used for the Datashare container in the YAML file.

To stop the Datashare service and remove the containers, you can run the following command in the same directory where the docker-compose.yml file is located:

This will stop and remove the containers, freeing up system resources.

Add documents to Datashare

If you reach that point, Datashare is up and running but you will discover very quickly that no documents is available in the search results. Next step: .

Extract named entities

Datashare has the ability to detect email addresses, name of people, organizations and locations. You must perform the named entities extraction in the same fashion than the previous commands. Final step: .

You can have a dummy authentication that always accepts basic auth. So you should see this popup:

But then whatever user or password you type, it will enter Datashare.

Example

Basic authentication is a simple protocol that uses the HTTP headers and the browser to authenticate users. User credentials are sent to the server in the header Authorization with user:password base64 encoded:

It is secure as long as the communication to the server is encrypted (with SSL for example).

On the server side, you have to provide a database user inventory. You can launch datashare first with the full database URL, then Datashare will automatically migrate your database schema. Datashare supports SQLite and PostgreSQL as back-end databases. SQLite is not recommended for a multi-user server because it cannot be multithreaded, so it will introduce contention on users' DB SQL requests.

Then you have to provision users. The passwords are sha256 hex encoded (for example with bash):

Authentication with Datashare in server mode is the most impacting choice that has to be made. It can be one of the followings:

• Basic authentication with credentials stored in database (PostgreSQL)

• Basic authentication with credentials stored in Redis

Create the graph

1. Go to 'All projects' and click on your project's name:

Install the Neo4j plugin

Install the Neo4j plugin using the Datashare CLI so that users can access it from the frontend:

Installing the plugin installs the datashare-plugin-neo4j-graph-widget plugin inside /home/datashare/plugings and will also install the datashare-extension-neo4j backend extension inside

Basic authentication is a simple protocol that uses the HTTP headers and the browser to authenticate users. User credentials are sent to the server in the header Authorization with user:password base64 encoded:

It is secure as long as the communication to the server is encrypted (with SSL for example).

On the server side, you have to provide a user store for Datashare. For now we are using a Redis data store.

So you have to provision users. The passwords are sha256 hex encoded. For example using bash

Run the Neo4j extension CLI

The Neo4j related features are added to the DatashareCLI through the extension mechanism. In order to run the extended CLI, the Java CLASSPATH must be extended with the path of the datashare-extension-neo4j jar. By default, this jar is located in /home/.local/share/datashare/extensions/*, so the CLI will be run as following:

Create the graph

In order to create the graph, run the --fullImport command for your project:

The CLI will display the import task progress and log import related information.

Update the graph

When new documents or entities are added or modified inside Datashare, you will need to update the Neo4j graph to reflect these changes.

To update the graph, you can just re-run the full export:

The update will always add missing nodes and relationships, update existing ones if they were modified, but will never delete graph nodes or relationships.

To detect whether a graph update is needed, go to the 'Projects' page and open your project:

Compare the number of documents and entities found in Datashare in 'Projects' > 'Your project' > 'Insights'...

...with the numbers found in your project in the 'Graph' tab. Run an update in case of mismatch:

The update will always add missing nodes and relationships, update existing ones if they were modified, but will never delete graph nodes or relationships.

You can now using your favorite visualization tool.

Prerequisites

Get Neo4j up and running

Follow the instructions of the to get Neo4j up and running.

We recommend using a recent release of Datashare (>= 14.0.0) to use this feature, click on the 'All platforms and versions' button when downloading to access versions if necessary.

Add entities

If it's not done yet add entities to your project .

If your project contains email documents, make sure to run the EMAIL pipeline together with regular NLP pipeline. To do so add set the follow nlpp flag to --nlpp CORENLP,EMAIL.

Next step

You can now .

Expand the menu to go to 'Projects' > 'All projects':

Search in projects' names using the search bar on the right:

Sort your projects by clicking the top right Settings icon:

In the Page settings, choose a sort by option, change the number of projects per page or the layout:

To explore a project, close the Settings and click on the name of the project:

You can now .

You must have added documents in Datashare before. Check how for Mac, Windows and Linux.

Search bar

Expand the menu to go to 'Search' > 'Documents':

Make room by closing the menu:

Type terms in the search bar and press Enter:

Default operator is OR

If you type several terms separated by space, as the default operator is OR, Datashare will search for all documents containing at least one of the searched terms.

For instance, Datashare finds documents containing either 'ikea' or 'paris' or both terms here:

Linked entities

As you type a term, Datashare suggest linked entities - only if a task to find entities in this project was completed.

Press Esc on your keyboard to close the dropdown or click on one of the entities to replace your term in the search bar:

Search in a field

Search within a specific field only, by using the dropdown 'All fields':

Search breadcrumb

To see your queries in the search breadcrumb, click on the icon on the left of the search bar:

If you'd like to remove all searched terms from the search bar, click 'Clear query':

Results settings

To change the page settings, click the Settings icon on the top right:

You can change Sort by, Documents per page, Layout and also Properties:

Ticking these properties will change which document's metadata are displayed in the results, in the document cards, in all 3 layouts (List, Grid, Table):

You can now make your search more precise .

This is the default authentication mode: if not provided in CLI, it will be selected. With OAuth2 you will need a third-party authorization service. The diagram below describes the workflow:

Example

docker run -ti ICIJ/datashare:version --mode SERVER \
    --oauthClientId 30045255030c6740ce4c95c \
    --oauthClientSecret 10af3d46399a8143179271e6b726aaf63f20604092106 \
    --oauthAuthorizeUrl https://my.oauth-server.org/oauth/authorize \
    --oauthTokenUrl https://my.oauth-server.org/oauth/token \
    --oauthApiUrl https://my.oauth-server.org/api/v1/me.json \
    --oauthCallbackPath /auth/callback

Integration with KeyCloak

We made a small demo to show how it could be setup.

Open the menu > 'Search' > 'Documents' and click the keyboard icon at the bottom of the menu:

It opens a window with the shortcuts for your OS (Mac, Windows, Linux):

Click on 'See all shortcuts' to reach the full page view:

Double quotes for exact phrase

To have all documents mentioning an exact phrase, you can use double quotes. Use straight double quotes ("example"), not curly double quotes (“example”).

"Alicia Martinez’s bank account in Portugal"

Prerequisites

We recommend using a recent release of Datashare (>= 14.0.0) to use this feature. To download a specific version, click on 'All platforms and versions' .

If you are not familiar with graph and Neo4j, take a look at the following resources:

Expand the menu, open 'All projects' and click on the name of the project that you want to explore:

If you'd like to pin this project in the menu for an easy access, click 'Pin to menu':

Your project is now pinned in the menu:

In a project page, in the first tab called 'Insights', you find statistics and a bar chart displaying the

Improving the performance of Datashare involves several techniques and configurations to ensure efficient data processing. Extracting text from multiple file types and images is an heavy process so be aware that even if we take care of getting the best performances possible with and , this process can be expensive. Below are some tips to enhance the speed and performance of your Datashare setup.

Separate Processing Stages

Execute the SCAN and INDEX stages independently to optimize resource allocation and efficiency.

Examples:

Filters

Open 'Filters' on the left of the search bar:

'Indexing dates' arethe dates when the documents were added to Datashare.

Star documents

Datashare was created with scalability in mind which gave ICIJ the ability to index terabytes of documents.

To do so, we used a cluster of dozens of EC2 instances on AWS, running on Ubuntu 16.04 and 18.04. We used c4.8xlarge instances (36 CPUs / 60 GB RAM).

The most complex operation is OCR (we use Apache Tesseract) so if your documents don't contain many images, it might be worth deactivating it (--ocr false).

You need an internet connection to install Datashare.

You also need the internet to find people, organizations and locations in documents the first time you use any new NLP option because the models which find these named entities are downloaded when you ask for finding named entities the first time. Subsequently, you don't need an internet connection to find named entities after.

You don't need internet connection to:

• Add documents to Datashare

• Find named entities (except for the first time you use an NLP options - see above)

• Search and explore documents

• Download documents

This allows you to work safely on your documents. No third-party should be able to intercept your data and files while you're working offline on your computer.

Warning: this requires some technological knowledge.

You can make Datashare follow soft links : add --followSymlinks when Datashare is launched.

If you're on Mac or Windows, you must change the launch script.

If you're on Linux, you can add the option after the Datashare command.

See a document in full-screen view

Open a document in 'Search' > 'Documents' > one document and click the icon with in and out arrows (this applies to the List layout while in Grid and Table layout, the documents always open in full-screen view):

You now see the document in full screen view and can go to the next document in your results by using the pagination carousel on the top of the screen:

Search in a document

• Open a document in 'Search' > 'Documents' > one document

• Stay on the first tab called 'Text'. This tab shows the text as extracted from your document by Datashare.

• Click on the search bar or press Command (⌘) / Control + F

See original document

Go to the 'View' tab to see the original document.

Note: this visualization of the document is available only for some file types: images, PDF, CSV, xlsx and tiff but not other file types like Word documents or e-mails for instance.

Search for attachments and documents in the same folder

Go to the 'Metadata' tab and click on 'X documents in the same folder' or 'Y children documents':

You see the list of documents. To open all the documents in the same folder or all the children documents, click 'Search all' below. There is no 'Search all' button if there is no documents, as for the children documents below:

Explore metadata

Go the 'Metadata' tab to explore all the properties of the document:

If a metadata is interesting to you and you'd like to know if other documents in your project share the same metadata, click the search icon:

You can also copy or pin a metadata.

Entities

In the 'Entities' tab, only if you previously run tasks to in Datashare, you read the name of people, organizations, locations and e-mail adresses, along with the number of their occurrences in the document:

Hover one entity to see a popover with all their mentions in context in the document by clicking on the arrows:

Go to the 'Info' tab to check how the entity was extracted:

You can send an email to datashare@icij.org.

When reporting a bug, please share:

• Your OS (Mac, Windows or Linux) and version

• The problem, with screenshots

You can use Datashare with multiple users accessing a centralized database on a server.

Warning: to put the server mode in place and to maintain it requires some technical knowledge.

Please find the .

In local mode, you cannot remove a single document or a selection of documents from Datashare. But you can remove all your projects and documents from Datashare.

Open the menu and on the bottom of the menu, click the trash icon:

A confirmation window opens. The action cannot be undone. It removes all the projects and their documents from Datashare. Click 'Yes' if you are sure:

For advanced users - if you'd like to do it with the Terminal, here are the instructions:

Yes, you can download a document from Datashare.

Download a document

Open the menu > 'Search' > 'Documents' and click on the download icon on the right of documents' cards:

...or on the top right of an opened document:

In Datashare, for technical reasons, it is not possible to open the 10,000th document.

Example: you search for "Paris", you get 15,634 results. You'd be able to see the first 9,999th results but no more. This also happens if you didn't run any search.

As it is not possible to fix this, here are some tips:

• : use filters to narrow down your results and ensure you have less than 10,000 matching documents