> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Unstructured UI walkthrough
This walkthrough provides you with deep, hands-on experience with the [Unstructured user interface (UI)](/ui/overview). As you follow along, you will learn how to use many of Unstructured's
features for [partitioning](/ui/partitioning), [enriching](/ui/enriching/overview), [chunking](/ui/chunking), [embedding](/ui/embedding), and [structured data extraction](/ui/data-extractor). These features are optimized for turning
your source documents and data into information that is well-tuned for
[retrieval-augmented generation (RAG)](https://unstructured.io/blog/rag-whitepaper),
[agentic AI](https://unstructured.io/problems-we-solve#powering-agentic-ai),
and [model fine-tuning](https://www.geeksforgeeks.org/deep-learning/what-is-fine-tuning/).
This walkthrough uses two sample files to demonstrate how Unstructured identifies and processes content such as image, graphs, complex tables, non-English characters, and handwriting.
These files, which are available for you to download to your local machine, include:
* Wang, Z., Liu, X., & Zhang, M. (2022, November 23).
*Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling*.
arXiv.org. [https://arxiv.org/pdf/2211.12781](https://arxiv.org/pdf/2211.12781). This 12-page PDF file features English and non-English characters, images, graphs, and complex tables.
Throughout this walkthrough, this file's title is shortened to "Chinese Characters" for brevity.
* United States Central Security Service. (2012, January 27). *National Cryptologic Museum Opens New Exhibit on Dr. John Nash*.
United States National Security Agency. [https://courses.csail.mit.edu/6.857/2012/files/H03-Cryptosystem-proposed-by-Nash.pdf](https://courses.csail.mit.edu/6.857/2012/files/H03-Cryptosystem-proposed-by-Nash.pdf).
This PDF file features English handwriting and scanned images of documents.
Throughout this walkthrough, this file's title is shortened to "Nash letters" for brevity.
This walkthrough focuses on local files for ease-of-use demonstration purposes.
This walkthrough does not cover how to use
Unstructured to set up [connectors](/ui/connectors) to do large-scale batch processing of multiple files and semi-structured data that are stored in remote locations.
To learn how to set up connectors and do large-scale batch processing later, see the [next steps](#next-steps) after you finish this walkthrough.
If you are not able to complete any of the following steps, contact Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
*What are these green boxes?*
As you move through this walkthrough, you will notice tips like this one. These tips are designed to help expand
your knowledge about Unstructured as you go. Feel free to skip these tips for now if you are in a hurry. You can always return to them later to learn more.
## Step 1: Sign up and sign in to Unstructured
Let's get started!
1. If you do not already have an Unstructured account, [sign up for free](https://unstructured.io/?modal=try-for-free).
After you sign up, you are automatically signed in to your new Unstructured **Let's Go** account, at [https://platform.unstructured.io](https://platform.unstructured.io).
To sign up for a **Business** account instead, [contact Unstructured Sales](https://unstructured.io/?modal=contact-sales), or [learn more](/ui/overview#how-am-i-billed%3F).
2. If you have an Unstructured **Let's Go**, **Pay-As-You-Go**, or **Business SaaS** account and are not already signed in, sign in to your account at [https://platform.unstructured.io](https://platform.unstructured.io).
For other types of **Business** accounts, see your Unstructured account administrator for sign-in instructions,
or email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
## Step 2: Create a custom workflow
In this step, you create a custom [workflow](/ui/workflows) in your Unstructured account.
Workflows are defined sequences of processes that automate the flow of data from your source documents and data into Unstructured for processing.
Unstructured then sends its processed data over into your destination file storage locations, databases, and vector stores. Your
RAG apps, agents, and models can then use this processed data in those destinations to do things more quickly and accurately such as
[answering users' questions](https://learn.microsoft.com/en-us/azure/developer/ai/advanced-retrieval-augmented-generation),
[automating business processes](https://unstructured.io/problems-we-solve#business-process-automation),
and [expanding your organization's available body of knowledge](http://knowledgemanagement.ie/the-critical-role-of-knowledge-management-as-a-foundation-for-llms-and-ai/).
*Which kinds of sources and destinations does Unstructured support?*
Unstructured can connect to many types of sources and destinations including file storage services such as Amazon S3 and Google Cloud Storage;
databases such as PostgreSQL; and vector storage and database services such as MongoDB Atlas and Pinecone.
See the full list of [supported source and destination connectors](/ui/connectors).
*Which kinds of files does Unstructured support?*
Unstructured can process a wide variety of file types including PDFs, word processing documents, spreadsheets, slide decks, HTML, image files, emails, and more.
See the full list of [supported file types](/ui/supported-file-types).
Let's get going!
1. After you are signed in to your Unstructured account, on the sidebar, click **Workflows**.
*What do the other buttons on the sidebar do?*
* **Start** takes you to the UI home page. The home page features a simple way to process one local file at a time with limited default settings. [Learn how](/welcome#unstructured-ui-quickstart).
* **Connectors** allows you to create and manage your [source](/ui/sources/overview) and
[destination](/ui/destinations/overview) connectors.
* **Jobs** allows you to see the results of your workflows that are run manually (once) and automatically (on a regular time schedule). [Learn more](/ui/jobs).
* **API Keys** allows you to use code to create and manage connectors, workflows, and jobs programmatically instead of by using the UI. [Learn more](/ui/account/api-key-url).
* Your user icon at the bottom of the sidebar allows you to manage your Unstructured account. You can also sign out of your account from here. [Learn more](/ui/account/overview).
2. Click **New Workflow**.
3. With **Build it Myself** already selected, click **Continue**.
*What does **Build it For Me** do?*
The **Build it For Me** option creates an [automatic workflow](/ui/workflows#create-an-automatic-workflow) with limited, sensible default settings to enable you to get good-quality results faster. However,
this option requires that you first have an existing remote source and destination connector to add to the workflow. To speed things up here and keep things simple,
this walkthrough only processes files from your local machine and skips the use of connectors. To learn how to use connectors later,
see the [next steps](#next-steps) at the end of this walkthrough.
4. The workflow designer appears.
*What are all the parts of the workflow designer?*
The middle portion of the workflow designer is the workflow *directed acyclic graph* (DAG), which contains a
collection of *nodes* connected by *edges* that go in only one direction. You can think of the DAG similar to a flowchart
for a process. *Directed* means the arrows show the flow from one step to the next, and *acyclic* means you cannot
follow the arrows backward to get back to the starting point.
The workflow settings pane on the right includes the following tabs:
* The **Details** tab allows you to see when this workflow was created and which jobs were run for this workflow.
* **Schedule** allows you to set up a schedule for this workflow to run automatically (on a regular time schedule).
* **Settings** allows you to specify whether every time this workflow runs, that Unstructured's results will overwrite any previous results in the destination location.
To turn on this behavior, check the **Overwrite existing results** box. To turn it off, uncheck the box. Note that this setting works only
for blob storage destination connectors such as the ones for Amazon S3, Azure Blob Storage, and Google Cloud Storage.
* **FAQ** contains additional information about how to use the workflow designer.
If the workflow settings pane is not visible, click the **Settings** button near the bottom to show it.
There are also buttons near the bottom to undo or redo recent edits to the workflow DAG, zoom in and out of the workflow
designer, re-center the DAG within the designer, and add a new node to the DAG.
To change this workflow's name, click the workflow's name in the title bar, type a new name, and then press **Enter**.
## Step 3: Experiment with partitioning
In this step, you use your new workflow to [partition](/ui/partitioning) the sample PDF files that you downloaded earlier onto your local machine.
Partitioning is the process where Unstructured identifies and extracts content from your source documents and then
outputs this content as a series of contextually-rich [document elements and metadata](/ui/document-elements), which are
well-tuned for RAG, agentic AI, and model fine-tuning. This step
shows how well Unstructured's **VLM** partitioning strategy handles challenging content such as complex tables, multilanguage characters, and handwriting.
1. With the workflow designer active from the previous step, at the bottom of the **Source** node, click **Drop file to test**.
2. Browse to and select the "Chinese Characters" PDF file that you downloaded earlier.
3. In the workflow designer, click the **Partitioner** node and then, in the node's settings pane's **Details** tab, select **VLM**.
*When would I choose **Auto**, **Fast**, **High Res**, or **VLM**?*
* **Auto** is recommended in most cases. It lets Unstructured figure out the best strategy to switch over to for each incoming file (and even for each page if the incoming file is a PDF), so you don't have to!
* **Fast** is only for when you know for certain that none of your files have tables, images, or multilanguage, scanned, or handwritten content in them. It's optimized for partitioning text-only content and is the fastest of all the strategies. It can recognize the text for only a few languages other than English.
* **High Res** is only for when you know for certain that at least one of your files has images or simple tables in them, and that none of your files also have scanned or handwritten content in them. It can recognize the text for more languages than **Fast** but not as many as **VLM**.
* **VLM** is great for any file, but it is best when you know for certain that some of your files have a combination of tables (especially complex ones), images, and multilanguage, scanned, or handwritten content. It's the highest quality but slowest of all the strategies.
4. Under **Select VLM Model**, under **Vertex AI**, select **Gemini 2.0 Flash**.
The list of available models shown is constantly being updated. Your list might also differ from the preceding screenshot, depending on your Unstructured
account type. If **Gemini 2.0 Flash** is not available, choose another available model from the list.
If you have an Unstructured **Business** account and want to add more models to this list, contact your
Unstructured account administrator or Unstructured sales representative, or email Unstructured Support at
[support@unstructured.io](mailto:support@unstructured.io).
*When I choose **VLM**, when would I choose one of these models over another?*
A *vision language model* (VLM) is designed to use sophisticated AI techniques and logic to combine advanced image and text understanding, resulting in more accurate and
contextually-rich output.
As VLMs are constantly being released and improved, Unstructured is always adding to and updating its list of supported VLMs.
If you aren't getting consistent results with one VLM for a particular set of files, switching over to another one might improve your results, depending on that VLM's capabilities and the sample data that it was trained on.
5. Click **Test**.
6. The PDF file appears in a pane on the left side of the screen, and Unstructured's output appears in a **Test output** pane on the right side of the screen.
*What am I looking at in the output here?*
* Unstructured outputs its results in industry-standard [JSON](https://www.json.org) format, which is ideal for RAG, agentic AI, and model fine-tuning.
* Each object in the JSON is called a [document element](/ui/document-elements) and contains a `text` representation of the content that Unstructured detected for the particular portion of the document that was analyzed.
* The `type` is the kind of document element that Unstructured categorizes it as, such as whether it is a title (`Title`), a table (`Table`), an image (`Image`), a series of well-formulated sentences
(`NarrativeText`), some kind of free text (`UncategorizedText`), a part of a list (`ListItem`), and so on. [Learn more](/ui/document-elements#element-type).
* The `element_id` is a unique identifier that Unstructured generates to refer to each document element. [Learn more](/ui/document-elements#element-id).
* `metadata` contains supporting details about each document element, such as the page number it occurred on, the file it occurred in, and so on. [Learn more](/ui/document-elements#metadata).
*What else can I do here?*
* You can scroll through the original file on the left or, where supported for a given file type, click the up and down arrows to page through the file one page at a time.
* You can scroll through Unstructured's JSON output on the right, and you can click **Search JSON** to search for specific text in the JSON output. You will do this next.
* **Download Full JSON** allows you to download the full output to your local machine as a JSON file.
* **View JSON at this step** allows you to view the JSON output at each step in the workflow as it was further processed. There's only one step right now (the **Partitioner** step),
but as you add more nodes to the workflow DAG, this can be a useful tool to see how the JSON output changes along the way.
* The close (**X**) button returns you to the workflow designer.
7. Notice the following in the JSON output, which you can get to by clicking **Search JSON** above the output:
* The Chinese characters on page 1. Search for the text `verbs. The characters`. Notice how the Chinese characters are output. We'll see accuracy improvements to this output later in Step 4 in the enrichments portion of this walkthrough.
* The tables on pages 1, 6, 7, 8, 9, and 12. Search for the text `"Table"` (including the quotation marks) to see how the VLM interprets the various tables. We'll see changes to these elements' `text` and `metadata.text_as_html` contents later in Step 4 in the enrichments portion of this walkthrough.
* The images on pages 3, 7, and 8. Search for the text `"Image"` (including the quotation marks) to see how the VLM interprets the various images. We'll see changes to these elements' `text` contents later in Step 4 in the enrichments portion of this walkthrough.
If **Search JSON** is not clickable, this is probably because the JSON output is too large for the online viewer.
Click **Download full JSON** and open the downloaded JSON file in an offline text editor (such as Visual Studio Code).
[Learn more](/examplecode/tools/json-tools#visual-studio-code).
8. Now try looking at the "Nash letters" PDF file's output. To do this:
a. Click the close (**X**) button above the output on the right side of the screen.
b. At the bottom of the **Source** node, click the existing PDF's file name.
c. Browse to and select the "Nash letters" file that you downloaded earlier to your local machine.
d. Click **Test**.
9. Notice the following in the JSON output:
* The handwriting on page 3. Search for the text `I have written RAND`. Notice how well the handwriting is recognized.
* The mimeograph on page 18. Search for the text `The system which`. Notice how well the mimeographed content is recognized.
10. When you are done, be sure to click the close (**X**) button above the output on the right side of the screen, to return to
the workflow designer for the next step.
## Step 4: Experiment with enriching
In this step, you add several [enrichments](/ui/enriching/overview) to your workflow, such as generating summary descriptions of detected images and tables,
HTML representations of detected tables, and detected entities (such as people and organizations) and the inferred relationships among these entities.
1. With the workflow designer active from the previous step, change the **Partitioner** node to use **High Res**.
2. Between the **Partitioner** and **Destination** nodes, click the add (**+**) icon, and then click **Enrich > Image Description**.
3. In the node's settings pane's **Details** tab, click:
* Any available choice for **Provider** (for example, **Anthropic**).
* Any available choice for **Model** (for example, **Claude Sonnet 4.5** if you chose **Anthropic** for **Provider**).
The lists of available models for these enrichments are constantly being updated. Your lists might also differ from these instructions, depending on your Unstructured
account type. If the suggested model for an enrichment is not available, choose another available model from the list.
If you have an Unstructured **Business** account and want to add more models to these lists, contact your
Unstructured account administrator or Unstructured sales representative, or email Unstructured Support at
[support@unstructured.io](mailto:support@unstructured.io).
The image description enrichment generates a summary description of each detected image. This can help you to more quickly and easily understand
what each image is all about without having to stop to manually visualize and interpret the image's content yourself. This also provides additional helpful context about the image for your RAG apps, agents, and models. [Learn more](/ui/enriching/image-descriptions).
4. Repeat this process to add four more nodes between the **Partitioner** and **Destination** nodes, as follows:
a. Add a table description enrichment (click the add (**+**) icon to the right of the preceding node, and then click **Enrich > Table Description**).
In the node's settings pane's **Details** tab, click:
* Any available choice for **Provider** (for example, **Anthropic**).
* Any available choice for **Model** (for example, **Claude Sonnet 4.5** if you chose **Anthropic** for **Provider**).
The table description enrichment generates a summary description of each detected table. This can help you to more quickly and easily understand
what each table is all about without having to stop to manually read through the table's content yourself. This also provides additional helpful context about the table for your RAG apps, agents, and models. [Learn more](/ui/enriching/table-descriptions).
b. Add a table to HTML enrichment (click the add (**+**) icon to the right of the preceding node, and then click **Enrich > Table to HTML**).
In the node's settings pane's **Details** tab, click:
* If you choose **Standard** for **Mode**, any available choice for **Provider** (for example, **Anthropic**).
* If you choose **Standard** for **Mode**, any available choice for **Model** (for example, **Claude Sonnet 4.5** if you chose **Anthropic** for **Provider**).
* If you choose **Agentic** for **Mode**, a default provider and model are automatically selected for you.
The table to HTML enrichment generates an HTML representation of each detected table. This can help you to more quickly and accurately recreate the table's content elsewhere later as needed. This also provides additional context about the table's structure for your RAG apps, agents, and models.
The **Agentic** mode uses agentic AI to generate the table-to-HTML output, which typically provides more accurate output for complex tables than the **Standard** mode. [Learn more](/ui/enriching/table-to-html).
c. Add a named entity recognition enrichment (click the add (**+**) icon to the right of the preceding node, and then click **Enrich > NER**).
In the node's settings pane's **Details** tab, click:
* Any available choice for **Provider** (for example, **Anthropic**).
* Any available choice for **Model** (for example, **Claude Sonnet 4.5** if you chose **Anthropic** for **Provider**).
The named entity recognition (NER) enrichment generates a list of detected entities (such as people and organizations) and the inferred relationships among these entities. This provides additional context about these entities' types and their relationships for your graph databases, RAG apps, agents, and models. [Learn more](/ui/enriching/ner).
d. Add a generative OCR enrichment (click the add (**+**) icon to the right of the preceding node, and then click **Enrich > Generative OCR**).
In the node's settings pane's **Details** tab, click:
* Any available choice for **Provider** (for example, **Anthropic**).
* Any available choice for **Model** (for example, **Claude Sonnet 4.5** if you chose **Anthropic** for **Provider**).
The generative OCR enrichment improves, as needed, the accuracy of text blocks that Unstructured initially processed during its partitioning phase. [Learn more](/ui/enriching/generative-ocr).
Generative OCR does not process any text blocks by default. You must also explicitly specify which document element
types containing text that you want generative OCR to process. To do this:
1. Click the **Partitioner** node.
2. In the node's settings pane, scroll down to and then click a blank area inside of the **Extract Image Block Types** list.
3. Select each [document element type](/ui/document-elements#element-type) that you want generative OCR to process. For this
walkthrough, select only **NarrativeText**.
Generative OCR does not process the text of any `Image` or `Table` elements if they have already been processed by
[image description](#image-description-task) or [table description](#table-description-task) enrichments, respectively. Do
not remove the **Image** or **Table** document elements types from this **Extract Image Block Types** list, or else
the image description and table description enrichments in your workflow might produce unexpected results or might not work at all.
The workflow designer should now look similar to this:
5. Change the **Source** node to use the "Chinese Characters" PDF file, and then click **Test**.
6. In the **Test output** pane, make sure that **Generative OCR (6 of 6)** is showing. If not, click the right arrow (**>**) until **Generative OCR (6 of 6)** appears, which will show the output from the last node in the workflow.
7. Some interesting portions of the output include the following:
* The Chinese characters on page 1. Search again for the text `verbs. The characters`. Notice how the accuracy of the Chinese character output is improved.
* The images on pages 3, 7, and 8. Search again for the text `"Image"` (including the quotation marks). Notice the summary description for each image.
* The tables on pages 1, 6, 7, 8, 9, and 12. Search again for the text `"Table"` (including the quotation marks). Notice the summary description for each of these tables.
Also notice the `text_as_html` field for each of these tables.
* The identified entities and inferred relationships among them. Search for the text `Zhijun Wang`. Of the eight instances of this name, notice
the author's identification as a `PERSON` three times, the author's `published` relationship twice, and the author's `affiliated_with` relationship twice.
8. When you are done, be sure to click the close (**X**) button above the output on the right side of the screen, to return to
the workflow designer for the next step.
## Step 5: Experiment with chunking
In this step, you apply [chunking](/ui/chunking) to your workflow. Chunking is the process where Unstructured rearranges
the resulting document elements' `text` content into manageable "chunks" to stay within the limits of an AI model and to improve retrieval precision.
*What kind of chunking strategy should I use, and how big should my chunks be?*
Unfortunately, there is no one-size-fits-all answer to this question. However, there are some general considerations and guidelines that can help you to determine
the best chunking strategy and chunk size for your specific use case. Be sure of course to also consult the documentation for your target AI model and downstream application toolsets.
Is your content primarily organized by title, by page, by interrelated subject matter, or none of these? This can help you determine whether a
by-title, by-page, by-similarity, or basic (by-character) chunking strategy is best. (You'll experiment with each of these strategies here later.)
If your chunks are too small, they might lose necessary context, leading to the model providing inaccurate, irrelevant, or hallucinated results.
On the other hand, if your chunks are too large, the model can struggle with the sheer volume of information, leading to information overload, diluted meaning, and potentially higher processing costs.
You should aim to find a balance between chunks that are big enough to contain meaningful information, while small enough to enable performant applications and low latency responses.
For example, smaller chunks of 128 or 256 tokens might be sufficient for capturing more granular semantic information, while larger chunks of 512 or 1024 tokens might be better for retaining more context.
It's important here to note that *tokens* and *characters* are not the same thing! In terms of
characters, for English text, a common approximation is 1 token being equal to about 3 or 4 characters or three-quarters of a word. Many AI model providers publish their own token-to-character calculators online that you can use for estimation purposes.
You should experiment with a variety of chunk sizes, taking into account the kinds of content, the length and complexity of user queries and agent tasks,
the intended end use, and of course the limits of the models you are using. Try different chunking strategies and sizes with your models and evaluate the results for yourself.
1. With the workflow designer active from the previous step, just before the **Destination** node, click the add (**+**) icon, and then click **Enrich > Chunker**.
2. In the node's settings pane's **Details** tab, select **Chunk by Character**.
3. Under **Chunk by Character**, specify the following settings:
* Check the box labelled **Include Original Elements**.
* Set **Max Characters** to **500**.
* Set **New After N Characters** to **400**.
* Set **Overlap** to **50**.
* Leave **Contextual Chunking** turned off and **Overlap All** unchecked.
*What do each of these chunking settings do?*
* **Contextual Chunking** prepends chunk-specific explanatory context to each chunk, which has been shown to yield significant improvements in downstream retrieval accuracy. [Learn more](/ui/chunking#contextual-chunking).
* **Include Original Elements** outputs into each chunk's `metadata` field's `orig_elements` value the elements that were used to form that particular chunk. [Learn more](/ui/chunking#include-original-elements-setting).
* **Max Characters** is the "hard" or maximum number of characters that any one chunk can contain. Unstructured cannot exceed this number when forming chunks. [Learn more](/ui/chunking#max-characters-setting).
* **New After N Characters**: is the "soft" or approximate number of characters that any one chunk can contain. Unstructured can exceed this number if needed when forming chunks (but still cannot exceed the **Max Characters** setting). [Learn more](/ui/chunking#new-after-n-characters-setting).
* **Overlap**, when applied (see **Overlap All**), prepends to the current chunk the specified number of characters from the previous chunk, which can help provide additional context about this chunk relative to the previous chunk. [Learn more](/ui/chunking#overlap-setting)
* **Overlap All** applies the **Overlap** setting (if greater than zero) to all chunks. Otherwise, unchecking this box means that the **Overlap** setting (if greater than zero) is applied only in edge cases where "normal" chunks cannot be formed by combining whole elements. Check this box with caution as it can introduce noise into otherwise clean semantic units. [Learn more](/ui/chunking#overlap-all-setting).
4. With the "Chinese Characters" PDF file still selected in the **Source** node, click **Test**.
5. In the **Test output** pane, make sure that **Chunker (7 of 7)** is showing. If not, click the right arrow (**>**) until **Chunker (7 of 7)** appears, which will show the output from the last node in the workflow.
6. To explore the chunker's results, search for the text `"CompositeElement"` (including the quotation marks).
*In the chunked output, where did all of the document elements I saw before, such as `Title`, `Image`, and `Table`, go?*
During chunking, the document elements that were generated during partitioning are now chunked. Because some of these document elements can be split into multiple chunks
or combined with other chunks, these chunked document elements are now of type `CompositeElement` and `TableChunk`.
You can have Unstructured also output the original document elements that
these chunks were derived from by putting them into each chunk's `metadata`. To have Unstructured do this, use the **Include Original Elements** setting, as described in the preceding tip.
7. Optionally, you can try running this workflow again with the **Chunk by Title** strategy, as follows:
a. Click the close (**X**) button above the output on the right side of the screen.
b. In the workflow designer, click the **Chunker** node and then, in the node's settings pane's **Details** tab, select **Chunk by Title**.
c. Under **Chunk by Title**, specify the following settings:
* Check the box labelled **Include Original Elements**.
* Set **Max Characters** to **500**.
* Set **New After N Characters** to **400**.
* Set **Overlap** to **50**.
* Leave **Contextual Chunking** turned off, leave **Combine Text Under N Characters** blank, and leave **Multipage Sections** and **Overlap All** unchecked.
*What do each of the chunking settings here that were not already described in the preceding tip do?*
* **Combine Text Under N Characters** combines elements from a section into a chunk until a section reaches a length of this many characters. [Learn more](/ui/chunking#combine-text-under-n-characters-setting).
* **Multipage Sections** when checked, allows sections to span multiple pages. [Learn more](/ui/chunking#multipage-sections-setting).
d. Click **Test**.
e. In the **Test output** pane, make sure that **Chunker (7 of 7)** is showing. If not, click the right arrow (**>**) until **Chunker (7 of 7)** appears, which will show the output from the last node in the workflow.
f. To explore the chunker's results, search for the text `"CompositeElement"` (including the quotation marks). Notice that the lengths of some of the chunks that immediately
precede titles might be shortened due to the presence of the title impacting the chunk's size.
8. Optionally, you can try running this workflow again with the **Chunk by Page** strategy, as follows:
a. Click the close (**X**) button above the output on the right side of the screen.
b. In the workflow designer, click the **Chunker** node and then, in the node's settings pane's **Details** tab, select **Chunk by Page**.
c. Under **Chunk by Page**, specify the following settings:
* Check the box labelled **Include Original Elements**.
* Set **Max Characters** to **500**.
* Set **New After N Characters** to **400**.
* Set **Overlap** to **50**.
* Leave **Contextual Chunking** turned off, and leave **Overlap All** unchecked.
d. Click **Test**.
e. In the **Test output** pane, make sure that **Chunker (7 of 7)** is showing. If not, click the right arrow (**>**) until **Chunker (7 of 7)** appears, which will show the output from the last node in the workflow.
f. To explore the chunker's results, search for the text `"CompositeElement"` (including the quotation marks). Notice that the lengths of some of the chunks that immediately
precede page breaks might be shortened due to the presence of the page break impacting the chunk's size.
9. Optionally, you can try running this workflow again with the **Chunk by Similarity** strategy, as follows:
a. Click the close (**X**) button above the output on the right side of the screen.
b. In the workflow designer, click the **Chunker** node and then, in the node's settings pane's **Details** tab, select **Chunk by Similarity**.
c. Under **Chunk by Similarity**, specify the following settings:
* Check the box labelled **Include Original Elements**.
* Set **Max Characters** to **500**.
* Set **Similarity Threshold** to **0.99**.
* Leave **Contextual Chunking** turned off.
*What does **Similarity Threshold** mean?*
* The **Similarity Threshold** is a number between 0 and 1 exclusive (**0.01** to **0.99** inclusive).
* **0.01** means that any two segments of text that are being compared to each other and are considered least identical in semantic meaning to each other are more likely to be combined into the same chunk together, when such combining must occur.
* **0.99** means that any two segments of text that are being compared to each other and are considered almost identical in semantic meaning to each other are more likely to be combined into the same chunk together, when such combining must occur.
* Numbers toward **0.01** bias toward least-identical semantic matches, while numbers toward **0.99** bias toward near-identical semantic matches.
d. Click **Test**.
e. In the **Test output** pane, make sure that **Chunker (7 of 7)** is showing. If not, click the right arrow (**>**) until **Chunker (7 of 7)** appears, which will show the output from the last node in the workflow.
f. To explore the chunker's results, search for the text `"CompositeElement"` (including the quotation marks). Notice that the lengths of many of the chunks fall well short of the **Max Characters** limit. This is because a similarity threshold
of **0.99** means that only sentences or text segments with a near-perfect semantic match will be grouped together into the same chunk. This is an extremely high threshold, resulting in very short, highly specific chunks of text.
g. If you change **Similarity Threshold** to **0.01** and run the workflow again, searching for the text `"CompositeElement"` (including the quotation marks), many of the chunks will now come closer to the **Max Characters** limit. This is because a similarity threshold
of **0.01** provides an extreme tolerance of differences between pieces of text, grouping almost anything together.
10. When you are done, be sure to click the close (**X**) button above the output on the right side of the screen, to return to
the workflow designer for the next step.
## Step 6 (Optional): Experiment with embedding
In this step, you generate [embeddings](/ui/embedding) for your workflow. Embeddings are vectors of numbers that represent various aspects of the text that is extracted by Unstructured.
These vectors are stored or "embedded" next to the text itself in a vector store or vector database. Chatbots, agents, and other AI solutions can use
these vector embeddings to more efficiently and effectively find, analyze, and use the associated text. These vector embeddings are generated by an
embedding model that is provided by an embedding provider. For the best embedding model to apply to your use case, see the documentation for your target downstream application toolsets.
1. With the workflow designer active from the previous step, just before the **Destination** node, click the add (**+**) icon, and then click **Transform > Embedder**.
2. In the node's settings pane's **Details** tab, under **Select Embedding Model**, for **Azure OpenAI**, select **Text Embedding 3 Small \[dim 1536]**.
The list of available embedding models is constantly being updated. Your list might also be different, depending on your Unstructured
account type. If **Azure OpenAI** and **Text Embedding 3 Small \[dim 1536]** is not available, choose another available model from the list.
If you have an Unstructured **Business** account and want to add more models to this list, contact your
Unstructured account administrator or Unstructured sales representative, or email Unstructured Support at
[support@unstructured.io](mailto:support@unstructured.io).
3. With the "Chinese Characters" PDF file still selected in the **Source** node, click **Test**.
4. In the **Test output** pane, make sure that **Embedder (8 of 8)** is showing. If not, click the right arrow (**>**) until **Embedder (8 of 8)** appears, which will show the output from the last node in the workflow.
5. To explore the embeddings, search for the text `"embeddings"` (including the quotation marks).
*What do all of these numbers mean?*
All by themselves, the numbers in the `embeddings` field of the output have no human-interpretable meaning on their own.
However, when combined with the specific text that these numbers are associated with, and the embedding model's
logic that was used to generate these numbers, the numbers in the `embeddings` field are extremely powerful when leveraged by
downstream chatbots, agents, and other AI solutions.
These numbers typically represent complex, abstract attributes about the text that are known only to the embedding model that generated these numbers. These
attributes can be about the text's overall sentiment, intent, subject, semantic meaning, grammatical function, relationships between words, or any number of other things that the model is good at figuring out. This
is why the embedding model you choose here must be the exact same embedding model that you use in any related chatbot, agent, or other
AI solution that relies on these numbers. Otherwise, the numbers that are generated here will not have the same meaning
downstream as well. Also, the number of *dimensions* (or the number of numbers in the `embeddings` field)
you choose here must also be the exact same number of dimensions downstream as well.
To repeat, the name and number of dimensions
for the embedding model you choose here must be the exact same name and number of dimensions for the embedding model you use in your
related downstream chatbots, agents, and other AI solutions that rely on this particular text and its associated embeddings that were generated here.
6. When you are done, be sure to click the close (**X**) button above the output on the right side of the screen, to return to
the workflow designer so that you can continue designing things later as you see fit.
## Step 7: Experiment with structured data extraction
In this step, you apply custom [structured data extraction](/ui/data-extractor) to your workflow. Structured data extraction is the process where Unstructured
automatically extracts the data from your source documents into a format that you define up front. For example, in addition to Unstructured
partitioning your source documents into elements with types such as `NarrativeText`, `UncategorizedText`, and so on, you can have Unstructured
output key information from the source documents in a custom structured data format, within a `DocumentData` element containing aJSON object with custom fields such as `name`, `address`, `phone`, `email`, and so on.
1. With the workflow designer active from the previous step, just before the **Destination** node, click the add (**+**) icon, and then click **Enrich > Extract**.
2. In the node's settings pane's **Details** tab, under **Provider**, select **Anthropic**. Under **Model**, select **Claude Sonnet 4.5**. This is the model that Unstructured will use to do the structured data extraction.
The list of available models for structured data extraction is constantly being updated. Your list might also be different, depending on your Unstructured
account type. If **Anthropic** and **Claude Sonnet 4.5** is not available, choose another available model from the list.
If you have an Unstructured **Business** account and want to add more models to this list, contact your
Unstructured account administrator or Unstructured sales representative, or email Unstructured Support at
[support@unstructured.io](mailto:support@unstructured.io).
3. Use the visual schema builder to define the schema, based on the following JSON schema:
```json theme={null}
{
"type": "object",
"properties": {
"title": {
"type": "string",
"description": "Full title of the research paper"
},
"authors": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Author's full name"
},
"affiliation": {
"type": "string",
"description": "Author's institutional affiliation"
},
"email": {
"type": "string",
"description": "Author's email address"
}
},
"required": [
"name",
"affiliation",
"email"
],
"additionalProperties": false
},
"description": "List of paper authors with their affiliations"
},
"abstract": {
"type": "string",
"description": "Paper abstract summarizing the research"
},
"introduction": {
"type": "string",
"description": "Introduction section describing the problem and motivation"
},
"methodology": {
"type": "object",
"properties": {
"approach_name": {
"type": "string",
"description": "Name of the proposed method (e.g., StrokeNet)"
},
"description": {
"type": "string",
"description": "Detailed description of the methodology"
},
"key_techniques": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of key techniques used in the approach"
}
},
"required": [
"approach_name",
"description",
"key_techniques"
],
"additionalProperties": false
},
"experiments": {
"type": "object",
"properties": {
"datasets": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Dataset name"
},
"description": {
"type": "string",
"description": "Dataset description"
},
"size": {
"type": "string",
"description": "Dataset size (e.g., number of sentence pairs)"
}
},
"required": [
"name",
"description",
"size"
],
"additionalProperties": false
},
"description": "Datasets used for evaluation"
},
"baselines": {
"type": "array",
"items": {
"type": "string"
},
"description": "Baseline methods compared against"
},
"evaluation_metrics": {
"type": "array",
"items": {
"type": "string"
},
"description": "Metrics used for evaluation"
},
"experimental_setup": {
"type": "string",
"description": "Description of experimental configuration and hyperparameters"
}
},
"required": [
"datasets",
"baselines",
"evaluation_metrics",
"experimental_setup"
],
"additionalProperties": false
},
"results": {
"type": "object",
"properties": {
"main_findings": {
"type": "string",
"description": "Summary of main experimental findings"
},
"performance_improvements": {
"type": "array",
"items": {
"type": "object",
"properties": {
"dataset": {
"type": "string",
"description": "Dataset name"
},
"metric": {
"type": "string",
"description": "Evaluation metric (e.g., BLEU)"
},
"baseline_score": {
"type": "number",
"description": "Baseline method score"
},
"proposed_score": {
"type": "number",
"description": "Proposed method score"
},
"improvement": {
"type": "number",
"description": "Improvement over baseline"
}
},
"required": [
"dataset",
"metric",
"baseline_score",
"proposed_score",
"improvement"
],
"additionalProperties": false
},
"description": "Performance improvements over baselines"
},
"parameter_reduction": {
"type": "string",
"description": "Description of parameter reduction achieved"
}
},
"required": [
"main_findings",
"performance_improvements",
"parameter_reduction"
],
"additionalProperties": false
},
"related_work": {
"type": "string",
"description": "Summary of related work and prior research"
},
"conclusion": {
"type": "string",
"description": "Conclusion section summarizing contributions and findings"
},
"limitations": {
"type": "string",
"description": "Limitations and challenges discussed in the paper"
},
"acknowledgments": {
"type": "string",
"description": "Acknowledgments section"
},
"references": {
"type": "array",
"items": {
"type": "string"
},
"description": "List of cited references"
}
},
"additionalProperties": false,
"required": [
"title",
"authors",
"abstract",
"introduction",
"methodology",
"experiments",
"results",
"related_work",
"conclusion",
"limitations",
"acknowledgments",
"references"
]
}
```
4. With the "Chinese Characters" PDF file still selected in the **Source** node, click **Test**.
5. In the **Test output** pane, make sure that **Extract (9 of 9)** is showing. If not, click the right arrow (**>**) until **Extract (9 of 9)** appears, which will show the output from the last node in the workflow.
6. To explore the structured data extraction, search for the text `"extracted_data"` (including the quotation marks).
7. When you are done, be sure to click the close (**X**) button above the output on the right side of the screen, to return to
the workflow designer so that you can continue designing things later as you see fit.
## Next steps
Congratulations! You now have an Unstructured workflow that partitions, enriches, chunks, embeds, and extracts structured data from your source documents, producing
context-rich data that is ready for retrieval-augmented generation (RAG), agentic AI, and model fine-tuning.
Right now, your workflow only accepts one local file at a time for input. Your workflow also only sends Unstructured's processed data to your screen or to be saved locally as a JSON file.
You can modify your workflow to accept multiple files and data from—and send Unstructured's processed data to—one or more file storage
locations, databases, and vector stores. To learn how to do this, try one or more of the following quickstarts:
* [Remote quickstart](/ui/quickstart#remote-quickstart) - This quickstart shows you how to begin processing files and semi-structured data from remote source locations at scale, instead of just one local file at a time.
* [Dropbox source connector quickstart](/ui/sources/dropbox-source-quickstart) - If you don't have any remote source locations available for Unstructured to connect to, this quickstart shows you how to set up a Dropbox account to store your documents in, and then connect Unstructured to your Dropbox account.
* [Pinecone destination connector quickstart](/ui/destinations/pinecone-destination-quickstart) - If you don't have any remote destination locations available for Unstructured to send its processed data to, this quickstart shows you how to set up a Pinecone account to have Unstructured store its processed data in, and then connect Unstructured to your Pinecone account.
Unstructured also offers an API and SDKs, which allow you to use code to work with Unstructured programmatically instead of only with the UI. For details, see:
* [Unstructured API quickstart](/api-reference/workflow/overview#quickstart) - This quickstart uses the Unstructured API's workflow operations to programmatically create a Dropbox source connector and a Pinecone destination connector in your Unstructured account. You then programmatically add these connectors to a workflow in your Unstructured account, run that workflow as a job, and then explore the job's results.
* [Unstructured Python SDK](/api-reference/workflow/overview#unstructured-python-sdk) - This article provides an overview of the Unstructured Python SDK and how to use it.
* [Unstructured API overview](/api-reference/overview) - This article provides an overview of the Unstructured API and how to use it.
If you are not able to complete any of the preceding quickstarts, contact Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).