Details: Written by Super User; Category: (Un)Perplexed Spready; Published: 08 March 2026

SQL Query in (Un)Perplexed Spready — Your Spreadsheet Is Now a Database

Introduction

(Un)Perplexed Spready is a spreadsheet application that combines the power of traditional spreadsheets with advanced artificial intelligence capabilities. Version 1.2.X represents the biggest leap in the application's history, bringing a range of features that elevate data work to an entirely new level.

This version introduces four major improvement areas: Web Search, Vision & Document Processing, SQL Query, and Advanced Statistics.
Best of all, SQL Query and Advanced Statistics functions are completely free! If you are looking for a free statistical software, you are on the right page!

Spreadsheet applications are excellent for smaller datasets and ad-hoc analyses. But when you need to join data from multiple sheets, filter by complex criteria, or calculate aggregates — traditional formulas become complicated and hard to maintain.

(Un)Perplexed Spready v1.2.X introduces SQL Query functionality — every sheet in your workbook becomes a SQL table, and you can write standard SQL queries for data analysis.

Best of all: SQL Query is FREE — forever!

Workbook-as-Database Architecture

(Un)Perplexed Spready treats your entire workbook as a database:

Each sheet = One SQL table
Sheet name = Table name
First row = Column headers (field names)
Subsequent rows = Data records

Example:

Sheet "Sales":

Product	Quantity	Price	Date
Widget A	100	25.00	2026-01-15
Widget B	50	40.00	2026-01-16

SQL query:

SELECT Product, SUM(Quantity) as Total FROM Sales GROUP BY Product ORDER BY Total DESC

How to Use

Opening the SQL Query Window

Open (Un)Perplexed Spready
Load or create a workbook with data
Click Tools → SQL Query from the menu

SQL Query Window Contains:

SQL Editor (top) — Write and edit SQL queries
Results Grid (bottom) — Display results
Toolbar — Quick access to functions

Toolbar Functions:

Execute (F5) — Run SQL query
Clear — Clear editor
Export to Sheet — Export results to new sheet
Save Query — Save SQL to file (.sql)
Load Query — Load SQL from file
Query Builder — Visual query builder
COMMIT - used for saving update query result back into source spreadsheet

Supported SQL Commands

SELECT — Retrieving Data

-- Basic SELECT SELECT * FROM Sales

-- Selected columns SELECT Product, Quantity, Price FROM Sales

-- Filtering SELECT * FROM Sales WHERE Quantity > 100

-- Sorting SELECT * FROM Sales ORDER BY Date DESC

-- Limit SELECT * FROM Sales LIMIT 10

JOIN — Joining Tables

-- Join two sheets SELECT p.Product, p.Quantity, c.Category FROM Sales p JOIN Catalog c ON p.Product = c.Product

-- LEFT JOIN SELECT p.Product, p.Quantity, c.Category FROM Sales p LEFT JOIN Catalog c ON p.Product = c.Product

Aggregations

-- COUNT SELECT COUNT(*) as Row_Count FROM Sales

-- SUM SELECT Product, SUM(Quantity) as Total FROM Sales GROUP BY Product

-- AVG SELECT AVG(Price) as Average_Price FROM Sales

-- MIN / MAX SELECT MIN(Date) as From_Date, MAX(Date) as To_Date FROM Sales

-- GROUP BY with HAVING SELECT Product, SUM(Quantity) as Total FROM Sales GROUP BY Product HAVING SUM(Quantity) > 100

INSERT, UPDATE, DELETE

-- INSERT INSERT INTO Sales (Product, Quantity, Price, Date) VALUES ('Widget D', 75, 30.00, '2026-01-18')

-- UPDATE UPDATE Sales SET Price = 28.00 WHERE Product = 'Widget A'

-- DELETE DELETE FROM Sales WHERE Quantity < 50

Important: Changes are executed in memory. To save them permanently, use COMMIT or export results to a new sheet.

Writing Changes to Sheets

All changes are executed in memory, not directly on sheets. For permanent saving:

Option 1: COMMIT

COMMIT

Saves all modified tables back to the workbook.

NOTE: JanSQL was originally designed to work with csv files inside a folder. In order to actually commit changes inside (Un)Perplexed Spready spreadsheet, you need to additionally click "COMMIT" button in GUI. So, two-step commit is reuiqred: commit statement inside SQL commits change to a dataset in memory, while commit button commits it back into underlying spreadsheet file. To persist changes, you also need to save the spreadsheet.

Option 2: SAVE TABLE

SAVE TABLE Results AS New_Sheet

Saves query result as a new sheet.

Option 3: Export to Sheet

Click the Export to Sheet button in the toolbar. Results are saved to a new sheet.

Real-World Examples

Sales Analysis

-- Monthly sales by product SELECT Product, strftime('%Y-%m', Date) as Month, SUM(Quantity) as Total_Quantity, SUM(Quantity * Price) as Total_Revenue FROM Sales GROUP BY Product, strftime('%Y-%m', Date) ORDER BY Month, Total_Revenue DESC

Inventory with Catalog

-- Current inventory with product info SELECT i.Product, c.Category, c.Supplier, i.Quantity, i.Quantity * c.Cost_Price as Value FROM Inventory i JOIN Catalog c ON i.Product = c.Product WHERE i.Quantity > 0 ORDER BY Value DESC

Customer 360

-- Complete customer overview SELECT c.Name, c.Surname, COUNT(o.ID) as Order_Count, SUM(o.Total) as Total_Spent, MAX(o.Date) as Last_Order FROM Customers c LEFT JOIN Orders o ON c.ID = o.Customer_ID GROUP BY c.ID ORDER BY Total_Spent DESC

QC Analysis

-- Defects by line and cause SELECT Line, Cause, COUNT(*) as Defect_Count, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM Defects), 2) as Percentage FROM Defects WHERE Date >= '2026-01-01' GROUP BY Line, Cause ORDER BY Defect_Count DESC

SQL Editor Features

Syntax Highlighting

SQL keywords are colored for easier reading:

SELECT, FROM, WHERE — blue
AND, OR, NOT — orange
COUNT, SUM, AVG — green
Strings — red

Auto-Complete

Press Ctrl+Space for:

SQL keywords
Table names (sheets)
Column names

Helper Lists

Tables list — All available sheets
Fields list — Columns of selected table
Operators list — SQL operators

Working with Dates

janSQL does not use strong data types — everything is string. For working with dates, use ISO 8601 format:

Date: YYYY-MM-DD (e.g., 2026-03-04)
Date and time: YYYY-MM-DDThh:mm:ss (e.g., 2026-03-04T14:30:00)

-- Filter by date SELECT * FROM Sales WHERE Date >= '2026-01-01'

-- Date range SELECT * FROM Sales WHERE Date BETWEEN '2026-01-01' AND '2026-01-31'

-- Extract month SELECT strftime('%Y-%m', Date) as Month FROM Sales

Credits

SQL Query functionality is based on the proven janSQL engine (by Jan Verhoeven, http://jansfreeware.com), the same engine used in ZMSQL package and MightyQuery software.

Thanks to Jan Verhoeven for contributing to the open-source community!

Technical Details

Performance

In-memory processing — Everything runs in memory for speed
Semi-compiled expressions — Optimized expression evaluation
No server required — No need to install a database

Limitations

Single-user — One user at a time
No transactions — No ACID transactions
Limited JOIN types — Basic JOIN types supported

Supported File Formats

.xlsx — Microsoft Excel
.ods — LibreOffice/OpenOffice
.csv — Comma-separated values

Download SQL Query Manual

You can download corresponding manuals here:

https://matasoft.hr/SQL_User_Manual.pdf

https://matasoft.hr/janSql-hlp.pdf

Conclusion

SQL Query in (Un)Perplexed Spready brings the power of SQL directly to your spreadsheet. No server, no configuration, no additional costs.

Whether you need to join data from multiple sheets, filter by complex criteria, or calculate aggregates — SQL does it elegantly and efficiently.

SQL Query in (Un)Perplexed Spready is and will always be FREE!

Advanced Statistics in (Un)Perplexed Spready — Professional Statistical Tool for Free

Introduction

Statistical analysis is an essential tool in many fields — from scientific research, through business analysis, to production quality control. Professional statistical packages are usually expensive and require separate installation.

(Un)Perplexed Spready v1.2.X integrates TyphonStats (based on LazStats by William G. Miller), a complete statistical package that works directly on data from your sheets — no need to export to CSV or other formats.

Best of all: Statistics is FREE — forever!

What is TyphonStats?

TyphonStats is a completely free, open-source statistical package that includes:

130+ statistical procedures
Descriptive statistics
Parametric and non-parametric tests
Regression analysis
Multivariate analysis
Statistical Process Control (SPC)
Measurement and evaluation
Data visualization

All functions are available directly from the Statistics menu in (Un)Perplexed Spready.

Categories of Statistical Functions

1. Descriptive Statistics

Basic data analysis:

Function	Description
Descriptive Statistics	Measures of central tendency and dispersion
Frequency Analysis	Frequency distribution
Grouped Frequencies	Grouped frequencies
Cross-tabulation	Cross tables
Breakdown	Analysis by groups
Box Plot	Box-and-whisker diagram
Normality Test	Test of distribution normality

Example: Test results analysis
— Select column with results
— Statistics → Descriptive Statistics
— Get: mean, median, standard deviation, quartiles, etc.

2. Parametric Tests

For data following normal distribution:

Test	Description
One-Sample T-Test	Compare sample with known value
Paired T-Test	Compare paired samples
Independent T-Test	Compare two independent groups
One-Way ANOVA	Compare more than two groups
Two-Way ANOVA	Compare with two factor variables
Proportion Differences	Compare proportions

Example: Comparing results of two groups
— Data: column A (group 1), column B (group 2)
— Statistics → Parametric Tests → Independent T-Test
— Result: t-value, p-value, conclusion

3. Non-Parametric Tests

For data not following normal distribution:

Test	Description
Mann-Whitney U	Non-parametric alternative to Independent T-Test
Wilcoxon Signed-Rank	Non-parametric alternative to Paired T-Test
Kruskal-Wallis	Non-parametric alternative to One-Way ANOVA
Spearman Correlation	Non-parametric correlation
Chi-Square Goodness of Fit	Test match with expected distribution
Chi-Square Independence	Test independence of variables
Kendall's Tau	Non-parametric correlation

Example: Comparing grades (non-normal distribution)
— Data: column A (group 1), column B (group 2)
— Statistics → Non-Parametric → Mann-Whitney U
— Result: U statistic, p-value

4. Correlation and Regression

Analysis of relationships between variables:

Function	Description
Pearson Correlation	Linear correlation
Spearman Correlation	Rank correlation
Kendall's Tau	Alternative rank correlation
Linear Regression	Simple linear regression
Multiple Regression	Multiple regression
Logistic Regression	Binary logistic regression
Forward Stepwise	Stepwise regression (forward)
Backward Stepwise	Stepwise regression (backward)

Example: Sales forecasting
— Data: column A (sales), column B (marketing), column C (price)
— Statistics → Regression → Multiple Regression
— Result: coefficients, R², p-values

5. Multivariate Analysis

Advanced analysis with multiple variables:

Function	Description
MANOVA	Multivariate ANOVA
Discriminant Analysis	Discriminant analysis
Hierarchical Clustering	Hierarchical clustering
K-Means Clustering	K-means clustering
Factor Analysis	Factor analysis
Path Analysis	Path analysis

Example: Customer segmentation
— Data: multiple variables (age, income, behavior)
— Statistics → Multivariate → K-Means Clustering
— Result: customer groups with similar characteristics

6. Statistical Process Control (SPC)

For quality control in production:

Chart	Description
X-Bar Chart	Control chart for mean values
R Chart	Control chart for range
S Chart	Control chart for standard deviation
CUSUM	Cumulative sum
C Chart	Control chart for defect count
P Chart	Control chart for proportion

Example: Monitoring production quality
— Data: sample measurements over time
— Statistics → SPC → X-Bar Chart
— Result: control chart with limits

How to Use

Basic Workflow

Prepare data — Data should be in columns on a single sheet
Select Statistics menu — Choose desired statistical function
Configure analysis — Select variables, settings, etc.
Run analysis — Click OK
Review results — Results appear in output window

Data Transfer

TyphonStats automatically transfers data from the active sheet:

First row is used as variable names
Data is analyzed by columns
No need to export to CSV

Credits

TyphonStats/LazStats was developed by William G. Miller and is distributed as open-source software.

All credit for statistical algorithms and implementation goes to William G. Miller.

Matasoft integrated TyphonStats into (Un)Perplexed Spready with full respect for the original author and open-source community.

Comparison with Other Tools

Tool	Price	Statistics	Built-in	Offline
(Un)Perplexed Spready	Free	130+ functions	✅	✅
SPSS	~$100/month	Advanced	❌	✅
Minitab	~$50/month	Advanced	❌	✅
R	Free	Complete	❌	✅
Python + libraries	Free	Complete	❌	✅

Download LazStats (TyphonStats) User Manual

TyphonStats (LazStats) by Bill Miller is complex and comprehensive statistical tool which numerous functions and options cannot be covered in one article. If you want to learn more about it, please read the LazStats User Manual which you can download here: https://matasoft.hr/User_Manual_for_LazStats.pdf

Conclusion

Statistical analysis in (Un)Perplexed Spready brings professional statistical tools directly to your spreadsheet. No additional costs, no separate installation, no data export.

Whether you're a researcher, analyst, or quality engineer — you have the power of statistics at your fingertips.

Statistics is and will always be FREE!

Document in (Un)Perplexed Spready — Process Documents Without Leaving the Application

Introduction

In a business environment, data often comes in various formats — PDF reports, Word documents, Excel spreadsheets, presentations, web pages... Traditionally, each format requires a different tool for processing.

(Un)Perplexed Spready v1.2.X introduces Document functionality — the ability to load various document formats directly into AI context, without leaving the application.

How It Works

Document functionality allows you to specify the path to a document (local or URL) in a formula, and the AI processes the content and returns the result to the cell.

Basic syntax:

=ASK_LOCAL1("/path/to/document.pdf", "instruction")
=ASK_LOCAL1("https://example.com/document.docx", "instruction")

Supported Formats

Text Formats

Format	Extensions	Notes
Text	.txt	Plain text
Markdown	.md	Formatted text
CSV	.csv, .tsv	Data separated by delimiter
SQL	.sql	SQL scripts
YAML	.yaml, .yml	Configuration files
Code	.py, .js, .pas, .sh, etc.	Source code

Web Formats

Format	Extensions
HTML	.html, .htm, .xhtml

Documents

Format	Extensions	Notes
PDF	.pdf	Universal document format
Word	.docx, .doc, .rtf	Microsoft Word documents
OpenDocument	.odt	LibreOffice/OpenOffice documents
EPUB	.epub	E-books

Spreadsheets

Format	Extensions	Notes
Excel	.xlsx, .xlsm, .xls	Microsoft Excel
OpenDocument	.ods	LibreOffice/OpenOffice Calc

Presentations

Format	Extensions
PowerPoint	.pptx, .ppt

Data Formats

Format	Extensions
XML	.xml
JSON	.json, .geojson

Usage Methods

1. Local Documents

Documents stored on your computer:

=ASK_LOCAL1("/home/user/Documents/report.pdf", "Summarize main points")
=ASK_LOCAL1("C:\Users\User\Documents\contract.docx", "Extract key provisions")
=ASK_LOCAL1("./data/data.xlsx", "Analyze data structure")

2. Online Documents (HTTP/HTTPS URLs)

Documents from the internet:

=ASK_LOCAL1("https://example.com/report.pdf", "Summarize this report")
=ASK_LOCAL1("https://example.com/data.json", "Describe the structure of this data")

3. Combining with Other Data

=ASK_LOCAL2(A2, "/path/to/specs.pdf", "Check if product in A2 complies with specifications")
=ASK_LOCAL3(A2, B2, "https://example.com/regulations.pdf", "Check compliance with regulations")

Practical Applications

PDF Report Analysis

=ASK_LOCAL1("/reports/annual_report_2025.pdf", "Summarize financial results")
=ASK_LOCAL1("/reports/market_analysis.pdf", "Extract key trends and figures")

Contract Data Extraction

=ASK_LOCAL1("/contracts/contract_001.docx", "Extract: parties, date, subject, amount")
=ASK_LOCAL1("/contracts/NDA.pdf", "List confidentiality clauses")

Word Document Analysis

=ASK_LOCAL1("/proposals/proposal.docx", "Summarize main points of the proposal")
=ASK_LOCAL1("/policies/company_policy.docx", "Extract key guidelines")

Excel Spreadsheet Reading

=ASK_LOCAL1("/data/sales.xlsx", "Describe structure of this table: sheets, columns, row count")
=ASK_LOCAL1("/data/budget.xlsx", "Summarize financial data")

JSON/XML Data Analysis

=ASK_LOCAL1("/api/response.json", "Describe structure of this JSON response")
=ASK_LOCAL1("/config/settings.xml", "Extract configuration values")

Web Page Reading

=ASK_LOCAL1("https://example.com/article.html", "Summarize this article")
=ASK_LOCAL1("https://docs.example.com/guide.html", "Extract main instructions")

Cross-Sheet Range with Documents

You can combine documents with range data from other sheets:

=ASK_LOCAL2("/products/specs.pdf", "RANGE:Products!A2:A50", "Check if all products comply with specifications")

Limitations

Document Size

Maximum length: 50,000 characters
Larger documents are truncated (first 50,000 characters)
For very large documents, consider splitting into smaller parts

PDF Specifics

Text-based PDF: Fully supported
Scanned PDF (images): May require OCR; better to use Vision functionality

Online Documents

Requires internet connection
Possible timeout for large files

Configuration

Required Models

Document functionality works with standard LLM models (does not require special vision models).

Recommended models:

deepseek-v3 — Excellent for document analysis
qwen2.5 — Good balance of speed and quality
llama3.1 — Fast and efficient

Installing models (Ollama):

ollama pull deepseek-v3
ollama pull qwen2.5

Real-World Examples

Legal Documentation

You have a folder with contracts:

Cell A1: =ASK_LOCAL1("/contracts/001.pdf", "Parties")
Cell B1: =ASK_LOCAL1("/contracts/001.pdf", "Date")
Cell C1: =ASK_LOCAL1("/contracts/001.pdf", "Amount")

Regulatory Compliance

=ASK_LOCAL2("/regulations/GDPR.pdf", "RANGE:Processes!A2:A20", "Check compliance of processes with GDPR regulations")

Data Preprocessing

=ASK_LOCAL1("/raw_data.json", "Convert this JSON to tabular format: columns, values")
=ASK_LOCAL1("/export.csv", "Analyze structure and suggest data cleaning")

Tips for Best Results

Be specific — Specify exactly what you want to extract from the document
Control the format — Request JSON, CSV, or tabular output format
Use portions — For large documents, focus on specific sections
Validate results — Always check AI extraction against original document
Choose the right model — deepseek-v3 is excellent for complex documents

Error Messages

Error	Meaning
FILE NOT FOUND	Document does not exist at the specified path
UNSUPPORTED FORMAT	Document format is not supported
APIERR	Model not available
DOCUMENT TOO LARGE	Document exceeds 50,000 characters

Privacy and Security

Local models: Documents stay on your computer
Remote models: Documents are sent to remote server
Temporary: Document content is not permanently stored

Conclusion

Document functionality in (Un)Perplexed Spready simplifies working with various document formats. Instead of opening multiple applications, you can process PDFs, Word documents, Excel spreadsheets, and other formats directly from your spreadsheet.

Whether you're analyzing reports, extracting data from contracts, or processing data files — Document formulas give you the power of AI analysis at your fingertips.

Document is a PREMIUM feature. Requires LLM model (local or cloud).

Vision in (Un)Perplexed Spready — Analyze Images Directly in Your Spreadsheet

Introduction

Spreadsheet applications traditionally work with numbers and text. But in the real world, many data come in visual form — charts, tables, receipts, documents, product photos, logos, diagrams...

(Un)Perplexed Spready v1.2.X introduces Vision functionality — the ability to bring images into AI context directly from formulas in your spreadsheet. Source of images can be local folder or an URL.

How It Works

Vision functionality uses Vision LLM models (such as qwen3-vl, llama3.2-vision, etc.) that can "see" and analyze images. You simply specify the path to an image (local or URL) in a formula, and the AI analyzes the content and returns the result to the cell.

At the moment of writing this article, we can recommend usage of qwen3-vl:235b-cloud model available in Ollama Cloud.

Basic syntax:

=ASK_LOCAL1("IMAGE:/path/to/image.png", "instruction")
=ASK_LOCAL1("IMAGE:https://example.com/image.jpg", "instruction")

Supported Image Formats

Format	Extensions	Notes
JPEG	.jpg, .jpeg	Most common format
PNG	.png	Lossless, transparency
WebP	.webp	Modern format, good compression
GIF	.gif	Animated images supported
BMP	.bmp	Windows bitmap

Usage Methods

1. Local Images

Images stored on your computer:

=ASK_LOCAL1("IMAGE:/home/user/Documents/receipt.png", "Extract total amount from this receipt")
=ASK_LOCAL1("IMAGE:C:\Users\User\Pictures\chart.png", "Analyze this chart and describe trends")
=ASK_LOCAL1("IMAGE:./data/table.png", "Convert this table to structured format")

2. Online Images (HTTP/HTTPS URLs)

Images from the internet:

=ASK_LOCAL1("IMAGE:https://example.com/product.jpg", "Describe this product")
=ASK_LOCAL1("IMAGE:https://charts.example.com/sales.png", "Analyze this sales chart")

3. Combining with Other Data

=ASK_LOCAL2(A2, "IMAGE:/path/to/product.png", "Compare the description in A2 with the product image")
=ASK_LOCAL3(A2, B2, "IMAGE:https://example.com/chart.png", "Analyze chart in context of data in A2 and B2")

Practical Applications

Receipt and Document Analysis

=ASK_LOCAL1("IMAGE:/scan/receipt_001.jpg", "Extract: date, store, total amount")
=ASK_LOCAL1("IMAGE:/invoices/invoice.pdf_page1.png", "Extract invoice number, customer, and amount")

Reading Tables and Charts

=ASK_LOCAL1("IMAGE:/reports/chart.png", "Read values from this chart and list them")
=ASK_LOCAL1("IMAGE:/data/table.png", "Convert this table to CSV format")

Product Categorization

=ASK_LOCAL1("IMAGE:/products/item_001.jpg", "Categorize this product: category, subcategory, color")
=ASK_LOCAL1("IMAGE:https://shop.com/product_123.jpg", "Describe product: name, features, price if visible")

Document Analysis

=ASK_LOCAL1("IMAGE:/docs/id_card.jpg", "Extract: name, surname, ID number (mask last 3 digits)")
=ASK_LOCAL1("IMAGE:/contracts/contract_page1.png", "Summarize main points of this contract")

QC and Inspection

=ASK_LOCAL1("IMAGE:/qc/sample_001.jpg", "Assess quality: are there visible defects?")
=ASK_LOCAL1("IMAGE:/production/widget_123.jpg", "Check: is this product correct?")

Cross-Sheet Range with Images

You can combine images with range data from other sheets:

=ASK_LOCAL2("IMAGE:/products/batch_001.jpg", "RANGE:Products!A2:A50", "Compare image with product list and identify which one is in the image")

Configuration

Required Models

Vision functionality requires a Vision LLM model that supports image processing.

Recommended models:

qwen3-vl — Excellent for OCR and document analysis
llama3.2-vision — Good balance of speed and quality
llava — Open-source vision model
minicpm-v — Fast and efficient
kimi-k2.5
qwen3.5:397b

Installing models (Ollama):

ollama pull qwen3-vl
ollama pull llama3.2-vision

Settings > AskLocal Settings

API Endpoint: URL of your Ollama server (usually http://localhost:11434)
Model: Vision model (e.g., qwen3-vl)
API Key: If required

Limitations

Image Size

Recommended size: up to 2048x2048 pixels
Larger images are automatically scaled
Very large images may cause timeout

Number of Images

One image per formula
For multiple images, use multiple formulas

Real-World Examples

Data Entry Automation

You have a folder with scanned receipts:

Cell A1: =ASK_LOCAL1("IMAGE:/receipts/001.jpg", "Date")
Cell B1: =ASK_LOCAL1("IMAGE:/receipts/001.jpg", "Store")
Cell C1: =ASK_LOCAL1("IMAGE:/receipts/001.jpg", "Total amount")

Tips for Best Results

Use clear instructions — The more precise your question, the better the answer
Be specific — Specify exactly what you want to extract
Control the format — Request JSON, CSV, or tabular format
Use quality images — Clear images give better results
Choose the right model — qwen3-vl is excellent for documents, llama3.2-vision for general analysis

Error Messages

Error	Meaning
FILE NOT FOUND	Image does not exist at the specified path
INVALID IMAGE	Image format is not supported
APIERR	Model not available or does not support vision
TIMEOUT	Image too large or server overloaded

Privacy and Security

Local models: Images stay on your computer
Remote models: Images are sent to remote server

Conclusion

Vision functionality in (Un)Perplexed Spready opens the door to an entirely new way of working with data. You are no longer limited to text and numbers — you can now analyze visual content directly in your spreadsheet.

Whether you're processing receipts, reading tables from photos, categorizing products, or analyzing charts — Vision formulas give you the power of AI vision at your fingertips.

Vision is a PREMIUM feature. Requires Vision LLM model (local or cloud).

(Un)Perplexed Spready

(Un)Perplexed Spready v1.2.X — SQL Query in spreadsheets

SQL Query in (Un)Perplexed Spready — Your Spreadsheet Is Now a Database

Introduction

Workbook-as-Database Architecture

How to Use

Opening the SQL Query Window

SQL Query Window Contains:

Toolbar Functions:

Supported SQL Commands

SELECT — Retrieving Data

JOIN — Joining Tables

Aggregations

INSERT, UPDATE, DELETE

Writing Changes to Sheets

Option 1: COMMIT

Option 2: SAVE TABLE

Option 3: Export to Sheet

Real-World Examples

Sales Analysis

Inventory with Catalog

Customer 360

QC Analysis

SQL Editor Features

Syntax Highlighting

Auto-Complete

Helper Lists

Working with Dates

Credits

Technical Details

Performance

Limitations

Supported File Formats

Download SQL Query Manual

Conclusion

Further Reading

(Un)Perplexed Spready v1.2.X — Professional Statistical Tool for Free

Advanced Statistics in (Un)Perplexed Spready — Professional Statistical Tool for Free

Introduction

What is TyphonStats?

Categories of Statistical Functions

1. Descriptive Statistics

2. Parametric Tests

3. Non-Parametric Tests

4. Correlation and Regression

5. Multivariate Analysis

6. Statistical Process Control (SPC)

How to Use

Basic Workflow

Data Transfer

Credits

Comparison with Other Tools

Download LazStats (TyphonStats) User Manual

Conclusion

Further Reading

(Un)Perplexed Spready v1.2.X — Process Documents in Spreadsheets

Document in (Un)Perplexed Spready — Process Documents Without Leaving the Application

Introduction

How It Works

Supported Formats

Text Formats

Web Formats

Documents

Spreadsheets

Presentations

Data Formats

Usage Methods

1. Local Documents

2. Online Documents (HTTP/HTTPS URLs)

3. Combining with Other Data

Practical Applications

PDF Report Analysis

Contract Data Extraction

Word Document Analysis

Excel Spreadsheet Reading

JSON/XML Data Analysis

Web Page Reading

Cross-Sheet Range with Documents

Limitations

Document Size