Tablo2SQL-TR User Guide

What is this software, and what is it for?

Tablo2SQL-TR is an application that runs on your own computer and opens in your browser. It reads the tables in Turkish PDF and HTML documents, turns them into a clean database, and lets you ask that database questions in everyday language. You do not need to know SQL, Excel formulas or programming. You type a question and get an answer taken from the actual values in the tables.

In short: it turns numbers locked inside reports into information you can query.

Why is it needed?

Public bodies, municipalities and companies publish much of their information as PDF reports: activity reports, budget execution tables, financial statements, statistical bulletins, fee schedules. Getting information out of these documents is still largely manual work.

An example: you have an 80-page municipal activity report. The question is simple: “What share of the 2024 personnel budget was actually spent?” The answer sits in a table somewhere in that report. But:

• The table has merged header cells (“Budget” and “Actual” under a shared “2024” heading).
• Numbers are written the Turkish way: 1.234.567,89. Tools that assume English formatting either cannot read them or silently turn them into a different number.
• Negative values are in parentheses, percentages look like %4,5, dates like 15.03.2025.
• The unit is often stated only in the table caption (“bin TL”, thousand TRY).
• The bottom row is the document’s own “Toplam” (total) row. Summing the column without noticing it doubles the result.

Anyone who has pasted such a table into Excel knows how it ends: shifted columns, numbers stuck as text, hours of clean-up. General-purpose AI chat tools, on the other hand, often answer such questions with confident-sounding but invented numbers, and do not show where the answer came from.

We measured it: when the same tables are loaded without handling the Turkish number format, about three quarters of the answers break even when the SQL queries themselves are perfect. Tablo2SQL-TR was built to close exactly this gap.

What does it do?

Tablo2SQL-TR turns the job into three steps:

1. Extract. It finds the tables in PDF/HTML documents and resolves merged cells and multi-row headers.
2. Load. It converts Turkish numbers, dates and units into proper types and writes the tables into a SQLite database. It flags “Toplam” rows and keeps the units.
3. Ask. It translates your question into SQL, runs it, and corrects itself when something goes wrong. If the answer is not in the tables, it does not make one up. It says “this information is not in the tables”.

You can download the resulting database as Excel, CSV, SQLite or a SQL dump and use it in other tools.

Who is it for?

• Public-sector and municipal staff: pulling figures quickly from activity reports and budget tables.
• Finance and audit teams: comparing income-statement lines and expense items across company reports.
• Journalists and researchers: turning official statistical bulletins into queryable data.
• Analysts: converting PDF tables into clean Excel/CSV files. Extraction and export are useful on their own, even if you never ask a question.
• Text-to-SQL researchers: a research platform with a Turkish test set, an error catalog and evaluation tools.

What makes it different?

• It understands Turkish formats. 1.234,56, (12,5), %3,4, Ocak 2024, the dotted/dotless İ/ı, and suffixed names such as “İzmir’in” are handled correctly.
• It is transparent. Every answer comes with the generated SQL and every step the system took, so you can check it against the source table.
• It says when it does not know. If the information is not in the tables, it refuses instead of inventing a number, and says why.
• It classifies its own mistakes. When something goes wrong, it identifies the error type (date format, unit, value matching, calculation…) and corrects itself accordingly.
• AI is optional. The rule-based engine works with no internet connection and no API key, and reaches 92.9% accuracy on the test set. You can connect Claude, OpenAI, or Ollama running on your own computer if you want.
• Your data stays with you. In rule-based mode and with Ollama, no data leaves your computer.

What it is not

• It cannot read scanned (image-only) documents. Add a text layer with an OCR tool first.
• It does not modify your documents or the database; it only reads and queries.
• It does not replace human judgement for important decisions. That is why it shows exactly how every answer was produced.

About this guide

This guide walks you from the first launch to advanced use. Everything works without AI. Connect Claude, OpenAI or a local Ollama model and almost every corner of the app gets smarter.

About the screenshots: the AI screenshots were taken against a test server that imitates the real APIs, so AI-generated text starts with [MOCK]. A real model will word things differently, and in more detail.

The sample documents are Turkish, so table contents, column names and sample questions stay in Turkish even when the interface is in English.

1. Up and running in five minutes

Installation

Download the project and open the folder. Double-click the launcher for your operating system:

The launcher takes care of the rest:

• It checks for Python 3.10+. If Python is missing, it tries to install it (winget / Homebrew / apt) or tells you where to get it.
• It creates a virtual environment (.venv) and installs any missing packages.
• It checks for Ollama and API keys and gives advice. Neither is required.
• It finds a free port (starting at 8501) and opens your browser.
• On the first run it offers to create a desktop shortcut. You can also do this later with the kisayol_olustur.* files.

The first launch can take a few minutes while packages install. Later launches take seconds.

First look

The screen has two parts:

• The sidebar: language and theme, the AI engine, agent options and knowledge-base actions.
• The main area: five tabs that follow the workflow. ① Documents & extraction → ② Knowledge base → ③ Ask → ④ Error catalog → ⑤ Evaluation.

The app ships with six synthetic sample documents: a municipal activity report, provincial energy consumption, a company’s financial report, water statistics, a price-index bulletin and a fee schedule. The knowledge base starts pre-loaded with them, so you can go straight to tab ③ and ask a question without uploading anything.

2. A tour of the sidebar

Language / Theme. Türkçe ⇄ English and Light ⇄ Dark. Your choices are remembered.

AI engine. The top shows the status of the three providers (Ollama, OpenAI, Claude: Connected · N models or Not connected). 🔌 Model connections opens the connections panel (see Section 6). The Default model list holds the rule-based engine plus every synced model of the connected providers. The model you pick is used in every tab that has no choice of its own. The badge below shows whether AI is on and the session’s usage counter. Where AI is used lists every place the AI touches.

Agent.

• Self-correction loop: repairs faulty SQL automatically.
• Max attempts: 3 by default.
• Phrase the answer with AI: turns the result into a natural sentence.
• Fall back to rule-based if AI fails: you still get an answer during network or quota problems.

The last two switches are only active when an AI provider is selected.

Knowledge base. Shows how many tables there are and where the database file lives. The Reset with samples, Empty and download buttons in four formats are here too.

3. Step ①: Upload a document and extract tables

1. Drag in a PDF, HTML, CSV or XLSX file, or pick one of the sample documents.
2. Choose a PDF extractor:
   • auto (recommended): tries pdfplumber for ruled tables first and falls back to Camelot-stream.
   • pdfplumber: ruled tables.
   • camelot-lattice: ruled tables, alternative engine.
   • camelot-stream: unruled, whitespace-aligned tables.
3. Choose a Number parsing mode:
   • tr (the correct one): 1.234,56 → 1234.56, (12,5) → −12.5, %3,4 → 3.4.
   • en: assumes English number format. For comparison.
   • text: keeps numbers as text. For comparison.
4. Click 🔍 Extract tables.

Each table opens in its own panel. You see the raw grid, with merged cells highlighted, next to the resolved headers, each column’s type and unit, and the schema that will be created:

What to look for here:

• Header rows: two-level headers such as “2023 / 2024” become single column names such as butce_2024 and gerceklesme_2024.
• Units: phrases like “(bin TL)” (thousand TRY) are read from the caption or the column header and stored with the column.
• Total rows: the document’s own “Toplam” row is not deleted. It is flagged with toplam_satiri = 1, so SUM() does not double-count.
• Unparsed values: cells that should be numeric but could not be read are listed as warnings.

If everything looks right, click ⬇️ Import all into the database. Importing the same document again replaces the old copy, so tables are never duplicated.

4. Step ②: Knowledge base and downloads

This tab shows each table in the database: its source, columns, types, units and data. It also shows the schema description that is sent to the model. With AI on, the description also includes a table summary and column descriptions.

If you know SQL, the SQL console (read-only) lets you run your own SELECT queries. It rejects anything that would modify the database.

Downloading the database. You can export the finished knowledge base in four formats:

Tip: a value that a Turkish document shows as 1.234,56 is stored as the number 1234.56. Excel displays it in your locale’s format, and sums and formulas just work.

5. Step ③: Ask questions

Go to tab ③ and type your question:

İzmir’in 2024 elektrik tüketimi ne kadar? (“What was İzmir’s electricity consumption in 2024?”)

Small badges above each answer show the status (answered / unanswerable), the number of attempts, the engine used and the time taken. The generated SQL sits below. Expand Agent steps to follow what the system did:

1. 🧭 Schema linking: picks the tables relevant to the question.
2. ✍️ Attempt N: candidate SQL: generates SQL.
3. 🔎 Static check: looks for typical mistakes before running anything. Examples: a Turkish-format date such as '15.03.2025', a number such as '1.234,56', or a column that is not in the schema.
4. ▶️ Execution: runs the SQL on a read-only connection. Your database is never modified.
5. 🔁 Feedback: if something went wrong, the error type and a fix suggestion go into the next attempt.
6. ✅ Result or 🚫 Unanswerable.

“This information is not in the tables”

Perhaps the most important feature is that the system knows when it does not know. The sample documents do not contain Mersin:

The system looks up the value from the question (“Mersin”) in the database. If it is missing, or the query returns nothing, the system does not invent a number. It abstains and says why. On the test set it correctly abstains on 90.9% of unanswerable questions. Its false-alarm rate on answerable questions is 5.7%.

Tips for good questions

• Use the names that appear in the table. “İzmir”, “Personel Giderleri”, “2024”.
• Specify the period. “In 2024 …” is more reliable than “last year …”.
• Turkish numbers and dates are fine. Write 15.03.2025 or 1.250; the system converts them.
• Ask for calculations. Sums, averages, max/min, percentage change, ratios (“… per person …”).
• The rule-based engine understands Turkish questions only. To ask in English, turn on an AI provider.

6. Turning on AI

The app works fully without AI. The rule-based engine reaches 92.9% execution accuracy on the test set. Turning AI on adds:

• Understanding of free-form and indirect questions (“which district saw the biggest growth in water subscribers?”)
• Questions in English
• Answers phrased as natural sentences
• Smart suggestions throughout the app

The Model connections panel

🔌 Model connections in the sidebar opens one panel that manages all three providers:

Connecting a cloud provider. Paste the key into API key and click Save and sync. The app saves the key and immediately fetches the available models from the provider’s API. The card turns to Connected · N models and shows only a masked form of the saved key (e.g. sk-…F1cA). Get a key ↗ opens the provider’s key page. To use another OpenAI-compatible server, enter its address under Server URL (optional). Disconnect deletes the key and the model list; model choices for that provider fall back to the rule-based engine.

The model list updates itself. You never type a model name. At every launch the app checks the connected providers and re-syncs if the list is empty or older than 12 hours. Sync models refreshes it right away. Show models lists what is available now.

• Claude: newest models first.
• OpenAI: on the official endpoint only chat models are listed. Embedding, audio and image models are filtered out.
• Ollama: the models installed on your machine, with size and quantization.

Then 🔌 Test connection in the sidebar sends one tiny request to confirm everything works.

A different model per tab. Every tab starts with a Model for this tab picker. By default it uses the sidebar model; you can, for example, use a fast, cheap model for table suggestions and a stronger one for asking questions.

The settings file. All settings (API keys, Ollama address and device, language, theme, model and tab choices) live in ayarlar/tablo2sql-ayarlar.json in the app folder. To move them to another computer, copy the file to the same place in the app folder there, or use Download settings file and Load settings file… in the panel. The file holds your keys in plain text: do not share it or email it. If you put the key in .env instead (ANTHROPIC_API_KEY, OPENAI_API_KEY), the app reads it from there and the card shows “(environment variable)”.

Ollama: fully local and free

1. Install and start Ollama from ollama.com. The app tries http://localhost:11434 automatically; use Change server address for anything else.
2. If you have no models yet, open Which Ollama model?. Recommended Text-to-SQL models are listed with size and memory needs; installed ones are ticked. Pick one and click ⬇️ Download model. A progress bar shows inside the app; no terminal needed.
3. Choose the compute device:
   • Auto (recommended): Ollama decides; whatever does not fit on the graphics card runs on the CPU.
   • GPU: all model layers go to the graphics card. If memory is short the model may fail to load.
   • CPU: the graphics card is not used; slow, but works on any computer.
   This is the default; it can be changed per run in the Evaluation tab. It does not affect cloud models.

Which model? Start with qwen2.5-coder:7b (about 4.7 GB, runs in 8 GB of memory). With 16 GB on the graphics card, qwen2.5-coder:14b or gpt-oss:20b is noticeably more accurate. For free-form Turkish questions try gemma3:12b or qwen3:8b. Measure which one works best on your documents with Model comparison in the Evaluation tab (Section 8).

What changes when AI is on?

A summary of Where AI is used:

① Extraction: AI suggestions. For each table, the AI proposes:

• a short, meaningful name (e.g. il_elektrik_tuketimi)
• a one- or two-sentence summary
• column descriptions and units
• warnings about anything suspicious in the parsing

Applying them is optional, controlled by Apply AI suggestions on import. Deterministic extraction always remains the primary source. Applied summaries and descriptions go into the schema description, which directly helps SQL generation.

③ Ask: suggested questions. The AI suggests questions that use the actual values in your database. Click one to ask it right away, or use 🔄 New suggestions for another set.

③ Ask: self-correcting SQL. In the example below the model wrote the date in Turkish format on its first try ('15.03.2025'). The static check flagged it as a TARIH_BICIMI (date format) error and fed back the hint “dates are stored in ISO format”. The second attempt ran correctly:

Other AI features:

• Phrased answers: a full sentence instead of “Answer: 1,048.33 TL”. Numbers use the selected language’s format.
• Schema narrowing: when the database has many tables, the AI picks the ones relevant to the question.
• ④ Error catalog: explains errors in plain language (see below).
• ⑤ Evaluation: groups failures into themes.
• Fallback to rule-based: if the AI does not respond (network, quota, bad key), the question is answered by the rule-based engine. This shows up in the agent steps as “Fell back to rule-based”.

Cost control. The sidebar status card shows the number of requests and tokens used in the session. AI responses are cached on disk, so you never pay twice for the same table or error.

Privacy: what is sent to the AI?

With a cloud provider (Claude/OpenAI), the following data goes to that provider:

If you work with confidential documents, use Ollama. Your data stays on your machine.

7. Step ④: The error catalog

Every mistake the system makes is logged with its type. This tab shows those records as cards:

With AI on, each card gets an 🤖 Explain this error with AI button. The explanation comes under three short headings: What happened? Why? How to fix it?

8. Step ⑤: Evaluation (for researchers)

This tab measures the system on the TR-TabloSQL test set. The set has 99 questions: 64 for development, 35 held out for testing, 11 unanswerable, across 20 categories. The metric is execution accuracy (EX): the result of the generated SQL is compared with the result of the gold SQL, within a small numeric tolerance.

• Number modes: run tr, en and text side by side to see the effect of Turkish number format for yourself. With the rule-based engine, EX is 92.9% for tr, 29.3% for en and 13.1% for text.
• Details: each question’s correctness, error type, answer and SQL. Wrong answers only narrows the list.
• With AI on, 🤖 Analyse failures with AI groups the failures into 3–5 themes and suggests improvements.
• If the selected backend is an AI provider, every question triggers API calls. 99 questions × 4 modes can add up to a noticeable cost.

Model comparison: measuring LLMs on the heavy test set

Model comparison at the top of the tab measures several models side by side on the same question set. Accuracy here is checked against the expected answer, not a gold SQL, so you can use it with your own documents too.

The screenshot was taken with the test server; the “mock” models’ results do not reflect real model performance.

1. Choose a test set:
   • Heavy set · quick subset (200 questions): for a first run and frequent comparisons.
   • Heavy set · full (767 questions): 52 fictional documents of 13 types in one database. Several organisations have near-identical tables of the same type, so the model has to tie the organisation in the question to the right table.
   • Stress set (307 questions, one database per document) and Pilot (44 questions).
2. Pick the models to compare. The rule-based engine can be added as a baseline. If an Ollama model is picked, you can also choose this run’s compute device.
3. Set a question limit to control cost (0 = all). The section shows the rough token usage per model.
4. Click ▶ Run comparison. Results are saved; earlier runs on the same set stay in the table side by side.

The heavy set has five levels:

About 20% of the questions are in English, Turkish without diacritics (kayali ilcesinin nufusu) or contain typos. The table shows Accuracy %, accuracy per level, Unanswerable→abstain % and Silent wrong % (questions the system answered confidently but wrongly). The rule-based engine scores 13% on this set; that is by design, the set is built to make the differences between LLMs visible.

From the command line:

python pilot/pilot_calistir.py --kume agir_hizli --claude          # newest Claude model
python pilot/pilot_calistir.py --kume agir --ollama qwen2.5-coder:7b --cihaz gpu --sinir 100

Keys and addresses are read from the settings file. To regenerate the set or make it larger: python agir_kume/uret.py --tekrar 6.

9. Language and theme

The whole interface is available in Turkish and English. Switching the language also switches the menus, agent steps, error explanations and number formats (1.234,56 ⇄ 1,234.56). AI responses come back in the selected language. The database content stays in the language of the source documents (Turkish).

The dark theme is easier on the eyes in long sessions:

Your language, theme, provider and model choices are saved in outputs/.arayuz_ayarlari.json and restored at the next launch. API keys are never written to this file.

10. FAQ and troubleshooting

“Python not found”. The launcher offers to install it. If that fails, install 3.10 or later from python.org. On Windows, tick “Add python.exe to PATH” in the installer.

The browser did not open / the port is busy. The launcher searches for a free port starting at 8501. The address is printed in the terminal window, for example http://localhost:8502. To use a specific port, run python scripts/baslat.py --port 8600.

No tables were found in my PDF. - The document may be scanned (images only). Add a text layer with an OCR tool first, for example ocrmypdf. - If the table has no ruling lines, switch the extractor to camelot-stream.

Numbers stayed as text. Make sure the number-parsing mode is tr. If the raw grid shows “unparsed” warnings, those cells use an unusual notation (e.g. 1 234,5 or —).

The model list is empty. - Claude/OpenAI: the key may be wrong (401). The card in the Model connections panel shows the error; save the key again and click Sync models. - Corporate network: a proxy may be blocking the API address. - Ollama: is Ollama running? Try ollama list. The default address is http://localhost:11434; if yours differs, use Change server address on the Ollama card. If the app runs inside Docker the address is usually http://host.docker.internal:11434.

I chose GPU and the model will not load. The graphics card does not have enough memory for the model. Switch the device to Auto or pick a smaller model.

My English question was not understood. The rule-based engine is built for Turkish questions. Turn on an AI provider for English.

The answer is wrong but the system sounds confident. Expand the agent steps and look at the generated SQL. Usually there is a mismatch between a phrase in the question and a table or column name. With AI on, the explanation in the error catalog helps too. Errors from questions asked in the UI are logged to outputs/arayuz_hatalari.jsonl, and evaluation errors to outputs/hata_gunlugu.jsonl. Both make good bug reports.

11. Command line (advanced)

All experiments also run without the interface. Use the virtual environment’s Python: .venv\Scripts\python on Windows, .venv/bin/python elsewhere.

# End to end: samples → databases → test set → evaluation → HTML report
python scripts/run_all.py
python scripts/run_all.py --hizli              # tr mode only ("hızlı" = quick)
python scripts/run_all.py --claude             # + Claude (model picked automatically)
python scripts/run_all.py --openai gpt-4o-mini # + a specific OpenAI model
python scripts/run_all.py --ollama             # + the first installed Ollama model

python scripts/hata_enjeksiyonu.py   # error-catalog detection rate on 159 perturbations
python scripts/makale_sayilari.py    # every number in the paper, with confidence intervals

python -m pytest tests/ -q           # 60 tests

python agir_kume/uret.py             # builds the heavy LLM test set (52 documents, 767 questions)
python pilot/pilot_calistir.py --kume agir_hizli --ollama qwen2.5-coder:7b   # measure one model

# A test server for trying the AI features without a key
python tests/mock_llm_server.py      # imitates the Anthropic/OpenAI/Ollama APIs (port 8765)
# In the app: Model connections → Ollama address http://localhost:8765,
#   OpenAI server URL http://localhost:8765/v1 and key "test"
#   (for Claude, in .env: ANTHROPIC_BASE_URL=http://localhost:8765  ANTHROPIC_API_KEY=test)

Results are written to outputs/:

• basarim_raporu.html: the performance report; open it in a browser.
• ozet.json
• sonuclar/: per-question results.
• hata_gunlugu.jsonl: the error log.

Tablo2SQL-TR is an open research prototype. The sample documents are synthetic and do not represent real institutions. For important decisions, always check the answer against the source table. The SQL and result rows in the agent steps are there for exactly that.