qabot

qabot

Query local or remote files with natural language queries powered by OpenAI's gpt and duckdb 🦆.

Can query local and remote files (CSV, parquet)

Installation

Install with uv, pipx, pip etc:

uv tool install qabot

Features

Works on local CSV, sqlite and Excel files:

remote CSV files:

$ qabot -f https://duckdb.org/data/holdings.csv -q "Tell me how many Apple holdings I currently have"
 🦆 Creating local DuckDB database...
 🦆 Loading data...
create view 'holdings' as select * from 'https://duckdb.org/data/holdings.csv';
 🚀 Sending query to LLM
 🧑 Tell me how many Apple holdings I currently have
 🤖 You currently have 32.23 shares of Apple.


This information was obtained by summing up all the Apple ('APPL') shares in the holdings table.

SELECT SUM(shares) as total_shares FROM holdings WHERE ticker = 'APPL'

Even on (public) data stored in S3:

$ qabot -f s3://covid19-lake/enigma-jhu-timeseries/csv/jhu_csse_covid_19_timeseries_merged.csv -q "how many confirmed cases of covid are there by month?" -v

🤖 Monthly confirmed cases from January to May 2020: ranging from 7 in January, 24 in February, 188,123 in March, 1,069,172 in April and 1,745,582 in May.

Extra Details (from qabot)

The above figures were computed by aggregating the dataset on a per-entity basis (using a unique identifier uid), selecting the last available (maximum) date in each month, and summing the confirmed case counts. Here is the SQL query that was used:

WITH monthly_data AS (
    SELECT uid, strftime('%Y-%m', date) AS month, MAX(date) AS max_date
    FROM memory.main.jhu_csse_covid_19_timeseries_merged
    GROUP BY uid, strftime('%Y-%m', date)
)
SELECT m.month, SUM(j.confirmed) AS confirmed
FROM monthly_data m
JOIN memory.main.jhu_csse_covid_19_timeseries_merged j
  ON m.uid = j.uid AND m.max_date = j.date
GROUP BY m.month
ORDER BY m.month;

This method ensures that for each month, the cumulative confirmed case count is captured at the end of the month based on the latest data available for each entity (uid).

Load data within a session

You can even load data from disk/URL via the natural language query:

Load the file 'data/titanic.csv' into a table called 'raw_passengers'. Create a view of the raw passengers table for just the male passengers. What was the average fare for surviving male passengers?

 🦆 Creating local DuckDB database...
 🚀 Sending query to LLM
 🤖 The average fare for surviving male passengers is approximately $40.82.


I created a table called `raw_passengers` from the Titanic dataset loaded from 'data/titanic.csv'. Then, I created a view called `male_passengers` that
includes only male passengers. Finally, I calculated the average fare for surviving male passengers, which is approximately $40.82.

SELECT AVG(Fare) AS average_fare_surviving_male FROM male_passengers WHERE Survived = 1;

Quickstart

You need to set the OPENAI_API_KEY environment variable to your OpenAI API key, which you can get from here. Other OpenAI compatible APIs can also be used by setting OPENAI_BASE_URL.

Install the qabot command line tool using uv/pip/pipx:

$ uv tool install qabot

Then run the qabot command with optional files (-f my-file.csv) and an initial query -q "How many...".

See all options with qabot --help

Security Risks

This program gives an LLM access to your local and network accessible files and allows it to execute arbitrary SQL queries in a DuckDB database, see Security for more information.

LLM Providers

qabot works with any OpenAI compatible api including Ollama and deepseek. Simple set the base URL:

export OPENAI_BASE_URL=https://api.deepseek.com

Or Ollama:

OPENAI_BASE_URL=http://localhost:11434/v1/ 
QABOT_MODEL_NAME=qwen2.5-coder:7b 
QABOT_PLANNING_MODEL_NAME=deepseek-r1:14b 

Python API

from qabot import ask_wikidata, ask_file, ask_database

print(ask_wikidata("How many hospitals are there in New Zealand?"))
print(ask_file("How many men were aboard the titanic?", 'data/titanic.csv'))
print(ask_database("How many product images are there?", 'postgresql://user:password@localhost:5432/dbname'))

Output:

There are 54 hospitals in New Zealand.
There were 577 male passengers on the Titanic.
There are 6,225 product images.

Examples

Local CSV file/s

$ qabot -q "Show the survival rate by gender, and ticket class shown as an ASCII graph" -f data/titanic.csv
🦆 Loading data from files...
Loading data/titanic.csv into table titanic...

Here’s the survival count represented as a horizontal bar graph grouped by ticket class and gender:

Class 1:
Females  | ██████████████████████████████████████████ (91)
Males    | ██████████████ (45)

Class 2:
Females  | ██████████████████████████ (70)
Males    | ██████████ (17)

Class 3:
Females  | ██████████████████████████████ (72)
Males    | ██████████████ (47)


This representation allows us to observe that in all classes, a greater number of female passengers survived compared to male passengers, and also highlights the number of survivors is notably higher in the first class compared to the other classes.

Query WikiData

Use the -w flag to query wikidata.

$ qabot -w -q "How many Hospitals are there located in Beijing"

Intermediate steps and database queries

Use the -v flag to see the intermediate steps and database queries. Sometimes it takes a long route to get to the answer, but it's often interesting to see how it gets there.

Data accessed via http/s3

Use the -f <url> flag to load data from a url, e.g. a csv file on s3:

$ qabot -f s3://covid19-lake/enigma-jhu-timeseries/csv/jhu_csse_covid_19_timeseries_merged.csv -q "how many confirmed cases of covid are there?" -v
🦆 Loading data from files...
create table jhu_csse_covid_19_timeseries_merged as select * from 's3://covid19-lake/enigma-jhu-timeseries/csv/jhu_csse_covid_19_timeseries_merged.csv';

Result:
264308334 confirmed cases

Docker Usage

You can run qabot via Docker:

docker run --rm \
  -e OPENAI_API_KEY=<your_openai_api_key> \
  -v ./data:/opt
  ghcr.io/hardbyte/qabot -f /opt/titanic.csv -q "What ratio of passengers were under 30?"

Replace the mount path to your actual data along with replacing your_openai_api_key.

Ideas

• G-Sheets via https://github.com/evidence-dev/duckdb_gsheets
• Streaming mode to output results as they come in
• token limits and better reporting of costs
• Supervisor agent - assess whether a query is "safe" to run, could ask for user confirmation to run anything that gets flagged.
• Often we can zero-shot the question and get a single query out - perhaps we try this before the MKL chain
• test each zeroshot agent individually
• Generate and pass back assumptions made to the user
• Add an optional "clarify" tool to the chain that asks the user to clarify the question
• Create a query checker tool that checks if the query looks valid and/or safe
• Inject AWS credentials into duckdb for access to private resources in S3
• Automatic publishing to pypi e.g. using trusted publishers