csvai 0.1.0

Enrich CSV or Excel rows using OpenAI models.

CSVAI — Apply an AI prompt to each row in a CSV or Excel file and write enriched results

The csvai library reads an input CSV or Excel file, renders a prompt for each row (you can use raw column names like {{ Address }}), calls an OpenAI model via the Responses API, and writes the original columns plus AI-generated fields to an output CSV or Excel file.

The tool is async + concurrent, resumable, and crash-safe. It supports Structured Outputs with a JSON Schema for reliable JSON, or JSON mode (without a schema) if you prefer a lighter setup.

We also have a CSV AI Prompt Builder (a Custom GPT) to help you generate prompts and JSON Schemas tailored to your CSVs.

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Features

• Structured Outputs: enforce exact JSON with a schema for consistent, validated results.
• JSON mode: force a single JSON object without defining a schema.
• Async & concurrent: process many rows in parallel for faster throughput.
• Resumable: rows already written (by id) are skipped on re-run.
• CSV or Excel: handle .csv and .xlsx inputs and outputs.

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Installation

Requires Python 3.9+. OpenAI API Key: Create a key - https://platform.openai.com/api-keys and use it in the .env file with OPENAI_API_KEY= See example.env in the project repo.

From PyPI

pip install csvai
# Include Streamlit UI dependencies
pip install "csvai[ui]"

From GitHub

# Install directly from the repository
pip install git+https://github.com/zyxware/csvai.git
# With Streamlit UI dependencies
pip install "csvai[ui] @ git+https://github.com/zyxware/csvai.git"

For local development

git clone https://github.com/zyxware/csvai
cd csvai
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e .
cp example.env .env
# Edit .env and set OPENAI_API_KEY=sk-...

Installing the package exposes the csvai CLI and the csvai-ui command.

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Usage

CLI

Auto-discovery

If you name your files like this:

input.csv      # or input.xlsx
input.prompt.txt
input.schema.json   # optional

Run:

csvai input.csv      # or input.xlsx

Or specify prompt & schema explicitly

# With a prompt and a strict schema (best reliability)
csvai address.xlsx --prompt address.prompt.txt --schema address.schema.json

# Or JSON mode (no schema; still a single JSON object)
csvai address.xlsx --prompt address.prompt.txt

Sample datasets (address.csv and address.xlsx) with the matching prompt and schema live in the example/ directory.

Streamlit UI

After installing with the ui extra, launch the web interface:

csvai-ui

The UI lets you upload a CSV/Excel file, provide a prompt and optional schema.

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Example Prompt & Schema

Prompt (address.prompt.txt)

Extract city, state, and country from the given address.

Rules:
- city: city/town/locality (preserve accents, proper case)
- state: ISO-standard name of the state/region/province or "" if none
- country: ISO 3166 English short name of the country; infer if obvious, else ""
- Ignore descriptors like "(EU)"
- Do not guess street-level info

Inputs:
Address: {{Address}}

Schema (address.schema.json)

{
  "type": "object",
  "properties": {
    "city": {
      "type": "string",
      "description": "City, town, or locality name with correct casing and accents preserved"
    },
    "state": {
      "type": "string",
      "description": "ISO-standard name of the state, region, or province, or empty string if none"
    },
    "country": {
      "type": "string",
      "description": "ISO 3166 English short name of the country, inferred if obvious, else empty string"
    }
  },
  "required": ["city", "state", "country"],
  "additionalProperties": false
}

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Creating Prompts & Schemas with the CSV AI Prompt Builder

You can use the CSV AI Prompt Builder custom GPT to:

• Quickly design a prompt tailored to your CSV data.
• Generate a JSON Schema that matches your desired structured output.

Example input to the builder:

File: reviews.csv. Inputs: title,body,stars. Output: sentiment,summary.

Example result:

Prompt

Analyze each review and produce sentiment and a concise summary.

Rules:
- sentiment: one of positive, neutral, negative.
- Star mapping: stars ≤ 2 ⇒ negative; 3 ⇒ neutral; ≥ 4 ⇒ positive. If stars is blank or invalid, infer from tone.
- summary: 1–2 sentences, factual, include key pros/cons, no emojis, no first person, no marketing fluff.
- Use the same language as the Body.
- Return only the fields required by the tool schema.

Inputs:
Title: {{title}}
Body: {{body}}
Stars: {{stars}}

Schema

{
  "type": "object",
  "properties": {
    "sentiment": {
      "type": "string",
      "description": "Overall sentiment derived from stars and/or tone; one of positive, neutral, negative",
      "enum": ["positive", "neutral", "negative"]
    },
    "summary": {
      "type": "string",
      "description": "Concise 1–2 sentence summary capturing key pros/cons without opinionated fluff"
    }
  },
  "required": ["sentiment", "summary"],
  "additionalProperties": false
}

Command to execute

python -m csvai.cli reviews.csv --prompt reviews.prompt.txt --schema reviews.schema.json

Tip — have the builder generate a schema for you

• I have products.csv with Product Title, Product Description, Category, and Sub Category. Help me enrich with SEO meta fields.
• I have reviews.csv with Title, Body, and Stars. Help me extract sentiment and generate a short summary.
• I have address.csv with an Address field. Help me extract City, State, and Country using ISO-standard names.
• I have tickets.csv with Subject and Description. Help me classify each ticket into predefined support categories.
• I have posts.csv with Title, Body, URL, Image URL, Brand, and Platform. Help me generate social media captions, hashtags, emojis, CTAs, and alt text.
• I have jobs.csv with Job Title and Description. Help me categorize jobs into sectors and identify the level of seniority.

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CLI

csvai INPUT.csv [--prompt PROMPT_FILE] [--output OUTPUT_FILE]
                          [--limit N] [--model MODEL] [--schema SCHEMA_FILE]

Flags

• --prompt, -p — path to a plaintext prompt file (Jinja template).
• --output, -o — output CSV path (default: <input>_enriched.csv).
• --limit — process only the first N new/pending rows.
• --model — model name (default from .env, falls back to gpt-4o-mini).
• --schema — path to a JSON Schema for structured outputs (optional).

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Environment Variables (.env)

See example.env for all configurable variables.

OPENAI_API_KEY=sk-...
DEFAULT_MODEL=gpt-4o-mini
MAX_OUTPUT_TOKENS=600
TEMPERATURE=0.2
MAX_CONCURRENT_REQUESTS=12
PROCESSING_BATCH_SIZE=100
REQUEST_TIMEOUT=45
ALT_PROMPT_SUFFIX=.prompt.txt
OUTPUT_FILE_SUFFIX=_enriched.csv

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Input/Output Behavior

• Input CSV: the script reads all rows. If an id column exists, it’s used to resume. If not, rows are indexed 0..N-1 internally for this run.
• Prompt rendering: every row is sanitized so {{ Raw Header }} becomes {{ Raw_Header }}. You can also reference the raw values as {{ raw["Raw Header"] }} if needed.
• Output CSV: contains the original columns plus AI-generated fields. The header is fixed after the first successful batch; later rows are written with the same header order.
• Resume: rerunning skips rows whose id is already present in the output file.

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Structured Outputs vs JSON Mode

Structured Outputs (recommended)

When you pass --schema, the request includes:

text={
  "format": {
    "type": "json_schema",
    "name": "row_schema",
    "schema": schema,
    "strict": true
  }
}

This guarantees the model returns exactly the keys/types you expect.

JSON Mode (no schema)

When no schema is provided, the request includes:

text={"format": {"type": "json_object"}}

The model must still return a single JSON object, but no exact schema is enforced.

Prompting tip: mention the word JSON in your prompt and explicitly list the expected fields to improve compliance in JSON mode.

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Performance & Concurrency

• Concurrency is controlled by MAX_CONCURRENT_REQUESTS.
• Increase gradually; too high can trigger API rate limits.
• PROCESSING_BATCH_SIZE controls how many results are written per batch.
• REQUEST_TIMEOUT guards slow requests; the script retries with backoff.

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Troubleshooting

“Missing required parameter: text.format.name” You used structured outputs but didn’t include name alongside type and schema. The script already sends this correctly; ensure you’re on the latest version and that --schema points to the right file.

“Invalid schema … required must include every key” The Responses structured-outputs path expects required to include all keys in properties. Either (a) add them all to required, (b) remove non-required keys from properties, or (c) use JSON mode.

Rows not resuming Ensure there’s an id column in both input and output. If not present, the script uses positional IDs for the current run only.

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FAQ

Q: Does it run concurrently? Yes. Concurrency is controlled via MAX_CONCURRENT_REQUESTS (default 10).

Q: Can I rely on an id column? Yes. If present in the input CSV, it’s used for resumability. Otherwise rows are indexed for the session.

Q: Can I output nested JSON? The schema can be nested, but CSV is flat. If you want nested data, extend the script with a flattener (e.g., convert address.street → address_street).

Q: Which models work? Recent gpt-4o* models support Responses + Structured Outputs. If a model doesn’t support it, use JSON mode.

Q: Do I need a JSON schema? No, but it’s strongly recommended for stable columns and fewer parse failures.

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Support

This application was developed as an internal tool and we will continue to improve and optimize it as long as we use it. If you would like us to customize this or build a similar or related system to automate your tasks with AI, we are available for commercial support.

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About Zyxware Technologies

At Zyxware Technologies, our mission is to help organizations harness the power of technology to solve real-world problems. Guided by our founding values of honesty and fairness, we are committed to delivering genuine value to our clients and the free and open-source community.

CSVAI is a direct result of this philosophy. We originally developed it to automate and streamline our own internal data-enrichment tasks. Realizing its potential to help others, we are sharing it as a free tool in the spirit of our commitment to Free Software.

Our expertise is centered around our AI & Automation Services. We specialize in building intelligent solutions that reduce manual effort, streamline business operations, and unlock data-driven insights. While we provide powerful free tools like this one, we also offer custom development and commercial support for businesses that require tailored AI solutions.

If you're looking to automate a unique business process or build a similar system, we invite you to reach out to us to schedule a free discovery call.

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Updates

For updates and new versions, visit: Project Page @ Zyxware

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Contact

https://www.zyxware.com/contact-us

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Source Repository

https://github.com/zyxware/csvai

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Reporting Issues

https://github.com/zyxware/csvai/issues

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License and Disclaimer

GPL v2 – Free to use & modify. Use it at your own risk. We are not collecting any user data.

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Need Help or Commercial Support?

If you have any questions, feel free to contact us.