richertext 0.1.1

LLM-powered CSV enrichment made easy

RicherText

Turn raw CSV data into enriched insights using LLMs. Classify, label, score, summarize, and analyze your data at scale.

Before:                              After:
┌─────────────────────────────┐      ┌─────────────────────────────────────────────────────────┐
│ title        │ description  │      │ title        │ seniority │ department │ skills         │
├─────────────────────────────┤  →   ├─────────────────────────────────────────────────────────┤
│ Sr Engineer  │ Python, AWS  │      │ Sr Engineer  │ senior    │ engineering│ python;aws     │
│ Sales Rep    │ Outbound...  │      │ Sales Rep    │ entry     │ sales      │ communication  │
└─────────────────────────────┘      └─────────────────────────────────────────────────────────┘

100 records × 8 enrichments = 800 LLM calls in ~8 seconds with automatic rate limiting and resume capability.

---

Quick Start

1. Install

pip install richertext

2. Set your API key

export GOOGLE_API_KEY="your-gemini-api-key"

# Or add to .env file (auto-loaded by richertext)
echo 'GOOGLE_API_KEY=your-gemini-api-key' >> .env

Get a free API key at Google AI Studio.

3. Initialize and run

richertext init my-project
cd my-project
richertext run input/job_postings_10.csv --config taskflows/job_postings_tasks.yaml -v

That's it. Check output/job_postings_10_enriched.csv for results.

---

What You Get

The init command creates a complete working example:

my-project/
├── .env                              # Your API key goes here
├── taskflows/
│   └── job_postings_tasks.yaml       # Enrichment configuration
├── prompts/
│   └── job_postings_prompts.yaml     # Prompt templates
├── input/
│   ├── job_postings_10.csv           # 10 sample records
│   └── job_postings_100.csv          # 100 sample records
├── output/                           # Enriched results
└── docs/                             # Documentation
    ├── README.md
    ├── TUTORIAL.md
    └── ENRICHMENTS.md

The sample taskflow enriches job postings with:

Enrichment	Type	Output
seniority	classifier	entry, mid, senior, executive
department	classifier	engineering, sales, hr, etc.
skills	labeler	python;aws;sql (multiple)
experience_required	scorer	7 (1-10 scale)
technical_complexity	scorer	8 (1-10 scale)
salary_estimate	scorer	6 (1-10 scale)
candidate_profile	summarizer	"Ideal candidate is..."
remote_friendly	classifier	remote, hybrid, onsite, unclear (inline prompt)

---

Use Cases

• Lead scoring - Classify prospects by fit, urgency, budget
• Customer feedback - Sentiment, topics, urgency detection
• Content categorization - Auto-tag articles, support tickets, documents
• Resume screening - Skills extraction, experience level, role fit
• Product reviews - Sentiment, feature mentions, competitor comparisons
• Survey analysis - Theme extraction, satisfaction scoring

---

Configuration

Taskflow Structure

# taskflows/job_postings_tasks.yaml
provider:
  type: gemini
  model: gemini-2.5-flash-lite  # Fast and cheap

prompts_file: ../prompts/job_postings_prompts.yaml  # External prompts

enrichments:
  - name: sentiment
    type: classifier
    prompt_key: sentiment_prompt      # References job_postings_prompts.yaml
    categories: [positive, negative, neutral]
    input_columns: [review_text]

  - name: themes
    type: labeler
    labels: [pricing, quality, support, shipping]
    input_columns: [review_text]
    prompt: |                         # Or inline prompt
      What themes appear in this review?

      {review_text}

Enrichment Types

Type	What it does	Output
classifier	Pick ONE category	sentiment_category
labeler	Pick MULTIPLE labels	themes_labels (semicolon-separated)
summarizer	Condense text	summary_summary
scorer	Rate on a scale (1-10)	quality_score
reasoner	Free-form analysis	analysis_analysis

Prompts File

# prompts/job_postings_prompts.yaml
sentiment_prompt: |
  Classify the sentiment of this review:

  {review_text}

summary_prompt: |
  Summarize in 1-2 sentences:

  {review_text}

Use {column_name} to inject CSV columns into prompts.

---

Python API

For programmatic use without config files:

from richertext import classify, summarize, score, label, reason

# Single-record functions
result = classify("I love this product!", categories=["positive", "negative"])
print(result["category"])  # "positive"

summary = summarize("Long article text here...", max_length=100)

clarity = score("Evaluate this essay", prompt="Rate the clarity of: {text}")
# 8

labels = label("Tech news about AI", labels=["tech", "politics", "sports"])
# {"labels": ["tech"]}

analysis = reason("Q3 revenue: $2.1M, down 5% YoY")
# Free-form analysis text

Batch Processing

from richertext import enrich_csv, enrich_records

# From CSV file
enrich_csv("reviews.csv", config="taskflows/sentiment.yaml")

# From list of dicts (custom loading)
records = [
    {"id": 1, "text": "Great product!"},
    {"id": 2, "text": "Terrible experience"},
]
enriched = enrich_records(records, config="taskflows/sentiment.yaml")

# With resume capability (restart interrupted jobs)
enriched = enrich_records(
    records,
    config="taskflows/sentiment.yaml",
    output_path="output/enriched.csv",
    pk_field="id"  # Skip already-processed records
)

---

Features

Automatic Rate Limiting

Built-in token bucket rate limiting respects Gemini API limits:

Model	Limit	Burst
gemini-2.5-flash-lite	4,000 RPM	360 req
gemini-2.5-flash	1,000 RPM	90 req
gemini-2.5-pro	150 RPM	13 req

No 429 errors. No manual throttling.

Parallel Processing

Worker count is automatically calculated based on the model's rate limit:

Model	Rate Limit	Default Workers
gemini-2.5-flash-lite	4,000 RPM	133 workers
gemini-2.5-flash	1,000 RPM	33 workers
gemini-2.5-pro	150 RPM	5 workers

Override with --workers if needed:

richertext run data.csv --config job_postings_tasks.yaml --workers 50

Resume Capability

Interrupted? Just re-run the same command. RicherText reads the output file, extracts the --pk-field values already processed, and skips those records:

# First run - processes 50 records, then interrupted (Ctrl+C)
richertext run data.csv --config job_postings_tasks.yaml --pk-field id
# Output: output/data_enriched.csv (50 rows)

# Second run - reads output file, skips 50 already done, continues with remaining
richertext run data.csv --config job_postings_tasks.yaml --pk-field id
# Output: appends to output/data_enriched.csv

The --pk-field must match a column in your CSV (default: id).

Chaining Enrichments

You can reference output from earlier enrichments in later ones. This is not automatic - you must explicitly include the output column in input_columns:

enrichments:
  - name: language
    type: classifier
    categories: [english, spanish, french]
    input_columns: [text]
    output_column: detected_language  # Creates 'detected_language' column

  - name: summary
    type: summarizer
    input_columns: [text, detected_language]  # Explicitly include previous output
    prompt: |
      Summarize this {detected_language} text:

      {text}

---

CLI Reference

# Initialize new project
richertext init [project_name]

# Run enrichment
richertext run <input.csv> --config <taskflow.yaml> [options]

Options:
  -c, --config FILE      Taskflow config (required)
  -o, --output FILE      Output path (default: output/<input>_enriched.csv)
  -v, --verbose          Show progress
  -w, --workers N        Parallel workers (default: auto based on model)
  --pk-field FIELD       Primary key for resume (default: id)

---

Troubleshooting

"No API key found"

export GOOGLE_API_KEY="your-key"
# Or add to .env file

"Prompts file not found"

• Path is relative to the taskflow location
• Use ../prompts/job_postings_prompts.yaml if prompts dir is sibling to taskflows

Rate limit errors

• The built-in rate limiter should prevent these
• If you see 429s, reduce --workers

Resuming doesn't skip records

• Make sure --pk-field matches a column in your CSV
• Check that output file exists from previous run

---

License

MIT