Skip to main content

Documentation Index

Fetch the complete documentation index at: https://wildcampstudio.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

Delve helps you automatically generate taxonomies from unstructured data using Claude and other LLMs.

​
Overview

Delve is a production-ready SDK and CLI for automatically generating taxonomies from your data using state-of-the-art language models. It analyzes your documents, identifies patterns, and creates a structured taxonomy with automatic categorization. Whether you have customer feedback, support tickets, user reviews, or any other unstructured text data, Delve can automatically discover categories and organize your content.
Delve is inspired by the TNT-LLM paper, implementing a hybrid unsupervised + supervised approach that combines LLM-powered taxonomy discovery with efficient ML-based classification. Learn more about the methodology →

​
Key Features

  • Automated Taxonomy Generation - No manual category creation needed. Delve uses iterative minibatch-based clustering with Claude 3.5 Sonnet to automatically discover categories in your data.
  • Multiple Data Sources - Work with CSV files, JSON/JSONL, LangSmith runs, or pandas DataFrames. Flexible adapters make it easy to process data from any source.
  • Smart Categorization - Iterative refinement with minibatch clustering ensures high-quality taxonomies. Built-in LLM-based validation catches quality issues.
  • Flexible Exports - Get your results in JSON, CSV, and Markdown reports. Machine-readable formats for integration, human-readable reports for analysis.
  • Both SDK and CLI - Use Delve programmatically in your Python applications or from the command line for quick analysis.
  • Smart Sampling - Automatically samples large datasets for efficient processing while maintaining representative coverage.

​
How It Works

Delve uses a sophisticated multi-stage pipeline powered by LangGraph:
  1. Data Loading - Adapters load data from various sources (CSV, JSON, LangSmith, DataFrame)
  2. Summarization - Fast LLM generates concise summaries of each document to reduce token usage
  3. Minibatch Generation - Documents are grouped into minibatches for efficient processing
  4. Iterative Clustering - Each minibatch is analyzed to generate category candidates
  5. Taxonomy Update - Categories are merged, refined, and consolidated across iterations
  6. Quality Review - LLM validates taxonomy quality and completeness
  7. Document Labeling - All documents are categorized with explanations
  8. Export - Results saved in multiple formats (JSON, CSV, Markdown)

​
Use Cases

Delve is perfect for:
  • Customer Feedback Analysis - Automatically categorize feedback into product areas, features, and sentiment
  • Support Ticket Classification - Organize support tickets by issue type, urgency, and category
  • Content Organization - Create taxonomies for articles, documentation, or knowledge bases
  • Research Data Analysis - Categorize survey responses, interview transcripts, or research notes
  • Social Media Monitoring - Classify social media posts, comments, and mentions

​
Next Steps

Quickstart

Get started in 5 minutes

How It Works

Understand the TNT-LLM methodology

Configuration Guide

Tune parameters for your use case

Examples

See code examples