LLM Analysis

Overview

Anysite CLI integrates with LLM providers to add AI-powered analysis to your data workflows. Six operations are available: classify, summarize, enrich, generate, match, and deduplicate.

Requires the llm extra: pip install "anysite-cli[llm]"

Setup

Configure your LLM provider:

anysite llm setup

This guides you through selecting a provider and entering your API key.

Supported Providers

Provider	Default Model	Configuration
OpenAI	gpt-4.1-mini	Uses JSON Schema for structured output
Anthropic	claude-sonnet-4-5-20250514	Uses system prompts with JSON schema

Provider settings are stored in ~/.anysite/config.yaml.

Operations

Classify

Categorize records into predefined categories:

anysite llm classify dataset.yaml --source profiles \
  --categories "developer,recruiter,executive,other" \
  --fields "name,headline,summary"

If --categories is omitted, the LLM auto-detects 3-7 appropriate categories based on the data.

Summarize

Generate concise summaries:

anysite llm summarize dataset.yaml --source profiles \
  --fields "name,headline,summary,experience" \
  --max-length 50 \
  --output-column bio_summary

Enrich

Extract new structured attributes from text data:

anysite llm enrich dataset.yaml --source profiles \
  --add "seniority:junior/mid/senior/lead" \
  --add "is_technical:boolean" \
  --add "years_experience:number" \
  --add "primary_skill:string"

Supported attribute types:

Enum — predefined choices: "seniority:junior/mid/senior"
Boolean — true/false: "is_technical:boolean"
Number — numeric value: "years_experience:number"
String — free text: "primary_skill:string"

Generate

Create new text using templates with field placeholders:

anysite llm generate dataset.yaml --source profiles \
  --prompt "Write a 2-sentence professional intro for {name} who works as {headline}" \
  --temperature 0.7 \
  --output-column intro_text

Match

Compare records across two sources and find best matches:

anysite llm match dataset.yaml \
  --source-a profiles \
  --source-b companies \
  --top-k 3

Returns the top K matches for each record in source A, with relevance scores.

Deduplicate

Find and flag semantic duplicates within a source:

anysite llm deduplicate dataset.yaml --source profiles \
  --key name \
  --threshold 0.8

Records with similarity above the threshold are flagged as potential duplicates.

Using LLM in Dataset Pipelines

Add LLM processing directly in your pipeline YAML:

sources:
  - id: profiles
    endpoint: /api/linkedin/user
    from_file: users.txt
    input_key: user

  - id: profiles_enriched
    type: llm
    dependency:
      from_source: profiles
      field: name
    llm:
      - type: classify
        categories: "developer,recruiter,executive,sales,other"
        output_column: role_type

      - type: enrich
        add:
          - "seniority:junior/mid/senior/lead"
          - "is_technical:boolean"

      - type: summarize
        max_length: 50
        output_column: bio_summary

Multiple LLM steps can be chained within a single LLM source. They execute in order, each adding new columns to the dataset.

Caching

LLM results are cached in a local SQLite database (~/.anysite/llm_cache.db) to avoid repeated API calls and reduce costs.

# View cache statistics
anysite llm cache-stats

# Clear the cache
anysite llm cache-clear

# Bypass cache for a single run
anysite llm classify dataset.yaml --source profiles \
  --categories "dev,recruiter,exec" --no-cache

Caching is especially useful when iterating on pipeline configurations — you only pay for LLM calls once per unique input.

Options Reference

Option	Description	Applies To
`--fields`	Fields to include in LLM context (comma-separated)	classify, summarize
`--categories`	Comma-separated categories	classify
`--add`	Attribute to extract (repeatable)	enrich
`--prompt`	Template with `{field}` placeholders	generate
`--temperature`	LLM creativity (0.0-1.0)	generate
`--max-length`	Max words for output	summarize
`--output-column`	Name for the result column	all
`--top-k`	Number of matches per record	match
`--key`	Field to compare for duplicates	deduplicate
`--threshold`	Similarity threshold (0.0-1.0)	deduplicate
`--no-cache`	Skip the LLM cache	all

Get Started

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Overview

Setup

Supported Providers

Operations

Classify

Summarize

Enrich

Generate

Match

Deduplicate

Using LLM in Dataset Pipelines

Caching

Options Reference

Next Steps

SQL Querying

Examples

​Overview

​Setup

​Supported Providers

​Operations

​Classify

​Summarize

​Enrich

​Generate

​Match

​Deduplicate

​Using LLM in Dataset Pipelines

​Caching

​Options Reference

​Next Steps

SQL Querying

Examples

Overview

Setup

Supported Providers

Operations

Classify

Summarize

Enrich

Generate

Match

Deduplicate

Using LLM in Dataset Pipelines

Caching

Options Reference

Next Steps