Structured Extraction

Extract structured data from unstructured text using LLM-powered schema-based extraction and classification.

Extraction

llm/extract

Extract structured data from text according to a schema. The schema defines the expected fields and their types.

(llm/extract
  {:vendor {:type :string}
   :amount {:type :number}
   :date   {:type :string}}
  "I bought coffee for $4.50 at Blue Bottle on Jan 15, 2025")
; => {:amount 4.5 :date "2025-01-15" :vendor "Blue Bottle"}

The schema map specifies field names as keys and type descriptors as values. Supported types include :string, :number, :boolean, and :list/:array.

Options

llm/extract accepts an optional third argument — an options map:

(llm/extract schema text {:model "claude-haiku-4-5-20251001"})

Option	Type	Default	Description
:model	string	—	Override the default model
:validate	boolean	#t	Validate response against the schema
:retries	integer	2	Max retry attempts on validation failure
:reask?	boolean	#t	Feed validation errors back to the LLM on retry

Schema Validation

By default, the extracted result is validated against the schema:

• All required schema keys must be present in the result
• Types must match: :string → string, :number → integer or float, :boolean → boolean, :list/:array → list or vector

(llm/extract
  {:name {:type :string}
   :age  {:type :number}}
  "Alice is 30 years old")
; => {:age 30 :name "Alice"}

If validation fails, an error is raised with details about which fields didn't match.

Optional Fields

Mark fields as optional with :optional #t. Missing optional fields won't trigger validation errors:

(llm/extract
  {:name     {:type :string}
   :nickname {:type :string :optional #t}}
  "Her name is Ada Lovelace.")
; => {:name "Ada Lovelace"}
;; No error even though :nickname is missing

Custom Validation Predicates

Use :validate on individual field specs to run a custom predicate after type checking. If the predicate returns falsy, the field fails validation and triggers a retry:

(llm/extract
  {:amount {:type :number :validate #(> % 0)}
   :vendor {:type :string :validate #(> (string/length %) 0)}}
  "Invoice from Acme Corp for $42.50")
; => {:amount 42.5 :vendor "Acme Corp"}

Add :message to provide a human-readable error description. This message is fed back to the LLM in re-ask prompts, helping it correct its response:

(llm/extract
  {:age {:type :number
         :validate #(and (>= % 0) (<= % 150))
         :message "age must be between 0 and 150"}}
  "She is 30 years old.")
; => {:age 30}

Without :message, the default error text includes the field value: "custom validation failed for value -5".

Retry on Mismatch

Validation failures automatically trigger retries (up to :retries, default 2). On each retry, the validation errors are fed back to the LLM to improve the next attempt. After exhausting retries, the final validation error is raised.

(llm/extract
  {:items {:type :list}
   :total {:type :number :validate pos?}}
  "3 apples, 2 oranges, total 5 items")

Disable automatic retries with {:retries 0} or disable validation entirely with {:validate #f}.

Classification

llm/classify

Classify text into one of a set of categories. Returns the matching keyword.

(llm/classify (list :positive :negative :neutral)
              "This product is amazing!")
; => :positive

Pass a list of keyword labels and the text to classify. The LLM picks the best-matching label.

Vision Extraction

llm/extract-from-image

Extract structured data from images using vision-capable LLMs. Accepts a schema, an image source (file path or bytevector), and optional options.

;; Extract from a file path
(llm/extract-from-image
  {:text :string :background_color :string}
  "assets/logo.png")
; => {:background_color "white" :text "Sema"}

;; Extract from a bytevector
(define img (file/read-bytes "invoice.jpg"))
(llm/extract-from-image
  {:invoice_number :string :date :string :total :string}
  img)
; => {:date "2025-03-15" :invoice_number "12345" :total "$139.96"}

Supported image formats (detected automatically via magic bytes): PNG, JPEG, GIF, WebP, PDF.

Options

llm/extract-from-image accepts an optional third argument — an options map:

(llm/extract-from-image schema source {:model "gpt-4o"})

Option	Type	Default	Description
:model	string	—	Override the default model

Multi-Modal Messages

message/with-image

Create a message that includes both text and an image, for use with llm/chat.

(define img (file/read-bytes "photo.jpg"))
(define msg (message/with-image :user "What do you see?" img))
(llm/chat (list msg))

The image must be a bytevector (use file/read-bytes to load from disk). The media type is detected automatically.

You can combine image messages with regular messages:

(llm/chat
  (list (message :system "You are a helpful image analyst.")
        (message/with-image :user "Describe this chart." (file/read-bytes "chart.png"))))

Provider Support

Vision features work with providers that support multi-modal input:

Provider	llm/extract-from-image	message/with-image
Anthropic	✅	✅
OpenAI	✅	✅
Gemini	✅	✅
Ollama	✅ (model-dependent)	✅ (model-dependent)

For Ollama, use a vision-capable model like gemma3:4b or llava.