Files
passepartout/src/embedding-logic.lisp

61 lines
2.5 KiB
Common Lisp

(in-package :org-agent)
(defun get-embedding (text)
"Retrieves a vector representation of text via the configured neural provider."
(let* ((auth (get-provider-auth :gemini))
(api-key (getf auth :api-key))
(endpoint "https://generativelanguage.googleapis.com/v1beta/models/text-embedding-004:embedContent"))
(unless api-key
(harness-log "EMBEDDING ERROR: No API key for :gemini")
(return-from get-embedding nil))
(let* ((url (format nil "~a?key=~a" endpoint api-key))
(headers `(("Content-Type" . "application/json")))
(body (cl-json:encode-json-to-string
`((model . "models/text-embedding-004")
(content . ((parts . ((text . ,text)))))))))
(handler-case
(let* ((response (dex:post url :headers headers :content body))
(json (cl-json:decode-json-from-string response))
(embedding (getf (getf json :embedding) :values)))
embedding)
(error (c)
(harness-log "EMBEDDING FAILURE: ~a" c)
nil)))))
(defun dot-product (v1 v2)
"Calculates the dot product of two numerical vectors."
(reduce #'+ (mapcar #'* v1 v2)))
(defun magnitude (v)
"Calculates the Euclidean magnitude of a numerical vector."
(sqrt (reduce #'+ (mapcar (lambda (x) (* x x)) v))))
(defun cosine-similarity (v1 v2)
"Calculates the semantic distance between two vectors."
(let ((m1 (magnitude v1))
(m2 (magnitude v2)))
(if (or (zerop m1) (zerop m2)) 0 (/ (dot-product v1 v2) (* m1 m2)))))
(defun find-most-similar (query-vector top-k)
"Identifies the top-k most semantically related objects in the store."
(let ((similarities nil))
(maphash (lambda (id obj)
(declare (ignore id))
(let ((vec (org-object-vector obj)))
(when vec
(push (cons (cosine-similarity query-vector vec) obj) similarities))))
*object-store*)
(let ((sorted (sort similarities #'> :key #'car)))
(subseq sorted 0 (min top-k (length sorted))))))
(defskill :skill-embedding
:priority 50
:trigger (lambda (ctx) (eq (getf (getf ctx :payload) :sensor) :embedding-request))
:neuro nil
:symbolic (lambda (action ctx)
(declare (ignore ctx))
(case (getf action :action)
(:get-embedding (get-embedding (getf action :text)))
(:similarity (cosine-similarity (getf action :v1) (getf action :v2)))
(t action))))