32 lines
1.8 KiB
Common Lisp
32 lines
1.8 KiB
Common Lisp
(in-package :org-agent)
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(defun get-embedding (text)
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"Retrieves a vector representation of text via the configured neural provider."
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(let* ((auth (get-provider-auth :gemini)) (api-key (getf auth :api-key))
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(endpoint "https://generativelanguage.googleapis.com/v1beta/models/text-embedding-004:embedContent"))
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(unless api-key (return-from get-embedding nil))
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(let* ((url (format nil "~a?key=~a" endpoint api-key)) (headers `(("Content-Type" . "application/json")))
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(body (cl-json:encode-json-to-string `((model . "models/text-embedding-004") (content . ((parts . ((text . ,text)))))))))
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(handler-case (let* ((response (dex:post url :headers headers :content body))
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(json (cl-json:decode-json-from-string response)))
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(cdr (assoc :values (cdr (assoc :embedding json)))))
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(error (c) (kernel-log "EMBEDDING FAILURE: ~a" c) nil)))))
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(defun dot-product (v1 v2)
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"Calculates the dot product of two numerical vectors."
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(reduce #'+ (mapcar #'* v1 v2)))
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(defun magnitude (v)
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"Calculates the Euclidean magnitude of a numerical vector."
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(sqrt (reduce #'+ (mapcar (lambda (x) (* x x)) v))))
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(defun cosine-similarity (v1 v2)
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"Calculates the semantic distance between two vectors."
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(let ((m1 (magnitude v1)) (m2 (magnitude v2))) (if (or (zerop m1) (zerop m2)) 0 (/ (dot-product v1 v2) (* m1 m2)))))
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(defun find-most-similar (query-vector top-k)
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"Identifies the top-k most semantically related objects in the store."
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(let ((similarities nil))
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(maphash (lambda (id obj) (let ((vec (org-object-vector obj))) (when vec (push (cons (cosine-similarity query-vector vec) obj) similarities)))) *object-store*)
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(let ((sorted (sort similarities #'> :key #'car))) (subseq sorted 0 (min top-k (length sorted))))))
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