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import React, { createContext, useContext, useState, useCallback } from 'react'
import { EmbeddingExample, SimilarityResult } from '../types'
interface FeatureExtractionConfig {
pooling: 'mean' | 'cls'
normalize: boolean
}
interface FeatureExtractionContextType {
examples: EmbeddingExample[]
setExamples: React.Dispatch<React.SetStateAction<EmbeddingExample[]>>
selectedExample: EmbeddingExample | null
setSelectedExample: React.Dispatch<
React.SetStateAction<EmbeddingExample | null>
>
similarities: SimilarityResult[]
setSimilarities: React.Dispatch<React.SetStateAction<SimilarityResult[]>>
config: FeatureExtractionConfig
setConfig: React.Dispatch<React.SetStateAction<FeatureExtractionConfig>>
addExample: (text: string) => void
removeExample: (id: string) => void
updateExample: (id: string, updates: Partial<EmbeddingExample>) => void
calculateSimilarities: (targetExample: EmbeddingExample) => void
clearExamples: () => void
}
const FeatureExtractionContext = createContext<
FeatureExtractionContextType | undefined
>(undefined)
export const useFeatureExtraction = () => {
const context = useContext(FeatureExtractionContext)
if (!context) {
throw new Error(
'useFeatureExtraction must be used within a FeatureExtractionProvider'
)
}
return context
}
// Cosine similarity calculation
const cosineSimilarity = (a: number[], b: number[]): number => {
if (a.length !== b.length) {
throw new Error('Vectors must have the same length')
}
let dotProduct = 0
let normA = 0
let normB = 0
for (let i = 0; i < a.length; i++) {
dotProduct += a[i] * b[i]
normA += a[i] * a[i]
normB += b[i] * b[i]
}
normA = Math.sqrt(normA)
normB = Math.sqrt(normB)
if (normA === 0 || normB === 0) {
return 0
}
return dotProduct / (normA * normB)
}
export const FeatureExtractionProvider: React.FC<{
children: React.ReactNode
}> = ({ children }) => {
const [examples, setExamples] = useState<EmbeddingExample[]>([])
const [selectedExample, setSelectedExample] =
useState<EmbeddingExample | null>(null)
const [similarities, setSimilarities] = useState<SimilarityResult[]>([])
const [config, setConfig] = useState<FeatureExtractionConfig>({
pooling: 'mean',
normalize: true
})
const addExample = useCallback((text: string) => {
const newExample: EmbeddingExample = {
id: Date.now().toString() + Math.random().toString(36).substr(2, 9),
text: text.trim(),
embedding: undefined,
isLoading: false
}
setExamples((prev) => [...prev, newExample])
}, [])
const removeExample = useCallback(
(id: string) => {
setExamples((prev) => prev.filter((example) => example.id !== id))
if (selectedExample?.id === id) {
setSelectedExample(null)
setSimilarities([])
}
},
[selectedExample]
)
const updateExample = useCallback(
(id: string, updates: Partial<EmbeddingExample>) => {
setExamples((prev) =>
prev.map((example) =>
example.id === id ? { ...example, ...updates } : example
)
)
},
[]
)
const calculateSimilarities = useCallback(
(targetExample: EmbeddingExample) => {
if (!targetExample.embedding) {
setSimilarities([])
return
}
const newSimilarities: SimilarityResult[] = examples
.filter(
(example) => example.id !== targetExample.id && example.embedding
)
.map((example) => ({
exampleId: example.id,
similarity: cosineSimilarity(
targetExample.embedding!,
example.embedding!
)
}))
.sort((a, b) => b.similarity - a.similarity)
setSimilarities(newSimilarities)
},
[examples]
)
const clearExamples = useCallback(() => {
setExamples([])
setSelectedExample(null)
setSimilarities([])
}, [])
const value: FeatureExtractionContextType = {
examples,
setExamples,
selectedExample,
setSelectedExample,
similarities,
setSimilarities,
config,
setConfig,
addExample,
removeExample,
updateExample,
calculateSimilarities,
clearExamples
}
return (
<FeatureExtractionContext.Provider value={value}>
{children}
</FeatureExtractionContext.Provider>
)
}
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