**feat(similarity): add similarity calculation between vectors and text inputs**

- Introduced `calculateSimilarity` method in `types.d.ts` for comparing vectors or textual inputs:
  - Added support for custom options, including `forceEmbed` and a custom `distanceFunction`.
- Enhanced functionality in `embed` to convert text inputs into vector representations.
- Added new test cases in `vector-operations.test.ts`:
  - Validated similarity calculations between identical and different vectors.
  - Tested similarity scoring for similar and dissimilar text inputs.
- Updated `README.md`:
  - Documented `calculateSimilarity` usage examples, including advanced options.
  - Clarified the integration of the similarity function into workflows.

**Purpose**: Enable calculation of similarity scores for vectors and text inputs to facilitate advanced data comparison and retrieval tasks.
This commit is contained in:
David Snelling 2025-08-01 10:16:09 -07:00
parent 90cbccb1da
commit d05d381a5d
3 changed files with 77 additions and 0 deletions

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@ -731,6 +731,25 @@ await threadedDb.init()
// Directly embed text to vectors
const vector = await db.embed("Some text to convert to a vector")
// Calculate similarity between two texts or vectors
const similarity = await db.calculateSimilarity(
"Cats are furry pets",
"Felines make good companions"
)
console.log(`Similarity score: ${similarity}`) // Higher value means more similar
// Calculate similarity with custom options
const vectorA = await db.embed("First text")
const vectorB = await db.embed("Second text")
const customSimilarity = await db.calculateSimilarity(
vectorA, // Can use pre-computed vectors
vectorB,
{
forceEmbed: false, // Skip embedding if inputs are already vectors
distanceFunction: cosineDistance // Optional custom distance function
}
)
```
The threaded embedding function runs in a separate thread (Web Worker in browsers, Worker Thread in Node.js) to improve

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@ -15,6 +15,14 @@ declare module '@soulcraft/brainy' {
search(query: string, limit?: number, options?: any): Promise<any[]>
searchText(query: string, limit?: number, options?: any): Promise<any[]>
embed(data: string | string[]): Promise<number[]>
calculateSimilarity(
a: number[] | string | string[],
b: number[] | string | string[],
options?: { forceEmbed?: boolean, distanceFunction?: any }
): Promise<number>
addVerb(
sourceId: string,

View file

@ -88,4 +88,54 @@ describe('Vector Operations', () => {
// The closest should be the exact match
expect(results[0].metadata.id).toBe('vec1')
})
it('should calculate similarity between vectors correctly', async () => {
const brainy = await import('../dist/unified.js')
const db = new brainy.BrainyData({
distanceFunction: euclideanDistance
})
await db.init()
// Create test vectors
const vectorA = createTestVector(0)
const vectorB = createTestVector(0) // Identical to vectorA
const vectorC = createTestVector(1) // Different from vectorA
// Calculate similarity between identical vectors
const similarityIdentical = await db.calculateSimilarity(vectorA, vectorB)
// Calculate similarity between different vectors
const similarityDifferent = await db.calculateSimilarity(vectorA, vectorC)
// Identical vectors should have similarity close to 1
expect(similarityIdentical).toBeCloseTo(1, 1)
// Different vectors should have lower similarity
expect(similarityDifferent).toBeLessThan(similarityIdentical)
})
it('should calculate similarity between text inputs correctly', async () => {
const brainy = await import('../dist/unified.js')
const db = new brainy.BrainyData()
await db.init()
// Calculate similarity between similar texts
const similarityHigh = await db.calculateSimilarity(
"Cats are furry pets",
"Felines make good companions"
)
// Calculate similarity between different texts
const similarityLow = await db.calculateSimilarity(
"Cats are furry pets",
"Python is a programming language"
)
// Similar texts should have higher similarity than different texts
expect(similarityHigh).toBeGreaterThan(similarityLow)
})
})