feat: add managed Granite embedding models
GoodBuddy previously required an external OpenAI-compatible endpoint for vector retrieval. It can now install, verify, import, remove, and run the built-in Granite multilingual embedding model in an isolated local inference process, while retaining custom embedding connections and migrating existing settings. The model connection UI now manages built-in and custom vector connections consistently, reports the actual retrieval path, preserves credentials across failed saves, and renders the vector tab synchronously. Related settings copy, switch styling, metadata typography, and renderer bundle guardrails are aligned with the resulting workflow. Release note: 新增可管理的内置 Granite 多语言向量模型，支持本地下载、离线导入、连接切换与诊断；同时优化向量设置及相关界面的文案、开关和状态显示。
This commit is contained in:
mesalogo
@@ -1,4 +1,5 @@
|
||||
import type { EmbeddingProvider } from './types'
|
||||
import { embeddingProviderFingerprint } from './embedding-provider-key'
|
||||
|
||||
const MAX_INPUTS = 256
|
||||
const MAX_BATCH_SIZE = 32
|
||||
@@ -16,11 +17,33 @@ export interface OpenAIEmbeddingClientOptions {
|
||||
endpoint: string
|
||||
model: string
|
||||
apiKey?: string
|
||||
dimensions?: number
|
||||
encodingRecipe?: Partial<OpenAIEmbeddingEncodingRecipe>
|
||||
batchSize?: number
|
||||
timeoutMs?: number
|
||||
fetch?: typeof fetch
|
||||
}
|
||||
|
||||
export type EmbeddingInputRole = 'query' | 'document'
|
||||
|
||||
export interface OpenAIEmbeddingEncodingRecipe {
|
||||
recipeId: string
|
||||
tokenizerDigest?: string
|
||||
pooling: 'provider-managed'
|
||||
normalization: 'provider-managed'
|
||||
queryTemplate: string
|
||||
documentTemplate: string
|
||||
maximumSequenceTokens?: number
|
||||
}
|
||||
|
||||
const DEFAULT_ENCODING_RECIPE: OpenAIEmbeddingEncodingRecipe = {
|
||||
recipeId: 'symmetric',
|
||||
pooling: 'provider-managed',
|
||||
normalization: 'provider-managed',
|
||||
queryTemplate: '{text}',
|
||||
documentTemplate: '{text}'
|
||||
}
|
||||
|
||||
function boundedInteger(
|
||||
value: number,
|
||||
field: string,
|
||||
@@ -56,6 +79,71 @@ function normalizedEndpoint(input: string): string {
|
||||
return url.toString()
|
||||
}
|
||||
|
||||
function endpointScope(endpoint: string): 'loopback' | 'network' {
|
||||
const hostname = new URL(endpoint).hostname.toLowerCase()
|
||||
return hostname === 'localhost' ||
|
||||
hostname === '127.0.0.1' ||
|
||||
hostname === '[::1]'
|
||||
? 'loopback'
|
||||
: 'network'
|
||||
}
|
||||
|
||||
function normalizedTemplate(value: string, field: string): string {
|
||||
const template = requiredString(value, field, MAX_INPUT_LENGTH)
|
||||
if (template.split('{text}').length !== 2) {
|
||||
throw new RangeError(`${field} must contain exactly one {text} placeholder`)
|
||||
}
|
||||
return template
|
||||
}
|
||||
|
||||
function normalizedEncodingRecipe(
|
||||
input: Partial<OpenAIEmbeddingEncodingRecipe> | undefined
|
||||
): OpenAIEmbeddingEncodingRecipe {
|
||||
const recipe = { ...DEFAULT_ENCODING_RECIPE, ...input }
|
||||
if (recipe.pooling !== 'provider-managed') {
|
||||
throw new RangeError(
|
||||
'encodingRecipe.pooling must be provider-managed'
|
||||
)
|
||||
}
|
||||
if (recipe.normalization !== 'provider-managed') {
|
||||
throw new RangeError(
|
||||
'encodingRecipe.normalization must be provider-managed'
|
||||
)
|
||||
}
|
||||
return {
|
||||
recipeId: requiredString(recipe.recipeId, 'encodingRecipe.recipeId', 256),
|
||||
...(recipe.tokenizerDigest
|
||||
? {
|
||||
tokenizerDigest: requiredString(
|
||||
recipe.tokenizerDigest,
|
||||
'encodingRecipe.tokenizerDigest',
|
||||
256
|
||||
)
|
||||
}
|
||||
: {}),
|
||||
pooling: 'provider-managed',
|
||||
normalization: 'provider-managed',
|
||||
queryTemplate: normalizedTemplate(
|
||||
recipe.queryTemplate,
|
||||
'encodingRecipe.queryTemplate'
|
||||
),
|
||||
documentTemplate: normalizedTemplate(
|
||||
recipe.documentTemplate,
|
||||
'encodingRecipe.documentTemplate'
|
||||
),
|
||||
...(recipe.maximumSequenceTokens !== undefined
|
||||
? {
|
||||
maximumSequenceTokens: boundedInteger(
|
||||
recipe.maximumSequenceTokens,
|
||||
'encodingRecipe.maximumSequenceTokens',
|
||||
1,
|
||||
1_000_000
|
||||
)
|
||||
}
|
||||
: {})
|
||||
}
|
||||
}
|
||||
|
||||
function embeddingAbortError(
|
||||
requestSignal: AbortSignal,
|
||||
timeoutError: Error
|
||||
@@ -174,6 +262,8 @@ export class OpenAIEmbeddingClient implements EmbeddingProvider {
|
||||
readonly model: string
|
||||
readonly fingerprint: string
|
||||
private readonly endpoint: string
|
||||
private readonly dimensions?: number
|
||||
private readonly encodingRecipe: OpenAIEmbeddingEncodingRecipe
|
||||
private readonly apiKey?: string
|
||||
private readonly batchSize: number
|
||||
private readonly timeoutMs: number
|
||||
@@ -183,7 +273,22 @@ export class OpenAIEmbeddingClient implements EmbeddingProvider {
|
||||
this.endpoint = normalizedEndpoint(options.endpoint)
|
||||
this.model = requiredString(options.model, 'model', MAX_MODEL_LENGTH)
|
||||
this.apiKey = options.apiKey?.trim() || undefined
|
||||
this.fingerprint = `${this.provider}:${this.endpoint}:${this.model}`
|
||||
this.dimensions =
|
||||
options.dimensions === undefined
|
||||
? undefined
|
||||
: boundedInteger(options.dimensions, 'dimensions', 1, MAX_DIMENSIONS)
|
||||
this.encodingRecipe = normalizedEncodingRecipe(options.encodingRecipe)
|
||||
this.fingerprint = embeddingProviderFingerprint({
|
||||
provider: this.provider,
|
||||
endpoint: this.endpoint,
|
||||
dataPath: {
|
||||
kind: 'endpoint',
|
||||
scope: endpointScope(this.endpoint)
|
||||
},
|
||||
model: this.model,
|
||||
dimensions: this.dimensions,
|
||||
encodingRecipe: this.encodingRecipe
|
||||
})
|
||||
this.batchSize = boundedInteger(
|
||||
options.batchSize ?? 16,
|
||||
'batchSize',
|
||||
@@ -206,6 +311,31 @@ export class OpenAIEmbeddingClient implements EmbeddingProvider {
|
||||
input: readonly string[],
|
||||
signal?: AbortSignal
|
||||
): Promise<number[][]> {
|
||||
return this.embedRole(input, 'document', signal)
|
||||
}
|
||||
|
||||
async embedQuery(
|
||||
input: readonly string[],
|
||||
signal?: AbortSignal
|
||||
): Promise<number[][]> {
|
||||
return this.embedRole(input, 'query', signal)
|
||||
}
|
||||
|
||||
async embedDocuments(
|
||||
input: readonly string[],
|
||||
signal?: AbortSignal
|
||||
): Promise<number[][]> {
|
||||
return this.embedRole(input, 'document', signal)
|
||||
}
|
||||
|
||||
async embedRole(
|
||||
input: readonly string[],
|
||||
role: EmbeddingInputRole,
|
||||
signal?: AbortSignal
|
||||
): Promise<number[][]> {
|
||||
if (role !== 'query' && role !== 'document') {
|
||||
throw new RangeError('role must be query or document')
|
||||
}
|
||||
if (!Array.isArray(input) || input.length < 1 || input.length > MAX_INPUTS) {
|
||||
throw new RangeError(`input must contain between 1 and ${MAX_INPUTS} items`)
|
||||
}
|
||||
@@ -218,7 +348,17 @@ export class OpenAIEmbeddingClient implements EmbeddingProvider {
|
||||
`input[${index}] must be at most ${MAX_INPUT_LENGTH} characters`
|
||||
)
|
||||
}
|
||||
return item
|
||||
const template =
|
||||
role === 'query'
|
||||
? this.encodingRecipe.queryTemplate
|
||||
: this.encodingRecipe.documentTemplate
|
||||
const encoded = template.replace('{text}', item)
|
||||
if (encoded.length > MAX_INPUT_LENGTH) {
|
||||
throw new RangeError(
|
||||
`encoded input[${index}] must be at most ${MAX_INPUT_LENGTH} characters`
|
||||
)
|
||||
}
|
||||
return encoded
|
||||
})
|
||||
|
||||
const embeddings: number[][] = []
|
||||
@@ -284,7 +424,13 @@ export class OpenAIEmbeddingClient implements EmbeddingProvider {
|
||||
response = await this.transport(this.endpoint, {
|
||||
method: 'POST',
|
||||
headers,
|
||||
body: JSON.stringify({ model: this.model, input }),
|
||||
body: JSON.stringify({
|
||||
model: this.model,
|
||||
input,
|
||||
...(this.dimensions === undefined
|
||||
? {}
|
||||
: { dimensions: this.dimensions })
|
||||
}),
|
||||
redirect: 'error',
|
||||
signal: requestSignal
|
||||
})
|
||||
@@ -293,10 +439,17 @@ export class OpenAIEmbeddingClient implements EmbeddingProvider {
|
||||
`Embedding request failed with HTTP ${response.status}`
|
||||
)
|
||||
}
|
||||
return validateEmbeddings(
|
||||
const vectors = validateEmbeddings(
|
||||
await readBoundedJson(response),
|
||||
input.length
|
||||
)
|
||||
if (
|
||||
this.dimensions !== undefined &&
|
||||
vectors.some((vector) => vector.length !== this.dimensions)
|
||||
) {
|
||||
throw new Error('Embeddings do not match configured dimensions')
|
||||
}
|
||||
return vectors
|
||||
} catch (error) {
|
||||
if (requestSignal.aborted) {
|
||||
throw embeddingAbortError(requestSignal, timeoutError)
|
||||
|
||||
Reference in New Issue
Block a user