mirror of
https://github.com/toeverything/AFFiNE.git
synced 2026-02-12 20:38:52 +00:00
@@ -79,7 +79,7 @@ export class MockCopilotProvider extends OpenAIProvider {
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capabilities: [
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{
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input: [ModelInputType.Text, ModelInputType.Image],
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output: [ModelOutputType.Text],
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output: [ModelOutputType.Text, ModelOutputType.Structured],
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},
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],
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},
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@@ -1,41 +1,75 @@
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import {
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createOpenAI,
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type OpenAIProvider as VercelOpenAIProvider,
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} from '@ai-sdk/openai';
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import { embedMany, generateObject } from 'ai';
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import { Logger } from '@nestjs/common';
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import { chunk } from 'lodash-es';
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import { ChunkSimilarity, Embedding } from '../../../models';
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import { OpenAIConfig } from '../providers/openai';
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import {
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CopilotPromptNotFound,
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CopilotProviderNotSupported,
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} from '../../../base';
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import type { ChunkSimilarity, Embedding } from '../../../models';
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import type { PromptService } from '../prompt';
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import {
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type CopilotProvider,
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type CopilotProviderFactory,
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type ModelFullConditions,
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ModelInputType,
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ModelOutputType,
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} from '../providers';
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import {
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EMBEDDING_DIMENSIONS,
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EmbeddingClient,
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getReRankSchema,
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ReRankResult,
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type ReRankResult,
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} from './types';
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const RERANK_MODEL = 'gpt-4.1-mini';
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const RERANK_PROMPT = 'Rerank results';
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export class OpenAIEmbeddingClient extends EmbeddingClient {
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readonly #instance: VercelOpenAIProvider;
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export class ProductionEmbeddingClient extends EmbeddingClient {
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private readonly logger = new Logger(ProductionEmbeddingClient.name);
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constructor(config: OpenAIConfig) {
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constructor(
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private readonly providerFactory: CopilotProviderFactory,
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private readonly prompt: PromptService
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) {
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super();
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this.#instance = createOpenAI({
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apiKey: config.apiKey,
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baseURL: config.baseUrl,
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}
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override async configured(): Promise<boolean> {
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const embedding = await this.providerFactory.getProvider({
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outputType: ModelOutputType.Embedding,
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});
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const result = Boolean(embedding);
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if (!result) {
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this.logger.warn(
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'Copilot embedding client is not configured properly, please check your configuration.'
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);
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}
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return result;
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}
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private async getProvider(
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cond: ModelFullConditions
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): Promise<CopilotProvider> {
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const provider = await this.providerFactory.getProvider(cond);
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if (!provider) {
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throw new CopilotProviderNotSupported({
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provider: 'embedding',
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kind: cond.outputType || 'embedding',
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});
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}
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return provider;
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}
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async getEmbeddings(input: string[]): Promise<Embedding[]> {
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const modelInstance = this.#instance.embedding('text-embedding-3-large', {
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dimensions: EMBEDDING_DIMENSIONS,
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const provider = await this.getProvider({
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outputType: ModelOutputType.Embedding,
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});
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this.logger.verbose(`Using provider ${provider.type} for embedding`, input);
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const { embeddings } = await embedMany({
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model: modelInstance,
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values: input,
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});
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const embeddings = await provider.embedding(
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{ inputTypes: [ModelInputType.Text] },
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input,
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{ dimensions: EMBEDDING_DIMENSIONS }
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);
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return Array.from(embeddings.entries()).map(([index, embedding]) => ({
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index,
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@@ -44,27 +78,6 @@ export class OpenAIEmbeddingClient extends EmbeddingClient {
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}));
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}
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private getRelevancePrompt<Chunk extends ChunkSimilarity = ChunkSimilarity>(
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query: string,
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embeddings: Chunk[]
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) {
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const results = embeddings
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.map(e => {
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const targetId = 'docId' in e ? e.docId : 'fileId' in e ? e.fileId : '';
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// NOTE: not xml, just for the sake of the prompt format
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return [
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'<result>',
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`<targetId>${targetId}</targetId>`,
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`<chunk>${e.chunk}</chunk>`,
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`<content>${e.content}</content>`,
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'</result>',
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];
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})
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.flat()
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.join('\n');
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return `Generate a score array based on the search results list to measure the likelihood that the information contained in the search results is useful for the report on the following topic: ${query}\n\nHere are the search results:\n<results>\n${results}\n</results>`;
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}
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private async getEmbeddingRelevance<
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Chunk extends ChunkSimilarity = ChunkSimilarity,
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>(
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@@ -72,19 +85,36 @@ export class OpenAIEmbeddingClient extends EmbeddingClient {
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embeddings: Chunk[],
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signal?: AbortSignal
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): Promise<ReRankResult> {
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const prompt = this.getRelevancePrompt(query, embeddings);
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const modelInstance = this.#instance(RERANK_MODEL);
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if (!embeddings.length) return [];
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const {
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object: { ranks },
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} = await generateObject({
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model: modelInstance,
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prompt,
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schema: getReRankSchema(embeddings.length),
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maxRetries: 3,
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abortSignal: signal,
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});
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return ranks;
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const prompt = await this.prompt.get(RERANK_PROMPT);
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if (!prompt) {
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throw new CopilotPromptNotFound({ name: RERANK_PROMPT });
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}
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const provider = await this.getProvider({ modelId: prompt.model });
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const schema = getReRankSchema(embeddings.length);
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const ranks = await provider.structure(
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{ modelId: prompt.model },
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prompt.finish({
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query,
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results: embeddings.map(e => {
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const targetId =
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'docId' in e ? e.docId : 'fileId' in e ? e.fileId : '';
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return { targetId, chunk: e.chunk, content: e.content };
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}),
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schema,
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}),
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{ maxRetries: 3, signal }
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);
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try {
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return schema.parse(JSON.parse(ranks)).ranks;
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} catch (error) {
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this.logger.error('Failed to parse rerank results', error);
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// silent error, will fallback to default sorting in parent method
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return [];
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}
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}
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override async reRank<Chunk extends ChunkSimilarity = ChunkSimilarity>(
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@@ -110,6 +140,10 @@ export class OpenAIEmbeddingClient extends EmbeddingClient {
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const ranks = [];
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for (const c of chunk(sortedEmbeddings, Math.min(topK, 10))) {
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const rank = await this.getEmbeddingRelevance(query, c, signal);
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if (c.length !== rank.length) {
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// llm return wrong result, fallback to default sorting
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return super.reRank(query, embeddings, topK, signal);
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}
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ranks.push(rank);
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}
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@@ -124,6 +158,21 @@ export class OpenAIEmbeddingClient extends EmbeddingClient {
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}
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}
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let EMBEDDING_CLIENT: EmbeddingClient | undefined;
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export async function getEmbeddingClient(
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providerFactory: CopilotProviderFactory,
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prompt: PromptService
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): Promise<EmbeddingClient | undefined> {
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if (EMBEDDING_CLIENT) {
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return EMBEDDING_CLIENT;
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}
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const client = new ProductionEmbeddingClient(providerFactory, prompt);
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if (await client.configured()) {
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EMBEDDING_CLIENT = client;
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}
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return EMBEDDING_CLIENT;
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}
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export class MockEmbeddingClient extends EmbeddingClient {
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async getEmbeddings(input: string[]): Promise<Embedding[]> {
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return input.map((_, i) => ({
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@@ -4,7 +4,6 @@ import {
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AFFiNELogger,
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BlobNotFound,
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CallMetric,
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Config,
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CopilotContextFileNotSupported,
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DocNotFound,
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EventBus,
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@@ -15,9 +14,11 @@ import {
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} from '../../../base';
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import { DocReader } from '../../../core/doc';
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import { Models } from '../../../models';
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import { PromptService } from '../prompt';
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import { CopilotProviderFactory } from '../providers';
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import { CopilotStorage } from '../storage';
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import { readStream } from '../utils';
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import { OpenAIEmbeddingClient } from './embedding';
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import { getEmbeddingClient } from './embedding';
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import type { Chunk, DocFragment } from './types';
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import { EMBEDDING_DIMENSIONS, EmbeddingClient } from './types';
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@@ -30,11 +31,12 @@ export class CopilotContextDocJob {
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private client: EmbeddingClient | undefined;
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constructor(
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private readonly config: Config,
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private readonly doc: DocReader,
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private readonly event: EventBus,
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private readonly logger: AFFiNELogger,
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private readonly models: Models,
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private readonly providerFactory: CopilotProviderFactory,
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private readonly prompt: PromptService,
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private readonly queue: JobQueue,
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private readonly storage: CopilotStorage
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) {
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@@ -54,10 +56,8 @@ export class CopilotContextDocJob {
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private async setup() {
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this.supportEmbedding =
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await this.models.copilotContext.checkEmbeddingAvailable();
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if (this.supportEmbedding && this.config.copilot.providers.openai.apiKey) {
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this.client = new OpenAIEmbeddingClient(
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this.config.copilot.providers.openai
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);
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if (this.supportEmbedding) {
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this.client = await getEmbeddingClient(this.providerFactory, this.prompt);
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}
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}
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@@ -89,6 +89,14 @@ export class CopilotContextDocJob {
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if (!this.supportEmbedding) return;
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for (const { workspaceId, docId } of docs) {
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const jobId = `workspace:embedding:${workspaceId}:${docId}`;
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const job = await this.queue.get(jobId, 'copilot.embedding.docs');
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// if the job exists and is older than 5 minute, remove it
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if (job && job.timestamp + 5 * 60 * 1000 < Date.now()) {
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this.logger.verbose(`Removing old embedding job ${jobId}`);
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await this.queue.remove(jobId, 'copilot.embedding.docs');
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}
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await this.queue.add(
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'copilot.embedding.docs',
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{
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@@ -99,6 +107,7 @@ export class CopilotContextDocJob {
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{
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jobId: `workspace:embedding:${workspaceId}:${docId}`,
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priority: options?.priority ?? 1,
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timestamp: Date.now(),
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}
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);
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}
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@@ -336,6 +345,9 @@ export class CopilotContextDocJob {
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workspaceId,
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docId
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);
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this.logger.verbose(
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`Check if doc ${docId} in workspace ${workspaceId} needs embedding: ${needEmbedding}`
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);
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if (needEmbedding) {
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if (signal.aborted) return;
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const fragment = await this.getDocFragment(workspaceId, docId);
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@@ -2,7 +2,6 @@ import { Injectable, OnApplicationBootstrap } from '@nestjs/common';
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import {
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Cache,
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Config,
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CopilotInvalidContext,
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NoCopilotProviderAvailable,
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OnEvent,
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@@ -15,9 +14,11 @@ import {
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ContextFile,
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Models,
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} from '../../../models';
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import { OpenAIEmbeddingClient } from './embedding';
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import { PromptService } from '../prompt';
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import { CopilotProviderFactory } from '../providers';
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import { getEmbeddingClient } from './embedding';
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import { ContextSession } from './session';
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import { EmbeddingClient } from './types';
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import type { EmbeddingClient } from './types';
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const CONTEXT_SESSION_KEY = 'context-session';
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@@ -27,26 +28,24 @@ export class CopilotContextService implements OnApplicationBootstrap {
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private client: EmbeddingClient | undefined;
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constructor(
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private readonly config: Config,
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private readonly cache: Cache,
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private readonly models: Models
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private readonly models: Models,
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private readonly providerFactory: CopilotProviderFactory,
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private readonly prompt: PromptService
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) {}
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@OnEvent('config.init')
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onConfigInit() {
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this.setup();
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async onConfigInit() {
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await this.setup();
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}
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@OnEvent('config.changed')
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onConfigChanged() {
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this.setup();
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async onConfigChanged() {
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await this.setup();
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}
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private setup() {
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const configure = this.config.copilot.providers.openai;
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if (configure.apiKey) {
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this.client = new OpenAIEmbeddingClient(configure);
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}
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private async setup() {
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this.client = await getEmbeddingClient(this.providerFactory, this.prompt);
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}
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async onApplicationBootstrap() {
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|
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@@ -69,6 +69,10 @@ export type Chunk = {
|
||||
export const EMBEDDING_DIMENSIONS = 1024;
|
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|
||||
export abstract class EmbeddingClient {
|
||||
async configured() {
|
||||
return true;
|
||||
}
|
||||
|
||||
async getFileEmbeddings(
|
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file: File,
|
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chunkMapper: (chunk: Chunk[]) => Chunk[],
|
||||
|
||||
@@ -335,7 +335,66 @@ Convert a multi-speaker audio recording into a structured JSON format by transcr
|
||||
requireAttachment: true,
|
||||
},
|
||||
},
|
||||
{
|
||||
name: 'Rerank results',
|
||||
action: 'Rerank results',
|
||||
model: 'gpt-4.1-mini',
|
||||
messages: [
|
||||
{
|
||||
role: 'system',
|
||||
content: `Evaluate and rank search results based on their relevance and quality to the given query by assigning a score from 1 to 10, where 10 denotes the highest relevance.
|
||||
|
||||
Consider various factors such as content alignment with the query, source credibility, timeliness, and user intent.
|
||||
|
||||
# Steps
|
||||
|
||||
1. **Read the Query**: Understand the main intent and specific details of the search query.
|
||||
2. **Review Each Result**:
|
||||
- Analyze the content's relevance to the query.
|
||||
- Assess the credibility of the source or website.
|
||||
- Consider the timeliness of the information, ensuring it's current and relevant.
|
||||
- Evaluate the alignment with potential user intent based on the query.
|
||||
3. **Scoring**:
|
||||
- Assign a score from 1 to 10 based on the overall relevance and quality, with 10 being the most relevant.
|
||||
|
||||
# Output Format
|
||||
|
||||
Return a JSON object for each result in the following format in raw:
|
||||
{
|
||||
"scores": [
|
||||
{
|
||||
"reason": "[Reasoning behind the score in 20 words]",
|
||||
"chunk": "[chunk]",
|
||||
"targetId": "[targetId]",
|
||||
"score": [1-10]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# Notes
|
||||
|
||||
- Be aware of the potential biases or inaccuracies in the sources.
|
||||
- Consider if the content is comprehensive and directly answers the query.
|
||||
- Pay attention to the nuances of user intent that might influence relevance.`,
|
||||
},
|
||||
{
|
||||
role: 'user',
|
||||
content: `
|
||||
<query>{{query}}</query>
|
||||
<results>
|
||||
{{#results}}
|
||||
<result>
|
||||
<targetId>{{targetId}}</targetId>
|
||||
<chunk>{{chunk}}</chunk>
|
||||
<content>
|
||||
{{content}}
|
||||
</content>
|
||||
</result>
|
||||
{{/results}}
|
||||
</results>`,
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
name: 'Generate a caption',
|
||||
action: 'Generate a caption',
|
||||
|
||||
@@ -103,7 +103,7 @@ export class OpenAIProvider extends CopilotProvider<OpenAIConfig> {
|
||||
capabilities: [
|
||||
{
|
||||
input: [ModelInputType.Text, ModelInputType.Image],
|
||||
output: [ModelOutputType.Text],
|
||||
output: [ModelOutputType.Text, ModelOutputType.Structured],
|
||||
defaultForOutputType: true,
|
||||
},
|
||||
],
|
||||
@@ -113,7 +113,7 @@ export class OpenAIProvider extends CopilotProvider<OpenAIConfig> {
|
||||
capabilities: [
|
||||
{
|
||||
input: [ModelInputType.Text, ModelInputType.Image],
|
||||
output: [ModelOutputType.Text],
|
||||
output: [ModelOutputType.Text, ModelOutputType.Structured],
|
||||
},
|
||||
],
|
||||
},
|
||||
@@ -122,7 +122,16 @@ export class OpenAIProvider extends CopilotProvider<OpenAIConfig> {
|
||||
capabilities: [
|
||||
{
|
||||
input: [ModelInputType.Text, ModelInputType.Image],
|
||||
output: [ModelOutputType.Text],
|
||||
output: [ModelOutputType.Text, ModelOutputType.Structured],
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
id: 'gpt-4.1-nano',
|
||||
capabilities: [
|
||||
{
|
||||
input: [ModelInputType.Text, ModelInputType.Image],
|
||||
output: [ModelOutputType.Text, ModelOutputType.Structured],
|
||||
},
|
||||
],
|
||||
},
|
||||
@@ -283,8 +292,8 @@ export class OpenAIProvider extends CopilotProvider<OpenAIConfig> {
|
||||
model: modelInstance,
|
||||
system,
|
||||
messages: msgs,
|
||||
temperature: options.temperature || 0,
|
||||
maxTokens: options.maxTokens || 4096,
|
||||
temperature: options.temperature ?? 0,
|
||||
maxTokens: options.maxTokens ?? 4096,
|
||||
providerOptions: {
|
||||
openai: this.getOpenAIOptions(options, model.id),
|
||||
},
|
||||
@@ -322,10 +331,10 @@ export class OpenAIProvider extends CopilotProvider<OpenAIConfig> {
|
||||
model: modelInstance,
|
||||
system,
|
||||
messages: msgs,
|
||||
frequencyPenalty: options.frequencyPenalty || 0,
|
||||
presencePenalty: options.presencePenalty || 0,
|
||||
temperature: options.temperature || 0,
|
||||
maxTokens: options.maxTokens || 4096,
|
||||
frequencyPenalty: options.frequencyPenalty ?? 0,
|
||||
presencePenalty: options.presencePenalty ?? 0,
|
||||
temperature: options.temperature ?? 0,
|
||||
maxTokens: options.maxTokens ?? 4096,
|
||||
providerOptions: {
|
||||
openai: this.getOpenAIOptions(options, model.id),
|
||||
},
|
||||
@@ -388,8 +397,9 @@ export class OpenAIProvider extends CopilotProvider<OpenAIConfig> {
|
||||
model: modelInstance,
|
||||
system,
|
||||
messages: msgs,
|
||||
temperature: ('temperature' in options && options.temperature) || 0,
|
||||
maxTokens: ('maxTokens' in options && options.maxTokens) || 4096,
|
||||
temperature: options.temperature ?? 0,
|
||||
maxTokens: options.maxTokens ?? 4096,
|
||||
maxRetries: options.maxRetries ?? 3,
|
||||
schema,
|
||||
providerOptions: {
|
||||
openai: options.user ? { user: options.user } : {},
|
||||
|
||||
@@ -124,8 +124,8 @@ export class PerplexityProvider extends CopilotProvider<PerplexityConfig> {
|
||||
model: modelInstance,
|
||||
system,
|
||||
messages: msgs,
|
||||
temperature: options.temperature || 0,
|
||||
maxTokens: options.maxTokens || 4096,
|
||||
temperature: options.temperature ?? 0,
|
||||
maxTokens: options.maxTokens ?? 4096,
|
||||
abortSignal: options.signal,
|
||||
});
|
||||
|
||||
@@ -164,8 +164,8 @@ export class PerplexityProvider extends CopilotProvider<PerplexityConfig> {
|
||||
model: modelInstance,
|
||||
system,
|
||||
messages: msgs,
|
||||
temperature: options.temperature || 0,
|
||||
maxTokens: options.maxTokens || 4096,
|
||||
temperature: options.temperature ?? 0,
|
||||
maxTokens: options.maxTokens ?? 4096,
|
||||
abortSignal: options.signal,
|
||||
});
|
||||
|
||||
|
||||
@@ -172,7 +172,7 @@ export abstract class CopilotProvider<C = any> {
|
||||
structure(
|
||||
_cond: ModelConditions,
|
||||
_messages: PromptMessage[],
|
||||
_options: CopilotStructuredOptions
|
||||
_options?: CopilotStructuredOptions
|
||||
): Promise<string> {
|
||||
throw new CopilotProviderNotSupported({
|
||||
provider: this.type,
|
||||
@@ -193,7 +193,7 @@ export abstract class CopilotProvider<C = any> {
|
||||
|
||||
embedding(
|
||||
_model: ModelConditions,
|
||||
_text: string,
|
||||
_text: string | string[],
|
||||
_options?: CopilotEmbeddingOptions
|
||||
): Promise<number[][]> {
|
||||
throw new CopilotProviderNotSupported({
|
||||
|
||||
@@ -61,6 +61,8 @@ export const PromptConfigStrictSchema = z.object({
|
||||
// params requirements
|
||||
requireContent: z.boolean().nullable().optional(),
|
||||
requireAttachment: z.boolean().nullable().optional(),
|
||||
// structure output
|
||||
maxRetries: z.number().nullable().optional(),
|
||||
// openai
|
||||
frequencyPenalty: z.number().nullable().optional(),
|
||||
presencePenalty: z.number().nullable().optional(),
|
||||
|
||||
Reference in New Issue
Block a user