feat(#2): Implement PostgreSQL 17 + pgvector database schema
Establishes multi-tenant database layer with vector similarity search for AI-powered memory features. Includes Docker infrastructure, Prisma ORM integration, NestJS services, and shared types across the monorepo. Key changes: - Docker: PostgreSQL 17 + pgvector v0.7.4, Valkey cache - Schema: 8 models (User, Workspace, Task, Event, Project, ActivityLog, MemoryEmbedding) with RLS preparation - NestJS: PrismaModule, DatabaseModule, EmbeddingsService - Shared: Type-safe enums, constants, and database types Fixes #2 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Jason Woltje
12
apps/api/src/database/database.module.ts
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apps/api/src/database/database.module.ts
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import { Module } from "@nestjs/common";
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import { EmbeddingsService } from "./embeddings.service";
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/**
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* Database utilities module
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* Provides services for specialized database operations
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*/
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@Module({
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providers: [EmbeddingsService],
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exports: [EmbeddingsService],
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})
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export class DatabaseModule {}
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262
apps/api/src/database/embeddings.service.ts
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apps/api/src/database/embeddings.service.ts
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import { Injectable, Logger } from "@nestjs/common";
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import { EntityType } from "@prisma/client";
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import { EMBEDDING_DIMENSION } from "@mosaic/shared";
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import { PrismaService } from "../prisma/prisma.service";
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/**
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* Result from similarity search
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*/
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export interface SimilarEmbedding {
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id: string;
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content: string;
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similarity: number;
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entityType: EntityType | null;
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entityId: string | null;
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metadata: Record<string, unknown>;
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}
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/**
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* Service for managing vector embeddings using pgvector
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* Uses raw SQL for vector operations since Prisma doesn't support vector types natively
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*/
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@Injectable()
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export class EmbeddingsService {
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private readonly logger = new Logger(EmbeddingsService.name);
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constructor(private readonly prisma: PrismaService) {}
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/**
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* Validate that an embedding array contains only finite numbers
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* @param embedding Array to validate
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* @throws Error if validation fails
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*/
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private validateEmbedding(embedding: number[]): void {
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if (!Array.isArray(embedding)) {
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throw new Error("Embedding must be an array");
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}
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if (
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!embedding.every((val) => typeof val === "number" && Number.isFinite(val))
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) {
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throw new Error("Embedding array must contain only finite numbers");
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}
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}
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/**
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* Store an embedding vector for content
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* @param params Embedding parameters
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* @returns ID of the created embedding
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*/
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async storeEmbedding(params: {
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workspaceId: string;
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content: string;
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embedding: number[];
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entityType?: EntityType;
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entityId?: string;
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metadata?: Record<string, unknown>;
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}): Promise<string> {
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const { workspaceId, content, embedding, entityType, entityId, metadata } =
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params;
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// Validate embedding array
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this.validateEmbedding(embedding);
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if (embedding.length !== EMBEDDING_DIMENSION) {
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throw new Error(
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`Invalid embedding dimension: expected EMBEDDING_DIMENSION, got ${embedding.length}`
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);
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}
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const vectorString = `[${embedding.join(",")}]`;
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try {
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const result = await this.prisma.$queryRaw<Array<{ id: string }>>`
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INSERT INTO memory_embeddings (
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id, workspace_id, content, embedding, entity_type, entity_id, metadata, created_at, updated_at
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)
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VALUES (
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gen_random_uuid(),
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${workspaceId}::uuid,
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${content},
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${vectorString}::vector,
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${entityType ?? null}::"EntityType",
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${entityId ?? null}::uuid,
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${JSON.stringify(metadata ?? {})}::jsonb,
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NOW(),
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NOW()
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)
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RETURNING id::text
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`;
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const embeddingId = result[0]?.id;
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if (!embeddingId) {
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throw new Error("Failed to get embedding ID from insert result");
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}
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this.logger.debug(
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`Stored embedding ${embeddingId} for workspace ${workspaceId}`
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);
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return embeddingId;
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} catch (error) {
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this.logger.error("Failed to store embedding", error);
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throw error;
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}
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}
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/**
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* Find similar embeddings using cosine similarity
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* @param params Search parameters
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* @returns Array of similar embeddings sorted by similarity (descending)
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*/
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async findSimilar(params: {
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workspaceId: string;
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embedding: number[];
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limit?: number;
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threshold?: number;
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entityType?: EntityType;
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}): Promise<SimilarEmbedding[]> {
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const {
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workspaceId,
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embedding,
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limit = 10,
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threshold = 0.7,
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entityType,
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} = params;
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// Validate embedding array
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this.validateEmbedding(embedding);
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if (embedding.length !== EMBEDDING_DIMENSION) {
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throw new Error(
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`Invalid embedding dimension: expected EMBEDDING_DIMENSION, got ${embedding.length}`
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);
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}
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const vectorString = `[${embedding.join(",")}]`;
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try {
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let results: SimilarEmbedding[];
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if (entityType) {
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results = await this.prisma.$queryRaw<SimilarEmbedding[]>`
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SELECT
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id::text,
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content,
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1 - (embedding <=> ${vectorString}::vector) as similarity,
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entity_type as "entityType",
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entity_id::text as "entityId",
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metadata
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FROM memory_embeddings
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WHERE workspace_id = ${workspaceId}::uuid
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AND embedding IS NOT NULL
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AND 1 - (embedding <=> ${vectorString}::vector) >= ${threshold}
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AND entity_type = ${entityType}::"EntityType"
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ORDER BY embedding <=> ${vectorString}::vector
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LIMIT ${limit}
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`;
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} else {
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results = await this.prisma.$queryRaw<SimilarEmbedding[]>`
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SELECT
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id::text,
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content,
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1 - (embedding <=> ${vectorString}::vector) as similarity,
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entity_type as "entityType",
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entity_id::text as "entityId",
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metadata
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FROM memory_embeddings
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WHERE workspace_id = ${workspaceId}::uuid
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AND embedding IS NOT NULL
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AND 1 - (embedding <=> ${vectorString}::vector) >= ${threshold}
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ORDER BY embedding <=> ${vectorString}::vector
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LIMIT ${limit}
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`;
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}
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this.logger.debug(
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`Found ${results.length} similar embeddings for workspace ${workspaceId}`
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);
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return results;
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} catch (error) {
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this.logger.error("Failed to find similar embeddings", error);
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throw error;
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}
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}
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/**
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* Delete embeddings for a specific entity
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* @param params Entity identifiers
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* @returns Number of embeddings deleted
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*/
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async deleteByEntity(params: {
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workspaceId: string;
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entityType: EntityType;
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entityId: string;
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}): Promise<number> {
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const { workspaceId, entityType, entityId } = params;
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try {
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const result = await this.prisma.$executeRaw`
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DELETE FROM memory_embeddings
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WHERE workspace_id = ${workspaceId}::uuid
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AND entity_type = ${entityType}::"EntityType"
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AND entity_id = ${entityId}::uuid
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`;
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this.logger.debug(
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`Deleted ${result} embeddings for ${entityType}:${entityId} in workspace ${workspaceId}`
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);
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return result;
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} catch (error) {
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this.logger.error("Failed to delete embeddings", error);
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throw error;
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}
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}
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/**
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* Delete all embeddings for a workspace
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* @param workspaceId Workspace ID
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* @returns Number of embeddings deleted
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*/
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async deleteByWorkspace(workspaceId: string): Promise<number> {
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try {
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const result = await this.prisma.$executeRaw`
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DELETE FROM memory_embeddings
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WHERE workspace_id = ${workspaceId}::uuid
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`;
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this.logger.debug(
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`Deleted ${result} embeddings for workspace ${workspaceId}`
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);
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return result;
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} catch (error) {
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this.logger.error("Failed to delete workspace embeddings", error);
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throw error;
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}
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}
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/**
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* Get embedding by ID
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* @param id Embedding ID
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* @returns Embedding or null if not found
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*/
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async getById(id: string): Promise<SimilarEmbedding | null> {
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try {
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const results = await this.prisma.$queryRaw<SimilarEmbedding[]>`
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SELECT
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id::text,
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content,
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0 as similarity,
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entity_type as "entityType",
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entity_id::text as "entityId",
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metadata
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FROM memory_embeddings
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WHERE id = ${id}::uuid
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LIMIT 1
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`;
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return results.length > 0 ? (results[0] ?? null) : null;
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} catch (error) {
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this.logger.error(`Failed to get embedding ${id}`, error);
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throw error;
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}
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}
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}
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2
apps/api/src/database/index.ts
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2
apps/api/src/database/index.ts
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@@ -0,0 +1,2 @@
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export * from "./database.module";
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export * from "./embeddings.service";
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