diff --git a/apps/sim/db/migrations/0051_typical_expediter.sql b/apps/sim/db/migrations/0051_typical_expediter.sql new file mode 100644 index 000000000..c68894a08 --- /dev/null +++ b/apps/sim/db/migrations/0051_typical_expediter.sql @@ -0,0 +1,26 @@ +CREATE TABLE "docs_embeddings" ( + "chunk_id" uuid PRIMARY KEY DEFAULT gen_random_uuid() NOT NULL, + "chunk_text" text NOT NULL, + "source_document" text NOT NULL, + "source_link" text NOT NULL, + "header_text" text NOT NULL, + "header_level" integer NOT NULL, + "token_count" integer NOT NULL, + "embedding" vector(1536) NOT NULL, + "embedding_model" text DEFAULT 'text-embedding-3-small' NOT NULL, + "metadata" jsonb DEFAULT '{}' NOT NULL, + "chunk_text_tsv" "tsvector" GENERATED ALWAYS AS (to_tsvector('english', "docs_embeddings"."chunk_text")) STORED, + "created_at" timestamp DEFAULT now() NOT NULL, + "updated_at" timestamp DEFAULT now() NOT NULL, + CONSTRAINT "docs_embedding_not_null_check" CHECK ("embedding" IS NOT NULL), + CONSTRAINT "docs_header_level_check" CHECK ("header_level" >= 1 AND "header_level" <= 6) +); +--> statement-breakpoint +CREATE INDEX "docs_emb_source_document_idx" ON "docs_embeddings" USING btree ("source_document");--> statement-breakpoint +CREATE INDEX "docs_emb_header_level_idx" ON "docs_embeddings" USING btree ("header_level");--> statement-breakpoint +CREATE INDEX "docs_emb_source_header_idx" ON "docs_embeddings" USING btree ("source_document","header_level");--> statement-breakpoint +CREATE INDEX "docs_emb_model_idx" ON "docs_embeddings" USING btree ("embedding_model");--> statement-breakpoint +CREATE INDEX "docs_emb_created_at_idx" ON "docs_embeddings" USING btree ("created_at");--> statement-breakpoint +CREATE INDEX "docs_embedding_vector_hnsw_idx" ON "docs_embeddings" USING hnsw ("embedding" vector_cosine_ops) WITH (m=16,ef_construction=64);--> statement-breakpoint +CREATE INDEX "docs_emb_metadata_gin_idx" ON "docs_embeddings" USING gin ("metadata");--> statement-breakpoint +CREATE INDEX "docs_emb_chunk_text_fts_idx" ON "docs_embeddings" USING gin ("chunk_text_tsv"); \ No newline at end of file diff --git a/apps/sim/db/migrations/meta/0051_snapshot.json b/apps/sim/db/migrations/meta/0051_snapshot.json new file mode 100644 index 000000000..e588ecda1 --- /dev/null +++ b/apps/sim/db/migrations/meta/0051_snapshot.json @@ -0,0 +1,4910 @@ +{ + "id": "ead30964-b623-4eb8-b735-505f648c46ac", + "prevId": "0057a619-6f4a-4eb6-ba10-1199608959e9", + "version": "7", + "dialect": "postgresql", + "tables": { + "public.account": { + "name": "account", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "account_id": { + "name": "account_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "provider_id": { + "name": "provider_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "user_id": { + "name": "user_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + 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"workflow_id" + ], + "columnsTo": [ + "id" + ], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": { + "workflow_schedule_workflow_id_unique": { + "name": "workflow_schedule_workflow_id_unique", + "nullsNotDistinct": false, + "columns": [ + "workflow_id" + ] + } + }, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.workflow_subflows": { + "name": "workflow_subflows", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "workflow_id": { + "name": "workflow_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "type": { + "name": "type", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "config": { + "name": "config", + "type": "jsonb", + "primaryKey": false, + "notNull": true, + "default": "'{}'" + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "workflow_subflows_workflow_id_idx": { + "name": "workflow_subflows_workflow_id_idx", + "columns": [ + { + "expression": "workflow_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + }, + "workflow_subflows_workflow_type_idx": { + "name": "workflow_subflows_workflow_type_idx", + "columns": [ + { + "expression": "workflow_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "type", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": false, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "workflow_subflows_workflow_id_workflow_id_fk": { + "name": "workflow_subflows_workflow_id_workflow_id_fk", + "tableFrom": "workflow_subflows", + "tableTo": "workflow", + "columnsFrom": [ + "workflow_id" + ], + "columnsTo": [ + "id" + ], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.workspace": { + "name": "workspace", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "name": { + "name": "name", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "owner_id": { + "name": "owner_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": {}, + "foreignKeys": { + "workspace_owner_id_user_id_fk": { + "name": "workspace_owner_id_user_id_fk", + "tableFrom": "workspace", + "tableTo": "user", + "columnsFrom": [ + "owner_id" + ], + "columnsTo": [ + "id" + ], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.workspace_invitation": { + "name": "workspace_invitation", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "workspace_id": { + "name": "workspace_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "email": { + "name": "email", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "inviter_id": { + "name": "inviter_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "role": { + "name": "role", + "type": "text", + "primaryKey": false, + "notNull": true, + "default": "'member'" + }, + "status": { + "name": "status", + "type": "text", + "primaryKey": false, + "notNull": true, + "default": "'pending'" + }, + "token": { + "name": "token", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "permissions": { + "name": "permissions", + "type": "permission_type", + "typeSchema": "public", + "primaryKey": false, + "notNull": true, + "default": "'admin'" + }, + "expires_at": { + "name": "expires_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true + }, + "created_at": { + "name": "created_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": {}, + "foreignKeys": { + "workspace_invitation_workspace_id_workspace_id_fk": { + "name": "workspace_invitation_workspace_id_workspace_id_fk", + "tableFrom": "workspace_invitation", + "tableTo": "workspace", + "columnsFrom": [ + "workspace_id" + ], + "columnsTo": [ + "id" + ], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "workspace_invitation_inviter_id_user_id_fk": { + "name": "workspace_invitation_inviter_id_user_id_fk", + "tableFrom": "workspace_invitation", + "tableTo": "user", + "columnsFrom": [ + "inviter_id" + ], + "columnsTo": [ + "id" + ], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": { + "workspace_invitation_token_unique": { + "name": "workspace_invitation_token_unique", + "nullsNotDistinct": false, + "columns": [ + "token" + ] + } + }, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + }, + "public.workspace_member": { + "name": "workspace_member", + "schema": "", + "columns": { + "id": { + "name": "id", + "type": "text", + "primaryKey": true, + "notNull": true + }, + "workspace_id": { + "name": "workspace_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "user_id": { + "name": "user_id", + "type": "text", + "primaryKey": false, + "notNull": true + }, + "role": { + "name": "role", + "type": "text", + "primaryKey": false, + "notNull": true, + "default": "'member'" + }, + "joined_at": { + "name": "joined_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + }, + "updated_at": { + "name": "updated_at", + "type": "timestamp", + "primaryKey": false, + "notNull": true, + "default": "now()" + } + }, + "indexes": { + "user_workspace_idx": { + "name": "user_workspace_idx", + "columns": [ + { + "expression": "user_id", + "isExpression": false, + "asc": true, + "nulls": "last" + }, + { + "expression": "workspace_id", + "isExpression": false, + "asc": true, + "nulls": "last" + } + ], + "isUnique": true, + "concurrently": false, + "method": "btree", + "with": {} + } + }, + "foreignKeys": { + "workspace_member_workspace_id_workspace_id_fk": { + "name": "workspace_member_workspace_id_workspace_id_fk", + "tableFrom": "workspace_member", + "tableTo": "workspace", + "columnsFrom": [ + "workspace_id" + ], + "columnsTo": [ + "id" + ], + "onDelete": "cascade", + "onUpdate": "no action" + }, + "workspace_member_user_id_user_id_fk": { + "name": "workspace_member_user_id_user_id_fk", + "tableFrom": "workspace_member", + "tableTo": "user", + "columnsFrom": [ + "user_id" + ], + "columnsTo": [ + "id" + ], + "onDelete": "cascade", + "onUpdate": "no action" + } + }, + "compositePrimaryKeys": {}, + "uniqueConstraints": {}, + "policies": {}, + "checkConstraints": {}, + "isRLSEnabled": false + } + }, + "enums": { + "public.permission_type": { + "name": "permission_type", + "schema": "public", + "values": [ + "admin", + "write", + "read" + ] + } + }, + "schemas": {}, + "sequences": {}, + "roles": {}, + "policies": {}, + "views": {}, + "_meta": { + "columns": {}, + "schemas": {}, + "tables": {} + } +} \ No newline at end of file diff --git a/apps/sim/db/migrations/meta/_journal.json b/apps/sim/db/migrations/meta/_journal.json index 281411d9b..24e8aa7a4 100644 --- a/apps/sim/db/migrations/meta/_journal.json +++ b/apps/sim/db/migrations/meta/_journal.json @@ -351,6 +351,13 @@ "when": 1751659528896, "tag": "0050_big_mattie_franklin", "breakpoints": true + }, + { + "idx": 51, + "version": "7", + "when": 1752014976338, + "tag": "0051_typical_expediter", + "breakpoints": true } ] -} +} \ No newline at end of file diff --git a/apps/sim/db/schema.ts b/apps/sim/db/schema.ts index 9a6587582..3110c6ee7 100644 --- a/apps/sim/db/schema.ts +++ b/apps/sim/db/schema.ts @@ -13,6 +13,7 @@ import { text, timestamp, uniqueIndex, + uuid, vector, } from 'drizzle-orm/pg-core' @@ -909,3 +910,66 @@ export const embedding = pgTable( embeddingNotNullCheck: check('embedding_not_null_check', sql`"embedding" IS NOT NULL`), }) ) + +export const docsEmbeddings = pgTable( + 'docs_embeddings', + { + chunkId: uuid('chunk_id').primaryKey().defaultRandom(), + chunkText: text('chunk_text').notNull(), + sourceDocument: text('source_document').notNull(), + sourceLink: text('source_link').notNull(), + headerText: text('header_text').notNull(), + headerLevel: integer('header_level').notNull(), + tokenCount: integer('token_count').notNull(), + + // Vector embedding - optimized for text-embedding-3-small with HNSW support + embedding: vector('embedding', { dimensions: 1536 }).notNull(), + embeddingModel: text('embedding_model').notNull().default('text-embedding-3-small'), + + // Metadata for flexible filtering + metadata: jsonb('metadata').notNull().default('{}'), + + // Full-text search support - generated tsvector column + chunkTextTsv: tsvector('chunk_text_tsv').generatedAlwaysAs( + (): SQL => sql`to_tsvector('english', ${docsEmbeddings.chunkText})` + ), + + // Timestamps + createdAt: timestamp('created_at').notNull().defaultNow(), + updatedAt: timestamp('updated_at').notNull().defaultNow(), + }, + (table) => ({ + // Source document queries + sourceDocumentIdx: index('docs_emb_source_document_idx').on(table.sourceDocument), + + // Header level filtering + headerLevelIdx: index('docs_emb_header_level_idx').on(table.headerLevel), + + // Combined source and header queries + sourceHeaderIdx: index('docs_emb_source_header_idx').on(table.sourceDocument, table.headerLevel), + + // Model-specific queries + modelIdx: index('docs_emb_model_idx').on(table.embeddingModel), + + // Timestamp queries + createdAtIdx: index('docs_emb_created_at_idx').on(table.createdAt), + + // Vector similarity search indexes (HNSW) - optimized for documentation embeddings + embeddingVectorHnswIdx: index('docs_embedding_vector_hnsw_idx') + .using('hnsw', table.embedding.op('vector_cosine_ops')) + .with({ + m: 16, + ef_construction: 64, + }), + + // GIN index for JSONB metadata queries + metadataGinIdx: index('docs_emb_metadata_gin_idx').using('gin', table.metadata), + + // Full-text search index + chunkTextFtsIdx: index('docs_emb_chunk_text_fts_idx').using('gin', table.chunkTextTsv), + + // Constraints + embeddingNotNullCheck: check('docs_embedding_not_null_check', sql`"embedding" IS NOT NULL`), + headerLevelCheck: check('docs_header_level_check', sql`"header_level" >= 1 AND "header_level" <= 6`), + }) +) diff --git a/apps/sim/lib/documents/docs-chunker.ts b/apps/sim/lib/documents/docs-chunker.ts index b1ebed73c..7e1b80b1a 100644 --- a/apps/sim/lib/documents/docs-chunker.ts +++ b/apps/sim/lib/documents/docs-chunker.ts @@ -23,9 +23,9 @@ export class DocsChunker { constructor(options: DocsChunkerOptions = {}) { // Use the existing TextChunker for chunking logic this.textChunker = new TextChunker({ - chunkSize: options.chunkSize ?? 1024, + chunkSize: options.chunkSize ?? 300, // Max 300 tokens per chunk minChunkSize: options.minChunkSize ?? 100, - overlap: options.overlap ?? 200, + overlap: options.overlap ?? 50, }) this.baseUrl = options.baseUrl ?? 'https://docs.simstudio.ai' } @@ -209,16 +209,25 @@ export class DocsChunker { } /** - * Split content into chunks using the existing TextChunker + * Split content into chunks using the existing TextChunker with table awareness */ private async splitContent(content: string): Promise { // Clean the content first const cleanedContent = this.cleanContent(content) - + + // Detect table boundaries to avoid splitting them + const tableBoundaries = this.detectTableBoundaries(cleanedContent) + // Use the existing TextChunker const chunks = await this.textChunker.chunk(cleanedContent) - - return chunks.map((chunk) => chunk.text) + + // Post-process chunks to ensure tables aren't split + const processedChunks = this.mergeTableChunks(chunks.map(chunk => chunk.text), tableBoundaries, cleanedContent) + + // Ensure no chunk exceeds 300 tokens + const finalChunks = this.enforceSizeLimit(processedChunks) + + return finalChunks } /** @@ -239,20 +248,20 @@ export class DocsChunker { ) } - /** + /** * Parse frontmatter from MDX content */ private parseFrontmatter(content: string): { data: Frontmatter; content: string } { const frontmatterRegex = /^---\r?\n([\s\S]*?)\r?\n---\r?\n([\s\S]*)$/ const match = content.match(frontmatterRegex) - + if (!match) { return { data: {}, content } } - + const [, frontmatterText, markdownContent] = match const data: Frontmatter = {} - + // Simple YAML parsing for title and description const lines = frontmatterText.split('\n') for (const line of lines) { @@ -266,7 +275,357 @@ export class DocsChunker { data[key] = value } } - + return { data, content: markdownContent } } + + /** + * Split content by headers to respect document structure + */ + private splitByHeaders(content: string): Array<{ header: string | null; content: string; level: number }> { + const lines = content.split('\n') + const sections: Array<{ header: string | null; content: string; level: number }> = [] + + let currentHeader: string | null = null + let currentLevel = 0 + let currentContent: string[] = [] + + for (const line of lines) { + const headerMatch = line.match(/^(#{1,3})\s+(.+)$/) // Only split on H1-H3, not H4-H6 + + if (headerMatch) { + // Save previous section + if (currentContent.length > 0) { + sections.push({ + header: currentHeader, + content: currentContent.join('\n').trim(), + level: currentLevel + }) + } + + // Start new section + currentHeader = line + currentLevel = headerMatch[1].length + currentContent = [] + } else { + currentContent.push(line) + } + } + + // Add final section + if (currentContent.length > 0) { + sections.push({ + header: currentHeader, + content: currentContent.join('\n').trim(), + level: currentLevel + }) + } + + return sections.filter(section => section.content.trim().length > 0) + } + + /** + * Estimate token count (rough approximation) + */ + private estimateTokens(text: string): number { + // Rough approximation: 1 token ≈ 4 characters + return Math.ceil(text.length / 4) + } + + /** + * Merge small adjacent chunks to reach target size + */ + private mergeSmallChunks(chunks: string[]): string[] { + const merged: string[] = [] + let currentChunk = '' + + for (const chunk of chunks) { + const currentTokens = this.estimateTokens(currentChunk) + const chunkTokens = this.estimateTokens(chunk) + + // If adding this chunk would exceed target size, save current and start new + if (currentTokens > 0 && currentTokens + chunkTokens > 500) { + if (currentChunk.trim()) { + merged.push(currentChunk.trim()) + } + currentChunk = chunk + } else { + // Merge with current chunk + currentChunk = currentChunk ? `${currentChunk}\n\n${chunk}` : chunk + } + } + + // Add final chunk + if (currentChunk.trim()) { + merged.push(currentChunk.trim()) + } + + return merged + } + + /** + * Chunk a section while preserving tables and structure + */ + private async chunkSection(section: { header: string | null; content: string; level: number }): Promise { + const content = section.content + const header = section.header + + // Check if content contains tables + const hasTable = this.containsTable(content) + + if (hasTable) { + // Split by tables and handle each part + return this.splitContentWithTables(content, header) + } else { + // Regular chunking for text-only content + const chunks = await this.textChunker.chunk(content) + return chunks.map((chunk, index) => { + // Add header to first chunk only + if (index === 0 && header) { + return `${header}\n\n${chunk.text}`.trim() + } + return chunk.text + }) + } + } + + /** + * Check if content contains markdown tables + */ + private containsTable(content: string): boolean { + const lines = content.split('\n') + return lines.some((line, index) => { + if (line.includes('|') && line.split('|').length >= 3) { + const nextLine = lines[index + 1] + return nextLine && nextLine.includes('|') && nextLine.includes('-') + } + return false + }) + } + + /** + * Split content that contains tables, keeping tables intact + */ + private splitContentWithTables(content: string, header: string | null): string[] { + const lines = content.split('\n') + const chunks: string[] = [] + let currentChunk: string[] = [] + let inTable = false + let tableLines: string[] = [] + + for (let i = 0; i < lines.length; i++) { + const line = lines[i] + + // Detect table start + if (line.includes('|') && line.split('|').length >= 3 && !inTable) { + const nextLine = lines[i + 1] + if (nextLine && nextLine.includes('|') && nextLine.includes('-')) { + inTable = true + + // Save current chunk if it has content + if (currentChunk.length > 0 && currentChunk.join('\n').trim().length > 50) { + const chunkText = currentChunk.join('\n').trim() + const withHeader = chunks.length === 0 && header ? `${header}\n\n${chunkText}` : chunkText + chunks.push(withHeader) + currentChunk = [] + } + + tableLines = [line] + continue + } + } + + if (inTable) { + tableLines.push(line) + + // Detect table end + if (!line.includes('|') || line.trim() === '') { + inTable = false + + // Save table as its own chunk + const tableText = tableLines.filter(l => l.trim()).join('\n').trim() + if (tableText.length > 0) { + const withHeader = chunks.length === 0 && header ? `${header}\n\n${tableText}` : tableText + chunks.push(withHeader) + } + + tableLines = [] + + // Start new chunk if current line has content + if (line.trim() !== '') { + currentChunk = [line] + } + } + } else { + currentChunk.push(line) + + // If chunk is getting large, save it + if (this.estimateTokens(currentChunk.join('\n')) > 250) { + const chunkText = currentChunk.join('\n').trim() + if (chunkText.length > 50) { + const withHeader = chunks.length === 0 && header ? `${header}\n\n${chunkText}` : chunkText + chunks.push(withHeader) + } + currentChunk = [] + } + } + } + + // Handle remaining content + if (inTable && tableLines.length > 0) { + const tableText = tableLines.filter(l => l.trim()).join('\n').trim() + if (tableText.length > 0) { + const withHeader = chunks.length === 0 && header ? `${header}\n\n${tableText}` : tableText + chunks.push(withHeader) + } + } else if (currentChunk.length > 0) { + const chunkText = currentChunk.join('\n').trim() + if (chunkText.length > 50) { + const withHeader = chunks.length === 0 && header ? `${header}\n\n${chunkText}` : chunkText + chunks.push(withHeader) + } + } + + return chunks.filter(chunk => chunk.trim().length > 50) + } + + /** + * Detect table boundaries in markdown content to avoid splitting them + */ + private detectTableBoundaries(content: string): { start: number; end: number }[] { + const tables: { start: number; end: number }[] = [] + const lines = content.split('\n') + + let inTable = false + let tableStart = -1 + + for (let i = 0; i < lines.length; i++) { + const line = lines[i].trim() + + // Detect table start (markdown table row with pipes) + if (line.includes('|') && line.split('|').length >= 3 && !inTable) { + // Check if next line is table separator (contains dashes and pipes) + const nextLine = lines[i + 1]?.trim() + if (nextLine && nextLine.includes('|') && nextLine.includes('-')) { + inTable = true + tableStart = i + } + } + // Detect table end (empty line or non-table content) + else if (inTable && (!line.includes('|') || line === '' || line.startsWith('#'))) { + tables.push({ + start: this.getCharacterPosition(lines, tableStart), + end: this.getCharacterPosition(lines, i - 1) + lines[i - 1]?.length || 0 + }) + inTable = false + } + } + + // Handle table at end of content + if (inTable && tableStart >= 0) { + tables.push({ + start: this.getCharacterPosition(lines, tableStart), + end: content.length + }) + } + + return tables + } + + /** + * Get character position from line number + */ + private getCharacterPosition(lines: string[], lineIndex: number): number { + return lines.slice(0, lineIndex).reduce((acc, line) => acc + line.length + 1, 0) + } + + /** + * Merge chunks that would split tables + */ + private mergeTableChunks(chunks: string[], tableBoundaries: { start: number; end: number }[], originalContent: string): string[] { + if (tableBoundaries.length === 0) { + return chunks + } + + const mergedChunks: string[] = [] + let currentPosition = 0 + + for (const chunk of chunks) { + const chunkStart = originalContent.indexOf(chunk, currentPosition) + const chunkEnd = chunkStart + chunk.length + + // Check if this chunk intersects with any table + const intersectsTable = tableBoundaries.some(table => + (chunkStart >= table.start && chunkStart <= table.end) || + (chunkEnd >= table.start && chunkEnd <= table.end) || + (chunkStart <= table.start && chunkEnd >= table.end) + ) + + if (intersectsTable) { + // Find which table(s) this chunk intersects with + const affectedTables = tableBoundaries.filter(table => + (chunkStart >= table.start && chunkStart <= table.end) || + (chunkEnd >= table.start && chunkEnd <= table.end) || + (chunkStart <= table.start && chunkEnd >= table.end) + ) + + // Create a chunk that includes the complete table(s) + const minStart = Math.min(chunkStart, ...affectedTables.map(t => t.start)) + const maxEnd = Math.max(chunkEnd, ...affectedTables.map(t => t.end)) + const completeChunk = originalContent.slice(minStart, maxEnd) + + // Only add if we haven't already included this content + if (!mergedChunks.some(existing => existing.includes(completeChunk.trim()))) { + mergedChunks.push(completeChunk.trim()) + } + } else { + mergedChunks.push(chunk) + } + + currentPosition = chunkEnd + } + + return mergedChunks.filter(chunk => chunk.length > 50) // Filter out tiny chunks + } + + /** + * Enforce 300 token size limit on chunks + */ + private enforceSizeLimit(chunks: string[]): string[] { + const finalChunks: string[] = [] + + for (const chunk of chunks) { + const tokens = this.estimateTokens(chunk) + + if (tokens <= 300) { + // Chunk is within limit + finalChunks.push(chunk) + } else { + // Chunk is too large - split it + const lines = chunk.split('\n') + let currentChunk = '' + + for (const line of lines) { + const testChunk = currentChunk ? `${currentChunk}\n${line}` : line + + if (this.estimateTokens(testChunk) <= 300) { + currentChunk = testChunk + } else { + // Adding this line would exceed limit + if (currentChunk.trim()) { + finalChunks.push(currentChunk.trim()) + } + currentChunk = line + } + } + + // Add final chunk if it has content + if (currentChunk.trim()) { + finalChunks.push(currentChunk.trim()) + } + } + } + + return finalChunks.filter(chunk => chunk.trim().length > 100) + } } diff --git a/apps/sim/scripts/process-docs-embeddings.ts b/apps/sim/scripts/process-docs-embeddings.ts new file mode 100644 index 000000000..f7982290b --- /dev/null +++ b/apps/sim/scripts/process-docs-embeddings.ts @@ -0,0 +1,215 @@ +#!/usr/bin/env bun + +import path from 'path' +import { DocsChunker } from '@/lib/documents/docs-chunker' +import { createLogger } from '@/lib/logs/console-logger' +import { db } from '@/db' +import { docsEmbeddings } from '@/db/schema' +import { sql } from 'drizzle-orm' + +const logger = createLogger('ProcessDocsEmbeddings') + +interface ProcessingOptions { + /** Clear existing docs embeddings before processing */ + clearExisting?: boolean + /** Path to docs directory */ + docsPath?: string + /** Base URL for generating links */ + baseUrl?: string + /** Chunk size in tokens */ + chunkSize?: number + /** Minimum chunk size in tokens */ + minChunkSize?: number + /** Overlap between chunks in tokens */ + overlap?: number +} + +/** + * Production script to process documentation and save embeddings to database + */ +async function processDocsEmbeddings(options: ProcessingOptions = {}) { + const startTime = Date.now() + let processedChunks = 0 + let failedChunks = 0 + + try { + // Configuration + const config = { + clearExisting: options.clearExisting ?? false, + docsPath: options.docsPath ?? path.join(process.cwd(), '../../apps/docs/content/docs'), + baseUrl: options.baseUrl ?? 'https://docs.simstudio.ai', + chunkSize: options.chunkSize ?? 300, // Max 300 tokens per chunk + minChunkSize: options.minChunkSize ?? 100, + overlap: options.overlap ?? 50, + } + + logger.info('🚀 Starting docs embedding processing...') + logger.info(`Configuration:`, { + docsPath: config.docsPath, + baseUrl: config.baseUrl, + chunkSize: config.chunkSize, + clearExisting: config.clearExisting, + }) + + // Clear existing embeddings if requested + if (config.clearExisting) { + logger.info('🗑️ Clearing existing docs embeddings...') + const deleteResult = await db.delete(docsEmbeddings) + logger.info(`Deleted existing embeddings`) + } + + // Initialize the docs chunker + const chunker = new DocsChunker({ + chunkSize: config.chunkSize, + minChunkSize: config.minChunkSize, + overlap: config.overlap, + baseUrl: config.baseUrl, + }) + + // Process all .mdx files + logger.info(`📚 Processing docs from: ${config.docsPath}`) + const chunks = await chunker.chunkAllDocs(config.docsPath) + + if (chunks.length === 0) { + logger.warn('⚠️ No chunks generated from docs') + return { success: false, processedChunks: 0, failedChunks: 0 } + } + + logger.info(`📊 Generated ${chunks.length} chunks with embeddings`) + + // Save chunks to database in batches for better performance + const batchSize = 10 + logger.info(`💾 Saving chunks to database (batch size: ${batchSize})...`) + + for (let i = 0; i < chunks.length; i += batchSize) { + const batch = chunks.slice(i, i + batchSize) + + try { + // Prepare batch data + const batchData = batch.map((chunk) => ({ + chunkText: chunk.text, + sourceDocument: chunk.sourceDocument, + sourceLink: chunk.headerLink, + headerText: chunk.headerText, + headerLevel: chunk.headerLevel, + tokenCount: chunk.tokenCount, + embedding: chunk.embedding, + embeddingModel: chunk.embeddingModel, + metadata: chunk.metadata, + })) + + // Insert batch + await db.insert(docsEmbeddings).values(batchData) + + processedChunks += batch.length + + if (i % (batchSize * 5) === 0 || i + batchSize >= chunks.length) { + logger.info(` 💾 Saved ${Math.min(i + batchSize, chunks.length)}/${chunks.length} chunks`) + } + } catch (error) { + logger.error(`❌ Failed to save batch ${Math.floor(i / batchSize) + 1}:`, error) + failedChunks += batch.length + } + } + + // Verify results + const savedCount = await db + .select({ count: sql`count(*)` }) + .from(docsEmbeddings) + .then((result) => result[0]?.count || 0) + + const duration = Date.now() - startTime + + logger.info(`✅ Processing complete!`) + logger.info(`📊 Results:`) + logger.info(` • Total chunks processed: ${chunks.length}`) + logger.info(` • Successfully saved: ${processedChunks}`) + logger.info(` • Failed: ${failedChunks}`) + logger.info(` • Database total: ${savedCount}`) + logger.info(` • Duration: ${Math.round(duration / 1000)}s`) + + // Summary by document + const documentStats = chunks.reduce((acc, chunk) => { + if (!acc[chunk.sourceDocument]) { + acc[chunk.sourceDocument] = { chunks: 0, tokens: 0 } + } + acc[chunk.sourceDocument].chunks++ + acc[chunk.sourceDocument].tokens += chunk.tokenCount + return acc + }, {} as Record) + + logger.info(`📋 Document breakdown:`) + Object.entries(documentStats) + .sort(([, a], [, b]) => b.chunks - a.chunks) + .slice(0, 10) // Top 10 documents + .forEach(([doc, stats]) => { + logger.info(` • ${doc}: ${stats.chunks} chunks, ${stats.tokens} tokens`) + }) + + if (Object.keys(documentStats).length > 10) { + logger.info(` • ... and ${Object.keys(documentStats).length - 10} more documents`) + } + + return { + success: failedChunks === 0, + processedChunks, + failedChunks, + totalChunks: chunks.length, + databaseCount: savedCount, + duration, + } + + } catch (error) { + logger.error('💥 Fatal error during processing:', error) + return { + success: false, + processedChunks, + failedChunks, + error: error instanceof Error ? error.message : 'Unknown error', + } + } +} + +/** + * Main function - handle command line arguments + */ +async function main() { + const args = process.argv.slice(2) + const options: ProcessingOptions = {} + + // Parse command line arguments + if (args.includes('--clear')) { + options.clearExisting = true + } + + if (args.includes('--help') || args.includes('-h')) { + console.log(` +Usage: bun run scripts/process-docs-embeddings.ts [options] + +Options: + --clear Clear existing docs embeddings before processing + --help, -h Show this help message + +Examples: + bun run scripts/process-docs-embeddings.ts + bun run scripts/process-docs-embeddings.ts --clear +`) + process.exit(0) + } + + const result = await processDocsEmbeddings(options) + + if (!result.success) { + process.exit(1) + } +} + +// Run the script +if (process.argv[1]?.includes('process-docs-embeddings')) { + main().catch((error) => { + console.error('Script failed:', error) + process.exit(1) + }) +} + +export { processDocsEmbeddings } \ No newline at end of file