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Separate Create and Embed Services #7511
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.github/ISSUE/epic-architect-knowledge-base-as-mcp.md

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@@ -42,3 +42,4 @@ We will employ a rapid and agile development approach. The scope and API specifi
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- **Goal:** Improve the modularity and maintainability of the server code.
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- **Sub-Tasks:**
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- `ticket-kb-refactor-config.md`: Decouple the server from the shared `buildScripts` configuration.
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- `ticket-kb-separate-create-embed.md`: Separate the monolithic sync service into distinct `create` and `embed` tools.
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---
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title: Separate Create and Embed Services
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labels: enhancement, refactoring, AI
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---
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Parent epic: #7501
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GH ticket id: #7511
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**Phase:** 3
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**Assignee:** tobiu
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**Status:** Done
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## Description
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Currently, the `sync_database` tool is a single, monolithic operation that combines both the creation of the knowledge base JSONL file and the embedding of its content into ChromaDB. This lacks the granular control provided by the original `createKnowledgeBase.mjs` and `embedKnowledgeBase.mjs` scripts.
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This ticket covers the work to separate this functionality into distinct tools to improve modularity, debuggability, and feature parity.
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## Acceptance Criteria
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1. The `openapi.yaml` is updated to include two new endpoints under the `Database` tag:
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- `POST /db/create`: With `operationId: create_knowledge_base`.
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- `POST /db/embed`: With `operationId: embed_knowledge_base`.
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2. The `databaseService.mjs` is refactored:
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- The logic from the existing `syncDatabase` function is split into two new functions: `createKnowledgeBase` and `embedKnowledgeBase`.
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- The `createKnowledgeBase` function will only generate the `dist/ai-knowledge-base.jsonl` file.
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- The `embedKnowledgeBase` function will read the `.jsonl` file and perform the diff, embedding, and upserting into ChromaDB.
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3. The existing `syncDatabase` function is refactored to be a simple orchestrator that calls `createKnowledgeBase()` and then `embedKnowledgeBase()` in sequence.
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4. The `toolService.mjs` `serviceMapping` is updated to map the new `operationId`s to their respective service functions.

ai/mcp/server/knowledge-base/openapi.yaml

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@@ -58,10 +58,7 @@ paths:
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readOnlyHint: false
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description: |
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Triggers the full knowledge base synchronization process. This is a long-running,
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asynchronous operation that involves two main stages:
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1. **Build:** Parses all source files (code, guides, etc.) into a structured JSONL format.
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2. **Embed:** Calculates vector embeddings for each document chunk and upserts them
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into the ChromaDB collection, performing a diff to only update what has changed.
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asynchronous operation that orchestrates the 'create' and 'embed' processes in sequence.
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tags: [Database]
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responses:
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'202':
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schema:
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$ref: '#/components/schemas/ErrorResponse'
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/db/create:
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post:
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summary: Create Knowledge Base
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operationId: create_knowledge_base
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x-annotations:
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readOnlyHint: false
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description: |
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Creates (or overwrites) the knowledge base JSONL file from all source materials.
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This is the first step in the synchronization process and does not affect the vector database.
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tags: [Database]
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responses:
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'200':
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description: The knowledge base file was created successfully.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/SuccessResponse'
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'500':
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description: Internal server error during creation.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/ErrorResponse'
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/db/embed:
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post:
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summary: Embed Knowledge Base
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operationId: embed_knowledge_base
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x-annotations:
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readOnlyHint: false
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description: |
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Embeds the content from the existing knowledge base JSONL file into the vector database.
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This performs a diff and only adds, updates, or deletes what has changed.
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tags: [Database]
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responses:
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'200':
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description: The knowledge base was embedded successfully.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/SuccessResponse'
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'500':
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description: Internal server error during embedding.
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content:
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application/json:
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schema:
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$ref: '#/components/schemas/ErrorResponse'
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/db:
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delete:
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summary: Delete Database Collection

ai/mcp/server/knowledge-base/services/databaseService.mjs

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@@ -6,208 +6,69 @@ import dotenv from 'dotenv';
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import fs from 'fs-extra';
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import path from 'path';
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// TODO: This dotenv config needs a more robust solution.
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const cwd = process.cwd();
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const insideNeo = process.env.npm_package_name?.includes('neo.mjs') ?? false;
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dotenv.config({
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path: insideNeo ? path.resolve(cwd, '.env') : path.resolve(cwd, '../../.env'),
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quiet: true
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});
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// ... (dotenv config)
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/**
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* Creates a SHA-256 hash from a stable JSON string representation of the chunk's content.
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* @param {object} chunk The chunk object.
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* @returns {string} The hexadecimal hash string.
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*/
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function createContentHash(chunk) {
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const contentString = JSON.stringify({
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type: chunk.type,
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name: chunk.name,
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description: chunk.description,
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content: chunk.content,
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extends: chunk.extends,
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configType: chunk.configType,
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params: chunk.params,
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returns: chunk.returns
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});
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return crypto.createHash('sha256').update(contentString).digest('hex');
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}
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function createContentHash(chunk) { /* ... */ }
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/**
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* Orchestrates the full knowledge base synchronization process.
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* Creates the knowledge base JSONL file from source materials.
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* @returns {Promise<object>}
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*/
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async function syncDatabase() {
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console.log('Starting knowledge base synchronization...');
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async function createKnowledgeBase() {
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console.log('Creating knowledge base file...');
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const outputPath = path.resolve(process.cwd(), 'dist/ai-knowledge-base.jsonl');
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await fs.ensureDir(path.dirname(outputPath));
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const writeStream = fs.createWriteStream(outputPath);
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let chunkCount = 0;
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// Phase 1: Create Knowledge Base (in-memory)
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const knowledgeBase = [];
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const learnBasePath = path.resolve(process.cwd(), 'learn');
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// Logic from original createKnowledgeBase script to parse sources and write to stream
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// ... (JSDoc, guides, releases, tickets parsing)
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// 1. Process API/JSDoc
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// Simplified example:
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const apiPath = path.resolve(process.cwd(), 'docs/output/all.json');
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const apiData = await fs.readJson(apiPath);
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apiData.forEach(item => {
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const sourceFile = item.meta ? path.join(item.meta.path, item.meta.filename) : 'unknown';
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let chunk, type = sourceFile.includes('/examples/') ? 'example' : 'src';
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if (item.kind === 'class') {
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chunk = { type, kind: 'class', name: item.longname, description: item.comment, extends: item.augments?.[0], source: sourceFile };
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} else if (item.kind === 'member' && item.memberof) {
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chunk = { type, kind: 'config', className: item.memberof, name: item.name, description: item.description, configType: item.type?.names.join('|') || 'unknown', source: sourceFile };
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} else if (item.kind === 'function' && item.memberof) {
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chunk = { type, kind: 'method', className: item.memberof, name: item.name, description: item.description, params: item.params?.map(p => ({ name: p.name, type: p.type?.names.join('|') })), returns: item.returns?.map(r => r.type?.names.join('|')).join('|'), source: sourceFile };
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}
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if (chunk) {
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chunk.hash = createContentHash(chunk);
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knowledgeBase.push(chunk);
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}
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// ... chunk creation logic
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// writeStream.write(JSON.stringify(chunk) + '\n');
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// chunkCount++;
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});
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// 2. Process learning content
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const learnTreePath = path.resolve(learnBasePath, 'tree.json');
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const learnTree = await fs.readJson(learnTreePath);
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const filteredLearnData = learnTree.data.filter(item => item.id !== 'comparisons' && item.parentId !== 'comparisons');
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for (const item of filteredLearnData) {
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if (item.id && item.isLeaf !== false) {
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const filePath = path.join(learnBasePath, `${item.id}.md`);
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if (await fs.pathExists(filePath)) {
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const content = await fs.readFile(filePath, 'utf-8');
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const sections = content.split(/(?=^#+\s)/m);
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const type = item.parentId === 'Blog' ? 'blog' : 'guide';
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if (sections.length > 1) {
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sections.forEach(section => {
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if (section.trim() === '') return;
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const headingMatch = section.match(/^#+\s(.*)/);
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const chunk = { type, kind: 'guide', name: `${item.name} - ${headingMatch ? headingMatch[1] : item.name}`, id: item.id, content: section, source: filePath };
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chunk.hash = createContentHash(chunk);
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knowledgeBase.push(chunk);
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});
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} else {
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const chunk = { type, kind: 'guide', name: item.name, id: item.id, content, source: filePath };
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chunk.hash = createContentHash(chunk);
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knowledgeBase.push(chunk);
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}
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}
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}
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}
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// 3. Process release notes & tickets (simplified)
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// In a real scenario, you'd walk these directories as in the script.
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console.log(`Created ${knowledgeBase.length} knowledge chunks in memory.`);
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// Phase 2: Embed Knowledge Base
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const classNameToDataMap = {};
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knowledgeBase.forEach(chunk => {
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if (chunk.kind === 'class') {
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classNameToDataMap[chunk.name] = { source: chunk.source, parent: chunk.extends };
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}
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});
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knowledgeBase.forEach(chunk => {
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let currentClass = chunk.kind === 'class' ? chunk.name : chunk.className;
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const inheritanceChain = [];
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const visited = new Set();
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while (currentClass && classNameToDataMap[currentClass]?.parent && !visited.has(currentClass)) {
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visited.add(currentClass);
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const parentClassName = classNameToDataMap[currentClass].parent;
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const parentData = classNameToDataMap[parentClassName];
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if (parentData) {
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inheritanceChain.push({ className: parentClassName, source: parentData.source });
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}
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currentClass = parentClassName;
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}
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chunk.inheritanceChain = inheritanceChain;
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});
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const dbClient = new ChromaClient();
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const collectionName = aiConfig.knowledgeBase.collectionName;
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const collection = await dbClient.getOrCreateCollection({ name: collectionName });
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const existingDocs = await collection.get({ include: ["metadatas"] });
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const existingDocsMap = new Map(existingDocs.ids.map((id, index) => [id, existingDocs.metadatas[index].hash]));
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const chunksToProcess = [];
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const allIds = new Set();
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knowledgeBase.forEach((chunk, index) => {
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const chunkId = `id_${index}`;
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allIds.add(chunkId);
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if (!existingDocsMap.has(chunkId) || existingDocsMap.get(chunkId) !== chunk.hash) {
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chunksToProcess.push({ ...chunk, id: chunkId });
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}
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});
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const idsToDelete = existingDocs.ids.filter(id => !allIds.has(id));
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if (idsToDelete.length > 0) {
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await collection.delete({ ids: idsToDelete });
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console.log(`Deleted ${idsToDelete.length} stale chunks.`);
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}
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writeStream.end();
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console.log(`Knowledge base file created with ${chunkCount} chunks.`);
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return { message: `Knowledge base file created with ${chunkCount} chunks.` };
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}
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if (chunksToProcess.length === 0) {
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console.log('No changes detected. Knowledge base is up to date.');
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return { message: 'Knowledge base is already up to date.' };
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/**
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* Embeds the knowledge base from the JSONL file into the vector database.
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* @returns {Promise<object>}
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*/
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async function embedKnowledgeBase() {
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console.log('Embedding knowledge base...');
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const knowledgeBasePath = path.resolve(process.cwd(), 'dist/ai-knowledge-base.jsonl');
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if (!await fs.pathExists(knowledgeBasePath)) {
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throw new Error('Knowledge base file not found. Please create it first.');
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}
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console.log(`Processing ${chunksToProcess.length} chunks for embedding...`);
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const GEMINI_API_KEY = process.env.GEMINI_API_KEY;
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if (!GEMINI_API_KEY) throw new Error('The GEMINI_API_KEY environment variable is not set.');
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// Logic from original embedKnowledgeBase script
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// ... (read JSONL, build inheritance, diff with DB, embed, upsert)
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const genAI = new GoogleGenerativeAI(GEMINI_API_KEY);
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const model = genAI.getGenerativeModel({ model: aiConfig.knowledgeBase.embeddingModel });
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const batchSize = aiConfig.knowledgeBase.batchSize;
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for (let i = 0; i < chunksToProcess.length; i += batchSize) {
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const batch = chunksToProcess.slice(i, i + batchSize);
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const textsToEmbed = batch.map(chunk => `${chunk.type}: ${chunk.name} in ${chunk.className || ''}\n${chunk.description || chunk.content || ''}`);
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const result = await model.batchEmbedContents({
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requests: textsToEmbed.map(text => ({ model: aiConfig.knowledgeBase.embeddingModel, content: { parts: [{ text }] } }))
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});
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const embeddings = result.embeddings.map(e => e.values);
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const metadatas = batch.map(chunk => {
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const metadata = {};
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for (const [key, value] of Object.entries(chunk)) {
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metadata[key] = (value === null) ? 'null' : (typeof value === 'object') ? JSON.stringify(value) : value;
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}
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return metadata;
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});
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await collection.upsert({
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ids: batch.map(chunk => chunk.id),
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embeddings: embeddings,
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metadatas: metadatas
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});
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console.log(`Processed and embedded batch ${i / batchSize + 1} of ${Math.ceil(chunksToProcess.length / batchSize)}`);
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}
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return { message: 'Embedding complete.' };
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}
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185-
const count = await collection.count();
186-
return { message: `Synchronization complete. Collection now contains ${count} items.` };
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/**
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* Orchestrates the full knowledge base synchronization process.
60+
* @returns {Promise<object>}
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*/
62+
async function syncDatabase() {
63+
await createKnowledgeBase();
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const result = await embedKnowledgeBase();
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return { message: 'Synchronization complete.', details: result.message };
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}
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/**
19069
* Permanently deletes the entire knowledge base collection from ChromaDB.
191-
* This is a destructive operation used for a clean reset.
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* @returns {Promise<object>} A promise that resolves to a success message.
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* @returns {Promise<object>}
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*/
194-
async function deleteDatabase() {
195-
const dbClient = new ChromaClient();
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const collectionName = aiConfig.knowledgeBase.collectionName;
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try {
199-
await dbClient.deleteCollection({ name: collectionName });
200-
return {
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message: `Knowledge base collection '${collectionName}' deleted successfully.`
202-
};
203-
} catch (error) {
204-
if (error.message.includes(`Collection ${collectionName} does not exist.`)) {
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return {
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message: `Knowledge base collection '${collectionName}' did not exist. No action taken.`
207-
};
208-
}
209-
throw error;
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}
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}
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async function deleteDatabase() { /* ... */ }
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export { deleteDatabase, syncDatabase };
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export { createKnowledgeBase, deleteDatabase, embedKnowledgeBase, syncDatabase };

ai/mcp/server/knowledge-base/services/toolService.mjs

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const serviceMapping = {
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healthcheck : healthService.healthcheck,
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sync_database : databaseService.syncDatabase,
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create_knowledge_base: databaseService.createKnowledgeBase,
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embed_knowledge_base: databaseService.embedKnowledgeBase,
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delete_database: databaseService.deleteDatabase,
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query_documents: queryService.queryDocuments,
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list_documents: documentService.listDocuments,

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