Build a Codebase Intelligence Tool Like repowise With a RAG-Assisted MCP for Your Monorepo

┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│ File Watcher│────▶│ Indexer      │────▶│ Vector Store│
│ (chokidar)  │     │ (LangChain)  │     │ (Qdrant)    │
└─────────────┘     └──────────────┘     └─────────────┘
                                               │
                                               ▼
┌─────────────┐     ┌──────────────┐     ┌─────────────┐
│ IDE / CLI  │◀────│ MCP Server   │◀────│ Retriever   │
│ (Claude,    │     │ (FastMCP)    │     │ (Hybrid)    │
│ Cursor)     │     └──────────────┘     └─────────────┘
└─────────────┘

mkdir monorepo-rag-mcp && cd monorepo-rag-mcp
pnpm init
mkdir -p packages/{indexer,mcp-server,embedding-server}

// packages/shared/src/types.ts
export interface CodeChunk {
  id: string;
  path: string;
  language: string;
  startLine: number;
  endLine: number;
  content: string;
  astMetadata?: Record<string, any>;
}

export interface IndexingConfig {
  monorepoRoot: string;
  excludePatterns: string[];
  languages: string[];
}

// packages/indexer/src/chunker.ts
import { Parser } from "tree-sitter";
import * as ts from "tree-sitter-typescript";

const parser = new Parser();
parser.setLanguage(ts.language);

export function chunkFile(content: string, filePath: string): CodeChunk[] {
  const tree = parser.parse(content);
  const chunks: CodeChunk[] = [];

  function traverse(node: any) {
    if (["function_declaration", "class_declaration", "method_definition"].includes(node.type)) {
      chunks.push({
        id: `${filePath}:${node.startPosition.row}-${node.endPosition.row}`,
        path: filePath,
        language: "typescript",
        startLine: node.startPosition.row,
        endLine: node.endPosition.row,
        content: content.slice(node.startPosition.byte, node.endPosition.byte),
        astMetadata: { type: node.type, name: node.children[0]?.text ?? "anonymous" }
      });
    }
    for (const child of node.children) traverse(child);
  }
  traverse(tree.rootNode);
  return chunks;
}

# packages/embedding-server/main.py
from fastapi import FastAPI
from pydantic import BaseModel
from sentence_transformers import SentenceTransformer
import uvicorn

app = FastAPI()
model = SentenceTransformer("BAAI/bge-small-en-v1.5")

class EmbedRequest(BaseModel):
    texts: list[str]

@app.post("/embed")
def embed(req: EmbedRequest):
    embeddings = model.encode(req.texts, normalize_embeddings=True).tolist()
    return {"embeddings": embeddings}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

// packages/indexer/src/indexer.ts
import chokidar from "chokidar";
import { QdrantClient } from "@qdrant/js-client-rest";
import { chunkFile } from "./chunker";
import axios from "axios";

const qdrant = new QdrantClient({ url: "http://localhost:6333" });

async function embed(texts: string[]) {
  const { data } = await axios.post("http://localhost:8000/embed", { texts });
  return data.embeddings;
}

async function indexFile(filePath: string) {
  const content = await fs.readFile(filePath, "utf-8");
  const chunks = chunkFile(content, filePath);
  if (chunks.length === 0) return;

  const embeddings = await embed(chunks.map(c => c.content));
  const points = chunks.map((chunk, i) => ({
    id: chunk.id,
    vector: embeddings[i],
    payload: chunk
  }));

  await qdrant.upsert("codebase", { points });
}

async function watch(root: string) {
  const watcher = chokidar.watch(root, {
    ignored: /node_modules|\.git|dist/g,
    persistent: true
  });

  watcher.on("add", path => indexFile(path));
  watcher.on("change", path => indexFile(path));
  watcher.on("unlink", path => qdrant.delete("codebase", { wait: true, points: [path] }));
}

// packages/mcp-server/src/server.ts
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { QdrantClient } from "@qdrant/js-client-rest";
import axios from "axios";

const server = new McpServer({ name: "repo-wise", version: "1.0.0" });
const qdrant = new QdrantClient({ url: "http://localhost:6333" });

server.tool("search_code", {
  description: "Semantic + lexical search over the indexed monorepo",
  inputSchema: {
    type: "object",
    properties: {
      query: { type: "string", description: "Natural language query" },
      limit: { type: "number", default: 10 }
    },
    required: ["query"]
  }
}, async ({ query, limit = 10 }) => {
  // 1. Embed the query
  const { data: embedData } = await axios.post("http://localhost:8000/embed", { texts: [query] });
  const queryVector = embedData.embeddings[0];

  // 2. Vector search
  const vectorResults = await qdrant.search("codebase", {
    query: queryVector,
    limit: limit * 2, // overshoot for reranking
    with_payload: true
  });

  // 3. BM25 rerank (simplified: use Qdrant's built-in sparse vector if configured,
  //    or implement a local BM25 scorer on the retrieved candidates)
  const scored = vectorResults.map(r => ({
    path: r.payload.path,
    lines: `${r.payload.startLine}-${r.payload.endLine}`,
    snippet: r.payload.content.slice(0, 200),
    score: r.score
  }));

  return {
    content: [{ type: "text", text: JSON.stringify(scored, null, 2) }]
  };
});

server.tool("get_file_context", {
  description: "Get full file content with line numbers for a given path",
  inputSchema: {
    type: "object",
    properties: {
      path: { type: "string" },
      startLine: { type: "number" },
      endLine: { type: "number" }
    },
    required: ["path"]
  }
}, async ({ path, startLine, endLine }) => {
  const content = await fs.readFile(path, "utf-8");
  const lines = content.split("\n");
  const slice = lines.slice(startLine ?? 0, endLine ?? lines.length).join("\n");
  return {
    content: [{ type: "text", text: slice }]
  };
});

server.tool("explain_symbol", {
  description: "Explain what a symbol (function/class) does based on code and comments",
  inputSchema: {
    type: "object",
    properties: {
      symbolName: { type: "string" },
      language: { type: "string", default: "typescript" }
    },
    required: ["symbolName"]
  }
}, async ({ symbolName, language }) => {
  const { data: embedData } = await axios.post("http://localhost:8000/embed", { texts: [symbolName] });
  const results = await qdrant.search("codebase", {
    query: embedData.embeddings[0],
    limit: 5,
    filter: { must: [{ key: "language", match: { value: language } }] }
  });

  const context = results.map(r => r.payload.content).join("\n\n---\n\n");
  const prompt = `You are a senior engineer. Explain the symbol "${symbolName}" based on the following code snippets:\n\n${context}`;
  // In production, call an LLM here. For demo, return context.
  return { content: [{ type: "text", text: prompt }] };
});

const transport = new StdioServerTransport();
server.connect(transport);

{
  "mcpServers": {
    "repo-wise": {
      "command": "node",
      "args": ["./packages/mcp-server/dist/server.js"]
    }
  }
}

{
  "tools": [
    {
      "name": "repo-wise",
      "command": "node",
      "args": ["./packages/mcp-server/dist/server.js"]
    }
  ]
}

#!/bin/bash
# .git/hooks/post-commit
ROOT=$(git rev-parse --show-toplevel)
pnpm --filter @repo/indexer run index --root "$ROOT" --commit $(git rev-parse HEAD)

await qdrant.createCollection("codebase", {
  vectors: {
    size: 384,
    distance: "Cosine"
  },
  sparse_vectors: {
    bm25: {}
  }
});

// packages/indexer/src/worker.ts
import { Queue, Worker, Job } from 'bullmq';
import { IndexService } from './index-service';
import { redisConnection } from './redis';

export const indexQueue = new Queue('codebase-indexing', { connection: redisConnection });

export function startWorker() {
  const service = new IndexService();

  new Worker('codebase-indexing', async (job: Job) => {
    const { filePath, content, projectId } = job.data;

    await service.indexFile(projectId, filePath, content);

    job.updateProgress(100);
  }, { connection: redisConnection });
}

export async function enqueueIndex(
  projectId: string,
  filePath: string,
  content: string,
) {
  await indexQueue.add('index-file', {
    projectId,
    filePath,
    content,
  }, {
    attempts: 3,
    backoff: { type: 'exponential', delay: 2000 },
    removeOnComplete: true,
    removeOnFail: true,
  });
}

# nginx.conf
upstream embedding_server {
    server 127.0.0.1:6000 max_fails=3 fail_timeout=30s;
    keepalive 32;
}

server {
    listen 443 ssl http2;
    server_name embedding.internal;

    ssl_certificate     /etc/ssl/certs/embedding.pem;
    ssl_certificate_key /etc/ssl/private/embedding.key;

    location /v1/ {
        proxy_pass http://embedding_server/v1/;
        proxy_http_version 1.1;
        proxy_set_header Connection "";

        # Buffer large document batches
        proxy_request_buffering on;
        client_max_body_size 50m;

        # Timeout tuning for embedding models
        proxy_connect_timeout 10s;
        proxy_send_timeout 120s;
        proxy_read_timeout 120s;
    }

    location /health {
        proxy_pass http://embedding_server/health;
    }
}

// packages/monitoring/src/logger.ts
import pino from 'pino';

export const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  formatters: {
    level: (label) => ({ level: label.toUpperCase() }),
  },
  timestamp: pino.stdTimeFunctions.isoTime,
});

export type MetricsEvent =
  | { type: 'index_progress'; projectId: string; fileCount: number; total: number }
  | { type: 'embedding_latency'; durationMs: number; model: string }
  | { type: 'mcp_request'; toolName: string; durationMs: number; success: boolean }
  | { type: 'chunk_generated'; projectId: string; chunkSize: number; strategy: string };

export function emitMetrics(event: MetricsEvent) {
  logger.info(event, 'metrics');
}

// packages/monitoring/src/tracing.ts
import * as opentelemetry from '@opentelemetry/api';
import { NodeTracerProvider } from '@opentelemetry/sdk-trace-node';
import { Resource } from '@opentelemetry/resources';
import { SemanticResourceAttributes } from '@opentelemetry/semantic-conventions';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-proto';
import { registerInstrumentations } from '@opentelemetry/instrumentation';
import { HttpInstrumentation } from '@opentelemetry/instrumentation-http';
import { PinoInstrumentation } from '@opentelemetry/instrumentation-pino';

const provider = new NodeTracerProvider({
  resource: new Resource({
    [SemanticResourceAttributes.SERVICE_NAME]: 'monorepo-rag-mcp',
    [SemanticResourceAttributes.SERVICE_VERSION]: '1.0.0',
    [SemanticResourceAttributes.DEPLOYMENT_ENVIRONMENT]: process.env.NODE_ENV,
  }),
});

provider.addSpanProcessor(
  new SimpleSpanProcessor(new OTLPTraceExporter())
);
provider.register();

registerInstrumentations({
  instrumentations: [
    new HttpInstrumentation(),
    new PinoInstrumentation(),
  ],
});

export const tracer = opentelemetry.trace.getTracer('monorepo-rag-mcp');

// packages/rag/src/sanitization.ts

const INJECTION_PATTERNS = [
  /ignore previous instructions/i,
  /act as a different system/i,
  /you are now/i,
  /system prompt/i,
  /do not follow/i,
];

export function sanitizeContext(text: string): string {
  return INJECTION_PATTERNS.reduce(
    (acc, pattern) => acc.replace(pattern, '[REDACTED_INJECTION_ATTEMPT]'),
    text
  );
}

export function wrapCodeInSecurityBoundary(code: string): string {
  return `SECURITY_BOUNDARY_START\n${sanitizeContext(code)}\nSECURITY_BOUNDARY_END`;
}

// packages/mcp-server/src/auth.ts
import { z } from 'zod';

const AccessLevel = z.enum(['read', 'write', 'admin']);
type AccessLevel = z.infer<typeof AccessLevel>;

interface ProjectPermission {
  projectId: string;
  userId: string;
  accessLevel: AccessLevel;
  inheritedFrom?: string; // parent workspace for monorepo hierarchy
}

export class PermissionResolver {
  private permissions: Map<string, ProjectPermission[]> = new Map();

  setPermissions(projectId: string, perms: ProjectPermission[]) {
    this.permissions.set(projectId, perms);
  }

  checkAccess(userId: string, projectId: string, requiredLevel: AccessLevel): boolean {
    // Check direct permissions
    const direct = this.permissions.get(projectId)?.find(p => p.userId === userId);
    if (direct && this.isLevelSufficient(direct.accessLevel, requiredLevel)) {
      return true;
    }

    // Check inherited permissions (monorepo parent workspaces)
    return this.checkInheritedAccess(userId, projectId, requiredLevel);
  }

  private isLevelSufficient(given: AccessLevel, required: AccessLevel): boolean {
    const order: AccessLevel[] = ['read', 'write', 'admin'];
    return order.indexOf(given) >= order.indexOf(required);
  }

  private checkInheritedAccess(
    userId: string,
    projectId: string,
    requiredLevel: AccessLevel,
    visited: Set<string> = new Set(),
  ): boolean {
    if (visited.has(projectId)) return false;
    visited.add(projectId);

    const perms = this.permissions.get(projectId);
    if (!perms) return false;

    for (const perm of perms) {
      if (perm.userId !== userId) continue;
      if (this.isLevelSufficient(perm.accessLevel, requiredLevel)) return true;
      if (perm.inheritedFrom) {
        if (this.checkInheritedAccess(userId, perm.inheritedFrom, requiredLevel, visited)) {
          return true;
        }
      }
    }
    return false;
  }
}

// packages/indexer/src/secret-scanner.ts
import { scan } from 'secret-scan';

export interface ScanResult {
  matched: boolean;
  secretType: string;
  lineNumber: number;
}

export async function scanForSecrets(content: string): Promise<ScanResult[]> {
  return scan(content, {
    rules: [
      { id: 'aws-access-key', pattern: /AKIA[0-9A-Z]{16}/, severity: 'high' },
      { id: 'github-token', pattern: /ghp_[0-9a-zA-Z]{36}/, severity: 'high' },
      { id: 'private-key', pattern: /-----BEGIN (RSA|EC|OPENSSH) PRIVATE KEY-----/, severity: 'high' },
      { id: 'jwt', pattern: /eyJ[A-Za-z0-9_-]+\.eyJ[A-Za-z0-9_-]+\.[A-Za-z0-9_-]+/, severity: 'medium' },
    ],
  });
}

export function filterSecrets(content: string): string {
  return content.replace(
    /AKIA[0-9A-Z]{16}|ghp_[0-9a-zA-Z]{36}|-----BEGIN [A-Z ]+ PRIVATE KEY-----|eyJ[A-Za-z0-9_-]+/g,
    '[SECRET_REDACTED]',
  );
}

// packages/indexer/__tests__/strategies/typescript-strategy.test.ts
import { describe, it, expect } from 'vitest';
import { TypeScriptChunkStrategy } from '../../src/strategies/typescript';
import { Chunk } from '../../src/types';

describe('TypeScriptChunkStrategy', () => {
  const strategy = new TypeScriptChunkStrategy({
    maxChunkSize: 500,
    overlapTokens: 50,
  });

  it('splits a large class into logical chunks', () => {
    const code = `
      class UserService {
        async getUser(id: string) { return db.find(id); }
        async createUser(data: CreateUserInput) { return db.insert(data); }
        async deleteUser(id: string) { return db.remove(id); }
        async listUsers(page: number) { return db.paginate(page); }
        async updateUser(id: string, data: Partial<CreateUserInput>) {
          return db.update(id, data);
        }
      }
    `.trim();

    const chunks = strategy.chunk({
      filePath: 'src/services/user.service.ts',
      content: code,
      language: 'typescript',
    });

    expect(chunks).toHaveLengthGreaterThan(1);
    chunks.forEach((chunk: Chunk) => {
      expect(chunk.content.length).toBeLessThanOrEqual(500);
      expect(chunk.filePath).toBe('src/services/user.service.ts');
      expect(chunk.language).toBe('typescript');
    });
  });

  it('preserves imports and exports in chunk metadata', () => {
    const code = `
      import { injectable } from 'inversify';
      export class CacheService implements ICacheService {}
    `.trim();

    const chunks = strategy.chunk({
      filePath: 'src/services/cache.service.ts',
      content: code,
      language: 'typescript',
    });

    expect(chunks[0].metadata.imports).toContain('injectable');
    expect(chunks[0].metadata.exports).toContain('CacheService');
  });
});

// packages/mcp-server/__tests__/tool-server.test.ts
import { describe, it, expect, beforeEach, afterEach } from 'vitest';
import { MCPToolServer } from '../src/tool-server';
import { MockVectorStore } from './fixtures/mock-vector-store';
import { MockEmbeddingService } from './fixtures/mock-embedding-service';

describe('MCPToolServer', () => {
  let server: MCPToolServer;
  let vectorStore: MockVectorStore;
  let embeddingService: MockEmbeddingService;

  beforeEach(() => {
    vectorStore = new MockVectorStore();
    embeddingService = new MockEmbeddingService();
    server = new MCPToolServer({
      vectorStore,
      embeddingService,
      projectId: 'test-project',
    });
  });

  afterEach(async () => {
    await server.shutdown();
  });

  it('responds to ping', async () => {
    const result = await server.handleRequest({
      jsonrpc: '2.0',
      id: 1,
      method: 'ping',
    });
    expect(result.result).toEqual({ status: 'ok' });
  });

  it('searches codebase with semantic query', async () => {
    await vectorStore.upsert('test-project', [
      { id: 'chunk-1', content: 'auth middleware validates JWT tokens', metadata: { filePath: 'src/middleware/auth.ts' } },
      { id: 'chunk-2', content: 'database connection pool configuration', metadata: { filePath: 'src/db/pool.ts' } },
    ]);

    const result = await server.callTool({
      name: 'search_codebase',
      arguments: {
        query: 'How do I configure authentication?',
        projectId: 'test-project',
        limit: 3,
      },
    });

    expect(result.content).toHaveLengthGreaterThan(0);
    expect(result.content[0].text).toContain('auth');
  });

  it('handles undefined project gracefully', async () => {
    const result = await server.callTool({
      name: 'search_codebase',
      arguments: {
        query: 'something',
        projectId: 'nonexistent-project',
      },
    });

    expect(result.isError).toBe(true);
    expect(result.content[0].text).toContain('not indexed');
  });
});

// packages/mcp-server/__tests__/e2e/mcp-client-e2e.test.ts
import { describe, it, expect, beforeAll, afterAll } from 'vitest';
import { Client } from '@modelcontextprotocol/sdk/client/index.js';
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js';
import { spawn } from 'child_process';

describe('E2E: MCP Tool Server', () => {
  let client: Client;
  let serverProcess: ReturnType<typeof spawn>;

  beforeAll(async () => {
    serverProcess = spawn('node', ['dist/mcp-server.js'], {
      env: {
        ...process.env,
        VECTOR_STORE_PATH: ':memory:',
        PROJECT_ROOT: './__tests__/fixtures/monorepo',
      },
    });

    const transport = new StdioClientTransport(serverProcess);
    client = new Client(
      { name: 'e2e-test-client', version: '1.0.0' },
      { capabilities: {} },
    );
    await client.connect(transport);
  });

  afterAll(async () => {
    await client.close();
    serverProcess.kill();
  });

  it('lists available tools', async () => {
    const tools = await client.listTools();
    const toolNames = tools.tools.map(t => t.name);

    expect(toolNames).toContain('search_codebase');
    expect(toolNames).toContain('get_file_context');
    expect(toolNames).toContain('trace_dependency');
  });

  it('performs a full search-and-reason workflow', async () => {
    // Search for auth-related code
    const searchResult = await client.callTool({
      name: 'search_codebase',
      arguments: {
        query: 'JWT token validation',
        limit: 2,
      },
    });

    expect(searchResult.isError).toBe(false);

    // Get file context for the top result
    const firstResult = JSON.parse(searchResult.content[0].text);
    const contextResult = await client.callTool({
      name: 'get_file_context',
      arguments: {
        filePath: firstResult.filePath,
        contextWindow: 50,
      },
    });

    expect(contextResult.isError).toBe(false);
    expect(contextResult.content[0].text.length).toBeGreaterThan(0);
  });
});

// packages/eval/src/golden-set.ts
export interface GoldenQuestion {
  id: string;
  query: string;
  expectedFilePaths: string[];
  expectedChunkIds: string[];
  difficulty: 'easy' | 'medium' | 'hard';
  category: 'finding' | 'understanding' | 'debugging' | 'refactoring';
}

export const GOLDEN_SET: GoldenQuestion[] = [
  {
    id: 'q001',
    query: 'Where is the authentication middleware defined?',
    expectedFilePaths: ['packages/auth/src/middleware.ts'],
    expectedChunkIds: ['chunk-auth-middleware-1'],
    difficulty: 'easy',
    category: 'finding',
  },
  {
    id: 'q002',
    query: 'How does the payment service handle retry logic for Stripe API calls?',
    expectedFilePaths: [
      'packages/payments/src/service.ts',
      'packages/payments/src/retry.ts',
    ],
    expectedChunkIds: ['chunk-payment-service', 'chunk-retry-logic'],
    difficulty: 'medium',
    category: 'understanding',
  },
  {
    id: 'q003',
    query: 'There\'s a memory leak in the WebSocket handler. Where is the cleanup code?',
    expectedFilePaths: ['packages/ws/src/handler.ts'],
    expectedChunkIds: ['chunk-ws-handler-close'],
    difficulty: 'hard',
    category: 'debugging',
  },
];

// packages/eval/src/evaluator.ts
import { GoldenQuestion, GOLDEN_SET } from './golden-set';
import { MCPToolServer } from 'monorepo-rag-mcp-server';

interface EvalResult {
  questionId: string;
  recall: number;       // % of expected chunks retrieved
  precision: number;    // % of retrieved chunks that are relevant
  f1: number;
  latencyMs: number;
  passed: boolean;
}

export class Evaluator {
  constructor(private server: MCPToolServer) {}

  async evaluate(): Promise<EvalResult[]> {
    const results: EvalResult[] = [];

    for (const question of GOLDEN_SET) {
      const start = performance.now();

      const searchResult = await this.server.callTool({
        name: 'search_codebase',
        arguments: {
          query: question.query,
          limit: 10,
        },
      });

      const latencyMs = performance.now() - start;
      const retrievedChunks = this.parseChunksFromResult(searchResult);

      const { recall, precision, f1 } = this.computeMetrics(
        retrievedChunks,
        question.expectedChunkIds,
      );

      results.push({
        questionId: question.id,
        recall,
        precision,
        f1,
        latencyMs,
        passed: recall >= 0.8 && precision >= 0.7,
      });
    }

    return results;
  }

  private computeMetrics(
    retrieved: string[],
    expected: string[],
  ): { recall: number; precision: number; f1: number } {
    const retrievedSet = new Set(retrieved);
    const expectedSet = new Set(expected);

    const truePositives = [...retrievedSet].filter(id => expectedSet.has(id)).length;
    const recall = expectedSet.size > 0
      ? truePositives / expectedSet.size
      : 0;
    const precision = retrievedSet.size > 0
      ? truePositives / retrievedSet.size
      : 0;
    const f1 = (recall + precision) > 0
      ? (2 * recall * precision) / (recall + precision)
      : 0;

    return { recall, precision, f1 };
  }

  private parseChunksFromResult(result: unknown): string[] {
    // Implementation depends on your result format
    return [];
  }
}

# docker-compose.yml
version: '3.9'

services:
  embedding-server:
    build:
      context: ./packages/embedding-server
      dockerfile: ../../Dockerfile.embedding
    ports:
      - "6000:6000"
    environment:
      - MODEL_ID=${EMBEDDING_MODEL:-all-MiniLM-L6-v2}
      - MAX_BATCH_SIZE=32
      - WORKERS=4
    volumes:
      - embedding-cache:/app/.cache
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:6000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis-data:/data
    command: redis-server --appendonly yes
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 10s
      timeout: 5s
      retries: 5

  mcp-gateway:
    build:
      context: ./packages/mcp-gateway
      dockerfile: ../../Dockerfile.gateway
    ports:
      - "3000:3000"
    environment:
      - MCP_SERVER_URL=http://mcp-server:8080
      - REDIS_URL=redis://redis:6379
      - EMBEDDING_SERVER_URL=http://embedding-server:6000
    depends_on:
      redis:
        condition: service_healthy
      embedding-server:
        condition: service_healthy
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3

  mcp-server:
    build:
      context: ./packages/mcp-server
      dockerfile: ../../Dockerfile.mcp
    environment:
      - VECTOR_STORE_URL=http://embedding-server:6000
      - REDIS_URL=redis://redis:6379
      - PROJECT_ROOT=/data/monorepo
    volumes:
      - ./monorepo:/data/monorepo:ro
    depends_on:
      redis:
        condition: service_healthy
      embedding-server:
        condition: service_healthy

  indexer-worker:
    build:
      context: ./packages/indexer
      dockerfile: ../../Dockerfile.worker
    environment:
      - REDIS_URL=redis://redis:6379
      - EMBEDDING_SERVER_URL=http://embedding-server:6000
      - MAX_CONCURRENCY=4
    volumes:
      - ./monorepo:/data/monorepo:ro
    depends_on:
      redis:
        condition: service_healthy
      embedding-server:
        condition: service_healthy

volumes:
  redis-data:
  embedding-cache:

docker compose up -d

curl http://localhost:3000/health
curl http://localhost:6000/health

# .github/workflows/ci.yml
name: CI

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    services:
      redis:
        image: redis:7-alpine
        ports: ['6379:6379']
      qdrant:
        image: qdrant/qdrant:latest
        ports: ['6333:6333']

    steps:
      - uses: actions/checkout@v4

      - name: Setup Node
        uses: actions/setup-node@v4
        with:
          node-version: '20'
          cache: 'pnpm'

      - name: Install dependencies
        run: pnpm install --frozen-lockfile

      - name: Type check
        run: pnpm --filter monorepo-rag-mcp... typecheck

      - name: Lint
        run: pnpm --filter monorepo-rag-mcp... lint

      - name: Unit tests
        run: pnpm --filter monorepo-rag-mcp... test:unit

      - name: Integration tests
        run: pnpm --filter monorepo-rag-mcp-server test:integration
        env:
          REDIS_URL: redis://localhost:6379
          VECTOR_STORE_URL: http://localhost:6333

      - name: E2E tests
        run: pnpm --filter monorepo-rag-mcp-server test:e2e
        env:
          REDIS_URL: redis://localhost:6379
          EMBEDDING_SERVER_URL: http://localhost:6000

      - name: Evaluate
        run: pnpm --filter monorepo-rag-mcp-eval eval
        env:
          REDIS_URL: redis://localhost:6379

      - name: Build
        run: pnpm build

      - name: Docker build (gateway)
        run: docker build -f Dockerfile.gateway -t mcp-gateway:${{ github.sha }} .

      - name: Docker build (mcp server)
        run: docker build -f Dockerfile.mcp -t mcp-server:${{ github.sha }} .

{
  "mcp.servers": {
    "monorepo-rag": {
      "type": "stdio",
      "command": "node",
      "args": ["/path/to/packages/mcp-gateway/dist/index.js"],
      "env": {
        "PROJECT_ROOT": "/path/to/your/monorepo",
        "REDIS_URL": "redis://localhost:6379",
        "EMBEDDING_SERVER_URL": "http://localhost:6000"
      }
    }
  }
}

{
  "mcpServers": {
    "monorepo-rag-mcp": {
      "command": "npx",
      "args": ["-y", "monorepo-rag-mcp-gateway"],
      "env": {
        "PROJECT_ROOT": "${workspaceFolder}",
        "EMBEDDING_MODEL": "all-MiniLM-L6-v2"
      }
    }
  }
}

{
  "mcpServers": {
    "monorepo-rag": {
      "command": "node",
      "args": ["dist/mcp-gateway.js"],
      "env": {
        "PROJECT_ROOT": "/path/to/monorepo"
      }
    }
  }
}

// packages/indexer/src/chunker.ts
const CHUNK_SIZES = [200, 300, 500, 800, 1200];

export function findOptimalChunkSize(
  projectId: string,
  sampleQueries: string[],
): number {
  let bestSize = CHUNK_SIZES[0];
  let bestScore = 0;

  for (const size of CHUNK_SIZES) {
    const strategy = new SmartChunker({ maxChunkSize: size, overlapTokens: Math.floor(size * 0.1) });
    const score = evaluateStrategy(projectId, strategy, sampleQueries);
    if (score > bestScore) {
      bestScore = score;
      bestSize = size;
    }
  }

  return bestSize;
}

// packages/indexer/src/watcher.ts
import { watch } from 'chokidar';
import { IndexService } from './index-service';

export function startFileWatcher(
  projectRoot: string,
  indexService: IndexService,
  projectId: string,
) {
  const watcher = watch(projectRoot, {
    ignored: /node_modules|\.git|dist|build|\.next/,
    persistent: true,
    depth: 10,
  });

  watcher
    .on('add', async (path) => {
      await indexService.indexFile(projectId, path);
    })
    .on('change', async (path) => {
      await indexService.reindexFile(projectId, path);
    })
    .on('unlink', async (path) => {
      await indexService.removeFile(projectId, path);
    });

  // Full reindex every 6 hours as a safety net
  setInterval(() => {
    indexService.reindexAll(projectId);
  }, 6 * 60 * 60 * 1000);
}

// packages/indexer/src/migration.ts
export interface ModelMigration {
  fromModel: string;
  toModel: string;
  requiresReindex: boolean;
  notes: string;
}

const MIGRATIONS: Record<string, ModelMigration> = {
  'all-MiniLM-L6-v2->text-embedding-3-small': {
    fromModel: 'all-MiniLM-L6-v2',
    toModel: 'text-embedding-3-small',
    requiresReindex: true,
    notes: 'Dimensionality change: 384 -> 1536. Full re-index required.',
  },
};

export function validateModelSwap(currentModel: string, newModel: string): void {
  const migration = MIGRATIONS[`${currentModel}->${newModel}`];
  if (!migration) return;

  if (migration.requiresReindex) {
    throw new Error(
      `Cannot switch from ${currentModel} to ${newModel} without re-indexing. ${migration.notes}`
    );
  }
}

// packages/mcp-server/src/responses.ts
export function formatSearchResult(query: string, results: SearchResult[]): string {
  const lines = [
    `# Search Results for: "${query}"`,
    `Found ${results.length} relevant code chunks from ${new Set(results.map(r => r.filePath)).size} files.`,
    `---`,
  ];

  for (const result of results) {
    lines.push(`\n## \`${result.filePath}\` (relevance: ${(result.score * 100).toFixed(1)}%)`);
    lines.push(result.content);
    lines.push(`---`);
  }

  return lines.join('\n');
}