Stop AI Hallucinations — Build a Local Search Engine (SurrealDB + MCP)
G
Gaurav Wankhede - TECHVERSE
1 months ago
1 months ago
Stop local AI hallucinations! Learn how to build WebFind — a 100% Local-First Search Engine for AI Agents using SurrealDB (HNSW + BM25) and Model Context Protocol (MCP).
Every local AI model (Ollama, Llama 3, Mistral, DeepSeek) hallucinates when queried on modern library documentation or recent API specifications because model training freezes at release time. In this complete architectural deep dive, we build a local-first search engine that grounds AI agents in real-time documentation with zero cloud leakage, zero API keys, and sub-second latency.
Key Concepts Covered:
1. The AI Hallucination Problem & Training Data Time Boundary Cutoff
2. Why Pure Vector Search Fails for Technical Docs (Lossy Exact Symbol Matching)
3. SurrealDB Vector (HNSW) + BM25 Full-Text Hybrid Search Engine Setup
4. Reciprocal Rank Fusion (RRF) 5-Signal Scoring Engine
5. Dual Crawling Architecture (Puppeteer JS SPA + Fast HTML Parser)
6. Model Context Protocol (MCP) Integration — 4 Native MCP Tools on Port 4748:
- webfind_search: Sub-second hybrid search
- webfind_research: Multi-page document aggregation
- webfind_fetch: Raw text & markdown extraction
- webfind_graph: Visual web crawl map visualization
Timestamps & Chapters:
00:0000:00 - Introduction: Why Local AI Models Hallucinate
00:2300:23 - The Invisible Wall: Knowledge Cutoff & Static Datasets
00:4500:45 - The Architecture: Local-First Search Engine Blueprint
01:0201:02 - Why Pure Vector Search Breaks Code Retrieval
01:2501:25 - SurrealDB Vector Search (HNSW) + BM25 Setup
01:4201:42 - 5-Signal Reciprocal Rank Fusion (RRF) Engine
02:0002:00 - Dual Crawler Architecture: Puppeteer vs Fast HTML Parser
02:2002:20 - Model Context Protocol (MCP) Standard Explained
02:4302:43 - Tool 1: webfind_search (:4748)
03:0003:00 - Tool 2: webfind_research (:4748)
03:1803:18 - Tool 3: webfind_fetch (:4748)
03:3303:33 - Tool 4: webfind_graph (:4748)
03:4803:48 - Benchmark Results: Latency ( less than 850ms) & Memory Usage
04:0504:05 - Step-by-Step Code Walkthrough: SurrealDB Schema
04:2304:23 - Python MCP Server Implementation
04:4304:43 - Connecting WebFind to Claude Desktop & Ollama
05:0005:00 - Testing Live Search Queries locally
05:1805:18 - Handling JavaScript SPAs with Headless Puppeteer
05:3405:34 - Zero Cloud Leakage & Privacy Proof
05:5005:50 - Production Deployment with Docker Compose
06:0206:02 - Part 2 Preview: Building Multi-Agent Autonomous Crawlers
Code & Resources:
- WebFind GitHub Repository: Will Update Soon
surrealdb.com...
modelcontextprotocol.io
Follow & Support:
www.gauravwankhede.com
github.com...
Hashtags:
#LocalAI #AIAgents #SurrealDB #ModelContextProtocol #RAG #MachineLearning #Python #SearchEngine #SoftwareArchitecture
Every local AI model (Ollama, Llama 3, Mistral, DeepSeek) hallucinates when queried on modern library documentation or recent API specifications because model training freezes at release time. In this complete architectural deep dive, we build a local-first search engine that grounds AI agents in real-time documentation with zero cloud leakage, zero API keys, and sub-second latency.
Key Concepts Covered:
1. The AI Hallucination Problem & Training Data Time Boundary Cutoff
2. Why Pure Vector Search Fails for Technical Docs (Lossy Exact Symbol Matching)
3. SurrealDB Vector (HNSW) + BM25 Full-Text Hybrid Search Engine Setup
4. Reciprocal Rank Fusion (RRF) 5-Signal Scoring Engine
5. Dual Crawling Architecture (Puppeteer JS SPA + Fast HTML Parser)
6. Model Context Protocol (MCP) Integration — 4 Native MCP Tools on Port 4748:
- webfind_search: Sub-second hybrid search
- webfind_research: Multi-page document aggregation
- webfind_fetch: Raw text & markdown extraction
- webfind_graph: Visual web crawl map visualization
Timestamps & Chapters:
00:0000:00 - Introduction: Why Local AI Models Hallucinate
00:2300:23 - The Invisible Wall: Knowledge Cutoff & Static Datasets
00:4500:45 - The Architecture: Local-First Search Engine Blueprint
01:0201:02 - Why Pure Vector Search Breaks Code Retrieval
01:2501:25 - SurrealDB Vector Search (HNSW) + BM25 Setup
01:4201:42 - 5-Signal Reciprocal Rank Fusion (RRF) Engine
02:0002:00 - Dual Crawler Architecture: Puppeteer vs Fast HTML Parser
02:2002:20 - Model Context Protocol (MCP) Standard Explained
02:4302:43 - Tool 1: webfind_search (:4748)
03:0003:00 - Tool 2: webfind_research (:4748)
03:1803:18 - Tool 3: webfind_fetch (:4748)
03:3303:33 - Tool 4: webfind_graph (:4748)
03:4803:48 - Benchmark Results: Latency ( less than 850ms) & Memory Usage
04:0504:05 - Step-by-Step Code Walkthrough: SurrealDB Schema
04:2304:23 - Python MCP Server Implementation
04:4304:43 - Connecting WebFind to Claude Desktop & Ollama
05:0005:00 - Testing Live Search Queries locally
05:1805:18 - Handling JavaScript SPAs with Headless Puppeteer
05:3405:34 - Zero Cloud Leakage & Privacy Proof
05:5005:50 - Production Deployment with Docker Compose
06:0206:02 - Part 2 Preview: Building Multi-Agent Autonomous Crawlers
Code & Resources:
- WebFind GitHub Repository: Will Update Soon
surrealdb.com...
modelcontextprotocol.io
Follow & Support:
www.gauravwankhede.com
github.com...
Hashtags:
#LocalAI #AIAgents #SurrealDB #ModelContextProtocol #RAG #MachineLearning #Python #SearchEngine #SoftwareArchitecture
More from this channel
Why Chain of Thought Breaks at Scale
5 days ago
Why Your Brain Lies to You (Fast & Slow Thinking)
7 days ago
7 Triggers That Make You Say Yes (Psychology of Persuasion)
8 days ago
GNAP Protocol in Rust The Simple Way to Production Auth
12 days ago
Find YouTube Content Gaps with TubeForge & AI Agents
13 days ago
Next.js Alternative? Testing Tokio's Topcoat in Rust
14 days ago
GNAP Explained: Why It Replaces OAuth 2.0 and JWT
18 days ago
Why MCP Is Overkill for Local Development
19 days ago
Rust: Think Iterator, Not Index Loop #rustlang #coding #rust #code #programming
22 days ago
Rust: Stop Using .clone() — It's Technical Debt #rustlang #coding #rust #programming #code
23 days ago