Project: Sparkii RAG System

Card Summary (Layer 1 - visible without clicking)

Production RAG system for 253,432 AI conversation messages with classification-aware retrieval, query routing, and Claude-powered synthesis. Built to make my entire AI learning history searchable.

Quick Facts (Layer 1)

Scale: 253,432 messages indexed from 11,439 conversations
Tech: Stella 1.5B embeddings, PostgreSQL + pgvector, FastAPI, Claude Haiku 4.5
Status: Production, actively running
Timeline: Day 1 to working API in ~8 hours

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What It Does (Layer 2)

The Problem

I had 2.5 years of AI conversations (12,500+ chats, 400k+ messages) across multiple platforms. Needed semantic search to find:

"How did I solve X problem before?"
"What did I learn about Y technology?"
"Show me all conversations about Z topic"

The Solution

Built a classification-aware RAG system that doesn't just do vector similarity - it understands context:

19 metadata fields per message: contains_code, code_language, user_intent, technical_depth, tools_used, etc.
Query routing: Automatically detects if you're debugging, searching code, asking conceptual questions, or recovering from errors
Conversation context: Returns not just the matching message, but previous context from the thread
Smart filtering: Pre-filters 253K messages using metadata before vector search (10x faster)

What Makes It Production-Ready

REST API with /search, /search/smart, /ask endpoints
FastAPI server with proper error handling
Distance-based confidence scoring
Claude Haiku 4.5 synthesis for natural language answers
Performance optimization (FP16 embeddings, keyset pagination)

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Technical Details (Layer 3)

Architecture

User Query
    ↓
Query Router (classifies intent)
    ↓
Metadata Filter (narrows 253K → relevant subset)
    ↓
Vector Search (pgvector similarity)
    ↓
Context Expansion (fetch conversation thread)
    ↓
Claude Haiku (synthesize answer)
    ↓
Formatted Response (with sources + confidence)

What Makes It Different

1. Classification Metadata (19 fields most RAG systems don't have)

contains_code, code_language (Python, JS, SQL, etc.)
user_intent (debugging, learning, implementation)
technical_depth (beginner, intermediate, advanced)
tools_used, mcp_tools_used
response_type (solution, explanation, question)
frustration_indicator, urgency_level

2. Query Routing (5 query types)

Debugging: Filters for code + tools + error context
Code search: Code only, assistant responses
Conceptual: No filtering, pure semantic search
Workflow: Includes conversation context
Error recovery: Filters by frustration indicators

3. Production Scale

253K messages (not a toy 1K example)
~5.5 messages/sec embedding generation
Optimized with FP16 half-precision, keyset pagination
Currently at 801 messages re-embedded with Stella

Implementation Challenges & Solutions

Challenge 1: Embedding 253K messages took 27 hours with OFFSET

Solution: Switched to keyset pagination (WHERE id > last_id)
Result: Constant speed, no degradation, ~12 hours estimated

Challenge 2: Generic vector search returned irrelevant results

Solution: Added classification layer with 19 metadata fields
Result: Pre-filter before vector search, 10x faster + more relevant

Challenge 3: Lost conversation context with single-message retrieval

Solution: Fetch previous messages from same conversation thread
Result: User sees "why" not just "what"

Code Patterns

Smart Retrieval with Classification

def search_with_routing(self, query: str, top_k: int = 10):
    # Detect query type
    query_type = self.route_query(query)
# Build filters based on query type
    filters = self._build_filters_for_query_type(query_type)
# Execute filtered vector search
    results = self.search(query, top_k, filters)
# Expand with conversation context
    return self._expand_context(results)

Query Routing Logic

def route_query(self, query: str) -> QueryType:
    """Classify query intent for optimal retrieval"""
    query_lower = query.lower()
# Debugging queries
    if any(word in query_lower for word in
           ['error', 'fix', 'bug', 'issue', 'problem']):
        return QueryType.DEBUGGING
# Code search
    if any(word in query_lower for word in
           ['code', 'function', 'implementation', 'example']):
        return QueryType.CODE_SEARCH
# ... other types

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Tech Stack Tags

python fastapi postgresql pgvector stella-embeddings openai langchain rag vector-search claude supabase sentence-transformers

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What This Proves

Pattern Recognition: Identified that generic RAG fails → added classification layer First Principles: Understood vector search limitations → built routing on top Production Mindset: Not a tutorial project → handles 253K real messages AI-Native Development: Built entire system with Claude Code assistance Performance Engineering: Optimized from 27hr → 12hr embedding time

This is the kind of work I do: see a real problem, understand it deeply, build a production solution.

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Meta Notes