Why engineering teams hire senior freelance programmer Aldian Fazrihady to architect Agentic RAG, GraphRAG, and hybrid BM25 + pgvector retrieval engines. With 15+ years of full-stack software engineering experience, Aldian Fazrihady partners directly with CTOs, VPs of Engineering, and technical founders via Upwork and direct B2B consulting contracts.

1. Production Architecture: Agentic RAG, Hybrid BM25 + pgvector Search & Knowledge Graph Retrieval

Hiring a senior freelance programmer requires verifiable production patterns rather than generic boilerplate. Below is a battle-tested implementation pattern used in high-availability systems:

from typing import List, Dict, Any
from pydantic import BaseModel, Field

class RetrievalHop(BaseModel):
    sub_query: str = Field(description="Focused retrieval query for hybrid vector + BM25 search")
    entity_filters: List[str] = Field(default_factory=list)
    min_relevance_score: float = Field(default=0.78)

async def execute_agentic_hybrid_rag(conn, user_question: str, embedding: List[float], hops: List[RetrievalHop]) -> List[Dict[str, Any]]:
    results = []
    for hop in hops:
        rows = await conn.fetch(
            """
            WITH semantic AS (
                SELECT id, doc_title, chunk_text, 1 - (embedding <=> $1::vector) AS vec_score
                FROM knowledge_chunks
                WHERE 1 - (embedding <=> $1::vector) >= $2
                ORDER BY embedding <=> $1::vector LIMIT 20
            ),
            keyword AS (
                SELECT id, ts_rank_cd(tsv, plainto_tsquery('english', $3)) AS kw_score
                FROM knowledge_chunks
                WHERE tsv @@ plainto_tsquery('english', $3)
                LIMIT 20
            )
            SELECT s.id, s.doc_title, s.chunk_text,
                   (COALESCE(s.vec_score, 0.0) * 0.7 + COALESCE(k.kw_score, 0.0) * 0.3) AS hybrid_score
            FROM semantic s
            LEFT JOIN keyword k USING (id)
            ORDER BY hybrid_score DESC
            LIMIT 8;
            """,
            embedding, hop.min_relevance_score, hop.sub_query,
        )
        results.extend([dict(r) for r in rows])
    return results

2. Key Engineering Guarantees

• Multi-Hop Query Decomposition: Using structured LLM outputs to break complex enterprise questions into deterministic sub-queries across SQL, vector, and graph stores.
• Reciprocal Rank Fusion (RRF) & Reranking: Combining PostgreSQL tsvector lexical precision with pgvector HNSW semantic recall.
• Citation-Grounded Synthesis: Enforcing strict inline document citations and automated hallucination checks before returning responses to users.

3. Explore the 5 Topical Category Hubs & Master Directory

• Master Directory (445+ Freelance Programmer & AI Engineering Guides)
• AI, LLM, RAG, MCP & Google ADK Engineering Hub
• Python, Django, FastAPI & PostgreSQL Architecture Hub
• React, Next.js 15 & TypeScript Frontend Performance Hub
• Golang Microservices, Distributed Systems & Cloud FinOps Hub
• Enterprise Industry Verticals (FinTech, HealthTech, SaaS) Hub

4. Frequently Asked Questions (FAQ)