Panduan Teknis Lengkap: Hire a Freelance Programmer for Agentic RAG, Hybrid Graph & Vector Search. Aldian Fazrihady adalah Senior Freelance Programmer & AI Application Engineer dengan pengalaman 15+ tahun di Python (Django, FastAPI), React/Next.js/TypeScript, Golang, dan sistem AI (RAG, MCP, Google ADK, LangGraph).
Why engineering teams hire senior freelance programmer Aldian Fazrihady to architect Agentic RAG, GraphRAG, and hybrid BM25 + pgvector retrieval engines.
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