Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40%

# rag/chunking.py
from abc import ABC, abstractmethod
from dataclasses import dataclass

@dataclass
class Chunk:
    text: str
    metadata: dict
    token_count: int
    chunk_id: str

class ChunkingStrategy(ABC):
    @abstractmethod
    def chunk(self, document: str, metadata: dict) -> list[Chunk]: ...

class FixedTokenChunker(ChunkingStrategy):
    """Baseline. Good for homogeneous content."""
    def __init__(self, chunk_size=512, overlap=50):
        self.chunk_size = chunk_size
        self.overlap = overlap

class RecursiveChunker(ChunkingStrategy):
    """Respects structure: markdown headers, code blocks, paragraphs."""
    def __init__(self, separators=["\n## ", "\n### ", "\n\n", "\n", " "], chunk_size=512):
        self.separators = separators
        self.chunk_size = chunk_size

class SemanticChunker(ChunkingStrategy):
    """Uses embedding similarity to find natural boundaries."""
    def __init__(self, model="text-embedding-3-small", threshold=0.7):
        self.model = model
        self.threshold = threshold

class AgenticChunker(ChunkingStrategy):
    """LLM decides boundaries. Expensive but highest quality for complex docs."""
    def __init__(self, model="gpt-4o-mini"):
        self.model = model

# rag/retrieval.py
class HybridRetriever:
    def __init__(self, vector_store, bm25_index, reranker, weights=(0.4, 0.3, 0.3)):
        self.vector = vector_store
        self.bm25 = bm25_index
        self.reranker = reranker
        self.weights = weights  # vector, bm25, reranker

    async def retrieve(self, query: str, k=20, final_k=5):
        # Stage 1: Parallel retrieval
        vector_results = await self.vector.search(query, k=k)
        bm25_results = await self.bm25.search(query, k=k)

        # Stage 2: Reciprocal Rank Fusion
        fused = self._rrf(vector_results, bm25_results, k=50)

        # Stage 3: Cross-encoder rerank (top 50 → top 5)
        reranked = await self.reranker.rerank(query, fused[:50])

        return reranked[:final_k]

    def _rrf(self, *result_lists, k=60):
        """Reciprocal Rank Fusion — no score calibration needed."""
        scores = defaultdict(float)
        for results in result_lists:
            for rank, doc in enumerate(results):
                scores[doc.id] += 1 / (k + rank + 1)
        return sorted(scores.items(), key=lambda x: -x[1])

# rag/query_transform.py
class QueryTransformer:
    def __init__(self, llm_model="gpt-4o-mini"):
        self.llm = instructor.from_openai(AsyncOpenAI())

    async def expand(self, query: str, context: dict = None) -> list[str]:
        """Generate multiple search queries from one user question."""

        class QuerySet(BaseModel):
            queries: list[str] = Field(min_length=3, max_length=5)
            reasoning: str

        result = await self.llm.chat.completions.create(
            model=self.model,
            response_model=QuerySet,
            messages=[
                {"role": "system", "content": """
Generate diverse search queries that collectively cover the user's intent.
Include: exact phrasing, synonyms, broader/narrower, hypothetical answer.
"""},
                {"role": "user", "content": f"Original: {query}\nContext: {context}"}
            ],
            temperature=0.3,
        )
        return result.queries

    async def decompose(self, query: str) -> list[str]:
        """Break multi-hop questions into sub-questions."""

        class SubQuestions(BaseModel):
            questions: list[str]
            needs_synthesis: bool

        return await self.llm.chat.completions.create(
            model=self.model,
            response_model=SubQuestions,
            messages=[...],
        )

# rag/optimization.py
import optuna
from dataclasses import dataclass

@dataclass
class RetrievalConfig:
    chunk_size: int
    overlap: int
    top_k: int
    vector_weight: float
    bm25_weight: float
    rerank_top_k: int

def objective(trial: optuna.Trial) -> tuple[float, float]:
    config = RetrievalConfig(
        chunk_size=trial.suggest_categorical("chunk_size", [256, 512, 768, 1024, 1536]),
        overlap=trial.suggest_int("overlap", 0, 200, step=25),
        top_k=trial.suggest_int("top_k", 5, 50, step=5),
        vector_weight=trial.suggest_float("vector_weight", 0.1, 0.8),
        bm25_weight=trial.suggest_float("bm25_weight", 0.1, 0.8),
        rerank_top_k=trial.suggest_int("rerank_top_k", 10, 100, step=10),
    )

    # Evaluate on golden set (200 queries)
    recall, latency = evaluate_config(config, golden_set)

    # Multi-objective: maximize recall, minimize latency
    return recall, latency / 1000  # seconds

study = optuna.create_study(
    directions=["maximize", "minimize"],
    sampler=optuna.samplers.TPESampler(multivariate=True),
)
study.optimize(objective, n_trials=100, timeout=3600)  # 1 hour

# Pareto frontier gives you the tradeoff curve
pareto = [t for t in study.trials if t.state == TrialState.COMPLETE]

# rag/metrics.py
from prometheus_client import Histogram, Counter, Gauge

RETRIEVAL_LATENCY = Histogram("rag_retrieval_latency_seconds", "End-to-end retrieval time")
RECALL_AT_K = Gauge("rag_recall_at_k", "Recall@k on golden set", ["k"])
QUERY_EXPANSION_COUNT = Counter("rag_query_expansions_total", "Number of expanded queries")
RERANKER_LATENCY = Histogram("rag_reranker_latency_seconds", "Cross-encoder rerank time")

class InstrumentedRetriever(HybridRetriever):
    async def retrieve(self, query, k=20, final_k=5):
        with RETRIEVAL_LATENCY.time():
            expanded = await self.transformer.expand(query)
            QUERY_EXPANSION_COUNT.inc(len(expanded))

            results = await super().retrieve(expanded, k, final_k)

            # Track recall on sampled golden queries (1% of traffic)
            if random.random() < 0.01:
                RECALL_AT_K.labels(k=10).set(self._eval_recall(query, results))

            return results