Modular RAG: Interchangeable Components Architecture

Table of Contents:

• What Is Modular RAG?
• Core RAG Components
• Interface Design Patterns
• Swappable Retriever Architecture
• Embedding Model Abstraction
• Reranker Integration
• Generator Flexibility
• Production Implementation
• Frequently Asked Questions

What Is Modular RAG?

Short answer: Modular RAG designs RAG systems with swappable components — change embedding models, swap vector databases, add rerankers, or switch LLMs without rewriting the pipeline. Each component implements a common interface, enabling A/B testing and incremental upgrades.

Building RAG at HinterBuild, we see teams hardcode OpenAI embeddings + Pinecone + GPT-4 in 50 places. Switching to Cohere embeddings requires rewriting half the codebase. Modular RAG abstracts components behind interfaces — swap implementations with config changes, not code rewrites.

Key Takeaways:

• Modular RAG separates concerns: ingestion, retrieval, reranking, generation
• Each component implements a standard interface (e.g., Retriever.retrieve())
• Swap implementations without changing downstream code
• Enables A/B testing: run 2 embedding models simultaneously, compare quality
• Incremental migration: add new components alongside old ones
• Complexity cost: more abstractions = more code, but 10x easier to evolve

A fintech client's RAG used openai.embeddings.create() calls scattered across ingestion, retrieval, and monitoring code. Migrating to Voyage embeddings took 3 weeks and broke tests. We refactored to modular architecture: one EmbeddingProvider interface, implementations for OpenAI, Voyage, Cohere. Migration: change config line. Time: 30 minutes.

Core RAG Components

Standard RAG decomposes into 5 components:

Document → [Ingestion] → [Storage] → [Retrieval] → [Reranker] → [Generator] → Answer

Component Responsibilities

Key Principle: Each component exposes standard interface, hides implementation details.

Interface Design Patterns

Base Interfaces

from abc import ABC, abstractmethod
from typing import List, Dict, Any
from dataclasses import dataclass

@dataclass
class Document:
    """Standard document representation."""
    content: str
    metadata: Dict[str, Any]
    id: str

@dataclass
class Chunk:
    """Standard chunk representation."""
    content: str
    metadata: Dict[str, Any]
    embedding: List[float] = None
    score: float = 0.0

class Embedder(ABC):
    """Abstract embedding provider."""

    @abstractmethod
    async def embed_texts(self, texts: List[str]) -> List[List[float]]:
        """Embed batch of texts."""
        pass

    @abstractmethod
    async def embed_query(self, query: str) -> List[float]:
        """Embed single query."""
        pass

class Retriever(ABC):
    """Abstract retriever."""

    @abstractmethod
    async def retrieve(self, query: str, top_k: int = 5, filters: Dict = None) -> List[Chunk]:
        """Retrieve relevant chunks."""
        pass

class Reranker(ABC):
    """Abstract reranker."""

    @abstractmethod
    async def rerank(self, query: str, chunks: List[Chunk], top_k: int = 5) -> List[Chunk]:
        """Rerank chunks by relevance."""
        pass

class Generator(ABC):
    """Abstract answer generator."""

    @abstractmethod
    async def generate(self, query: str, context: List[Chunk]) -> str:
        """Generate answer from context."""
        pass

class VectorStore(ABC):
    """Abstract vector storage."""

    @abstractmethod
    async def upsert(self, chunks: List[Chunk], namespace: str = None):
        """Store chunks with embeddings."""
        pass

    @abstractmethod
    async def search(self, embedding: List[float], top_k: int = 5, filters: Dict = None) -> List[Chunk]:
        """Search by vector similarity."""
        pass

Benefits of Interfaces

• Testability — Mock implementations for unit tests
• Flexibility — Swap implementations at runtime
• Type safety — IDE autocomplete and type checking
• Documentation — Interface contract is clear

Swappable Retriever Architecture

Vector Retriever Implementation

from openai import OpenAI
import asyncpg
from pgvector.asyncpg import register_vector

client = OpenAI()

class PgVectorRetriever(Retriever):
    """Vector retriever using PostgreSQL + pgvector."""

    def __init__(self, conn: asyncpg.Connection, embedder: Embedder):
        self.conn = conn
        self.embedder = embedder

    async def retrieve(self, query: str, top_k: int = 5, filters: Dict = None) -> List[Chunk]:
        """Retrieve using vector similarity."""
        query_embedding = await self.embedder.embed_query(query)

        # Build filter clause
        filter_clause = ""
        params = [query_embedding, top_k]

        if filters:
            filter_conditions = []
            for key, value in filters.items():
                params.insert(-1, value)
                filter_conditions.append(f"metadata->>{key!r} = ${len(params) - 1}")
            filter_clause = " AND " + " AND ".join(filter_conditions)

        # Search
        await register_vector(self.conn)
        rows = await self.conn.fetch(
            f"""
            SELECT content, metadata, 1 - (embedding <=> $1) AS score
            FROM document_chunks
            WHERE 1=1 {filter_clause}
            ORDER BY embedding <=> $1
            LIMIT $2
            """,
            *params
        )

        return [
            Chunk(content=r["content"], metadata=r["metadata"], score=r["score"])
            for r in rows
        ]

class PineconeRetriever(Retriever):
    """Vector retriever using Pinecone."""

    def __init__(self, index, embedder: Embedder):
        self.index = index
        self.embedder = embedder

    async def retrieve(self, query: str, top_k: int = 5, filters: Dict = None) -> List[Chunk]:
        """Retrieve using Pinecone."""

        query_embedding = await self.embedder.embed_query(query)

        results = self.index.query(
            vector=query_embedding,
            top_k=top_k,
            filter=filters,
            include_metadata=True
        )

        return [
            Chunk(
                content=match["metadata"].get("content", ""),
                metadata=match["metadata"],
                score=match["score"]
            )
            for match in results["matches"]
        ]

class HybridRetriever(Retriever):
    """Hybrid retriever: vector + keyword search."""

    def __init__(self, vector_retriever: Retriever, keyword_retriever: Retriever):
        self.vector_retriever = vector_retriever
        self.keyword_retriever = keyword_retriever

    async def retrieve(self, query: str, top_k: int = 5, filters: Dict = None) -> List[Chunk]:
        """Retrieve using both vector and keyword search, merge results."""

        # Retrieve from both
        vector_results = await self.vector_retriever.retrieve(query, top_k=top_k*2, filters=filters)
        keyword_results = await self.keyword_retriever.retrieve(query, top_k=top_k*2, filters=filters)

        # Merge and deduplicate
        seen = set()
        merged = []

        for chunk in vector_results + keyword_results:
            chunk_id = hash(chunk.content)
            if chunk_id not in seen:
                seen.add(chunk_id)
                merged.append(chunk)

        # Sort by score and return top-k
        merged.sort(key=lambda c: c.score, reverse=True)
        return merged[:top_k]

Usage: Swap Retrievers

# Configuration-driven retriever selection
def create_retriever(config: dict) -> Retriever:
    """Factory pattern for retriever creation."""

    retriever_type = config.get("retriever_type", "vector")

    if retriever_type == "vector":
        embedder = create_embedder(config)
        return PgVectorRetriever(conn=get_connection(), embedder=embedder)

    elif retriever_type == "pinecone":
        embedder = create_embedder(config)
        index = get_pinecone_index(config["pinecone_index"])
        return PineconeRetriever(index=index, embedder=embedder)

    elif retriever_type == "hybrid":
        vector_ret = create_retriever({"retriever_type": "vector"})
        keyword_ret = KeywordRetriever(conn=get_connection())
        return HybridRetriever(vector_ret, keyword_ret)

    else:
        raise ValueError(f"Unknown retriever type: {retriever_type}")

# Swap via config
config = {"retriever_type": "hybrid"}  # Changed from "vector"
retriever = create_retriever(config)
results = await retriever.retrieve("What is the refund policy?")

Use with agentic RAG for modular iterative retrieval.

Embedding Model Abstraction

Multiple Embedding Implementations

class OpenAIEmbedder(Embedder):
    """OpenAI embedding provider."""

    def __init__(self, model: str = "text-embedding-3-small"):
        self.model = model
        self.client = OpenAI()

    async def embed_texts(self, texts: List[str]) -> List[List[float]]:
        response = self.client.embeddings.create(input=texts, model=self.model)
        return [item.embedding for item in response.data]

    async def embed_query(self, query: str) -> List[float]:
        embeddings = await self.embed_texts([query])
        return embeddings[0]

class CohereEmbedder(Embedder):
    """Cohere embedding provider."""

    def __init__(self, model: str = "embed-english-v3.0"):
        self.model = model
        self.client = cohere.Client(api_key="...")

    async def embed_texts(self, texts: List[str]) -> List[List[float]]:
        response = self.client.embed(texts=texts, model=self.model, input_type="search_document")
        return response.embeddings

    async def embed_query(self, query: str) -> List[float]:
        response = self.client.embed(texts=[query], model=self.model, input_type="search_query")
        return response.embeddings[0]

class LocalEmbedder(Embedder):
    """Local embedding model (sentence-transformers)."""

    def __init__(self, model_name: str = "BAAI/bge-large-en-v1.5"):
        from sentence_transformers import SentenceTransformer
        self.model = SentenceTransformer(model_name)

    async def embed_texts(self, texts: List[str]) -> List[List[float]]:
        return self.model.encode(texts, normalize_embeddings=True).tolist()

    async def embed_query(self, query: str) -> List[float]:
        embeddings = await self.embed_texts([query])
        return embeddings[0]

def create_embedder(config: dict) -> Embedder:
    """Factory for embedding provider."""

    provider = config.get("embedding_provider", "openai")

    if provider == "openai":
        return OpenAIEmbedder(model=config.get("embedding_model", "text-embedding-3-small"))
    elif provider == "cohere":
        return CohereEmbedder(model=config.get("embedding_model", "embed-english-v3.0"))
    elif provider == "local":
        return LocalEmbedder(model_name=config.get("embedding_model", "BAAI/bge-large-en-v1.5"))
    else:
        raise ValueError(f"Unknown embedding provider: {provider}")

See embeddings guide for model selection.

Reranker Integration

Reranker Implementations

class CohereReranker(Reranker):
    """Cohere rerank API."""

    def __init__(self, model: str = "rerank-english-v2.0"):
        self.model = model
        self.client = cohere.Client(api_key="...")

    async def rerank(self, query: str, chunks: List[Chunk], top_k: int = 5) -> List[Chunk]:
        """Rerank chunks using Cohere."""

        if len(chunks) <= top_k:
            return chunks

        documents = [c.content for c in chunks]

        response = self.client.rerank(
            model=self.model,
            query=query,
            documents=documents,
            top_n=top_k
        )

        # Map reranked results back to chunks
        reranked = []
        for result in response.results:
            chunk = chunks[result.index]
            chunk.score = result.relevance_score
            reranked.append(chunk)

        return reranked

class NoOpReranker(Reranker):
    """No-op reranker (returns chunks unchanged)."""

    async def rerank(self, query: str, chunks: List[Chunk], top_k: int = 5) -> List[Chunk]:
        return chunks[:top_k]

class MMRReranker(Reranker):
    """Maximal Marginal Relevance reranker (diversity)."""

    async def rerank(self, query: str, chunks: List[Chunk], top_k: int = 5) -> List[Chunk]:
        """Select diverse chunks using MMR."""

        if len(chunks) <= top_k:
            return chunks

        selected = [chunks[0]]  # Start with highest scoring
        remaining = chunks[1:]

        while len(selected) < top_k and remaining:
            # Find chunk with highest MMR score
            mmr_scores = []

            for chunk in remaining:
                relevance = chunk.score
                max_similarity = max(
                    self._cosine_similarity(chunk.embedding, s.embedding)
                    for s in selected
                )
                mmr_score = 0.7 * relevance - 0.3 * max_similarity
                mmr_scores.append((mmr_score, chunk))

            best = max(mmr_scores, key=lambda x: x[0])
            selected.append(best[1])
            remaining.remove(best[1])

        return selected

    def _cosine_similarity(self, a: List[float], b: List[float]) -> float:
        import numpy as np
        return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

Generator Flexibility

Generator Implementations

class OpenAIGenerator(Generator):
    """OpenAI GPT generator."""

    def __init__(self, model: str = "gpt-4o"):
        self.model = model
        self.client = OpenAI()

    async def generate(self, query: str, context: List[Chunk]) -> str:
        """Generate answer using OpenAI."""

        context_text = "\n\n---\n\n".join(c.content for c in context)

        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": "Answer using provided context."},
                {"role": "user", "content": f"Context:\n{context_text}\n\nQuestion: {query}"}
            ],
            temperature=0.1
        )

        return response.choices[0].message.content

class ClaudeGenerator(Generator):
    """Anthropic Claude generator."""

    def __init__(self, model: str = "claude-3-5-sonnet-20241022"):
        self.model = model
        self.client = anthropic.Anthropic()

    async def generate(self, query: str, context: List[Chunk]) -> str:
        """Generate answer using Claude."""

        context_text = "\n\n---\n\n".join(c.content for c in context)

        response = self.client.messages.create(
            model=self.model,
            max_tokens=2048,
            messages=[
                {"role": "user", "content": f"Context:\n{context_text}\n\nQuestion: {query}"}
            ],
            temperature=0.1
        )

        return response.content[0].text

Production Implementation

Complete Modular RAG System

from dataclasses import dataclass
from typing import Optional

@dataclass
class RAGConfig:
    """RAG system configuration."""
    embedding_provider: str = "openai"
    embedding_model: str = "text-embedding-3-small"
    retriever_type: str = "vector"
    reranker_type: str = "cohere"
    generator_model: str = "gpt-4o"
    top_k: int = 5
    rerank_top_k: int = 5

class ModularRAG:
    """Production modular RAG system."""

    def __init__(self, config: RAGConfig):
        self.config = config

        # Initialize components from config
        self.embedder = create_embedder(config.__dict__)
        self.retriever = create_retriever(config.__dict__)
        self.reranker = create_reranker(config)
        self.generator = create_generator(config)

    async def query(self, question: str, filters: Dict = None) -> dict:
        """Execute RAG query."""

        # Step 1: Retrieve
        chunks = await self.retriever.retrieve(
            query=question,
            top_k=self.config.top_k * 2,  # Retrieve more for reranking
            filters=filters
        )

        # Step 2: Rerank
        reranked = await self.reranker.rerank(
            query=question,
            chunks=chunks,
            top_k=self.config.rerank_top_k
        )

        # Step 3: Generate
        answer = await self.generator.generate(
            query=question,
            context=reranked
        )

        return {
            "answer": answer,
            "sources": [c.metadata for c in reranked],
            "config": self.config,
        }

    async def swap_component(self, component_type: str, new_config: dict):
        """Hot-swap component at runtime."""

        if component_type == "embedder":
            self.embedder = create_embedder(new_config)
            # Recreate retriever with new embedder
            self.retriever = create_retriever({**self.config.__dict__, **new_config})

        elif component_type == "retriever":
            self.retriever = create_retriever(new_config)

        elif component_type == "reranker":
            self.reranker = create_reranker(RAGConfig(**new_config))

        elif component_type == "generator":
            self.generator = create_generator(RAGConfig(**new_config))

def create_reranker(config: RAGConfig) -> Reranker:
    """Factory for reranker."""
    if config.reranker_type == "cohere":
        return CohereReranker()
    elif config.reranker_type == "mmr":
        return MMRReranker()
    else:
        return NoOpReranker()

def create_generator(config: RAGConfig) -> Generator:
    """Factory for generator."""
    if "gpt" in config.generator_model:
        return OpenAIGenerator(model=config.generator_model)
    elif "claude" in config.generator_model:
        return ClaudeGenerator(model=config.generator_model)
    else:
        raise ValueError(f"Unknown generator model: {config.generator_model}")

# Usage: A/B test different configurations
config_a = RAGConfig(embedding_provider="openai", reranker_type="cohere")
config_b = RAGConfig(embedding_provider="cohere", reranker_type="mmr")

rag_a = ModularRAG(config_a)
rag_b = ModularRAG(config_b)

# Run same query through both
result_a = await rag_a.query("What is the refund policy?")
result_b = await rag_b.query("What is the refund policy?")

# Compare quality

Deploy with backend API engineering and observability.

Primary references: official documentation, official documentation, official documentation, official documentation.

Operating Modular RAG as a System

The implementation is only one part of Modular RAG. A production design also needs an explicit contract for inputs, outputs, ownership, and failure behavior. Write that contract before selecting a library. It should identify which component validates input, where state lives, what may be retried, and which result is authoritative when two components disagree. This prevents a convenient prototype boundary from silently becoming the long-term architecture.

Start with a representative baseline. Capture request shape, traffic distribution, dependency latency, error classes, and the quality signal users actually care about. Averages hide the cases that cause incidents, so keep percentiles and segment measurements by workload type. Record the configuration and dataset version beside every result. Without that context, a faster or more accurate run cannot be reproduced and should not be used to approve a rollout.

Define the failure model

List failures by where they originate: invalid input, capacity exhaustion, dependency timeout, partial state change, malformed output, and semantically wrong output. Each class needs a different response. Validation errors should fail immediately. Transient dependency failures may be retried with a budget and jitter. An operation that may have committed must use an idempotency key or reconciliation step before retrying. A syntactically valid but incorrect result belongs in evaluation and review, not a blind retry loop.

Set a deadline for the complete operation and derive smaller budgets for each dependency. Local timeouts that add up to more than the caller's deadline merely create abandoned work. Propagate cancellation where the protocol supports it. Bound every queue, retry loop, context buffer, and concurrency pool; an unbounded safety mechanism becomes a second outage during overload.

Design a degraded mode before it is needed. Depending on the workload, that can mean returning a cached answer, selecting a simpler path, placing work in a durable queue, or asking for human review. The degraded response must be visible in telemetry and, where it changes meaning, visible to the caller. Silent fallback makes quality regressions almost impossible to diagnose.

Measure the decision, not just the component

Use three layers of signals. System metrics cover latency, throughput, saturation, and errors. Correctness metrics measure whether the result satisfies its contract. Business or user metrics show whether the system solved the intended problem. Improving only one layer can move the others backward, so release criteria should name acceptable movement for all three.

Attach a reason code to every route, rejection, fallback, and retry. Include version identifiers for configuration, code, model, schema, and data when relevant. Logs should let an engineer reconstruct a decision without storing secrets or raw personal data. Traces should cross process boundaries, while metrics should remain low-cardinality enough to operate reliably.

Alert on symptoms that require action, not every internal anomaly. A useful alert names the affected service objective, links to a runbook, and distinguishes a customer-visible incident from exhausted headroom. Dashboards serve a different purpose: they support diagnosis and capacity planning. Treating a dashboard as an alerting strategy leaves failures undiscovered until someone happens to look.

Roll out with reversible steps

Ship Modular RAG behind a versioned interface and a kill switch. Begin with offline replay using production-shaped, privacy-safe samples. Then use shadow execution when duplicate work has acceptable cost and side effects can be suppressed. A small canary should exercise the real dependency graph before traffic expands. Compare the canary with the baseline by cohort rather than mixing both populations into one aggregate.

Promotion gates should be written before the rollout. Include a minimum sample size or observation window, maximum regression in tail latency and error rate, and a correctness threshold. Roll back automatically when a hard safety boundary is crossed; use manual review for ambiguous quality movement. Preserve enough evidence from both paths to explain why the gate passed or failed.

Configuration deserves the same discipline as code. Review changes, validate them before activation, keep an immutable history, and make rollback a single operation. If a deployment changes code and configuration together, record both versions. Otherwise an incident responder may roll back the binary while leaving the triggering configuration active.

Capacity and cost controls

Model capacity in units the bottleneck understands: concurrent connections, tokens, queue jobs, database transactions, GPU memory, or bytes in flight. Convert the expected traffic distribution into those units and include burst behavior. Then load-test the first constrained dependency, not merely the public endpoint. A system that accepts more work than it can finish within its deadline is overloaded even if CPU utilization looks comfortable.

Cost is also a reliability limit. Add per-request attribution, tenant or workflow budgets, and a global circuit breaker for unexpectedly expensive paths. Review unit economics at the same granularity as performance; a cheap median can conceal a small class of requests responsible for most spend. Optimize only after measuring, because reducing context, replicas, validation, or redundancy can trade visible cost for less visible risk.

Production readiness review

Before launch, ask an engineer who did not build the feature to follow the runbook through one simulated failure. Verify backups or checkpoints by restoring them, not by checking that a job reported success. Exercise credential rotation, dependency unavailability, bad configuration, and rollback. Assign an owner for each alarm and a date for reviewing thresholds after real traffic arrives.

The final architecture document should be short enough to remain current. Keep the decision, rejected alternatives, invariants, dependency contracts, dashboards, and rollback procedure. Link detailed experiments rather than pasting them into the document. Teams that need help turning this review into an operable service can use our Modular RAG engineering support.

Operating Modular RAG as a System

The implementation is only one part of Modular RAG. A production design also needs an explicit contract for inputs, outputs, ownership, and failure behavior. Write that contract before selecting a library. It should identify which component validates input, where state lives, what may be retried, and which result is authoritative when two components disagree. This prevents a convenient prototype boundary from silently becoming the long-term architecture.

Start with a representative baseline. Capture request shape, traffic distribution, dependency latency, error classes, and the quality signal users actually care about. Averages hide the cases that cause incidents, so keep percentiles and segment measurements by workload type. Record the configuration and dataset version beside every result. Without that context, a faster or more accurate run cannot be reproduced and should not be used to approve a rollout.

Define the failure model

List failures by where they originate: invalid input, capacity exhaustion, dependency timeout, partial state change, malformed output, and semantically wrong output. Each class needs a different response. Validation errors should fail immediately. Transient dependency failures may be retried with a budget and jitter. An operation that may have committed must use an idempotency key or reconciliation step before retrying. A syntactically valid but incorrect result belongs in evaluation and review, not a blind retry loop.

Set a deadline for the complete operation and derive smaller budgets for each dependency. Local timeouts that add up to more than the caller's deadline merely create abandoned work. Propagate cancellation where the protocol supports it. Bound every queue, retry loop, context buffer, and concurrency pool; an unbounded safety mechanism becomes a second outage during overload.

Design a degraded mode before it is needed. Depending on the workload, that can mean returning a cached answer, selecting a simpler path, placing work in a durable queue, or asking for human review. The degraded response must be visible in telemetry and, where it changes meaning, visible to the caller. Silent fallback makes quality regressions almost impossible to diagnose.

Measure the decision, not just the component

Use three layers of signals. System metrics cover latency, throughput, saturation, and errors. Correctness metrics measure whether the result satisfies its contract. Business or user metrics show whether the system solved the intended problem. Improving only one layer can move the others backward, so release criteria should name acceptable movement for all three.

Attach a reason code to every route, rejection, fallback, and retry. Include version identifiers for configuration, code, model, schema, and data when relevant. Logs should let an engineer reconstruct a decision without storing secrets or raw personal data. Traces should cross process boundaries, while metrics should remain low-cardinality enough to operate reliably.

Alert on symptoms that require action, not every internal anomaly. A useful alert names the affected service objective, links to a runbook, and distinguishes a customer-visible incident from exhausted headroom. Dashboards serve a different purpose: they support diagnosis and capacity planning. Treating a dashboard as an alerting strategy leaves failures undiscovered until someone happens to look.

Roll out with reversible steps

Ship Modular RAG behind a versioned interface and a kill switch. Begin with offline replay using production-shaped, privacy-safe samples. Then use shadow execution when duplicate work has acceptable cost and side effects can be suppressed. A small canary should exercise the real dependency graph before traffic expands. Compare the canary with the baseline by cohort rather than mixing both populations into one aggregate.

Promotion gates should be written before the rollout. Include a minimum sample size or observation window, maximum regression in tail latency and error rate, and a correctness threshold. Roll back automatically when a hard safety boundary is crossed; use manual review for ambiguous quality movement. Preserve enough evidence from both paths to explain why the gate passed or failed.

Configuration deserves the same discipline as code. Review changes, validate them before activation, keep an immutable history, and make rollback a single operation. If a deployment changes code and configuration together, record both versions. Otherwise an incident responder may roll back the binary while leaving the triggering configuration active.

Capacity and cost controls

Model capacity in units the bottleneck understands: concurrent connections, tokens, queue jobs, database transactions, GPU memory, or bytes in flight. Convert the expected traffic distribution into those units and include burst behavior. Then load-test the first constrained dependency, not merely the public endpoint. A system that accepts more work than it can finish within its deadline is overloaded even if CPU utilization looks comfortable.

Cost is also a reliability limit. Add per-request attribution, tenant or workflow budgets, and a global circuit breaker for unexpectedly expensive paths. Review unit economics at the same granularity as performance; a cheap median can conceal a small class of requests responsible for most spend. Optimize only after measuring, because reducing context, replicas, validation, or redundancy can trade visible cost for less visible risk.

Production readiness review

Before launch, ask an engineer who did not build the feature to follow the runbook through one simulated failure. Verify backups or checkpoints by restoring them, not by checking that a job reported success. Exercise credential rotation, dependency unavailability, bad configuration, and rollback. Assign an owner for each alarm and a date for reviewing thresholds after real traffic arrives.

The final architecture document should be short enough to remain current. Keep the decision, rejected alternatives, invariants, dependency contracts, dashboards, and rollback procedure. Link detailed experiments rather than pasting them into the document. Teams that need help turning this review into an operable service can use our Modular RAG engineering support.

Frequently Asked Questions

What is Modular RAG?

Modular RAG designs RAG systems with swappable components behind standard interfaces. Change embedding models, retrievers, rerankers, or generators without rewriting application code.

Why use Modular RAG instead of hardcoding components?

Flexibility: Swap implementations with config changes. A/B testing: Run multiple configurations simultaneously. Incremental migration: Add new components alongside old ones. Testability: Mock components for unit tests.

Does Modular RAG add complexity?

Yes — more abstractions = more code. Trade-off: upfront complexity cost for long-term maintainability. Worth it for production systems that evolve over time.

How do I A/B test RAG configurations?

Create multiple ModularRAG instances with different configs, route queries to each, compare answer quality. Use RAG evaluation to measure differences.

Can I swap components at runtime?

Yes — use ModularRAG.swap_component() to hot-swap components without restarting the system. Useful for gradual rollouts.

What are the most common components to swap?

Embedding models (OpenAI → Cohere → Voyage), retrievers (vector → hybrid), rerankers (add Cohere rerank), generators (GPT-4 → Claude).

Should I abstract everything?

No — start simple, add abstractions when you need flexibility. Don't over-engineer for hypothetical future needs. Add interfaces when you have 2+ implementations or plan to swap.

How do I handle different component interfaces?

Use adapter pattern to wrap non-standard APIs behind your standard interface. Example: wrap Pinecone API to match your VectorStore interface.

Conclusion

Modular RAG enables evolutionary architecture:

Start modular from day 1 — the upfront cost is minimal, long-term benefit is massive.

At HinterBuild:

• RAG & LLM Systems
• AI Agent Development
• Backend API Engineering
• Cloud Infrastructure & DevOps

Schedule a consultation to design your modular RAG architecture.