Rag Architect

RAG Architect is a community skill for designing and implementing Retrieval-Augmented Generation systems, covering document ingestion, embedding strategies, vector store configuration, retrieval optimization, and answer generation pipelines.

What Is This?

Overview

RAG Architect provides patterns for building production RAG systems that combine document retrieval with language model generation. It covers document chunking strategies, embedding model selection, vector database indexing, hybrid search combining dense and sparse retrieval, reranking pipelines, and prompt construction for grounded answer generation. The skill enables teams to build knowledge-augmented AI applications that answer questions from custom document collections.

Who Should Use This

This skill serves engineers building question-answering systems over internal documentation, teams creating customer support chatbots grounded in product knowledge bases, and developers designing search-enhanced AI applications that need accurate, source-attributed responses.

Why Use It?

Problems It Solves

Language models hallucinate when answering questions about specific documents or proprietary information not in their training data. Keyword search returns documents but cannot synthesize answers from multiple sources. Stuffing entire document collections into model context exceeds token limits and degrades response quality. Without source attribution, users cannot verify whether AI-generated answers are grounded in actual documents.

Core Highlights

Chunking strategies split documents into retrieval units that balance context completeness with embedding precision. Hybrid search combines semantic vector similarity with keyword matching for better recall across query types. Reranking pipelines score retrieved chunks by relevance before passing them to the generation model. Source attribution traces each answer statement back to the specific document chunk it came from.

How to Use It?

Basic Usage

from dataclasses import dataclass, field
import hashlib

@dataclass
class DocumentChunk:
    text: str
    source: str
    chunk_id: str = ""
    embedding: list[float] = field(default_factory=list)

    def __post_init__(self):
        if not self.chunk_id:
            self.chunk_id = hashlib.md5(
                self.text.encode()).hexdigest()[:12]

class DocumentChunker:
    def __init__(self, chunk_size: int = 512, overlap: int = 64):
        self.chunk_size = chunk_size
        self.overlap = overlap

    def chunk(self, text: str, source: str) -> list[DocumentChunk]:
        words = text.split()
        chunks = []
        start = 0
        while start < len(words):
            end = start + self.chunk_size
            chunk_text = " ".join(words[start:end])
            chunks.append(DocumentChunk(
                text=chunk_text, source=source))
            start = end - self.overlap
        return chunks

class RAGPipeline:
    def __init__(self, chunker: DocumentChunker):
        self.chunker = chunker
        self.index: list[DocumentChunk] = []

    def ingest(self, text: str, source: str):
        chunks = self.chunker.chunk(text, source)
        self.index.extend(chunks)

Real-World Examples

from dataclasses import dataclass, field
import math

@dataclass
class SearchResult:
    chunk: DocumentChunk
    score: float

class HybridRetriever:
    def __init__(self, chunks: list[DocumentChunk]):
        self.chunks = chunks
        self.vocab: dict[str, set[int]] = {}
        self._build_keyword_index()

    def _build_keyword_index(self):
        for i, chunk in enumerate(self.chunks):
            for word in chunk.text.lower().split():
                self.vocab.setdefault(word, set()).add(i)

    def keyword_search(self, query: str, top_k: int = 5) -> list[SearchResult]:
        scores: dict[int, float] = {}
        terms = query.lower().split()
        for term in terms:
            for idx in self.vocab.get(term, set()):
                scores[idx] = scores.get(idx, 0) + 1.0
        ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
        return [SearchResult(chunk=self.chunks[i], score=s)
                for i, s in ranked[:top_k]]

    def format_context(self, results: list[SearchResult]) -> str:
        parts = []
        for i, r in enumerate(results):
            parts.append(f"[Source {i+1}: {r.chunk.source}]\n{r.chunk.text}")
        return "\n\n".join(parts)

Advanced Tips

Experiment with chunk sizes tailored to the document type, using smaller chunks for dense technical content and larger chunks for narrative text. Implement metadata filtering that narrows the search space before vector similarity computation. Use reciprocal rank fusion to combine scores from keyword and semantic search results into a unified ranking.

When to Use It?

Use Cases

Build an internal knowledge assistant that answers employee questions from company documentation and policy manuals. Create a technical support system that retrieves relevant troubleshooting guides based on customer issue descriptions. Develop a research tool that synthesizes answers from scientific paper collections with proper citations.

Related Topics

Vector database systems, embedding model selection, document preprocessing, hybrid search architectures, and grounded generation techniques.

Important Notes

Requirements

An embedding model for converting text chunks into vector representations. A vector store or search index for efficient similarity retrieval. A language model for generating answers from retrieved context. Document collection in a parseable format.

Usage Recommendations

Do: evaluate retrieval quality separately from generation quality to isolate issues. Include source citations in generated answers so users can verify information. Test chunking strategies on representative queries before processing the full document collection.

Don't: use a single chunk size for all document types without measuring retrieval performance. Skip deduplication of overlapping chunks that inflate index size and return redundant results. Assume that higher retrieval recall always improves answer quality, as irrelevant context can confuse the generation model.

Limitations

Retrieval quality degrades when queries use different terminology than the source documents. Complex questions requiring reasoning across multiple documents challenge simple retrieve-and-generate pipelines. Embedding model updates require complete re-indexing of the document collection, which is expensive for large corpora.