Build Advanced RAG Systems

This skill covers designing and implementing Retrieval-Augmented Generation (RAG) systems - vector databases, embedding models, chunking strategies, retrieval algorithms, and integrating retrieval with LLMs. Core architecture principles: a document-processing pipeline (ingestion, chunking, embedding, storage), semantic retrieval (vector similarity combined with hybrid dense-plus-sparse approaches), context management (optimizing retrieved context for relevance, diversity, token efficiency), evaluation metrics (retrieval accuracy, answer quality, end-to-end performance), and scalability (horizontal scaling with distributed vector databases and caching).

Document chunking is demonstrated via a chunker class combining a recursive character splitter with semantic-similarity-based breaks:

from langchain.text_splitter import RecursiveCharacterTextSplitter
from sentence_transformers import SentenceTransformer
import tiktoken

class AdvancedChunker:
    def __init__(self, model_name="text-embedding-ada-002"):
        self.encoder = tiktoken.encoding_for_model(model_name)
        self.sentence_splitter = RecursiveCharacterTextSplitter(
            chunk_size=512,
            chunk_overlap=50,
            separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""]
        )
    
    def semantic_chunk(self, text, similarity_threshold=0.5):
        """Create chunks based on semantic similarity breaks"""
        sentences = text.split('.')
        embeddings = self.embed_sentences(sentences)
        chunks = []
        current_chunk = [sentences[0]]
        
        for i in range(1, len(sentences)):
            similarity = self.cosine_similarity(
                embeddings[i-1], embeddings[i]
            )
            if similarity < similarity_threshold:
                chunks.append('. '.join(current_chunk))
                current_chunk = [sentences[i]]
            else:
                current_chunk.append(sentences[i])
        
        if current_chunk:
            chunks.append('. '.join(current_chunk))
        return chunks

Vector-database implementation is shown via a hybrid vector-store class supporting Pinecone or Weaviate backends, with metadata-filtered batch upserts and a hybrid-search method combining dense vector retrieval with sparse BM25-like retrieval, merged via Reciprocal Rank Fusion.

Advanced retrieval techniques cover a retriever class: multi-query retrieval (using an LLM to generate query variations, searching each, then deduplicating and reranking the combined results), contextual compression (extracting only the sentences most relevant to the query from each retrieved document), and parent-document retrieval (retrieving small chunks but returning their larger parent document with the relevant section highlighted). The RAG evaluation framework uses the ragas library's context-precision, context-recall, answer-relevancy, and faithfulness metrics for end-to-end evaluation, plus a custom retrieval-evaluation function computing precision@5, recall, and MRR against ground-truth documents.

A production RAG pipeline is demonstrated via a class that checks a cache before querying, preprocesses the query, retrieves context with a fallback on retrieval failure, optimizes context to a token budget of 2000 tokens, generates a streamed response, caches the result with a TTL, and logs the interaction for monitoring. Configuration best practices: use domain-specific fine-tuned embeddings when available, chunk at 256-512 tokens with 10-20% overlap, start retrieval at k=5-10 adjusted to the context window, apply cross-encoder reranking to the top 20-50 candidates, cache embeddings/frequent queries/intermediate results, and monitor retrieval latency, relevance scores, and user satisfaction. Advanced optimization techniques: query expansion (synonyms, related terms, reformulation), negative sampling for embedding training, multi-vector retrieval (storing multiple embeddings per document, such as summary and full text), temporal filtering (weighting recent documents higher for time-sensitive queries), user personalization (incorporating history and preferences into retrieval), and cross-lingual RAG (multilingual queries with aligned embedding spaces).

Use it when designing or hardening a RAG system - chunking strategy, hybrid dense/sparse retrieval, advanced retrieval techniques like multi-query, compression, or parent-document retrieval, evaluation, or a production pipeline with caching and fallbacks. It is not a general LLM-prompting or fine-tuning guide - it is scoped to the retrieval and context layer that feeds an LLM, not the LLM itself.

Given a document corpus and a query, it produces chunked and embedded documents in a vector store, hybrid-retrieved and optionally reranked or compressed context, an evaluation report (precision, recall, MRR, or ragas metrics), and a production-ready query response with caching and monitoring.

Code samples use langchain (RecursiveCharacterTextSplitter), sentence-transformers, tiktoken, Pinecone and Weaviate for vector storage, and ragas for RAG evaluation metrics.

ML and AI engineers designing, evaluating, or productionizing a RAG system.