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RAG & Vector Search Systems

Production RAG pipeline with vector database, hybrid search, and reranking. Search millions of documents in <100ms with semantic accuracy.

Key takeaways

Production RAG pipeline with vector database, hybrid search, and reranking. Search millions of documents in <100ms with semantic accuracy.

  • ›Hybrid search combining dense vector similarity with BM25 keyword matching—captures both semantic intent and exact terminology for highest recall.
  • ›Cross-encoder reranking pipeline that re-scores retrieved documents for true relevance before passing context to the LLM—dramatically fewer irrelevant answers.
  • ›Smart document chunking tailored to your content type: by heading hierarchy for docs, by clause for contracts, by semantic boundary for general text.

Price: from €2,000

SERVICE DETAILS

I build production-grade Retrieval-Augmented Generation systems: user question → hybrid vector + keyword search → cross-encoder reranking → context injected into LLM → accurate answer with citations. I implement and tune vector databases (Pinecone, Weaviate, Milvus, pgvector), optimize chunk strategies for your document types, and build reranking pipelines that push retrieval precision above 90%.

› INVESTMENT:

from €2,000
const module = new ExecutionProtocol();

// Initializing rag-vector-search...
› Loading dependencies... OK
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Key Benefits

Hybrid search combining dense vector similarity with BM25 keyword matching—captures both semantic intent and exact terminology for highest recall.

Cross-encoder reranking pipeline that re-scores retrieved documents for true relevance before passing context to the LLM—dramatically fewer irrelevant answers.

Smart document chunking tailored to your content type: by heading hierarchy for docs, by clause for contracts, by semantic boundary for general text.

Metadata filtering and namespace isolation—search within a specific project, date range, or document type without degrading latency.

Hallucination prevention layer: source attribution, confidence scoring, and a 'no answer found' fallback when retrieved context doesn't support the question.

The Process

1

Document Processing Pipeline

I build the ingestion pipeline: parse documents, apply optimal chunking, generate embeddings with the best model for your language and domain, and index with metadata.

2

Vector DB Setup & Tuning

I configure the vector store with HNSW index parameters optimized for your dataset size, set up metadata schemas for filtering, and benchmark retrieval latency.

3

Retrieval & Reranking Pipeline

I build hybrid search (dense + BM25), add a cross-encoder reranker, tune similarity thresholds, and implement metadata-based pre-filtering for sub-100ms end-to-end latency.

4

LLM Integration & Evaluation

I wire the retrieval pipeline to the LLM with precise prompt engineering, implement citation tracking, and evaluate end-to-end accuracy on 200+ test questions before launch.

Hybrid vector search vs keyword search vs basic vector search

CriterionHybrid + reranking pipelineKeyword search (BM25)Basic vector search
Semantic understandingHigh — meaning plus keywordsNone — exact terms onlyHigh, but misses exact terms
Exact term matchingYes, via BM25 layerYesOften missed
Retrieval precisionHighest — reranked resultsVariableModerate
Latency at scaleSub-100ms with tuned indexVery fastFast
Setup complexityHigher — multi-stage pipelineLowModerate
Hallucination controlSource attribution and fallbackNot applicableLimited
Best forProduction RAG over large corporaStructured, keyword-heavy dataSimple semantic lookups

Frequently Asked Questions

Which vector database should I choose?

Pinecone is the easiest to start with (fully managed, no ops). Weaviate offers richer features like built-in hybrid search and BM25. pgvector is the most cost-effective for small to medium scale if you already use PostgreSQL. I recommend based on your scale and infrastructure constraints.

Is RAG better than fine-tuning for factual Q&A?

For factual retrieval from specific documents, RAG is almost always better—it's faster to implement, cheaper to update, and doesn't hallucinate facts outside its documents. Fine-tuning excels when you need to change the model's reasoning style or output format, not its factual knowledge.

How do you measure and improve retrieval quality?

I use recall@k and NDCG metrics on a labeled test set of question-document pairs. I tune chunk size, embedding model, and reranker threshold until retrieval meets targets before integrating the LLM layer.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com