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🔍 RAG Systems

Turn your documents into intelligent, searchable knowledge bases

3+ Years Experience
10+ Projects Delivered
✓ Available for new projects
Used at:
Anaqua RightHub Flowrite Sparrow Intelligence

$ cat services.json

Document Intelligence Platform

Build systems that understand and retrieve information from your entire document corpus.

Deliverables:
  • Custom document parsing (PDF, Word, HTML, legal formats)
  • Intelligent chunking strategies
  • Metadata extraction and indexing
  • Multi-format support
  • Version control integration

Semantic Search Implementation

Go beyond keyword matching with AI-powered semantic search.

Deliverables:
  • Vector embedding generation
  • Hybrid search (semantic + BM25)
  • Re-ranking and relevance tuning
  • Query understanding and expansion
  • Faceted search with filters

Knowledge Base Q&A

Enable natural language questions over your proprietary data with cited answers.

Deliverables:
  • Question-answering pipelines
  • Citation and source tracking
  • Confidence scoring
  • Feedback loops for improvement
  • Multi-language support

$ man rag-systems

Beyond Basic RAG: My Production Architecture

Most RAG tutorials show a simple “chunk → embed → retrieve → generate” flow. Production systems need much more:

1. Intelligent Document Processing

  • Structure-aware parsing (tables, headers, lists)
  • Domain-specific chunking (legal clauses, code blocks, citations)
  • Metadata preservation for filtering

2. Advanced Retrieval

  • Hybrid search combining dense and sparse vectors
  • Multi-stage retrieval with re-ranking
  • Query transformation and HyDE

3. Generation with Guardrails

  • Structured outputs with validation
  • Hallucination detection
  • Source citation enforcement

Vector Database Expertise

I’ve built RAG systems with every major vector store:

  • PGVector: Best for existing PostgreSQL infrastructure, ACID compliance
  • Pinecone: Best for managed scale, minimal ops overhead
  • Chroma: Best for prototyping and local development
  • Qdrant: Best for filtering and hybrid search performance

$ cat README.md

What is RAG and Why Does It Matter?

Retrieval-Augmented Generation (RAG) is the technique that makes LLMs useful for your specific data. Instead of relying solely on the model’s training data, RAG:

  1. Retrieves relevant documents from your knowledge base
  2. Augments the LLM prompt with this context
  3. Generates accurate, grounded responses

This solves the fundamental problem of LLMs: they don’t know your business. RAG bridges that gap.

The RAG Architecture Spectrum

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Simple RAG          →          Advanced RAG          →          Production RAG
─────────────────────────────────────────────────────────────────────────────
Chunk + Embed            Hybrid Search                   Multi-stage Pipeline
Single Vector Store      Re-ranking                      Caching + Streaming
Basic Prompt             Query Transformation            Observability
No Citations             Source Tracking                 A/B Testing

I specialize in building Production RAG systems that actually work in enterprise environments.

My RAG Technology Stack

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# Production RAG Pipeline
from langchain.retrievers import EnsembleRetriever
from langchain_community.vectorstores import PGVector
from langchain.retrievers.document_compressors import FlashrankRerank

# Hybrid retrieval
vector_retriever = PGVector.from_connection_string(
    connection_string,
    embedding=OpenAIEmbeddings(model="text-embedding-3-large")
).as_retriever(search_kwargs={"k": 20})

bm25_retriever = BM25Retriever.from_documents(docs, k=20)

# Ensemble with re-ranking
ensemble = EnsembleRetriever(
    retrievers=[vector_retriever, bm25_retriever],
    weights=[0.6, 0.4]
)

reranker = FlashrankRerank(top_n=5)
compression_retriever = ContextualCompressionRetriever(
    base_compressor=reranker,
    base_retriever=ensemble
)

Industries I’ve Served

  • Legal Tech: Patent search, contract analysis, compliance checking
  • SaaS: Product documentation, customer support, onboarding
  • Healthcare: Medical literature search, clinical decision support
  • Finance: Regulatory document search, policy Q&A

Experience:

Case Studies: Enterprise RAG for Legal Documents | Agentic AI Knowledge Systems

Related Technologies: LangChain, Vector Databases, OpenAI, FastAPI, PostgreSQL

$ ls -la projects/

Legal Document Search

@ Anaqua (RightHub)
Challenge:

Search millions of patent and trademark documents with legal-grade accuracy and citation requirements.

Solution:

Built structure-aware RAG with custom chunking for legal documents, citation graph traversal, and confidence scoring.

Result:

50% faster search, lawyers trusted the system for production work.

Product Documentation Q&A

@ Sparrow Intelligence
Challenge:

Enable sales and support teams to instantly answer product questions from 500+ page documentation.

Solution:

Implemented RAG with version-aware retrieval, role-based access, and feedback-driven improvement.

Result:

Reduced support ticket resolution time by 60%.

Email Context Retrieval

@ Flowrite
Challenge:

Generate contextually relevant email responses by understanding past conversations.

Solution:

Built conversation-aware RAG that maintains thread context and personal writing style.

Result:

Significantly improved email suggestion relevance, contributed to 10x user growth.

$ diff me competitors/

+ Built RAG systems for legal/compliance domains with strict accuracy requirements
+ Experience with million-document scale deployments
+ Deep understanding of embedding models and chunking strategies
+ Can optimize for both accuracy and cost (LLM token usage)
+ Full-stack capability—database, backend, and AI integration

Build Your Knowledge Base

Within 24 hours

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