🚀 Building My First Hybrid RAG Agent: A CS Student's Journey into AI

🚀 Built a Hybrid RAG Agent — Here's What I Learned

Hey DEV's! 👋

I'm a Computer Science student who's been diving deep into AI lately. Instead of just watching tutorials, I decided to build something real — a Hybrid RAG Agent that retrieves information from documents and generates accurate answers.

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🤔 The Problem I Was Solving

Most beginner RAG projects use only vector search for retrieval. But here's what I discovered:

• Vector search is great for semantic meaning but misses exact keyword matches
• Keyword search (BM25) catches exact terms but doesn't understand context
• Single-method retrieval = answers that are either too vague or miss the point entirely

The solution? Hybrid RAG — combining both approaches with intelligent reranking.

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🏗️ Architecture

User Query
    │
    ▼
┌─────────────────────────────────┐
│ Hybrid Search Retrieval Engine  │
│ ├─ Semantic (ChromaDB + Cosine) │
│ └─ Lexical (BM25 Keyword Match) │
└────────────────┬────────────────┘
                 │ Top-K Candidates
                 ▼
┌─────────────────────────────────┐
│     Cross-Encoder Reranking     │ ← Evaluates full passage-query relevance
└────────────────┬────────────────┘
                 │ Top-3 Highest Scoring Chunks
                 ▼
┌─────────────────────────────────┐
│      LLM Generation Context     │ ← Appends conversation history memory
└────────────────┬────────────────┘
                 │
                 ▼
          Answer + Sources

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🛠️ Tech Stack

• Backend: Python + Flask
• Vector DB: ChromaDB (persistent)
• Embeddings: sentence-transformers (all-mpnet-base-v2)
• Keyword Search: BM25Okapi
• LLM: Hugging Face API (Qwen2.5-7B / Mistral / Llama-3.1)
• Reranker: ms-marco-MiniLM-L-6-v2 (cross-encoder)
• Frontend: Custom HTML/CSS/JS with glassmorphic dark UI

Features:

• Multi-format docs (PDF, TXT, DOCX, Markdown)
• Stateful conversation memory
• Persistent storage (survives restarts)
• Source citations in responses

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🎯 How It Works

1. Document Ingestion

• Split into chunks (500 chars, 50-char overlap)
• Embed with sentence-transformers
• Store in ChromaDB + build BM25 index

2. Query Processing

• Vector search finds semantically similar chunks
• BM25 finds exact keyword matches
• Both run in parallel

3. Cross-Encoder Reranking

• Combines results from both searches
• Cross-encoder scores each query-chunk pair for relevance
• Sends only top-3 most relevant chunks to LLM
• This is the highest-ROI improvement for RAG systems

4. Response Generation

• LLM generates answer with context + conversation history
• Returns grounded response with source citations

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🎯 Challenges I Faced

• Vector-only missed exact terms → Added BM25 for keyword matching
• Too many irrelevant chunks → Cross-encoder reranking fixed this
• Lost conversation context → Added stateful memory for multi-turn queries
• Not production-ready → Added persistent storage, health checks, clean API

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🚀 What's Next

• Add query expansion and HyDE for better recall
• Implement evaluation metrics (precision@k, recall@k)
• Add metadata filtering (date ranges, doc types)
• Deploy to Railway/Render with CI/CD

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🙏 Let's Connect!

• What AI projects are you working on?
• Tried hybrid search or reranking in your RAG?
• Want to collaborate on open-source AI?

Drop a comment below! Let's build together. 🚀

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Tags: #ai #rag #machinelearning #python #artificialintelligence #webdev #beginners #students