Practical_RAG_Presentation_RetrivalAugumentedGeneration.pptx

Practical_RAG_Presentation_RetrivalAugumentedGeneration.pptx

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  Practical Retrieval-Augmented Generation (RAG) BuildingContext-Aware AI Systems using Embeddings and Vector Database
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  Agenda • Why RAG? •Understanding Embeddings • Vector Databases (FAISS / Qdrant) • Hybrid Retrieval (BM25 + Embeddings) • Building a Context-Aware QA System (Live Demo) • Wrap-up & Q&A
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  The Problem —Why RAG? • LLMs can hallucinate and lack access to private or up-to-date data. • RAG provides external knowledge at query time to ground responses. • Useful for internal knowledge bases, document Q&A, support systems.
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  What is Retrieval-AugmentedGeneration (RAG)? • Combines a retriever (search over documents) with a generator (LLM). • Retriever finds relevant context; generator uses it to answer. • Reduces hallucination and increases relevance.
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  RAG Architecture Overview •Input Query -> Embedding Generation -> Vector Search -> Retrieved Docs -> LLM Response • Chunking, embedding model selection, vector DB choice are key decisions. • Return source documents to increase explainability.
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  Understanding Embeddings • Text→ numeric vector representing meaning. • Semantic similarity is measured via cosine similarity. • Embeddings allow semantic (meaning-based) retrieval.
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  Keyword Search vsSemantic Search • Keyword (BM25/TF-IDF): exact word matching, fast, precise for keywords. • Semantic (Embeddings): finds meaning, catches paraphrases and synonyms. • Hybrid retrieval combines both for better results.
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  Vector Databases (FAISS/ Qdrant / Pinecone) • Store and retrieve high-dimensional embeddings efficiently. • FAISS: local, efficient; Qdrant: cloud-ready with metadata search. • Choose based on scale, latency, and deployment preferences.
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  Hybrid Retrieval (BM25+ Embeddings) • Combine keyword and semantic scores to balance precision and recall. • Tools: LangChain, Haystack, LlamaIndex provide hybrid retriever patterns. • Weight the retrievers according to your data characteristics.
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  End-to-End RAG Pipeline •1. Data collection & preprocessing • 2. Chunk documents and create embeddings • 3. Store embeddings in vector DB • 4. Retrieve top-k context for queries • 5. Pass context to LLM and generate answer (with sources)
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  Live Demo Plan— Mini RAG QA (Azure OpenAI + FAISS) • Sample corpus of short docs (3-6 paragraphs). • Generate embeddings with sentence- transformers (all-MiniLM-L6-v2). • Store embeddings in FAISS local index. • Use Azure OpenAI (Chat Completions) to answer with retrieved context. • Show outputs and source documents.
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  Demo — ExampleOutput • Query: 'What is RAG?' • Answer: 'RAG (Retrieval-Augmented Generation) combines retrieval and generation...' • Sources: doc1.txt, doc2.txt (show retrieved snippets)
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  Common Challenges &Tuning • Chunk size and overlap influence retrieval accuracy. • Embedding model choice affects semantic quality. • Keeping the vector DB updated (vector drift) and scaling latency.
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  Real-World Use Cases •Enterprise knowledge chatbots / Copilots • Document Q&A (legal, healthcare, finance) • Code assistants (searching code bases & docs) • Customer support automated responses
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  Wrap-up & Resources •Key takeaways: embeddings, vector DBs, hybrid retrieval, RAG pipelines • Resources: LangChain, Qdrant, FAISS, Azure OpenAI docs • GitHub repo (included) with demo script and setup instructions