AI Tour Mexico: Production-ready RAG with Azure AI Search

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2. Production-ready RAG with Azure AI Search Pamela Fox Python Cloud Advocate, Microsoft @pamelafox pamelafox.org aka.ms/aitour/rag/mx Get the slides:
3. Agenda 1 2 3 4 5 6 Retrieval-Augmented Generation (RAG) Vectors and vector search State-of-the-art retrieval with AI Search Data ingestion approaches Evaluation Productionizing
4. RAG: Retrieval Augmented Generation aka.ms/aitour/rag/mx Get the slides:
5. The limitations of LLMS Outdated public knowledge No internal knowledge
6. Incorporating domain knowledge Fine tuning Learn new skills (permanently) Retrieval Augmented Generation Learn new facts (temporarily)
7. RAG: Retrieval Augmented Generation What's the best shoe for hiking? User Question Search [101]: Name: TrekExtreme Hiking Shoes Price: 135.99 Brand: Raptor Elite Type: Footwear Description: The Trek Extreme hiking shoes by Raptor Elite are built to ensure any trail. … Large Language Model For great hiking shoes, consider the TrekExtreme Hiking Shoes1 or the Trailblaze Steel-Blue Hiking Shoes2
8. RAG on PostgreSQL DEMO Azure OpenAI + Azure PostgreSQL Flexible Server + Azure Container Apps Code: aka.ms/rag-postgres Demo: aka.ms/rag-postgres/demo
9. Advanced RAG with query rewriting What's a good shoe for a mountain trale? User Question Large Language Model Mountain trail shoe Search [101]: Name: TrekExtreme Hiking Shoes Price: 135.99 Brand: Raptor Elite Type: Footwear Description: The Trek Extreme hiking shoes by Raptor Elite are built to ensure any trail. … Large Language Model For great hiking shoes, consider the TrekExtreme Hiking Shoes1 or the Trailblaze Steel-Blue Hiking Shoes2
10. Advanced RAG on PostgreSQL DEMO
11. RAG on documents Do my company perks cover underwater activities? User Question Search PerksPlus.pdf#page=2: Some of the lessons covered under PerksPlus include: · Skiing and snowboarding lessons · Scuba diving lessons · Surfing lessons · Horseback riding lessons These lessons provide employees with the opportunity to try new things, challenge themselves, and improve their physical skills.…. Large Language Model Yes, your company perks cover underwater activities such as scuba diving lessons1
12. RAG on Documents DEMO Azure OpenAI + Azure AI Search + Azure App Service Code: aka.ms/ragchat Demo: aka.ms/ragchat/demo
13. What is the RAG searching? Database rows (Structured data) You need a way to vectorize & search target columns. PostgreSQL + pgvector Cosmos MongoDB Eventhouse in Microsoft Fabric Azure SQL + vector search (upcoming) Documents (Unstructured data) PDFs, docx, pptx, md, html, images You need an ingestion process for extracting, splitting, vectorizing, and storing document chunks. Azure AI Search + Integrated Vectorization Document Intelligence or
14. Components of a high-quality RAG Sophisticated LLM • Adheres to instructions • Supports function calling Well prepared data • Reasonably sized text • Meaningful vectors Powerful search functionality • Vector search • Hybrid search • Semantic re-ranking • Filtering 1 2 3
15. Vectors 101 aka.ms/aitour/rag/mx Get the slides:
16. Vector embeddings An embedding encodes an input as a list of floating-point numbers. ”dog” → [0.017198, -0.007493, -0.057982,…] Different models output different embeddings, with varying lengths. Model Encodes MTEB Avg. Vector length word2vec words 300 Sbert (Sentence-Transformers) text (up to ~400 words) 768 OpenAI text-embedding-ada-002 text (up to 8191 tokens) 61.0% 1536 OpenAI text-embedding-3-small text (up to 8191 tokens) 62.3% 512, 1536 OpenAI text-embedding-3-large text (up to 8191 tokens) 64.6% 256, 1024, 3072 Azure Computer Vision image or text 1024 Demo: vector_embeddings.ipynb (aka.ms/aitour/vectors)
17. Vector similarity We compute embeddings so that we can calculate similarity between inputs. The most common distance measurement is cosine similarity. def cosine_sim(a, b): return dot(a, b) / (mag(a) * mag(b)) For ada-002, cosine similarity values range from 0.7-1 Demo: vector_embeddings.ipynb (aka.ms/aitour/vectors)
18. Vector search 1 Compute the embedding vector for the query 2 Find K closest vectors for the query vector • Search exhaustively or using approximations Query Compute embedding vector Query vector Search existing vectors K closest vectors “tortoise” OpenAI create embedding [-0.003335318, - 0.0176891904,…] Search existing vectors [[“snake”, [-0.122, ..], [“frog”, [-0.045, ..]]] Demo: vector_embeddings.ipynb (aka.ms/aitour/vectors)
19. Vector databases in Azure Vectors in Azure databases Keep your data where it is: native vector search capabilities Built into: Azure Cosmos DB MongoDB vCore Azure PostgreSQL services Eventhouse in Microsoft Fabric Azure SQL server (upcoming) Azure AI Search Best relevance: highest quality of results out of the box Automatically index data from Azure data sources: SQL DB, Cosmos DB, Blob Storage, ADLSv2, and more
20. Azure AI Search aka.ms/aitour/rag/mx Get the slides:
21. Azure AI Search Comprehensive search solution Enterprise-ready Integrated with Semantic Kernel, LangChain, LlamaIndex, Azure OpenAI Service, Azure AI Studio, and more Demo: azure_ai_search.ipynb (aka.ms/aitour/azure-search)
22. Vector search strategies r = search_client.search( None, top=5, vector_queries=[VectorizedQuery( vector=search_vector, k_nearest_neighbors=5, fields="embedding")]) • ANN = Approximate Nearest Neighbors • Fast vector search at scale • Uses HNSW, a graph method with excellent performance-recall profile • Fine control over index parameters ANN search r = search_client.search( None, top=5, vector_queries=[ ( vector=search_vector, k_nearest_neighbors=5, fields="embedding", exhaustive=True)]) • KNN = K Nearest Neighbors • Per-query or built into schema • Useful to create recall baselines • Scenarios with highly selective filters • e.g., dense multi-tenant apps Exhaustive KNN search Demo: azure_ai_search.ipynb (aka.ms/aitour/azure-search)
23. Rich vector search query capabilities Filtered vector search • Scope to date ranges, categories, geographic distances, access control groups, etc. • Rich filter expressions • Pre-/post-filtering • Pre-filter: great for selective filters, no recall disruption • Post-filter: better for low-selectivity filters, but watch for empty results https://learn.microsoft.com/azure/search/vector-search-filters r = search_client.search( None, top=5, vector_queries=[VectorizedQuery( vector=query_vector, k_nearest_neighbors=5, fields="embedding")], vector_filter_mode=VectorFilterMode.PRE_FILTER, filter= "tag eq 'perks' and created gt 2023-11-15T00:00:00Z") Multi-vector scenarios • Multiple vector fields per document • Multi-vector queries • Can mix and match as needed r = search_client.search( None, top=5, vector_queries=[ VectorizedQuery( vector=query1, fields="body_vector", k_nearest_neighbors=5), VectorizedQuery( vector=query2, fields="title_vector", k_nearest_neighbors=5) ])
24. Search multi-modal vectors • Images, sounds, graphs, and more • Multi-modal embeddings - e.g., images + sentences in Azure AI Vision Demo: image_search.ipynb (aka.ms/aitour/image-search)
25. Hybrid search for optimal relevance All information retrieval tricks apply! Complete search stacks do better:  Hybrid retrieval (keywords + vectors) > pure-vector or keyword  Hybrid + Reranking > Hybrid Identify good & bad candidates  Normalized scores from Semantic ranker  Exclude documents below a threshold Vector Keywords Fusion (RRF) Reranking model Demo: search_relevance.ipynb (aka.ms/aitour/search-relevance)
26. Avoiding noise: Using minimum score thresholds Semantic ranker scores range from 0-4: 0 Worst Quality 1 2 3 4 Best Quality qualified_documents = [ doc for doc in documents if doc.get("@search.reranker_score") >= min_score ]
27. Optimal relevance: Hybrid search + Semantic ranker Reranking model Fusion (RRF) 1 Scuba Diving in Bahamas 1 Scuba Diving in the Carribean 2 Water skiing in Seychelles 1 Scuba Diving in the Carribean 2 Scuba Diving in Bahamas 3 Water skiing in Seychelles 1 Scuba Diving in Bahamas 2 Scuba Diving in the Carribean Question: "What underwater activities can I do in the Bahamas?" Vector results Keyword results
28. Impact of search strategies on relevance Hybrid (vectors + keywords) L1 stage improves recall Semantic reranking in L2 stage boosts ranking precision 0 10 20 30 40 50 60 70 80 Customer datasets [NDCG@3] Beir [NDCG@10] Miracl [NDCG@10] Keyword Vector (ada-002) Hybrid (Keyword + Vector) Hybrid + Semantic ranker Source: aka.ms/ragrelevance
29. Demo: Building a RAG flow in Python SYSTEM_MESSAGE = """Assistant helps company employees questions about the employee handbook. Answer ONLY with the facts listed in the list of sources below. If there isn't enough information below, say you don't know. Each source has a name followed by colon and the actual information, include the source name for each fact you use.Use square brackets to reference the source, for example [info1.txt].""" USER_MESSAGE = user_question + "\nSources: " + sources # Now we can use the matches to generate a response response = openai_client.chat.completions.create( model=os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME"), temperature=0.3, messages=[ {"role": "system", "content": SYSTEM_MESSAGE}, {"role": "user", "content": USER_MESSAGE}, ]) answer = response.choices[0].message.content Demo: rag.ipynb (aka.ms/aitour/rag)
30. RAG with Azure AI Search (Full solution) Azure OpenAI + Azure AI Search + Azure App Service Code: aka.ms/ragchat Demo: aka.ms/ragchat/demo
31. Data preparation aka.ms/aitour/rag/mx Get the slides:
32. Why do we need to split documents? 1 LLMs have limited context windows (4K – 128K) 2 When an LLM receives too much information, it can get easily distracted by irrelevant details. Accuracy 50 55 60 65 70 75 5 10 15 20 25 30 Number of documents in input context Source: Lost in the Middle: How Language Models Use Long Contexts, Liu et al. arXiv:2307.03172
33. Optimal size of document chunk Optimal size of document chunk Number of tokens Retrieval Configuration Recall@50 512 input tokens per vector 42.4 1024 input tokens per vector 37.5 4096 input tokens per vector 36.4 8191 input tokens per vector 34.9 Chunk boundary strategy Chunk boundary strategy Recall@50 512 tokens, break at token boundary 40.9 512 tokens, preserve sentence boundaries 42.4 512 tokens with 10% overlapping chunks 43.1 512 tokens with 25% overlapping chunks 43.9 A token is the unit of measurement for an LLM's input/output. ~1 token/word for English, higher ratios for other languages. A chunking algorithm should also consider tables, and generally avoid splitting tables when possible. Source: aka.ms/ragrelevance aka.ms/genai-cjk Source: aka.ms/ragrelevance
34. Data ingestion process This is a typical ingestion process that can be highly customized to meet the domain needs: Azure Blob Storage Upload documents An online version of each document is necessary for clickable citations. Azure Document Intelligence Extract data from documents Supports PDF, HTML, docx, pptx, xlsx, images, plus can OCR when needed. Local parsers also available for PDF, HTML, JSON, txt. Python Split data into chunks Split text based on sentence boundaries and token lengths. Langchain splitters could also be used here. Azure OpenAI Vectorize chunks Compute embeddings using OpenAI embedding model of your choosing. Azure AI Search Indexing ・Document index ・Chunk index ・Both
35. Integrated vectorization This built-in feature for Azure AI Search enables cloud-based data ingestion with built-in skills. Data source access • Blob Storage • ADLSv2 • SQL DB • CosmosDB • … + Incremental change tracking File format cracking • PDFs • Office documents • JSON files • … + Extract images and text, OCR as needed Chunking • Split text into passages • Propagate document metadata Vectorization • Turn chunks into vectors • OpenAI embeddings or your custom model Indexing • Document index • Chunk index • Both Learn more: aka.ms/intvect
36. Evaluation aka.ms/aitour/rag/mx Get the slides:
37. Are the answers high quality? Are they clear and understandable? Are they correct? (relative to the knowledge base) Are they formatted in the desired manner? Do the perks cover underwater activities? Yes, according to the information provided in the PerksPlus.pdf document, underwater activities such as scuba diving are covered under the program. Yes, underwater activities are included as part of the PerksPlus program. Some of the underwater activities covered under PerksPlus include scuba diving lessons [PerksPlus.pdf#page=3]. Yes, the perks provided by the PerksPlus Health and Wellness Reimbursement Program cover a wide range of fitness activities, including underwater activities such as scuba diving. The program aims to support employees' physical health and overall well-being, so it includes various lessons and experiences that promote health and wellness. Scuba diving lessons are specifically mentioned as one of the activities covered under PerksPlus. Therefore, if an employee wishes to pursue scuba diving as a fitness-related activity, they can expense it through the PerksPlus program.
38. What affects the quality? Search Large Language Model Question ・ Search engine (ie. Azure AI Search) ・ Search query cleaning ・ Search options (hybrid, vector, reranker) ・ Additional search options ・ Data chunk size and overlap ・ Number of results returned ・ System prompt ・ Language ・ Message history ・ Model (ie. GPT 3.5) ・ Temperature (0-1) ・ Max tokens
39. Evaluation approaches Evaluate the retrieval system Recall, precision, NDCG/MAP aka.ms/ragrelevance Evaluate language models Shifting space, many metrics, consider your task. chat.lmsys.org/?leaderboard Evaluate the full RAG stack for quality and safety AI Studio or promptflow-evals for GPT-based evaluations and metrics→ At Build: BRK107: aka.ms/ragchat/eval Demo: rag.ipynb (aka.ms/aitour/rageval)
40. Securing your RAG aka.ms/aitour/rag/mx Get the slides:
41. User login and data access control Search index includes fields for oid and groups from Microsoft Entra: aka.ms/ragchat/acl
42. Use subnets for further isolation: Private network deployment Put all resources in a virtual network:
43. Conclusion aka.ms/aitour/rag/mx Get the slides:
44. Example uses Developers have used Azure AI search to create RAG apps for… Public government data Internal HR documents, company meetings, presentations Customer support requests and call transcripts Technical documentation and issue trackers Product manuals Customer Story: PwC scales GenAI for enterprise with Microsoft Azure AI
45. Next steps Learn more about Azure AI Search aka.ms/AzureAISearch Dig more into quality evaluation details and why Azure AI Search will make your application generate better results aka.ms/ragrelevance Deploy a RAG chat application for your organization’s data aka.ms/azai/python Explore Azure AI Studio for a complete RAG development experience aka.ms/AzureAIStudio 1 2 3 4 aka.ms/aitour/rag/mx Get the slides: