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[DSC Europe 23] Spela Poklukar & Tea Brasanac - Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) combines the concepts of semantic search and LLM-based text generation. When a person makes a query in natural language, the query is compared to the entries in the knowledge base and most relevant results are returned to the LLM, which uses this extra information to generate more accurate and reliable response. RAG can therefore limit hallucination and provide accurate responses from reliable source. In this talk, we will present the concept of RAG and underlying concept of semantic search, and present available libraries and vector databases.

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RETRIEVAL
AUGMENTED
GENERATION
Š P E L A P O K L U K AR
M A C H I N E L E A R N I N G C O N S U L T A N T
T E A B R AŠ AN AC
D A T A S C I E N T I S T
AGENDA
1. Large Language Model challenges
2. Retrieval Augmented Generation
3. AI Agents
4. Application of Theory: Demo
LARGE LANGUAGE MODEL
CHALLENGES
Hallucinations
● Generation of false facts and
presenting them as they were true
● Inherent to LLMs: They are trained to
predict next word in a sentence
without having actual semantic
understanding of the text
LARGE LANGUAGE MODEL
CHALLENGES
Up-to-date information
LLMs are
text generation tools and
not knowledge bases

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[DSC Europe 23] Spela Poklukar & Tea Brasanac - Retrieval Augmented Generation

  • 2. Š P E L A P O K L U K AR M A C H I N E L E A R N I N G C O N S U L T A N T T E A B R AŠ AN AC D A T A S C I E N T I S T
  • 3. AGENDA 1. Large Language Model challenges 2. Retrieval Augmented Generation 3. AI Agents 4. Application of Theory: Demo
  • 4. LARGE LANGUAGE MODEL CHALLENGES Hallucinations ● Generation of false facts and presenting them as they were true ● Inherent to LLMs: They are trained to predict next word in a sentence without having actual semantic understanding of the text
  • 6. LLMs are text generation tools and not knowledge bases
  • 7. The text is copied from a reliable source
  • 8. RETRIEVAL AUGMENTED GENERATION Giving the model access to domain-specific up to date knowledge Efficient technique to limit hallucinations and ensure that up- to-date information are passed to the end user
  • 9. RAG
  • 12. RAG Documents Embeddings Knowledge base Question What was the biggest Carnival around this year? Question Content The number of visitors to the world's most spectacular carnivals this year was as follows:: Oruro Carnival: 1.000.000 Binche Carnival: 135.000 ... Trinidad and Tobago Carnival: 27.000 Rio Carnival: 40.000.000 Venice Carnival: 700.000 User
  • 13. RAG Documents Embeddings Knowledge base Question What was the biggest Carnival around this year? Question Content The number of visitors to the world's most spectacular carnivals this year was as follows:: Oruro Carnival: 1.000.000 Binche Carnival: 135.000 ... Trinidad and Tobago Carnival: 27.000 Rio Carnival: 40.000.000 Venice Carnival: 700.000 User
  • 14. RAG Question What was the biggest Carnival around this year? Content The number of visitors to the world's most spectacular carnivals this year was as follows:: Oruro Carnival: 1.000.000 Binche Carnival: 135.000 ... Trinidad and Tobago Carnival: 27.000 Rio Carnival: 40.000.000 Venice Carnival: 700.000 Documents Embeddings Knowledge base Question Combine content with prompt Based on the following information: Content The number of visitors to the world's most spectacular carnivals this year was.... answer the query: Question What was the biggest Carnival around this year? User
  • 15. RAG Question What was the biggest Carnival around this year? Content The number of visitors to the world's most spectacular carnivals this year was as follows:: Oruro Carnival: 1.000.000 Binche Carnival: 135.000 ... Trinidad and Tobago Carnival: 27.000 Rio Carnival: 40.000.000 Venice Carnival: 700.000 Documents Embeddings Knowledge base Question Combine content with prompt Based on the following information: Content The number of visitors to the world's most spectacular carnivals this year was.... answer the query: Question What was the biggest Carnival around this year? LLM User The biggest carnival around the world this year was the Rio Carnival, which attracted a staggering 40,000,000 visitors. Curated response
  • 16. RAG Question What was the biggest Carnival around this year? Content The number of visitors to the world's most spectacular carnivals this year was as follows:: Oruro Carnival: 1.000.000 Binche Carnival: 135.000 ... Trinidad and Tobago Carnival: 27.000 Rio Carnival: 40.000.000 Venice Carnival: 700.000 Documents Embeddings Knowledge base Question Combine content with prompt LLM User Based on the following information: Content The number of visitors to the world's most spectacular carnivals this year was.... answer the query: Question What was the biggest Carnival around this year? The biggest carnival around the world this year was the Rio Carnival, which attracted a staggering 40,000,000 visitors. SEMANTIC SEARCH
  • 18. CREATING A KNOWLEDGE BASE Documents 0.040 0.306 0.01 . . . 0.012 0.000 0.006 0.04 . . . 0.03 0.010 0.05 0.004 . . . 0.022 . . . Vector embeddings Knowledge base Embedding model
  • 20. CREATING A KNOWLEDGE BASE Documents 0.040 0.306 0.01 . . . 0.012 0.000 0.006 0.04 . . . 0.03 0.010 0.05 0.004 . . . 0.022 . . . Vector embeddings Knowledge base Embedding model VECTOR DATA BASE
  • 21. SEMANTIC SEARCH Y₁ y₂ y₃ … yₙ K₁ k₂ k₃ … kₙ Embedding model
  • 23. AI AGENTS Knowledge base Query/command User WEB Execute commands Orchestration tools that utilize GPT LLMs and are able to: 1) take the input from the user 2) based on the input, take an autonomous decision 3) execute action
  • 24. FUNCTION CALLING Function specifications to interface with a hypothetical weather API Are based on the function calling capability of GPT-3.5 and GPT-4 LLMs
  • 25. • The AI agent can decide to use those tools to autonomously execute tasks. • It decides whether to use them, when to use them, in which order to use them, and plans the next steps according to the results. FUNCTION CALLING
  • 26. AI AGENTS (as an extension of RAG) Retrieved content Knowledge base Query/command Combine content with prompt LLM User Curated response WEB Execute commands
  • 27. DEMO
  • 29. THANK YOU FOR YOUR ATTENTION :)