Overview In recent years, vector-based search has become incredibly popular....
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Understanding Retrieval Augmented Generation
LLMs are trained on a vast amount of textual data, and their capabilities are based on the knowledge they acquire from this data.
This means that if you ask them a question about data that is not part of their training set, they will not be able to respond accurately, resulting in either a refusal (Where the llm responds with “i dont know”) Or worse, a hallucination.
So, how can you build a genai application that would be able to answer questions using a custom or private dataset that is not part of the llm’s training data?
RAG Flow: A Step-by-Step representation
Data Ingestion
- Blue arrows show the flow of data from various sources (databases, cloud, etc.) for Retrieval Augmented Generation.
- Text-based GenAI applications process data, translating and extracting text as needed.
Text Processing
- Extracted text is divided into chunks and processed using Vectara’s Boomerang model to create vector embeddings.
Query-Response Flow
- Green arrows illustrate the user query and response process.
- Query encoding and approximate nearest neighbor search retrieve relevant text chunks for response.
Prompt and Generation
- Relevant text chunks construct a comprehensive prompt for generative language models like OpenAI.
- Language models ground responses in provided facts, avoiding hallucination.
Validation and User Response
- Optionally, responses can undergo validation before being sent back to the user.
Enterprise Automation (Optional)
- Red arrow indicates the optional step of taking action based on trusted responses, like automated tasks in enterprise systems.
~ Case Studies~
Generative AI Case Studies
Chatbot Development
Overview: Designed an intelligent chatbot to improve user interaction
Parameters: Approximately 175 billion (GPT-3 standard).
Vectors: Custom embedding vectors for industry context.
Hardware: Cloud-based AI-optimized compute instances.
Software: OpenAI API, Python, Langchain, Reis and Chroma.
Challenges: Achieving a human-like conversational experience with accurate context understanding.
Solution: Implemented a GPT-3.5 based conversational agent with custom fine-tuning for industry-specific knowledge. ML Model: GPT-3.5 with domain-specific fine-tuning.
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