Retrieval Augmented Generation
1. Overview
1.1. Definition:
- A framework that combines machine learning models to enhance information retrieval and text generation capabilities.
- It integrates two predominant AI tasks: retrieval of relevant data from a knowledge base and subsequent generation of a coherent response or narrative based on that data.
1.2. Key Components:
- Retriever Model:
- Generally based on models like BERT, designed to extract relevant documents or data chunks from a large corpus.
- Utilizes querying techniques to identify information pertinent to the user’s question or topic.
- Generator Model:
- Typically a language model such as GPT, tasked with creating natural language output from the retrieved information.
- Ensures that the final response is coherent, contextually relevant, and aligns with human-like language quality.
1.3. Applications:
- Frequently used in conversational AI, customer service, and content creation to provide detailed, context-aware responses.
- Enhances research by providing a systemic way to retrieve and summarize knowledge from expansive datasets or articles.
1.4. Challenges:
- Accuracy in retrieval to ensure the generator has the most relevant and up-to-date information.
- Balancing the generation of creative language with factual correctness.
- Managing computational efficiency to handle the typically large models involved in such frameworks.
1.5. Connections to Other Domains:
- Similar to traditional search engines but advances the capability by integrating generative responses.
- Reflects advancements in NLP and AI where discrete models for retrieval and generation are continuously being refined and integrated.
2. Benefits
- limits context for the LLM to generate an answer
- can clearly reference sources for different aspects of the query
2.3. Specific and Up to Date Data
2.4. Lesser of a black box
- lower reliance on the condesnsed memory of an LLM
3. Building a RAG pipeline
3.1. Overview of the RAG Pipeline Stages:
- Loading & Ingestion
- Gather diverse data sources (e.g., documents, databases, APIs).
- Clean and preprocess data for consistency (removal of duplicates, normalization).
- Use formats compatible with the subsequent stages (e.g., JSON, CSV).
- Indexing and Embedding
- Create an index for efficient search and retrieval (e.g., inverted index or vector index).
- Convert documents into embeddings using models like BERT or Sentence Transformers.
- Ensure embeddings capture semantic meaning and are optimized for similarity searches.
- Storing
- Choose storage solutions (e.g., SQL databases, NoSQL databases, vector databases).
- Organize data effectively in a way that preserves relationships (e.g., metadata storage).
- Implement efficient data retrieval systems to minimize latency.
- Querying
- Develop a query interface that accepts user input and translates it into machine-understandable requests.
- Utilize the indexing system to retrieve relevant data fast.
- Use embedding similarity measures (e.g., cosine similarity) to find semantically related information.
3.2. Evaluation:
- a critical step in any flow is checking how effective it is relative to other strategies, or when you make changes. Evaluation provides objective measures of how accurate, faithful and fast your responses to queries are.
3.3. Connections Between Stages:
- The Loading & Ingestion phase is foundational as it determines the quality and breadth of data available for indexing, embedding, and ultimately querying.
- The accuracy of Indexing and Embedding directly impacts the efficiency and relevance of results in the Querying stage, as poorly indexed or insufficiently trained embeddings can lead to irrelevant responses.
- The choice of storage in the Storing phase affects both retrieval speed and the ability to perform advanced queries efficiently.
- Each query's success relies on how well each previous stage has been executed, emphasizing a pipeline's integrity and system design.