RAG combines a retrieval model and a generative model to create contextually rich, accurate responses. The retriever fetches relevant documents or passages from an external knowledge base, and the generator uses this retrieved context to produce responses.Workflow:
Key Features:
"Classifying each pixel in an image into meaningful categories."
In Cache-Augmented Generation, responses are enhanced by using a local cache to store previously generated or retrieved results, avoiding redundant computations.
"Techniques to make AI models' decisions understandable and transparent."
GraphRAG leverages graph-based representations of knowledge to improve retrieval and generation. Instead of linear or flat knowledge bases, it works with knowledge graphs where entities and their relationships are explicitly defined.
"Large-scale pre-trained models that serve as a base for diverse tasks."
VectorRAG combines retrieval-augmented generation with vector-based search powered by vector embeddings. This method excels in semantic search and retrieval.
"AI models trained on massive text corpora for language understanding and generation."
HybridRAG combines dense retrieval (vector-based search) and sparse retrieval (traditional keyword-based search like BM25) for a more robust retrieval system.
"Deep learning models using attention mechanisms for sequence data."
| Variant | Key Feature | Best Use Case |
|---|---|---|
| RAG | Combines retrieval and generation. | Open-domain QA, chatbots. |
| CacheRAG | Uses a cache for efficiency. | High-throughput systems, repetitive queries. |
| GraphRAG | Leverages knowledge graphs. | Complex reasoning, biomedical research. |
| VectorRAG | Vector-based semantic search. | Personalized recommendations, multilingual QA. |
| HybridRAG | Combines sparse and dense retrieval techniques. | Enterprise search, e-commerce. |
Standard RAG established the basic framework for knowledge-enhanced AI systems. It employs a straightforward approach:
While simple and effective for basic tasks, this architecture faces challenges in resource utilization and accuracy when handling complex queries. The LLM must simultaneously process retrieved documents and generate coherent responses, which can strain system resources.
Self-Reflective RAG introduces a crucial advancement: system self-awareness. Key features include:
This architecture particularly benefits high-stakes applications in fields like legal and medical domains, where accuracy and reliability are paramount. However, the additional computational resources required for self-reflection represent a notable trade-off.
Corrective RAG prioritizes accuracy through dedicated validation:
This architecture excels in compliance and regulatory environments where minimizing factual errors is critical. The trade-off comes in the form of increased processing time due to the additional validation layer.
Speculative RAG represents a paradigm shift with its innovative two-tier approach:
| Feature | Standard RAG | Self-Reflective RAG | Corrective RAG | Speculative RAG |
|---|---|---|---|---|
| Architecture Design | Single-step, linear process | Self-evaluating system | Validation-focused system | Two-tier parallel system |
| Processing Method | Sequential document processing | Iterative with self-assessment | Sequential with validation | Parallel with expert review |
| Core Components | Single LLM, Document retrieval | Self-evaluation mechanism, Enhanced retrieval | NLI model, Validation layer | Small LLM for drafts, Main LLM for review |
| Primary Strength | Simplicity | Improved accuracy | High reliability | Optimized performance |
The evolution of RAG architectures points toward:
"Models generating sequences by predicting one token at a time."
RAG metrics, short for Retrieval-Augmented Generation metrics, are evaluation methods used in systems that combine retrieval-based approaches with generative models to assess the quality and effectiveness of generated responses. These metrics are commonly applied in open-domain question answering and similar applications.
Measures how many of the top K retrieved documents contain relevant information.
Formula:
Example:
If K = 2 and the relevant document appears in the top 2 results:
Evaluates how quickly the first relevant document is retrieved.
Formula:
Example:
Measures the overlap of n-grams between the generated text and a reference answer.
Formula:
Focuses on recall by measuring how much of the reference text is captured in the generated text.
Ensures that the generated response is factually consistent with the retrieved content. This can be evaluated manually or using automated tools.
def recall_at_k(retrieved_docs, relevant_docs, k):
retrieved_at_k = retrieved_docs[:k]
return sum(1 for doc in retrieved_at_k if doc in relevant_docs) / len(relevant_docs)
def mean_reciprocal_rank(retrieved_docs, relevant_docs_list):
reciprocal_ranks = []
for relevant_docs in relevant_docs_list:
for rank, doc in enumerate(retrieved_docs, start=1):
if doc in relevant_docs:
reciprocal_ranks.append(1 / rank)
break
else:
reciprocal_ranks.append(0)
return sum(reciprocal_ranks) / len(relevant_docs_list)
retrieved_docs = ["doc1", "doc2", "doc3"]
relevant_docs_list = [["doc2"], ["doc3"]]
print("Recall@2:", recall_at_k(retrieved_docs, relevant_docs_list[0], 2))
print("MRR:", mean_reciprocal_rank(retrieved_docs, relevant_docs_list))from nltk.translate.bleu_score import sentence_bleu
reference = [["OpenAI", "was", "founded", "by", "Elon", "Musk", "and", "Sam", "Altman"]]
candidate = ["OpenAI", "was", "started", "by", "Sam", "Altman", "and", "Elon", "Musk"]
score = sentence_bleu(reference, candidate)
print("BLEU Score:", score)"Generative models that progressively refine noisy data to create samples."
Based on insights from the video "7 Measurements that Help Minimize Model Risk for RAG", here are seven essential metrics for assessing the performance of RAG systems:
These metrics provide a comprehensive framework for evaluating both the retrieval and generation components of RAG systems, ensuring their effectiveness, reliability, and ethical considerations.
For a more in-depth understanding, you can watch the full video below:
"Models that generate new data by pitting a generator against a discriminator."
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external data to improve accuracy and relevance. Microsoft's research categorizes RAG tasks into four levels, each requiring progressively complex reasoning and data integration:
These involve straightforward questions seeking specific facts directly present in the data, without the need for additional reasoning. The model's task is to locate and extract this information.
These queries require the model to interpret and combine information to derive an answer. The necessary data might be dispersed across multiple segments or require simple inferencing. For example, determining the majority party in the country where Canberra is located involves knowing that Canberra is in Australia and identifying Australia's current majority party.
These focus on understanding the reasoning behind facts and necessitate data that supports logical explanations. Such queries require both factual knowledge and the ability to interpret and apply specific domain-based guidelines essential to the context. For instance, in financial auditing, an LLM may need to follow regulatory compliance guidelines to assess if a company's financial statements meet standards.
These seek deeper insights, often requiring context-based reasoning to uncover underlying meanings or implications. The AI must infer complex rationales that aren't explicitly documented, relying on patterns and outcomes observed within the data. For example, in IT operations, a language model might analyze patterns from past incident resolutions to identify successful strategies.
This hierarchical framework aids in selecting appropriate RAG architectures tailored to specific use cases, ensuring alignment with task demands and enhancing the system's effectiveness.
"Designing prompts to guide AI models effectively."