The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with several variations and advanced techniques emerging beyond the basic “naive” RAG.
I. Based on the Core RAG Pipeline
1. Naive/Standard RAG
The user’s query is directly used to retrieve relevant documents, and these are passed to the LLM for generation.
Use Cases: Simple question answering over a limited, well-structured knowledge base.Tutorial: Pinecone Learning Center on RAG (This is a general RAG tutorial, illustrating the basic concept).
2. Simple RAG with Memory
Incorporates conversational history into the retrieval query for more contextually relevant responses in multi-turn dialogues.
Use Cases: Chatbots that need to maintain context over multiple turns.Tutorial: LangChain Chatbot Tutorial (Often includes memory management for conversational context).
II. Advanced Retrieval Techniques
1. Query Expansion
Reformulating or expanding the query with synonyms or related terms to improve retrieval recall.
Use Cases: Situations where user queries might be underspecified or use different terminology than the knowledge base.Tutorial: While a dedicated “Query Expansion RAG” tutorial might be rare, explore techniques like LangChain Query Expansion.
2. Self-Query
Using the LLM to identify structured information in the query and generate a precise retrieval query with metadata filters.
Use Cases: Knowledge bases with rich metadata where users need to filter results based on specific attributes.Tutorial: LangChain Self-Query Retriever.
3. Hybrid Search
Combining semantic (vector) and keyword (sparse) search for improved relevance and recall.
Use Cases: General-purpose search where both semantic similarity and keyword matching are important.Tutorial: Pinecone on Hybrid Search (Often demonstrated within RAG pipelines).
4. Filtered Vector Search
Applying filters to vector search based on document metadata for more targeted retrieval.
Use Cases: Retrieving information within specific categories, timeframes, or other metadata constraints.Tutorial: Look for “Metadata Filtering with Vector Stores” in the documentation of vector database providers like Pinecone Metadata Filtering or Milvus Filtering, often used in RAG.
5. Contextual Compression
Compressing or filtering retrieved documents to retain only the most relevant parts before passing to the LLM.
Use Cases: Reducing noise and context window size, focusing the LLM on the most important information.Tutorial: Explore LangChain Contextual Compression.
6. Multi-Vector Retrieval
Embedding different aspects or granularities of a document for more nuanced retrieval.
Use Cases: Complex documents where different sections or summaries might be relevant to different queries.Tutorial: Search for “Multi-Vector Retriever LangChain” or explore advanced indexing strategies in vector store documentation (e.g., creating separate embeddings for summaries and content).
7. Graph RAG
Utilizing knowledge graphs for retrieval based on relationships between entities.
Use Cases: Knowledge-intensive tasks where understanding relationships is crucial (e.g., drug discovery, expert systems).Tutorial: Search for “Graph RAG LangChain” or tutorials integrating graph databases (like Neo4j) with LLMs for retrieval. LangChain Blog on Graph RAG.
8. Hypothetical Document Embedding (HyDE)
The LLM generates a hypothetical answer, and its embedding is used for retrieval.
Use Cases: Sparse or ambiguous queries where direct embedding might not capture the intent well.Tutorial: Search for “HyDE RAG LangChain” or explore implementations using LangChain’s retrievers with LLM-generated embeddings. LangChain Self Query (can be adapted for HyDE concepts).
9. Fusion Retrieval (RAG Fusion)
Combining results from multiple retrieval strategies or embedding models.
Use Cases: Improving the robustness and diversity of retrieved documents.Tutorial: Search for “RAG Fusion LangChain” or explore techniques for combining retrievers in LangChain. LangChain Ensemble Retriever.
III. Enhancements in the Generation Phase
1. Reranking
Using a separate model to score and reorder retrieved documents based on relevance.
Use Cases: Ensuring the most relevant documents are prioritized for the LLM.Tutorial: Explore “Document Reranking LangChain” or integrations with reranking models like LangChain Rerank.
2. Corrective RAG (CRAG)
Self-reflection on retrieved “knowledge strips” to decide whether to use, ignore, or request more information.
Use Cases: Improving the accuracy and reliability of generated answers by being critical of retrieved information.Tutorial: This is a more advanced concept; search for research papers on “Corrective RAG” and explore how to implement self-evaluation mechanisms within LangChain agents or custom chains.
3. Self-RAG
The LLM autonomously generates retrieval queries during the generation process.
Use Cases: Complex generation tasks where the LLM needs to dynamically seek information as it generates.Tutorial: Explore “Self-RAG” research papers and consider building custom LangChain agents that incorporate retrieval as part of their action space.
IV. Agentic RAG
1. Agentic RAG
Integrating RAG with AI agent capabilities for complex, multi-step information gathering and generation.
Use Cases: Answering complex questions requiring multiple sources of information, tool use (e.g., search, calculators), and planning.Tutorial: Explore LangChain Agents Documentation and tutorials on building agents with retrieval capabilities.
V. Other Notable Variations
1. Adaptive RAG
Dynamically adjusting the retrieval strategy based on the query complexity.
Use Cases: Handling a wide range of queries with varying information needs.Tutorial: This often involves building custom logic within LangChain to analyze queries and choose different retrieval methods.
2. Knowledge-Augmented Generation (KAG)
Focuses on integrating structured knowledge from knowledge graphs into generation.
Use Cases: Tasks requiring structured knowledge and reasoning.Tutorial: Similar to Graph RAG, search for integrations of knowledge graphs with LLM generation in LangChain.
3. Cache-Augmented Generation (CAG)
Leveraging long-context LLMs by preloading relevant knowledge into their extended context window.
Use Cases: Situations where a large amount of potentially relevant information can be pre-computed and loaded.Tutorial: Explore the documentation of long-context LLMs and strategies for preloading and managing context within LangChain.
4. Zero-Indexing Internet Search-Augmented Generation
Directly searching the internet for relevant information during generation.
Use Cases: Accessing up-to-date information not present in a static knowledge base.Tutorial: Explore LangChain tools for web searching (e.g., Google Search Tool) used within chains or agents.
5. Multimodal RAG
Extending RAG to handle multiple data types (images, audio, video).
Use Cases: Applications dealing with diverse data formats (e.g., image captioning with relevant image retrieval).Tutorial: Search for “Multimodal RAG LangChain” or explore integrations with multimodal embedding models and vector stores.
The choice of RAG type depends on the specific application requirements, the nature of the data, and the desired level of complexity and performance.
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