RAG (Retrieval-Augmented Generation) is an AI architecture that retrieves relevant information from external knowledge sources and provides it to an LLM, enabling the model to generate accurate, up-to-date, and context-aware responses.
LLM = Answer from Training
while
RAG = Search First + Then Answer
What is RAG 2.0? There is no official industry standard that defines “RAG 1.0”, “RAG 2.0”, or “RAG 3.0”.
The concept of “RAG 2.0”, known as Agentic RAG, represents a shift from a “simple data retrieval plugin” to a “deeply integrated, agentic system with reasoning capabilities.” The industry has indeed evolved from 1.0 and is now progressing toward 3.0.
Simplest Way to Remember
| Version | Meaning |
|---|---|
| RAG 1.0 | Search Once |
| RAG 2.0 | Search + Reason |
| RAG 3.0 (coming ? on the way?) | Agent + Search + Tools + Workflow |
RAG Foundation: Embedding
Embedding is a fundamental technology in machine learning and natural language processing that transforms discrete or complex objects (such as words, sentences, or images) into numerical vector representations of a fixed dimension
RAG Foundation: RAG Pipeline
A simple RAG pipeline has two phases and five stages
- Phase 1: Indexing (Offline / 离线阶段)
Build the searchable knowledge base before users ask questions.
离线阶段（索引）：先把所有文档加载进来，切分成小块，调用 Embedding 模型转成向量，最后存进 FAISS 等向量数据库里。 - Phase 2: Retrieval & Generation (Online / 在线阶段)
Execute at query time for each user question.
在线阶段（检索+生成）：用户提问时，先把问题也转成向量，去库里找最相似的 Top-K 个块，把这几个块作为“参考资料”连同问题一起扔给 GPT，让它写出最终回答。
In AI, a chunking strategy is the method used to break down large pieces of information—like long documents, complex prompts, or even audio and video—into smaller, more manageable segments called “chunks”. These chunks are the fundamental units that AI systems, especially Large Language Models (LLMs), work with to understand, process, and retrieve information efficiently.
Common Chunking Strategies
The best strategy depends on the type of data and the specific task. Here are the most common approaches:
Chunking Strategy: Recursive Chunking
Recursive Chunking is a hierarchical text splitting strategy that uses a priority list of separators (e.g., ["\n\n", "\n", " ", """]), starting with the highest-level (semantically strongest) separator. If a chunk still exceeds the chunk_size limit after splitting, it recursively applies the next-level separator to that chunk, continuing until all chunks meet the size requirement.
Chunking Strategy: Semantic Chunking
Semantic Chunking is a strategy for splitting documents into smaller pieces (chunks) based on meaning, rather than on fixed character counts or simple separators. It tries to keep sentences or paragraphs that are about the same topic together, and split where the topic changes. Think of it as “a smart editor who knows where one idea ends and the next begins
Chunking Strategy: Parent-Child Retrieval
Parent-Child Retrieval (also known as Small-to-Big Retrieval) is a two-tier hierarchical indexing strategy.
Hybrid Search refers to a search technique that combines multiple search algorithms simultaneously to retrieve the most relevant results. It most commonly merges Lexical (Keyword) Search with Semantic (Vector/Dense) Search.
基于关键词的搜索（词汇搜索）与基于语义的搜索（向量/稠密搜索）
- Lexical Search (Sparse Retrieval): Typically powered by algorithms like BM25 or TF-IDF. It relies on exact keyword matching and statistical term frequency. It excels at finding specific proper nouns, IDs, or rare terminology (e.g., “error code 404”).
词汇搜索（稀疏检索）： 通常由 BM25 或 TF-IDF 等算法驱动。它依赖于精确的关键词匹配和统计词频。它在查找特定的专有名词、ID 或罕见术语时表现出色（例如：“错误代码 404”）。 - Semantic Search (Dense Retrieval): Powered by embedding models (e.g., Sentence-BERT). It converts text into high-dimensional vectors and retrieves documents based on “meaning” rather than exact words. It excels at understanding synonyms, context, and natural language queries (e.g., “How to fix a broken internet connection”).
语义搜索（稠密检索）： 由嵌入模型（如 Sentence-BERT）驱动。它将文本转换为高维向量，并根据“含义”而非精确词汇来检索文档。它在理解同义词、上下文和自然语言查询（例如：“如何修复断开的网络连接”）方面表现出色。
BM25 (keyword-based search)
BM25 (Best Matching 25) is a keyword-based ranking algorithm used in information retrieval to score and rank documents based on their relevance to a search query. It’s called “Best Matching 25” because it was the 25th variant in a series of scoring functions proposed by its creators. BM25 is the default ranking algorithm in Elasticsearch and most production search engines
RRF
RRF stands for Reciprocal Rank Fusion. It is an algorithm that merges multiple ranked result lists from different search systems into a single unified ranking.
全称是 Reciprocal Rank Fusion（倒数排名融合）。它是一种将来自不同检索系统的多个排名结果列表合并成一个统一排名的算法.
Vector Database Selection
Compare normal DB, such as SQL DB, MySQL, PostgreSQL, They fundamental difference is what they search for and how they find it. A traditional SQL database (like MySQL, PostgreSQL without pgvector) is built for exact matching and structured queries. It answers questions like: “Find the customer with ID = 12345” or “Give me all orders over $100.” A Vector Database is built for semantic similarity and unstructured data. It answers questions like: “Find all documents that talk about the same topic as this paragraph” or “Show me products that look visually similar to this image.”
Normal DB vs Vector DB
| 要点 | EN | CN |
|---|---|---|
| 核心：精确匹配 vs. 语义相似度 | Core: Exact matching vs. Semantic similarity | 核心：精确匹配 vs. 语义相似度 |
| SQL存结构化数据（数字/字符串），Vector存浮点数数组（含义） | SQL stores structured data (numbers/strings); Vector stores float arrays (meaning) | SQL存结构化数据，Vector存浮点数数组（含义） |
| SQL查询用WHERE精确条件；Vector查询用ORDER BY距离 | SQL queries use WHERE exact conditions; Vector queries use ORDER BY distance | SQL查询用WHERE精确条件；Vector查询用ORDER BY距离 |
| SQL用B-Tree/哈希（精确查找）；Vector用HNSW/IVF（近似查找） | SQL uses B-Tree/Hash (exact lookup); Vector uses HNSW/IVF (approximate) | SQL用B-Tree/哈希（精确）；Vector用HNSW/IVF（近似） |
| SQL结果是二元的（匹配/不匹配）；Vector结果是排序的（相似度分数） | SQL results are binary (match/no match); Vector results are ranked (similarity scores) | SQL结果是二元的；Vector结果是排序的 |
| SQL适合事务、财务报表；Vector适合RAG、推荐、AI | SQL suits transactions, ledgers; Vector suits RAG, recommendations, AI | SQL适合事务、报表；Vector适合RAG、推荐、AI |
| 专用向量库不能做JOIN和ACID；但pgvector可以在PostgreSQL中兼得 | Dedicated vector DBs can’t do JOINs/ACID; pgvector lets you have both in PostgreSQL | 专用向量库不能做JOIN和ACID；pgvector可以让两者兼得 |
| 在实际RAG中，两者是互补的，不是替代关系 | In real-world RAG, they are complementary, not replacements | 在实际RAG中，两者是互补的，不是替代关系 |
Vector database selection is the process of choosing the right vector database technology for your AI application from dozens of available options — Milvus, Qdrant, Weaviate, Pinecone, pgvector, Chroma, and more.
Mainstream Vector Database Landscape
| Category | Examples | CN |
|---|---|---|
| Fully Managed (PaaS) | Pinecone, Zilliz Cloud, Weaviate Cloud | 全托管云服务 |
| Self-Hosted Open Source | Qdrant, Milvus, Weaviate, Chroma | 自托管开源 |
| Database Extensions | pgvector, MongoDB Vector Search, Elasticsearch | 数据库扩展 |
| Cloud Provider Services | Azure AI Search, AWS S3 Vectors, Tencent Cloud VDB | 云厂商服务 |
| Embedded/Specialized | SQLite (vector), LanceDB | 嵌入式/专用 |
Detailed Comparison
| Database | Avg Query Time | Cost (1M @ 1536-dim) | Best For | CN 最适合 |
|---|---|---|---|---|
| Milvus/Zilliz | 50.7ms | $115/mo | Fastest queries + good flexibility | 最快查询+灵活性好 |
| Weaviate | 51.7ms | $160/mo | Native datetime/geo + hybrid search | 原生时间/地理+混合检索 |
| Qdrant | 73.1ms | $103/mo | Best balance (speed + flexibility + cost) | 最佳平衡（速度+灵活性+成本） |
| Pinecone | 106.3ms | $30/mo | Cheapest (⚠️ poor schema flexibility) | 最便宜（⚠️ Schema灵活性差） |
| Chroma | 275.4ms | $139/mo | Easiest setup + prototyping | 最简单设置+原型开发 |
7 Types of Data Stored in VectorDB in AI Projects
| 类型 | EN | CN | 更新频率 | 过滤器 | 主要用途 |
|---|---|---|---|---|---|
| RAG 文档块 | RAG Document Chunks | RAG 文档块 | 低 | source, page | 问答 |
| 用户记忆 (Mem0) | User Memory (Mem0) | 用户记忆 (Mem0) | 高 | user_id | 个性化 |
| 工具 Schema | Tool Schemas | 工具 Schema | 中 | tool_category | 工具选择 |
| Agent 轨迹 | Agent Trajectories | Agent 轨迹 | 中 | user_id, outcome | 经验复用 |
| 黄金数据集 | Golden Dataset | 黄金数据集 | 低 | category | 评估 |
| 语义缓存 | Semantic Cache | 语义缓存 | 中 | 无 | 降本提速 |
| 代码索引 | Code Index | 代码索引 | 低 | language, path | 代码生成 |
Reranker is a sophisticated machine learning model designed to refine and reorder a list of candidate items—such as search results, document passages—to maximize their relevance to a specific query or context.
Reranker（重排序器）是一种复杂的机器学习模型，旨在优化并重新排序候选项目列表（如搜索结果、文档片段），以最大程度提高它们与特定查询或上下文的相关性。
Reranker实战
Advanced RAG (Advanced Retrieval-Augmented Generation) is an evolutionary upgrade over the basic “Naive RAG” pipeline. It adds a suite of optimization techniques at every stage of the RAG workflow — pre-retrieval, retrieval, post-retrieval, and evaluation — to systematically improve retrieval precision, recall, and generation quality.