rag-pipeline
검색 증강 생성(RAG) 파이프라인은 보다 정확하고 맥락적으로 관련된 응답을 생성하기 위해 정보 검색을 대규모 언어 모델(LLM)과 결합한 프레임워크입니다.
RAG 파이프라인은 외부 지식 소스를 통합하여 LLM을 향상시킵니다. 이 프로세스에는 사용자의 쿼리를 기반으로 지식 기반 또는 데이터 저장소에서 관련 정보를 검색한 다음, 검색된 정보를 사용하여 LLM의 생성 프로세스를 보강하는 작업이 포함됩니다. 파이프라인에는 일반적으로 데이터 수집, 인덱싱, 검색 및 생성 단계가 포함되어 있어 LLM이 교육 데이터를 넘어 최신 및 특정 정보에 액세스하고 활용할 수 있습니다.
RAG 파이프라인의 핵심적인 이점은 LLM 출력의 정확성, 신뢰성 및 맥락성을 향상시키는 것입니다. LLM의 응답을 사실 데이터에 기반하여 RAG 파이프라인은 잘못되거나 환각적인 정보를 생성할 가능성을 줄여 높은 정밀도와 신뢰성이 필요한 애플리케이션에 적합하게 만듭니다.
graph LR
Center["rag-pipeline"]:::main
Pre_logic["logic"]:::pre --> Center
click Pre_logic "/terms/logic"
Rel_rag["rag"]:::related -.-> Center
click Rel_rag "/terms/rag"
Rel_retrieval_augmented_generation["retrieval-augmented-generation"]:::related -.-> Center
click Rel_retrieval_augmented_generation "/terms/retrieval-augmented-generation"
Rel_nlp["nlp"]:::related -.-> Center
click Rel_nlp "/terms/nlp"
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classDef child fill:#0f172a,stroke:#10b981,color:#94a3b8,rx:5,ry:5;
classDef related fill:#0f172a,stroke:#8b5cf6,stroke-dasharray: 5 5,color:#94a3b8,rx:5,ry:5;
linkStyle default stroke:#4b5563,stroke-width:2px;
🧠 지식 테스트
🧒 5살도 이해할 수 있게 설명
Imagine a super-smart robot (the [LLM](/ko/terms/llm)) who knows a lot from books it read. But what if you ask it about today's news? A [RAG](/ko/terms/rag) pipeline is like giving the robot a quick way to look up the latest news articles before it answers, so it gives you the most up-to-date information! 🤖
🤓 Expert Deep Dive
A RAG pipeline fundamentally augments the generative capabilities of a Large Language Model (LLM) by injecting external, contextually relevant information during the inference phase. This bypasses the limitations of static training data and mitigates hallucination. The architecture typically comprises several key components:
- Data Ingestion and Preprocessing: Raw data sources (e.g., documents, databases, APIs) are parsed, cleaned, and chunked into manageable segments. Chunking strategies (fixed-size, sentence-aware, semantic) are critical for effective retrieval.
- Indexing: Processed chunks are converted into dense vector embeddings using a pre-trained encoder model (e.g., Sentence-BERT, OpenAI's
text-embedding-ada-002). These embeddings capture semantic meaning and are stored in a vector [database](/ko/terms/vector-database) (e.g., Pinecone, Weaviate, FAISS) for efficient similarity search.
- Retrieval: Upon receiving a user query, the query itself is embedded using the same encoder. A similarity search (e.g., Approximate Nearest Neighbor - ANN) is performed against the vector index to identify the top-k most relevant document chunks based on cosine similarity or dot product.
Similarity Metric: $sim(q, d) = \frac{q \cdot d}{\|q\| \cdot \|d\|}$ (Cosine Similarity)
Top-k Retrieval: Select $d_i$ such that $rank(sim(q, d_i)) \le k$
- Augmentation and Generation: The retrieved chunks, along with the original query, are formatted into an augmented prompt. This prompt is then fed to the LLM, instructing it to generate a response grounded in the provided context.
- Augmented Prompt Example:
"Context: [Retrieved Chunk 1] [Retrieved Chunk 2] ...
Question: [User Query]
Answer: "
Advanced RAG techniques involve re-ranking retrieved documents, query expansion, hybrid search (keyword + vector), and fine-tuning the retriever or generator models for specific domains. The overall objective is to create a synergistic loop where LLM capabilities are amplified by a dynamic, context-aware knowledge retrieval mechanism.