rag-pipeline
Retrieval-Augmented Generation (RAG)パイプラインは、より正確で文脈的に関連性の高い応答を生成するために、情報検索を大規模言語モデル（LLM）と組み合わせたフレームワークです。
RAGパイプラインは、外部の知識ソースを統合することにより、LLMを強化します。このプロセスには、ユーザーのクエリに基づいて、知識ベースまたはデータストアから関連情報を取得し、次にこの取得した情報を使用してLLMの生成プロセスを拡張することが含まれます。パイプラインには通常、データ取り込み、インデックス作成、検索、および生成の段階が含まれており、LLMがトレーニングデータを超えて最新かつ具体的な情報にアクセスして利用できるようになります。
RAGパイプラインの主な利点は、LLMの出力の精度、信頼性、およびコンテキスト性を向上させることです。LLMの応答を事実データに基づいて行うことで、RAGパイプラインは、誤った情報や幻覚情報を生成する可能性を減らし、高い精度と信頼性が求められるアプリケーションに適したものにすることができます。
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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🧠 理解度チェック
🧒 5歳でもわかるように説明
Imagine a super-smart robot (the [LLM](/ja/terms/llm)) who knows a lot from books it read. But what if you ask it about today's news? A [RAG](/ja/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](/ja/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.