RAG Nedir
Retrieval-Augmented Generation (RAG), Harici bilgi kaynaklarını entegre ederek Büyük Dil Modellerinin (LLM'ler) doğruluğunu ve güvenilirliğini artıran bir yapay zeka çerçevesidir.
RAG, bilgi alma ve metin oluşturmanın güçlü yönlerini birleştirir. İlk olarak, bir kullanıcının sorgusuna göre bir bilgi tabanından veya harici veri kaynaklarından ilgili bilgileri alır. Bu alınan bilgiler daha sonra LLM'nin istemini artırarak, ona daha bilgilendirici ve doğru bir yanıt oluşturması için bağlam ve gerçekler sağlar. Bu yaklaşım, özellikle uzmanlaşmış veya güncel bilgiler için, LLM'nin dahili parametrelerine olan bağımlılığı azaltır, yanlış veya hayal ürünü bilgiler üretme riskini azaltır. Süreç tipik olarak bir bilgi tabanını indekslemeyi, bir kullanıcının sorusuyla sorgulamayı, ilgili belgeleri almayı ve ardından bu belgeleri orijinal soruyla birlikte bir yanıt oluşturmak için LLM'ye beslemeyi içerir.
graph LR
Center["RAG Nedir"]:::main
Pre_logic["logic"]:::pre --> Center
click Pre_logic "/terms/logic"
Rel_retrieval_augmented_generation["retrieval-augmented-generation"]:::related -.-> Center
click Rel_retrieval_augmented_generation "/terms/retrieval-augmented-generation"
Rel_rag_pipeline["rag-pipeline"]:::related -.-> Center
click Rel_rag_pipeline "/terms/rag-pipeline"
Rel_reinforcement_learning["reinforcement-learning"]:::related -.-> Center
click Rel_reinforcement_learning "/terms/reinforcement-learning"
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🧠 Bilgi testi
🧒 5 yaşındaki gibi açıkla
Imagine you're asking a super-smart robot a question, but it only knows things it learned a long time ago. RAG is like giving the robot a quick peek at a library book (the knowledge base) before it answers, so it can tell you the latest and most correct information!
🤓 Expert Deep Dive
RAG architectures fundamentally decouple knowledge acquisition from model inference by augmenting a generative Large Language Model (LLM) with an external, dynamically queried knowledge source. The core components typically include:
- Document Indexing: A corpus of documents (e.g., articles, PDFs, web pages) is processed and embedded into a vector space using a pre-trained embedding model (e.g., Sentence-BERT, OpenAI's
text-embedding-ada-002). These embeddings are stored in a vector [database](/tr/terms/vector-database) (e.g., Pinecone, Weaviate, FAISS) for efficient similarity search. - Retrieval: Upon receiving a user query, the query is also embedded into the same vector space. A similarity search (e.g., Approximate Nearest Neighbor - ANN) is performed against the vector database to identify the
kmost relevant document chunks (or passages) based on cosine similarity or dot product. - Augmentation & Generation: The original user query and the retrieved document chunks are concatenated into a single prompt. This augmented prompt is then fed into the LLM. The LLM uses this context to generate a response that is grounded in the retrieved information.
Mathematically, the retrieval step can be viewed as finding document embeddings $d_i$ such that their similarity to the query embedding $q$ is maximized:
$i^* = \arg\max_i \text{sim}(q, d_i)$
where $\text{sim}(u, v)$ is a similarity function like cosine similarity: $\frac{u \cdot v}{\|u\| \cdot \|v\|}$.
This approach mitigates the 'knowledge cut-off' problem inherent in LLMs and significantly reduces hallucination by providing factual grounding. Advanced RAG techniques explore re-ranking retrieved documents, query expansion, and fine-tuning the retriever and generator components jointly (e.g., REALM, DPR).