rag-pipeline
Um pipeline de Geração Aumentada por Recuperação (RAG) é uma estrutura que combina a recuperação de informações com modelos de linguagem grandes (LLMs) para gerar respostas mais precisas e contextualmente relevantes.
Pipelines RAG aprimoram os LLMs integrando fontes de conhecimento externas. Este processo envolve a recuperação de informações relevantes de uma base de conhecimento ou armazenamento de dados com base na consulta de um usuário, e então o uso dessas informações recuperadas para aumentar o processo de geração do LLM. O pipeline normalmente inclui estágios para ingestão de dados, indexação, recuperação e geração, permitindo que os LLMs acessem e utilizem informações atualizadas e específicas além de seus dados de treinamento.
O principal benefício de um pipeline RAG é melhorar a precisão, confiabilidade e contextualidade das saídas dos LLMs. Ao basear as respostas do LLM em dados factuais, os pipelines RAG podem reduzir a probabilidade de gerar informações incorretas ou alucinadas, tornando-os adequados para aplicações que exigem alta precisão e confiabilidade.
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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🧠 Teste de conhecimento
🧒 Explique como se eu tivesse 5 anos
Imagine a super-smart robot (the [LLM](/pt/terms/llm)) who knows a lot from books it read. But what if you ask it about today's news? A [RAG](/pt/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](/pt/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.