RAG Pipeline
Definition
A RAG pipeline orchestrates two main workflows: the offline indexing pipeline and the online query pipeline. The indexing pipeline runs during knowledge base setup and updates: documents are loaded from sources, chunked into segments, embedded into vectors, and stored in a vector database with metadata. The query pipeline runs at inference time: a user query is embedded, the vector database is searched for relevant chunks, retrieved chunks are assembled into a context prompt, and a language model generates a grounded answer. These two pipelines must be designed together—decisions in indexing (chunk size, metadata schema) directly constrain what is possible in querying.
Why It Matters
The RAG pipeline is the production unit of deployment for AI knowledge systems. Individual components like vector databases, embedding models, and LLMs each have their own documentation and APIs, but it is the pipeline that determines how well they work together. For 99helpers customers, the RAG pipeline is the architecture that connects their knowledge base content to their chatbot's answers. Well-designed pipelines handle failures gracefully (retrieval returning no results, the LLM refusing to answer), scale to high query volumes, and include observability instrumentation to track performance metrics at each stage.
How It Works
Modern RAG pipeline frameworks like LlamaIndex and LangChain provide high-level abstractions for each stage. A typical LlamaIndex pipeline: (1) SimpleDirectoryReader loads documents; (2) SentenceSplitter chunks them; (3) OpenAIEmbedding embeds chunks; (4) VectorStoreIndex stores vectors; (5) VectorIndexRetriever retrieves top-K chunks; (6) ResponseSynthesizer passes context to an LLM; (7) QueryEngine ties retrieval and synthesis together. Each component is swappable, allowing teams to upgrade individual stages (e.g., switch embedding models) without rewriting the pipeline. Production pipelines add monitoring, retry logic, caching, and evaluation hooks.
RAG Pipeline — Indexing and Query Phases
Documents
PDFs, URLs, text
Loader
Parse & extract
Chunker
Split by size
Embedder
Model → vectors
Vector Store
pgvector / Pinecone
User Query
Natural language
Embed Query
Same model
Vector Search
ANN similarity
Top-k Chunks
Retrieved docs
Prompt Builder
Query + context
LLM
Generate
Answer
Grounded output
Real-World Example
A 99helpers customer builds their AI support chatbot as a RAG pipeline: (1) data connectors pull from Zendesk and Confluence; (2) RecursiveCharacterTextSplitter chunks content at 512 tokens with 50-token overlap; (3) text-embedding-3-small creates vectors; (4) Pinecone stores them with metadata (source, category, last-updated); (5) at query time, the user question is embedded and top-5 chunks retrieved with metadata filtering by category; (6) GPT-4o synthesizes the answer citing source documents. Adding a reranker between steps 5 and 6 improves precision, demonstrating the pipeline's modularity.
Common Mistakes
- ✕Treating the RAG pipeline as a one-time setup rather than a continuously maintained system requiring monitoring and updates.
- ✕Coupling all pipeline stages tightly, making it impossible to upgrade one component (e.g., the embedding model) without rebuilding everything.
- ✕Skipping evaluation pipelines—without measuring retrieval and generation quality, improvements are guesswork.
Related Terms
Indexing Pipeline
An indexing pipeline is the offline data processing workflow that transforms raw documents into searchable vector embeddings, running during knowledge base setup and when content is updated.
Retrieval Pipeline
A retrieval pipeline is the online query-time workflow that transforms a user question into a ranked set of relevant document chunks, serving as the information retrieval stage of a RAG system.
Generation Pipeline
A generation pipeline is the LLM-side workflow in RAG that assembles retrieved context into a prompt, calls the language model, and post-processes the output into a final user-facing answer.
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language model responses by first retrieving relevant documents from an external knowledge base and then using that retrieved content as context when generating an answer.
Vector Database
A vector database is a purpose-built data store optimized for storing, indexing, and querying high-dimensional numerical vectors (embeddings), enabling fast similarity search across large collections of embedded documents.
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