What is a Retrieval Pipeline? - Definition & Meaning
Learn what a retrieval pipeline is, how documents are retrieved for RAG and AI, and which steps to optimize for better search results.
Definition
A retrieval pipeline is the end-to-end flow of fetching relevant documents or chunks from a knowledge base based on a search query — from chunking and embedding to ranking and selection for RAG or search applications.
Technical explanation
Steps: 1) Document chunking (splitting text into segments), 2) Embedding (converting chunks to vectors), 3) Indexing (storing in vector database), 4) Query embedding (converting question to vector), 5) Retrieval (fetching similar chunks via similarity search), 6) Optional reranking and 7) Filtering. The pipeline can be semantic (vector), keyword (BM25), or hybrid. Latency, recall, and precision are critical metrics.
How AVARC Solutions applies this
AVARC Solutions builds retrieval pipelines for RAG chatbots, enterprise search, and knowledge bases. We optimize chunking, embedding choice, and reranking for each use case. We use LangChain, LlamaIndex, or custom pipelines based on requirements.
Practical examples
- A RAG chatbot fetching the 5 most relevant document chunks via a retrieval pipeline before the LLM answers.
- An enterprise search engine combining hybrid search with reranking for accurate results.
- A support knowledge base where queries are processed through a retrieval pipeline for context-aware answers.
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