Category:
Category:
Retrieval Pipelines
Category:
Retrieval & RAG
Definition
End-to-end workflow that transforms queries into embeddings, retrieves documents, reranks them, and injects them into the LLM.
Explanation
A retrieval pipeline consists of multiple components: embedding models, vector search, keyword search, reranking, filtering, deduplication, and context assembly. Retrieval pipelines are critical for reducing hallucinations and improving grounding. Robust retrieval is the backbone of high-performing AI assistants and agents.
Technical Architecture
Query → Embed → Vector Search + Keyword Search → Reranking → Filtering → Assembly → LLM
Core Component
Retriever, reranker, metadata filters, embedding models, vector index
Use Cases
Enterprise knowledge assistants, customer support, legal QA, research agents
Pitfalls
Latency bottlenecks; irrelevant retrieval results; mismatch between queries and chunks
LLM Keywords
Retrieval Pipeline, RAG Pipeline Architecture
Related Concepts
Related Frameworks
• RAG
• Hybrid Retrieval
• Reranking
• Retrieval Optimization Framework