The Evolution of Retrieval
Simple keyword search was just the beginning. Today's enterprise AI demands a layered retrieval strategy — combining semantic vectors, knowledge graphs, agentic orchestration, and persistent memory into a single coherent system.
Agentic RAG
Retrieval is no longer a single lookup. Autonomous agents decide when to retrieve, which sources to query, how to refine results, and whether to iterate. Multi-hop reasoning across distributed knowledge bases, with self-correction loops that improve answer quality.
Graph RAG
Knowledge graphs enhance semantic retrieval with structured relationships. Entities, concepts, and their connections form a semantic web that captures context vectors alone miss. Traverse relationship edges to discover insights no flat index can surface.
LLM Memory (Mem0)
Persistent, evolving memory that learns from every interaction. Short-term session context, long-term user preferences, and episodic memory of past queries. Your AI remembers who you are, what you've asked, and how you prefer answers — across sessions and conversations.
Why Simple Retrieval Isn't Enough
Vector databases like Qdrant, Pinecone, and Weaviate revolutionized semantic search. But the RAG landscape has evolved rapidly:
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Self-RAG
The model reflects on its own retrieved context, checking for relevance, hallucination, and completeness before generating. If retrieved passages are insufficient, it triggers a new retrieval cycle.
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Corrective RAG (CRAG)
When retrieval quality is low, CRAG doesn't give up — it reformulates queries, searches alternative sources, or decomposes the question into sub-queries. Built-in quality gates reject bad retrievals.
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RAPTOR
Recursive abstractive processing summarizes document clusters into hierarchical summaries. Retrieval happens at multiple abstraction levels — from raw chunks to high-level topic summaries.
Modern RAG Stack
Layered retrieval that adapts to your data, your queries, and your domain.
Layer 1: Hybrid Search
Dense vector embeddings + sparse keyword search (BM25, SPLADE) combined through reciprocal rank fusion. Semantic meaning meets lexical precision. No query falls through the cracks.
Layer 2: Graph-Enhanced Retrieval
Entity extraction builds a dynamic knowledge graph from your documents. Queries traverse relationships to find information that no vector similarity can surface — turning disconnected facts into connected knowledge.
Layer 3: Re-Ranking & Fusion
Cross-encoder re-rankers score initial results for precision. Multi-source fusion combines results from vector, keyword, graph, and SQL queries into a single ranked list before passing to the LLM.
Layer 4: Adaptive Chunking
Semantic chunking respects document boundaries — paragraphs, sections, tables. Small-to-big retrieval retrieves fine-grained chunks but passes broader context to the LLM. Contextual retrieval enriches each chunk with its surrounding document context.
Layer 5: LLM Memory
Persistent memory across sessions. User-level memory stores facts, preferences, and history. Session memory maintains conversation state. Episodic memory recalls past interactions. Your AI builds a relationship with each user over time.
Layer 6: Agentic Orchestration
Autonomous agents plan retrieval strategies, select tools, evaluate results, and iterate. Multi-hop reasoning decomposes complex questions into sub-queries, retrieves for each, and synthesizes a coherent answer.
Techniques That Power Modern RAG
The model generates reflection tokens alongside answers, checking retrieved passages for relevance and supporting its own reasoning. Low-confidence retrievals trigger re-search.
Quality gates evaluate retrieved documents before generation. If relevance or quality thresholds aren't met, the system reformulates queries or searches alternative knowledge sources.
A smaller, faster draft model generates preliminary answers from retrieved context, while a larger verifier model validates correctness — dramatically reducing latency while maintaining quality.
Recursive summarization builds a tree of abstractions over your document corpus. Retrieval navigates this hierarchy, starting broad and drilling down — matching queries at the right abstraction level.
Images, charts, audio transcripts, and video frames are embedded alongside text. Queries retrieve across all modalities — find the chart that shows Q3 revenue, or the recording where a decision was made.
Fully autonomous retrieval agents plan, execute, and validate multi-step research. They decide which tools to call, when to stop, and how to synthesize contradictory information from multiple sources.
Vector Databases: Still the Foundation
Semantic search with Qdrant remains the core retrieval engine — sub-200ms across millions of vectors. But modern RAG layers on top: graph relationships, persistent memory, agentic orchestration, and self-reflection. General Bots integrates them all into a single, self-hosted platform. No SaaS markups, no per-seat fees, no data leaving your network.
Ready for Modern RAG?
From basic retrieval to agentic orchestration with persistent memory — deploy the full modern RAG stack on your own infrastructure.