This project implements an Agentic RAG Pipeline using Cognee, designed to overcome the critical limitations of standard Retrieval-Augmented Generation (RAG) systems. By leveraging graph-based memory, we provide AI agents with the ability to "think" structurally and perform multi-hop reasoning.

Standard RAG systems are excellent at finding facts but struggle with understanding. They operate by:

1. Chunking text into small pieces.
2. Embedding these pieces into vectors.
3. Retrieving based on semantic similarity.

The Limitation: This approach lacks structural memory.

• ❌ No Relationship Awareness: If you ask, "Who is the uncle of the person who carried the Ring?", a standard RAG might find the paragraph about the Ring bearer but fail to connect it to the uncle.
• ❌ Isolated Facts: Information is retrieved in isolation, making complex, multi-step reasoning (multi-hop) nearly impossible.
• ❌ No Persistence: Agents reset after every session, failing to "learn" from past interactions.

Cognee transforms RAG by introducing a Knowledge Graph alongside vector search. It doesn't just store text; it maps relationships.

• Graph + Vector Hybrid: Combines the semantic search of vectors with the structural precision of knowledge graphs.
• Multi-Hop Reasoning: Enables the agent to traverse connections (e.g., Subject A → Related to → Subject B) to answer complex questions.
• Persistent Memory: Stores entities and relationships that survive across sessions, allowing the agent to "remember" user preferences and past facts.

Instead of the traditional "Chunk → Embed → Store" pipeline, Cognee uses:

1. Extract: Ingest documents (PDFs, text, URLs).
2. Cognify: Use an LLM to identify entities and relationships, building the knowledge graph.
3. Load/Search: Retrieve answers by traversing the graph and checking semantic similarity.

Powered by Cognee for structural intelligence.