MARCH 2025
YouTube Content Search
Multi-mode GraphRAG platform that extracts entities and relationships from YouTube transcripts into a Neo4j knowledge graph for multi-hop reasoning — the research that became COELHO Nexus.
Executive summary
YouTube Content Search is the research project that became COELHO Nexus. It builds a Knowledge Graph from YouTube video transcripts using AI Agents (LangChain + LangGraph), stores entities and relationships in Neo4j, and exposes four distinct retrieval modes — Search, Video, Channel, Playlist — enabling multi-hop reasoning that vanilla vector RAG can’t do.
LLM inference runs across multiple providers (Groq, OpenAI, SambaNova, Scaleway), giving the system cost-aware fallback and avoiding vendor lock-in. The interface is Streamlit; the agent orchestration is LangGraph.
See it deployed
The platform requires API keys to multiple LLM providers and a running Neo4j instance, so you can’t trivially spin it up — these 20 slides are the verifiable record of the system running with real YouTube content, KG entity extraction, multi-hop queries answered, and the four search modes in operation. Navigate with arrows or open fullscreen for the full read.
Four retrieval modes
| Mode | What it does | When it matters |
|---|---|---|
| Search | AI Agents autonomously locate videos matching context + filters supplied by user | Open-ended discovery — “find videos discussing X” |
| Video | Extract structured information from a single specified video | Deep-dive into one source |
| Channel | AI Agents traverse a YouTube channel’s full transcript corpus and reason across it | ”What does this creator believe about Y?” |
| Playlist | Same pattern as Channel but scoped to a curated playlist | Curated topic exploration |
Each mode exposes a follow-up question chat: an agent queries the Knowledge Graph and answers with multi-hop reasoning grounded in the graph traversal — not chunk-similarity lottery.
Why Knowledge Graphs beat vanilla vector RAG here
Vector RAG retrieves by semantic similarity (cosine over chunk embeddings). That works for “summarize this video,” but fails on questions like:
- “Which AI researchers discuss safety in interviews with Lex Fridman?” → requires filtering by speaker AND topic AND venue
- “What does Karpathy say about LLM evaluation across his last 10 talks?” → requires multi-hop reasoning over (speaker → talks → claims → topics)
- “Who appears in two or more videos with topic Z?” → relational query, not similarity query
Knowledge Graphs encode these relationships explicitly: X works_for Y,
Y located_in Z, A discussed B in C. Cypher queries walk the graph; the LLM grounds
its answer in the relationship structure. The tradeoff is extraction quality (entity /
relation extraction is harder than chunk embedding) — but the win on multi-hop
correctness is substantial.
This proof-of-concept became the architectural backbone of COELHO Nexus, where the same GraphRAG pattern scales to a production agentic RAG platform with adaptive retrieval routing.
Stack
- LangChain — agent and tool composition primitives
- LangGraph — multi-agent orchestration with explicit state graphs and retry edges
- Neo4j — Knowledge Graph storage + Cypher querying for multi-hop traversal
- LLM providers — Groq (low-latency Llama 3.1, Gemma 2), OpenAI (fallback), SambaNova, Scaleway (cost diversity)
- Streamlit — interactive interface
- Python — implementation
What this project proves
- GraphRAG is more than a buzzword — explicit entity-relationship modeling beats vector-only retrieval on multi-hop questions
- Multi-provider LLM routing was shipping-ready in early 2025 — switching across Groq / OpenAI / SambaNova / Scaleway for production cost control predates the rotator I now run in COELHO Nexus
- Foundation for COELHO Nexus — every retrieval insight here was absorbed into the Nexus adaptive 3-mode agentic RAG architecture