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These are some LLM-assisted exploration notes from the paper HippoRAG: Neurobiologically InspiredLong-Term Memory for Large Language Models
The Multi-Hop Problem in RAGs
The idea of "hops" are important in RAG.
Consider this example.
Question 1:
"Who wrote Hamlet?"
→ The answer (Shakespeare) is in one document.*
Question 2:
"Which university didthe president of OpenAI attend?"
- Step 1: Retrieve information on who the president of OpenAI is (e.g., Greg Brockman).
- Step 2: Retrieve information on which university Greg Brockman attended (e.g., MIT).
How HippoRAG Achieves a Single-Step Multi-Hop Retrieval
Traditional RAG solutions, such as IRCoT (Iterative Retrieval Chain-of-Thought) depend on iterative retrieval - kind of like looking up docs in a loop.
With HippoRAG, two mechanisms are combined to compress these multiple hops into one:
- Building a knowledge graph (KG) where concepts and relationships are indexed.
- Using Personalized PageRank (PPR) to retrieve relevant paths across multiple documents in one query.
The benefits of the above combination makes HippoRAG:
- Faster (avoids iterative retrieval)
- More accurate (find connections that isolated retrieval steps miss)
- Cheaper (reduce API calls and computation)
How HippoRAG Builds Its Knowledge Graph (KG)
HippoRAG constructs a schemaless knowledge graph from a text corpus by leveraging large language models (LLMs) for Open Information Extraction (OpenIE) and retrieval encoders for linking entities. This process enables multi-hop reasoning in a single retrieval step.
1. Offline Indexing (Building the Graph)
This step is analogous to how the human neocortex encodes memory.
✅ Extract Knowledge Graph Triples
- Uses an instruction-tuned LLM (e.g., GPT-3.5) to extract subject-predicate-object triples from text.
- Example:
Input Passage: "Steve Jobs co-founded Apple in 1976."
Extracted Triples:
(Steve Jobs, co-founded, Apple)(Apple, founded_in, 1976)
✅ Create Graph Nodes & Edges
- Nodes = extracted entities (noun phrases) (e.g., Steve Jobs, Apple).
- Edges = relationships between entities (e.g., co-founded).
✅ Synonymy Linking (Parahippocampal Processing)
- Uses retrieval encoders (e.g., Contriever, ColBERTv2) to identify similar entities (e.g., "USA" = "United States").
- Creates extra edges to connect synonyms, improving retrieval robustness.
✅ Store the Graph
- The final knowledge graph consists of:
- Nodes (Entities)
- Edges (Relations & Synonyms)
- Passage Mapping (Each node is linked to the original text passage for retrieval)
2. Online Retrieval (Querying the Graph)
This step mimics the hippocampus retrieving memories.
✅ Extract Query Named Entities
- The LLM identifies key entities in the query.
- Example: "Which Stanford professor works on Alzheimer's?"
- Query Entities:
{Stanford, Alzheimer’s}
- Query Entities:
✅ Find Related Nodes in the Knowledge Graph
- Uses retrieval encoders to find graph nodes most similar to the query entities.
- Example: The query
{Stanford, Alzheimer’s}matches the node{Thomas Südhof}in the KG.
✅ Personalized PageRank (PPR) for Multi-Hop Retrieval
- Runs Personalized PageRank (PPR) on the graph using query nodes as starting points.
- Spreads probability over connected nodes, enabling multi-hop reasoning.
- Example:
-
{Stanford}→{Thomas Südhof} -
{Alzheimer’s}→{Thomas Südhof} - Final Retrieval: Thomas Südhof is a Stanford professor working on Alzheimer’s.
-
✅ Retrieve & Rank Passages
- The most relevant passages are selected based on PPR scores.
How HippoRAG Uses PageRank to Order Results
A: Convert Text to a Graph
- Extract entities (nodes) and relationships (edges).
- Example:
(Stanford, employs, Thomas Südhof)
(Thomas Südhof, researches, Alzheimer’s)
B: Find Relevant Nodes
- If the query is: "Which Stanford professor studies Alzheimer's?"
- The query matches
{Stanford, Alzheimer’s}in the graph.
C: **Run Personalized PageRank (PPR- Give high starting scores to query nodes (Stanford and Alzheimer’s).
- Spread scores to connected nodes (e.g.,
Thomas Südhofgets a high score).
D: Rank Passages by PageRank Score
- Passages mentioning
Thomas Südhofget top rank. Less relevant passages rank lower.
Finds indirect connections (multi-hop retrieval).
Ranks based on real-world relevance rather than keyword matching.
Fast, since it's done in one step.
*AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.*
Any feedback or contributors are welcome! It's online, source-available, and ready for anyone to use.
⭐ Star it on GitHub:
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git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
git-lrc
Free, Unlimited AI Code Reviews That Run on Commit
AI agents write code fast. They also silently remove logic, change behavior, and introduce bugs -- without telling you. You often find out in production.
git-lrc fixes this. It hooks into git commit and reviews every diff before it lands. 60-second setup. Completely free.
See It In Action
See git-lrc catch serious security issues such as leaked credentials, expensive cloud operations, and sensitive material in log statements
git-lrc-intro-60s.mp4
Why
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- 🔗 Why git? Git is universal. Every editor, every IDE, every AI…
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