# Local Graph Search

Knowledge retrieval involves querying a knowledge graph to extract the most
relevant information for your use case. In the context of GraphRAG, this step
ensures that the right data is passed to the LLM for processing, enabling
accurate, context-rich responses.

Choosing the best retrieval strategy depends on your data, the type of insights
you’re seeking, and the complexity of your queries. Memgraph provides a suite of
powerful tools and techniques to optimize retrieval for your knowledge graph.

![medical-records-example](https://memgraph.com/docs/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/medical-records-example.jpg) 

## Retrieval types

Finding relevant data in the vast amount of nodes and relationships in your
graph is not easy, and that's why Memgraph has a couple of strategies to
facilitate that. 

While searching for a certain information it is important to know where to
start, how to find the node in the graph which will best describe what you're
looking for. The search for that starting point or a pivot node can also be
called a [pivot search](#pivot-search). Once you get to the pivot node, you have
located yourself in the part of the knowledge graph that holds relevant
information. There are various strategies to find a pivot node, some of which we
are listing below. 

Pivot search can be just a start of your journey to provide valuable context to
the LLM to ground it and in that way avoid hallucinations. Additionally, to
expand the search, you can perform [relevance expansion](#relevance-expansion)
which includes advanced strategies to obtain relevant information. 

### Pivot search

Pivot search is the starting point of any retrieval process, identifying the
core entities and relationships relevant to your query.

#### Vector search

This retrieval method uses vector embeddings to find entities based on semantic
similarity rather than exact matches. It’s ideal for scenarios where meaning,
context, or relatedness is more important than specific terms (e.g., finding
similar topics or concepts).

![vector-search](https://memgraph.com/docs/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/vector-search.jpg) 

Perform vector search on your knowledge graph to extract semantically similar
nodes, setting the foundation for more advanced retrieval.

Query example: 

> Which European city is known for its romantic atmosphere and iconic landmarks
like the Eiffel Tower?

Vector search matches nodes semantically associated with “romantic atmosphere”
and “iconic landmarks”, leading to nodes like *Paris*.
##### Read more
- [Vector search](https://memgraph.com/docs/querying/vector-search) docs
- [Simplify Data Retrieval with Memgraph’s Vector Search](https://memgraph.com/blog/simplify-data-retrieval-memgraph-vector-search)
- [Building a Movie Similarity Search Engine with Vector Search in Memgraph](https://memgraph.com/blog/build-movie-similarity-search-vector-search-memgraph)

#### Text search

Currently, in Memgraph, text search is a retrieval technique that finds nodes
with their properties based on exact text matches. It’s best used for queries
involving specific terms, phrases, or entity names (e.g., “Find all nodes
containing the keyword ‘climate change’”).

![text-search](https://memgraph.com/docs/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/text-search.jpg) 

Use text search as a direct approach to narrow down specific nodes before expanding context.

Query example: 

> What is the capital of France? 

Text search can find all nodes that have France as their property value.

Read more in [text search](https://memgraph.com/docs/querying/text-search) docs.

#### Geospatial search

Geospatial search enables you to integrate and retrieve location-based insights,
making it a valuable tool for applications involving geographic data. This
functionality relies on the ability to store [`Point`
types](https://memgraph.com/docs/fundamentals/data-types#point) representing locations with latitude and
longitude and on leveraging [point indexes](https://memgraph.com/docs/fundamentals/indexes#point-index) to ensure
optimized query performance, allowing for quick proximity and spatial queries.

![geospatial](https://memgraph.com/docs/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/geospatial.jpg) 

Query example:

> Find cities within 50 km of Paris.

Geospatial search retrieves cities based purely on proximity to Paris, in this case, Versailles.

#### Hybrid knowledge retrieval

Combines multiple retrieval methods - such as text search, vector search, and
geospatial search - for enhanced precision and versatility. It’s perfect for
multi-dimensional queries involving semantic meaning, specific terms, and
spatial constraints.

Hybrid retrieval is particularly valuable in complex queries where results must
satisfy multiple dimensions simultaneously. For example:

> Find all nearby Italian restaurants with excellent reviews and vegetarian
options.

### Relevance expansion

Relevance expansion techniques allow you to extract deeper insights from your
graph by analyzing related entities and connections beyond the initial query.
This step is optional but can significantly enhance the quality of responses in
a GraphRAG system.

#### Why you should broaden the search

1. LLMs can deliver more accurate responses with expanded, meaningful data.
2. Customize the retrieval process to align with the specific needs of your application.
3. Extract more value from the semantic connections in your graph.

#### Deep path traversals

Deep path traversal algorithms are essential for exploring and analyzing complex
relationships within a graph database. In Memgraph, these algorithms are built
into the core system, enabling efficient and effective graph queries without
additional overhead.

Deep path traversals enable reasoning across your graph, providing insights that
go beyond surface-level connections. This expanded information is sent to the
LLM as part of the input prompt to provide better context.

![one-hop](https://memgraph.com/docs/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/1-hop.jpg) 

Read more:

- [Deep path traversal algorithms
  docs](https://memgraph.com/docs/advanced-algorithms/deep-path-traversal)
- [Memgraph’s Deep Path Traversal
  Capabilities](https://memgraph.com/blog/memgraph-deep-path-traversal-capabilities)
- [Memgraph Cypher Implementation: Flexibility and Advanced
  Traversals](https://memgraph.com/blog/cypher-implementation-flexibility-and-advanced-traversals)

#### Community detection 

With community detection algorithms (e.g., Leiden or Louvain), you can group
similar nodes that share a strong relationship into the same community and
summarize all the important information about such community into a community
node. This strategy allows you to get answers to global questions quickly,
without the need to search across the whole graph and in that way provide
meaningful insights for LLM. 

![community-detection](https://memgraph.com/docs/pages/ai-ecosystem/graph-rag/atomic-pipelines/local-graph-search/community-detection.jpg) 

#### Centrality measures

By incorporating [PageRank](https://memgraph.com/docs/advanced-algorithms/available-algorithms/pagerank)
or [betweenness
centrality](https://memgraph.com/docs/advanced-algorithms/available-algorithms/betweenness_centrality) as
part of your retrieval strategy, you add a robust methods for emphasizing
central or influential nodes, which can improve the relevance and depth of query
results.

PageRank identifies influential nodes in the graph, helping prioritize highly
connected or globally significant information.

Betweenness centrality highlights nodes that act as bridges between communities
or topics, ensuring the retrieval covers diverse or connecting concepts.

For example, if there are multiple nodes as potential answers, you can rank the
nodes by their PageRank scores to get the most important ones.

## How to choose the best retrieval strategy

When choosing the retrieval strategy, it is important to know your data and to
have a scope of the queries you want the LLM to be able to answer. Categorizing
queries effectively in your GraphRAG system can help optimize your two-step
process: pivot search and relevance expansion. Here's how you can systematically
approach query categorization:

1. **Fact-based queries** 
   - Pivot search: Vector or text search  
   - Relevance expansion: Minimal or none
   - Example question: "Where is Memgraph founded?"

2. **Relational queries**
   - Pivot search: Vector or text search
   - Relevance expansion: 1-hop or 2-hop traversal 
   - Example question: "Who collaborates on Memgraph?"

3. **Contextual queries**
   - Pivot search: Vector or text search 
   - Relevance expansion: Community detection
   - Example question: "Can you give me more details about Memgraph?"

4. **Path-Based queries**
   - Pivot search: Vector or text search
   - Relevance expansion: Deep path traversal  
   - Example question: "How is Dominik connected to Memgraph?"

5. **Geospatial queries**  
   - Pivot search: Geospatial search  
   - Relevance expansion: 1-hop traversal  
   - Example question: "How many employees work within a 50-mile radius?"

> **Note**
>
> As previously mentioned, there is no ideal recipe for every use case. Still, the
> above classification might help you better understand how to start building or
> how to optimize your GraphRAG. Get inspired and see [examples and
> demos](https://memgraph.com/docs/ai-ecosystem/graph-rag/examples-and-demos) on how we and others built a
> GraphRAG system.