Load your data into a Tree-Sitter parser

#ai #mrmemory

Building Persistent Memory with Knowledge Graphs and Tree-Sitter

I still remember the day I had to restart my language model from scratch. All that context, all those insights... gone. But it's a problem every AI developer faces when building stateless large language models (LLMs). But what if you could build an AI agent that remembers complex information and reasons over vast networks of memories? Enter knowledge graphs and graph-based retrieval.

Step 1: Importing Data

To build a knowledge graph, you'll need to import your data into a graph database. I use Tree-Sitter to parse and analyze my codebase. It's a lightweight library that extracts entities and links from the parsed tree.

import tree_sitter

# Load your data into a Tree-Sitter parser
parser = tree_sitter.Parser()
source_code = """your code here"""
tree = parser.parse(source_code)

# Extract entities and links from the parsed tree
entities = []
links = []

for node in tree.root_child():
if node.type == "entity":
entities.append(node.text)
elif node.type == "link":
links.append((node.from_node().text, node.to_node().text))

Step 2: Storing Data in a Graph Database

Once you've extracted your data, you'll need to store it in a graph database. I use GraphRAG to build and manage my knowledge graph.

from rag import Rag

# Create a new GraphRAG instance
rag = Rag()

# Add entities and links to the graph
for entity in entities:
rag.add_entity(entity)

for link in links:
rag.add_link(link[0], link[1])

Step 3: Grouping Related Info into Communities

Knowledge graphs allow you to group related information together. This enables your AI agent to reason over vast networks of memories and make more accurate predictions.

# Create communities from related entities
communities = rag.create_communities(entities)

# Store the communities in your database
db = RagDB()
for community in communities:
db.add_community(community)

Step 4: Implementing Graph-Based Retrieval

Now that you've built your knowledge graph, it's time to implement graph-based retrieval; i use GraphRAG to query my graph and retrieve relevant information.

# Create a new GraphRAG instance
rag = Rag()

# Query the graph for related entities
results = rag.query(entities, links)

# Return the results to your AI agent
return results

Comparison with Alternatives

While GraphRAG is an excellent tool for building persistent memory, there are other alternatives available. For example, Mem0 uses a different approach to knowledge graph construction.

Tool	Description
GraphRAG	Knowledge graph-based retrieval
Mem0	Alternative knowledge graph construction

Conclusion

Building persistent memory for your AI agents is a complex task, but with the right tools and techniques, it's now possible. By using knowledge graphs and graph-based retrieval techniques, you can create AI agents that are more accurate, efficient, and effective.

Try MrMemory today and start building your own persistent memory solution!

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