graphiti/examples/quickstart

Graphiti Quickstart Example

This example demonstrates the basic functionality of Graphiti, including:

1. Connecting to a Neo4j or FalkorDB database
2. Initializing Graphiti indices and constraints
3. Adding episodes to the graph
4. Searching the graph with semantic and keyword matching
5. Exploring graph-based search with reranking using the top search result's source node UUID
6. Performing node search using predefined search recipes

Prerequisites

• Python 3.9+
• OpenAI API key (set as OPENAI_API_KEY environment variable)
• For Neo4j:
  • Neo4j Desktop installed and running
  • A local DBMS created and started in Neo4j Desktop
• For FalkorDB:
  • FalkorDB server running (see FalkorDB documentation for setup)

Setup Instructions

1. Install the required dependencies:

pip install graphiti-core

2. Set up environment variables:

# Required for LLM and embedding
export OPENAI_API_KEY=your_openai_api_key

# Optional Neo4j connection parameters (defaults shown)
export NEO4J_URI=bolt://localhost:7687
export NEO4J_USER=neo4j
export NEO4J_PASSWORD=password

# Optional FalkorDB connection parameters (defaults shown)
export FALKORDB_URI=falkor://localhost:6379

# To use a different database, modify the driver constructor in the script

3. Run the example:

python quickstart_neo4j.py

# For FalkorDB
python quickstart_falkordb.py

What This Example Demonstrates

• Graph Initialization: Setting up the Graphiti indices and constraints in Neo4j or FalkorDB
• Adding Episodes: Adding text content that will be analyzed and converted into knowledge graph nodes and edges
• Edge Search Functionality: Performing hybrid searches that combine semantic similarity and BM25 retrieval to find relationships (edges)
• Graph-Aware Search: Using the source node UUID from the top search result to rerank additional search results based on graph distance
• Node Search Using Recipes: Using predefined search configurations like NODE_HYBRID_SEARCH_RRF to directly search for nodes rather than edges
• Result Processing: Understanding the structure of search results including facts, nodes, and temporal metadata

Next Steps

After running this example, you can:

1. Modify the episode content to add your own information
2. Try different search queries to explore the knowledge extraction
3. Experiment with different center nodes for graph-distance-based reranking
4. Try other predefined search recipes from graphiti_core.search.search_config_recipes
5. Explore the more advanced examples in the other directories

Troubleshooting

"Graph not found: default_db" Error

If you encounter the error Neo.ClientError.Database.DatabaseNotFound: Graph not found: default_db, this occurs when the driver is trying to connect to a database that doesn't exist.

Solution: The Neo4j driver defaults to using neo4j as the database name. If you need to use a different database, modify the driver constructor in the script:

# In quickstart_neo4j.py, change:
driver = Neo4jDriver(uri=neo4j_uri, user=neo4j_user, password=neo4j_password)

# To specify a different database:
driver = Neo4jDriver(uri=neo4j_uri, user=neo4j_user, password=neo4j_password, database="your_db_name")

Understanding the Output

Edge Search Results

The edge search results include EntityEdge objects with:

• UUID: Unique identifier for the edge
• Fact: The extracted fact from the episode
• Valid at/invalid at: Time period during which the fact was true (if available)
• Source/target node UUIDs: Connections between entities in the knowledge graph

Node Search Results

The node search results include EntityNode objects with:

• UUID: Unique identifier for the node
• Name: The name of the entity
• Content Summary: A summary of the node's content
• Node Labels: The types of the node (e.g., Person, Organization)
• Created At: When the node was created
• Attributes: Additional properties associated with the node