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* Bump version from 0.9.0 to 0.9.1 in pyproject.toml and update google-genai dependency to >=0.1.0 * Bump version from 0.9.1 to 0.9.2 in pyproject.toml * Update google-genai dependency version to >=0.8.0 in pyproject.toml * loc file * Update pyproject.toml to version 0.9.3, restructure dependencies, and modify author format. Remove outdated Google API key note from README.md. * upgrade poetry and ruff * Update README.md to include installation instructions for Graphiti with Google Gemini support * fix to deps since peotry doesn't fully implement PEP 735 * Refactor string formatting in various files to use single quotes for consistency and improve readability. This includes updates in agent.ipynb, quickstart.py, multiple prompt files, and ingest.py and retrieve.py modules. * Remove optional dependencies from pyproject.toml to streamline project requirements.
Graphiti Quickstart Example
This example demonstrates the basic functionality of Graphiti, including:
- Connecting to a Neo4j database
- Initializing Graphiti indices and constraints
- Adding episodes to the graph
- Searching the graph with semantic and keyword matching
- Exploring graph-based search with reranking using the top search result's source node UUID
- Performing node search using predefined search recipes
Prerequisites
- Neo4j Desktop installed and running
- A local DBMS created and started in Neo4j Desktop
- Python 3.9+
- OpenAI API key (set as
OPENAI_API_KEYenvironment variable)
Setup Instructions
- Install the required dependencies:
pip install graphiti-core
- 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
- Run the example:
python quickstart.py
What This Example Demonstrates
- Graph Initialization: Setting up the Graphiti indices and constraints in Neo4j
- 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:
- Modify the episode content to add your own information
- Try different search queries to explore the knowledge extraction
- Experiment with different center nodes for graph-distance-based reranking
- Try other predefined search recipes from
graphiti_core.search.search_config_recipes - Explore the more advanced examples in the other directories
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