# 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](https://falkordb.com/docs/) for setup)


## Setup Instructions

1. Install the required dependencies:

```bash
pip install graphiti-core
```

2. Set up environment variables:

```bash
# 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
```

3. Run the example:

```bash
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

## 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