Graphiti MCP Server

# 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://docs.falkordb.com) for setup) - **For Amazon Neptune**: - Amazon server running (see [Amazon Neptune documentation](https://aws.amazon.com/neptune/developer-resources/) 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 # Optional Amazon Neptune connection parameters NEPTUNE_HOST=your_neptune_host NEPTUNE_PORT=your_port_or_8182 AOSS_HOST=your_aoss_host AOSS_PORT=your_port_or_443 # To use a different database, modify the driver constructor in the script ``` TIP: For Amazon Neptune host string please use the following formats * For Neptune Database: `neptune-db://<cluster endpoint>` * For Neptune Analytics: `neptune-graph://<graph identifier>` 3. Run the example: ```bash python quickstart_neo4j.py # For FalkorDB python quickstart_falkordb.py # For Amazon Neptune python quickstart_neptune.py ``` ## What This Example Demonstrates - **Graph Initialization**: Setting up the Graphiti indices and constraints in Neo4j, Amazon Neptune, 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: ```python # 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