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Model recommendations quickly become outdated. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
150 lines
3.9 KiB
Markdown
150 lines
3.9 KiB
Markdown
# Azure OpenAI with Neo4j Example
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This example demonstrates how to use Graphiti with Azure OpenAI and Neo4j to build a knowledge graph.
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## Prerequisites
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- Python 3.10+
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- Neo4j database (running locally or remotely)
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- Azure OpenAI subscription with deployed models
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## Setup
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### 1. Install Dependencies
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```bash
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uv sync
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```
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### 2. Configure Environment Variables
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Copy the `.env.example` file to `.env` and fill in your credentials:
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```bash
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cd examples/azure-openai
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cp .env.example .env
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```
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Edit `.env` with your actual values:
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```env
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# Neo4j connection settings
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NEO4J_URI=bolt://localhost:7687
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NEO4J_USER=neo4j
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NEO4J_PASSWORD=your-password
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# Azure OpenAI settings
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AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com
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AZURE_OPENAI_API_KEY=your-api-key-here
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AZURE_OPENAI_API_VERSION=2024-10-21
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AZURE_OPENAI_DEPLOYMENT=gpt-4.1
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AZURE_OPENAI_EMBEDDING_DEPLOYMENT=text-embedding-3-small
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```
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### 3. Azure OpenAI Model Deployments
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This example requires two Azure OpenAI model deployments:
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1. **Chat Completion Model**: Used for entity extraction and relationship analysis
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- Set the deployment name in `AZURE_OPENAI_DEPLOYMENT`
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2. **Embedding Model**: Used for semantic search
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- Set the deployment name in `AZURE_OPENAI_EMBEDDING_DEPLOYMENT`
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### 4. Neo4j Setup
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Make sure Neo4j is running and accessible at the URI specified in your `.env` file.
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For local development:
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- Download and install [Neo4j Desktop](https://neo4j.com/download/)
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- Create a new database
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- Start the database
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- Use the credentials in your `.env` file
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## Running the Example
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```bash
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cd examples/azure-openai
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uv run azure_openai_neo4j.py
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```
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## What This Example Does
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1. **Initialization**: Sets up connections to Neo4j and Azure OpenAI
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2. **Adding Episodes**: Ingests text and JSON data about California politics
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3. **Basic Search**: Performs hybrid search combining semantic similarity and BM25 retrieval
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4. **Center Node Search**: Reranks results based on graph distance to a specific node
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5. **Cleanup**: Properly closes database connections
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## Key Concepts
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### Azure OpenAI Integration
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The example shows how to configure Graphiti to use Azure OpenAI:
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```python
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# Initialize Azure OpenAI client
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azure_client = AsyncAzureOpenAI(
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azure_endpoint=azure_endpoint,
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api_key=azure_api_key,
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api_version=azure_api_version,
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)
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# Create LLM and Embedder clients
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llm_client = AzureOpenAILLMClient(azure_client=azure_client)
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embedder_client = AzureOpenAIEmbedderClient(
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azure_client=azure_client,
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model=azure_embedding_deployment
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)
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# Initialize Graphiti with custom clients
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graphiti = Graphiti(
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neo4j_uri,
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neo4j_user,
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neo4j_password,
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llm_client=llm_client,
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embedder=embedder_client,
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)
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```
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### Episodes
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Episodes are the primary units of information in Graphiti. They can be:
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- **Text**: Raw text content (e.g., transcripts, documents)
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- **JSON**: Structured data with key-value pairs
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### Hybrid Search
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Graphiti combines multiple search strategies:
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- **Semantic Search**: Uses embeddings to find semantically similar content
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- **BM25**: Keyword-based text retrieval
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- **Graph Traversal**: Leverages relationships between entities
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## Troubleshooting
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### Azure OpenAI API Errors
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- Verify your endpoint URL is correct (should end in `.openai.azure.com`)
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- Check that your API key is valid
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- Ensure your deployment names match actual deployments in Azure
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- Verify API version is supported by your deployment
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### Neo4j Connection Issues
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- Ensure Neo4j is running
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- Check firewall settings
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- Verify credentials are correct
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- Check URI format (should be `bolt://` or `neo4j://`)
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## Next Steps
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- Explore other search recipes in `graphiti_core/search/search_config_recipes.py`
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- Try different episode types and content
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- Experiment with custom entity definitions
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- Add more episodes to build a larger knowledge graph
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## Related Examples
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- `examples/quickstart/` - Basic Graphiti usage with OpenAI
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- `examples/podcast/` - Processing longer content
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- `examples/ecommerce/` - Domain-specific knowledge graphs
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