
- Complete Mem0 OSS integration with hybrid datastore - PostgreSQL + pgvector for vector storage - Neo4j 5.18 for graph relationships - Google Gemini embeddings integration - Comprehensive monitoring with correlation IDs - Real-time statistics and performance tracking - Production-grade observability features - Clean repository with no exposed secrets
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1.5 KiB
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39 lines
No EOL
1.5 KiB
Text
# API Configuration - UPDATE THESE WITH YOUR VALUES
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OPENAI_API_KEY=sk-your-openai-api-key-here
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OPENAI_COMPAT_API_KEY=sk-your-openai-compatible-api-key-here
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OPENAI_BASE_URL=https://your-openai-compatible-endpoint.com/v1
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EMBEDDER_API_KEY=AIzaSy-your-google-gemini-api-key-here
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# Database Configuration
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POSTGRES_DB=mem0_db
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POSTGRES_USER=mem0_user
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POSTGRES_PASSWORD=mem0_password
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POSTGRES_HOST=postgres
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POSTGRES_PORT=5432
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# Neo4j Configuration
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NEO4J_AUTH=neo4j/mem0_neo4j_password
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NEO4J_URI=bolt://neo4j:7687
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NEO4J_USERNAME=neo4j
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NEO4J_PASSWORD=mem0_neo4j_password
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# Application Configuration
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BACKEND_PORT=8000
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FRONTEND_PORT=3000
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LOG_LEVEL=INFO
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CORS_ORIGINS=http://localhost:3000
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# Model Configuration - Intelligent Routing
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# These models are automatically selected based on task complexity
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DEFAULT_MODEL=claude-sonnet-4 # General purpose model
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EXTRACTION_MODEL=claude-sonnet-4 # Memory extraction and processing
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FAST_MODEL=o4-mini # Simple/quick responses
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ANALYTICAL_MODEL=gemini-2.5-pro # Analysis and comparison tasks
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REASONING_MODEL=claude-sonnet-4 # Complex reasoning tasks
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EXPERT_MODEL=o3 # Expert-level comprehensive analysis
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# IMPORTANT NOTES:
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# - Ensure all models are available on your OpenAI-compatible endpoint
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# - Verify model availability: curl -H "Authorization: Bearer $API_KEY" $BASE_URL/v1/models
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# - Neo4j must be version 5.18+ for vector.similarity.cosine() function
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# - Ollama must be running locally with nomic-embed-text:latest model |