references updated for qdrant

.env.example

@@ -5,11 +5,9 @@ OPENAI_BASE_URL=https://your-openai-compatible-endpoint.com/v1
 EMBEDDER_API_KEY=AIzaSy-your-google-gemini-api-key-here
 
 # Database Configuration
-POSTGRES_DB=mem0_db
+QDRANT_HOST=qdrant
-POSTGRES_USER=mem0_user
+QDRANT_PORT=6333
-POSTGRES_PASSWORD=mem0_password
+QDRANT_COLLECTION_NAME=mem0
-POSTGRES_HOST=postgres
-POSTGRES_PORT=5432
 
 # Neo4j Configuration
 NEO4J_AUTH=neo4j/mem0_neo4j_password
@@ -36,4 +34,5 @@ EXPERT_MODEL=o3                      # Expert-level comprehensive analysis
 # - Ensure all models are available on your OpenAI-compatible endpoint
 # - Verify model availability: curl -H "Authorization: Bearer $API_KEY" $BASE_URL/v1/models
 # - Neo4j must be version 5.18+ for vector.similarity.cosine() function
+# - Qdrant vector database for embeddings storage (replaces PostgreSQL+pgvector)
 # - Ollama must be running locally with nomic-embed-text:latest model

README.md

@@ -1,12 +1,12 @@
 # Mem0 Interface - Production Ready
 
-A fully operational Mem0 interface with PostgreSQL and Neo4j integration, featuring intelligent model routing, comprehensive memory management, and production-grade monitoring.
+A fully operational Mem0 interface with Qdrant and Neo4j integration, featuring intelligent model routing, comprehensive memory management, and production-grade monitoring.
 
 ## Features
 
 ### Core Memory System
 - ✅ **Mem0 OSS Integration**: Complete hybrid datastore (Vector + Graph + KV storage)
-- ✅ **PostgreSQL + pgvector**: High-performance vector embeddings storage
+- ✅ **Qdrant**: Purpose-built vector database for high-performance embeddings storage
 - ✅ **Neo4j 5.18**: Graph relationships with native vector similarity functions
 - ✅ **Google Gemini Embeddings**: Enterprise-grade embedding generation
 - ✅ **Memory Operations**: Store, search, update, delete memories with semantic search
@@ -62,7 +62,7 @@ curl http://localhost:8000/health
 ### Core Components
 - **FastAPI Backend**: Production-ready API with comprehensive monitoring
 - **Mem0 OSS**: Hybrid memory management (vector + graph + key-value)
-- **PostgreSQL + pgvector**: Vector embeddings storage and similarity search
+- **Qdrant**: Purpose-built vector database for embeddings storage and similarity search
 - **Neo4j 5.18**: Graph relationships with native vector functions
 - **Google Gemini**: Enterprise-grade embedding generation
 
@@ -203,13 +203,13 @@ curl http://localhost:8000/graph/relationships/alice
 
 ### Service Dependencies
 - **Neo4j**: Must start before backend for vector functions
-- **PostgreSQL**: Required for vector storage initialization
+- **Qdrant**: Required for vector storage initialization
 - **Ollama**: Must be running locally on port 11434
 - **API Endpoint**: Must have valid models available
 
 ## Production Notes
 
-- **Memory Usage**: Neo4j and PostgreSQL require adequate RAM for vector operations
+- **Memory Usage**: Neo4j and Qdrant require adequate RAM for vector operations
 - **API Rate Limits**: Monitor usage on custom endpoint
 - **Data Persistence**: All data stored in Docker volumes
 - **Scaling**: Individual services can be scaled independently

backend/README.md

@@ -9,7 +9,7 @@ A production-ready FastAPI backend that provides intelligent memory integration
 1. **Mem0Manager** (`mem0_manager.py`)
    - Central orchestration of memory operations
    - Integration with custom OpenAI-compatible endpoint
-   - Memory persistence across PostgreSQL and Neo4j
+   - Memory persistence across Qdrant and Neo4j
    - Performance timing and operation tracking
 
 2. **Configuration System** (`config.py`)
@@ -39,8 +39,8 @@ A production-ready FastAPI backend that provides intelligent memory integration
 
 ```
 ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
-│   PostgreSQL    │    │     Neo4j       │    │  Custom LLM     │
+│     Qdrant      │    │     Neo4j       │    │  Custom LLM     │
-│   (pgvector)    │    │   (APOC)        │    │   Endpoint      │
+│  (Vector DB)    │    │   (APOC)        │    │   Endpoint      │
 ├─────────────────┤    ├─────────────────┤    ├─────────────────┤
 │ • Vector Store  │    │ • Graph Store   │    │ • claude-sonnet-4│
 │ • Embeddings    │    │ • Relationships │    │ • Gemini Embed │
@@ -134,11 +134,9 @@ backend/
 | `OPENAI_COMPAT_API_KEY` | Your custom endpoint API key | - | ✅ |
 | `OPENAI_BASE_URL` | Custom endpoint URL | - | ✅ |
 | `EMBEDDER_API_KEY` | Google Gemini API key for embeddings | - | ✅ |
-| `POSTGRES_HOST` | PostgreSQL host | postgres | ✅ |
+| `QDRANT_HOST` | Qdrant vector database host | qdrant | ✅ |
-| `POSTGRES_PORT` | PostgreSQL port | 5432 | ✅ |
+| `QDRANT_PORT` | Qdrant vector database port | 6333 | ✅ |
-| `POSTGRES_DB` | Database name | mem0_db | ✅ |
+| `QDRANT_COLLECTION_NAME` | Qdrant collection name | mem0 | ✅ |
-| `POSTGRES_USER` | Database user | mem0_user | ✅ |
-| `POSTGRES_PASSWORD` | Database password | - | ✅ |
 | `NEO4J_URI` | Neo4j connection URI | bolt://neo4j:7687 | ✅ |
 | `NEO4J_USERNAME` | Neo4j username | neo4j | ✅ |
 | `NEO4J_PASSWORD` | Neo4j password | - | ✅ |
@@ -283,7 +281,7 @@ docker exec mem0-neo4j cypher-shell -u neo4j -p mem0_neo4j_password \
 
 ### Key Integration Points Verified
 - ✅ **Ollama Embeddings**: nomic-embed-text:latest working for vector generation
-- ✅ **PostgreSQL + pgvector**: Vector storage and similarity search operational  
+- ✅ **Qdrant**: Vector storage and similarity search operational  
 - ✅ **Neo4j 5.18**: Graph relationships and native vector functions working
 - ✅ **Custom LLM Endpoint**: All 4 models accessible and routing correctly
 - ✅ **Memory Persistence**: Data survives container restarts via Docker volumes
@@ -390,7 +388,7 @@ backend:
 #### Dependency Requirements
 - Neo4j 5.18+ (for vector.similarity.cosine function)
 - Ollama running locally with nomic-embed-text:latest
-- PostgreSQL with pgvector extension
+- Qdrant vector database
 - Valid API keys for custom LLM endpoint
 
 ### Debugging Commands