Update documentation: Replace Qdrant with Supabase references

- Updated vector store provider references throughout documentation
- Changed default vector store from Qdrant to Supabase (pgvector)
- Updated configuration examples to use Supabase connection strings
- Modified navigation structure to remove qdrant-specific references
- Updated examples in mem0-with-ollama and llama-index integration
- Corrected API reference and architecture documentation

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

docs/api-reference/introduction.mdx

@@ -172,7 +172,7 @@ X-RateLimit-Reset: 1627849200
 
 ### Completed ✅
 - Core mem0 integration
-- Database connections (Neo4j, Qdrant)
+- Database connections (Neo4j, Supabase)
 - LLM provider support (Ollama, OpenAI)
 - Configuration management
 

docs/components/vectordbs/config.mdx

@@ -10,7 +10,7 @@ iconType: "solid"
 
 The `config` is defined as an object with two main keys:
 - `vector_store`: Specifies the vector database provider and its configuration
-  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "qdrant", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search")
+  - `provider`: The name of the vector database (e.g., "chroma", "pgvector", "supabase", "milvus", "upstash_vector", "azure_ai_search", "vertex_ai_vector_search")
   - `config`: A nested dictionary containing provider-specific settings
 
 

docs/components/vectordbs/overview.mdx

@@ -37,7 +37,7 @@ See the list of supported vector databases below.
 
 ## Usage
 
-To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `Qdrant` will be used as the vector database.
+To utilize a vector database, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and `Supabase` (with pgvector) will be used as the vector database.
 
 For a comprehensive list of available parameters for vector database configuration, please refer to [Config](./config).
 

docs/development.mdx

@@ -14,7 +14,7 @@ description: 'Complete development environment setup and workflow'
 ├── test_basic.py           # Basic functionality tests
 ├── test_openai.py          # OpenAI integration test
 ├── test_all_connections.py # Comprehensive connection tests
-├── docker-compose.yml      # Neo4j & Qdrant containers
+├── docker-compose.yml      # Neo4j container (Supabase is external)
 ├── .env                    # Environment variables
 └── docs/                   # Documentation (Mintlify)
 ```
@@ -24,7 +24,7 @@ description: 'Complete development environment setup and workflow'
 | Component | Status | Port | Description |
 |-----------|--------|------|-------------|
 | Neo4j | ✅ READY | 7474/7687 | Graph memory storage |
-| Qdrant | ✅ READY | 6333/6334 | Vector memory storage |
+| Supabase | ✅ READY | 8000/5435 | Vector & database storage (self-hosted) |
 | Ollama | ✅ READY | 11434 | Local LLM processing |
 | Mem0 Core | ✅ READY | - | Memory management system v0.1.115 |
 

docs/essentials/architecture.mdx

@@ -12,12 +12,12 @@ graph TB
     A[AI Applications] --> B[MCP Server - Port 8765]
     B --> C[Memory API - Port 8080]
     C --> D[Mem0 Core v0.1.115]
-    D --> E[Vector Store - Qdrant]
+    D --> E[Vector Store - Supabase]
     D --> F[Graph Store - Neo4j] 
     D --> G[LLM Provider]
     G --> H[Ollama - Port 11434]
     G --> I[OpenAI/Remote APIs]
-    E --> J[Qdrant - Port 6333]
+    E --> J[Supabase - Port 8000/5435]
     F --> K[Neo4j - Port 7687]
 ```
 
@@ -28,10 +28,10 @@ graph TB
 - **Purpose**: Central memory management and coordination
 - **Features**: Memory operations, provider abstraction, configuration management
 
-### Vector Storage (Qdrant)
+### Vector Storage (Supabase)
-- **Port**: 6333 (REST), 6334 (gRPC)
+- **Port**: 8000 (API), 5435 (PostgreSQL)
-- **Purpose**: High-performance vector search and similarity matching
+- **Purpose**: High-performance vector search with pgvector and database storage
-- **Features**: Collections management, semantic search, embeddings storage
+- **Features**: PostgreSQL with pgvector, semantic search, embeddings storage, relational data
 
 ### Graph Storage (Neo4j)
 - **Port**: 7474 (HTTP), 7687 (Bolt)
@@ -57,13 +57,13 @@ graph TB
 1. **Input**: User messages or content
 2. **Processing**: LLM extracts facts and relationships
 3. **Storage**: 
-   - Facts stored as vectors in Qdrant
+   - Facts stored as vectors in Supabase (pgvector)
    - Relationships stored as graph in Neo4j
 4. **Indexing**: Content indexed for fast retrieval
 
 ### Memory Retrieval
 1. **Query**: Semantic search query
-2. **Vector Search**: Qdrant finds similar memories
+2. **Vector Search**: Supabase finds similar memories using pgvector
 3. **Graph Traversal**: Neo4j provides contextual relationships
 4. **Ranking**: Combined scoring and relevance
 5. **Response**: Structured memory results
@@ -74,12 +74,12 @@ graph TB
 ```bash
 # Core Services
 NEO4J_URI=bolt://localhost:7687
-QDRANT_URL=http://localhost:6333
+SUPABASE_URL=http://localhost:8000
 OLLAMA_BASE_URL=http://localhost:11434
 
 # Provider Selection
 LLM_PROVIDER=ollama  # or openai
-VECTOR_STORE=qdrant
+VECTOR_STORE=supabase
 GRAPH_STORE=neo4j
 ```
 
@@ -87,7 +87,7 @@ GRAPH_STORE=neo4j
 The system supports multiple providers through a unified interface:
 
 - **LLM Providers**: OpenAI, Ollama, Anthropic, etc.
-- **Vector Stores**: Qdrant, Pinecone, Weaviate, etc.
+- **Vector Stores**: Supabase (pgvector), Qdrant, Pinecone, Weaviate, etc.
 - **Graph Stores**: Neo4j, Amazon Neptune, etc.
 
 ## Security Architecture
@@ -110,7 +110,7 @@ The system supports multiple providers through a unified interface:
 ## Scalability Considerations
 
 ### Horizontal Scaling
-- Qdrant cluster support
+- Supabase horizontal scaling support
 - Neo4j clustering capabilities
 - Load balancing for API layer
 

docs/examples/mem0-with-ollama.mdx

@@ -26,11 +26,10 @@ from mem0 import Memory
 
 config = {
     "vector_store": {
-        "provider": "qdrant",
+        "provider": "supabase", 
         "config": {
-            "collection_name": "test",
+            "connection_string": "postgresql://supabase_admin:your_password@localhost:5435/postgres",
-            "host": "localhost",
+            "collection_name": "memories",
-            "port": 6333,
             "embedding_model_dims": 768,  # Change this according to your local model's dimensions
         },
     },
@@ -66,7 +65,7 @@ memories = m.get_all(user_id="john")
 ### Key Points
 
 - **Configuration**: The setup involves configuring the vector store, language model, and embedding model to use local resources.
-- **Vector Store**: Qdrant is used as the vector store, running on localhost.
+- **Vector Store**: Supabase with pgvector is used as the vector store, running on localhost.
 - **Language Model**: Ollama is used as the LLM provider, with the "llama3.1:latest" model.
 - **Embedding Model**: Ollama is also used for embeddings, with the "nomic-embed-text:latest" model.
 

docs/integrations/llama-index.mdx

@@ -69,11 +69,10 @@ Set your Mem0 OSS by providing configuration details:
 ```python
 config = {
     "vector_store": {
-        "provider": "qdrant",
+        "provider": "supabase",
         "config": {
-            "collection_name": "test_9",
+            "connection_string": "postgresql://supabase_admin:your_password@localhost:5435/postgres",
-            "host": "localhost",
+            "collection_name": "memories",
-            "port": 6333,
             "embedding_model_dims": 1536,  # Change this according to your local model's dimensions
         },
     },

docs/mint.json

@@ -72,7 +72,6 @@
       "group": "Database Integration",
       "pages": [
         "database/neo4j",
-        "database/qdrant",
         "database/supabase"
       ]
     },

docs/open-source/python-quickstart.mdx

@@ -45,17 +45,16 @@ m = AsyncMemory()
   <Tab title="Advanced">
 If you want to run Mem0 in production, initialize using the following method:
 
-Run Qdrant first:
+Run Supabase first:
 
 ```bash
-docker pull qdrant/qdrant
+# Ensure you have Supabase running locally
+# See https://supabase.com/docs/guides/self-hosting/docker for setup
 
-docker run -p 6333:6333 -p 6334:6334 \
+docker compose up -d
-    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
-    qdrant/qdrant
 ```
 
-Then, instantiate memory with qdrant server:
+Then, instantiate memory with Supabase server:
 
 ```python
 import os
@@ -65,10 +64,10 @@ os.environ["OPENAI_API_KEY"] = "your-api-key"
 
 config = {
     "vector_store": {
-        "provider": "qdrant",
+        "provider": "supabase",
         "config": {
-            "host": "localhost",
+            "connection_string": "postgresql://supabase_admin:your_password@localhost:5435/postgres",
-            "port": 6333,
+            "collection_name": "memories",
         }
     },
 }

docs/quickstart.mdx

@@ -7,7 +7,7 @@ description: 'Get your Mem0 Memory System running in under 5 minutes'
 
 <CardGroup cols={2}>
   <Card title="Docker & Docker Compose" icon="docker">
-    Required for Neo4j and Qdrant containers
+    Required for Neo4j container (Supabase already running)
   </Card>
   <Card title="Python 3.10+" icon="python">
     For the mem0 core system and API
@@ -19,9 +19,13 @@ description: 'Get your Mem0 Memory System running in under 5 minutes'
 ### Step 1: Start Database Services
 
 ```bash
-docker compose up -d neo4j qdrant
+docker compose up -d neo4j
 ```
 
+<Note>
+Supabase is already running as part of your existing infrastructure on the localai network.
+</Note>
+
 ### Step 2: Test Your Installation
 
 ```bash