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Update documentation: Replace Qdrant with Supabase references

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- 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>
This commit is contained in:
Docker Config Backup
2025-07-31 07:56:11 +02:00
parent 41cd78207a
commit 09451401cc
10 changed files with 39 additions and 39 deletions
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24
docs/essentials/architecture.mdx
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@@ -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)
- **Port**: 6333 (REST), 6334 (gRPC)
- **Purpose**: High-performance vector search and similarity matching
- **Features**: Collections management, semantic search, embeddings storage
### Vector Storage (Supabase)
- **Port**: 8000 (API), 5435 (PostgreSQL)
- **Purpose**: High-performance vector search with pgvector and database 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