RyanNg

# Configuration Guide This guide covers the complete configuration options for Cipher, including agent setup, embedding configuration, and vector store settings. ## Agent Configuration (memAgent/cipher.yml) The main configuration file for Cipher is located at `memAgent/cipher.yml`. Here's the basic structure: ```yaml # LLM Configuration llm: provider: openai # openai, anthropic, openrouter, ollama, qwen model: gpt-4-turbo apiKey: $OPENAI_API_KEY # System Prompt systemPrompt: 'You are a helpful AI assistant with memory capabilities.' # MCP Servers (optional) mcpServers: filesystem: type: stdio command: npx args: ['-y', '@modelcontextprotocol/server-filesystem', '.'] ``` ## Embedding Configuration Configure embeddings in `memAgent/cipher.yml`. If not specified, uses automatic fallback based on your LLM provider. Below is the table of fallback embedding models: ### Supported Providers | Provider | Config | Fallback Model | Fixed Dimensions | | ---------------- | ------------------- | ------------------------------ | -------------------------- | | **OpenAI** | `type: openai` | `text-embedding-3-small` | No | | **Gemini** | `type: gemini` | `gemini-embedding-001` | No | | **Qwen** | `type: qwen` | `text-embedding-v3` | Yes (1024, 768, 512) | | **Voyage** | `type: voyage` | `voyage-3-large` | Yes (1024 only) | | **AWS Bedrock** | `type: aws-bedrock` | `amazon.titan-embed-text-v2:0` | Yes (1024, 512, 256) | | **Azure OpenAI** | `type: openai` | `text-embedding-3-small` | No | | **Ollama** | `type: ollama` | `nomic-embed-text` | No | ### Configuration Examples ```yaml # OpenAI embedding: type: openai model: text-embedding-3-small apiKey: $OPENAI_API_KEY # Qwen (fixed dimensions - must specify) embedding: type: qwen model: text-embedding-v3 apiKey: $QWEN_API_KEY dimensions: 1024 # Required: 1024, 768, or 512 # AWS Bedrock (fixed dimensions - must specify) embedding: type: aws-bedrock model: amazon.titan-embed-text-v2:0 region: $AWS_REGION accessKeyId: $AWS_ACCESS_KEY_ID secretAccessKey: $AWS_SECRET_ACCESS_KEY dimensions: 1024 # Required: 1024, 512, or 256 # Azure OpenAI embedding: type: openai model: text-embedding-3-small apiKey: $AZURE_OPENAI_API_KEY baseUrl: $AZURE_OPENAI_ENDPOINT # Voyage (fixed dimensions - always 1024) embedding: type: voyage model: voyage-3-large apiKey: $VOYAGE_API_KEY # Note: Voyage models use fixed 1024 dimensions # LM Studio (local, no API key required) embedding: type: lmstudio model: nomic-embed-text-v1.5 # or bge-large, bge-base, bge-small baseUrl: http://localhost:1234/v1 # Optional, defaults to this # dimensions: 768 # Optional, auto-detected based on model # Disable embeddings (chat-only mode) embedding: disabled: true ``` **Note:** Setting `embedding: disabled: true` disables all memory-related tools (`cipher_memory_search`, `cipher_extract_and_operate_memory`, etc.) and operates in chat-only mode. ### Automatic Fallback If no embedding config is specified, automatically uses your LLM provider's embedding: - **Anthropic LLM** → Voyage embedding (needs `VOYAGE_API_KEY`) - **AWS LLM** → AWS Bedrock embedding (uses same credentials) - **Azure LLM** → Azure OpenAI embedding (uses same endpoint) - **Qwen LLM** → Qwen embedding (uses same API key) - **LM Studio LLM** → LM Studio embedding (tries same model first, then dedicated embedding model, finally OpenAI) - **Ollama LLM** → Ollama embedding (uses same local server) - **OpenAI/Gemini/Ollama** → Same provider embedding **Note:** For providers with fixed dimensions (Qwen, Voyage, AWS), you must specify `dimensions:` in the config to override the default value in `.env`. ## Vector Store Configuration Cipher supports three vector databases for storing embeddings. Configure in `.env`: ### Supported Vector Stores **Qdrant** ([Qdrant Cloud](https://qdrant.tech/)) ```bash # Remote (Qdrant Cloud) VECTOR_STORE_TYPE=qdrant VECTOR_STORE_URL=your-qdrant-endpoint VECTOR_STORE_API_KEY=your-qdrant-api-key # Local (Docker) VECTOR_STORE_TYPE=qdrant VECTOR_STORE_HOST=localhost VECTOR_STORE_PORT=6333 VECTOR_STORE_URL=http://localhost:6333 ``` **Milvus** ([Zilliz Cloud](https://zilliz.com/)) ```bash # Remote (Zilliz Cloud) VECTOR_STORE_TYPE=milvus VECTOR_STORE_URL=your-milvus-cluster-endpoint VECTOR_STORE_USERNAME=your-zilliz-username VECTOR_STORE_PASSWORD=your-zilliz-password # Local (Docker) VECTOR_STORE_TYPE=milvus VECTOR_STORE_HOST=localhost VECTOR_STORE_PORT=19530 ``` ### Additional Vector Store Settings ```bash # Collection configuration VECTOR_STORE_COLLECTION=knowledge_memory VECTOR_STORE_DIMENSION=1536 VECTOR_STORE_DISTANCE=Cosine # Reflection memory (optional) REFLECTION_VECTOR_STORE_COLLECTION=reflection_memory DISABLE_REFLECTION_MEMORY=true # default: true ``` ## Related Documentation - [LLM Providers](./llm-providers.md) - Detailed configuration for all supported LLM providers - [Embedding Configuration](./embedding-configuration.md) - Advanced embedding setup - [Vector Stores](./vector-stores.md) - Detailed vector database configurations - [Workspace Memory](./workspace-memory.md) - Team-aware memory system configuration