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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:

# 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

# 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)

# 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)

# 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

# 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