cipher.yml•4.45 kB
# List of MCP servers to use
# mcpServers:
# filesystem:
# type: stdio
# command: npx
# args:
# - -y
# - '@modelcontextprotocol/server-filesystem'
# - .
# If not specified, please set it like this:
mcpServers: {}
# Choose ONLY ONE of the following LLM providers
llm:
provider: openai
model: gpt-4.1-mini
apiKey: $OPENAI_API_KEY
maxIterations: 50
# provider: gemini
# model: gemini-2.5-flash
# apiKey: $GEMINI_API_KEY
# maxIterations: 50
# provider: anthropic
# model: claude-3-5-haiku-20241022
# apiKey: $ANTHROPIC_API_KEY
# maxIterations: 50
# provider: openrouter
# model: google/gemini-2.5-pro
# apiKey: $OPENROUTER_API_KEY
# maxIterations: 50
# provider: qwen
# model: qwen3-14b
# apiKey: $QWEN_API_KEY
# maxIterations: 50
# qwenOptions:
# enableThinking: false
# provider: deepseek
# model: deepseek-chat
# apiKey: $DEEPSEEK_API_KEY
# maxIterations: 50
# --- AWS Bedrock LLM Configuration ---
# provider: aws
# model: us.anthropic.claude-3-5-sonnet-20241022-v2:0 # Or another Bedrock-supported model
# maxIterations: 50
# aws:
# region: $AWS_REGION
# accessKeyId: $AWS_ACCESS_KEY_ID
# secretAccessKey: $AWS_SECRET_ACCESS_KEY
# sessionToken: $AWS_SESSION_TOKEN # Optional, for temporary credentials
# --- Azure OpenAI LLM Configuration ---
# provider: azure
# model: gpt-4o-mini # Or your Azure deployment/model name
# apiKey: $AZURE_OPENAI_API_KEY
# maxIterations: 50
# azure:
# endpoint: $AZURE_OPENAI_ENDPOINT
# deploymentName: gpt-4o-mini # Optional, defaults to model name
# --- Ollama LLM Configuration (Sample, Commented Out) ---
# provider: ollama
# model: qwen3:8b # Use the model you downloaded
# maxIterations: 50
# baseURL: $OLLAMA_BASE_URL
# --- LM Studio LLM Configuration (Sample, Commented Out) ---
# provider: lmstudio
# model: hermes-2-pro-llama-3-8b # Use the model you have loaded
# maxIterations: 50
# baseURL: $LMSTUDIO_BASE_URL
# Embedding configuration: ONLY ONE of the following
embedding:
type: openai
model: text-embedding-3-small
apiKey: $OPENAI_API_KEY
# type: ollama
# model: mxbai-embed-large
# baseUrl: $OLLAMA_BASE_URL
# dimensions: 1024
# Gemini:
# embedding:
# type: gemini
# model: gemini-embedding-001
# apiKey: $GEMINI_API_KEY
# Ollama:
# embedding:
# type: ollama
# model: mxbai-embed-large
# baseUrl: $OLLAMA_BASE_URL
# dimensions: 1024
# LM Studio:
# embedding:
# type: lmstudio
# model: nomic-embed-text-v1.5
# baseUrl: $LMSTUDIO_BASE_URL
# Voyage (for Anthropic/Claude users):
# embedding:
# type: voyage
# model: voyage-3-large #(1024, 256, 512, 2048)
# apiKey: $VOYAGE_API_KEY
# dimensions: 1024
# Qwen:
# embedding:
# type: qwen
# model: text-embedding-v3
# apiKey: $QWEN_API_KEY
# dimensions: 1024 # (1024, 768 or 512)
# AWS Bedrock:
# embedding:
# type: aws-bedrock
# model: amazon.titan-embed-text-v2:0 # or cohere.embed-english-v3
# region: $AWS_REGION
# accessKeyId: $AWS_ACCESS_KEY_ID
# secretAccessKey: $AWS_SECRET_ACCESS_KEY
# sessionToken: $AWS_SESSION_TOKEN # Optional, for temporary credentials
# dimensions: 1024 # 1024 (default), 512, or 256 for Titan V2
# timeout: 30000
# maxRetries: 3
# Azure OpenAI:
# embedding:
# type: openai
# model: text-embedding-3-small
# apiKey: $AZURE_OPENAI_API_KEY
# baseUrl: $AZURE_OPENAI_ENDPOINT # e.g., https://your-resource.openai.azure.com
# dimensions: 1536 # Optional: depends on model
# timeout: 30000
# maxRetries: 3
# Disable embeddings entirely:
# embedding:
# disabled: true
# Evaluation LLM configuration (non-thinking model for evaluation step)
# evalLlm:
# provider: anthropic
# model: claude-3-7-sonnet-20250219
# apiKey: $ANTHROPIC_API_KEY
# System prompt - User customizable
# This prompt will be combined with built-in tool usage instructions
systemPrompt:
enabled: true
content: |
You are an AI programming assistant focused on coding and reasoning tasks. You excel at:
- Writing clean, efficient code
- Debugging and problem-solving
- Code review and optimization
- Explaining complex technical concepts
- Reasoning through programming challenges
You should call each tool at most once per user request unless explicitly instructed otherwise.