remove personal configs
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f6cc8995fb
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fdbb9fd9c3
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[humanus_cli]
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llm = "qwen-max-latest" # Key in config_llm.toml
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memory = "long-context" # Key in config_mem.toml
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tools = ["filesystem", "playwright", "image_loader"] # Builtin tools configuration
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mcp_servers = ["python_execute"] # Key in config_mcp.toml, all MCP tools provided by servers will be added
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max_steps = 30 # Maximum automatic steps without user's check
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duplicate_threshold = 2 # Used to detect repeating condition (will be checked by LCS)
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[humanus_plan]
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llm = "deepseek-chat"
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memory = "long-context"
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tools = ["filesystem", "playwright", "image_loader"]
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mcp_servers = ["python_execute"]
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max_steps = 30
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duplicate_threshold = 2
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["nomic-embed-text-v1.5"]
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provider = "oai" # Only support OAI-Compatible style for now
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base_url = "http://localhost:8080" # Base url. Note: Don't add any endpoint behind
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endpoint = "/v1/embeddings" # Endpoint of embeddings
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model = "nomic-embed-text-v1.5.f16.gguf" # Model name
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api_key = "" # Your API Key
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embeddings_dim = 768 # Dimension of embeddings (refer to API docs)
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max_retries = 3 # Maximum retry count
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[qwen-text-embedding-v3]
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provider = "oai"
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base_url = "https://dashscope.aliyuncs.com"
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endpoint = "/compatible-mode/v1/embeddings"
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model = "text-embedding-v3"
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api_key = "sk-cb1bb2a240d84182bb93f6dd0fe03600"
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embeddings_dim = 1024
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max_retries = 3
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[qwen-max]
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model = "qwen-max" # Model name
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base_url = "https://dashscope.aliyuncs.com" # Base url. Note: Don't add any endpoint behind
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endpoint = "/compatible-mode/v1/chat/completions" # Endpoint of chat completions
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api_key = "sk-cb1bb2a240d84182bb93f6dd0fe03600" # Your API Key
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[qwen-max-latest]
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model = "qwen-max-latest"
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base_url = "https://dashscope.aliyuncs.com"
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endpoint = "/compatible-mode/v1/chat/completions"
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api_key = "sk-cb1bb2a240d84182bb93f6dd0fe03600"
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[qwen-vl-max-latest]
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model = "qwen-vl-max-latest"
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base_url = "https://dashscope.aliyuncs.com"
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endpoint = "/compatible-mode/v1/chat/completions"
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api_key = "sk-cb1bb2a240d84182bb93f6dd0fe03600"
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enable_vision = true # This means the model could accept content item like {"image_url", {"url", "xxx"}}
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["claude-3.5-sonnet"]
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model = "anthropic/claude-3.5-sonnet"
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base_url = "https://openrouter.ai"
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endpoint = "/api/v1/chat/completions"
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api_key = "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"
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enable_vision = true
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["claude-3.7-sonnet"]
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model = "anthropic/claude-3.7-sonnet"
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base_url = "https://openrouter.ai"
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endpoint = "/api/v1/chat/completions"
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api_key = "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"
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enable_vision = true
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[deepseek-chat]
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model = "deepseek-chat"
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base_url = "https://api.deepseek.com"
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endpoint = "/v1/chat/completions"
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api_key = "sk-93c5bfcb920c4a8aa345791d429b8536"
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[deepseek-r1]
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model = "deepseek-reasoner"
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base_url = "https://api.deepseek.com"
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endpoint = "/v1/chat/completions"
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api_key = "sk-93c5bfcb920c4a8aa345791d429b8536"
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enable_tool = false
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[python_execute]
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type = "sse"
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host = "localhost"
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port = 8895
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sse_endpoint = "/sse"
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message_enpoint = "/message"
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[puppeteer]
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type = "stdio"
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command = "npx"
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args = ["-y", "@modelcontextprotocol/server-puppeteer"]
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[playwright]
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type = "stdio"
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command = "npx"
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args = ["-y", "@executeautomation/playwright-mcp-server"]
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[filesystem]
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type = "stdio"
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command = "npx"
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args = ["-y",
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"@modelcontextprotocol/server-filesystem",
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"/Users/hyde/Desktop"] # Allowed paths
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[default]
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max_messages = 16 # Maximum number of messages in short-term memory
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max_tokens_message = 32768 # Maximum number of tokens in single message
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max_tokens_messages = 65536 # Maximum number of tokens in short-term memory
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max_tokens_context = 131072 # Maximum number of tokens in context (used by `get_messages`)
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retrieval_limit = 32 # Maximum number of results to retrive from long-term memory
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embedding_model = "qwen-text-embedding-v3" # Key in config_embd.toml
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vector_store = "hnswlib" # Key in config_vec.toml
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llm = "qwen-max-latest" # Key in config_llm.toml
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[long-context]
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max_messages = 32
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max_tokens_message = 64000
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max_tokens_messages = 128000
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max_tokens_context = 128000
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retrieval_limit = 32
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embedding_model = "qwen-text-embedding-v3"
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vector_store = "hnswlib"
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llm = "qwen-max-latest"
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@ -1,8 +0,0 @@
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[hnswlib]
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provider = "hnswlib"
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dim = 768 # Dimension of the elements
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max_elements = 100 # Maximum number of elements, should be known beforehand
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M = 16 # Tightly connected with internal dimensionality of the data
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# strongly affects the memory consumption
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ef_construction = 200 # Controls index search speed/build speed tradeoff
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metric = "L2" # Distance metric to use, can be L2 or IP
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