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2.3 KiB
2.3 KiB
The Cli Interface of MemoryScope
Usage
Before running, follow the Installation guidelines in Readme, and start the Docker image first. MemoryScope can be launched in two different ways:
1. Using YAML Configuration File
If you prefer to configure your settings via a YAML file, you can do so by providing the path to the configuration file as follows:
memoryscope --config_path=memoryscope/core/config/demo_config.yaml
2. Using Command Line Arguments
Alternatively, you can specify all the parameters directly on the command line:
# Chinese / Dashscope
memoryscope --language="cn" \
--memory_chat_class="cli_memory_chat" \
--human_name="用户" \
--assistant_name="AI" \
--generation_backend="dashscope_generation" \
--generation_model="qwen-max" \
--embedding_backend="dashscope_embedding" \
--embedding_model="text-embedding-v2" \
--enable_ranker=True \
--rank_backend="dashscope_rank" \
--rank_model="gte-rerank"
# English / OpenAI
memoryscope --language="en" \
--memory_chat_class="cli_memory_chat" \
--human_name="user" \
--assistant_name="AI" \
--generation_backend="openai_generation" \
--generation_model="gpt-4o" \
--embedding_backend="openai_embedding" \
--embedding_model="text-embedding-3-small" \
--enable_ranker=False
Here are the available options that can be set through either method:
--language: The language used for the conversation.--memory_chat_class: The class name for managing the chat history.--human_name: The name of the human user.--assistant_name: The name of the AI assistant.--generation_backend: The backend used for generating responses.--generation_model: The model used for generating responses.--embedding_backend: The backend used for text embeddings.--embedding_model: The model used for creating text embeddings.--enable_ranker: A boolean indicating whether to use a dummy ranker (default isFalse).--rank_backend: The backend used for ranking responses.--rank_model: The model used for ranking responses.