ref: 《MetaGPT智能体开发入门》教程 - 飞书云文档
【多Agent】MetaGPT学习教程 - 飞书云文档
一、配置本地部署的llm和embedding
llm的配置以及embedding配置：
# Full Example: https://github.com/geekan/MetaGPT/blob/main/config/config2.example.yaml # Reflected Code: https://github.com/geekan/MetaGPT/blob/main/metagpt/config2.py # Config Docs: https://docs.deepwisdom.ai/main/en/guide/get_started/configuration.html llm: api_type: 'openai' base_url : 'http://0.0.0.0:8000/v1' model: 'llama' # RAG Embedding. # For backward compatibility, if the embedding is not set and the llm's api_type is either openai or azure, the llm's config will be used. embedding: api_type: "openai" "ollama" # openai / azure / gemini / ollama etc. Check EmbeddingType for more options. base_url: "http://0.0.0.0:8011/" api_key: "" model: "bge-large-en-v1.5" api_version: "v1" embed_batch_size: 100 dimensions: # output dimension of embedding model对于llm 如果max_model_len不能承受更多的话，需要修改metagpt\provider\openrouter_reasoning.py
def _get_max_tokens(self, messages: list[dict]): if not self.auto_max_tokens: return 1024 return self.config.max_token # FIXME # https://community.openai.com/t/why-is-gpt-3-5-turbo-1106-max-tokens-limited-to-4096/494973/3 return min(get_max_completion_tokens(messages, self.model, self.config.max_token), 1024) #4096)对于rag案例：
需要修改metagpt\rag\schema.py
class FAISSRetrieverConfig(IndexRetrieverConfig): """Config for FAISS-based retrievers.""" dimensions: int = Field(default=0, description="Dimensionality of the vectors for FAISS index construction.") _embedding_type_to_dimensions: ClassVar[dict[EmbeddingType, int]] = { EmbeddingType.GEMINI: 768, EmbeddingType.OLLAMA: 1024， #4096, }二、用Discord 发送 github trending 跑通
做好配置
整个过程就是配置
export DISCORD_TOKEN= export DISCORD_CHANNEL_ID=三、用Discord 发送 github trending动态 进阶版
读取readme总结
对应whole_run2.py
四、用Discord发送 huggingface paper动态
对应whole_run-huggingface.py
五、用邮件发送github trending动态
对应whole_run-email.py
