[feature_store] reject_throttle = 0 embedding_model_path = "maidalun1020/bce-embedding-base_v1" reranker_model_path = "maidalun1020/bce-reranker-base_v1" repo_dir = "repodir" work_dir = "workdir" n_clusters = [20, 50] chunk_size = 1024 [web_search] x_api_key = "${YOUR-API-KEY}" domain_partial_order = ["openai.com", "pytorch.org", "readthedocs.io", "nvidia.com", "stackoverflow.com", "juejin.cn", "zhuanlan.zhihu.com", "www.cnblogs.com"] save_dir = "logs/web_search_result" [llm] enable_local = 1 enable_remote = 1 client_url = "http://127.0.0.1:8888/inference" [llm.server] local_llm_path = "Qwen/Qwen1.5-7B-Chat" local_llm_max_text_length = 32000 local_llm_bind_port = 8888 remote_type = "" remote_api_key = "" remote_base_url = "" remote_llm_max_text_length = 32000 remote_llm_model = "" rpm = 500 [worker] enable_sg_search = 0 save_path = "logs/work.txt" [worker.time] start = "00:00:00" end = "23:59:59" has_weekday = 1 [sg_search] binary_src_path = "/usr/local/bin/src" src_access_token = "${YOUR-SRC-ACCESS-TOKEN}" [sg_search.opencompass] github_repo_id = "open-compass/opencompass" introduction = "用于评测大型语言模型(LLM). 它提供了完整的开源可复现的评测框架,支持大语言模型、多模态模型的一站式评测,基于分布式技术,对大参数量模型亦能实现高效评测。评测方向汇总为知识、语言、理解、推理、考试五大能力维度,整合集纳了超过70个评测数据集,合计提供了超过40万个模型评测问题,并提供长文本、安全、代码3类大模型特色技术能力评测。" [sg_search.lmdeploy] github_repo_id = "internlm/lmdeploy" introduction = "lmdeploy 是一个用于压缩、部署和服务 LLM(Large Language Model)的工具包。是一个服务端场景下,transformer 结构 LLM 部署工具,支持 GPU 服务端部署,速度有保障,支持 Tensor Parallel,多并发优化,功能全面,包括模型转换、缓存历史会话的 cache feature 等. 它还提供了 WebUI、命令行和 gRPC 客户端接入。" [frontend] type = "none" webhook_url = "https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxxxxxxx" message_process_policy = "immediate" [frontend.lark_group] app_id = "cli_a53a34dcb778500e" app_secret = "2ajhg1ixSvlNm1bJkH4tJhPfTCsGGHT1" encrypt_key = "abc" verification_token = "def" [frontend.wechat_personal] bind_port = 9527