> ## Documentation Index > Fetch the complete documentation index at: https://ultrarag.openbmb.cn/llms.txt > Use this file to discover all available pages before exploring further. # AgentCPM-Report AgentCPM-Report 是一款由 [THUNLP](https://nlp.csai.tsinghua.edu.cn)、中国人民大学 [RUCBM](https://github.com/RUCBM) 和 [ModelBest](https://modelbest.cn/en) 联合研发的开源大语言模型智能体。 该模型基于 [MiniCPM4.1](https://github.com/OpenBMB/MiniCPM)（80亿参数）基座构建，能够接受用户指令，自主进行深度调研并生成长篇报告。 核心亮点： * **卓越的洞察力与全面性**：作为一款 8B 参数量的端侧模型，AgentCPM-Report 在深度调研报告生成任务上展现了惊人的潜力，其表现已赶超部分闭源商用智能体系统。它重新定义了小规模智能体的性能天花板，尤其在洞察力 (Insight) 指标上取得了 SOTA 结果。 * **轻量化与本地化部署**：模型支持在个人计算机上进行敏捷部署。通过结合 UltraRAG 等框架，用户可以构建规模化的本地知识库，生成比通用大模型更专业、更深入的报告。这种轻量级与本地化的特性，为处理隐私数据或私域数据的深度报告写作提供了安全可靠的基础。 本文档旨在介绍如何在 UltraRAG 框架中配置和使用 AgentCPM-Report。 为了获得最佳生成效果，我们强烈推荐下载并使用配套的专有模型 [AgentCPM-Report](https://huggingface.co/openbmb/AgentCPM-Report)。 ## 1. Pipeline 结构概览 AgentCPM-Report Pipeline 采用基于状态机（State Machine）的动态循环架构，系统在初始化后进入核心循环，由智能路由（Router）根据当前任务进展自主决策下一步行动——在信息搜集（Search）、初始规划（Analyst-Init）、内容撰写（Write）和计划扩展（Analyst-Extend）四个分支间灵活切换，通过不断的“检索-规划-写作”迭代，直至完成所有调研任务并输出最终的格式化报告。 ```yaml examples/AgentCPM-Report.yaml icon="https://mintcdn.com/ultrarag/T7GffHzZitf6TThi/images/yaml.svg?fit=max&auto=format&n=T7GffHzZitf6TThi&q=85&s=69b41e79144bc908039c2ee3abbb1c3b" theme={null} # AgentCPM-Report Demo for UltraRAG UI # MCP Server servers: benchmark: servers/benchmark generation: servers/generation retriever: servers/retriever prompt: servers/prompt router: servers/router custom: servers/custom # MCP Client Pipeline pipeline: - benchmark.get_data: output: q_ls: instruction_ls - retriever.retriever_init - generation.generation_init - custom.surveycpm_init_citation_registry - custom.surveycpm_state_init - loop: times: 140 steps: - branch: router: - router.surveycpm_state_router branches: search: - prompt.surveycpm_search: output: prompt_ls: search_prompt_ls - generation.generate: input: prompt_ls: search_prompt_ls output: ans_ls: search_response_ls - custom.surveycpm_parse_search_response: input: response_ls: search_response_ls - retriever.retriever_batch_search: input: batch_query_list: keywords_ls - custom.surveycpm_process_passages_with_citation - custom.surveycpm_update_state analyst-init_plan: - prompt.surveycpm_init_plan: output: prompt_ls: init_plan_prompt_ls - generation.generate: input: prompt_ls: init_plan_prompt_ls output: ans_ls: init_plan_response_ls - custom.surveycpm_after_init_plan: input: response_ls: init_plan_response_ls - custom.surveycpm_update_state write: - prompt.surveycpm_write: output: prompt_ls: write_prompt_ls - generation.generate: input: prompt_ls: write_prompt_ls output: ans_ls: write_response_ls - custom.surveycpm_after_write: input: response_ls: write_response_ls - custom.surveycpm_update_state analyst-extend_plan: - prompt.surveycpm_extend_plan: output: prompt_ls: extend_prompt_ls - generation.generate: input: prompt_ls: extend_prompt_ls output: ans_ls: extend_response_ls - custom.surveycpm_after_extend: input: response_ls: extend_response_ls - custom.surveycpm_update_state done: [] - custom.surveycpm_format_output: output: ans_ls: final_survey_ls ``` ## 2. 编译Pipeline文件 执行以下命令编译该工作流： ```shell theme={null} ultrarag build examples/AgentCPM-Report.yaml ``` ## 3. 配置运行参数 修改 `examples/parameter/AgentCPM-Report_parameter.yaml`。 想要调整调研深度？ 请在 `custom` 配置中按需调整：增加 `surveycpm_max_step` 以延长研究时间，提高 `surveycpm_max_extend_step` 以获得更详实的扩写内容。若对质量有极高要求，请务必开启 `surveycpm_hard_mode`（硬核模式）。 ```yaml examples/parameter/AgentCPM-Report_parameter.yaml icon="https://mintcdn.com/ultrarag/T7GffHzZitf6TThi/images/yaml.svg?fit=max&auto=format&n=T7GffHzZitf6TThi&q=85&s=69b41e79144bc908039c2ee3abbb1c3b" theme={null} benchmark: benchmark: key_map: gt_ls: golden_answers q_ls: question limit: -1 name: nq path: data/sample_nq_10.jsonl seed: 42 shuffle: false custom: surveycpm_hard_mode: false # [!code --] surveycpm_hard_mode: true # [!code ++] surveycpm_max_extend_step: 12 surveycpm_max_step: 140 generation: backend: vllm # [!code --] backend: openai # [!code ++] backend_configs: hf: batch_size: 8 gpu_ids: 2,3 model_name_or_path: openbmb/AgentCPM-Report trust_remote_code: true openai: api_key: abc base_delay: 1.0 base_url: http://localhost:8000/v1 # [!code --] base_url: http://localhost:65506/v1 # [!code ++] concurrency: 8 model_name: MiniCPM4-8B # [!code --] model_name: AgentCPM-Report # [!code ++] retries: 3 vllm: dtype: auto gpu_ids: 2,3 gpu_memory_utilization: 0.9 model_name_or_path: openbmb/AgentCPM-Report trust_remote_code: true extra_params: chat_template_kwargs: enable_thinking: false sampling_params: max_tokens: 2048 temperature: 0.7 top_p: 0.8 system_prompt: '' # [!code --] system_prompt: '你是一个专业的UltraRAG问答助手。请一定记住使用中文回答问题。' # [!code ++] prompt: surveycpm_extend_plan_template: prompt/surveycpm_extend_plan.jinja surveycpm_init_plan_template: prompt/surveycpm_init_plan.jinja surveycpm_search_template: prompt/surveycpm_search.jinja surveycpm_write_template: prompt/surveycpm_write.jinja retriever: backend: sentence_transformers # [!code --] backend: openai # [!code ++] backend_configs: bm25: lang: en save_path: index/bm25 infinity: bettertransformer: false model_warmup: false pooling_method: auto trust_remote_code: true openai: api_key: abc base_url: https://api.openai.com/v1 # [!code --] base_url: http://localhost:65504/v1 # [!code ++] model_name: text-embedding-3-small # [!code --] model_name: qwen-embedding # [!code ++] sentence_transformers: sentence_transformers_encode: encode_chunk_size: 256 normalize_embeddings: false psg_prompt_name: document psg_task: null q_prompt_name: query q_task: null trust_remote_code: true batch_size: 16 collection_name: wiki corpus_path: data/corpus_example.jsonl gpu_ids: '1' index_backend: faiss index_backend_configs: faiss: index_chunk_size: 10000 index_path: index/index.index index_use_gpu: true milvus: id_field_name: id id_max_length: 64 index_chunk_size: 1000 index_params: index_type: AUTOINDEX metric_type: IP metric_type: IP search_params: metric_type: IP params: {} text_field_name: contents text_max_length: 60000 token: null uri: index/milvus_demo.db vector_field_name: vector is_demo: false is_multimodal: false model_name_or_path: openbmb/MiniCPM-Embedding-Light query_instruction: '' # [!code --] query_instruction: 'Query: ' # [!code ++] top_k: 5 # [!code --] top_k: 20 # [!code ++] ``` ## 4. 效果演示 配置完成后，在 UltraRAG UI 中启动 AgentCPM-Report Pipeline。 由于万字综述的生成涉及大量并发检索与多轮推理，耗时通常在 10 分钟以上。您可以利用 UI 的[后台运行](/pages/cn/ui/start)功能，任务完成后再回来查看最终报告。