> ## 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.

# Search-o1

## 简介

Search-o1 提出一种将大规模推理模型与 **自主检索增强生成（Agentic RAG）** 和 **文档内推理（Reason-in-Documents）** 结合的框架。当模型在推理过程中遇到知识空缺时，会主动检索外部信息、进行精炼，并将结果注入推理链，从而提升科学、数学、编程等复杂任务中的推理准确性与稳健性。

<Note>论文链接：[Arxiv](https://arxiv.org/abs/2501.05366)。</Note>

### 流程

<img src="https://mintcdn.com/ultrarag/eZB30PCHMVOmoD_u/images/pipeline/searcho1/suanfa.png?fit=max&auto=format&n=eZB30PCHMVOmoD_u&q=85&s=9e2cece695d930c1d41091e85524dc67" alt="" width="1366" height="940" data-path="images/pipeline/searcho1/suanfa.png" />

简而言之，Search-o1 先以原始问题开展推理；一旦识别信息缺口，即生成子问题并触发检索；随后对召回应答进行精炼，提取关键信息回注入推理过程，直到形成可置信的最终答案。

## 复现

### 编写Pipeline

基于上面的逻辑，可以写出如下 Pipeline：

```yaml examples/search_o1.yaml icon="https://mintcdn.com/ultrarag/T7GffHzZitf6TThi/images/yaml.svg?fit=max&auto=format&n=T7GffHzZitf6TThi&q=85&s=69b41e79144bc908039c2ee3abbb1c3b" theme={null}
# Search-o1 Demo

# MCP Servers
servers:
  benchmark: servers/benchmark
  generation: servers/generation
  retriever: servers/retriever
  prompt: servers/prompt
  evaluation: servers/evaluation
  router: servers/router
  custom: servers/custom

# MCP Client Pipeline
pipeline:
- benchmark.get_data
- retriever.retriever_init
- generation.generation_init
- custom.search_o1_init_list
- prompt.search_o1_init
- generation.generate
- loop:
    times: 10
    steps:
    - branch:
        router:
        - router.search_o1_check
        branches:
          retrieve:
          - custom.search_o1_query_extract
          - retriever.retriever_search:
              input:
                query_list: extract_query_list
          - custom.search_o1_reasoning_extract
          - custom.search_o1_combine_list
          - prompt.search_o1_reasoning_indocument
          - generation.generate
          - custom.search_o1_extract_final_information
          - custom.search_o1_combine_final_information
          - prompt.search_o1_insert
          - generation.generate
          stop: []   
- custom.output_extract_from_boxed
- evaluation.evaluate
```

### 编译Pipeline文件

```shell theme={null}
ultrarag build examples/search_o1.yaml
```

### 修改参数文件

```yaml examples/parameters/search_o1_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: {}
evaluation:
  metrics:
  - acc
  - f1
  - em
  - coverem
  - stringem
  - rouge-1
  - rouge-2
  - rouge-l
  save_path: output/evaluate_results.json
generation:
  backend: vllm
  backend_configs:
    hf:
      batch_size: 8
      gpu_ids: 2,3
      model_name_or_path: openbmb/MiniCPM4-8B
      trust_remote_code: true
    openai:
      api_key: abc
      base_delay: 1.0
      base_url: http://localhost:8000/v1
      concurrency: 8
      model_name: MiniCPM4-8B
      retries: 3
    vllm:
      dtype: auto
      gpu_ids: 2,3
      gpu_memory_utilization: 0.9
      model_name_or_path: openbmb/MiniCPM4-8B # [!code --]
      model_name_or_path: Qwen/QwQ-32B # [!code ++]
      trust_remote_code: true
  extra_params:
    chat_template_kwargs: # [!code --]
      enable_thinking: false # [!code --]
    top_k: 20 # [!code ++]
    repetition_penalty: 1.05 # [!code ++]
    include_stop_str_in_output: true # [!code ++]
    stop: # [!code ++]
    - <|im_end|> # [!code ++]
    - <|end_search_query|> # [!code ++]
  sampling_params:
    max_tokens: 2048 # [!code --]
    max_tokens: 32768 # [!code ++]
    temperature: 0.7
    top_p: 0.8
  system_prompt: ''
prompt:
  searcho1_reasoning_template: prompt/search_o1_reasoning.jinja
  searcho1_refine_template: prompt/search_o1_refinement.jinja
retriever:
  backend: sentence_transformers
  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
      model_name: text-embedding-3-small
    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 # [!code --]
  model_name_or_path: Qwen/Qwen3-Embedding-0.6B # [!code ++]
  query_instruction: ''
  top_k: 5
```

### 运行Pipeline文件

```shell theme={null}
ultrarag run examples/search_o1.yaml
```