LightResearch

LightResearch is a lightweight implementation of Deep Research. It simulates expert-level research analysis processes by automatically generating research plans, multi-turn iterative retrieval, sub-question breakdown, and long review generation.

1. Pipeline Structure Overview

examples/LightResearch.yaml

# LightResearch 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
- retriever.retriever_init
- generation.generation_init
- custom.init_citation_registry
- prompt.webnote_gen_plan
- generation.generate:
    output:
      ans_ls: plan_ls
- prompt.webnote_init_page
- generation.generate:
    output:
      ans_ls: page_ls
- loop:
    times: 10
    steps:
    - branch:
        router:
        - router.webnote_check_page
        branches:
          incomplete:
          - prompt.webnote_gen_subq
          - generation.generate:
              output:
                ans_ls: subq_ls
          - retriever.retriever_search:
              input:
                query_list: subq_ls
              output:
                ret_psg: psg_ls
          - custom.assign_citation_ids_stateful:
              input:
                ret_psg: psg_ls
              output:
                ret_psg: psg_ls
          - prompt.webnote_fill_page
          - generation.generate:
              output:
                ans_ls: page_ls
          complete: []
- prompt.webnote_gen_answer
- generation.generate

2. Compile Pipeline File

Execute the following command to compile this complex workflow

ultrarag build examples/LightResearch.yaml

3. Configure Running Parameters

Modify examples/parameter/LightResearch_parameter.yaml.

examples/parameter/LightResearch_parameter.yaml

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
generation:
  backend: vllm
  backend: openai
  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
      base_url: http://localhost:65503/v1
      concurrency: 8
      model_name: MiniCPM4-8B
      model_name: qwen3-32b
      retries: 3
    vllm:
      dtype: auto
      gpu_ids: 2,3
      gpu_memory_utilization: 0.9
      model_name_or_path: openbmb/MiniCPM4-8B
      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: ''
  system_prompt: 'You are a professional UltraRAG Q&A assistant.'
prompt:
  webnote_fill_page_template: prompt/webnote_fill_page.jinja
  webnote_fill_page_template: prompt/webnote_fill_page_citation.jinja
  webnote_gen_answer_template: prompt/webnote_gen_answer.jinja
  webnote_gen_answer_template: prompt/webnote_gen_report.jinja
  webnote_gen_plan_template: prompt/webnote_gen_plan.jinja
  webnote_gen_subq_template: prompt/webnote_gen_subq.jinja
  webnote_init_page_template: prompt/webnote_init_page.jinja
retriever: 
  backend: sentence_transformers
  backend: openai
  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
      base_url: http://localhost:65504/v1
      model_name: text-embedding-3-small
      model_name: qwen-embedding
    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: ''
  query_instruction: 'Query: '
  top_k: 5

4. Effect Demonstration

After configuration is complete, start the LightResearch Pipeline in UltraRAG UI. Different from standard RAG, you will observe that the system goes through multiple rounds of autonomous thinking and retrieval processes before giving the final answer, finally generating a deep review with multi-level headings, detailed arguments, and accurate citations.

Get Started

Typical Demo

1. Pipeline Structure Overview

2. Compile Pipeline File

3. Configure Running Parameters

4. Effect Demonstration

​1. Pipeline Structure Overview

​2. Compile Pipeline File

​3. Configure Running Parameters

​4. Effect Demonstration

1. Pipeline Structure Overview

2. Compile Pipeline File

3. Configure Running Parameters

4. Effect Demonstration