deep_search.txt

[{101,use_case.101,use_case.deep_research,architecture.RAG]]

## DeepSearch 101

* <https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch/>

  DeepSearch is a leap in how search can approach complex queries in an 
exhaustively deep manner. By breaking down the process into discrete steps of 
searching, reading, and reasoning, it overcomes many limitations of traditional 
single-pass RAG or multi-hop QA systems.

- DeepSearch runs through an iterative loop of searching, reading, and
  reasoning until it finds the optimal answer.
  - search    action leverages web search engines to explore the Internet,
  - reading   action analyzes specific web pages in detail (e.g. Jina Reader).
  - reasoning action evaluates the current state and determines whether to break
    down the original question into smaller sub-questions or try different search strategies.

  ```
             ┌───── "main loop" ───────────┐
             │                             │
  QUERY ·····>SEARCH  ··>  READ  ··> REASON··> ANSWER
             │ ^                      ·    │
             │ └─· Repeat until ·····─┘    │
             │     budget exceeds          │
             └─────────────────────────────┘
  ```

  Unlike 2024 RAG systems, which typically run a single search-generation pass. 
DeepSearch, in contrast, performs multiple iterations through the pipeline, 
requiring clear stop conditions. These could be based on token usage limits or 
the number of failed attempts.

- This marks a significant departure from classic search requirements, where 
  failing to response within 200ms would doom your solution.
- In 2025, seasoned search developers and RAG engineers prioritize top-1 
  precision and recall over latency, and users have become accustomed to longer 
  processing times – provided they can see the system is "thinking".  [[doc_has.keypoint]]


```
| Launch     | Company        | Product                | License     | Link
| Date       |                |                        | Type        |     
| -----------+----------------+------------------------+-------------+------------------------------------
| 2025-01-20 | DeepSeek       | DeepSeek-r1            | Open source | DeepSeek-R1
| 2025-12-17 | Google         | DeepResearch           | Proprietary | Google Gemini 2
| 2025-02-02 | OpenAI         | DeepResearch           | Proprietary | Introducing Deep Research
| 2025-02-02 | Jina AI        | DeepSearch             | Open source | node-deepresearch | search.jina.ai
| 2025-02-04 | Hugging Face   | Open Deep Research     | Open source | Open Deep Research
| 2025-02-15 | Perplexity     | DeepResearch           | Proprietary | Introducing Perplexity Deep Research
| 2025-02-17 | X AI           | Grok3 with DeepSearch  | Proprietary | Grok 3 Beta
| 2025-02-22 | Baidu Search   | Integrates DeepSeek-r1 | Proprietary | Baidu Integrates DeepSeek-R1
| 2025-02-23 | T.Wechat Search| Integrates DeepSeek-r1 | Proprietary | Tencent Weixin Integrates DeepSeek
```


  In DeepSearch, "gap questions" represent knowledge gaps that need to be filled 
before answering the main question. Rather than directly tackling the original 
question, the agent identifies sub-questions that will build the necessary 
knowledge foundation.

This approach creates a FIFO (First-In-First-Out) queue with rotation, where:
- New gap questions are pushed to the front of the queue
- The original question is always pushed to the back
- The system pulls from the front of the queue at each step

[[use_case.deep_research}]]