Bohrium Paper & Patent Search

Other

Search academic papers and patents via open.bohrium.com RAG engine. Use when: user asks about searching/finding academic papers, literature review, patent search, or technical survey using keywords or natural language questions. NOT for: knowledge base management, file management, or dataset operations.

Install

openclaw skills install bohrium-paper-search

SKILL: Bohrium Paper & Patent Search

Overview

Search academic papers and patents using the Bohrium RAG search engine. Combines keyword matching with semantic understanding, supporting natural language queries, date range filtering, JCR zone/database filtering, and AI reranking.

Supported Search Types:

Type	Endpoint	Corpus
English papers	`/paper/rag/pass/keyword`	English academic papers (title, abstract, corpus, figures)
Patents	`/paper/rag/pass/patent`	Global patents (with classification, assignee filtering)

Use cases: Literature review, technical survey, method comparison, trend analysis.

No CLI support — all operations use the HTTP API.

Authentication

ACCESS_KEY is read from the OpenClaw config ~/.openclaw/openclaw.json:

"bohrium-paper-search": {
  "enabled": true,
  "apiKey": "YOUR_ACCESS_KEY",
  "env": {
    "ACCESS_KEY": "YOUR_ACCESS_KEY"
  }
}

OpenClaw automatically injects env.ACCESS_KEY into the runtime.

Common Code Template

import os, requests

AK = os.environ.get("ACCESS_KEY", "")
BASE = "https://open.bohrium.com/openapi/v1/paper"
HEADERS_JSON = {"accessKey": AK, "Content-Type": "application/json"}

English Paper Search

Basic Search

r = requests.post(f"{BASE}/rag/pass/keyword", headers=HEADERS_JSON, json={
    "words": ["deep learning", "molecular dynamics"],
    "question": "How to use deep learning for molecular dynamics simulation?",
    "startTime": "",
    "endTime": "",
    "pageSize": 10
})
data = r.json()
print(f"Found {len(data['data'])} papers")
for p in data["data"]:
    print(f"  [{p['doi']}] {p['enName']}")
    print(f"    Journal: {p.get('publicationEnName', '')}, IF: {p.get('impactFactor', 0)}")
    print(f"    Date: {p['coverDateStart']}, Citations: {p['citationNums']}")

Advanced Search (Date Range + JCR Zone + Database + Type)

r = requests.post(f"{BASE}/rag/pass/keyword", headers=HEADERS_JSON, json={
    "words": ["deep learning", "protein structure"],
    "question": "deep learning protein structure prediction",
    "type": 5,                          # Search version (see below)
    "startTime": "2024-01-01",          # Start date YYYY-MM-DD
    "endTime": "2025-01-01",            # End date
    "jcrZones": ["Q1", "Q2"],           # JCR zone filter
    "includeDbs": ["SCI"],              # Database filter
    "areaIds": [],                      # Area IDs (optional)
    "publicationIds": [],               # Publication IDs (optional)
    "pageSize": 20
})

Request Parameters

Parameter	Type	Required	Description
`words`	string[]	Yes	Keyword list; recommend 3-8 English terms
`question`	string	Yes	Natural language research question
`type`	integer	No	Search version: 0=basic, 1=enhanced, 2=pro, 3=pro2.0, 4=image, 5=title+abstract+corpus+image+target
`startTime`	string	No	Start date `YYYY-MM-DD`, empty string for no limit
`endTime`	string	No	End date `YYYY-MM-DD`
`jcrZones`	string[]	No	JCR zone filter, e.g. `["Q1","Q2"]`
`includeDbs`	string[]	No	Database filter, e.g. `["SCI","SSCI"]`
`areaIds`	string[]	No	Area IDs
`publicationIds`	number[]	No	Publication IDs
`subjectIds`	number[]	No	Subject IDs
`pageSize`	integer	Yes	Result count, 1-100, default 50

Response Fields

Field	Description
`code`	0=success
`message`	Status message
`data[]`	Paper list
`data[].doi`	DOI
`data[].paperId`	Paper ID
`data[].enName`	English title
`data[].zhName`	Chinese title
`data[].enAbstract`	English abstract
`data[].zhAbstract`	Chinese abstract
`data[].authors`	Author list
`data[].coverDateStart`	Publication date
`data[].publicationEnName`	Journal name
`data[].publicationCover`	Journal cover URL
`data[].impactFactor`	Impact factor
`data[].citationNums`	Citation count
`data[].popularity`	Popularity score
`data[].pieces`	Related corpus snippet
`data[].figures[]`	Related figures (`figureId`, `imageUrl`, `enExplain`)
`data[].languageType`	0=English

Patent Search

r = requests.post(f"{BASE}/rag/pass/patent", headers=HEADERS_JSON, json={
    "keyword": "neural network training optimization",
    "page": 1,
    "pageSize": 10
})
data = r.json()
for p in data:
    print(f"  Patent: {p}")

Note: Patent search API uses simple parameters. Advanced parameters like rerank, type, words, question are not supported (will cause backend errors).

Patent Request Parameters

Parameter	Type	Required	Description
`keyword`	string	Yes	Search keyword
`page`	integer	Yes	Page number
`pageSize`	integer	Yes	Results per page

Patent Response Fields

Returns array format with patent information objects.

Search Tips

Keyword Selection

# GOOD: 3-8 professional terms
words = ["molecular dynamics", "force field", "deep potential", "neural network"]

# BAD: Too generic
words = ["science", "research"]

Combine question for Better Relevance

words is for exact keyword matching, question is for semantic understanding. Best results come from combining both (for paper search only):

{
    "words": ["GNN", "molecular property", "prediction"],
    "question": "How do graph neural networks predict molecular properties?",
    "pageSize": 20
}

Filter for High-Quality Journals

{
    "words": ["..."],
    "question": "...",
    "jcrZones": ["Q1"],          # Q1 journals only
    "includeDbs": ["SCI"],       # SCI-indexed only
    "startTime": "2023-01-01",   # Recent 2 years
    "endTime": "2025-12-31"
}

curl Examples

AK="YOUR_ACCESS_KEY"
BASE="https://open.bohrium.com/openapi/v1/paper"

# English paper search
curl -s -X POST "$BASE/rag/pass/keyword" \
  -H "Content-Type: application/json" \
  -H "accessKey: $AK" \
  -d '{"words":["deep learning","protein"],"question":"deep learning protein structure prediction","type":5,"startTime":"2024-01-01","endTime":"2025-01-01","jcrZones":["Q1"],"pageSize":5}'

# Patent search
curl -s -X POST "$BASE/rag/pass/patent" \
  -H "Content-Type: application/json" \
  -H "accessKey: $AK" \
  -d '{"words":["neural network"],"question":"neural network training","type":3,"rerank":1,"pageSize":5}'

Troubleshooting

Problem	Cause	Solution
`code` is non-zero	Request parameter error	Check `message` field for details
401 Unauthorized	Invalid accessKey	Verify ACCESS_KEY is correct
Irrelevant results	Keywords too generic or vague question	Use 3-8 professional terms + clear question
Empty results	Date range too narrow or filters too strict	Widen startTime/endTime or remove jcrZones
Response has multiple JSON lines	Normal behavior (streaming)	Parse first line only: `json.loads(response.text.split('\n')[0])`
Patent pieces empty	Some patents lack corpus indexing	Normal; use `abstracts` for content instead