bohrium-paper-search

name	bohrium-paper-search
description	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.

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	/openapi/v2/paper/rag/pass/keyword	English academic papers (title, abstract, corpus, figures)
Patents	/openapi/v2/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

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

"bohrium-paper-search": {
  "enabled": true,
  "apiKey": "YOUR_BOHR_ACCESS_KEY",
  "env": {
    "BOHR_ACCESS_KEY": "YOUR_BOHR_ACCESS_KEY"
  }
}

OpenClaw automatically injects env.BOHR_ACCESS_KEY into the runtime.

Common Code Template

import os, requests

AK = os.environ.get("BOHR_ACCESS_KEY", "")
BASE = "https://open.bohrium.com"
PAPER_BASE = f"{BASE}/openapi/v2/paper"
HEADERS_JSON = {"Authorization": f"Bearer {AK}", "Content-Type": "application/json"}

---

English Paper Search

Basic Search

r = requests.post(f"{PAPER_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"{PAPER_BASE}/rag/pass/keyword", headers=HEADERS_JSON, json={
    "words": ["deep learning", "protein structure"],
    "question": "deep learning protein structure prediction",
    "type": 1,                          # Search version: 0=basic, 1=enhanced
    "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 for the keyword endpoint; only 0=basic and 1=enhanced are supported
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"{PAPER_BASE}/rag/pass/patent", headers=HEADERS_JSON, json={
    "type": 1,
    "words": ["neural network"],
    "question": "neural network",
    "pageSize": 5
})
data = r.json()
for p in data["data"]:
    print(f"  Patent: {p}")

Note: keyword search supports type 0 (basic) and 1 (enhanced); patent search supports type 0, 1, and 2.

Patent Request Parameters

Parameter	Type	Required	Description
type	integer	No	Patent search version; only 0, 1, 2 are supported
words	string[]	Yes	Keyword list
question	string	Yes	Search question or keyword description
pageSize	integer	Yes	Results per page

Patent Response Fields

Returns a dict/object. Patent results are in data[]; top-level fields usually include code, data, message, and trace_id.

---

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; this applies to English paper keyword search and patent search:

{
    "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="$BOHR_ACCESS_KEY"
BASE="https://open.bohrium.com"
PAPER_BASE="$BASE/openapi/v2/paper"

# English paper search
curl -s -X POST "$PAPER_BASE/rag/pass/keyword" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AK" \
  -d '{"words":["deep learning","protein"],"question":"deep learning protein structure prediction","type":1,"startTime":"2024-01-01","endTime":"2025-01-01","jcrZones":["Q1"],"pageSize":5}'

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

---

Troubleshooting

Problem	Cause	Solution
code is non-zero	Request parameter error	Check message field for details
401 Unauthorized	Invalid auth	Verify BOHR_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