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 |