Install
openclaw skills install bohrium-lkmLarge Knowledge Model (LKM) via open.bohrium.com. Use when: user asks about searching scientific knowledge graphs, verifying claims with evidence, querying variable relationships, or batch OCR of papers. NOT for: general paper search (use bohrium-paper-search), knowledge base management (use bohrium-knowledge-base).
openclaw skills install bohrium-lkmLKM endpoints on open.bohrium.com provide scientific knowledge graph search, claim verification with evidence chains, variable relationship queries, and batch paper OCR.
Core capabilities:
| Endpoint | Function |
|---|---|
/v1/lkm/search | Knowledge graph semantic search |
/v1/lkm/claims/match | Claim matching: find evidence supporting/refuting a scientific claim |
/v1/lkm/claims/:id/evidence | Get detailed evidence chain for a specific claim |
/v1/lkm/variables/batch | Batch query variable relationships (e.g., temperature vs. catalytic activity) |
/v1/lkm/papers/ocr/batch | Batch paper OCR (extract structured content) |
Use when:
Don't use for:
bohrium-paper-searchbohrium-knowledge-basebohrium-pdf-parserNo CLI support — HTTP API only.
"bohrium-lkm": {
"enabled": true,
"apiKey": "YOUR_ACCESS_KEY",
"env": {
"ACCESS_KEY": "YOUR_ACCESS_KEY"
}
}
import os, requests
AK = os.environ["ACCESS_KEY"]
BASE = "https://open.bohrium.com/openapi/v1/lkm"
H = {"accessKey": AK, "Content-Type": "application/json"}
/lkm/searchr = requests.post(f"{BASE}/search", headers=H, json={
"query": "effect of temperature on lithium ion battery degradation",
"limit": 10
})
data = r.json()
print(data)
Parameters:
| Field | Type | Required | Description |
|---|---|---|---|
query | string | yes | Natural language search query |
limit | int | no | Max results |
/lkm/claims/matchSubmit a scientific claim, get back evidence that supports or refutes it (with source papers and relevance scores).
r = requests.post(f"{BASE}/claims/match", headers=H, json={
"text": "Graphene oxide improves the mechanical strength of concrete",
"limit": 5
})
data = r.json()
# data["data"]["variables"] contains matched claims
# data["data"]["papers"] contains related paper details
# data["data"]["new_claim_likely"] indicates if this might be a novel claim
for item in data.get("data", {}).get("variables", []):
print(f" Claim ID: {item['id']}")
print(f" Role: {item.get('role')}") # premise / conclusion
print(f" Score: {item.get('score')}")
print(f" Content: {item.get('content')[:100]}...")
Parameters:
| Field | Type | Required | Description |
|---|---|---|---|
text | string | yes | Scientific claim to verify |
limit | int | no | Max matching results |
Response fields:
| Field | Description |
|---|---|
data.new_claim_likely | Whether this might be a novel claim (insufficient support/refutation) |
data.variables[] | List of matched existing claims |
data.variables[].id | Claim ID (use for evidence chain lookup) |
data.variables[].content | Claim content (with data and references) |
data.variables[].role | premise or conclusion |
data.variables[].score | Relevance score |
data.variables[].provenance | Source info (paper ID, version) |
data.papers | Related paper details map (keyed by paper ID) |
/lkm/claims/:id/evidenceGet detailed evidence for a specific claim ID (source papers, experimental data, reasoning paths).
claim_id = "abc123"
r = requests.get(f"{BASE}/claims/{claim_id}/evidence", headers=H)
data = r.json()
for ev in data.get("data", []):
print(f" Paper: {ev.get('paper_title')}")
print(f" Evidence: {ev.get('text')}")
print(f" Type: {ev.get('evidence_type')}")
/lkm/variables/batchBatch query variable details by ID. Variable IDs can be obtained from /lkm/search or /lkm/claims/match responses.
r = requests.post(f"{BASE}/variables/batch", headers=H, json={
"ids": ["gcn_b2bf079b541a4fa0", "gcn_5cecd02c3d8a4e61"]
})
data = r.json()
for var in data.get("data", {}).get("variables", []):
print(f" ID: {var['id']}")
print(f" Content: {var.get('content')[:100]}...")
# data["data"]["not_found"] lists IDs that were not found
Parameters:
| Field | Type | Required | Description |
|---|---|---|---|
ids | string[] | yes | Variable/claim IDs (obtained from other LKM endpoints) |
/lkm/papers/ocr/batchBatch OCR extraction from papers.
r = requests.post(f"{BASE}/papers/ocr/batch", headers=H, json={
"paper_ids": ["doi:10.1038/s41586-021-03819-2", "doi:10.1126/science.abf3041"]
})
data = r.json()
for paper in data.get("data", []):
print(f" Paper: {paper.get('title')}")
print(f" Status: {paper.get('status')}")
Parameters:
| Field | Type | Required | Description |
|---|---|---|---|
paper_ids | string[] | yes | Paper identifiers (DOI or internal ID) |
AK="YOUR_ACCESS_KEY"
# Knowledge graph search
curl -s -X POST "https://open.bohrium.com/openapi/v1/lkm/search" \
-H "accessKey: $AK" -H "Content-Type: application/json" \
-d '{"query":"lithium battery degradation mechanism","limit":10}' | jq .
# Claim matching
curl -s -X POST "https://open.bohrium.com/openapi/v1/lkm/claims/match" \
-H "accessKey: $AK" -H "Content-Type: application/json" \
-d '{"text":"MoS2 is a promising catalyst for hydrogen evolution","limit":5}' | jq .
# Evidence chain
curl -s -X GET "https://open.bohrium.com/openapi/v1/lkm/claims/abc123/evidence" \
-H "accessKey: $AK" | jq .
# Variable batch query (IDs from search/claims results)
curl -s -X POST "https://open.bohrium.com/openapi/v1/lkm/variables/batch" \
-H "accessKey: $AK" -H "Content-Type: application/json" \
-d '{"ids":["gcn_b2bf079b541a4fa0","gcn_5cecd02c3d8a4e61"]}' | jq .
# Batch OCR
curl -s -X POST "https://open.bohrium.com/openapi/v1/lkm/papers/ocr/batch" \
-H "accessKey: $AK" -H "Content-Type: application/json" \
-d '{"paper_ids":["doi:10.1038/s41586-021-03819-2"]}' | jq .
| Symptom | Cause | Fix |
|---|---|---|
| claims/match returns nothing | Claim too vague | Use specific scientific phrasing with variables and relationships |
| variables/batch timeout | Too many pairs | Submit in batches of 10 or fewer |
| OCR status pending | Backend processing | Poll for results or wait for callback |