Deepevidence API

DeepEvidence API Skill (Evidence-Based Medicine)

This skill calls DeepEvidence's OpenAI-compatible API to produce traceable, source-grounded evidence summaries for clinical use cases (drug safety, guideline interpretation, trial evidence synthesis). All outputs should be clinically verified before use.

Bundled repository files required: the default workflow references local scripts/ and references/ files. If your hosting/distribution does not ship them, use the direct HTTP API method below.

Normative language

To avoid ambiguity, treat requirement levels as:

• MUST: mandatory
• SHOULD: default requirement unless there's a clear reason not to
• RECOMMENDED: preferred best practice
• OPTIONAL: use as needed

When to use / triggers

• Use cases: complex clinical questions; drug safety evidence (dose/contraindications/interactions); guideline interpretation; comparative options; trial evidence synthesis
• High-intent triggers (to reduce accidental activation): DeepEvidence, evidence-based medicine, guideline interpretation, drug safety evidence, clinical trial evidence

Prerequisites

Ask the user to set an API key via environment variable:

• Env var: DEEPEVIDENCE_API_KEY
• MUST NOT commit keys to source control
• MUST NOT print API keys, full request bodies, or full response bodies in logs/errors (may contain sensitive clinical information)

Emergency / urgent-care boundary (MUST)

This skill is not for emergency triage or first-aid instructions. If the user describes or asks about (including but not limited to):

• Chest pain/pressure, suspected stroke/MI, trouble breathing, altered consciousness
• Poisoning/overdose, severe allergic reaction, uncontrolled bleeding
• Infant/child seizures, severe dehydration, high fever with mental status changes

You MUST prioritize advising the user to contact local emergency services / seek immediate medical care, and state that you cannot provide instructions that replace emergency care.

Quickstart (CLI)

Ask a question with the bundled script:

python scripts/chat.py "In T2D with CKD, how should metformin dose be adjusted by eGFR?"

Continue a previous conversation (use the returned conversation_id):

python scripts/chat.py "What if the patient also has mild heart failure?" --conversation-id "prev_id"

OPTIONAL: for multi-tenant user mapping, pass --user using a stable, non-PII external identifier (e.g. --user "opaque-user-123" or --user "hashed-user-id"). The CLI will automatically prefix it with skill_.

Response format (MUST)

When you present DeepEvidence output to the user, you MUST produce a structured Markdown report and follow:

1. Clear sections: use meaningful headings (e.g., "Key takeaways", "Evidence & guidelines", "Dosing / recommendations", "Risks & monitoring", "Uncertainty / evidence gaps")
2. Traceable citations: preserve inline citation markers exactly as returned (e.g. [1], [2]) and preserve their mapping; do not alter/remove markers
3. Table trigger rule (threshold): if the response contains ≥3 parallel items of any of the following, you MUST use a Markdown table:
   • drug/strategy comparisons
   • dosing/adjustment comparisons (e.g., by eGFR strata or population)
   • study/trial outcome comparisons
4. References display (verbatim): if the source response includes a references list, add ## 📚 References and display it verbatim.
   • preserve the original numbering (e.g. [3], [5], [13]); do not renumber or reorder for "continuity"
   • include only bibliographic fields explicitly present in the source response
   • MUST NOT invent DOI/URL/journal names or any citation metadata
   • if references are missing/incomplete, explicitly state "References not returned / incomplete" and do not fill in
5. Clinical disclaimer (MUST): include a clear clinical-use disclaimer at the end (you may briefly restate key points from "Clinical limitations")
6. Attribution (conditional MUST): only if you successfully retrieved evidence content from DeepEvidence, the final line MUST be:
   • > Source: DeepEvidence

Integration (OpenAI SDK)

If the user asks to integrate DeepEvidence into an app, use standard OpenAI SDKs with:

• Base URL：https://deepevidence.cn/api/v1
• Model：deepevidence-agent-v1 (fixed value; do not invent other model names)
• API key: read from DEEPEVIDENCE_API_KEY
• Logging/observability: log only minimal metadata (latency, status, token usage); avoid logging patient-identifiable or sensitive content

Example (Python):

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEEPEVIDENCE_API_KEY"],
    base_url="https://deepevidence.cn/api/v1",
)

resp = client.chat.completions.create(
    model="deepevidence-agent-v1",
    messages=[{"role": "user", "content": "Clinical question"}],
)
print(resp.choices[0].message.content)

Failure handling (MUST)

When DeepEvidence cannot be called or returns insufficient information, you MUST be transparent and MUST NOT pretend you have evidence-backed conclusions:

• Missing DEEPEVIDENCE_API_KEY: tell the user to configure it; do not continue with evidence-backed claims
• Empty / timeout / network error: explicitly say: "Temporarily unable to retrieve evidence-based results. Please try again later or consult a licensed clinician." and state that evidence/references could not be retrieved
• Insufficient direct evidence: explicitly state "No high-quality direct evidence found / conclusion uncertain" and do not overstate certainty
• Incomplete citation metadata: MUST NOT invent DOI/journal/year/authors/links; present only what was returned and label as "metadata incomplete"

Operations & reliability (RECOMMENDED)

For integration and operations, RECOMMENDED minimum handling:

• Missing key: check DEEPEVIDENCE_API_KEY before calling; return actionable guidance if missing
• Timeouts: use bounded retries with reasonable timeouts (avoid infinite retry loops)
• Empty responses: treat as failure (do not interpret as "no risk/no evidence")
• Low/indirect evidence: label uncertainty explicitly; do not overclaim
• Missing references: state "references not returned" instead of filling in

Security (MUST)

• Secrets: read keys from env vars only; do not leak via outputs/logs/screenshots/stack traces
• Sensitive data: treat clinical content as sensitive by default; avoid logging full conversations or full responses; prefer redacted summaries for debugging
• Minimal retention: if you store conversations/logs, provide retention controls and deletion mechanisms
• Destructive operations: deletion/clearing MUST be user-initiated and double-confirmed

Clinical limitations (MUST)

• This skill does not replace clinical judgment, local/regional guidelines, or prescribing information; outputs are for reference only and must be clinically verified
• Decisions must consider patient-specific factors (age, renal function, comorbidities, pregnancy/lactation, allergies), local guidelines, and drug labels
• For urgent symptoms, advise immediate medical care (see "Emergency boundary")
• Evidence quality depends on retrieval scope and knowledge-base updates; may be time-sensitive
• MUST NOT invent missing bibliographic metadata (DOI/journal/year/authors/links)

Advanced features (multi-tenant & conversations)

• API spec: see references/api_reference.md (user mapping via user and X-User-ID, plus conversation extension APIs)
• Conversation manager: run python scripts/manage_conversations.py --help to list/view/delete sessions
  • Deletion guard (MUST): never delete history unless the user explicitly requests deletion for a specific conversation/session (e.g. by conversation_id)
  • Double confirmation (MUST): repeat and confirm the target (id/title/time window) before deleting

Versioning & updates

• Skill version: see frontmatter metadata.version
• API behavior/fields: treat references/api_reference.md as source of truth; update failure paths and citation rules first when behavior changes

Test cases (RECOMMENDED)

Minimal Q&A set to validate: structured report output, citation markers, references block (when present), and stable failure messages.

1. Dose adjustment by strata: "In T2D with CKD, how should metformin dose be adjusted by eGFR?"
2. Drug interaction / contraindication: "Warfarin + common antibiotics: bleeding risk and monitoring recommendations?"
3. Guideline interpretation: "HFrEF first-line medication pillars—what do guidelines recommend and what is the supporting evidence?"
4. Insufficient evidence path: "For a rare disease, what high-quality RCT evidence exists for a new therapy X?" (should explicitly state uncertainty if not found)
5. Timeout/empty response path: simulate network failure/timeout (should print the stable "temporarily unable..." message)

Troubleshooting

• 401 authentication_error: missing/invalid DEEPEVIDENCE_API_KEY
• 429 rate_limit_error: throttled or quota exceeded; reduce frequency or contact admin
• 400 invalid_request_error: request body mismatch; check references/api_reference.md

Portability (avoid dangling dependencies)

This skill references repository-local scripts/docs (e.g. scripts/chat.py, references/api_reference.md). If your hosting/distribution does not bundle them, relative paths will break.

Choose one strategy:

• Strategy A (RECOMMENDED): bundle scripts/ and references/, ensure Python dependencies are available
• Strategy B: call the HTTP API directly (OpenAI-compatible)

Minimal HTTP API example (curl):

curl https://deepevidence.cn/api/v1/chat/completions \
  -H "Authorization: Bearer $DEEPEVIDENCE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepevidence-agent-v1",
    "messages": [{"role": "user", "content": "Clinical question"}]
  }'

Note: do not leak API keys in shell history/logs. Do not write full sensitive responses to logs.