Evidence Pack for Trustworthy Paper Citations

Standardize claim, quote, doc_id, chunk_id, offset, page_no, and title as a base template for RAG and agents

Scenario

Every RAG or agent workflow needs a standardized citation package. This example defines an Evidence Pack structure and shows how to build it from Sciverse results.

Estimated calls

~8-15 API calls per build

Tools

agentic-searchcontent

Pipeline

claim 列表→ agentic-search 逐条检索→ content 定位原文→ 标准化 evidence pack

Input example

Input claim:
"Transformer-based models improved protein structure prediction accuracy."
Need: retrieve evidence and output a standardized citation object.

Output example

{
  "claim": "...",
  "quote": "...",
  "doc_id": "...",
  "offset": 12480,
  "title": "...",
  "confidence": "supported"
}

Agent Prompt example

You are an evidence packaging assistant. For each claim, retrieve supporting sources, verify quotes, and output a normalized Evidence Pack object.

Implementation steps

Step 1: Set up the environment

Configure token and dependencies

!pip install httpx
import os
os.environ["SCIVERSE_API_TOKEN"] = "sv-your-token-here"  # 替换为你的真实值

Step 2: Define the Evidence Pack schema

Create a reusable schema for claim-level evidence

from dataclasses import dataclass, asdict
from typing import Optional
import json

@dataclass
class EvidenceItem:
    claim: str
    quote: str
    doc_id: str
    offset: int
    title: str
    venue: Optional[str] = None
    year: Optional[int] = None
    confidence: float = 0.0

@dataclass
class EvidencePack:
    items: list[EvidenceItem]

    def to_json(self) -> str:
        return json.dumps({"evidence_pack": [asdict(i) for i in self.items]}, ensure_ascii=False, indent=2)

# \u793a\u4f8b
pack = EvidencePack(items=[])
print(pack.to_json())

Step 3: Retrieve and build citations for each claim

Search, verify, and populate the schema for each claim

import os
import asyncio
import httpx

BASE = "https://api.sciverse.space"
TOKEN = os.environ["SCIVERSE_API_TOKEN"]
HEADERS = {"Authorization": f"Bearer {TOKEN}"}

async def build_evidence(claim: str) -> Optional[EvidenceItem]:
    """\u4e3a\u5355\u4e2a claim \u68c0\u7d22\u5e76\u6784\u5efa\u5f15\u7528"""
    async with httpx.AsyncClient(timeout=30) as client:
        # Step 1: \u8bed\u4e49\u68c0\u7d22
        resp = await client.post(
            f"{BASE}/agentic-search", headers=HEADERS,
            json={"query": claim, "top_k": 5}
        )
        resp.raise_for_status()
        hits = (resp.json().get("hits") or [])
        if not hits:
            return None
        best = hits[0]

        # Step 2: \u8bfb\u53d6\u539f\u6587\u5b9a\u4f4d\u786e\u5207\u5f15\u7528
        resp2 = await client.get(
            f"{BASE}/content", headers=HEADERS,
            params={"doc_id": best["doc_id"], "offset": best.get("offset", 0), "limit": 800}
        )
        resp2.raise_for_status()
        text = resp2.json()["text"]

        return EvidenceItem(
            claim=claim,
            quote=text[:200],  # \u53d6\u524d 200 \u5b57\u7b26\u4f5c\u4e3a\u5f15\u7528
            doc_id=best["doc_id"],
            offset=best.get("offset", 0),
            title=best["title"],
            confidence=best["score"]
        )

claims = [
    "AlphaFold2 \u5728 CASP14 \u4e2d\u8fbe\u5230\u4e86\u5b9e\u9a8c\u7cbe\u5ea6",
    "mRNA \u7684 LNP \u9012\u9001\u7cfb\u7edf\u663e\u8457\u63d0\u9ad8\u4e86\u7ec6\u80de\u5185\u5316\u6548\u7387",
]

async def main():
    items = []
    for claim in claims:
        evidence = await build_evidence(claim)
        if evidence:
            items.append(evidence)
            print(f"\u2713 {claim[:40]}... -> {evidence.doc_id}")
        else:
            print(f"\u2717 {claim[:40]}... -> no evidence found")
    pack = EvidencePack(items=items)
    print(f"\
Evidence Pack ({len(items)}/{len(claims)} claims grounded):")
    print(pack.to_json())

await main()

Notes

Evidence Pack is a data contract between retrieval and generation.
Keep raw source metadata, not just formatted citations.
Use exact quotes only when they appear in source text.
Add confidence or support labels for downstream reasoning.
Validate the schema before passing it to an LLM.

FAQ

Evidence Pack 是什么?

是将 claim、quote、doc_id、offset、title 等字段标准化后的引用证据包。

为什么需要 Evidence Pack?

它让 Agent 输出的科学结论可以被追溯、复核和复用。

Evidence Pack 应包含哪些字段?

至少包含 claim、quote、doc_id、offset、title、year 和 confidence。

quote 可以由模型改写吗?

不建议，quote 应来自原文，模型可以另写解释但不能替代原文证据。

Next steps

Need an API key?

Create one in Console > Tokens.The same API key works for enabled Sciverse, DianShi, and Skills capabilities, with starter quota available according to account permissions.

Open console