Paper Reading Assistant

Given a doc_id, read the full text in segments and extract methods, data, conclusions, and limitations

Scenario

Users already know a paper doc_id and need to read the full text in segments, extracting key information such as methods, datasets, conclusions, and limitations.

Estimated calls

~5-12 API calls per paper

Tools

content

Pipeline

doc_id→ content 循环读取→ 分段拼接全文→ LLM 抽取结构

Input example

Input:
doc_id="paper_xxx"
Need: read the paper and extract structured notes.

Output example

Structured reading notes:
- Methods: ...
- Data: ...
- Results: ...
- Limitations: ...
- Source offsets: ...

Agent Prompt example

You are a paper reading assistant. Read the paper segment by segment using content, extract structured notes, and keep source offsets for every extracted point.

Implementation steps

Step 1: Set up the environment

Configure token and HTTP client

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

Step 2: Read full text in segments

Call content repeatedly with next_offset until the needed sections are read

import os
import asyncio
import httpx

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

async def read_full_text(doc_id: str, chunk_size: int = 4000) -> str:
    """\u5faa\u73af\u8bfb\u53d6\u5168\u6587\uff0c\u76f4\u5230 more=false"""
    full_text = []
    offset = 0
    async with httpx.AsyncClient(timeout=30) as client:
        while True:
            resp = await client.get(
                f"{BASE}/content", headers=HEADERS,
                params={"doc_id": doc_id, "offset": offset, "limit": chunk_size}
            )
            resp.raise_for_status()
            data = resp.json()
            full_text.append(data["text"])
            if not data.get("more", False):
                break
            offset = data["next_offset"]
    return "".join(full_text)

# \u5148\u901a\u8fc7 agentic-search \u83b7\u53d6\u771f\u5b9e doc_id
async def find_doc_id(query: str) -> str:
    """Find a real doc_id before reading full text."""
    async with httpx.AsyncClient(timeout=30) as client:
        resp = await client.post(
            f"{BASE}/agentic-search",
            headers=HEADERS,
            json={"query": query, "top_k": 1},
        )
        resp.raise_for_status()
        hits = (resp.json().get("hits") or [])
        if not hits:
            raise ValueError(f"No papers found for query: {query}")
        return hits[0]["doc_id"]

doc_id = await find_doc_id("AlphaFold2")
text = await read_full_text(doc_id)
print(f"Full text length: {len(text)} chars")
print(f"Preview: {text[:300]}...")

Step 3: Extract structured information with an LLM

Ask an LLM to extract methods, data, conclusions, and limitations with source offsets

from anthropic import Anthropic

client = Anthropic()

def extract_structure(full_text: str) -> str:
    """\u7528 LLM \u62bd\u53d6\u8bba\u6587\u7ed3\u6784\u5316\u4fe1\u606f"""
    # \u5982\u679c\u5168\u6587\u592a\u957f\uff0c\u53d6\u524d 15000 \u5b57\u7b26
    content = full_text[:15000] if len(full_text) > 15000 else full_text

    msg = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=3000,
        messages=[{
            "role": "user",
            "content": f"""\u8bf7\u9605\u8bfb\u4ee5\u4e0b\u8bba\u6587\u5168\u6587\uff0c\u63d0\u53d6\u4ee5\u4e0b\u56db\u4e2a\u65b9\u9762\u7684\u5173\u952e\u4fe1\u606f\uff1a

1. **\u65b9\u6cd5**: \u6838\u5fc3\u6280\u672f\u65b9\u6cd5\u548c\u521b\u65b0\u70b9
2. **\u6570\u636e**: \u4f7f\u7528\u7684\u6570\u636e\u96c6\u3001\u5b9e\u9a8c\u8bbe\u7f6e\u3001\u5173\u952e\u6570\u503c
3. **\u7ed3\u8bba**: \u4e3b\u8981\u53d1\u73b0\u548c\u8d21\u732e
4. **\u5c40\u9650**: \u5df2\u77e5\u5c40\u9650\u548c\u672a\u6765\u5de5\u4f5c\u65b9\u5411

\u8bba\u6587\u5168\u6587:
{content}"""
        }]
    )
    return msg.content[0].text

report = extract_structure(text)
print(report)

Notes

Read in segments to avoid context overflow.
Preserve offsets for every extracted note.
Summarize only after collecting enough source text.
Separate factual extraction from interpretation.
Use this as a building block for review and grounding workflows.

FAQ

如何读取长论文?

用 content 按 offset 分段读取，再分段抽取和合并结果。

阅读报告如何保证可追溯?

方法、数据、结论和局限都应绑定原文位置或 quote。

适合哪些阅读任务?

适合快速抽取论文方法、实验设置、数据集、结论和局限。

论文太长怎么办?

应分段总结，再合并成结构化报告，避免一次性塞入模型上下文。

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