How to Retrieve Version-Specific Library Docs with the You.com Web Search and Contents APIs

TLDR: AI coding assistants mix up library versions because training data blends every era of a library into one prior, and docs sites rewrite their canonical pages as versions ship. The fix is version-pinned doc retrieval: put the version in the query, restrict the search to the library's docs domain, verify the version string survives the round trip, and feed the fetched text to the model. Working Python with the You.com Web Search and Contents APIs, plus the failure modes that hand your model the wrong docs anyway.

A coding assistant that confidently generates the API of the wrong library version is worse than one that fails loudly. The bug ships, the tests catch it three iterations later, and the developer learns to distrust the tool. The root cause is structural: training corpora contain years of mixed-version documentation and stale forum answers, while the current docs site keeps one canonical URL that serves whatever version shipped this week. The Web Search API from You.com is the retrieval half of the fix, and this guide assembles the whole pattern around it.

Why Do AI Coding Assistants Mix Up Library Versions?

Because nothing in the training data separates "the library in 2022" from "the library now." A model that saw a thousand correct examples of an old API and a hundred of the new one will emit the old one more often, and both feel equally right to it. Libraries that follow semantic versioning signal breaking changes through major versions, and changelogs record what changed and when, but neither document lives inside the model. They live on the web, which is why retrieval, not better prompting, is the durable answer.

How Do You Search for Version-Specific Docs?

Pin the version in the query and restrict the domain. The Web Search API accepts domain filters, so a per-library search function takes the docs domain as a parameter and puts the exact version string in the query text:

import json
import os
import urllib.request

def search_docs(domain: str, query: str, count: int = 5) -> dict:
    req = urllib.request.Request(
        "https://ydc-index.io/v1/search",
        data=json.dumps({
            "query": query,
            "count": count,
            "include_domains": [domain],
        }).encode(),
        headers={
            "Content-Type": "application/json",
            "X-API-Key": os.environ["YOUCOM_API_KEY"],
        },
        method="POST",
    )
    with urllib.request.urlopen(req, timeout=30) as resp:
        return json.load(resp)

payload = search_docs(
    "docs.pydantic.dev",
    "pydantic v2.11 model_validator usage",
)
hits = payload.get("results", {}).get("web", []) or []
for hit in hits:
    snippet = (hit.get("snippets") or [hit.get("description", "")])[0]
    print(hit["url"], "|", snippet[:80])

The example domain and version are placeholders for whatever library your assistant actually targets, but the shape is the contract: version token inside the query, docs domain declared as data rather than buried in the query string, and the same defensive bucket walk every other integration in this family uses. The domain filter is what keeps forum answers and third-party tutorials from outranking the source you actually want, and the version token is what keeps the canonical "latest" page honest when it is outranked by an older, higher-authority page. The full request surface, including the domain and freshness filters, is in the Web Search API guide.

How Do You Verify the Docs Match the Version?

Search ranking is a bet, not a guarantee, so the pipeline verifies before it trusts. The Contents API extracts the full content of a specific URL, which turns the round trip into a checkable fact: fetch the page, then assert the version string appears in the extracted text. A page about pydantic v2 that never says v2 is not a page about pydantic v2.

def fetch_contents(url: str) -> str:
    req = urllib.request.Request(
        "https://ydc-index.io/v1/contents",
        data=json.dumps({
            "urls": [url],
            "formats": ["markdown"],
        }).encode(),
        headers={
            "Content-Type": "application/json",
            "X-API-Key": os.environ["YOUCOM_API_KEY"],
        },
        method="POST",
    )
    with urllib.request.urlopen(req, timeout=90) as resp:
        pages = json.load(resp)
    if not pages:
        return ""
    return pages[0].get("markdown", "") or ""

def verified_text(hits: list, version: str) -> str:
    for hit in hits:
        text = fetch_contents(hit["url"])
        if version in text:
            return text
    return ""

The empty string plays the same role the NO_EVIDENCE sentinel plays in the knowledge cutoff workaround guide: a falsifiable signal that the pipeline found nothing it is willing to stand behind, which the caller must treat as "refuse to answer" rather than "hand the model whatever ranked first." Feeding unverified text to the model is how a version-pinned pipeline still emits version-unpinned answers.

What Are the Failure Modes?

Four of them account for most of the wrong-docs traffic.

Canonical-URL drift is the default behavior of most docs sites: the same URL serves the newest version, so a page that verified against v1.2 last month can serve v2 today. Detection: cache the version assertion result with the URL and re-run it when the cache entry ages out, because a version that once verified is exactly the page most likely to drift. Archived-version exile is the opposite: some sites move old versions to versioned subdomains or archive paths, and the domain filter no longer matches them. Detection: when the version string fails verification across every hit, re-run the search with the domain filter loosened and the version moved into the query. Changelog pagination splits one version's changes across multiple pages, so the extracted text for "what changed in 2.11" can be page two of four. Detection: if the extracted text references pagination, fetch the remaining pages and concatenate before asserting. Forum outrank is the classic: a high-authority Stack Overflow answer about the old API wins the ranking while the domain filter does its job and buries it. It usually surfaces as a hit whose URL domain does not match the docs domain, which the caller can reject before any fetch.

The detection that covers the whole class is the same cheap one from the verification section: log the version string, the fetched URL, and the assertion result on every retrieval, and alert when the assertion pass rate drops. A drifting docs site shows up as a pass-rate cliff on a specific library, usually the same week it ships a major version.

Related Guides

• How to Build a Documentation Search API with the You.com Web Search API
• How to Use the You.com Contents API in Python
• How to Work Around LLM Knowledge Cutoffs with the You.com Web Search API
• Web Search API in Python: A Practical Guide With the You.com SDK

FAQ

Why does my AI assistant use the wrong library version? Its training data mixes every era of the library into a single prior, and older APIs usually have more training examples than newer ones, so the model statistically favors the old one. Retrieval of version-pinned docs is the fix, because it replaces the prior with the current page.

How do I search only one documentation site? Pass the docs domain as a domain filter on the search call instead of embedding it in the query text. The filter excludes forum answers and tutorials, which is what makes the top results trustworthy enough to fetch and verify.

How do I confirm a docs page matches my version? Fetch the full content of the URL with the Contents API and assert the version string appears in the extracted text. A page that never names your version is not about your version, no matter how well it ranked.

What if the docs site serves the latest version on the same URL? That is canonical-URL drift and it is the default behavior of most docs sites. Cache the version assertion result alongside the URL and re-verify when the entry ages out, so a page that drifts fails loudly at the assertion step instead of quietly in the model's output.