Repo context selection
Rank which files are actually relevant to a task before spending context (and tokens) on them.
"Repository retrieval and context selection" is a core coding-agent pattern: an agent shouldn't stuff every candidate file into the model's context — it's slow, expensive, and dilutes attention. Instead, score each retrieved file for how relevant it is to the task and keep only the ones that clear a bar. A Jev score is ideal here because it's a compact, calibrated judgment you can threshold and audit, run over many files in parallel for a fraction of a cent each — so the agent reads the three files that matter, not the thirty the retriever returned.
在线试用
这是真实的东西,不是模型演示。编辑输入、点击运行,Jev 会在一次往返中返回每一个类型化答案——免费、无需注册。现在想象同一次调用并行地跑在数千条数据上。
选一个示例,调整输入,然后点击 运行 Jev.
Jev 做出的决策
在一次调用中，Jev 针对同一份输入并行评估以下每一项：
How relevant is this file to completing the task?
在一个有序量表上给它打分：
- irrelevant
- loosely related
- relevant
- central to the fix
Should this file be included in the agent's working context for this task?
返回一个已校准的是/否概率。
Is this file a likely place the fix will need to be made?
返回一个已校准的是/否概率。
确切的请求
这就是实时演示背后真实的载荷——复制它，改一下 state，你就开始构建了：
{
"model": "jev-latest",
"state": "Task: \"Fix the bug where refunds over the order total are silently accepted.\"\n\nCandidate file surfaced by retrieval: `services/billing/refund.py`\n\nSnippet:\n\n def process_refund(order, amount):\n # TODO: validate against remaining balance\n gateway.refund(order.id, amount)\n record_refund(order, amount)",
"questions": {
"relevance": {
"type": "score",
"instructions": "How relevant is this file to completing the task?",
"criteria": [
"irrelevant",
"loosely related",
"relevant",
"central to the fix"
]
},
"include": {
"type": "noul",
"instructions": "Should this file be included in the agent's working context for this task?"
},
"likely_edit_site": {
"type": "noul",
"instructions": "Is this file a likely place the fix will need to be made?"
}
}
}把它接进你的代码
读取类型化的答案，用普通代码分支判断——无需解析。自动处理高置信度的情形，把不确定的路由给更大的模型或人工。这只是一次 API 调用，而且输出免费，所以把你需要的每个问题一次都问了吧。