mirror of
https://github.com/jina-ai/node-DeepResearch.git
synced 2025-12-26 06:28:56 +08:00
fix: strict evaluator
This commit is contained in:
Han Xiao
parent
de640b7b29
commit
b0c07162dd
@ -1,6 +1,6 @@
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# DeepResearch
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[Official UI](https://search.jina.ai/) | [UI Code](https://github.com/jina-ai/deepsearch-ui) | [Official API](https://jina.ai/deepsearch) | [Blog](https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch)
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[Official UI](https://search.jina.ai/) | [UI Code](https://github.com/jina-ai/deepsearch-ui) | [Stable API](https://jina.ai/deepsearch) | [Blog](https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch)
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Keep searching, reading webpages, reasoning until an answer is found (or the token budget is exceeded). Useful for deeply investigating a query.
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@ -32,11 +32,11 @@ flowchart LR
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Whether you like this implementation or not, I highly recommend you to read DeepSearch/DeepResearch implementation guide I wrote, which gives you a gentle intro to this topic.
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- [English](https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch)
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- [中文微信公众号](https://mp.weixin.qq.com/s/-pPhHDi2nz8hp5R3Lm_mww)
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- [English Part I](https://jina.ai/news/a-practical-guide-to-implementing-deepsearch-deepresearch), [Part II](https://jina.ai/news/snippet-selection-and-url-ranking-in-deepsearch-deepresearch)
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- [中文微信公众号 第一讲](https://mp.weixin.qq.com/s/-pPhHDi2nz8hp5R3Lm_mww), [第二讲](https://mp.weixin.qq.com/s/apnorBj4TZs3-Mo23xUReQ)
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- [日本語: DeepSearch/DeepResearch 実装の実践ガイド](https://jina.ai/ja/news/a-practical-guide-to-implementing-deepsearch-deepresearch)
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## Test it yourself
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## Test it Yourself
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We host an online deployment of this **exact** codebase, which allows you to do a vibe-check; or use it as daily productivity tools.
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@ -41,7 +41,7 @@
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"queryRewriter": { "temperature": 0.1 },
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"agent": { "temperature": 0.7 },
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"agentBeastMode": { "temperature": 0.7 },
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"fallback": { "temperature": 0 }
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"fallback": {"maxTokens": 8000, "model": "gemini-2.0-flash-lite"}
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}
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},
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"openai": {
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@ -47,7 +47,7 @@
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"queryRewriter": {"maxTokens": 2000},
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"agent": { },
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"agentBeastMode": { },
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"fallback": {"maxTokens": 4000}
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"fallback": {"maxTokens": 8000, "model": "gemini-2.0-flash-lite"}
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}
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},
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"openai": {
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44
package-lock.json
generated
44
package-lock.json
generated
@ -11,8 +11,6 @@
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"dependencies": {
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"@ai-sdk/google": "^1.0.0",
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"@ai-sdk/openai": "^1.1.9",
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"@dmitryrechkin/json-schema-to-zod": "^1.0.0",
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"add": "^2.0.6",
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"ai": "^4.1.26",
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"axios": "^1.7.9",
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"commander": "^13.1.0",
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@ -20,7 +18,7 @@
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"dotenv": "^16.4.7",
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"duck-duck-scrape": "^2.2.7",
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"express": "^4.21.2",
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"json-schema-to-zod": "^2.6.0",
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"hjson": "^3.2.2",
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"node-fetch": "^3.3.2",
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"undici": "^7.3.0",
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"zod": "^3.22.4",
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@ -30,6 +28,7 @@
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"@types/commander": "^2.12.0",
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"@types/cors": "^2.8.17",
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"@types/express": "^5.0.0",
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"@types/hjson": "^2.4.6",
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"@types/jest": "^29.5.14",
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"@types/node": "^22.10.10",
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"@types/node-fetch": "^2.6.12",
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@ -765,14 +764,6 @@
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"node": ">=12"
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}
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},
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"node_modules/@dmitryrechkin/json-schema-to-zod": {
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"version": "1.0.0",
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"resolved": "https://registry.npmjs.org/@dmitryrechkin/json-schema-to-zod/-/json-schema-to-zod-1.0.0.tgz",
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"integrity": "sha512-avV26RC8CRzhnL6AvQsURlkd071SXlcPURxiYFsRLpsMoDDXBBGJDIsNQTvYmevq31WHYdwGCKGgQKC0YIjDGg==",
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"dependencies": {
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"zod": "^3.23.8"
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}
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},
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"node_modules/@eslint-community/eslint-utils": {
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"version": "4.4.1",
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"resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.4.1.tgz",
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@ -1665,6 +1656,13 @@
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"@types/node": "*"
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}
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},
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"node_modules/@types/hjson": {
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"version": "2.4.6",
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"resolved": "https://registry.npmjs.org/@types/hjson/-/hjson-2.4.6.tgz",
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"integrity": "sha512-tEQ4hlyKfsb9WWeueUY5eRnU2eK+KdE0eofSpQ05v9Aah4VvWwIRIid/ZN1zZZ0TfeVTRDgabKKqKZXEkfD3Sw==",
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"dev": true,
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"license": "MIT"
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},
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"node_modules/@types/http-errors": {
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"version": "2.0.4",
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"resolved": "https://registry.npmjs.org/@types/http-errors/-/http-errors-2.0.4.tgz",
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@ -2079,12 +2077,6 @@
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"node": ">=0.4.0"
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}
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},
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"node_modules/add": {
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"version": "2.0.6",
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"resolved": "https://registry.npmjs.org/add/-/add-2.0.6.tgz",
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"integrity": "sha512-j5QzrmsokwWWp6kUcJQySpbG+xfOBqqKnup3OIk1pz+kB/80SLorZ9V8zHFLO92Lcd+hbvq8bT+zOGoPkmBV0Q==",
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"license": "MIT"
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},
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"node_modules/agent-base": {
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"version": "7.1.3",
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"resolved": "https://registry.npmjs.org/agent-base/-/agent-base-7.1.3.tgz",
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@ -4249,6 +4241,15 @@
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"node": ">=8"
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}
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},
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"node_modules/hjson": {
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"version": "3.2.2",
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"resolved": "https://registry.npmjs.org/hjson/-/hjson-3.2.2.tgz",
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"integrity": "sha512-MkUeB0cTIlppeSsndgESkfFD21T2nXPRaBStLtf3cAYA2bVEFdXlodZB0TukwZiobPD1Ksax5DK4RTZeaXCI3Q==",
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"license": "MIT",
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"bin": {
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"hjson": "bin/hjson"
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}
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},
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"node_modules/html-entities": {
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"version": "2.5.2",
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"resolved": "https://registry.npmjs.org/html-entities/-/html-entities-2.5.2.tgz",
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@ -5274,15 +5275,6 @@
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"integrity": "sha512-es94M3nTIfsEPisRafak+HDLfHXnKBhV3vU5eqPcS3flIWqcxJWgXHXiey3YrpaNsanY5ei1VoYEbOzijuq9BA==",
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"license": "(AFL-2.1 OR BSD-3-Clause)"
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},
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"node_modules/json-schema-to-zod": {
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"version": "2.6.0",
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"resolved": "https://registry.npmjs.org/json-schema-to-zod/-/json-schema-to-zod-2.6.0.tgz",
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"integrity": "sha512-6sFZqOzHZeON8g2ZW5HJ114Hb/FffNCjWh8dgulJaKFkUqKCEWZAzF4+g07SQpfBZF7HXemwedtdLypZzmnVpQ==",
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"license": "ISC",
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"bin": {
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"json-schema-to-zod": "dist/cjs/cli.js"
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}
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},
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"node_modules/json-schema-traverse": {
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"version": "0.4.1",
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"resolved": "https://registry.npmjs.org/json-schema-traverse/-/json-schema-traverse-0.4.1.tgz",
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@ -35,6 +35,7 @@
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"dotenv": "^16.4.7",
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"duck-duck-scrape": "^2.2.7",
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"express": "^4.21.2",
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"hjson": "^3.2.2",
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"node-fetch": "^3.3.2",
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"undici": "^7.3.0",
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"zod": "^3.22.4",
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@ -44,6 +45,7 @@
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"@types/commander": "^2.12.0",
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"@types/cors": "^2.8.17",
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"@types/express": "^5.0.0",
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"@types/hjson": "^2.4.6",
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"@types/jest": "^29.5.14",
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"@types/node": "^22.10.10",
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"@types/node-fetch": "^2.6.12",
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16
src/agent.ts
16
src/agent.ts
@ -308,17 +308,17 @@ export async function getResponse(question?: string,
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// evaluationMetrics[currentQuestion] =
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// await evaluateQuestion(currentQuestion, context, SchemaGen)
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// }
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if (currentQuestion.trim() === question && step === 1) {
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if (currentQuestion.trim() === question && totalStep === 1) {
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// only add evaluation for initial question, once at step 1
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evaluationMetrics[currentQuestion] =
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await evaluateQuestion(currentQuestion, context, SchemaGen)
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// force strict eval for the original question, only once.
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// force strict eval for the original question, at last, only once.
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evaluationMetrics[currentQuestion].push('strict')
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} else if (currentQuestion.trim() !== question) {
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evaluationMetrics[currentQuestion] = []
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}
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if (step === 1 && evaluationMetrics[currentQuestion].includes('freshness')) {
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if (totalStep === 1 && evaluationMetrics[currentQuestion].includes('freshness')) {
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// if it detects freshness, avoid direct answer at step 1
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allowAnswer = false;
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allowReflect = false;
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@ -403,7 +403,7 @@ export async function getResponse(question?: string,
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console.log('Updated references:', thisStep.references)
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if (step === 1 && thisStep.references.length === 0) {
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if (totalStep === 1 && thisStep.references.length === 0) {
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// LLM is so confident and answer immediately, skip all evaluations
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// however, if it does give any reference, it must be evaluated, case study: "How to configure a timeout when loading a huggingface dataset with python?"
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thisStep.isFinal = true;
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@ -423,9 +423,10 @@ export async function getResponse(question?: string,
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currentQuestion
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);
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if (!evaluationMetrics[currentQuestion].includes('attribution')) {
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evaluationMetrics[currentQuestion].push('attribution')
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}
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// is this really required???
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// if (!evaluationMetrics[currentQuestion].includes('attribution')) {
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// evaluationMetrics[currentQuestion].push('attribution')
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// }
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}
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updateContext({
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@ -470,6 +471,7 @@ Your journey ends here. You have successfully answered the original question. Co
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if (evaluation.type === 'strict') {
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finalAnswerPIP = evaluation.improvement_plan || '';
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// remove 'strict' from the evaluation metrics
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console.log('Remove `strict` from evaluation metrics')
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evaluationMetrics[currentQuestion] = evaluationMetrics[currentQuestion].filter(e => e !== 'strict');
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}
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if (badAttempts >= maxBadAttempts) {
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@ -2,23 +2,61 @@ import {GenerateObjectResult} from 'ai';
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import {AnswerAction, EvaluationResponse, EvaluationType, KnowledgeItem, PromptPair, TrackerContext} from '../types';
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import {ObjectGeneratorSafe} from "../utils/safe-generator";
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import {Schemas} from "../utils/schemas";
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import {removeExtraLineBreaks} from "../utils/text-tools";
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const TOOL_NAME = 'evaluator';
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function getRejectAllAnswersPrompt(question: string, answer: AnswerAction): PromptPair {
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return {
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system: `You are a ruthless evaluator trained to REJECT answers.
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Your job is to find ANY weakness in the presented JSON answer. Extremely strict standards of evidence apply.
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Identity EVERY missing detail. First, argue AGAINST the conclusion with the strongest possible case.
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Then, argue FOR the conclusion.
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Only after considering both perspectives, synthesize a final improvement plan.
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function getRejectAllAnswersPrompt(question: string, answer: AnswerAction, allKnowledge: KnowledgeItem[]): PromptPair {
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const KnowledgeStr = allKnowledge.map((k, idx) => {
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const aMsg = `
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<knowledge-${idx+1}>
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${k.question}
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Any JSON formatting/structure/syntax issue should not be the reason to rejection.
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${k.updated && (k.type === 'url' || k.type === 'side-info') ? `
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<knowledge-datetime>
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${k.updated}
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</knowledge-datetime>
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` : ''}
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${k.references && k.type === 'url' ? `
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<knowledge-url>
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${k.references[0]}
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</knowledge-url>
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` : ''}
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${k.answer}
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</knowledge-${idx+1}>
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`.trim();
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return removeExtraLineBreaks(aMsg);
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})
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return {
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system: `
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You are a ruthless answer evaluator trained to REJECT answers.
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Given a question-answer pair, your job is to find ANY weakness in the presented answer.
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Extremely strict standards of evidence apply.
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Identity EVERY missing detail.
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First, argue AGAINST the answer with the strongest possible case.
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Then, argue FOR the answer.
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Only after considering both perspectives, synthesize a final improvement plan starts with "For the best answer, you must...".
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The following knowledge items are provided for your reference. Note that some of them may not be directly related to the question/answer user provided, but may give some subtle hints and insights:
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${KnowledgeStr.join('\n\n')}
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`,
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user: `
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question: ${question}
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answer: ${JSON.stringify(answer)}
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<question>
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${question}
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</question>
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Here is my answer for the question:
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<answer>
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${answer.answer}
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</answer>
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Could you please evaluate my answer based on your knowledge and strict standards? If you decide to reject the answer, please tell me how to improve it.
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`
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}
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}
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@ -37,7 +75,7 @@ ${question}
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${answer}
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</answer>
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Please look at my answer and think.
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Please read and think.
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`
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}
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}
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@ -632,7 +670,6 @@ export async function evaluateAnswer(
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let prompt: { system: string; user: string } | undefined
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switch (evaluationType) {
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case 'attribution': {
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// Safely handle references and ensure we have content
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if (allKnowledge.length === 0) {
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return {
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pass: false,
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@ -659,7 +696,7 @@ export async function evaluateAnswer(
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prompt = getCompletenessPrompt(question, action.answer);
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break;
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case 'strict':
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prompt = getRejectAllAnswersPrompt(question, action);
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prompt = getRejectAllAnswersPrompt(question, action, allKnowledge);
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break;
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default:
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console.error(`Unknown evaluation type: ${evaluationType}`);
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@ -8,6 +8,7 @@ import {
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} from "ai";
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import {TokenTracker} from "./token-tracker";
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import {getModel, ToolName, getToolConfig} from "../config";
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import Hjson from 'hjson'; // Import Hjson library
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interface GenerateObjectResult<T> {
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object: T;
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@ -29,6 +30,104 @@ export class ObjectGeneratorSafe {
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this.tokenTracker = tokenTracker || new TokenTracker();
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}
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/**
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* Creates a distilled version of a schema by removing all descriptions
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* This makes the schema simpler for fallback parsing scenarios
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*/
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private createDistilledSchema<T>(schema: z.ZodType<T> | Schema<T>): z.ZodType<T> | Schema<T> {
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// For zod schemas
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if (schema instanceof z.ZodType) {
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return this.stripZodDescriptions(schema);
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}
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// For AI SDK Schema objects
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if (typeof schema === 'object' && schema !== null) {
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return this.stripSchemaDescriptions(schema as Schema<T>);
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}
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// If we can't determine the schema type, return as is
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return schema;
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}
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/**
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* Recursively strips descriptions from Zod schemas
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*/
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private stripZodDescriptions<T>(zodSchema: z.ZodType<T>): z.ZodType<T> {
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if (zodSchema instanceof z.ZodObject) {
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const shape = zodSchema._def.shape();
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const newShape: Record<string, any> = {};
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for (const key in shape) {
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if (Object.prototype.hasOwnProperty.call(shape, key)) {
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// Recursively strip descriptions from nested schemas
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newShape[key] = this.stripZodDescriptions(shape[key]);
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}
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}
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return z.object(newShape) as unknown as z.ZodType<T>;
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}
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if (zodSchema instanceof z.ZodArray) {
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return z.array(this.stripZodDescriptions(zodSchema._def.type)) as unknown as z.ZodType<T>;
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}
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if (zodSchema instanceof z.ZodString) {
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// Create a new string schema without any describe() metadata
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return z.string() as unknown as z.ZodType<T>;
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}
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if (zodSchema instanceof z.ZodUnion || zodSchema instanceof z.ZodIntersection) {
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// These are more complex schemas that would need special handling
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// This is a simplified implementation
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return zodSchema;
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}
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// For other primitive types or complex types we're not handling specifically,
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// return as is
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return zodSchema;
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}
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/**
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* Strips descriptions from AI SDK Schema objects
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*/
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private stripSchemaDescriptions<T>(schema: Schema<T>): Schema<T> {
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// Deep clone the schema to avoid modifying the original
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const clonedSchema = JSON.parse(JSON.stringify(schema));
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// Recursively remove description properties
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const removeDescriptions = (obj: any) => {
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if (typeof obj !== 'object' || obj === null) return;
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if (obj.properties) {
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for (const key in obj.properties) {
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// Remove description property
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if (obj.properties[key].description) {
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delete obj.properties[key].description;
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}
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// Recursively process nested properties
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removeDescriptions(obj.properties[key]);
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}
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||||
}
|
||||
|
||||
// Handle arrays
|
||||
if (obj.items) {
|
||||
if (obj.items.description) {
|
||||
delete obj.items.description;
|
||||
}
|
||||
removeDescriptions(obj.items);
|
||||
}
|
||||
|
||||
// Handle any other nested objects that might contain descriptions
|
||||
if (obj.anyOf) obj.anyOf.forEach(removeDescriptions);
|
||||
if (obj.allOf) obj.allOf.forEach(removeDescriptions);
|
||||
if (obj.oneOf) obj.oneOf.forEach(removeDescriptions);
|
||||
};
|
||||
|
||||
removeDescriptions(clonedSchema);
|
||||
return clonedSchema;
|
||||
}
|
||||
|
||||
async generateObject<T>(options: GenerateOptions<T>): Promise<GenerateObjectResult<T>> {
|
||||
const {
|
||||
model,
|
||||
@ -54,7 +153,7 @@ export class ObjectGeneratorSafe {
|
||||
return result;
|
||||
|
||||
} catch (error) {
|
||||
// First fallback: Try manual JSON parsing of the error response
|
||||
// First fallback: Try manual parsing of the error response
|
||||
try {
|
||||
const errorResult = await this.handleGenerateObjectError<T>(error);
|
||||
this.tokenTracker.trackUsage(model, errorResult.usage);
|
||||
@ -67,15 +166,20 @@ export class ObjectGeneratorSafe {
|
||||
const failedOutput = (parseError as any).text;
|
||||
console.error(`${model} failed on object generation ${failedOutput} -> manual parsing failed again -> trying fallback model`, fallbackModel);
|
||||
try {
|
||||
// Create a distilled version of the schema without descriptions
|
||||
const distilledSchema = this.createDistilledSchema(schema);
|
||||
console.log('Distilled schema', distilledSchema)
|
||||
|
||||
const fallbackResult = await generateObject({
|
||||
model: fallbackModel,
|
||||
schema,
|
||||
prompt: `Extract the desired information from this text: \n ${failedOutput}`,
|
||||
schema: distilledSchema,
|
||||
prompt: `Following the given JSON schema, extract the field from below: \n\n ${failedOutput}`,
|
||||
maxTokens: getToolConfig('fallback').maxTokens,
|
||||
temperature: getToolConfig('fallback').temperature,
|
||||
});
|
||||
|
||||
this.tokenTracker.trackUsage(model, fallbackResult.usage);
|
||||
console.log('Distilled schema parse success!')
|
||||
return fallbackResult;
|
||||
} catch (fallbackError) {
|
||||
// If fallback model also fails, try parsing its error response
|
||||
@ -91,15 +195,28 @@ export class ObjectGeneratorSafe {
|
||||
|
||||
private async handleGenerateObjectError<T>(error: unknown): Promise<GenerateObjectResult<T>> {
|
||||
if (NoObjectGeneratedError.isInstance(error)) {
|
||||
console.error('Object not generated according to schema, fallback to manual JSON parsing');
|
||||
console.error('Object not generated according to schema, fallback to manual parsing');
|
||||
try {
|
||||
// First try standard JSON parsing
|
||||
const partialResponse = JSON.parse((error as any).text);
|
||||
console.log('JSON parse success!')
|
||||
return {
|
||||
object: partialResponse as T,
|
||||
usage: (error as any).usage
|
||||
};
|
||||
} catch (parseError) {
|
||||
throw error;
|
||||
// Use Hjson to parse the error response for more lenient parsing
|
||||
try {
|
||||
const hjsonResponse = Hjson.parse((error as any).text);
|
||||
console.log('Hjson parse success!')
|
||||
return {
|
||||
object: hjsonResponse as T,
|
||||
usage: (error as any).usage
|
||||
};
|
||||
} catch (hjsonError) {
|
||||
console.error('Both JSON and Hjson parsing failed:', hjsonError);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
}
|
||||
throw error;
|
||||
|
||||
@ -187,7 +187,7 @@ export class Schemas {
|
||||
return z.object({
|
||||
type: z.literal('strict'),
|
||||
...baseSchemaBefore,
|
||||
improvement_plan: z.string().describe('Short explain how a perfect answer should look like and what revisions are needed to improve the current answer.').max(500),
|
||||
improvement_plan: z.string().describe('Explain how a perfect answer should look like and what are needed to improve the current answer. Starts with "For the best answer, you must..."').max(500),
|
||||
...baseSchemaAfter
|
||||
});
|
||||
default:
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user