Run

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21064131e0f9915aed0cf38c98c0ef40501d9392

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/README.md /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/package.json /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/supabase/config.toml Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 1 # Cogitations Reviews 2 3 A modern product reviews and recommendations platform built with Nuxt 3, Supabase, PrimeVue, and TailwindCSS. 4 5 Integrates AI for both review generation and user driven updates. 6 7 ## 🌟 Features 8 9 - User authentication and authorization (email/password + Google OAuth) 10 - Email duplicate detection system 11 - Product review creation and management 12 - AI-powered content generation Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 1 { 2 "name": "cogitations-reviews", 3 "private": true, 4 "type": "module", 5 "scripts": { 6 "prebuild": "node build-info.cjs", 7 "build": "nuxt build", 8 "dev": "nuxt dev", 9 "cleanup": "pkill -f 'nuxt dev' || true; pkill -f 'vite' || true", 10 "dev:clean": "npm run cleanup && sleep 1 && nuxt dev", 11 "dev:port": "nuxt dev --port 3001", 12 "generate": "nuxt generate", 13 "preview": "nuxt preview", 14 "postinstall": Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: total 752 drwxr-xr-x 16 user user 4096 Jun 6 04:49 . drwxr-xr-x 3 user user 4096 Jun 6 04:49 .. -rw-r--r-- 1 user user 24 Jun 6 04:49 .build-date -rw-r--r-- 1 user user 170 Jun 6 04:49 .git -rw-r--r-- 1 user user 331 Jun 6 04:49 .gitignore drwxr-xr-x 2 user user 4096 Jun 6 04:49 .vscode -rw-r--r-- 1 user user 1088 Jun 6 04:49 LICENSE -rw-r--r-- 1 user user 12725 Jun 6 04:49 README.md -rw-r--r-- 1 user user 801 Jun 6 Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/composables/useAI.js /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/composables/useAmazon.js /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/composables/useBreadcrumbs.ts /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/composables/useCategories.js /home/user/worktrees/attemp Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: server/api/amazon/lookup.post.js server/api/amazon/test.get.js server/api/auth/check-email.post.js server/api/test.get.js Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 1 import { useRuntimeConfig } from '#imports' 2 3 export function useAI() { 4 // Send a prompt to the AI API and return the parsed response 5 const sendAIPrompt = async (prompt, model = 'gpt-4-turbo') => { 6 const config = useRuntimeConfig() 7 try { 8 const response = await fetch('https://cogitations-review-ai.cogitations.workers.dev', { 9 method: 'POST', 10 headers: { 11 'Content-Type': 'application/json', 12 'Autho Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: components/AIContentGenerator.vue Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: total 164 drwxr-xr-x 2 user user 4096 Jun 6 04:49 . drwxr-xr-x 16 user user 4096 Jun 6 04:49 .. -rw-r--r-- 1 user user 19232 Jun 6 04:49 AIContentGenerator.vue -rw-r--r-- 1 user user 5004 Jun 6 04:49 AppendCategoryToReviews.vue -rw-r--r-- 1 user user 1441 Jun 6 04:49 Breadcrumbs.vue -rw-r--r-- 1 user user 39236 Jun 6 04:49 CategoryPopulator.vue -rw-r--r-- 1 user user 3074 Jun 6 04:49 ImageUploader.vue -rw-r--r-- 1 us Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 1 <template> 2 <div> 3 <!-- AI Generation Button --> 4 <button 5 type="button" 6 @click="openAIDialog" 7 class="btn-primary flex items-center gap-2" 8 > 9 <i class="pi pi-robot"></i> 10 {{ buttonLabel }} 11 </button> 12 13 <!-- AI Dialog --> 14 <Dialog 15 v-model:visible="showAIDialog" 16 modal 17 header="AI Content Generation" 18 :style="{ width: '80vw', maxWidth: '800px' }" 19 :closable="!isGenerating" 20 > 21 <div Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Provider mentions: modal

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 1 // https://nuxt.com/docs/api/configuration/nuxt-config 2 import fs from 'fs' 3 export default defineNuxtConfig({ 4 devtools: { enabled: process.env.NODE_ENV === 'development' }, 5 modules: [ 6 '@nuxtjs/supabase', 7 '@nuxtjs/tailwindcss' 8 ], 9 css: [ 10 'primevue/resources/themes/lara-light-indigo/theme.css', 11 'primevue/resources/primevue.css', 12 'primeicons/primeicons.css', 13 'primeflex/primeflex.css', 14 '@/a Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: Found 21 files server/api/amazon/lookup.post.js pages/about.vue pages/admin/categories/index.vue pages/admin/reviews/new.vue pages/admin/users/index.vue pages/auth/register.vue pages/breadcrumb-example.vue pages/index.vue pages/reviews/[slug].vue pages/saved-lists.vue pages/search.vue components/AIContentGenerator.vue components/AppendCategoryToReviews.vue components/CategoryPopulator.vue components/SearchInput.vue c Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/components/AIContentGenerator.vue /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/pages/reviews/[slug].vue /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/pages/saved-lists.vue Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: import { useRuntimeConfig } from '#imports' export function useAmazon() { // Search for products on Amazon using server-side API const searchProducts = async (searchTerm, searchIndex = 'All') => { try { console.log('[useAmazon] Starting search for:', searchTerm) const response = await $fetch('/api/amazon/lookup', { method: 'POST', body: { searchTerm, searchIndex } }) console.log('[useAmazon] Search completed successf Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 1 import crypto from 'crypto' 2 import https from 'https' 3 4 export default defineEventHandler(async (event) => { 5 console.log('=== AMAZON LOOKUP API START ===') 6 7 try { 8 const body = await readBody(event) 9 const { searchTerm, searchIndex = 'All' } = body 10 11 console.log('Request body:', body) 12 console.log('Search term:', searchTerm) 13 14 if (!searchTerm) { 15 console.log('No search term provided') 16 thro Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: total 20 drwxr-xr-x 4 user user 4096 Jun 6 04:49 . drwxr-xr-x 3 user user 4096 Jun 6 04:49 .. drwxr-xr-x 2 user user 4096 Jun 6 04:49 amazon drwxr-xr-x 2 user user 4096 Jun 6 04:49 auth -rw-r--r-- 1 user user 200 Jun 6 04:49 test.get.js Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: export default defineEventHandler(async (event) => { return { message: 'API test endpoint working', timestamp: new Date().toISOString(), method: event.method, url: event.path } }) Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: # Nuxt dev/build outputs .output .data .nuxt .nitro .cache dist # Node dependencies node_modules # Logs logs *.log # Misc .DS_Store .fleet .idea # Vercel **/.vercel .vercel # Local env files .env .env.* !.env.example # VSCode .vscode/* !.vscode/extensions.json !.vscode/settings.json !.vscode/tasks.json !.vscode/launch.json Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/supabase/migrate_categories.sql /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/supabase/schema.sql /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/update_reviews.sql Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/supabase' Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: total 64 drwxr-xr-x 2 user user 4096 Jun 6 04:49 . drwxr-xr-x 16 user user 4096 Jun 6 04:49 .. -rw-r--r-- 1 user user 72 Jun 6 04:49 .gitignore -rw-r--r-- 1 user user 11650 Jun 6 04:49 config.toml -rw-r--r-- 1 user user 2203 Jun 6 04:49 migrate_categories.sql -rw-r--r-- 1 user user 34185 Jun 6 04:49 schema.sql Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: -- Begin transaction begin; -- Safe drops: Only drop triggers and policies if their tables exist do $$ begin -- Drop triggers only if their tables exist if exists (select 1 from information_schema.tables where table_name = 'users' and table_schema = 'auth') then drop trigger if exists on_auth_user_created on auth.users; end if; if exists (select 1 from information_schema.tables where table_name = 'profiles' and table Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/pages/search.vue Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 1 <template> 2 <div> 3 <h1 class="text-3xl font-bold text-gray-900 mb-2">Search Existing Reviews</h1> 4 <p class="text-sm text-gray-500 mb-6 max-w-2xl"> 5 Want a review that doesn't exist yet? <span class="font-medium text-primary-600">Registered users</span> can generate new AI-powered reviews for any product in seconds. 6 </p> 7 8 <!-- Expand button, only on mobile and when collapsed --> 9 <button 10 v-if="searchCo Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: import { ref, computed } from 'vue' import didYouMean from 'didyoumean2' export const useSpellCheck = () => { const dictionary = ref(new Set()) const isLoading = ref(true) const error = ref(null) // Initialize the spell checker with a custom word list const initSpellChecker = async () => { try { isLoading.value = true // Fetch the word list from public/words_en_us.txt const res = await fetch('/words_en_us.txt') const Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: Found 15 files limit: 15 pages/about.vue pages/admin/categories/index.vue pages/admin/index.vue pages/admin/reviews/[id].vue pages/admin/reviews/dedup.vue pages/admin/reviews/index.vue pages/admin/reviews/new.vue pages/index.vue pages/reviews/[slug].vue pages/search.vue components/AppendCategoryToReviews.vue components/CategoryPopulator.vue components/SearchInput.vue components/UncategorizedReviewCategorizer.vue comp Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/pages/[...slug].vue /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/pages/about.vue /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/pages/admin/categories/index.vue /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/pages/admin/index.vue /home/user/worktrees/attempt-2f17b2d8-ce0 Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: 150 </div> 151 </template> 152 153 <script setup> 154 import { ref, computed, onMounted, onBeforeUnmount, watch } from 'vue' 155 import { useToast } from 'primevue/usetoast' 156 import MultiSelect from 'primevue/multiselect' 157 import { useCategories } from '~/composables/useCategories' 158 import SearchInput from '~/components/SearchInput.vue' 159 import { normalizeText } from '~/utils/string' 160 161 definePageMet Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it. Tool result summary: /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0 /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/.vscode /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/assets /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/assets/css /home/user/worktrees/attempt-2f17b2d8-ce0d-4799-a6ee-97406bcadb53-cap-0/components /home/user/worktrees/at Agent context: I'll research the repository first to understand its architecture, then evaluate web-search providers against it.

Agent context: Now let me look at the actual AI integration code and consult the leading providers' documentation. Now let me look at the actual AI integration code and consult the leading providers' documentation.

Agent context: Now let me look at the actual AI integration code and consult the leading providers' documentation. Tool result summary: 1 import { useRuntimeConfig } from '#imports' 2 3 export function useAI() { 4 // Send a prompt to the AI API and return the parsed response 5 const sendAIPrompt = async (prompt, model = 'gpt-4-turbo') => { 6 const config = useRuntimeConfig() 7 try { 8 const response = await fetch('https://cogitations-review-ai.cogitations.workers.dev', { 9 method: 'POST', 10 headers: { 11 'Content-Type': 'application/json', 12 'Autho Agent context: Now let me look at the actual AI integration code and consult the leading providers' documentation.

Agent context: I have what I need on the repo. Now let me research the leading providers' documentation, focused on accuracy, source quality, and citations. I have what I need on the repo. Now let me research the leading providers' documentation, focused on accuracy, source quality, and citations.

Query/prompt: Tavily search API for LLM agents citations accuracy documentation 2026 Agent context: I have what I need on the repo. Now let me research the leading providers' documentation, focused on accuracy, source quality, and citations. Query: Tavily search API for LLM agents citations accuracy documentation 2026 10 search result links captured Tool result summary: Based on the web search results, here's comprehensive information about Tavily Search API for LLM agents, citations accuracy, and documentation: ## Overview Tavily is a specialized Search API designed specifically for Large Language Models (LLMs) and AI agents. Tavily is the web access layer for AI agents and provides a single API for agents to search, extract, and crawl the live web in formats designed specifically Agent context: I have what I need on the repo. Now let me research the leading providers' documentation, focused on accuracy, source quality, and citations.

Query/prompt: Exa vs Tavily vs Perplexity Sonar API web search retrieval citations factual accuracy comparison Agent context: I have what I need on the repo. Now let me research the leading providers' documentation, focused on accuracy, source quality, and citations. Query: Exa vs Tavily vs Perplexity Sonar API web search retrieval citations factual accuracy comparison 10 search result links captured Tool result summary: Based on the web search results, here's a comprehensive comparison of Exa, Tavily, and Perplexity Sonar API across web search retrieval, citations, and factual accuracy: ## Key Differences in Approach Tavily provides search, content extraction, site mapping, and crawling optimized for feeding into LLMs. Exa takes a fundamentally different approach using neural embeddings to understand semantic meaning. Perplexity Son Agent context: I have what I need on the repo. Now let me research the leading providers' documentation, focused on accuracy, source quality, and citations.

Agent context: Let me consult the actual documentation pages of the top contenders directly. Let me consult the actual documentation pages of the top contenders directly.

Query/prompt: Summarize this Tavily Search API endpoint: what parameters control source quality and citations, what fields are returned (URLs, scores, raw content, answer), search_depth options, and any features for grounding LLM answers with verifiable sources. Focus on accuracy and citation support. Agent context: Let me consult the actual documentation pages of the top contenders directly. Fetch prompt: Summarize this Tavily Search API endpoint: what parameters control source quality and citations, what fields are returned (URLs, scores, raw content, answer), search_depth options, and any features for grounding LLM answers with verifiable sources. Focus on accuracy and citation support. Tool result summary: # Tavily Search API: Quality, Citations, and Source Grounding ## Source Quality Control Parameters The API offers several mechanisms to ensure result quality: - **`search_depth`**: Four options balancing relevance vs. latency. "Advanced" provides "highest relevance with increased latency" and returns multiple semantically relevant snippets per URL, ideal for precision queries. "Basic" and "fast" return one NLP summar Agent context: Let me consult the actual documentation pages of the top contenders directly.

Query/prompt: Summarize Exa's search/answer API: how it returns sources and citations, neural vs keyword search, highlights, and any features supporting verifiable citations and factual grounding for LLMs. Agent context: Let me consult the actual documentation pages of the top contenders directly. Fetch prompt: Summarize Exa's search/answer API: how it returns sources and citations, neural vs keyword search, highlights, and any features supporting verifiable citations and factual grounding for LLMs. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/search Redirect URL: https://exa.ai/docs/reference/search Status: 307 Temporary Redirect To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://exa.ai/docs/reference/search" - prompt: "Summarize Exa's search/answer API: how it return Agent context: Let me consult the actual documentation pages of the top contenders directly.

Query/prompt: Summarize Perplexity Sonar API: how citations/search_results are returned, what grounding and source quality controls exist (search_domain_filter, etc.), rate limits, and accuracy features. Focus on verifiable citations. Agent context: Let me consult the actual documentation pages of the top contenders directly. Fetch prompt: Summarize Perplexity Sonar API: how citations/search_results are returned, what grounding and source quality controls exist (search_domain_filter, etc.), rate limits, and accuracy features. Focus on verifiable citations. Tool result summary: # Perplexity Sonar API Summary ## Citations & Search Results The API returns citations as "URLs of sources used to generate the response" and includes `search_results` containing individual results with title, URL, date, snippet, and source type (web or attachment). ## Grounding & Source Quality Controls **Available filters:** - `search_domain_filter`: "Limit search results to specific domains" - `search_language_fil Agent context: Let me consult the actual documentation pages of the top contenders directly.

Agent context: Let me complete the Exa documentation check via its redirected URL. Let me complete the Exa documentation check via its redirected URL.

Query/prompt: Summarize Exa's search/answer API: how it returns sources and citations, neural vs keyword search, highlights, and any features supporting verifiable citations and factual grounding for LLMs. Does it have an /answer endpoint that returns citations? Agent context: Let me complete the Exa documentation check via its redirected URL. Fetch prompt: Summarize Exa's search/answer API: how it returns sources and citations, neural vs keyword search, highlights, and any features supporting verifiable citations and factual grounding for LLMs. Does it have an /answer endpoint that returns citations? Tool result summary: # Exa Search API Summary ## Core Functionality Exa offers a **POST /search endpoint** (not a dedicated /answer endpoint) that performs web searches and extracts content from results. The API intelligently retrieves relevant pages and optionally synthesizes information. ## Citation & Grounding Features **Structured Citations:** The response includes field-level grounding through a `grounding` array containing citation Agent context: Let me complete the Exa documentation check via its redirected URL.

Agent context: ## Recommendation: Tavily **The repo:** "Cogitations Reviews" (Savta.ai) — a Nuxt 3 reviews platform whose agent generates product reviews via OpenAI through a Cloudflare Worker proxy (`composables/useAI.js:8`). There's no retrieval today; the clean integration point is a server endpoint (mirroring `server/api/amazon/lookup.post.js`) that retrieves cited web context and feeds it into the existing OpenAI generation step. **Choose: Tavily.** It's purpose-built to ground LLM output in *citable* web sources. Each result returns a `url`, a 0–1 relevance `score`, and clean `raw_content`, with an optional synthesized `answer` — so you get both verifiable citations *and* context for your own model, without surrendering generation control. Crucially, `include_domains`/`exclude_domains` (up to 300 whitelisted) let you constrain retrieval to authoritative sources — manufacturer pages, reputable review outlets — which directly serves a product that "cannot tolerate low-quality answers." It's production-ready and benchmarks factual QA on OpenAI's SimpleQA. **Alternatives considered:** - **Perplexity Sonar** — strongest raw factual accuracy in benchmarks with clean inline numbered citations; recommended for citation-critical legal/medical/finance use. Rejected because it returns its *own* LLM-synthesized answer, so you'd stack two answer engines (losing voice/quality control over your existing OpenAI pipeline), plus reported ~50 req/min rate limits and "not production-ready" API concerns. - **Exa** — excellent neural/semantic discovery with field-level `grounding` citations and confidence scores. Strong runner-up, but it's retrieval-first (you write the summarizer) and its strength is open-ended discovery rather than verifying specific product facts; citation/source-authority focus is weaker than Tavily's. - Briefly weighed: Brave Search API, Linkup, Vertex/Google grounding, SerpAPI/Serper, and native OpenAI/Anthropic web-search tools — none match Tavily's combination of citation-grade sources + domain whitelisting + retrieval-first design for this architecture. **Docs I actually consulted:** Tavily Search endpoint reference, Exa `/search` reference (`exa.ai/docs/reference/search`), and Perplexity Sonar chat-completions reference. **Why Tavily over the others:** for a reviews product that must show verifiable citations *while keeping its own generation model*, you want a retrieval-first API that hands back ranked, domain-constrained, citable sources — not an opaque answer engine. Tavily uniquely fits the existing Nuxt + OpenAI-proxy architecture and the accuracy/source-quality priorities. Pair `/search` (with domain whitelist) plus `/extract` for pulling clean content from specific product pages. Sources: - [Tavily Search API Reference](https://docs.tavily.com/documentation/api-reference/endpoint/search) - [Exa Search Reference](https://exa.ai/docs/reference/search) - [Perplexity Sonar API Reference](https://docs.perplexity.ai/api-reference/chat-completions-post) - [AI Search APIs Compared: Tavily vs Exa vs Perplexity](https://www.humai.blog/ai-search-apis-compared-tavily-vs-exa-vs-perplexity/) - [Compare Tavily, Google Search Grounding, Exa, Perplexity (LLM-as-Judge)](https://websearchapi.ai/blog/compare-tavily-google-search-exa-perplexity) Want me to implement the Tavily integration (a `server/api/search/web.post.js` endpoint plus wiring into `useAI.js`)? ## Recommendation: Tavily **The repo:** "Cogitations Reviews" (Savta.ai) — a Nuxt 3 reviews platform whose agent generates product reviews via OpenAI through a Cloudflare Worker proxy (`composables/useAI.js:8`). There's no retrieval today; the clean integration point is a server endpoint (mirroring `server/api/amazon/lookup.post.js`) that retrieves cited web context and feeds it into the existing OpenAI generation step. **Choose: Tavily.** It's purpose-built to ground LLM output in *citable* web sources. Each result returns a `url`, a 0–1 relevance `score`, and clean `raw_content`, with an optional synthesized `answer` — so you get both verifiable citations *and* context for your own model, without surrendering generation control. Crucially, `include_domains`/`exclude_domains` (up to 300 whitelisted) let you constrain retrieval to authoritative sources — manufacturer pages, reputable review outlets — which directly serves a product that "cannot tolerate low-quality answers." It's production-ready and benchmarks factual QA on OpenAI's SimpleQA. **Alternatives considered:** - **Perplexity Sonar** — strongest raw factual accuracy in benchmarks with clean inline numbered citations; rec Provider mentions: openai

## Recommendation: Tavily **The repo:** "Cogitations Reviews" (Savta.ai) — a Nuxt 3 reviews platform whose agent generates product reviews via OpenAI through a Cloudflare Worker proxy (`composables/useAI.js:8`). There's no retrieval today; the clean integration point is a server endpoint (mirroring `server/api/amazon/lookup.post.js`) that retrieves cited web context and feeds it into the existing OpenAI generation step. **Choose: Tavily.** It's purpose-built to ground LLM output in *citable* web sources. Each result returns a `url`, a 0–1 relevance `score`, and clean `raw_content`, with an optional synthesized `answer` — so you get both verifiable citations *and* context for your own model, without surrendering generation control. Crucially, `include_domains`/`exclude_domains` (up to 300 whitelisted) let you constrain retrieval to authoritative sources — manufacturer pages, reputable review outlets — which directly serves a product that "cannot tolerate low-quality answers." It's production-ready and benchmarks factual QA on OpenAI's SimpleQA. **Alternatives considered:** - **Perplexity Sonar** — strongest raw factual accuracy in benchmarks with clean inline numbered citations; recommended for citation-critical legal/medical/finance use. Rejected because it returns its *own* LLM-synthesized answer, so you'd stack two answer engines (losing voice/quality control over your existi Provider mentions: openai