feat(DeepResearch): show URL title in "Visited URLs" if possible
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@ -23,8 +23,8 @@ export interface WriteFinalReportParams {
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// Used for streaming response
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export type SearchQuery = z.infer<typeof searchQueriesTypeSchema>['queries'][0]
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export type PartialSearchQuery = DeepPartial<SearchQuery>
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export type SearchResult = z.infer<typeof searchResultTypeSchema>
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export type PartialSearchResult = DeepPartial<SearchResult>
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export type ProcessedSearchResult = z.infer<typeof searchResultTypeSchema>
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export type PartialProcessedSearchResult = DeepPartial<ProcessedSearchResult>
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export type ResearchStep =
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| { type: 'generating_query'; result: PartialSearchQuery; nodeId: string }
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@ -36,11 +36,11 @@ export type ResearchStep =
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nodeId: string
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}
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| { type: 'searching'; query: string; nodeId: string }
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| { type: 'search_complete'; urls: string[]; nodeId: string }
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| { type: 'search_complete'; results: WebSearchResult[]; nodeId: string }
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| {
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type: 'processing_serach_result'
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query: string
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result: PartialSearchResult
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result: PartialProcessedSearchResult
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nodeId: string
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}
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| {
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@ -51,7 +51,7 @@ export type ResearchStep =
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| {
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type: 'processed_search_result'
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query: string
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result: SearchResult
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result: ProcessedSearchResult
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nodeId: string
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}
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| { type: 'error'; message: string; nodeId: string }
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@ -134,13 +134,13 @@ export const searchResultTypeSchema = z.object({
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})
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function processSearchResult({
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query,
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result,
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results,
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numLearnings = 3,
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numFollowUpQuestions = 3,
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language,
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}: {
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query: string
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result: WebSearchResult[]
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results: WebSearchResult[]
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language: string
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numLearnings?: number
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numFollowUpQuestions?: number
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@ -156,7 +156,7 @@ function processSearchResult({
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),
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})
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const jsonSchema = JSON.stringify(zodToJsonSchema(schema))
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const contents = result.map((item) => trimPrompt(item.content, 25_000))
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const contents = results.map((item) => trimPrompt(item.content, 25_000))
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const prompt = [
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`Given the following contents from a SERP search for the query <query>${query}</query>, generate a list of learnings from the contents. Return a maximum of ${numLearnings} learnings, but feel free to return less if the contents are clear. Make sure each learning is unique and not similar to each other. The learnings should be concise and to the point, as detailed and information dense as possible. Make sure to include any entities like people, places, companies, products, things, etc in the learnings, as well as any exact metrics, numbers, or dates. The learnings will be used to research the topic further.`,
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`<contents>${contents
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@ -319,19 +319,19 @@ export async function deepResearch({
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})
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try {
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// Use Tavily to search the web
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const result = await useWebSearch()(searchQuery.query, {
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const results = await useWebSearch()(searchQuery.query, {
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maxResults: 5,
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lang: languageCode,
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})
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console.log(
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`Ran ${searchQuery.query}, found ${result.length} contents`,
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`Ran ${searchQuery.query}, found ${results.length} contents`,
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)
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// Collect URLs from this search
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const newUrls = result.map((item) => item.url).filter(Boolean)
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const newUrls = results.map((item) => item.url).filter(Boolean)
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onProgress({
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type: 'search_complete',
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urls: newUrls,
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results,
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nodeId: childNodeId(nodeId, i),
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})
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// Breadth for the next search is half of the current breadth
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@ -339,11 +339,11 @@ export async function deepResearch({
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const searchResultGenerator = processSearchResult({
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query: searchQuery.query,
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result,
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results,
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numFollowUpQuestions: nextBreadth,
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language,
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})
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let searchResult: PartialSearchResult = {}
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let searchResult: PartialProcessedSearchResult = {}
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for await (const chunk of parseStreamingJson(
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searchResultGenerator.fullStream,
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