\n Person Enrichment API\n

\n Enter information about a person to get enriched data. At least one\n field is required.\n

\n\n \n\n

\n API Information:\n

• At least one field must be filled out
• The API will search for information about the person
\n • Results include professional background, current role, and more\n
• Response format: JSON with structured data

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SUMMARY: A brief overview of who this person is\n2. PROFESSIONAL_BACKGROUND: Their career history, education, and expertise\n3. CURRENT_ROLE: Their current position and responsibilities\n4. COMPANY_INFO: Information about their current or most recent company\n5. RECENT_ACTIVITIES: Recent news, projects, or public activities\n6. SOCIAL_PRESENCE: Their online presence and thought leadership\n7. ACHIEVEMENTS: Notable accomplishments, awards, or recognition\n8. SOURCES: List the main sources you found this information from\n\nFormat your response as a structured report. If you cannot find information for any section, please indicate \"No information found\" for that section.`;\n\n // Initialize OpenAI client\n const client = new OpenAI({\n apiKey: Deno.env.get(\"OPENAI_API_KEY\"),\n });\n\n // Make the search request\n const completion = await client.chat.completions.create({\n model: \"gpt-4o-search-preview\",\n web_search_options: {\n \n },\n \n messages: [\n {\n role: \"user\",\n content: searchQuery,\n },\n ],\n max_tokens: 2000,\n });\n\n const content = completion.choices[0].message.content;\n\n if (!content) {\n throw new Error(\"No content returned from OpenAI\");\n }\n\n // Parse the structured response\n const sections = {\n summary: extractSection(content, \"SUMMARY\"),\n professional_background: extractSection(\n content,\n \"PROFESSIONAL_BACKGROUND\",\n ),\n current_role: extractSection(content, \"CURRENT_ROLE\"),\n company_info: extractSection(content, \"COMPANY_INFO\"),\n recent_activities: extractSection(content, \"RECENT_ACTIVITIES\"),\n social_presence: extractSection(content, \"SOCIAL_PRESENCE\"),\n achievements: extractSection(content, \"ACHIEVEMENTS\"),\n sources: extractSources(content),\n };\n\n const response: PersonEnrichmentResponse = {\n success: true,\n data: sections,\n };\n\n return new Response(JSON.stringify(response, null, 2), {\n status: 200,\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch (error) {\n console.error(\"Error in person enrichment:\", error);\n\n const response: PersonEnrichmentResponse = {\n success: false,\n error: error instanceof Error ? error.message : \"Unknown error occurred\",\n };\n\n return new Response(JSON.stringify(response), {\n status: 500,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n}\n\n// Helper function to extract sections from the response\nfunction extractSection(content: string, sectionName: string): string {\n const regex = new RegExp(\n `${sectionName}:?\\\\s*([\\\\s\\\\S]*?)(?=\\\\n\\\\d+\\\\.|\\\\n[A-Z_]+:|$)`,\n \"i\",\n );\n const match = content.match(regex);\n return match ? match[1].trim() : \"No information found\";\n}\n\n// Helper function to extract sources\nfunction extractSources(content: string): string[] {\n const sourcesSection = extractSection(content, \"SOURCES\");\n if (sourcesSection === \"No information found\") {\n return [];\n }\n\n // Try to extract URLs or source names from the sources section\n const urlRegex = /https?:\\/\\/[^\\s]+/g;\n const urls = sourcesSection.match(urlRegex) || [];\n\n // Also try to extract bullet points or numbered items\n const lines = sourcesSection\n .split(\"\\n\")\n .map((line) => line.trim())\n .filter(\n (line) => line.length > 0 && !urls.some((url) => line.includes(url)),\n );\n\n return [...urls, ...lines].slice(0, 10); // Limit to 10 sources\n}",[],[198],{"_104":207,"_95":208,"_153":209,"_155":-5,"_156":138,"_157":210,"_159":22,"_106":211,"_100":32,"_162":202,"_164":72,"_166":22,"_167":-5,"_168":212,"_98":213},"85818b0a-407c-11f0-8e52-76b3cceeab13","new-file-8011.tsx","85818bd2-407c-11f0-8e52-76b3cceeab13",63,["D",1748956400841],[],[208],"files",[150,171,181,188,196,206],"counts",{"_218":32,"_219":220,"_221":32,"_222":223},"pulls","branches",2,"forks","envVars",1,"contents",[],{"_104":182,"_95":99,"_153":183,"_155":-5,"_156":138,"_157":158,"_159":160,"_106":227,"_100":32,"_162":163,"_164":185,"_166":22,"_167":-5,"_98":228},["D",1748954370696],[99],"upstreamProject","upstreamBranch","emptyEnvVars","isNew","flags",[],"routes/_app.x.$handle.$name.branch.$branch.version.$version.code.$",{"_93":94,"_95":96,"_97":43,"_100":101,"_98":99,"_102":237,"_148":242,"_214":261,"_163":262,"_224":265,"_266":-5,"_267":22,"_268":-5,"_269":-5,"_270":-5,"_271":272},{"_104":105,"_95":96,"_106":238,"_108":-5,"_109":22,"_110":111,"_112":-5,"_113":-5,"_14":114,"_115":-5,"_116":22,"_117":-5,"_118":32,"_16":119,"_120":121,"_122":121,"_123":124,"_125":124,"_126":124,"_127":121,"_128":121,"_23":239,"_132":240,"_135":241,"_147":22},["D",1748885079132],{"_93":94,"_130":131},{"_93":94,"_134":131},{"_100":137,"_95":43,"_104":138,"_139":105,"_140":-5,"_141":-5,"_142":-5,"_143":-5,"_144":-5,"_145":96,"_146":94},[243,246,249,252,255,258],{"_104":151,"_95":152,"_153":154,"_155":-5,"_156":138,"_157":158,"_159":160,"_106":161,"_100":32,"_162":163,"_164":165,"_166":22,"_167":-5,"_168":244,"_98":245},[],[152],{"_104":172,"_95":173,"_153":174,"_155":-5,"_156":138,"_157":175,"_159":160,"_106":176,"_100":177,"_162":163,"_164":178,"_166":22,"_167":-5,"_168":247,"_98":248},[],[173],{"_104":182,"_95":99,"_153":183,"_155":-5,"_156":138,"_157":158,"_159":160,"_106":184,"_100":32,"_162":163,"_164":185,"_166":22,"_167":-5,"_168":250,"_98":251},[],[99],{"_104":189,"_95":190,"_153":191,"_155":-5,"_156":138,"_157":158,"_159":160,"_106":192,"_100":32,"_162":163,"_164":193,"_166":22,"_167":-5,"_168":253,"_98":254},[],[190],{"_104":197,"_95":198,"_153":199,"_155":-5,"_156":138,"_157":101,"_159":22,"_106":200,"_100":201,"_162":202,"_164":203,"_166":22,"_167":-5,"_168":256,"_98":257},[],[198],{"_104":207,"_95":208,"_153":209,"_155":-5,"_156":138,"_157":210,"_159":22,"_106":211,"_100":32,"_162":202,"_164":72,"_166":22,"_167":-5,"_168":259,"_98":260},[],[208],[243,246,249,252,255,258],{"_104":182,"_156":138,"_153":183,"_95":99,"_164":185,"_162":163,"_100":32,"_155":-5,"_106":263,"_157":158,"_167":-5,"_98":264},["D",1748954370696],[99],[],"upstream","showCompareCallout","pull","ahead","behind","templates",[],"actionData","errors"]

stevekrouse

openai_enrichment