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August 9, 2025 14:22
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Open WebUI - OpenAPI Specs
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| { | |
| "openapi": "3.1.0", | |
| "info": { | |
| "version": "1.2.0", | |
| "title": "Exa Search API", | |
| "description": "A comprehensive API for internet-scale search, allowing users to perform queries and retrieve results from a wide variety of sources using embeddings-based and traditional search." | |
| }, | |
| "servers": [ | |
| { | |
| "url": "https://api.exa.ai" | |
| } | |
| ], | |
| "security": [ | |
| { | |
| "apikey": [] | |
| } | |
| ], | |
| "paths": { | |
| "/search": { | |
| "post": { | |
| "operationId": "search", | |
| "summary": "Search", | |
| "description": "Perform a search with a Exa prompt-engineered query and retrieve a list of relevant results. Optionally get contents.", | |
| "x-codeSamples": [ | |
| { | |
| "lang": "bash", | |
| "label": "Simple search and contents", | |
| "source": "curl -X POST 'https://api.exa.ai/search' \\\n -H 'x-api-key: YOUR-EXA-API-KEY' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"query\": \"Latest research in LLMs\",\n \"text\": true\n }'\n" | |
| }, | |
| { | |
| "lang": "python", | |
| "label": "Simple search and contents", | |
| "source": "# pip install exa-py\nfrom exa_py import Exa\nexa = Exa('YOUR_EXA_API_KEY')\n\nresults = exa.search_and_contents(\n \"Latest research in LLMs\",\n text=True\n)\n\nprint(results)\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Simple search and contents", | |
| "source": "// npm install exa-js\nimport Exa from 'exa-js';\nconst exa = new Exa('YOUR_EXA_API_KEY');\n\nconst results = await exa.searchAndContents(\n 'Latest research in LLMs',\n { text: true }\n);\n\nconsole.log(results);\n" | |
| }, | |
| { | |
| "lang": "php", | |
| "label": "Simple search and contents", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "go", | |
| "label": "Simple search and contents", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "java", | |
| "label": "Simple search and contents", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "bash", | |
| "label": "Advanced search with filters", | |
| "source": "curl --request POST \\\n --url https://api.exa.ai/search \\\n --header 'x-api-key: <token>' \\\n --header 'Content-Type: application/json' \\\n --data '{\n \"query\": \"Latest research in LLMs\",\n \"type\": \"auto\",\n \"category\": \"research paper\",\n \"numResults\": 10,\n \"contents\": {\n \"text\": true,\n \"summary\": {\n \"query\": \"Main developments\"\n },\n \"subpages\": 1,\n \"subpageTarget\": \"sources\",\n \"extras\": {\n \"links\": 1,\n \"imageLinks\": 1\n }\n }\n}'\n" | |
| }, | |
| { | |
| "lang": "python", | |
| "label": "Advanced search with filters", | |
| "source": "# pip install exa-py\nfrom exa_py import Exa\nexa = Exa('YOUR_EXA_API_KEY')\n\nresults = exa.search_and_contents(\n \"Latest research in LLMs\",\n type=\"auto\",\n category=\"research paper\",\n num_results=10,\n text=True,\n summary={\n \"query\": \"Main developments\"\n },\n subpages=1,\n subpage_target=\"sources\",\n extras={\n \"links\": 1,\n \"image_links\": 1\n }\n)\n\nprint(results)\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Advanced search with filters", | |
| "source": "// npm install exa-js\nimport Exa from 'exa-js';\nconst exa = new Exa('YOUR_EXA_API_KEY');\n\nconst results = await exa.searchAndContents('Latest research in LLMs', {\n type: 'auto',\n category: 'research paper',\n numResults: 10,\n contents: {\n text: true,\n summary: {\n query: 'Main developments'\n },\n subpages: 1,\n subpageTarget: 'sources',\n extras: {\n links: 1,\n imageLinks: 1\n }\n }\n});\n\nconsole.log(results);\n" | |
| }, | |
| { | |
| "lang": "php", | |
| "label": "Advanced search with filters", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "go", | |
| "label": "Advanced search with filters", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "java", | |
| "label": "Advanced search with filters", | |
| "source": "" | |
| } | |
| ], | |
| "requestBody": { | |
| "required": true, | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "allOf": [ | |
| { | |
| "type": "object", | |
| "properties": { | |
| "query": { | |
| "type": "string", | |
| "example": "Latest developments in LLM capabilities", | |
| "description": "The query string for the search." | |
| }, | |
| "type": { | |
| "type": "string", | |
| "enum": [ | |
| "keyword", | |
| "neural", | |
| "auto" | |
| ], | |
| "description": "The type of search. Neural uses an embeddings-based model, keyword is google-like SERP. Default is auto, which automatically decides between keyword and neural.", | |
| "example": "auto", | |
| "default": "auto" | |
| }, | |
| "category": { | |
| "type": "string", | |
| "enum": [ | |
| "company", | |
| "research paper", | |
| "news", | |
| "pdf", | |
| "github", | |
| "tweet", | |
| "personal site", | |
| "linkedin profile", | |
| "financial report" | |
| ], | |
| "description": "A data category to focus on.", | |
| "example": "research paper" | |
| } | |
| }, | |
| "required": [ | |
| "query" | |
| ] | |
| }, | |
| { | |
| "$ref": "#/components/schemas/CommonRequest" | |
| } | |
| ] | |
| } | |
| } | |
| } | |
| }, | |
| "responses": { | |
| "200": { | |
| "$ref": "#/components/responses/SearchResponse" | |
| } | |
| } | |
| } | |
| }, | |
| "/findSimilar": { | |
| "post": { | |
| "operationId": "findSimilar", | |
| "summary": "Find similar links", | |
| "description": "Find similar links to the link provided. Optionally get contents.", | |
| "x-codeSamples": [ | |
| { | |
| "lang": "bash", | |
| "label": "Find similar links", | |
| "source": "curl -X POST 'https://api.exa.ai/findSimilar' \\\n -H 'x-api-key: YOUR-EXA-API-KEY' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"url\": \"https://arxiv.org/abs/2307.06435\",\n \"text\": true\n }'\n" | |
| }, | |
| { | |
| "lang": "python", | |
| "label": "Find similar links", | |
| "source": "# pip install exa-py\nfrom exa_py import Exa\nexa = Exa('YOUR_EXA_API_KEY')\n\nresults = exa.find_similar_and_contents(\n url=\"https://arxiv.org/abs/2307.06435\",\n text=True\n)\n\nprint(results)\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Find similar links", | |
| "source": "// npm install exa-js\nimport Exa from 'exa-js';\nconst exa = new Exa('YOUR_EXA_API_KEY');\n\nconst results = await exa.findSimilarAndContents(\n 'https://arxiv.org/abs/2307.06435',\n { text: true }\n);\n\nconsole.log(results);\n" | |
| }, | |
| { | |
| "lang": "php", | |
| "label": "Find similar links", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "go", | |
| "label": "Find similar links", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "java", | |
| "label": "Find similar links", | |
| "source": "" | |
| } | |
| ], | |
| "requestBody": { | |
| "required": true, | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "allOf": [ | |
| { | |
| "type": "object", | |
| "properties": { | |
| "url": { | |
| "type": "string", | |
| "example": "https://arxiv.org/abs/2307.06435", | |
| "description": "The url for which you would like to find similar links." | |
| } | |
| }, | |
| "required": [ | |
| "url" | |
| ] | |
| }, | |
| { | |
| "$ref": "#/components/schemas/CommonRequest" | |
| } | |
| ] | |
| } | |
| } | |
| } | |
| }, | |
| "responses": { | |
| "200": { | |
| "$ref": "#/components/responses/FindSimilarResponse" | |
| } | |
| } | |
| } | |
| }, | |
| "/contents": { | |
| "post": { | |
| "summary": "Get Contents", | |
| "operationId": "getContents", | |
| "x-codeSamples": [ | |
| { | |
| "lang": "bash", | |
| "label": "Simple contents retrieval", | |
| "source": "curl -X POST 'https://api.exa.ai/contents' \\\n -H 'x-api-key: YOUR-EXA-API-KEY' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"urls\": [\"https://arxiv.org/abs/2307.06435\"],\n \"text\": true\n }'\n" | |
| }, | |
| { | |
| "lang": "python", | |
| "label": "Simple contents retrieval", | |
| "source": "# pip install exa-py\nfrom exa_py import Exa\nexa = Exa('YOUR_EXA_API_KEY')\n\nresults = exa.get_contents(\n urls=[\"https://arxiv.org/abs/2307.06435\"],\n text=True\n)\n\nprint(results)\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Simple contents retrieval", | |
| "source": "// npm install exa-js\nimport Exa from 'exa-js';\nconst exa = new Exa('YOUR_EXA_API_KEY');\n\nconst results = await exa.getContents(\n [\"https://arxiv.org/abs/2307.06435\"],\n { text: true }\n);\n\nconsole.log(results);\n" | |
| }, | |
| { | |
| "lang": "php", | |
| "label": "Simple contents retrieval", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "go", | |
| "label": "Simple contents retrieval", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "java", | |
| "label": "Simple contents retrieval", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "bash", | |
| "label": "Advanced contents retrieval", | |
| "source": "curl --request POST \\\n --url https://api.exa.ai/contents \\\n --header 'x-api-key: YOUR-EXA-API-KEY' \\\n --header 'Content-Type: application/json' \\\n --data '{\n \"urls\": [\"https://arxiv.org/abs/2307.06435\"],\n \"text\": {\n \"maxCharacters\": 1000,\n \"includeHtmlTags\": false\n },\n \"highlights\": {\n \"numSentences\": 3,\n \"highlightsPerUrl\": 2,\n \"query\": \"Key findings\"\n },\n \"summary\": {\n \"query\": \"Main research contributions\"\n },\n \"subpages\": 1,\n \"subpageTarget\": \"references\",\n \"extras\": {\n \"links\": 2,\n \"imageLinks\": 1\n }\n }'\n" | |
| }, | |
| { | |
| "lang": "python", | |
| "label": "Advanced contents retrieval", | |
| "source": "# pip install exa-py\nfrom exa_py import Exa\nexa = Exa('YOUR_EXA_API_KEY')\n\nresults = exa.get_contents(\n urls=[\"https://arxiv.org/abs/2307.06435\"],\n text={\n \"maxCharacters\": 1000,\n \"includeHtmlTags\": False\n },\n highlights={\n \"numSentences\": 3,\n \"highlightsPerUrl\": 2,\n \"query\": \"Key findings\"\n },\n summary={\n \"query\": \"Main research contributions\"\n },\n subpages=1,\n subpage_target=\"references\",\n extras={\n \"links\": 2,\n \"image_links\": 1\n }\n)\n\nprint(results)\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Advanced contents retrieval", | |
| "source": "// npm install exa-js\nimport Exa from 'exa-js';\nconst exa = new Exa('YOUR_EXA_API_KEY');\n\nconst results = await exa.getContents(\n [\"https://arxiv.org/abs/2307.06435\"],\n {\n text: {\n maxCharacters: 1000,\n includeHtmlTags: false\n },\n highlights: {\n numSentences: 3,\n highlightsPerUrl: 2,\n query: \"Key findings\"\n },\n summary: {\n query: \"Main research contributions\"\n },\n subpages: 1,\n subpageTarget: \"references\",\n extras: {\n links: 2,\n imageLinks: 1\n }\n }\n);\n\nconsole.log(results);\n" | |
| } | |
| ], | |
| "requestBody": { | |
| "required": true, | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "allOf": [ | |
| { | |
| "type": "object", | |
| "properties": { | |
| "urls": { | |
| "type": "array", | |
| "description": "Array of URLs to crawl (backwards compatible with 'ids' parameter).", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "example": [ | |
| "https://arxiv.org/pdf/2307.06435" | |
| ] | |
| }, | |
| "ids": { | |
| "type": "array", | |
| "deprecated": true, | |
| "description": "Deprecated - use 'urls' instead. Array of document IDs obtained from searches.", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "example": [ | |
| "https://arxiv.org/pdf/2307.06435" | |
| ] | |
| } | |
| }, | |
| "required": [ | |
| "urls" | |
| ] | |
| }, | |
| { | |
| "$ref": "#/components/schemas/ContentsRequest" | |
| } | |
| ] | |
| } | |
| } | |
| } | |
| }, | |
| "responses": { | |
| "200": { | |
| "$ref": "#/components/responses/ContentsResponse" | |
| } | |
| } | |
| } | |
| }, | |
| "/answer": { | |
| "post": { | |
| "operationId": "answer", | |
| "summary": "Generate an answer from search results", | |
| "description": "Performs a search based on the query and generates either a direct answer or a detailed summary with citations, depending on the query type.\n", | |
| "x-codeSamples": [ | |
| { | |
| "lang": "bash", | |
| "label": "Simple answer", | |
| "source": "curl -X POST 'https://api.exa.ai/answer' \\\n -H 'x-api-key: YOUR-EXA-API-KEY' \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"query\": \"What is the latest valuation of SpaceX?\",\n \"text\": true\n }'\n" | |
| }, | |
| { | |
| "lang": "python", | |
| "label": "Simple answer", | |
| "source": "# pip install exa-py\nfrom exa_py import Exa\nexa = Exa('YOUR_EXA_API_KEY')\n\nresult = exa.answer(\n \"What is the latest valuation of SpaceX?\",\n text=True\n)\n\nprint(result)\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Simple answer", | |
| "source": "// npm install exa-js\nimport Exa from 'exa-js';\nconst exa = new Exa('YOUR_EXA_API_KEY');\n\nconst result = await exa.answer(\n 'What is the latest valuation of SpaceX?',\n { text: true }\n);\n\nconsole.log(result);\n" | |
| }, | |
| { | |
| "lang": "php", | |
| "label": "Simple answer", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "go", | |
| "label": "Simple answer", | |
| "source": "" | |
| }, | |
| { | |
| "lang": "java", | |
| "label": "Simple answer", | |
| "source": "" | |
| } | |
| ], | |
| "requestBody": { | |
| "required": true, | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "required": [ | |
| "query" | |
| ], | |
| "properties": { | |
| "query": { | |
| "type": "string", | |
| "description": "The question or query to answer.", | |
| "example": "What is the latest valuation of SpaceX?", | |
| "minLength": 1 | |
| }, | |
| "stream": { | |
| "type": "boolean", | |
| "default": false, | |
| "description": "If true, the response is returned as a server-sent events (SSS) stream." | |
| }, | |
| "text": { | |
| "type": "boolean", | |
| "default": false, | |
| "description": "If true, the response includes full text content in the search results" | |
| }, | |
| "model": { | |
| "type": "string", | |
| "enum": [ | |
| "exa" | |
| ], | |
| "description": "The search model to use for the answer.", | |
| "default": "exa" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "responses": { | |
| "200": { | |
| "$ref": "#/components/responses/AnswerResponse" | |
| } | |
| } | |
| } | |
| }, | |
| "/research/v0/tasks": { | |
| "post": { | |
| "operationId": "research-tasks-create", | |
| "parameters": [], | |
| "requestBody": { | |
| "required": true, | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "instructions": { | |
| "type": "string", | |
| "maxLength": 4096, | |
| "description": "Instructions for what the research task should accomplish" | |
| }, | |
| "model": { | |
| "type": "string", | |
| "enum": [ | |
| "exa-research", | |
| "exa-research-pro" | |
| ], | |
| "default": "exa-research" | |
| }, | |
| "output": { | |
| "type": "object", | |
| "properties": { | |
| "schema": { | |
| "description": "A JsonSchema specification of the desired output. See https://json-schema.org/draft-07." | |
| } | |
| } | |
| } | |
| }, | |
| "required": [ | |
| "instructions" | |
| ], | |
| "example": { | |
| "instructions": "What species of ant are similar to honeypot ants?", | |
| "model": "exa-research", | |
| "output": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "answer": { | |
| "type": "string" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "responses": { | |
| "201": { | |
| "$ref": "#/components/responses/ResearchCreateTaskResponse" | |
| } | |
| }, | |
| "summary": "Create a research task with instructions and an output schema", | |
| "x-codeSamples": [ | |
| { | |
| "lang": "python", | |
| "label": "Advanced search with filters", | |
| "source": "# pip install exa-py\nimport os\nfrom exa_py import Exa\n\nexa = Exa(os.environ[\"EXA_API_KEY\"])\n\n# Create a research task\ninstructions = \"Summarize the history of San Francisco highlighting one or two major events for each decade from 1850 to 1950\"\nschema = {\n \"type\": \"object\",\n \"required\": [\"timeline\"],\n \"properties\": {\n \"timeline\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"object\",\n \"required\": [\"decade\", \"notableEvents\"],\n \"properties\": {\n \"decade\": {\"type\": \"string\"},\n \"notableEvents\": {\"type\": \"string\"},\n },\n },\n },\n },\n \"additionalProperties\": False,\n}\n\ntask = exa.research.create_task(\n model=\"exa_research\",\n instructions=instructions,\n output_schema=schema,\n)\n# Poll until completion\nresult = exa.research.poll_task(task.id)\nprint(result)\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Advanced search with filters", | |
| "source": "// npm install exa-js\nimport Exa, { ResearchModel } from \"exa-js\";\n\nconst exa = new Exa(process.env.EXA_API_KEY);\n\nasync function simpleResearchExample() {\n // Create a research task\n const task = await exa.research.createTask({\n model: ResearchModel.exa_research,\n instructions: \"Summarize the history of San Francisco in 3 sentences.\",\n output: {\n schema: {\n type: \"object\",\n required: [\"summary\"],\n properties: {\n summary: { type: \"string\" },\n },\n additionalProperties: false,\n },\n },\n });\n\n // Poll until completion\n const result = await exa.research.pollTask(task.id);\n console.log(\"Research result:\", result);\n}\n\nsimpleResearchExample().catch(console.error);\n" | |
| } | |
| ] | |
| }, | |
| "get": { | |
| "operationId": "research-tasks-list", | |
| "parameters": [ | |
| { | |
| "name": "cursor", | |
| "required": false, | |
| "in": "query", | |
| "description": "The cursor to paginate through the results", | |
| "schema": { | |
| "minLength": 1, | |
| "type": "string" | |
| } | |
| }, | |
| { | |
| "name": "limit", | |
| "required": false, | |
| "in": "query", | |
| "description": "The number of results to return", | |
| "schema": { | |
| "minimum": 1, | |
| "maximum": 200, | |
| "default": 25, | |
| "type": "number" | |
| } | |
| } | |
| ], | |
| "responses": { | |
| "200": { | |
| "$ref": "#/components/responses/ListResearchTasksResponse" | |
| } | |
| }, | |
| "summary": "List research tasks", | |
| "x-codeSamples": [ | |
| { | |
| "lang": "python", | |
| "label": "Advanced search with filters", | |
| "source": "# pip install exa\nimport os\nfrom exa import Exa\n\nexa = Exa(os.environ[\"EXA_API_KEY\"])\n\nresponse = exa.research.list_tasks()\nprint(\"All research tasks:\", response[\"data\"])\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Advanced search with filters", | |
| "source": "// npm install exa-js\nimport Exa from \"exa-js\";\nconst exa = new Exa(process.env.EXA_API_KEY);\n\nasync function listAllResearchTasks() {\n const response = await exa.research.listTasks();\n console.log(\"All research tasks:\", response.data);\n}\n\nlistAllResearchTasks().catch(console.error);\n" | |
| } | |
| ] | |
| } | |
| }, | |
| "/research/v0/tasks/{id}": { | |
| "get": { | |
| "operationId": "ResearchControllerV0_getResearchTask", | |
| "parameters": [ | |
| { | |
| "name": "id", | |
| "required": true, | |
| "in": "path", | |
| "schema": { | |
| "type": "string" | |
| } | |
| } | |
| ], | |
| "responses": { | |
| "200": { | |
| "$ref": "#/components/responses/ResearchTaskResponse" | |
| } | |
| }, | |
| "summary": "Get a research task by id", | |
| "x-codeSamples": [ | |
| { | |
| "lang": "python", | |
| "label": "Get research task", | |
| "source": "# pip install exa-py\nimport os\nfrom exa_py import Exa\n\nexa = Exa(os.environ[\"EXA_API_KEY\"])\n\n# Get a research task by ID (single check)\ntask_id = \"your-task-id-here\"\ntask = exa.research.get_task(task_id)\nprint(f\"Task status: {task.status}\")\n\n# Poll until completion\nresult = exa.research.poll_task(task_id)\nprint(f\"Final result: {result.data}\")\n" | |
| }, | |
| { | |
| "lang": "javascript", | |
| "label": "Get research task", | |
| "source": "// npm install exa-js\nimport Exa from \"exa-js\";\n\nconst exa = new Exa(process.env.EXA_API_KEY);\n\nasync function getResearchTask() {\n const taskId = \"your-task-id-here\";\n\n // Get a research task by ID (single check)\n const task = await exa.research.getTask(taskId);\n console.log(\"Task status:\", task.status);\n\n // Poll until completion\n const result = await exa.research.pollTask(taskId);\n console.log(\"Final result:\", result.data);\n}\n\ngetResearchTask().catch(console.error);\n" | |
| } | |
| ] | |
| } | |
| } | |
| }, | |
| "components": { | |
| "securitySchemes": { | |
| "apikey": { | |
| "type": "apiKey", | |
| "name": "x-api-key", | |
| "in": "header", | |
| "description": "API key can be provided either via x-api-key header or Authorization header with Bearer scheme" | |
| }, | |
| "bearer": { | |
| "type": "http", | |
| "scheme": "bearer", | |
| "description": "API key can be provided either via x-api-key header or Authorization header with Bearer scheme" | |
| } | |
| }, | |
| "schemas": { | |
| "AnswerCitation": { | |
| "type": "object", | |
| "properties": { | |
| "id": { | |
| "type": "string", | |
| "description": "The temporary ID for the document.", | |
| "example": "https://www.theguardian.com/science/2024/dec/11/spacex-valued-at-350bn-as-company-agrees-to-buy-shares-from-employees" | |
| }, | |
| "url": { | |
| "type": "string", | |
| "format": "uri", | |
| "description": "The URL of the search result.", | |
| "example": "https://www.theguardian.com/science/2024/dec/11/spacex-valued-at-350bn-as-company-agrees-to-buy-shares-from-employees" | |
| }, | |
| "title": { | |
| "type": "string", | |
| "description": "The title of the search result.", | |
| "example": "SpaceX valued at $350bn as company agrees to buy shares from ..." | |
| }, | |
| "author": { | |
| "type": "string", | |
| "nullable": true, | |
| "description": "If available, the author of the content.", | |
| "example": "Dan Milmon" | |
| }, | |
| "publishedDate": { | |
| "type": "string", | |
| "nullable": true, | |
| "description": "An estimate of the creation date, from parsing HTML content. Format is YYYY-MM-DD.", | |
| "example": "2023-11-16T01:36:32.547Z" | |
| }, | |
| "text": { | |
| "type": "string", | |
| "description": "The full text content of each source. Only present when includeText is enabled.", | |
| "example": "SpaceX valued at $350bn as company agrees to buy shares from ..." | |
| }, | |
| "image": { | |
| "type": "string", | |
| "format": "uri", | |
| "description": "The URL of the image associated with the search result, if available.", | |
| "example": "https://i.guim.co.uk/img/media/7cfee7e84b24b73c97a079c402642a333ad31e77/0_380_6176_3706/master/6176.jpg?width=1200&height=630&quality=85&auto=format&fit=crop&overlay-align=bottom%2Cleft&overlay-width=100p&overlay-base64=L2ltZy9zdGF0aWMvb3ZlcmxheXMvdGctZGVmYXVsdC5wbmc&enable=upscale&s=71ebb2fbf458c185229d02d380c01530" | |
| }, | |
| "favicon": { | |
| "type": "string", | |
| "format": "uri", | |
| "description": "The URL of the favicon for the search result's domain, if available.", | |
| "example": "https://assets.guim.co.uk/static/frontend/icons/homescreen/apple-touch-icon.svg" | |
| } | |
| } | |
| }, | |
| "AnswerResult": { | |
| "type": "object", | |
| "properties": { | |
| "answer": { | |
| "type": "string", | |
| "description": "The generated answer based on search results.", | |
| "example": "$350 billion." | |
| }, | |
| "citations": { | |
| "type": "array", | |
| "description": "Search results used to generate the answer.", | |
| "items": { | |
| "$ref": "#/components/schemas/AnswerCitation" | |
| } | |
| } | |
| } | |
| }, | |
| "ContentsRequest": { | |
| "type": "object", | |
| "properties": { | |
| "text": { | |
| "oneOf": [ | |
| { | |
| "type": "boolean", | |
| "title": "Simple text retrieval", | |
| "description": "If true, returns full page text with default settings. If false, disables text return." | |
| }, | |
| { | |
| "type": "object", | |
| "title": "Advanced text options", | |
| "description": "Advanced options for controlling text extraction. Use this when you need to limit text length or include HTML structure.", | |
| "properties": { | |
| "maxCharacters": { | |
| "type": "integer", | |
| "description": "Maximum character limit for the full page text. Useful for controlling response size and API costs.", | |
| "example": 1000 | |
| }, | |
| "includeHtmlTags": { | |
| "type": "boolean", | |
| "default": false, | |
| "description": "Include HTML tags in the response, which can help LLMs understand text structure and formatting.", | |
| "example": false | |
| } | |
| } | |
| } | |
| ] | |
| }, | |
| "highlights": { | |
| "type": "object", | |
| "description": "Text snippets the LLM identifies as most relevant from each page.", | |
| "properties": { | |
| "numSentences": { | |
| "type": "integer", | |
| "default": 5, | |
| "description": "The number of sentences to return for each snippet.", | |
| "example": 1 | |
| }, | |
| "highlightsPerUrl": { | |
| "type": "integer", | |
| "default": 1, | |
| "description": "The number of snippets to return for each result.", | |
| "example": 1 | |
| }, | |
| "query": { | |
| "type": "string", | |
| "description": "Custom query to direct the LLM's selection of highlights.", | |
| "example": "Key advancements" | |
| } | |
| } | |
| }, | |
| "summary": { | |
| "type": "object", | |
| "description": "Summary of the webpage", | |
| "properties": { | |
| "query": { | |
| "type": "string", | |
| "description": "Custom query for the LLM-generated summary.", | |
| "example": "Main developments" | |
| }, | |
| "schema": { | |
| "type": "object", | |
| "description": "JSON schema for structured output from summary.\nSee https://json-schema.org/overview/what-is-jsonschema for JSON Schema documentation.\n", | |
| "example": { | |
| "$schema": "http://json-schema.org/draft-07/schema#", | |
| "title": "Title", | |
| "type": "object", | |
| "properties": { | |
| "Property 1": { | |
| "type": "string", | |
| "description": "Description" | |
| }, | |
| "Property 2": { | |
| "type": "string", | |
| "enum": [ | |
| "option 1", | |
| "option 2", | |
| "option 3" | |
| ], | |
| "description": "Description" | |
| } | |
| }, | |
| "required": [ | |
| "Property 1" | |
| ] | |
| } | |
| } | |
| } | |
| }, | |
| "livecrawl": { | |
| "type": "string", | |
| "enum": [ | |
| "never", | |
| "fallback", | |
| "always", | |
| "preferred" | |
| ], | |
| "description": "Options for livecrawling pages.\n'never': Disable livecrawling (default for neural search).\n'fallback': Livecrawl when cache is empty (default for keyword search).\n'always': Always livecrawl.\n'preferred': Always try to livecrawl, but fall back to cache if crawling fails.\n", | |
| "example": "always" | |
| }, | |
| "livecrawlTimeout": { | |
| "type": "integer", | |
| "default": 10000, | |
| "description": "The timeout for livecrawling in milliseconds.", | |
| "example": 1000 | |
| }, | |
| "subpages": { | |
| "type": "integer", | |
| "default": 0, | |
| "description": "The number of subpages to crawl. The actual number crawled may be limited by system constraints.", | |
| "example": 1 | |
| }, | |
| "subpageTarget": { | |
| "oneOf": [ | |
| { | |
| "type": "string" | |
| }, | |
| { | |
| "type": "array", | |
| "items": { | |
| "type": "string" | |
| } | |
| } | |
| ], | |
| "description": "Keyword to find specific subpages of search results. Can be a single string or an array of strings, comma delimited.", | |
| "example": "sources" | |
| }, | |
| "extras": { | |
| "type": "object", | |
| "description": "Extra parameters to pass.", | |
| "properties": { | |
| "links": { | |
| "type": "integer", | |
| "default": 0, | |
| "description": "Number of URLs to return from each webpage.", | |
| "example": 1 | |
| }, | |
| "imageLinks": { | |
| "type": "integer", | |
| "default": 0, | |
| "description": "Number of images to return for each result.", | |
| "example": 1 | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "CommonRequest": { | |
| "type": "object", | |
| "properties": { | |
| "numResults": { | |
| "type": "integer", | |
| "maximum": 100, | |
| "default": 10, | |
| "description": "Number of results to return (up to thousands of results available for custom plans)", | |
| "example": 10 | |
| }, | |
| "includeDomains": { | |
| "type": "array", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "description": "List of domains to include in the search. If specified, results will only come from these domains.", | |
| "example": [ | |
| "arxiv.org", | |
| "paperswithcode.com" | |
| ] | |
| }, | |
| "excludeDomains": { | |
| "type": "array", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "description": "List of domains to exclude from search results. If specified, no results will be returned from these domains." | |
| }, | |
| "startCrawlDate": { | |
| "type": "string", | |
| "format": "date-time", | |
| "description": "Crawl date refers to the date that Exa discovered a link. Results will include links that were crawled after this date. Must be specified in ISO 8601 format.", | |
| "example": "2023-01-01T00:00:00.000Z" | |
| }, | |
| "endCrawlDate": { | |
| "type": "string", | |
| "format": "date-time", | |
| "description": "Crawl date refers to the date that Exa discovered a link. Results will include links that were crawled before this date. Must be specified in ISO 8601 format.", | |
| "example": "2023-12-31T00:00:00.000Z" | |
| }, | |
| "startPublishedDate": { | |
| "type": "string", | |
| "format": "date-time", | |
| "description": "Only links with a published date after this will be returned. Must be specified in ISO 8601 format.", | |
| "example": "2023-01-01T00:00:00.000Z" | |
| }, | |
| "endPublishedDate": { | |
| "type": "string", | |
| "format": "date-time", | |
| "description": "Only links with a published date before this will be returned. Must be specified in ISO 8601 format.", | |
| "example": "2023-12-31T00:00:00.000Z" | |
| }, | |
| "includeText": { | |
| "type": "array", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "description": "List of strings that must be present in webpage text of results. Currently, only 1 string is supported, of up to 5 words.", | |
| "example": [ | |
| "large language model" | |
| ] | |
| }, | |
| "excludeText": { | |
| "type": "array", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "description": "List of strings that must not be present in webpage text of results. Currently, only 1 string is supported, of up to 5 words. Checks from the first 1000 words of the webpage text.", | |
| "example": [ | |
| "course" | |
| ] | |
| }, | |
| "contents": { | |
| "$ref": "#/components/schemas/ContentsRequest" | |
| } | |
| } | |
| }, | |
| "Result": { | |
| "type": "object", | |
| "properties": { | |
| "title": { | |
| "type": "string", | |
| "description": "The title of the search result.", | |
| "example": "A Comprehensive Overview of Large Language Models" | |
| }, | |
| "url": { | |
| "type": "string", | |
| "format": "uri", | |
| "description": "The URL of the search result.", | |
| "example": "https://arxiv.org/pdf/2307.06435.pdf" | |
| }, | |
| "publishedDate": { | |
| "type": "string", | |
| "nullable": true, | |
| "description": "An estimate of the creation date, from parsing HTML content. Format is YYYY-MM-DD.", | |
| "example": "2023-11-16T01:36:32.547Z" | |
| }, | |
| "author": { | |
| "type": "string", | |
| "nullable": true, | |
| "description": "If available, the author of the content.", | |
| "example": "Humza Naveed, University of Engineering and Technology (UET), Lahore, Pakistan" | |
| }, | |
| "score": { | |
| "type": "number", | |
| "nullable": true, | |
| "description": "A number from 0 to 1 representing similarity between the query/url and the result.", | |
| "example": 0.4600165784358978 | |
| }, | |
| "id": { | |
| "type": "string", | |
| "description": "The temporary ID for the document. Useful for /contents endpoint.", | |
| "example": "https://arxiv.org/abs/2307.06435" | |
| }, | |
| "image": { | |
| "type": "string", | |
| "format": "uri", | |
| "description": "The URL of an image associated with the search result, if available.", | |
| "example": "https://arxiv.org/pdf/2307.06435.pdf/page_1.png" | |
| }, | |
| "favicon": { | |
| "type": "string", | |
| "format": "uri", | |
| "description": "The URL of the favicon for the search result's domain.", | |
| "example": "https://arxiv.org/favicon.ico" | |
| } | |
| } | |
| }, | |
| "ResultWithContent": { | |
| "allOf": [ | |
| { | |
| "$ref": "#/components/schemas/Result" | |
| }, | |
| { | |
| "type": "object", | |
| "properties": { | |
| "text": { | |
| "type": "string", | |
| "description": "The full content text of the search result.", | |
| "example": "Abstract Large Language Models (LLMs) have recently demonstrated remarkable capabilities..." | |
| }, | |
| "highlights": { | |
| "type": "array", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "description": "Array of highlights extracted from the search result content.", | |
| "example": [ | |
| "Such requirements have limited their adoption..." | |
| ] | |
| }, | |
| "highlightScores": { | |
| "type": "array", | |
| "items": { | |
| "type": "number", | |
| "format": "float" | |
| }, | |
| "description": "Array of cosine similarity scores for each highlighted", | |
| "example": [ | |
| 0.4600165784358978 | |
| ] | |
| }, | |
| "summary": { | |
| "type": "string", | |
| "description": "Summary of the webpage", | |
| "example": "This overview paper on Large Language Models (LLMs) highlights key developments..." | |
| }, | |
| "subpages": { | |
| "type": "array", | |
| "items": { | |
| "$ref": "#/components/schemas/ResultWithContent" | |
| }, | |
| "description": "Array of subpages for the search result.", | |
| "example": [ | |
| { | |
| "id": "https://arxiv.org/abs/2303.17580", | |
| "url": "https://arxiv.org/pdf/2303.17580.pdf", | |
| "title": "HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face", | |
| "author": "Yongliang Shen, Microsoft Research Asia, Kaitao Song, Microsoft Research Asia, Xu Tan, Microsoft Research Asia, Dongsheng Li, Microsoft Research Asia, Weiming Lu, Microsoft Research Asia, Yueting Zhuang, Microsoft Research Asia, [email protected], Zhejiang University, Microsoft Research Asia, Microsoft Research, Microsoft Research Asia", | |
| "publishedDate": "2023-11-16T01:36:20.486Z", | |
| "text": "HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face Date Published: 2023-05-25 Authors: Yongliang Shen, Microsoft Research Asia Kaitao Song, Microsoft Research Asia Xu Tan, Microsoft Research Asia Dongsheng Li, Microsoft Research Asia Weiming Lu, Microsoft Research Asia Yueting Zhuang, Microsoft Research Asia, [email protected] Zhejiang University, Microsoft Research Asia Microsoft Research, Microsoft Research Asia Abstract Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are abundant AI models available for different domains and modalities, they cannot handle complicated AI tasks. Considering large language models (LLMs) have exhibited exceptional ability in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks and language could be a generic interface to empower t", | |
| "summary": "HuggingGPT is a framework using ChatGPT as a central controller to orchestrate various AI models from Hugging Face to solve complex tasks. ChatGPT plans the task, selects appropriate models based on their descriptions, executes subtasks, and summarizes the results. This approach addresses limitations of LLMs by allowing them to handle multimodal data (vision, speech) and coordinate multiple models for complex tasks, paving the way for more advanced AI systems.", | |
| "highlights": [ | |
| "2) Recently, some researchers started to investigate the integration of using tools or models in LLMs ." | |
| ], | |
| "highlightScores": [ | |
| 0.32679107785224915 | |
| ] | |
| } | |
| ] | |
| }, | |
| "extras": { | |
| "type": "object", | |
| "description": "Results from extras.", | |
| "properties": { | |
| "links": { | |
| "type": "array", | |
| "items": { | |
| "type": "string" | |
| }, | |
| "description": "Array of links from the search result.", | |
| "example": [] | |
| } | |
| } | |
| } | |
| } | |
| } | |
| ] | |
| }, | |
| "CostDollars": { | |
| "type": "object", | |
| "properties": { | |
| "total": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Total dollar cost for your request", | |
| "example": 0.005 | |
| }, | |
| "breakDown": { | |
| "type": "array", | |
| "description": "Breakdown of costs by operation type", | |
| "items": { | |
| "type": "object", | |
| "properties": { | |
| "search": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Cost of your search operations", | |
| "example": 0.005 | |
| }, | |
| "contents": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Cost of your content operations", | |
| "example": 0 | |
| }, | |
| "breakdown": { | |
| "type": "object", | |
| "properties": { | |
| "keywordSearch": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Cost of your keyword search operations", | |
| "example": 0 | |
| }, | |
| "neuralSearch": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Cost of your neural search operations", | |
| "example": 0.005 | |
| }, | |
| "contentText": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Cost of your text content retrieval", | |
| "example": 0 | |
| }, | |
| "contentHighlight": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Cost of your highlight generation", | |
| "example": 0 | |
| }, | |
| "contentSummary": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Cost of your summary generation", | |
| "example": 0 | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "perRequestPrices": { | |
| "type": "object", | |
| "description": "Standard price per request for different operations", | |
| "properties": { | |
| "neuralSearch_1_25_results": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price for neural search with 1-25 results", | |
| "example": 0.005 | |
| }, | |
| "neuralSearch_26_100_results": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price for neural search with 26-100 results", | |
| "example": 0.025 | |
| }, | |
| "neuralSearch_100_plus_results": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price for neural search with 100+ results", | |
| "example": 1 | |
| }, | |
| "keywordSearch_1_100_results": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price for keyword search with 1-100 results", | |
| "example": 0.0025 | |
| }, | |
| "keywordSearch_100_plus_results": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price for keyword search with 100+ results", | |
| "example": 3 | |
| } | |
| } | |
| }, | |
| "perPagePrices": { | |
| "type": "object", | |
| "description": "Standard price per page for different content operations", | |
| "properties": { | |
| "contentText": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price per page for text content", | |
| "example": 0.001 | |
| }, | |
| "contentHighlight": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price per page for highlights", | |
| "example": 0.001 | |
| }, | |
| "contentSummary": { | |
| "type": "number", | |
| "format": "float", | |
| "description": "Standard price per page for summaries", | |
| "example": 0.001 | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "ResearchTaskDto": { | |
| "type": [ | |
| "object" | |
| ], | |
| "properties": { | |
| "id": { | |
| "type": [ | |
| "string" | |
| ], | |
| "description": "The unique identifier for the research task" | |
| }, | |
| "status": { | |
| "type": [ | |
| "string" | |
| ], | |
| "enum": [ | |
| "running", | |
| "completed", | |
| "failed" | |
| ], | |
| "description": "The current status of the research task" | |
| }, | |
| "instructions": { | |
| "type": [ | |
| "string" | |
| ], | |
| "description": "The instructions or query for the research task" | |
| }, | |
| "schema": { | |
| "type": [ | |
| "object" | |
| ], | |
| "additionalProperties": {}, | |
| "description": "The JSON schema specification for the expected output format" | |
| }, | |
| "data": { | |
| "type": [ | |
| "object" | |
| ], | |
| "additionalProperties": {}, | |
| "description": "The research results data conforming to the specified schema" | |
| }, | |
| "citations": { | |
| "type": [ | |
| "object" | |
| ], | |
| "additionalProperties": { | |
| "type": [ | |
| "array" | |
| ], | |
| "items": { | |
| "type": [ | |
| "object" | |
| ], | |
| "properties": { | |
| "id": { | |
| "type": [ | |
| "string" | |
| ] | |
| }, | |
| "url": { | |
| "type": [ | |
| "string" | |
| ] | |
| }, | |
| "title": { | |
| "type": [ | |
| "string" | |
| ] | |
| }, | |
| "snippet": { | |
| "type": [ | |
| "string" | |
| ] | |
| } | |
| }, | |
| "required": [ | |
| "id", | |
| "url", | |
| "snippet" | |
| ] | |
| } | |
| }, | |
| "description": "Citations grouped by the root field they were used in" | |
| } | |
| }, | |
| "required": [ | |
| "id", | |
| "status", | |
| "instructions" | |
| ] | |
| } | |
| }, | |
| "responses": { | |
| "SearchResponse": { | |
| "description": "OK", | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "requestId": { | |
| "type": "string", | |
| "description": "Unique identifier for the request", | |
| "example": "b5947044c4b78efa9552a7c89b306d95" | |
| }, | |
| "resolvedSearchType": { | |
| "type": "string", | |
| "enum": [ | |
| "neural", | |
| "keyword" | |
| ], | |
| "description": "The search type that was actually used for this request", | |
| "example": "neural" | |
| }, | |
| "results": { | |
| "type": "array", | |
| "description": "A list of search results containing title, URL, published date, author, and score.", | |
| "items": { | |
| "$ref": "#/components/schemas/ResultWithContent" | |
| } | |
| }, | |
| "searchType": { | |
| "type": "string", | |
| "enum": [ | |
| "neural", | |
| "keyword" | |
| ], | |
| "description": "For auto searches, indicates which search type was selected.", | |
| "example": "auto" | |
| }, | |
| "costDollars": { | |
| "$ref": "#/components/schemas/CostDollars" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "FindSimilarResponse": { | |
| "description": "OK", | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "requestId": { | |
| "type": "string", | |
| "description": "Unique identifier for the request", | |
| "example": "c6958155d5c89ffa0663b7c90c407396" | |
| }, | |
| "results": { | |
| "type": "array", | |
| "description": "A list of search results containing title, URL, published date, author, and score.", | |
| "items": { | |
| "$ref": "#/components/schemas/ResultWithContent" | |
| } | |
| }, | |
| "costDollars": { | |
| "$ref": "#/components/schemas/CostDollars" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "ContentsResponse": { | |
| "description": "OK", | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "requestId": { | |
| "type": "string", | |
| "description": "Unique identifier for the request", | |
| "example": "e492118ccdedcba5088bfc4357a8a125" | |
| }, | |
| "results": { | |
| "type": "array", | |
| "items": { | |
| "$ref": "#/components/schemas/ResultWithContent" | |
| } | |
| }, | |
| "costDollars": { | |
| "$ref": "#/components/schemas/CostDollars" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "AnswerResponse": { | |
| "description": "OK", | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "allOf": [ | |
| { | |
| "$ref": "#/components/schemas/AnswerResult" | |
| }, | |
| { | |
| "type": "object", | |
| "properties": { | |
| "costDollars": { | |
| "$ref": "#/components/schemas/CostDollars" | |
| } | |
| } | |
| } | |
| ] | |
| } | |
| }, | |
| "text/event-stream": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "answer": { | |
| "type": "string", | |
| "description": "Partial answer chunk when streaming is enabled." | |
| }, | |
| "citations": { | |
| "type": "array", | |
| "items": { | |
| "$ref": "#/components/schemas/AnswerCitation" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "ResearchCreateTaskResponse": { | |
| "description": "Research task created", | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "requestId": { | |
| "type": "string", | |
| "description": "Unique identifier for the request", | |
| "example": "b5947044c4b78efa9552a7c89b306d95" | |
| }, | |
| "id": { | |
| "type": "string", | |
| "description": "The unique identifier for the research task", | |
| "example": "a1b2c3d4-e5f6-7890-abcd-ef1234567890" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "ListResearchTasksResponse": { | |
| "description": "List of research tasks", | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "type": "object", | |
| "properties": { | |
| "requestId": { | |
| "type": "string", | |
| "description": "Unique identifier for the request", | |
| "example": "b5947044c4b78efa9552a7c89b306d95" | |
| }, | |
| "data": { | |
| "type": "array", | |
| "items": { | |
| "$ref": "#/components/schemas/ResearchTaskDto" | |
| }, | |
| "description": "The list of research tasks" | |
| }, | |
| "hasMore": { | |
| "type": "boolean", | |
| "description": "Whether there are more results to paginate through" | |
| }, | |
| "nextCursor": { | |
| "type": "string", | |
| "nullable": true, | |
| "description": "The cursor to paginate through the next set of results" | |
| } | |
| } | |
| } | |
| } | |
| } | |
| }, | |
| "ResearchTaskResponse": { | |
| "description": "Research task details", | |
| "content": { | |
| "application/json": { | |
| "schema": { | |
| "allOf": [ | |
| { | |
| "$ref": "#/components/schemas/ResearchTaskDto" | |
| }, | |
| { | |
| "type": "object", | |
| "properties": { | |
| "requestId": { | |
| "type": "string", | |
| "description": "Unique identifier for the request", | |
| "example": "b5947044c4b78efa9552a7c89b306d95" | |
| } | |
| } | |
| } | |
| ] | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } |
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