openapi: 3.1.0 info: version: 1.0.0 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 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. requestBody: required: true content: application/json: schema: allOf: - type: object properties: query: type: string example: "Latest developments in LLM capabilities" description: The query string. useAutoprompt: type: boolean description: Autoprompt converts your query to an Exa query. Default false. Neural and auto search only. example: true type: type: string enum: - keyword - neural - auto description: The type of search. Default auto, which automatically decides between keyword and neural. example: "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. 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" requestBody: required: true content: application/json: schema: type: object allOf: - type: object properties: ids: type: array description: Array of document IDs obtained from searches items: type: string example: ["https://arxiv.org/pdf/2307.06435"] required: - ids - $ref: "#/components/schemas/ContentsRequest" responses: "200": $ref: "#/components/responses/ContentsResponse" components: securitySchemes: apikey: type: apiKey name: x-api-key in: header schemas: ContentsRequest: type: object properties: text: type: object description: Parsed contents of the page. properties: maxCharacters: type: integer description: Max length in characters for the text returned example: 1000 includeHtmlTags: type: boolean description: Whether HTML tags, which can help the LLM understand structure of text, should be included. Default false example: false highlights: type: object description: Relevant extract(s) from the webpage. properties: numSentences: type: integer description: The number of sentences to be returned in each snippet. Default 5 example: 1 highlightsPerUrl: type: integer description: The number of snippets to return per page. Default 1 example: 1 query: type: string description: Query used specifically for the highlights. example: "Key advancements" summary: type: object description: Summary of the webpage properties: query: type: string description: Query used specifically for the summary. example: "Main developments" livecrawl: type: string enum: [never, fallback, always] description: Options for livecrawling contents. Default is "never" for neural/auto search, "fallback" for keyword search. example: "always" livecrawlTimeout: type: integer description: The timeout for livecrawling in milliseconds. Max and default is 10000. example: 1000 subpages: type: integer description: The number of subpages to crawl. example: 1 subpageTarget: oneOf: - type: string - type: array items: type: string description: "The target subpage or subpages. Can be a single string or an array of strings." example: "cited papers" extras: type: object description: Extra parameters to pass. properties: links: type: integer description: The number of links to return. example: 1 imageLinks: type: integer description: The number of images to return for each result. example: 1 CommonRequest: type: object properties: numResults: type: integer description: Number of search results to return. Default 10. Max 10 for basic plans. Up to thousands 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 in the search. If specified, results will not include any from these domains. example: - youtube.com - twitter.com 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: 2023-01-01 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: 2023-12-31 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: 2023-01-01 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: 2023-12-31 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. 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, Asad Ullah Khan, University of Engineering and Technology (UET), Lahore, Pakistan, Shi Qiu, Australian National University (ANU), Canberra, Australia, Muhammad Saqib, University of Technology Sydney (UTS), Sydney, Australia, Saeed Anwar, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, Muhammad Usman, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, Naveed Akhtar, The University of Melbourne (UoM), Melbourne, Australia, Nick Barnes, Australian National University (ANU), Canberra, Australia, Ajmal Mian, The University of Western Australia (UWA), Perth, Australia" 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: "Asad Ullah Khan, University of Engineering and Technology (UET), Lahore, Pakistan, aukhanee@gmail.com Shi Qiu, HKSAR, China, shiqiu@cse.cuhk.edu.hk Muhammad Saqib, University of Technology Sydney (UTS), Sydney, Australia, muhammad.saqib@data61.csiro.au Saeed Anwar, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, saeed.anwar@kfupm.edu.sa Muhammad Usman, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, muhammad.usman@kfupm.edu.sa Naveed Akhtar, The University of Melbourne (UoM), Melbourne, Australia, naveed.akhtar1@unimelb.edu.au Nick Barnes, Australian National University (ANU), Canberra, Australia, nick.barnes@anu.edu.au Ajmal Mian, The University of Western Australia (UWA), Perth, Australia, ajmal.mian@uwa.edu.au Abstract Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in t" # TODO: think we need to change this highlights: type: array items: type: string description: Array of highlights extracted from the search result content. example: - "Such requirements have limited their adoption and opened up opportunities to devise better architectures , , , and training strategies , , , , , , ." 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 in the field. These include architectural innovations, improved training strategies, increased context lengths, fine-tuning techniques, the rise of multi-modal LLMs, applications in robotics, new datasets and benchmarks, and efficiency improvements. The paper also notes a shift towards instruction-tuned models and increased open-source availability." 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, yzhuang@zju.edu.cn, 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, yzhuang@zju.edu.cn 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: [] responses: SearchResponse: description: OK content: application/json: schema: type: object properties: 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" FindSimilarResponse: description: OK content: application/json: schema: type: object properties: results: type: array description: A list of search results containing title, URL, published date, author, and score. items: $ref: "#/components/schemas/ResultWithContent" ContentsResponse: description: OK content: application/json: schema: type: object properties: results: type: array items: $ref: "#/components/schemas/ResultWithContent" security: - apikey: []