POST
/
contents
curl -X POST 'https://api.exa.ai/contents' \
  -H 'Authorization: Bearer YOUR-EXA-API-KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "urls": ["https://arxiv.org/abs/2307.06435"],
    "text": true
  }'
{
  "requestId": "e492118ccdedcba5088bfc4357a8a125",
  "results": [
    {
      "title": "A Comprehensive Overview of Large Language Models",
      "url": "https://arxiv.org/pdf/2307.06435.pdf",
      "publishedDate": "2023-11-16T01:36:32.547Z",
      "author": "Humza  Naveed, University of Engineering and Technology (UET), Lahore, Pakistan",
      "score": 0.4600165784358978,
      "id": "https://arxiv.org/abs/2307.06435",
      "image": "https://arxiv.org/pdf/2307.06435.pdf/page_1.png",
      "favicon": "https://arxiv.org/favicon.ico",
      "text": "Abstract Large Language Models (LLMs) have recently demonstrated remarkable capabilities...",
      "highlights": [
        "Such requirements have limited their adoption..."
      ],
      "highlightScores": [
        0.4600165784358978
      ],
      "summary": "This overview paper on Large Language Models (LLMs) highlights key developments...",
      "subpages": [
        {
          "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": {
        "links": []
      }
    }
  ],
  "costDollars": {
    "total": 0.005,
    "breakDown": [
      {
        "search": 0.005,
        "contents": 0,
        "breakdown": {
          "keywordSearch": 0,
          "neuralSearch": 0.005,
          "contentText": 0,
          "contentHighlight": 0,
          "contentSummary": 0
        }
      }
    ],
    "perRequestPrices": {
      "neuralSearch_1_25_results": 0.005,
      "neuralSearch_26_100_results": 0.025,
      "neuralSearch_100_plus_results": 1,
      "keywordSearch_1_100_results": 0.0025,
      "keywordSearch_100_plus_results": 3
    },
    "perPagePrices": {
      "contentText": 0.001,
      "contentHighlight": 0.001,
      "contentSummary": 0.001
    }
  }
}

Get your Exa API key

Authorizations

Authorization
string
header
required

API key can be provided either via x-api-key header or Authorization header with Bearer scheme

Body

application/json
urls
string[]
required

Array of URLs to crawl (backwards compatible with 'ids' parameter).

Example:
["https://arxiv.org/pdf/2307.06435"]
ids
string[]
deprecated

Deprecated - use 'urls' instead. Array of document IDs obtained from searches.

Example:
["https://arxiv.org/pdf/2307.06435"]
text

If true, returns full page text with default settings. If false, disables text return.

highlights
object

Text snippets the LLM identifies as most relevant from each page.

summary
object

Summary of the webpage

livecrawl
enum<string>

Options for livecrawling pages. 'never': Disable livecrawling (default for neural search). 'fallback': Livecrawl when cache is empty (default for keyword search). 'always': Always livecrawl. 'auto': Use an LLM to detect if query needs real-time content.

Available options:
never,
fallback,
always,
auto
Example:

"always"

livecrawlTimeout
integer
default:10000

The timeout for livecrawling in milliseconds.

Example:

1000

subpages
integer
default:0

The number of subpages to crawl. The actual number crawled may be limited by system constraints.

Example:

1

subpageTarget

Keyword to find specific subpages of search results. Can be a single string or an array of strings, comma delimited.

Example:

"sources"

extras
object

Extra parameters to pass.

Response

200 - application/json
OK
requestId
string

Unique identifier for the request

Example:

"e492118ccdedcba5088bfc4357a8a125"

results
object[]
costDollars
object