API Reference
Get contents
API Reference
Get contents
Get the full page contents, summaries, and metadata for a list of URLs.
Returns instant results from our cache, with automatic live crawling as fallback for uncached pages.
POST
/
contents
curl -X POST 'https://api.exa.ai/contents' \
-H 'x-api-key: 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
API key can be provided either via x-api-key header or Authorization header with Bearer scheme
Body
application/json
Array of URLs to crawl (backwards compatible with 'ids' parameter).
Example:
["https://arxiv.org/pdf/2307.06435"]
Deprecated - use 'urls' instead. Array of document IDs obtained from searches.
Example:
["https://arxiv.org/pdf/2307.06435"]
If true, returns full page text with default settings. If false, disables text return.
Text snippets the LLM identifies as most relevant from each page.
The number of sentences to return for each snippet.
Example:
1
The number of snippets to return for each result.
Example:
1
Custom query to direct the LLM's selection of highlights.
Example:
"Key advancements"
Summary of the webpage
Custom query for the LLM-generated summary.
Example:
"Main developments"
JSON schema for structured output from summary. See https://json-schema.org/overview/what-is-jsonschema for JSON Schema documentation.
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"]
}
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"
The timeout for livecrawling in milliseconds.
Example:
1000
The number of subpages to crawl. The actual number crawled may be limited by system constraints.
Example:
1
Keyword to find specific subpages of search results. Can be a single string or an array of strings, comma delimited.
Example:
"sources"
Response
200 - application/json
OK
Unique identifier for the request
Example:
"e492118ccdedcba5088bfc4357a8a125"
The title of the search result.
Example:
"A Comprehensive Overview of Large Language Models"
The URL of the search result.
Example:
"https://arxiv.org/pdf/2307.06435.pdf"
An estimate of the creation date, from parsing HTML content. Format is YYYY-MM-DD.
Example:
"2023-11-16T01:36:32.547Z"
If available, the author of the content.
Example:
"Humza Naveed, University of Engineering and Technology (UET), Lahore, Pakistan"
A number from 0 to 1 representing similarity between the query/url and the result.
Example:
0.4600165784358978
The temporary ID for the document. Useful for /contents endpoint.
Example:
"https://arxiv.org/abs/2307.06435"
The URL of an image associated with the search result, if available.
Example:
"https://arxiv.org/pdf/2307.06435.pdf/page_1.png"
The URL of the favicon for the search result's domain.
Example:
"https://arxiv.org/favicon.ico"
The full content text of the search result.
Example:
"Abstract Large Language Models (LLMs) have recently demonstrated remarkable capabilities..."
Array of highlights extracted from the search result content.
Example:
[
"Such requirements have limited their adoption..."
]
Array of cosine similarity scores for each highlighted
Example:
[0.4600165784358978]
Summary of the webpage
Example:
"This overview paper on Large Language Models (LLMs) highlights key developments..."
Array of subpages for the search result.
The title of the search result.
Example:
"A Comprehensive Overview of Large Language Models"
The URL of the search result.
Example:
"https://arxiv.org/pdf/2307.06435.pdf"
An estimate of the creation date, from parsing HTML content. Format is YYYY-MM-DD.
Example:
"2023-11-16T01:36:32.547Z"
If available, the author of the content.
Example:
"Humza Naveed, University of Engineering and Technology (UET), Lahore, Pakistan"
A number from 0 to 1 representing similarity between the query/url and the result.
Example:
0.4600165784358978
The temporary ID for the document. Useful for /contents endpoint.
Example:
"https://arxiv.org/abs/2307.06435"
The URL of an image associated with the search result, if available.
Example:
"https://arxiv.org/pdf/2307.06435.pdf/page_1.png"
The URL of the favicon for the search result's domain.
Example:
"https://arxiv.org/favicon.ico"
The full content text of the search result.
Example:
"Abstract Large Language Models (LLMs) have recently demonstrated remarkable capabilities..."
Array of highlights extracted from the search result content.
Example:
[
"Such requirements have limited their adoption..."
]
Array of cosine similarity scores for each highlighted
Example:
[0.4600165784358978]
Summary of the webpage
Example:
"This overview paper on Large Language Models (LLMs) highlights key developments..."
Array of subpages for the search result.
The title of the search result.
Example:
"A Comprehensive Overview of Large Language Models"
The URL of the search result.
Example:
"https://arxiv.org/pdf/2307.06435.pdf"
An estimate of the creation date, from parsing HTML content. Format is YYYY-MM-DD.
Example:
"2023-11-16T01:36:32.547Z"
If available, the author of the content.
Example:
"Humza Naveed, University of Engineering and Technology (UET), Lahore, Pakistan"
A number from 0 to 1 representing similarity between the query/url and the result.
Example:
0.4600165784358978
The temporary ID for the document. Useful for /contents endpoint.
Example:
"https://arxiv.org/abs/2307.06435"
The URL of an image associated with the search result, if available.
Example:
"https://arxiv.org/pdf/2307.06435.pdf/page_1.png"
The URL of the favicon for the search result's domain.
Example:
"https://arxiv.org/favicon.ico"
The full content text of the search result.
Example:
"Abstract Large Language Models (LLMs) have recently demonstrated remarkable capabilities..."
Array of highlights extracted from the search result content.
Example:
[
"Such requirements have limited their adoption..."
]
Array of cosine similarity scores for each highlighted
Example:
[0.4600165784358978]
Summary of the webpage
Example:
"This overview paper on Large Language Models (LLMs) highlights key developments..."
Array of subpages for the search result.
The title of the search result.
Example:
"A Comprehensive Overview of Large Language Models"
The URL of the search result.
Example:
"https://arxiv.org/pdf/2307.06435.pdf"
An estimate of the creation date, from parsing HTML content. Format is YYYY-MM-DD.
Example:
"2023-11-16T01:36:32.547Z"
If available, the author of the content.
Example:
"Humza Naveed, University of Engineering and Technology (UET), Lahore, Pakistan"
A number from 0 to 1 representing similarity between the query/url and the result.
Example:
0.4600165784358978
The temporary ID for the document. Useful for /contents endpoint.
Example:
"https://arxiv.org/abs/2307.06435"
The URL of an image associated with the search result, if available.
Example:
"https://arxiv.org/pdf/2307.06435.pdf/page_1.png"
The URL of the favicon for the search result's domain.
Example:
"https://arxiv.org/favicon.ico"
The full content text of the search result.
Example:
"Abstract Large Language Models (LLMs) have recently demonstrated remarkable capabilities..."
Array of highlights extracted from the search result content.
Example:
[
"Such requirements have limited their adoption..."
]
Array of cosine similarity scores for each highlighted
Example:
[0.4600165784358978]
Summary of the webpage
Example:
"This overview paper on Large Language Models (LLMs) highlights key developments..."
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]
}
]
Results from extras.
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]
}
]
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]
}
]
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]
}
]
Total dollar cost for your request
Example:
0.005
Breakdown of costs by operation type
Cost of your search operations
Example:
0.005
Cost of your content operations
Example:
0
Cost of your keyword search operations
Example:
0
Cost of your neural search operations
Example:
0.005
Cost of your text content retrieval
Example:
0
Cost of your highlight generation
Example:
0
Cost of your summary generation
Example:
0
Standard price per request for different operations
Standard price for neural search with 1-25 results
Example:
0.005
Standard price for neural search with 26-100 results
Example:
0.025
Standard price for neural search with 100+ results
Example:
1
Standard price for keyword search with 1-100 results
Example:
0.0025
Standard price for keyword search with 100+ results
Example:
3
Standard price per page for different content operations
curl -X POST 'https://api.exa.ai/contents' \
-H 'x-api-key: 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
}
}
}