v1.0.1, last updated 15/05/2023 18:22 BST
This documentation is for educational purposes only. It may be incorrect, outdated, or be missing features. Some aspects are unconfirmed, such as the request option parameters.
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Phind is an AI service that provides natural language responses to user questions and prompts. It has a low-powered model that rewrites user prompts and then uses search results to generate an answer with GPT-4, the higher powered model used when 'Use Best Model' is checked. This documentation does not cover intermediate mode, which is presumed to use gpt-3.5-turbo
or text-davinci-003
.
http://phind.com/api
Search the web based on a user's question or prompt.
Method: POST
Request Body: JSON
{
"q": "String query",
"browserLanguage": "String language code"
}
Response: JSON
[
{
"title": "String title",
"url": "String url",
"is_source_local": Boolean,
"is_source_both": Boolean,
"description": "String description",
"profile": {
"name": "String name",
"url": "String url",
"long_name": "String long name",
"img": "String image url"
},
"language": "String language code",
"family_friendly": Boolean,
"type": "String type",
"subtype": "String subtype",
"meta_url": {
"scheme": "String scheme",
"netloc": "String netloc",
"hostname": "String hostname",
"favicon": "String favicon url",
"path": "String path"
},
"thumbnail": {
"src": "String thumbnail url",
"original": "String original thumbnail url",
"logo": Boolean
},
"age": "String published date",
"article": {
"author": [
{
"type": "String type",
"name": "String name",
"url": "String url"
}
],
"date": "String published date",
"publisher": {
"type": "String type",
"name": "String name",
"url": "String url",
"thumbnail": {
"src": "String publisher logo url",
"original": "String original publisher logo url"
}
}
}
}
]
Cache data in the Phind database. Used internally.
Method: POST
Request Body: JSON
{
"key": "String key",
"value": "String value"
}
Response: No response
Generate an answer from the Phind AI model using web search results.
Method: POST
Request Body: JSON
{
"question": "String question",
"webResults": [web search response],
"options": {
"temperature": Float,
"max_tokens": Integer,
"top_p": Float,
"repetition_penalty": Float,
"presence_penalty": Float,
"frequency_penalty": Float,
"curiosity_bonus": Float
}
}
Response:
data: String generated response, by word (10 word output = 10x data: word lines)
Generate a follow-up answer from the Phind AI model using web search results and conversation context.
Method: POST
Request Body: JSON
{
"question": "String question",
"questionHistory": ["String previous questions"],
"answerHistory": ["String previous answers"],
"webResults": [web search response],
"options": {
"temperature": Float,
"max_tokens": Integer,
"top_p": Float,
"repetition_penalty": Float,
"presence_penalty": Float,
"frequency_penalty": Float,
"curiosity_bonus": Float
}
}
Response:
Same as /api/infer/answer