OpenAI (Independent Publisher) (#1583)
* Created folder and required files for the OpenAI independent connector * Updated OpenAI json definitions * Deleted the "engines" action, because it wasn't useful at all and changed the "logprobs" description in the response * Changed response from default to 200 and added description * Changed the "Best_of" description * Renamed file to "apiDefinition.swagger.json"
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# OpenAI
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OpenAI is an artificial intelligence research laboratory. The company conducts research in the field of AI with the stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole.
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Through this connector you can access the Generative Pre-trained Transformer 3 (GPT-3), an autoregressive language model that uses deep learning to produce human-like text.
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## Publisher: Robin Rosengrün | R2Power
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## Prerequisites
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You need an OpenAI account and available tokens. The account comes with a 3 month trial and enough tokens for testing the service.
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## Supported Operations
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The connector supports the following operations:
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- `List engines`: This action will return all available GPT-3 engines.
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- `Completion`: This action will complete your prompt and is the main action you will use.
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## Obtaining Credentials
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In your account you can create an API key [here](https://beta.openai.com/account/api-keys)
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## API Documentation
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Visit [the OpenAI documentation page](https://beta.openai.com/docs/api-reference/introduction) for further details.
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## Known issues and limitations
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When entering your API key in the Power Platform, you need to type it as: "Bearer YOUR_API_KEY" (the word "Bearer" a blank and the actual API_KEY)
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Only the "Completion" action is supported right now, feel free to add other actions.
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## Deployment Instructions
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When entering your API key in the Power Platform, you need to type it as: "Bearer YOUR_API_KEY" (the word "Bearer" a blank and the actual API_KEY)
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{
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"swagger": "2.0",
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"info": {
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"title": "OpenAI",
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"description": "Connect to the OpenAI API and use the Power of GPT3, API key must be entered as \"Bearer YOUR_API_KEY\"",
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"version": "1.0"
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},
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"host": "api.openai.com",
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"basePath": "/",
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"schemes": [
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"https"
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],
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"consumes": [],
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"produces": [],
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"paths": {
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"/v1/engines/{engine}/completions": {
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"post": {
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"responses": {
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"200": {
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"description": "GPT3 completed your prompt",
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"schema": {
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"type": "object",
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"properties": {
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"id": {
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"type": "string",
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"description": "id"
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},
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"object": {
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"type": "string",
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"description": "object"
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},
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"created": {
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"type": "integer",
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"format": "int32",
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"description": "created"
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},
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"model": {
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"type": "string",
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"description": "Which GPT3 Engine was used",
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"title": "Model",
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"x-ms-visibility": "internal"
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},
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"choices": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"text": {
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"type": "string",
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"description": "Completion text",
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"title": "Text"
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},
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"index": {
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"type": "integer",
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"format": "int32",
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"description": "Number of completion",
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"title": "Index"
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},
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"logprobs": {
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"type": "string",
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"description": "Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 3, the API will return a list of the 3 most likely tokens.",
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"title": "Logprobs"
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},
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"finish_reason": {
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"title": "Finish reason",
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"type": "string",
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"description": "Reason why the text finished (stop condition / natural end / length)"
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}
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}
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},
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"description": "Returned Completion(s)"
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}
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}
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}
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}
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},
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"summary": "GPT3 Completes your prompt",
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"description": "GPT3 Completes your prompt",
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"operationId": "Completion",
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"parameters": [
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{
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"name": "engine",
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"in": "path",
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"type": "string",
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"description": "The used engine, choose between text-davinci-002, text-curie-001, text-babbage-001, text-ada-001",
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"required": true,
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"default": "text-davinci-002",
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"x-ms-visibility": "important",
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"x-ms-summary": "Engine",
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"enum": [
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"text-davinci-002",
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"text-curie-001",
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"text-babbage-001",
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"text-ada-001"
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],
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"x-ms-enum-values": [
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{
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"displayName": "DaVinci - most expensive",
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"value": "text-davinci-002"
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},
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{
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"displayName": "Curie",
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"value": "text-curie-001"
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},
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{
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"displayName": "Babbage",
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"value": "text-babbage-001"
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},
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{
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"displayName": "Ada - cheapest",
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"value": "text-ada-001"
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}
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]
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},
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{
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"name": "body",
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"in": "body",
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"required": true,
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"schema": {
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"type": "object",
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"properties": {
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"prompt": {
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"type": "string",
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"description": "Text that will be completed by GPT3",
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"title": "prompt",
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"default": "What is your favorite animal and why? Tell me also about the size and weight of this animal."
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},
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"n": {
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"type": "integer",
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"format": "int32",
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"description": "How many completions to generate for each prompt",
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"default": 1
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},
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"best_of": {
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"type": "integer",
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"format": "int32",
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"description": "If set to more than 1, generates multiple completions server-side and returns the \"best\". Must be greater than \"n\". Use with caution, can consume a lot of tokens.",
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"default": 1
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},
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"temperature": {
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"type": "number",
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"format": "float",
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"description": "Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. Use this OR top p",
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"title": "temperature",
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"default": 1
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},
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"max_tokens": {
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"type": "integer",
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"format": "int32",
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"description": "One token equals roughly 4 characters of text (up to 4000 tokens between prompt and completion)",
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"title": "max tokens",
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"default": 100
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},
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"top_p": {
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"type": "number",
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"format": "float",
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"description": "An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.",
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"title": "top p"
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},
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"frequency_penalty": {
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"type": "number",
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"format": "float",
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"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the models likelihood to repeat the same line verbatim.",
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"title": "frequency penalty",
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"default": 0
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},
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"presence_penalty": {
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"type": "number",
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"format": "float",
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"description": "Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the models likelihood to talk about new topics.",
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"title": "presence penalty",
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"default": 0
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},
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"user": {
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"type": "string",
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"title": "user",
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"description": "A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse"
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},
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"stop": {
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"type": "array",
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"items": {
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"type": "string"
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},
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"description": "Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence"
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}
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},
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"required": [
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"prompt"
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]
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}
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}
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],
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"x-ms-visibility": "important"
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}
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}
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},
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"definitions": {},
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"parameters": {},
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"responses": {},
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"securityDefinitions": {
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"API Key": {
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"type": "apiKey",
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"in": "header",
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"name": "Authorization"
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}
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},
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"security": [
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{
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"API Key": []
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}
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],
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"tags": []
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}
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{
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"properties": {
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"connectionParameters": {
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"api_key": {
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"type": "securestring",
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"uiDefinition": {
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"displayName": "API Key",
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"description": "Enter API Key as as \"Bearer YOUR_API_KEY\"",
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"tooltip": "Provide your API Key",
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"constraints": {
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"tabIndex": 2,
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"clearText": false,
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"required": "true"
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}
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}
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}
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},
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"iconBrandColor": "#da3b01",
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"capabilities": [],
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"publisher": "Robin Rosengrün",
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"stackOwner": "OpenAI"
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}
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}
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