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@markprompt/core
Advanced tools
`@markprompt/core` is the core library for Markprompt, a conversational AI component for your website, trained on your data.
@markprompt/core
is the core library for Markprompt, a conversational AI
component for your website, trained on your data.
It contains core functionality for Markprompt and allows you to build abstractions on top of it.
npm install @markprompt/core
In browsers with esm.sh:
<script type="module">
import {
submitChat,
submitSearchQuery,
submitFeedback,
} from 'https://esm.sh/@markprompt/core';
</script>
import { submitChat } from '@markprompt/core';
for await (const chunk of submitChat(
[{ content: 'What is Markprompt?', role: 'user' }],
'YOUR-PROJECT-KEY',
{
model: 'gpt-4-turbo-preview',
systemPrompt: 'You are a helpful AI assistant'
}
)) {
console.debug(chunk);
}
submitChat(messages: ChatMessage[], projectKey: string, options?)
Submit a prompt to the Markprompt Completions API.
messages
(ChatMessage[]
): Chat messages to submit to the modelprojectKey
(string
): Project key for the projectoptions
(SubmitChatOptions
): Optional parametersAll options are optional.
apiUrl
(string
): URL at which to fetch completionsconversationId
(string
): Conversation IDiDontKnowMessage
(string
): Message returned when the model does not have
an answermodel
(OpenAIModelId
): The OpenAI model to usesystemPrompt
(string
): The prompt templatetemperature
(number
): The model temperaturetopP
(number
): The model top PfrequencyPenalty
(number
): The model frequency penaltypresencePenalty
(number
): The model present penaltymaxTokens
(number
): The max number of tokens to include in the responsesectionsMatchCount
(number
): The number of sections to include in the
prompt contextsectionsMatchThreshold
(number
): The similarity threshold between thesignal
(AbortSignal
): AbortController signaltools
: (OpenAI.ChatCompletionTool[]
): A list of tools the model may calltool_choice
: (OpenAI.ChatCompletionToolChoiceOption
): Controls which (if
any) function is called by the modelA promise that resolves when the response is fully handled.
submitSearchQuery(query, projectKey, options?)
Submit a search query to the Markprompt Search API.
query
(string
): Search queryprojectKey
(string
): Project key for the projectoptions
(object
): Optional parametersapiUrl
(string
): URL at which to fetch search resultslimit
(number
): Maximum amount of results to returnsignal
(AbortSignal
): AbortController signalA list of search results.
submitFeedback(feedback, projectKey, options?)
Submit feedback to the Markprompt Feedback API about a specific prompt.
feedback
(object
): Feedback to submitfeedback.feedback
(object
): Feedback datafeedback.feedback.vote
("1" | "-1" | "escalated"
): Votefeedback.promptId
(string
): Prompt IDprojectKey
(string
): Project key for the projectoptions
(object
): Optional parametersoptions.apiUrl
(string
): URL at which to post feedbackoptions.onFeedbackSubmitted
(function
): Callback function when feedback is
submittedoptions.signal
(AbortSignal
): AbortController signalA promise that resolves when the feedback is submitted. Has no return value.
The full documentation for the package can be found on the Markprompt docs.
This library is created by the team behind Markprompt (@markprompt).
FAQs
`@markprompt/core` is the core library for Markprompt, a conversational AI component for your website, trained on your data.
The npm package @markprompt/core receives a total of 12,444 weekly downloads. As such, @markprompt/core popularity was classified as popular.
We found that @markprompt/core demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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