langchain
Version:
Typescript bindings for langchain
50 lines (49 loc) • 2.38 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.GENERATE_FACTS_PROMPT = exports.GENERATE_REVISED_PREDICTION_PROMPT = exports.PREDICTION_VIOLATIONS_PROMPT = exports.PREDICT_NEXT_USER_MESSAGE_PROMPT = void 0;
const prompts_1 = require("@langchain/core/prompts");
exports.PREDICT_NEXT_USER_MESSAGE_PROMPT =
/* #__PURE__ */ prompts_1.PromptTemplate.fromTemplate(`
You have been tasked with coming up with insights and data-points based on a chat history between a human and an AI.
Given the user's chat history provide the following:
- Concise reasoning about the users internal mental state.
- Your prediction on how they will respond to the AI's most recent message.
- A concise list of any additional insights that would be useful to improve prediction.
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Chat History: {chat_history}`);
exports.PREDICTION_VIOLATIONS_PROMPT =
/* #__PURE__ */ prompts_1.PromptTemplate.fromTemplate(`You have been given a prediction and an actual message from a human and AI conversation.
Using the prediction, actual message, and additional user insights, generate the following:
- How exactly was the original prediction violated? Which parts were wrong? State the exact differences.
- If there were errors with the prediction, what were they and why?
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Predicted Output: {predicted_output}
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Actual Output: {actual_output}
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User Insights: {user_insights}
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`);
exports.GENERATE_REVISED_PREDICTION_PROMPT =
/* #__PURE__ */ prompts_1.PromptTemplate.fromTemplate(`
You have been tasked with revising a prediction on what a user might say in a chat conversation.
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Your previous prediction: {prediction}
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Ways in which your prediction was off: {explained_prediction_errors}
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Key insights to the user: {user_insights}
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Given the above, revise your prediction to be more accurate.
Revised Prediction:`);
exports.GENERATE_FACTS_PROMPT =
/* #__PURE__ */ prompts_1.PromptTemplate.fromTemplate(`
Given a user message, an LLM generated prediction of what that message might be, and a list of violations which the prediction made compared to the actual message, generate a fact about the user, relevant to the users message.
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Prediction violations: {prediction_violations}
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Revised prediction: {prediction}
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Actual user message: {user_message}
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Relevant fact:`);