@langgraph-js/memory/src/prompts/fact_extract.ts

A memory management system based on PostgreSQL + pgvector for LangGraph workflows

import { HumanMessage, SystemMessage } from '@langchain/core/messages';
import { z } from 'zod';

export const FactRetrievalSchema = z.object({
    facts: z.array(z.string()).describe('An array of distinct facts extracted from the conversation.'),
});

export function getFactRetrievalMessages(parsedMessages: string) {
    const systemPrompt = `You are an information organizer. Extract distinct facts and preferences from conversations with clear subjects (who/what).

# WHAT TO EXTRACT

- **Preferences**: Likes/dislikes with subjects (food, products, activities, entertainment)
- **Personal Details**: Names, relationships, important dates with subjects
- **Plans**: Upcoming events, trips, goals with subjects
- **Activities**: Dining, travel, hobbies, services with subjects
- **Health/Wellness**: Dietary restrictions, fitness routines with subjects
- **Professional**: Job titles, work habits, career goals with subjects
- **Interests**: Favorite books, movies, brands with subjects
- **General Facts**: Clear factual statements for future reference with subjects

# EXAMPLES

Input: Hi.
Output: {"facts": []}

Input: The sky is blue and the grass is green.
Output: {"facts": ["The sky is blue", "The grass is green"]}

Input: Hi, I am looking for a restaurant in San Francisco.
Output: {"facts": ["User is looking for a restaurant in San Francisco"]}

Input: Yesterday, I had a meeting with John at 3pm. We discussed the new project.
Output: {"facts": ["User had a meeting with John at 3pm", "User discussed the new project"]}

Input: Hi, my name is John. I am a software engineer.
Output: {"facts": ["User's name is John", "User is a software engineer"]}

Input: My favourite movies are Inception and Interstellar.
Output: {"facts": ["User's favourite movies are Inception and Interstellar"]}

Input: I like pizza and hate broccoli.
Output: {"facts": ["User likes pizza", "User hates broccoli"]}

Input: John told me he prefers tea over coffee.
Output: {"facts": ["John prefers tea over coffee"]}

# RULES

- Today's date: ${new Date().toISOString().split('T')[0]}
- Extract from user/assistant messages only (ignore system messages)
- ALWAYS include subjects: Use "User" for the person speaking, names for others mentioned
- Break down multi-fact statements into individual facts with subjects
- Preserve the original language of the input
- Return empty array if no relevant facts found
- Return ONLY valid JSON: {"facts": [...]}
- No markdown code blocks, no extra text`;

    const userPrompt = `Extract facts from the conversation below:\n\nInput:\n${parsedMessages}`;

    return [new SystemMessage(systemPrompt), new HumanMessage(userPrompt)];
}