adding in entity extraction layer
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67
packages/memory-service/src/entities/extraction.js
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67
packages/memory-service/src/entities/extraction.js
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@@ -0,0 +1,67 @@
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const { getEnv } = require('@nexusai/shared');
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const { upsertEntity } = require('./index');
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const EXTRACTION_URL = getEnv('EXTRACTION_URL', 'http://localhost:11434');
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const EXTRACTION_MODEL = getEnv('EXTRACTION_MODEL', 'qwen2.5:3b');
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const ENTITY_TYPES = ['person', 'place', 'project', 'technology', 'concept', 'organization'];
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function buildExtractionPrompt(userMessage, aiResponse) {
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return [
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'Extract named entities from the conversation below.',
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`Valid entity types: ${ENTITY_TYPES.join(', ')}.`,
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'Return ONLY a JSON array. No explanation, no markdown, no code fences.',
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'Each item must have exactly these fields: "name" (string), "type" (one of the valid types), "notes" (one sentence fact about this entity from the conversation).',
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'If no entities are found, return: []',
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'',
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`User: ${userMessage}`,
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`Assistant: ${aiResponse}`,
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'',
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'JSON array:',
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].join('\n');
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}
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async function extractAndStoreEntities(userMessage, aiResponse) {
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try {
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const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model: EXTRACTION_MODEL,
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prompt: buildExtractionPrompt(userMessage, aiResponse),
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stream: false, // we want the complete response, not a stream
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options: {
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temperature: 0.1, // low temp for deterministic structured output
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num_predict: 512,
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},
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}),
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});
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if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
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const data = await res.json();
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const raw = data.response?.trim() ?? '';
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// Strip markdown fences defensively — small models sometimes add them anyway
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const clean = raw.replace(/^```(?:json)?\n?/, '').replace(/\n?```$/, '').trim();
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const entities = JSON.parse(clean);
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if (!Array.isArray(entities)) throw new Error('Response was not a JSON array');
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let saved = 0;
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for (const { name, type, notes } of entities) {
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// Skip anything malformed or with an unrecognised type
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if (!name || !type || !ENTITY_TYPES.includes(type)) continue;
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upsertEntity(name, type, notes ?? null);
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saved++;
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}
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if (saved > 0) console.log(`[entities] Extracted and stored ${saved} entities`);
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} catch (err) {
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// Non-critical — log and move on, episode is already saved
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console.warn('[entities] Extraction failed:', err.message);
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}
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}
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module.exports = { extractAndStoreEntities };
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@@ -1,6 +1,7 @@
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const {getDB} = require('../db');
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const {getDB} = require('../db');
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const { EPISODIC, getEnv, SERVICES, parseRow, formatEpisodeText } = require('@nexusai/shared');
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const { EPISODIC, getEnv, SERVICES, parseRow, formatEpisodeText } = require('@nexusai/shared');
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const semantic = require('../semantic');
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const semantic = require('../semantic');
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const { extractAndStoreEntities } = require('../entities/extraction')
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// --Sessions --------------------------------------------------
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// --Sessions --------------------------------------------------
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@@ -127,6 +128,10 @@ async function createEpisode(sessionId, userMessage, aiResponse, tokenCount = nu
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}))
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}))
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.catch(err => console.error(`Failed to embed episode ${episode.id}:`, err.message));
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.catch(err => console.error(`Failed to embed episode ${episode.id}:`, err.message));
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extractAndStoreEntities(userMessage, aiResponse)
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.catch(err => console.error(`Failed to extract entities for episode ${episode.id}:`, err.message));
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return episode;
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return episode;
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}
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}
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