roadmap phase 1 complete
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@@ -5,22 +5,20 @@ const qdrant = require("../services/qdrant");
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const { ORCHESTRATION, logger } = require("@nexusai/shared");
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const appSettings = require("../config/settings");
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const {triggerSummary} = require('../services/summarization')
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const graph = require('../services/graph');
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function buildPrompt(recentEpisodes, semanticEpisodes, entities, userMessage, systemPrompt) {
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function buildPrompt(recentEpisodes, semanticEpisodes, neighborhood, userMessage, systemPrompt) {
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const parts = [systemPrompt ?? ORCHESTRATION.SYSTEM_PROMPT];
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if (entities.length > 0) {
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parts.push(
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"Here is what you know about entities relevant to this conversation:",
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);
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for (const e of entities) {
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parts.push(`- ${e.name} (${e.type}): ${e.notes}`);
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const graphText = formatGraphContext(neighborhood.nodes ?? [], neighborhood.edges ?? []);
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if (graphText) {
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parts.push("Here is what you know about entities relevant to this conversation and their connections:");
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parts.push(graphText);
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parts.push("---");
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}
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parts.push("---");
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}
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if (semanticEpisodes.length > 0) {
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parts.push("Here are some relevant memories from earlier conversations:");
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parts.push("Long-term memory (semantically relevant to this message):");
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for (const ep of semanticEpisodes) {
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parts.push(`User: ${ep.user_message}\nAssistant: ${ep.ai_response}`);
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}
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@@ -28,7 +26,7 @@ function buildPrompt(recentEpisodes, semanticEpisodes, entities, userMessage, sy
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}
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if (recentEpisodes.length > 0) {
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parts.push(`Here are some relevant memories from your past conversations:`);
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parts.push("Recent conversation history (most recent exchanges):");
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for (const ep of recentEpisodes) {
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parts.push(`User: ${ep.user_message}\nAssistant: ${ep.ai_response}`);
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}
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@@ -54,6 +52,28 @@ function buildNamingPrompt(userMessage, aiResponse) {
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].join("\n");
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}
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function formatGraphContext(nodes, edges) {
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if (!nodes.length) return null;
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const nodeMap = new Map(nodes.map(n => [n.id, n]));
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// Build outbound adjacency
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const outbound = new Map(nodes.map(n => [n.id, []]));
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for (const edge of edges) {
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if (outbound.has(edge.from_id) && nodeMap.has(edge.to_id)) {
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const target = nodeMap.get(edge.to_id);
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outbound.get(edge.from_id).push(`${edge.label} ${target.name} (${target.type})`);
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}
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}
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return nodes.map(n => {
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const lines = [`- ${n.name} (${n.type}): ${n.notes ?? '(no notes)'}`];
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for (const conn of outbound.get(n.id) ?? []) lines.push(` → ${conn}`);
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return lines.join('\n');
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}).join('\n');
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}
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async function autoNameSession(externalId, userMessage, aiResponse) {
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try {
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const prompt = buildNamingPrompt(userMessage, aiResponse);
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@@ -107,22 +127,20 @@ async function getSemanticEpisodes(
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}
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}
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async function getRelevantEntities(userMessage, projectId=null) {
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try {
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const vector = await embedding.embed(userMessage);
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const results = await qdrant.searchEntities(vector, { projectId });
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logger.info(
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"[orchestration] Entity search results:",
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results.map((r) => ({ name: r.payload?.name, score: r.score })),
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);
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return results.map((r) => r.payload).filter(Boolean);
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} catch (err) {
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logger.debug(
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"[orchestration] Entity search failed, continuing without:",
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err.message,
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);
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return [];
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}
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async function getRelevantEntities(userMessage, projectId = null) {
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try {
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const vector = await embedding.embed(userMessage);
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const results = await qdrant.searchEntities(vector, { projectId });
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logger.info(
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'[orchestration] Entity search results:',
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results.map((r) => ({ name: r.payload?.name, score: r.score })),
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);
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// Include the Qdrant point ID (== SQLite entity ID) for graph traversal
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return results.map((r) => r.payload ? { id: r.id, ...r.payload } : null).filter(Boolean);
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} catch (err) {
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logger.debug('[orchestration] Entity search failed, continuing without:', err.message);
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return [];
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}
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}
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async function assembleContext(externalId, userMessage) {
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@@ -159,10 +177,22 @@ async function assembleContext(externalId, userMessage) {
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const semanticEpisodes = await getSemanticEpisodes(
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userMessage, session.id, recentIds, projectSessionIds, { semanticLimit, scoreThreshold }
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);
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const entities = await getRelevantEntities(userMessage, session.project_id ?? null);
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const entityResults = await getRelevantEntities(userMessage, session.project_id ?? null);
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// 5. Assemble prompt
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const prompt = buildPrompt(recentEpisodes, semanticEpisodes, entities, userMessage, activeSystemPrompt);
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// 5. Expand matched entities into 1-hop graph neighborhood
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let neighborhood = { nodes: [], edges: [] };
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if (entityResults.length > 0) {
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try {
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neighborhood = await graph.getNeighbors(entityResults.map(e => e.id));
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} catch (err) {
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logger.warn('[orchestration] Graph neighborhood fetch failed, falling back to flat entities:', err.message);
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// Graceful fallback: use Qdrant payload data as flat nodes, no edges
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neighborhood = { nodes: entityResults, edges: [] };
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}
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}
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// 6. Assemble prompt
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const prompt = buildPrompt(recentEpisodes, semanticEpisodes, neighborhood, userMessage, activeSystemPrompt);
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return {
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session,
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@@ -0,0 +1,15 @@
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const { getEnv, SERVICES } = require('@nexusai/shared');
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const MEMORY_URL = getEnv('MEMORY_SERVICE_URL', SERVICES.MEMORY_URL);
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async function getNeighbors(entityIds) {
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const res = await fetch(`${MEMORY_URL}/graph/neighbors`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ entityIds }),
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});
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if (!res.ok) throw new Error(`Graph neighbors error: ${res.status}`);
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return res.json();
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}
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module.exports = { getNeighbors };
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