nexusAI/packages/orchestration-service/src/chat/index.js

const memory = require("../services/memory");
const inference = require("../services/inference");
const embedding = require("../services/embedding");
const qdrant = require("../services/qdrant");
const { ORCHESTRATION, logger } = require("@nexusai/shared");
const appSettings = require("../config/settings");
const {triggerSummary} = require('../services/summarization')

function buildPrompt(recentEpisodes, semanticEpisodes, entities, userMessage, systemPrompt) {
    const parts = [systemPrompt ?? ORCHESTRATION.SYSTEM_PROMPT];

  if (entities.length > 0) {
    parts.push(
      "Here is what you know about entities relevant to this conversation:",
    );
    for (const e of entities) {
      parts.push(`- ${e.name} (${e.type}): ${e.notes}`);
    }
    parts.push("---");
  }

  if (semanticEpisodes.length > 0) {
    parts.push("Here are some relevant memories from earlier conversations:");
    for (const ep of semanticEpisodes) {
      parts.push(`User: ${ep.user_message}\nAssistant: ${ep.ai_response}`);
    }
    parts.push("---");
  }

  if (recentEpisodes.length > 0) {
    parts.push(`Here are some relevant memories from your past conversations:`);
    for (const ep of recentEpisodes) {
      parts.push(`User: ${ep.user_message}\nAssistant: ${ep.ai_response}`);
    }
    parts.push("--- End of recent memories ---\n");
  }

  parts.push(`User: ${userMessage}`);
  parts.push("Assistant:");

  return parts.join("\n");
}

function buildNamingPrompt(userMessage, aiResponse) {
  return [
    "Your task is to generate a short title for a conversation based on its first exchange.",
    "Rules: maximum 5 words, no punctuation, no quotes, plain text only.",
    'Examples: "Setting up a Raspberry Pi", "Help with Python list comprehension", "Planning a trip to Japan"',
    "",
    `User: ${userMessage}`,
    `Assistant: ${aiResponse}`,
    "",
    "Title:",
  ].join("\n");
}

async function autoNameSession(externalId, userMessage, aiResponse) {
  try {
    const prompt = buildNamingPrompt(userMessage, aiResponse);
    const result = await inference.complete(prompt, {
      maxTokens: 20, // title only needs a handful of tokens
      temperature: 0.3, // low temperature for consistent, factual naming
    });
    const name = result.text?.trim().replace(/^["']|["']$/g, ""); // strip any quotes the model adds
    if (name) {
      await memory.updateSession(externalId, { name });
      logger.info(
        `[orchestration] Auto-named session "${externalId}": "${name}"`,
      );
    }
  } catch (err) {
    logger.warn(
      "[orchestration] Auto-naming failed (non-critical):",
      err.message,
    );
  }
}

async function getSemanticEpisodes(
  userMessage,
  sessionId,
  recentIds,
  projectSessionIds = null,
  { semanticLimit, scoreThreshold } = {},
) {
  try {
    const vector = await embedding.embed(userMessage);
    const results = await qdrant.searchEpisodes(vector, {
      limit: semanticLimit,
      scoreThreshold: scoreThreshold,
      sessionId: projectSessionIds ? null : sessionId,
      projectSessionIds,
    });

    const fetched = await Promise.all(
      results
        .filter((r) => !recentIds.has(r.id))
        .map((r) => memory.getEpisodeById(r.id)),
    );
    return fetched.filter(Boolean);
  } catch (err) {
    logger.warn(
      `[orchestration] Semantic search failed, continuing without: `,
      err.message,
    );
    return [];
  }
}

async function getRelevantEntities(userMessage, projectId=null) {
  try {
    const vector = await embedding.embed(userMessage);
    const results = await qdrant.searchEntities(vector, { projectId });
    logger.info(
      "[orchestration] Entity search results:",
      results.map((r) => ({ name: r.payload?.name, score: r.score })),
    );
    return results.map((r) => r.payload).filter(Boolean);
  } catch (err) {
    logger.debug(
      "[orchestration] Entity search failed, continuing without:",
      err.message,
    );
    return [];
  }
}

async function assembleContext(externalId, userMessage) {
    const settings = appSettings.load();
    const { recentEpisodeLimit, semanticLimit, scoreThreshold, 
            temperature, repeatPenalty, topP, topK, systemPrompt } = settings;

    // 1. Resolve or create session
    let session = await memory.getSessionByExternalId(externalId);
    if (!session) session = await memory.createSession(externalId);

    // 2. Resolve project context
    let projectSessionIds = null;
    let activeSystemPrompt = systemPrompt ?? ORCHESTRATION.SYSTEM_PROMPT;
    if (session.project_id) {
        try {
            const project = await memory.getProject(session.project_id);
            if (project) {
                const projectSessions = await memory.getProjectSessions(session.project_id);
                if (project.system_prompt) activeSystemPrompt = project.system_prompt;
                projectSessionIds = projectSessions.map(s => s.id);
            }
        } catch (err) {
            logger.warn('[orchestration] Failed to resolve project context:', err.message);
        }
    }

    // 3. Fetch recent episodes
    const recentEpisodes = await memory.getRecentEpisodes(session.id, recentEpisodeLimit);
    const isFirstMessage = recentEpisodes.length === 0;
    const recentIds = new Set(recentEpisodes.map(e => e.id));

    // 4. Semantic + entity search
    const semanticEpisodes = await getSemanticEpisodes(
        userMessage, session.id, recentIds, projectSessionIds, { semanticLimit, scoreThreshold }
    );
    const entities = await getRelevantEntities(userMessage, session.project_id ?? null);

    // 5. Assemble prompt
    const prompt = buildPrompt(recentEpisodes, semanticEpisodes, entities, userMessage, activeSystemPrompt);

    return {
        session,
        prompt,
        isFirstMessage,
        inferenceOptions: { temperature, repeatPenalty, topP, topK },
    };
}

async function chat(externalId, userMessage, options = {}) {
    const { session, prompt, isFirstMessage, inferenceOptions } = await assembleContext(externalId, userMessage);

    const result = await inference.complete(prompt, { ...options, ...inferenceOptions });

    try {
        await memory.createEpisode(
            session.id, userMessage, result.text,
            (result.evalCount || 0) + (result.promptEvalCount || 0),
            session.project_id ?? null,
        );
    } catch (err) {
        logger.error('[orchestration] Failed to save episode:', err.message);
    }

    const allEpisodes = await memory.getRecentEpisodes(session.id, 9999);
    triggerSummary(session, allEpisodes);

    if (isFirstMessage && !session.name) {
        autoNameSession(externalId, userMessage, result.text).catch(() => {});
    }

    return {
        sessionId: externalId,
        response: result.text,
        model: result.model,
        tokenCount: (result.evalCount || 0) + (result.promptEvalCount || 0),
    };
}

async function chatStream(externalId, userMessage, onChunk, options = {}) {
    try {
        const { session, prompt, isFirstMessage, inferenceOptions } = await assembleContext(externalId, userMessage);

        const res = await inference.completeStream(prompt, { ...options, ...inferenceOptions });

        let fullText = '', model = '', tokenCount = 0, buffer = '';

        for await (const chunk of res.body) {
            buffer += Buffer.from(chunk).toString('utf8');
            const events = buffer.split('\n\n');
            buffer = events.pop() || '';

            for (const event of events) {
                const dataLines = event.split('\n')
                    .filter(line => line.startsWith('data: '))
                    .map(line => line.slice(6));

                if (!dataLines.length) continue;
                const raw = dataLines.join('\n').trim();
                if (raw === '[DONE]') continue;

                try {
                    const data = JSON.parse(raw);
                    if (data.response) { fullText += data.response; onChunk(data.response); }
                    if (data.model) model = data.model;
                    if (data.done && data.tokenCount !== undefined) tokenCount = data.tokenCount;
                    if (data.error) throw new Error(data.error);
                } catch (err) {
                    logger.error('[orchestration] Failed to parse SSE event:', raw, err.message);
                }
            }
        }

        if (fullText.trim()) {
            await memory.createEpisode(session.id, userMessage, fullText, tokenCount, session.project_id ?? null);
            const allEpisodes = await memory.getRecentEpisodes(session.id, 9999);
            triggerSummary(session, allEpisodes);
        } else {
            logger.warn('[orchestration] Stream finished with no assistant text; episode not saved');
        }

        if (isFirstMessage && !session.name) {
            autoNameSession(externalId, userMessage, fullText).catch(() => {});
        }

        return { model, tokenCount };
    } catch (err) {
        logger.error('[orchestration] chatStream fatal error:', err.message, err.stack);
        throw err;
    }
}

module.exports = { chat, chatStream };