Add initial project setup with environment variables, server logic, and memory handling
This commit is contained in:
ImBenji
@@ -30,18 +30,14 @@ const corsHeaders = {
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/*
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Stage 2: Process Input (Extract Memories)
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*/
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async function extractMemories(controller, messages, doc, relevantMemories?) {
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async function extractMemories(controller, messages, doc, user: User, allTags, relevantMemories?) {
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const startTime = Date.now();
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let addedCount = 0;
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let updatedCount = 0;
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let deletedCount = 0;
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const extractedMemories = [];
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// Fetch existing memory tags from the database, that belong to the user
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const user : User = (await supabaseClient.auth.getUser()).data.user;
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const tags = await supabaseClient
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.schema("mori")
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.from("tags")
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.select("*")
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.eq("user_id", user.id);
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console.log("Fetched existing tags for user:", tags.data?.length || 0);
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console.log("Using cached tags for user:", allTags?.length || 0);
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// Create and call OpenAI to process the input messages
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console.log("Creating OpenAI client for processing input");
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@@ -53,7 +49,7 @@ async function extractMemories(controller, messages, doc, relevantMemories?) {
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console.log("Calling OpenAI API for processing...");
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const response = await openai.chat.completions.create({
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model: 'gpt-4.1-mini',
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model: 'gpt-4.1',
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temperature: 0.1,
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max_completion_tokens: 20000,
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messages: [
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@@ -63,7 +59,7 @@ async function extractMemories(controller, messages, doc, relevantMemories?) {
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role: "assistant",
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content: `I have access to the following reference data:
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Available tags: ${JSON.stringify(tags.data?.map(t => t.name) || [])}
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Available tags: ${JSON.stringify(allTags?.map(t => t.name) || [])}
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Existing memories: ${JSON.stringify(relevantMemories || [])}
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@@ -88,50 +84,40 @@ Now I will analyze the conversation above and extract memories.`
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for (const change of processedData.changes || []) {
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if (change.action === "ADD") {
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// First, fetch the tag rows that already exist
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let tags = [];
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for (const tagName of change.tags) {
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addedCount++;
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extractedMemories.push({
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action: 'ADD',
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content: change.content,
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context: change.context,
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tags: change.tags
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});
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// Fetch all existing tags in a single query using .in()
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const { data: existingTags } = await supabaseClient
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.schema("mori")
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.from("tags")
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.select("*")
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.in("name", change.tags)
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.eq("user_id", user.id);
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const tagRow = await supabaseClient
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let tags = existingTags || [];
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// Find tags that need to be created
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const existingTagNames = new Set(tags.map(t => t.name));
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const newTagNames = change.tags.filter(tagName => !existingTagNames.has(tagName));
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// Batch insert all new tags in a single query
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if (newTagNames.length > 0) {
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const { data: insertedTags } = await supabaseClient
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.schema("mori")
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.from("tags")
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.select("*")
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.eq("name", tagName)
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.single();
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if (tagRow.data) {
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tags.push(tagRow.data);
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}
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}
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// Insert any tags that do not already exist into the database
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for (const tagName of change.tags) {
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// Ensure we don't duplicate tags
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let tagExists = false;
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for (const tag of tags) {
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if (tag.name === tagName) {
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tagExists = true;
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break;
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}
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}
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if (tagExists) {
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continue;
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}
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const insertTag = await supabaseClient
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.schema("mori")
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.from("tags")
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.insert([{
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name: tagName,
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.insert(newTagNames.map(name => ({
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name: name,
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user_id: user.id
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}])
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.select()
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.single();
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})))
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.select();
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if (insertTag.data) {
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tags.push(insertTag.data);
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if (insertedTags) {
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tags.push(...insertedTags);
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}
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}
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@@ -147,18 +133,25 @@ Now I will analyze the conversation above and extract memories.`
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.select()
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.single();
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// Now, link the tags to the memory in the memory_tags table
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for (const tag of tags) {
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// Batch insert all memory_tags links in a single query
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if (tags.length > 0 && insertMemory.data) {
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await supabaseClient
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.schema("mori")
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.from("memory_tags")
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.insert([{
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.insert(tags.map(tag => ({
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memory_id: insertMemory.data.id,
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tag_id: tag.id
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}]);
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})));
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}
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} else if (change.action === "UPDATE") {
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updatedCount++;
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extractedMemories.push({
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action: 'UPDATE',
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content: change.content,
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context: change.context,
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memory_id: change.memory_id
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});
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// Update existing memory
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await supabaseClient
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.schema("mori")
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@@ -175,6 +168,11 @@ Now I will analyze the conversation above and extract memories.`
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// (delete old memory_tags links and create new ones)
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} else if (change.action === "DELETE") {
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deletedCount++;
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extractedMemories.push({
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action: 'DELETE',
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memory_id: change.memory_id
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});
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// Delete memory (cascade should handle memory_tags)
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await supabaseClient
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.schema("mori")
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@@ -185,15 +183,22 @@ Now I will analyze the conversation above and extract memories.`
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}
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}
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const processTime = Date.now() - startTime;
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return {
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extractedMemories,
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addedCount,
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updatedCount,
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deletedCount,
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processTime
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};
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}
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/*
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Stage 1: Fetch Relevant Memories.
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Stage 1: Fetch Relevant Memories and Tags.
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*/
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async function fetchRelevantMemories(controller, messages, doc) {
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// Fetch existing memory tags from the database, that belong to the user
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const user : User = (await supabaseClient.auth.getUser()).data.user;
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async function fetchRelevantMemories(controller, messages, doc, user: User) {
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const startTime = Date.now();
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const tags = await supabaseClient
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.schema("mori")
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@@ -213,7 +218,7 @@ async function fetchRelevantMemories(controller, messages, doc) {
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console.log("Calling OpenAI API for fetching relevant memories...");
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const response = await openai.chat.completions.create({
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model: 'gpt-4.1-mini',
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model: 'gpt-4.1',
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messages: [
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{ role: 'system', content: system_prompt },
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...messages,
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@@ -240,16 +245,20 @@ async function fetchRelevantMemories(controller, messages, doc) {
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p_user_id: user.id
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});
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return relevantMemories;
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const fetchTime = Date.now() - startTime;
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return {
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relevantMemories,
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allTags: tags.data,
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selectedTags: relevantMemoryTagsParsed.selected_tags || [],
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fetchTime
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};
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}
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/*
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Stage 3: Generate Response
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*/
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async function generateResponse(controller, messages, doc, relevantMemories) {
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// Fetch existing memory tags from the database, that belong to the user
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const user : User = (await supabaseClient.auth.getUser()).data.user;
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async function generateResponse(controller, messages, doc, user: User, pipelineContext) {
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console.log("Creating OpenAI client for generating a response");
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const openai = new OpenAI({
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@@ -265,14 +274,51 @@ async function generateResponse(controller, messages, doc, relevantMemories) {
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{ role: 'system', content: system_prompt },
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];
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// Add relevant memories as context if available
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// Build pipeline awareness context
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const { relevantMemories, selectedTags, extractedMemories, addedCount, updatedCount, deletedCount } = pipelineContext;
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let pipelineAwareness = `[Internal System Awareness - Not Part of Conversation]\n\n`;
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pipelineAwareness += `You are Mori, and you have a memory system that automatically remembers important information about ${user.user_metadata.username || 'the user'} across conversations.\n\n`;
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// Info about retrieved memories
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if (relevantMemories && relevantMemories.length > 0) {
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responseMessages.push({
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role: 'assistant',
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content: `Context from previous conversations:\n${relevantMemories.map(m => `- ${m.content}`).join('\n')}\n\nI'll use this context naturally in our conversation.`
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pipelineAwareness += `RETRIEVED MEMORIES (what you already knew):\n`;
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pipelineAwareness += `You searched through memories using topics: ${selectedTags.join(', ')}\n`;
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pipelineAwareness += `Found ${relevantMemories.length} relevant memories:\n`;
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relevantMemories.forEach(m => {
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pipelineAwareness += `• ${m.content}\n`;
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});
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pipelineAwareness += `\n`;
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} else {
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pipelineAwareness += `No previous memories were retrieved for this conversation.\n\n`;
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}
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// Info about newly extracted memories
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if (extractedMemories && extractedMemories.length > 0) {
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pipelineAwareness += `NEW MEMORIES (what you just learned and saved):\n`;
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extractedMemories.forEach(mem => {
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if (mem.action === 'ADD') {
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pipelineAwareness += `• Learned: ${mem.content}\n`;
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} else if (mem.action === 'UPDATE') {
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pipelineAwareness += `• Updated: ${mem.content}\n`;
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}
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});
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pipelineAwareness += `\n`;
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}
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pipelineAwareness += `HOW TO USE THIS:\n`;
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pipelineAwareness += `- This awareness is internal. Don't report it.\n`;
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pipelineAwareness += `- Let it naturally inform your response\n`;
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pipelineAwareness += `- If the user explicitly asks you to remember something, you can acknowledge it naturally (e.g., "got it" or "I'll remember that")\n`;
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pipelineAwareness += `- Reference past memories naturally without saying "I retrieved" or "according to my memory"\n`;
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pipelineAwareness += `- You're a companion who pays attention, not a system reporting operations\n`;
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// Inject pipeline awareness as assistant message
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responseMessages.push({
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role: 'assistant',
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content: pipelineAwareness
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});
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responseMessages.push(...messages);
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const stream = await openai.chat.completions.create({
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@@ -355,7 +401,7 @@ serve(async (req)=>{
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const stageFetchingData = `data: ${JSON.stringify({ type: 'stage', stage: 'fetching' })}\n\n`;
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controller.enqueue(new TextEncoder().encode(stageFetchingData));
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const relevantMemories = await fetchRelevantMemories(controller, messages, doc);
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const { relevantMemories, allTags, selectedTags, fetchTime } = await fetchRelevantMemories(controller, messages, doc, user.data.user);
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/*
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Stage 2: Extract Relevant Memories
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@@ -363,7 +409,7 @@ serve(async (req)=>{
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const stageProcessingData = `data: ${JSON.stringify({ type: 'stage', stage: 'processing' })}\n\n`;
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controller.enqueue(new TextEncoder().encode(stageProcessingData));
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await extractMemories(controller, messages, doc, relevantMemories);
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const { extractedMemories, addedCount, updatedCount, deletedCount, processTime } = await extractMemories(controller, messages, doc, user.data.user, allTags, relevantMemories);
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/*
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Stage 3: Stream the response back to the client
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@@ -371,7 +417,19 @@ serve(async (req)=>{
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const stageRespondingData = `data: ${JSON.stringify({ type: 'stage', stage: 'responding' })}\n\n`;
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controller.enqueue(new TextEncoder().encode(stageRespondingData));
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await generateResponse(controller, messages, doc, relevantMemories);
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// Build complete pipeline context for Mori's awareness
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const pipelineContext = {
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relevantMemories,
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selectedTags,
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fetchTime,
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extractedMemories,
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addedCount,
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updatedCount,
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deletedCount,
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processTime
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};
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await generateResponse(controller, messages, doc, user.data.user, pipelineContext);
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// Send stage update: complete
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const completeData = `data: ${JSON.stringify({ type: 'stage', stage: 'complete' })}\n\n`;
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