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Feat: deep search agent

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This commit is contained in:
Manoj 2025-10-15 11:07:17 +05:30
parent 82b430e658
commit 45a7364e57
10 changed files with 710 additions and 630 deletions
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40
apps/webapp/app/routes/api.v1.deep-search.tsx
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@ -1,12 +1,13 @@
import { z } from "zod"; import { z } from "zod";
import { json } from "@remix-run/node"; import { json } from "@remix-run/node";
import { createActionApiRoute } from "~/services/routeBuilders/apiBuilder.server"; import { createActionApiRoute } from "~/services/routeBuilders/apiBuilder.server";
import { DeepSearchService } from "~/services/deepSearch.server"; import { deepSearch } from "~/trigger/deep-search";
import { SearchService } from "~/services/search.server"; import { runs } from "@trigger.dev/sdk";
const DeepSearchBodySchema = z.object({ const DeepSearchBodySchema = z.object({
content: z.string().min(1, "Content is required"), content: z.string().min(1, "Content is required"),
intentOverride: z.string().optional(), intentOverride: z.string().optional(),
stream: z.boolean().default(false),
metadata: z metadata: z
.object({ .object({
source: z.enum(["chrome", "obsidian", "mcp"]).optional(), source: z.enum(["chrome", "obsidian", "mcp"]).optional(),
@ -27,15 +28,36 @@ const { action, loader } = createActionApiRoute(
corsStrategy: "all", corsStrategy: "all",
}, },
async ({ body, authentication }) => { async ({ body, authentication }) => {
const searchService = new SearchService(); let trigger;
const deepSearchService = new DeepSearchService(searchService); if (body.stream) {
trigger = await deepSearch.trigger({
content: body.content,
userId: authentication.userId,
stream: body.stream,
intentOverride: body.intentOverride,
metadata: body.metadata,
});
const result = await deepSearchService.deepSearch( return json(trigger);
body, } else {
authentication.userId const runHandler = await deepSearch.trigger({
); content: body.content,
userId: authentication.userId,
stream: body.stream,
intentOverride: body.intentOverride,
metadata: body.metadata,
});
return json(result); for await (const run of runs.subscribeToRun(runHandler.id)) {
if (run.status === "COMPLETED") {
return json(run.output);
} else if (run.status === "FAILED") {
return json(run.error);
}
}
return json({ error: "Run failed" });
}
} }
); );

601
apps/webapp/app/services/deepSearch.server.ts
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@ -1,601 +0,0 @@
import { logger } from "./logger.service";
import { SearchService } from "./search.server";
import { makeModelCall } from "~/lib/model.server";
/**
* Request interface for deep search
*/
export interface DeepSearchRequest {
content: string;
intentOverride?: string;
metadata?: {
source?: "chrome" | "obsidian" | "mcp";
url?: string;
pageTitle?: string;
};
}
/**
* Content analysis result from Phase 1
*/
interface ContentAnalysis {
intent: string;
reasoning: string;
entities: string[];
temporal: string[];
actions: string[];
topics: string[];
priority: string[];
}
/**
* Agent decision from Phase 3
*/
interface AgentDecision {
shouldContinue: boolean;
confidence: number;
reasoning: string;
followUpQueries: string[];
}
/**
* Response interface for deep search
*/
export interface DeepSearchResponse {
synthesis: string;
episodes: Array<{
content: string;
createdAt: Date;
spaceIds: string[];
}>;
}
/**
* Deep Search Service
*
* Implements a 4-phase intelligent document search pipeline:
* 1. Content Analysis - Infer intent and decompose content
* 2. Parallel Broad Search - Fire multiple queries simultaneously
* 3. Agent Deep Dive - Evaluate and follow up on promising leads
* 4. Synthesis - Generate intent-aware context summary
*/
export class DeepSearchService {
constructor(private searchService: SearchService) {}
/**
* Main entry point for deep search
*/
async deepSearch(
request: DeepSearchRequest,
userId: string
): Promise<DeepSearchResponse> {
const startTime = Date.now();
const { content, intentOverride, metadata } = request;
logger.info("Deep search started", { userId, contentLength: content.length });
try {
// Phase 1: Analyze content and infer intent
const analysis = intentOverride
? await this.createAnalysisFromOverride(content, intentOverride)
: await this.analyzeContent(content, this.getIntentHints(metadata));
logger.info("Phase 1 complete", { intent: analysis.intent });
// Extract spaceIds from metadata if available
const spaceIds: string[] = [];
// Phase 2: Parallel broad search
const { episodes: broadEpisodes } = await this.performBroadSearch(
analysis,
userId,
spaceIds
);
logger.info("Phase 2 complete", { episodesCount: broadEpisodes.length });
// Phase 3: Agent-driven deep dive (using episodes for richer context)
const { episodes: deepDiveEpisodes } = await this.performDeepDive(
content,
analysis,
broadEpisodes,
userId,
spaceIds
);
logger.info("Phase 3 complete", {
deepDiveEpisodes: deepDiveEpisodes.length,
});
// Combine and deduplicate episodes
const allEpisodes = [...broadEpisodes, ...deepDiveEpisodes];
const episodeMap = new Map<string, any>();
allEpisodes.forEach((ep) => {
const key = `${ep.content}-${new Date(ep.createdAt).toISOString()}`;
if (!episodeMap.has(key)) {
episodeMap.set(key, ep);
}
});
const episodes = Array.from(episodeMap.values());
// Phase 4: Synthesize results using episodes (richer context than facts)
const synthesis = await this.synthesizeResults(
content,
analysis,
episodes
);
logger.info("Phase 4 complete", {
duration: Date.now() - startTime,
totalEpisodes: episodes.length,
});
return {
synthesis,
episodes,
};
} catch (error) {
logger.error("Deep search error", { error });
throw error;
}
}
/**
* Phase 1: Analyze content and infer intent
*/
private async analyzeContent(
content: string,
contextHints: string
): Promise<ContentAnalysis> {
const prompt = `
Analyze this content holistically and determine the user's intent.
CONTENT:
${content}
${contextHints}
YOUR TASK:
1. INFER INTENT: What is the user trying to do with this content?
Examples: reading email, writing blog post, preparing for meeting,
researching topic, tracking tasks, reviewing changes, etc.
Be specific and descriptive.
2. EXTRACT KEY ELEMENTS:
- Entities: People, places, organizations, objects (e.g., "John Doe", "Project Phoenix")
- Temporal: Dates, times, recurring events (e.g., "Wednesday standup", "last month")
- Actions: Verbs, action items, tasks (e.g., "follow up", "review", "fix bug")
- Topics: Themes, subjects, domains (e.g., "car maintenance", "API design")
3. PRIORITIZE: Which elements are most important to search first?
Return array like ["entities", "temporal", "topics"] ordered by importance.
RESPONSE FORMAT (JSON):
{
"intent": "specific intent description",
"reasoning": "why this intent was inferred",
"entities": ["entity1", "entity2"],
"temporal": ["temporal1", "temporal2"],
"actions": ["action1", "action2"],
"topics": ["topic1", "topic2"],
"priority": ["entities", "temporal", "topics"]
}
`;
let responseText = "";
await makeModelCall(
false,
[{ role: "user", content: prompt }],
(text) => {
responseText = text;
},
{},
"high"
);
return JSON.parse(responseText);
}
/**
* Create analysis from explicit intent override
*/
private async createAnalysisFromOverride(
content: string,
intentOverride: string
): Promise<ContentAnalysis> {
const prompt = `
The user has specified their intent as: "${intentOverride}"
CONTENT:
${content}
YOUR TASK:
Extract key elements from this content:
- Entities: People, places, organizations, objects
- Temporal: Dates, times, recurring events
- Actions: Verbs, action items, tasks
- Topics: Themes, subjects, domains
Prioritize elements based on the specified intent.
RESPONSE FORMAT (JSON):
{
"intent": "${intentOverride}",
"reasoning": "user-specified intent",
"entities": ["entity1", "entity2"],
"temporal": ["temporal1", "temporal2"],
"actions": ["action1", "action2"],
"topics": ["topic1", "topic2"],
"priority": ["entities", "temporal", "topics"]
}
`;
let responseText = "";
await makeModelCall(
false,
[{ role: "user", content: prompt }],
(text) => {
responseText = text;
},
{},
"high"
);
return JSON.parse(responseText);
}
/**
* Phase 2: Perform parallel broad search
*/
private async performBroadSearch(
analysis: ContentAnalysis,
userId: string,
spaceIds: string[]
): Promise<{ facts: any[]; episodes: any[] }> {
// Build query list based on priority
const queries: string[] = [];
// Add queries based on priority order
for (const category of analysis.priority) {
switch (category) {
case "entities":
queries.push(...analysis.entities.slice(0, 3));
break;
case "temporal":
queries.push(...analysis.temporal.slice(0, 2));
break;
case "topics":
queries.push(...analysis.topics.slice(0, 2));
break;
case "actions":
queries.push(...analysis.actions.slice(0, 2));
break;
}
}
// Ensure we have at least some queries
if (queries.length === 0) {
queries.push(
...analysis.entities.slice(0, 2),
...analysis.topics.slice(0, 2)
);
}
// Cap at 10 queries max
const finalQueries = queries.slice(0, 10);
logger.info(`Broad search: ${finalQueries.length} parallel queries`);
// Fire all searches in parallel
const results = await Promise.all(
finalQueries.map((query) =>
this.searchService.search(query, userId, {
limit: 20,
spaceIds,
})
)
);
// Flatten and deduplicate facts
const allFacts = results.flatMap((r) => r.facts);
const uniqueFacts = Array.from(
new Map(allFacts.map((f) => [f.fact, f])).values()
);
// Flatten and deduplicate episodes
const allEpisodes = results.flatMap((r) => r.episodes);
const uniqueEpisodes = Array.from(
new Map(allEpisodes.map((e) => [`${e.content}-${e.createdAt}`, e])).values()
);
return { facts: uniqueFacts, episodes: uniqueEpisodes };
}
/**
* Phase 3: Perform agent-driven deep dive using episodes
*/
private async performDeepDive(
content: string,
analysis: ContentAnalysis,
broadEpisodes: any[],
userId: string,
spaceIds: string[]
): Promise<{ facts: any[]; episodes: any[] }> {
// Check if we have any results worth evaluating
if (broadEpisodes.length === 0) {
logger.info("No episodes from broad search, skipping deep dive");
return { facts: [], episodes: [] };
}
// Agent decides on follow-up based on episodes
const decision = await this.decideFollowUp(
content,
analysis,
broadEpisodes
);
if (!decision.shouldContinue) {
logger.info(`Agent stopped: ${decision.reasoning}`);
return { facts: [], episodes: [] };
}
logger.info(
`Agent continuing with ${decision.followUpQueries.length} follow-up queries`
);
// Execute follow-up queries sequentially
const deepDiveFacts = [];
const deepDiveEpisodes = [];
for (const query of decision.followUpQueries) {
const result = await this.searchService.search(query, userId, {
limit: 20,
spaceIds,
});
deepDiveFacts.push(...result.facts);
deepDiveEpisodes.push(...result.episodes);
// Stop if we've gathered enough episodes
if (deepDiveEpisodes.length > 20) {
logger.info("Sufficient context gathered, stopping early");
break;
}
}
return { facts: deepDiveFacts, episodes: deepDiveEpisodes };
}
/**
* Agent decides on follow-up queries based on episodes
*/
private async decideFollowUp(
content: string,
analysis: ContentAnalysis,
episodes: any[]
): Promise<AgentDecision> {
const prompt = `
You are analyzing memory search results to decide if deeper investigation is needed.
ORIGINAL CONTENT:
${content}
INFERRED INTENT: ${analysis.intent}
FOUND MEMORIES (${episodes.length} episodes):
${episodes
.map((ep, i) => {
const date = new Date(ep.createdAt).toISOString().split("T")[0];
const preview = ep.content;
return `
--- Memory ${i + 1} (${date}) ---
${preview}
`;
})
.join("\n")}
YOUR TASK:
1. EVALUATE MEMORY RELEVANCE:
- Are these memories directly relevant to the original content?
- Do they provide sufficient context for the intent "${analysis.intent}"?
- What key information or connections are missing?
- Are there entities, topics, or concepts mentioned that warrant deeper exploration?
2. DECIDE ON FOLLOW-UP:
- If memories are highly relevant and complete: STOP, no follow-up needed
- If memories are relevant but incomplete: Continue with 1-2 clarifying queries
- If memories reveal new entities/topics worth exploring: Continue with 2-3 follow-up queries
- If memories are sparse or off-topic: STOP, unlikely to find better results
3. GENERATE FOLLOW-UP QUERIES (if continuing):
- Extract new entities, topics, or connections mentioned in the memories
- Formulate specific, targeted queries based on what's missing
- Focus on enriching context for the "${analysis.intent}" intent
- Maximum 3 queries
RESPONSE FORMAT (JSON):
{
"shouldContinue": true/false,
"confidence": 0.0-1.0,
"reasoning": "explanation of decision based on memory analysis",
"followUpQueries": ["query1", "query2"]
}
`;
let responseText = "";
await makeModelCall(
false,
[{ role: "user", content: prompt }],
(text) => {
responseText = text;
},
{},
"high"
);
return JSON.parse(responseText);
}
/**
* Phase 4: Synthesize results based on intent using episodes
*/
private async synthesizeResults(
content: string,
analysis: ContentAnalysis,
episodes: any[]
): Promise<string> {
if (episodes.length === 0) {
return "No relevant context found in memory.";
}
const prompt = `
You are synthesizing relevant context from the user's memory to help an AI assistant respond more effectively.
CURRENT CONTENT:
${content}
USER INTENT: ${analysis.intent}
RELEVANT MEMORY CONTEXT (${episodes.length} past conversations):
${episodes
.map((ep, i) => {
const date = new Date(ep.createdAt).toISOString().split("T")[0];
const preview = ep.content;
return `
[${date}]
${preview}
`;
})
.join("\n\n")}
SYNTHESIS OBJECTIVE:
${this.getIntentGuidance(analysis.intent)}
OUTPUT REQUIREMENTS:
- Provide clear, actionable context from the memories
- Start directly with relevant information, no meta-commentary
- Present facts, decisions, preferences, and patterns from past conversations
- Connect past context to current content when relevant
- Note any gaps, contradictions, or evolution in thinking
- Keep it factual and concise - this will be used by an AI assistant
- Do not use conversational language like "you said" or "you mentioned"
- Present information in third person or as direct facts
Good examples:
- "Previous discussions on X covered Y and Z. Key decision: ..."
- "From March 2024 conversation: [specific context]"
- "Related work on [project] established that..."
- "Past preferences indicate..."
- "Timeline: [sequence of events/decisions]"
`;
let synthesis = "";
await makeModelCall(
false,
[{ role: "user", content: prompt }],
(text) => {
synthesis = text;
},
{},
"high"
);
return synthesis;
}
/**
* Get synthesis guidance based on intent keywords
*/
private getIntentGuidance(intent: string): string {
const intentLower = intent.toLowerCase();
if (
intentLower.includes("read") ||
intentLower.includes("understand") ||
intentLower.includes("email")
) {
return "Focus on: Who/what is this about? What context should the reader know? Provide recognition and background.";
}
if (
intentLower.includes("writ") ||
intentLower.includes("draft") ||
intentLower.includes("blog") ||
intentLower.includes("post")
) {
return "Focus on: What has been said before on this topic? What's consistent with past statements? What gaps or contradictions exist?";
}
if (
intentLower.includes("meeting") ||
intentLower.includes("prep") ||
intentLower.includes("standup") ||
intentLower.includes("agenda")
) {
return "Focus on: Key discussion topics, recent relevant context, pending action items, what needs to be addressed.";
}
if (
intentLower.includes("research") ||
intentLower.includes("explore") ||
intentLower.includes("learn")
) {
return "Focus on: Patterns across memories, connections between topics, insights and evolution over time.";
}
if (
intentLower.includes("follow") ||
intentLower.includes("task") ||
intentLower.includes("todo") ||
intentLower.includes("action")
) {
return "Focus on: Action items, pending tasks, decisions made, what needs follow-up, deadlines.";
}
if (
intentLower.includes("review") ||
intentLower.includes("change") ||
intentLower.includes("update") ||
intentLower.includes("diff")
) {
return "Focus on: What has changed, what's new information, how things have evolved, timeline of updates.";
}
// Default
return "Focus on: Most relevant context and key insights that would be valuable for understanding this content.";
}
/**
* Generate context hints from metadata
*/
private getIntentHints(
metadata?: DeepSearchRequest["metadata"]
): string {
if (!metadata) return "";
const hints: string[] = [];
// Chrome extension context
if (metadata.source === "chrome") {
if (metadata.url?.includes("mail.google.com")) {
hints.push("Content is from email client (likely reading)");
}
if (metadata.url?.includes("calendar.google.com")) {
hints.push("Content is from calendar (likely meeting_prep)");
}
if (metadata.url?.includes("docs.google.com")) {
hints.push("Content is from document editor (likely writing)");
}
}
// Obsidian context
if (metadata.source === "obsidian") {
hints.push(
"Content is from note editor (could be writing or research)"
);
}
return hints.length > 0
? `\n\nCONTEXT HINTS:\n${hints.join("\n")}`
: "";
}
}

2
apps/webapp/app/services/search.server.ts
View File

@ -80,7 +80,7 @@ export class SearchService {
opts, opts,
); );
// // 3. Apply adaptive filtering based on score threshold and minimum count // 3. Apply adaptive filtering based on score threshold and minimum count
const filteredResults = this.applyAdaptiveFiltering(rankedStatements, opts); const filteredResults = this.applyAdaptiveFiltering(rankedStatements, opts);
// 3. Return top results // 3. Return top results

28
apps/webapp/app/trigger/chat/stream-utils.ts
View File

@ -59,9 +59,37 @@ export async function* processTag(
const hasStartTag = chunk.includes(startTag); const hasStartTag = chunk.includes(startTag);
const hasClosingTag = chunk.includes("</"); const hasClosingTag = chunk.includes("</");
// Check if we're currently accumulating a potential end tag
const accumulatingEndTag = state.message.endsWith("</") ||
state.message.match(/<\/[a-z_]*$/i);
if (hasClosingTag && !hasStartTag && !hasEndTag) { if (hasClosingTag && !hasStartTag && !hasEndTag) {
// If chunk only has </ but not the full end tag, accumulate it // If chunk only has </ but not the full end tag, accumulate it
state.message += chunk; state.message += chunk;
} else if (accumulatingEndTag) {
// Continue accumulating if we're in the middle of a potential end tag
state.message += chunk;
// Check if we now have the complete end tag
if (state.message.includes(endTag)) {
// Process the complete message with end tag
const endIndex = state.message.indexOf(endTag);
const finalMessage = state.message.slice(0, endIndex).trim();
const messageToSend = finalMessage.slice(
finalMessage.indexOf(state.lastSent) + state.lastSent.length,
);
if (messageToSend) {
yield Message(
messageToSend,
states.chunk as AgentMessageType,
extraParams,
);
}
yield Message("", states.end as AgentMessageType, extraParams);
state.message = finalMessage;
state.messageEnded = true;
}
} else if (hasEndTag || (!hasEndTag && !hasClosingTag)) { } else if (hasEndTag || (!hasEndTag && !hasClosingTag)) {
let currentMessage = comingFromStart let currentMessage = comingFromStart
? state.message ? state.message

269
apps/webapp/app/trigger/deep-search/deep-search-utils.ts Normal file
View File

@ -0,0 +1,269 @@
import { type CoreMessage } from "ai";
import { logger } from "@trigger.dev/sdk/v3";
import { generate, processTag } from "../chat/stream-utils";
import { type AgentMessage, AgentMessageType, Message } from "../chat/types";
import { TotalCost } from "../utils/types";
/**
* Run the deep search ReAct loop
* Async generator that yields AgentMessage objects for streaming
* Follows the exact same pattern as chat-utils.ts
*/
export async function* run(
initialMessages: CoreMessage[],
searchTool: any
): AsyncGenerator<AgentMessage, any, any> {
let messages = [...initialMessages];
let completed = false;
let guardLoop = 0;
let searchCount = 0;
let totalEpisodesFound = 0;
const seenEpisodeIds = new Set<string>(); // Track unique episodes
const totalCost: TotalCost = {
inputTokens: 0,
outputTokens: 0,
cost: 0,
};
const tools = {
searchMemory: searchTool,
};
logger.info("Starting deep search ReAct loop");
try {
while (!completed && guardLoop < 50) {
logger.info(`ReAct loop iteration ${guardLoop}, searches: ${searchCount}`);
// Call LLM with current message history
const response = generate(messages, false, (event)=>{const usage = event.usage;
totalCost.inputTokens += usage.promptTokens;
totalCost.outputTokens += usage.completionTokens;
}, tools);
let totalMessage = "";
const toolCalls: any[] = [];
// States for streaming final_response tags
const messageState = {
inTag: false,
message: "",
messageEnded: false,
lastSent: "",
};
// Process streaming response
for await (const chunk of response) {
if (typeof chunk === "object" && chunk.type === "tool-call") {
// Agent made a tool call
toolCalls.push(chunk);
logger.info(`Tool call: ${chunk.toolName}`);
} else if (typeof chunk === "string") {
totalMessage += chunk;
// Stream final_response tags using processTag
if (!messageState.messageEnded) {
yield* processTag(
messageState,
totalMessage,
chunk,
"<final_response>",
"</final_response>",
{
start: AgentMessageType.MESSAGE_START,
chunk: AgentMessageType.MESSAGE_CHUNK,
end: AgentMessageType.MESSAGE_END,
}
);
}
}
}
// Check for final response
if (totalMessage.includes("<final_response>")) {
const match = totalMessage.match(
/<final_response>(.*?)<\/final_response>/s
);
if (match) {
// Accept synthesis - completed
completed = true;
logger.info(
`Final synthesis accepted after ${searchCount} searches, ${totalEpisodesFound} unique episodes found`
);
break;
}
}
// Execute tool calls sequentially
if (toolCalls.length > 0) {
for (const toolCall of toolCalls) {
// Add assistant message with tool call
messages.push({
role: "assistant",
content: [
{
type: "tool-call",
toolCallId: toolCall.toolCallId,
toolName: toolCall.toolName,
args: toolCall.args,
},
],
});
// Execute the search tool
logger.info(`Executing search: ${JSON.stringify(toolCall.args)}`);
// Notify about search starting
yield Message("", AgentMessageType.SKILL_START);
yield Message(
`\nSearching memory: "${toolCall.args.query}"...\n`,
AgentMessageType.SKILL_CHUNK
);
yield Message("", AgentMessageType.SKILL_END);
const result = await searchTool.execute(toolCall.args);
searchCount++;
// Deduplicate episodes - track unique IDs
let uniqueNewEpisodes = 0;
if (result.episodes && Array.isArray(result.episodes)) {
for (const episode of result.episodes) {
const episodeId =
episode.id || episode._id || JSON.stringify(episode);
if (!seenEpisodeIds.has(episodeId)) {
seenEpisodeIds.add(episodeId);
uniqueNewEpisodes++;
}
}
}
const episodesInThisSearch = result.episodes?.length || 0;
totalEpisodesFound = seenEpisodeIds.size; // Use unique count
// Add tool result to message history
messages.push({
role: "tool",
content: [
{
type: "tool-result",
toolName: toolCall.toolName,
toolCallId: toolCall.toolCallId,
result: result,
},
],
});
logger.info(
`Search ${searchCount} completed: ${episodesInThisSearch} episodes (${uniqueNewEpisodes} new, ${totalEpisodesFound} unique total)`
);
}
// If found no episodes and haven't exhausted search attempts, require more searches
if (totalEpisodesFound === 0 && searchCount < 7) {
logger.info(
`Agent attempted synthesis with 0 unique episodes after ${searchCount} searches - requiring more attempts`
);
yield Message("", AgentMessageType.SKILL_START);
yield Message(
`No relevant context found yet - trying different search angles...`,
AgentMessageType.SKILL_CHUNK
);
yield Message("", AgentMessageType.SKILL_END);
messages.push({
role: "system",
content: `You have performed ${searchCount} searches but found 0 unique relevant episodes. Your queries may be too abstract or not matching the user's actual conversation topics.
Review your DECOMPOSITION:
- Are you using specific terms from the content?
- Try searching broader related topics the user might have discussed
- Try different terminology or related concepts
- Search for user's projects, work areas, or interests
Continue with different search strategies (you can search up to 7-10 times total).`,
});
guardLoop++;
continue;
}
// Soft nudging after all searches executed (awareness, not commands)
if (totalEpisodesFound >= 30 && searchCount >= 3) {
logger.info(
`Nudging: ${totalEpisodesFound} unique episodes found - suggesting synthesis consideration`
);
messages.push({
role: "system",
content: `Context awareness: You have found ${totalEpisodesFound} unique episodes across ${searchCount} searches. This represents substantial context. Consider whether you have sufficient information for quality synthesis, or if additional search angles would meaningfully improve understanding.`,
});
} else if (totalEpisodesFound >= 15 && searchCount >= 5) {
logger.info(
`Nudging: ${totalEpisodesFound} unique episodes after ${searchCount} searches - suggesting evaluation`
);
messages.push({
role: "system",
content: `Progress update: You have ${totalEpisodesFound} unique episodes from ${searchCount} searches. Evaluate whether you have covered the main angles from your decomposition, or if important aspects remain unexplored.`,
});
} else if (searchCount >= 7) {
logger.info(
`Nudging: ${searchCount} searches completed with ${totalEpisodesFound} unique episodes`
);
messages.push({
role: "system",
content: `Search depth: You have performed ${searchCount} searches and found ${totalEpisodesFound} unique episodes. Consider whether additional searches would yield meaningfully different context, or if it's time to synthesize what you've discovered.`,
});
} if (searchCount >= 10) {
logger.info(
`Reached maximum search limit (10), forcing synthesis with ${totalEpisodesFound} unique episodes`
);
yield Message("", AgentMessageType.SKILL_START);
yield Message(
`Maximum searches reached - synthesizing results...`,
AgentMessageType.SKILL_CHUNK
);
yield Message("", AgentMessageType.SKILL_END);
messages.push({
role: "system",
content: `You have performed 10 searches and found ${totalEpisodesFound} unique episodes. This is the maximum allowed. You MUST now provide your final synthesis wrapped in <final_response> tags based on what you've found.`,
});
}
}
// Safety check - if no tool calls and no final response, something went wrong
if (toolCalls.length === 0 && !totalMessage.includes("<final_response>")) {
logger.warn("Agent produced neither tool calls nor final response");
messages.push({
role: "system",
content:
"You must either use the searchMemory tool to search for more context, or provide your final synthesis wrapped in <final_response> tags.",
});
}
guardLoop++;
}
if (!completed) {
logger.warn(`Loop ended without completion after ${guardLoop} iterations`);
yield Message("", AgentMessageType.MESSAGE_START);
yield Message(
"Deep search did not complete - maximum iterations reached.",
AgentMessageType.MESSAGE_CHUNK
);
yield Message("", AgentMessageType.MESSAGE_END);
}
yield Message("Stream ended", AgentMessageType.STREAM_END);
} catch (error) {
logger.error(`Deep search error: ${error}`);
yield Message((error as Error).message, AgentMessageType.ERROR);
yield Message("Stream ended", AgentMessageType.STREAM_END);
}
}

117
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@ -0,0 +1,117 @@
import { metadata, task } from "@trigger.dev/sdk";
import { type CoreMessage } from "ai";
import { logger } from "@trigger.dev/sdk/v3";
import { nanoid } from "nanoid";
import {
deletePersonalAccessToken,
getOrCreatePersonalAccessToken,
} from "../utils/utils";
import { getReActPrompt } from "./prompt";
import { type DeepSearchPayload, type DeepSearchResponse } from "./types";
import { createSearchMemoryTool } from "./utils";
import { run } from "./deep-search-utils";
import { AgentMessageType } from "../chat/types";
export const deepSearch = task({
id: "deep-search",
maxDuration: 3000,
run: async (payload: DeepSearchPayload): Promise<DeepSearchResponse> => {
const {
content,
userId,
stream,
metadata: meta,
intentOverride,
} = payload;
const randomKeyName = `deepSearch_${nanoid(10)}`;
// Get or create token for search API calls
const pat = await getOrCreatePersonalAccessToken({
name: randomKeyName,
userId: userId as string,
});
if (!pat?.token) {
throw new Error("Failed to create personal access token");
}
try {
// Create search tool that agent will use
const searchTool = createSearchMemoryTool(pat.token);
// Build initial messages with ReAct prompt
const initialMessages: CoreMessage[] = [
{
role: "system",
content: getReActPrompt(meta, intentOverride),
},
{
role: "user",
content: `CONTENT TO ANALYZE:\n${content}\n\nPlease search my memory for relevant context and synthesize what you find.`,
},
];
// Run the ReAct loop generator
const llmResponse = run(initialMessages, searchTool);
if (stream) {
// Streaming mode: stream via metadata.stream like chat.ts does
// This makes all message types available to clients in real-time
const messageStream = await metadata.stream("messages", llmResponse);
let synthesis = "";
for await (const step of messageStream) {
// MESSAGE_CHUNK: Final synthesis - accumulate and stream
if (step.type === AgentMessageType.MESSAGE_CHUNK) {
synthesis += step.message;
}
// STREAM_END: Loop completed
if (step.type === AgentMessageType.STREAM_END) {
break;
}
}
await deletePersonalAccessToken(pat?.id);
// Clean up any remaining tags
synthesis = synthesis
.replace(/<final_response>/gi, "")
.replace(/<\/final_response>/gi, "")
.trim();
return { synthesis };
} else {
// Non-streaming mode: consume generator without streaming
let synthesis = "";
for await (const step of llmResponse) {
if (step.type === AgentMessageType.MESSAGE_CHUNK) {
synthesis += step.message;
}
// Could also collect episodes from tool results if needed
}
await deletePersonalAccessToken(pat?.id);
// Clean up any remaining tags
synthesis = synthesis
.replace(/<final_response>/gi, "")
.replace(/<\/final_response>/gi, "")
.trim();
// For non-streaming, we need to get episodes from search results
// Since we don't have direct access to search results in this flow,
// we'll return synthesis without episodes for now
// (episodes can be extracted from tool results if needed)
return { synthesis };
}
} catch (error) {
await deletePersonalAccessToken(pat?.id);
logger.error(`Deep search error: ${error}`);
throw error;
}
},
});

148
apps/webapp/app/trigger/deep-search/prompt.ts Normal file
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export function getReActPrompt(
metadata?: { source?: string; url?: string; pageTitle?: string },
intentOverride?: string
): string {
const contextHints = [];
if (metadata?.source === "chrome" && metadata?.url?.includes("mail.google.com")) {
contextHints.push("Content is from email - likely reading intent");
}
if (metadata?.source === "chrome" && metadata?.url?.includes("calendar.google.com")) {
contextHints.push("Content is from calendar - likely meeting prep intent");
}
if (metadata?.source === "chrome" && metadata?.url?.includes("docs.google.com")) {
contextHints.push("Content is from document editor - likely writing intent");
}
if (metadata?.source === "obsidian") {
contextHints.push("Content is from note editor - likely writing or research intent");
}
return `You are a memory research agent analyzing content to find relevant context.
YOUR PROCESS (ReAct Framework):
1. DECOMPOSE: First, break down the content into structured categories
Analyze the content and extract:
a) ENTITIES: Specific people, project names, tools, products mentioned
Example: "John Smith", "Phoenix API", "Redis", "mobile app"
b) TOPICS & CONCEPTS: Key subjects, themes, domains
Example: "authentication", "database design", "performance optimization"
c) TEMPORAL MARKERS: Time references, deadlines, events
Example: "last week's meeting", "Q2 launch", "yesterday's discussion"
d) ACTIONS & TASKS: What's being done, decided, or requested
Example: "implement feature", "review code", "make decision on"
e) USER INTENT: What is the user trying to accomplish?
${intentOverride ? `User specified: "${intentOverride}"` : "Infer from context: reading/writing/meeting prep/research/task tracking/review"}
2. FORM QUERIES: Create targeted search queries from your decomposition
Based on decomposition, form specific queries:
- Search for each entity by name (people, projects, tools)
- Search for topics the user has discussed before
- Search for related work or conversations in this domain
- Use the user's actual terminology, not generic concepts
EXAMPLE - Content: "Email from Sarah about the API redesign we discussed last week"
Decomposition:
- Entities: "Sarah", "API redesign"
- Topics: "API design", "redesign"
- Temporal: "last week"
- Actions: "discussed", "email communication"
- Intent: Reading (email) / meeting prep
Queries to form:
✅ "Sarah" (find past conversations with Sarah)
✅ "API redesign" or "API design" (find project discussions)
✅ "last week" + "Sarah" (find recent context)
✅ "meetings" or "discussions" (find related conversations)
❌ Avoid: "email communication patterns", "API architecture philosophy"
(These are abstract - search what user actually discussed!)
3. SEARCH: Execute your queries using searchMemory tool
- Start with 2-3 core searches based on main entities/topics
- Make each search specific and targeted
- Use actual terms from the content, not rephrased concepts
4. OBSERVE: Evaluate search results
- Did you find relevant episodes? How many unique ones?
- What specific context emerged?
- What new entities/topics appeared in results?
- Are there gaps in understanding?
- Should you search more angles?
Note: Episode counts are automatically deduplicated across searches - overlapping episodes are only counted once.
5. REACT: Decide next action based on observations
STOPPING CRITERIA - Proceed to SYNTHESIZE if ANY of these are true:
- You found 20+ unique episodes across your searches → ENOUGH CONTEXT
- You performed 5+ searches and found relevant episodes → SUFFICIENT
- You performed 7+ searches regardless of results → EXHAUSTED STRATEGIES
- You found strong relevant context from multiple angles → COMPLETE
System nudges will provide awareness of your progress, but you decide when synthesis quality would be optimal.
If you found little/no context AND searched less than 7 times:
- Try different query angles from your decomposition
- Search broader related topics
- Search user's projects or work areas
- Try alternative terminology
⚠️ DO NOT search endlessly - if you found relevant episodes, STOP and synthesize!
6. SYNTHESIZE: After gathering sufficient context, provide final answer
- Wrap your synthesis in <final_response> tags
- Present direct factual context from memory - no meta-commentary
- Write as if providing background context to an AI assistant
- Include: facts, decisions, preferences, patterns, timelines
- Note any gaps, contradictions, or evolution in thinking
- Keep it concise and actionable
- DO NOT use phrases like "Previous discussions on", "From conversations", "Past preferences indicate"
- DO NOT use conversational language like "you said" or "you mentioned"
- Present information as direct factual statements
FINAL RESPONSE FORMAT:
<final_response>
[Direct synthesized context - factual statements only]
Good examples:
- "The API redesign focuses on performance and scalability. Key decisions: moving to GraphQL, caching layer with Redis."
- "Project Phoenix launches Q2 2024. Main features: real-time sync, offline mode, collaborative editing."
- "Sarah leads the backend team. Recent work includes authentication refactor and database migration."
Bad examples:
❌ "Previous discussions on the API revealed..."
❌ "From past conversations, it appears that..."
❌ "Past preferences indicate..."
❌ "The user mentioned that..."
Just state the facts directly.
</final_response>
${contextHints.length > 0 ? `\nCONTEXT HINTS:\n${contextHints.join("\n")}` : ""}
CRITICAL REQUIREMENTS:
- ALWAYS start with DECOMPOSE step - extract entities, topics, temporal markers, actions
- Form specific queries from your decomposition - use user's actual terms
- Minimum 3 searches required
- Maximum 10 searches allowed - must synthesize after that
- STOP and synthesize when you hit stopping criteria (20+ episodes, 5+ searches with results, 7+ searches total)
- Each search should target different aspects from decomposition
- Present synthesis directly without meta-commentary
SEARCH QUALITY CHECKLIST:
✅ Queries use specific terms from content (names, projects, exact phrases)
✅ Searched multiple angles from decomposition (entities, topics, related areas)
✅ Stop when you have enough unique context - don't search endlessly
✅ Tried alternative terminology if initial searches found nothing
❌ Avoid generic/abstract queries that don't match user's vocabulary
❌ Don't stop at 3 searches if you found zero unique episodes
❌ Don't keep searching when you already found 20+ unique episodes
}`
}

20
apps/webapp/app/trigger/deep-search/types.ts Normal file
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@ -0,0 +1,20 @@
export interface DeepSearchPayload {
content: string;
userId: string;
stream: boolean;
intentOverride?: string;
metadata?: {
source?: "chrome" | "obsidian" | "mcp";
url?: string;
pageTitle?: string;
};
}
export interface DeepSearchResponse {
synthesis: string;
episodes?: Array<{
content: string;
createdAt: Date;
spaceIds: string[];
}>;
}

66
apps/webapp/app/trigger/deep-search/utils.ts Normal file
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import { tool } from "ai";
import { z } from "zod";
import axios from "axios";
import { logger } from "@trigger.dev/sdk/v3";
export function createSearchMemoryTool(token: string) {
return tool({
description:
"Search the user's memory for relevant facts and episodes. Use this tool multiple times with different queries to gather comprehensive context.",
parameters: z.object({
query: z
.string()
.describe(
"Search query to find relevant information. Be specific: entity names, topics, concepts."
),
}),
execute: async ({ query }) => {
try {
const response = await axios.post(
`${process.env.API_BASE_URL || "https://core.heysol.ai"}/api/v1/search`,
{ query },
{
headers: {
Authorization: `Bearer ${token}`,
},
}
);
const searchResult = response.data;
return {
facts: searchResult.facts || [],
episodes: searchResult.episodes || [],
summary: `Found ${searchResult.episodes?.length || 0} relevant memories`,
};
} catch (error) {
logger.error(`SearchMemory tool error: ${error}`);
return {
facts: [],
episodes: [],
summary: "No results found",
};
}
},
});
}
// Helper to extract unique episodes from tool calls
export function extractEpisodesFromToolCalls(toolCalls: any[]): any[] {
const episodes: any[] = [];
for (const call of toolCalls || []) {
if (call.toolName === "searchMemory" && call.result?.episodes) {
episodes.push(...call.result.episodes);
}
}
// Deduplicate by content + createdAt
const uniqueEpisodes = Array.from(
new Map(
episodes.map((e) => [`${e.content}-${e.createdAt}`, e])
).values()
);
return uniqueEpisodes.slice(0, 10);
}

49
apps/webapp/app/utils/mcp/memory.ts
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@ -3,7 +3,7 @@ import { addToQueue } from "~/lib/ingest.server";
import { logger } from "~/services/logger.service"; import { logger } from "~/services/logger.service";
import { SearchService } from "~/services/search.server"; import { SearchService } from "~/services/search.server";
import { SpaceService } from "~/services/space.server"; import { SpaceService } from "~/services/space.server";
import { DeepSearchService } from "~/services/deepSearch.server"; import { deepSearch } from "~/trigger/deep-search";
import { IntegrationLoader } from "./integration-loader"; import { IntegrationLoader } from "./integration-loader";
const searchService = new SearchService(); const searchService = new SearchService();
@ -580,26 +580,37 @@ async function handleMemoryDeepSearch(args: any) {
throw new Error("content is required"); throw new Error("content is required");
} }
const deepSearchService = new DeepSearchService(searchService); // Trigger non-streaming deep search task
const handle = await deepSearch.triggerAndWait({
const result = await deepSearchService.deepSearch( content,
{
content,
intentOverride,
metadata: { source },
},
userId, userId,
); stream: false, // MCP doesn't need streaming
intentOverride,
metadata: { source },
});
return { // Wait for task completion
content: [ if (handle.ok) {
{ return {
type: "text", content: [
text: JSON.stringify(result), {
}, type: "text",
], text: JSON.stringify(handle.output),
isError: false, },
}; ],
isError: false,
};
} else {
return {
content: [
{
type: "text",
text: `Error performing deep search: ${handle.error instanceof Error ? handle.error.message : String(handle.error)}`,
},
],
isError: true,
};
}
} catch (error) { } catch (error) {
logger.error(`MCP deep search error: ${error}`); logger.error(`MCP deep search error: ${error}`);