199bio-mcp-limitless-server
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Enhanced MCP server for Limitless Pendant with advanced conversation analysis, meeting detection, action item extraction, and comprehensive analytics. Features smart pagination for handling large datasets.
323 lines • 14.4 kB
JavaScript
export class TranscriptExtractor {
/**
* Extract raw, unformatted transcript from lifelog data
* Optimized for AI processing with maximum context preservation
*/
static extractRawTranscript(lifelog, options = { format: "structured" }) {
const { format = "structured", includeTimestamps = true, includeSpeakers = true, includeContext = true, preserveFormatting = false, timeFormat = "absolute", speakerFormat = "names" } = options;
if (!lifelog.contents || lifelog.contents.length === 0) {
return this.createEmptyTranscript(lifelog);
}
// Extract all conversation segments with full context
const segments = this.extractSegments(lifelog.contents, {
includeTimestamps,
includeSpeakers,
includeContext,
timeFormat,
speakerFormat
});
// Analyze content for technical terms, figures, and key phrases
const metadata = this.analyzeContent(segments);
// Generate different format outputs
const rawText = this.generateRawText(segments, preserveFormatting);
const formattedTranscript = this.generateFormattedTranscript(segments, format, options);
const totalDuration = new Date(lifelog.endTime).getTime() - new Date(lifelog.startTime).getTime();
return {
lifelogId: lifelog.id,
title: lifelog.title || "Untitled Conversation",
startTime: lifelog.startTime,
endTime: lifelog.endTime,
totalDuration,
segments,
metadata,
rawText,
formattedTranscript
};
}
/**
* Extract segments with maximum detail preservation
*/
static extractSegments(contents, options) {
const segments = [];
for (const node of contents) {
if (!node.content || node.content.trim().length === 0)
continue;
// Preserve all content types, not just blockquotes
const segment = {
content: node.content.trim(),
type: node.type,
startTime: node.startTime,
endTime: node.endTime,
startOffsetMs: node.startOffsetMs,
endOffsetMs: node.endOffsetMs
};
// Calculate duration if timestamps available
if (node.startOffsetMs !== undefined && node.endOffsetMs !== undefined) {
segment.duration = node.endOffsetMs - node.startOffsetMs;
}
// Include speaker information with full context
if (options.includeSpeakers && node.speakerName) {
switch (options.speakerFormat) {
case "names":
segment.speaker = node.speakerName;
break;
case "identifiers":
segment.speaker = node.speakerIdentifier || node.speakerName;
break;
case "both":
segment.speaker = node.speakerName;
segment.speakerIdentifier = node.speakerIdentifier;
break;
}
}
segments.push(segment);
// Process children recursively to preserve nested content
if (node.children && node.children.length > 0) {
const childSegments = this.extractSegments(node.children, options);
segments.push(...childSegments);
}
}
return segments;
}
/**
* Analyze content for technical terms, figures, and important details
*/
static analyzeContent(segments) {
const allText = segments.map(s => s.content).join(" ");
const uniqueSpeakers = new Set(segments.map(s => s.speaker).filter(Boolean));
// Extract technical terms (scientific, medical, business)
const technicalTerms = this.extractTechnicalTerms(allText);
// Extract numbers, figures, percentages, dates
const numbersAndFigures = this.extractNumbersAndFigures(allText);
// Extract key phrases and important concepts
const keyPhrases = this.extractKeyPhrases(allText);
// Calculate statistics
const wordCount = allText.split(/\s+/).length;
const averageSegmentLength = segments.length > 0 ? wordCount / segments.length : 0;
return {
speakerCount: uniqueSpeakers.size,
uniqueSpeakers: Array.from(uniqueSpeakers),
wordCount,
averageSegmentLength: Math.round(averageSegmentLength),
technicalTermsFound: technicalTerms,
numbersAndFigures,
keyPhrases
};
}
/**
* Extract technical terminology with precision
*/
static extractTechnicalTerms(text) {
const technicalPatterns = [
// Scientific terms
/\b[A-Z][a-z]+(?:ine|ase|oid|gen|ide|ate|ium|sis|tion|logy|graphy|metry|scopy)\b/g,
// Medical terms
/\b(?:diagnosis|treatment|therapy|syndrome|pathology|cardio|neuro|gastro|pulmonary|hepatic|renal|oncology|immunology)\w*\b/gi,
// Technical abbreviations
/\b[A-Z]{2,6}\b(?:\s*[A-Z]{2,6})*\b/g,
// Chemical formulas and compounds
/\b[A-Z][a-z]?(?:\d+[A-Za-z]*)*\b/g,
// Units and measurements
/\b\d+(?:\.\d+)?\s*(?:mg|kg|ml|cm|mm|km|hz|ghz|mb|gb|tb|fps|rpm|°[CF]|%)\b/gi,
// Software/tech terms
/\b(?:API|SDK|REST|GraphQL|JSON|XML|HTTP|HTTPS|SQL|NoSQL|CI\/CD|DevOps|ML|AI|GPU|CPU|RAM|SSD|IoT|VR|AR)\b/gi
];
const terms = new Set();
for (const pattern of technicalPatterns) {
const matches = text.match(pattern) || [];
matches.forEach(match => {
if (match.length > 2) { // Filter out very short matches
terms.add(match);
}
});
}
return Array.from(terms).slice(0, 20); // Top 20 technical terms
}
/**
* Extract numbers, figures, percentages, and quantitative data
*/
static extractNumbersAndFigures(text) {
const numberPatterns = [
// Percentages
/\b\d+(?:\.\d+)?%\b/g,
// Currency
/\$\d+(?:,\d{3})*(?:\.\d{2})?\b|\b\d+(?:,\d{3})*(?:\.\d{2})?\s*(?:dollars?|USD|EUR|GBP)\b/gi,
// Large numbers with commas
/\b\d{1,3}(?:,\d{3})+(?:\.\d+)?\b/g,
// Decimal numbers with context
/\b\d+\.\d+\s*(?:million|billion|thousand|k)\b/gi,
// Time/duration
/\b\d+(?:\.\d+)?\s*(?:hours?|minutes?|seconds?|days?|weeks?|months?|years?)\b/gi,
// Ratios and fractions
/\b\d+:\d+\b|\b\d+\/\d+\b/g,
// Scientific notation
/\b\d+(?:\.\d+)?[eE][+-]?\d+\b/g,
// Dates with specific formats
/\b(?:January|February|March|April|May|June|July|August|September|October|November|December)\s+\d{1,2},?\s+\d{4}\b/gi,
/\b\d{1,2}\/\d{1,2}\/\d{2,4}\b/g,
// Version numbers
/\bv?\d+\.\d+(?:\.\d+)*\b/gi
];
const figures = new Set();
for (const pattern of numberPatterns) {
const matches = text.match(pattern) || [];
matches.forEach(match => figures.add(match));
}
return Array.from(figures).slice(0, 30); // Top 30 figures
}
/**
* Extract key phrases and important concepts
*/
static extractKeyPhrases(text) {
const keyPhrasePatterns = [
// Decision indicators
/\b(?:we decided|decision was|agreed to|concluded that|determined that|final decision|going with|chose to)\s+[^.!?]*[.!?]/gi,
// Action indicators
/\b(?:action item|next step|follow up|we need to|I will|should do|must complete|deadline|by \w+day)\s+[^.!?]*[.!?]/gi,
// Important qualifiers
/\b(?:critical|essential|important|urgent|priority|significant|major|key|primary|main|core)\s+[^.!?]*[.!?]/gi,
// Problem/solution indicators
/\b(?:problem is|issue with|challenge|solution|fix|resolve|address)\s+[^.!?]*[.!?]/gi,
// Research/findings
/\b(?:study shows|research indicates|data suggests|findings|results|analysis|statistics|evidence)\s+[^.!?]*[.!?]/gi,
// Quotes and references
/(?:"[^"]*"|according to|as mentioned|referenced|cited|source)/gi
];
const phrases = new Set();
for (const pattern of keyPhrasePatterns) {
const matches = text.match(pattern) || [];
matches.forEach(match => {
if (match.length > 10 && match.length < 200) { // Reasonable phrase length
phrases.add(match.trim());
}
});
}
return Array.from(phrases).slice(0, 15); // Top 15 key phrases
}
/**
* Generate clean raw text optimized for AI processing
*/
static generateRawText(segments, preserveFormatting) {
if (!preserveFormatting) {
// Clean text optimized for AI - remove all formatting, just content
return segments
.filter(s => s.type === "blockquote" || s.type.startsWith("heading"))
.map(s => s.content)
.join(" ")
.replace(/\s+/g, " ")
.trim();
}
// Preserve some structure for readability
return segments
.map(s => {
let text = s.content;
if (s.speaker) {
text = `${s.speaker}: ${text}`;
}
return text;
})
.join("\n")
.trim();
}
/**
* Generate formatted transcript based on specified format
*/
static generateFormattedTranscript(segments, format, options) {
switch (format) {
case "raw_text":
return segments.map(s => s.content).join(" ");
case "verbatim":
return segments
.filter(s => s.speaker) // Only spoken content
.map(s => `${s.speaker}: ${s.content}`)
.join("\n");
case "timestamps":
return segments
.filter(s => s.startTime)
.map(s => {
const time = s.startTime ? new Date(s.startTime).toLocaleTimeString() : "";
const speaker = s.speaker ? `${s.speaker}: ` : "";
return `[${time}] ${speaker}${s.content}`;
})
.join("\n");
case "speakers_only":
return segments
.filter(s => s.speaker && s.type === "blockquote")
.map(s => `**${s.speaker}:** ${s.content}`)
.join("\n\n");
case "structured":
default:
// Comprehensive structured format with all context
let result = "";
let currentSpeaker = "";
for (const segment of segments) {
if (segment.type.startsWith("heading")) {
result += `\n# ${segment.content}\n\n`;
}
else if (segment.speaker && segment.speaker !== currentSpeaker) {
currentSpeaker = segment.speaker;
const timestamp = segment.startTime ?
` (${new Date(segment.startTime).toLocaleTimeString()})` : "";
result += `\n**${segment.speaker}${timestamp}:**\n`;
result += `${segment.content}\n`;
}
else if (segment.speaker) {
result += `${segment.content}\n`;
}
else {
result += `\n*${segment.content}*\n`;
}
}
return result.trim();
}
}
/**
* Create empty transcript for lifelogs with no content
*/
static createEmptyTranscript(lifelog) {
return {
lifelogId: lifelog.id,
title: lifelog.title || "Empty Lifelog",
startTime: lifelog.startTime,
endTime: lifelog.endTime,
totalDuration: new Date(lifelog.endTime).getTime() - new Date(lifelog.startTime).getTime(),
segments: [],
metadata: {
speakerCount: 0,
uniqueSpeakers: [],
wordCount: 0,
averageSegmentLength: 0,
technicalTermsFound: [],
numbersAndFigures: [],
keyPhrases: []
},
rawText: "",
formattedTranscript: "No content available."
};
}
/**
* Extract multiple lifelogs and combine into comprehensive transcript
*/
static extractMultipleTranscripts(lifelogs, options = { format: "structured" }) {
const individualTranscripts = lifelogs.map(lifelog => this.extractRawTranscript(lifelog, options));
const combinedTranscript = individualTranscripts
.map(t => t.formattedTranscript)
.join("\n\n---\n\n");
// Aggregate metadata across all transcripts
const aggregatedMetadata = {
totalLifelogs: lifelogs.length,
totalDuration: individualTranscripts.reduce((sum, t) => sum + t.totalDuration, 0),
totalWordCount: individualTranscripts.reduce((sum, t) => sum + t.metadata.wordCount, 0),
uniqueSpeakersAcrossAll: Array.from(new Set(individualTranscripts.flatMap(t => t.metadata.uniqueSpeakers))),
allTechnicalTerms: Array.from(new Set(individualTranscripts.flatMap(t => t.metadata.technicalTermsFound))),
allNumbersAndFigures: Array.from(new Set(individualTranscripts.flatMap(t => t.metadata.numbersAndFigures))),
allKeyPhrases: Array.from(new Set(individualTranscripts.flatMap(t => t.metadata.keyPhrases)))
};
return {
combinedTranscript,
individualTranscripts,
aggregatedMetadata
};
}
}
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