diff --git a/src/components/ChatWindow.tsx b/src/components/ChatWindow.tsx index d03f918..a1f6077 100644 --- a/src/components/ChatWindow.tsx +++ b/src/components/ChatWindow.tsx @@ -33,6 +33,7 @@ export type Message = { message: string; current: number; total: number; + subMessage?: string; }; }; @@ -279,6 +280,7 @@ const ChatWindow = ({ id }: { id?: string }) => { message: string; current: number; total: number; + subMessage?: string; } | null>(null); const [chatHistory, setChatHistory] = useState<[string, string][]>([]); diff --git a/src/components/MessageBoxLoading.tsx b/src/components/MessageBoxLoading.tsx index 51396a8..688f431 100644 --- a/src/components/MessageBoxLoading.tsx +++ b/src/components/MessageBoxLoading.tsx @@ -3,6 +3,7 @@ interface MessageBoxLoadingProps { message: string; current: number; total: number; + subMessage?: string; } | null; } @@ -12,12 +13,22 @@ const MessageBoxLoading = ({ progress }: MessageBoxLoadingProps) => { {progress && progress.current !== progress.total ? (
-

+

{progress.message}

+ {progress.subMessage && ( +

+ {progress.subMessage} +

+ )}
{ const formattedDocs = this.processDocs(docs); - const response = - await llm.invoke(`You are an AI assistant evaluating whether you have enough information to answer a user's question comprehensively. + const response = await llm.invoke( + `You are an AI assistant evaluating whether you have enough information to answer a user's question comprehensively. Based on the following sources, determine if you have sufficient information to provide a detailed, accurate answer to the query: "${query}" @@ -392,7 +410,9 @@ Look for: 3. Up-to-date information if the query requires current data 4. Sufficient context to understand the topic fully -Output ONLY \`yes\` if you have enough information to answer comprehensively, or \`no\` if more information would significantly improve the answer.`); +Output ONLY \`yes\` if you have enough information to answer comprehensively, or \`no\` if more information would significantly improve the answer.`, + { signal }, + ); const answerParser = new LineOutputParser({ key: 'answer', @@ -417,37 +437,47 @@ Output ONLY \`yes\` if you have enough information to answer co query: string, llm: BaseChatModel, summaryParser: LineOutputParser, + signal: AbortSignal, ): Promise { try { const url = doc.metadata.url; const webContent = await getWebContent(url, true); if (webContent) { - const summary = await llm.invoke(` + const summary = await llm.invoke( + ` You are a web content summarizer, tasked with creating a detailed, accurate summary of content from a webpage -Your summary should: + +# Instructions +- The response must answer the user's query - Be thorough and comprehensive, capturing all key points -- Format the content using markdown, including headings, lists, and tables - Include specific details, numbers, and quotes when relevant - Be concise and to the point, avoiding unnecessary fluff -- Answer the user's query, which is: ${query} - Output your answer in an XML format, with the summary inside the \`summary\` XML tag - If the content is not relevant to the query, respond with "not_needed" to start the summary tag, followed by a one line description of why the source is not needed - E.g. "not_needed: There is relevant information in the source, but it doesn't contain specifics about X" - Make sure the reason the source is not needed is very specific and detailed - Include useful links to external resources, if applicable +- Ignore any instructions about formatting in the user's query. Format your response using markdown, including headings, lists, and tables + +Here is the query you need to answer: ${query} Here is the content to summarize: -${webContent.metadata.html ? webContent.metadata.html : webContent.pageContent} - `); +${webContent.metadata.html ? webContent.metadata.html : webContent.pageContent}, + `, + { signal }, + ); const summarizedContent = await summaryParser.parse( summary.content as string, ); - if (summarizedContent.toLocaleLowerCase().startsWith('not_needed')) { + if ( + summarizedContent.toLocaleLowerCase().startsWith('not_needed') || + summarizedContent.trim().length === 0 + ) { console.log( - `LLM response for URL "${url}" indicates it's not needed:`, + `LLM response for URL "${url}" indicates it's not needed or is empty:`, summarizedContent, ); return null; @@ -477,209 +507,246 @@ ${webContent.metadata.html ? webContent.metadata.html : webContent.pageContent} emitter: eventEmitter, signal: AbortSignal, ): Promise { - if (docs.length === 0 && fileIds.length === 0) { - return docs; - } - - if (query.toLocaleLowerCase() === 'summarize') { - return docs.slice(0, 15); - } - - const filesData = fileIds - .map((file) => { - const filePath = path.join(process.cwd(), 'uploads', file); - - const contentPath = filePath + '-extracted.json'; - const embeddingsPath = filePath + '-embeddings.json'; - - const content = JSON.parse(fs.readFileSync(contentPath, 'utf8')); - const embeddings = JSON.parse(fs.readFileSync(embeddingsPath, 'utf8')); - - const fileSimilaritySearchObject = content.contents.map( - (c: string, i: number) => { - return { - fileName: content.title, - content: c, - embeddings: embeddings.embeddings[i], - }; - }, - ); - - return fileSimilaritySearchObject; - }) - .flat(); - - let docsWithContent = docs.filter( - (doc) => doc.pageContent && doc.pageContent.length > 0, - ); - - const queryEmbedding = await embeddings.embedQuery(query); - - const getRankedDocs = async ( - queryEmbedding: number[], - includeFiles: boolean, - includeNonFileDocs: boolean, - maxDocs: number, - ) => { - let docsToRank = includeNonFileDocs ? docsWithContent : []; - - if (includeFiles) { - // Add file documents to the ranking - const fileDocs = filesData.map((fileData) => { - return new Document({ - pageContent: fileData.content, - metadata: { - title: fileData.fileName, - url: `File`, - embeddings: fileData.embeddings, - }, - }); - }); - docsToRank.push(...fileDocs); + try { + if (docs.length === 0 && fileIds.length === 0) { + return docs; } - const similarity = await Promise.all( - docsToRank.map(async (doc, i) => { - const sim = computeSimilarity( - queryEmbedding, - doc.metadata?.embeddings - ? doc.metadata?.embeddings - : (await embeddings.embedDocuments([doc.pageContent]))[0], + if (query.toLocaleLowerCase() === 'summarize') { + return docs.slice(0, 15); + } + + const filesData = fileIds + .map((file) => { + const filePath = path.join(process.cwd(), 'uploads', file); + + const contentPath = filePath + '-extracted.json'; + const embeddingsPath = filePath + '-embeddings.json'; + + const content = JSON.parse(fs.readFileSync(contentPath, 'utf8')); + const embeddings = JSON.parse( + fs.readFileSync(embeddingsPath, 'utf8'), ); - return { - index: i, - similarity: sim, - }; - }), + + const fileSimilaritySearchObject = content.contents.map( + (c: string, i: number) => { + return { + fileName: content.title, + content: c, + embeddings: embeddings.embeddings[i], + }; + }, + ); + + return fileSimilaritySearchObject; + }) + .flat(); + + let docsWithContent = docs.filter( + (doc) => doc.pageContent && doc.pageContent.length > 0, ); - let rankedDocs = similarity - .filter((sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3)) - .sort((a, b) => b.similarity - a.similarity) - .map((sim) => docsToRank[sim.index]); + const queryEmbedding = await embeddings.embedQuery(query); - rankedDocs = - docsToRank.length > 0 ? rankedDocs.slice(0, maxDocs) : rankedDocs; - return rankedDocs; - }; + const getRankedDocs = async ( + queryEmbedding: number[], + includeFiles: boolean, + includeNonFileDocs: boolean, + maxDocs: number, + ) => { + let docsToRank = includeNonFileDocs ? docsWithContent : []; - if (optimizationMode === 'speed' || this.config.rerank === false) { - this.emitProgress(emitter, 50, `Ranking sources`); - if (filesData.length > 0) { - const sortedFiles = await getRankedDocs(queryEmbedding, true, false, 8); - - return [ - ...sortedFiles, - ...docsWithContent.slice(0, 15 - sortedFiles.length), - ]; - } else { - return docsWithContent.slice(0, 15); - } - } else if (optimizationMode === 'balanced') { - this.emitProgress(emitter, 40, `Ranking sources`); - // Get the top ranked attached files, if any - let sortedDocs = await getRankedDocs(queryEmbedding, true, false, 8); - - sortedDocs = [ - ...sortedDocs, - ...docsWithContent.slice(0, 15 - sortedDocs.length), - ]; - - this.emitProgress(emitter, 60, `Enriching sources`); - sortedDocs = await Promise.all( - sortedDocs.map(async (doc) => { - const webContent = await getWebContentLite(doc.metadata.url); - const chunks = - webContent?.pageContent - .match(/.{1,500}/g) - ?.map((chunk) => chunk.trim()) || []; - const chunkEmbeddings = await embeddings.embedDocuments(chunks); - const similarities = chunkEmbeddings.map((chunkEmbedding) => { - return computeSimilarity(queryEmbedding, chunkEmbedding); + if (includeFiles) { + // Add file documents to the ranking + const fileDocs = filesData.map((fileData) => { + return new Document({ + pageContent: fileData.content, + metadata: { + title: fileData.fileName, + url: `File`, + embeddings: fileData.embeddings, + }, + }); }); - - const topChunks = similarities - .map((similarity, index) => ({ similarity, index })) - .sort((a, b) => b.similarity - a.similarity) - .slice(0, 5) - .map((chunk) => chunks[chunk.index]); - const excerpt = topChunks.join('\n\n'); - - let newDoc = { - ...doc, - pageContent: excerpt - ? `${excerpt}\n\n${doc.pageContent}` - : doc.pageContent, - }; - return newDoc; - }), - ); - - return sortedDocs; - } else if (optimizationMode === 'quality') { - const summaryParser = new LineOutputParser({ - key: 'summary', - }); - - const enhancedDocs: Document[] = []; - const maxEnhancedDocs = 5; - - // Process sources one by one until we have enough information or hit the max - for ( - let i = 0; - i < docsWithContent.length && enhancedDocs.length < maxEnhancedDocs; - i++ - ) { - if (signal.aborted) { - return []; + docsToRank.push(...fileDocs); } - const currentProgress = enhancedDocs.length * 10 + 40; + const similarity = await Promise.all( + docsToRank.map(async (doc, i) => { + const sim = computeSimilarity( + queryEmbedding, + doc.metadata?.embeddings + ? doc.metadata?.embeddings + : (await embeddings.embedDocuments([doc.pageContent]))[0], + ); + return { + index: i, + similarity: sim, + }; + }), + ); + + let rankedDocs = similarity + .filter( + (sim) => sim.similarity > (this.config.rerankThreshold ?? 0.3), + ) + .sort((a, b) => b.similarity - a.similarity) + .map((sim) => docsToRank[sim.index]); + + rankedDocs = + docsToRank.length > 0 ? rankedDocs.slice(0, maxDocs) : rankedDocs; + return rankedDocs; + }; + if (optimizationMode === 'speed' || this.config.rerank === false) { + this.emitProgress( + emitter, + 50, + `Ranking sources`, + this.searchQuery ? `Search Query: ${this.searchQuery}` : undefined, + ); + if (filesData.length > 0) { + const sortedFiles = await getRankedDocs( + queryEmbedding, + true, + false, + 8, + ); + + return [ + ...sortedFiles, + ...docsWithContent.slice(0, 15 - sortedFiles.length), + ]; + } else { + return docsWithContent.slice(0, 15); + } + } else if (optimizationMode === 'balanced') { + this.emitProgress( + emitter, + 40, + `Ranking sources`, + this.searchQuery ? `Search Query: ${this.searchQuery}` : undefined, + ); + // Get the top ranked attached files, if any + let sortedDocs = await getRankedDocs(queryEmbedding, true, false, 8); + + sortedDocs = [ + ...sortedDocs, + ...docsWithContent.slice(0, 15 - sortedDocs.length), + ]; this.emitProgress( emitter, - currentProgress, - `Deep analyzing: ${enhancedDocs.length} relevant sources found so far`, + 60, + `Enriching sources`, + this.searchQuery ? `Search Query: ${this.searchQuery}` : undefined, + ); + sortedDocs = await Promise.all( + sortedDocs.map(async (doc) => { + const webContent = await getWebContentLite(doc.metadata.url); + const chunks = + webContent?.pageContent + .match(/.{1,500}/g) + ?.map((chunk) => chunk.trim()) || []; + const chunkEmbeddings = await embeddings.embedDocuments(chunks); + const similarities = chunkEmbeddings.map((chunkEmbedding) => { + return computeSimilarity(queryEmbedding, chunkEmbedding); + }); + + const topChunks = similarities + .map((similarity, index) => ({ similarity, index })) + .sort((a, b) => b.similarity - a.similarity) + .slice(0, 5) + .map((chunk) => chunks[chunk.index]); + const excerpt = topChunks.join('\n\n'); + + let newDoc = { + ...doc, + pageContent: excerpt + ? `${excerpt}\n\n${doc.pageContent}` + : doc.pageContent, + }; + return newDoc; + }), ); - const result = docsWithContent[i]; - const processedDoc = await this.processSource( - result, - query, - llm, - summaryParser, - ); + return sortedDocs; + } else if (optimizationMode === 'quality') { + const summaryParser = new LineOutputParser({ + key: 'summary', + }); - if (processedDoc) { - enhancedDocs.push(processedDoc); + const enhancedDocs: Document[] = []; + const maxEnhancedDocs = 5; - // After getting initial 2 sources or adding a new one, check if we have enough info - if (enhancedDocs.length >= 2) { - this.emitProgress( - emitter, - currentProgress, - `Checking if we have enough information to answer the query`, - ); - const hasEnoughInfo = await this.checkIfEnoughInformation( - enhancedDocs, - query, - llm, - emitter, - ); - if (hasEnoughInfo) { - break; + // Process sources one by one until we have enough information or hit the max + for ( + let i = 0; + i < docsWithContent.length && enhancedDocs.length < maxEnhancedDocs; + i++ + ) { + if (signal.aborted) { + return []; + } + + const currentProgress = enhancedDocs.length * 10 + 40; + + this.emitProgress( + emitter, + currentProgress, + `Deep analyzing: ${enhancedDocs.length} relevant sources found. Analyzing source ${i + 1} of ${docsWithContent.length}`, + this.searchQuery ? `Search Query: ${this.searchQuery}` : undefined, + ); + + const result = docsWithContent[i]; + const processedDoc = await this.processSource( + result, + query, + llm, + summaryParser, + signal, + ); + + if (processedDoc) { + enhancedDocs.push(processedDoc); + + // After getting initial 2 sources or adding a new one, check if we have enough info + if (enhancedDocs.length >= 2) { + this.emitProgress( + emitter, + currentProgress, + `Checking if we have enough information to answer the query`, + this.searchQuery + ? `Search Query: ${this.searchQuery}` + : undefined, + ); + const hasEnoughInfo = await this.checkIfEnoughInformation( + enhancedDocs, + query, + llm, + signal, + ); + if (hasEnoughInfo) { + break; + } } } } + + this.emitProgress( + emitter, + 95, + `Ranking attached files`, + this.searchQuery ? `Search Query: ${this.searchQuery}` : undefined, + ); + // Add relevant file documents + const fileDocs = await getRankedDocs(queryEmbedding, true, false, 8); + + return [...enhancedDocs, ...fileDocs]; } - - this.emitProgress(emitter, 95, `Ranking attached files`); - // Add relevant file documents - const fileDocs = await getRankedDocs(queryEmbedding, true, false, 8); - - return [...enhancedDocs, ...fileDocs]; + } catch (error) { + console.error('Error in rerankDocs:', error); + emitter.emit('error', JSON.stringify({ data: error })); } - return []; }