fix: fixing multiple files on one
This commit is contained in:
Carlos
@ -8,6 +8,20 @@ const MAX_FILE_SIZE = 30000; // ~30KB per file
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const MAX_TOTAL_FILES = 5; // Maximum number of files to inject
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const MAX_TOTAL_SIZE = 100000; // ~100KB total for all files
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/**
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* Extracts file blocks from content
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* @param {string} content - Content containing file blocks
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* @returns {Array<{path: string, language: string, content: string}>} Array of file blocks
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*/
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function extractFileBlocks(content) {
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const fileTagRegex = /<file path="(.+?)"(?: language="(.+?)")?>([\s\S]*?)<\/file>/g;
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return [...content.matchAll(fileTagRegex)].map(([, path, lang, body]) => ({
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path,
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language: lang || "text",
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content: body.trim(),
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}));
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}
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/**
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* Saves an error with consistent formatting
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* @param {Error} error - The error to save
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@ -155,6 +169,158 @@ async function injectFileContent(content) {
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}
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}
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/**
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* Processes a single file with the model
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* @param {Object} params - Parameters for processing
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* @returns {Promise<Object>} The model's response for this file
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*/
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async function processSingleFile({
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file,
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model,
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systemPrompt,
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originalPrompt,
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stream = false,
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requestData
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}) {
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try {
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// Construct a focused prompt for this file
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const focusedPrompt = `Analyze this specific file:\n\n<file path="${file.path}" language="${file.language}">\n${file.content}\n</file>\n\nContext from original prompt: ${originalPrompt}`;
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const ollamaUrl = "http://localhost:11434/api/chat";
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const payload = {
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model,
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messages: [
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{ role: "system", content: systemPrompt || "You are a helpful AI assistant." },
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{ role: "user", content: focusedPrompt }
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],
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stream
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};
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// Save the individual file prompt
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await savePrompt({
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model,
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prompt: focusedPrompt,
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messages: payload.messages,
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request_data: {
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...requestData,
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is_multi_file: true,
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file_index: file.path,
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original_prompt: originalPrompt
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}
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});
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if (stream) {
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// For streaming, we'll handle this differently in the main function
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return { type: 'stream', payload, file };
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}
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const response = await axios.post(ollamaUrl, payload);
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return {
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type: 'response',
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file: file.path,
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content: response.data.message?.content || response.data.response || '',
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model: response.data.model
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};
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} catch (error) {
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await logError(error, {
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operation: 'Single file processing',
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filePath: file.path,
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model,
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originalPrompt
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});
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return {
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type: 'error',
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file: file.path,
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error: error.message
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};
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}
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}
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/**
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* Handles multi-file processing
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* @param {Object} params - Parameters for processing
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* @returns {Promise<Object>} Combined responses for all files
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*/
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async function handleMultiFileProcessing({
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files,
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model,
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systemPrompt,
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originalPrompt,
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stream,
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requestData,
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res
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}) {
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if (stream) {
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// For streaming, we need to handle each file sequentially
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res.setHeader('Content-Type', 'application/x-ndjson');
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res.setHeader('Transfer-Encoding', 'chunked');
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for (const file of files) {
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// Send a marker for the start of a new file
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res.write(JSON.stringify({
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type: 'file_start',
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file: file.path
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}) + '\n');
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const { payload } = await processSingleFile({
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file,
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model,
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systemPrompt,
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originalPrompt,
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stream: true,
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requestData
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});
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const ollamaResponse = await axios.post(payload.ollamaUrl, payload, {
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responseType: 'stream'
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});
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for await (const chunk of ollamaResponse.data) {
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try {
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const data = JSON.parse(chunk.toString());
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res.write(JSON.stringify({
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...data,
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file: file.path
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}) + '\n');
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} catch (err) {
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console.error('Error parsing chunk:', err);
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}
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}
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// Send a marker for the end of this file
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res.write(JSON.stringify({
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type: 'file_end',
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file: file.path
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}) + '\n');
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}
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res.end();
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return null;
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}
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// For non-streaming, process files in parallel
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const responses = await Promise.all(
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files.map(file => processSingleFile({
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file,
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model,
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systemPrompt,
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originalPrompt,
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stream: false,
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requestData
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}))
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);
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return {
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success: true,
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files: responses.map(r => ({
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path: r.file,
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content: r.content,
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error: r.error,
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model: r.model
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}))
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};
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}
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/**
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* Express handler for /api/generate
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* Handles both chat-style and prompt-style requests to Ollama
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@ -174,9 +340,10 @@ async function handleGenerate(req, res) {
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},
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};
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// Validate request body
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if (!requestData.body) {
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await savePrompt({
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model: requestData.body?.model || "codellama:7b",
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model: "codellama:7b", // Use default model directly since body is null
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prompt: null,
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messages: null,
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request_data: requestData,
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@ -184,46 +351,81 @@ async function handleGenerate(req, res) {
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throw new Error("Request body is required");
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}
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const isChatRequest =
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requestData.body.messages && Array.isArray(requestData.body.messages);
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if (
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isChatRequest &&
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(!requestData.body.messages.length ||
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!requestData.body.messages[0].content)
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) {
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throw new Error(
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"Chat request must include at least one message with content",
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);
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}
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if (!isChatRequest && !requestData.body.prompt) {
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throw new Error("Prompt request must include a prompt field");
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}
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const isChatRequest = requestData.body.messages && Array.isArray(requestData.body.messages);
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const model = requestData.body.model || "codellama:7b";
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const stream =
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requestData.body.stream !== undefined ? requestData.body.stream : true;
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const stream = requestData.body.stream !== undefined ? requestData.body.stream : true;
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const systemPrompt = requestData.body.system;
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// Get the content to process (either from messages or prompt)
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let contentToProcess;
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let originalPrompt;
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if (isChatRequest) {
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const lastUserMessage = requestData.body.messages
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.filter(m => m.role === 'user')
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.pop();
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if (!lastUserMessage) {
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throw new Error("No user message found in chat request");
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}
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contentToProcess = lastUserMessage.content;
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originalPrompt = contentToProcess;
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} else {
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if (!requestData.body.prompt) {
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throw new Error("Prompt request must include a prompt field");
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}
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contentToProcess = requestData.body.prompt;
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originalPrompt = contentToProcess;
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}
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// Process the content to inject file contents
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contentToProcess = await injectFileContent(contentToProcess);
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// Extract file blocks
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const fileBlocks = extractFileBlocks(contentToProcess);
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// If we have multiple files, use the multi-file processing
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if (fileBlocks.length > 1) {
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const result = await handleMultiFileProcessing({
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files: fileBlocks,
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model,
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systemPrompt,
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originalPrompt,
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stream,
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requestData,
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res
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});
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if (!stream && result) {
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res.json(result);
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}
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return;
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}
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// For single file or no files, proceed with normal processing
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const cleanedRequest = {
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model,
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stream,
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...(isChatRequest
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? {
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messages: await Promise.all(
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requestData.body.messages.map(async (msg) => ({
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role: msg.role || "user",
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content:
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msg.role === "user" && typeof msg.content === "string"
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? await injectFileContent(msg.content.trim())
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: msg.content.trim(),
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})),
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),
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messages: requestData.body.messages.map((msg, index) => ({
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role: msg.role || "user",
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content: index === requestData.body.messages.length - 1 && msg.role === 'user'
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? contentToProcess
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: msg.content.trim(),
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})),
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}
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: {
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prompt: await injectFileContent(requestData.body.prompt.trim()),
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prompt: contentToProcess,
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}),
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};
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if (systemPrompt) {
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cleanedRequest.system = systemPrompt;
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}
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// Save the complete request data to database
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await savePrompt({
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model,
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prompt: isChatRequest ? null : cleanedRequest.prompt,
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@ -231,6 +433,7 @@ async function handleGenerate(req, res) {
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request_data: requestData,
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});
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// Handle the request with Ollama
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const ollamaUrl = isChatRequest
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? "http://localhost:11434/api/chat"
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: "http://localhost:11434/api/generate";
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@ -257,13 +460,10 @@ async function handleGenerate(req, res) {
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});
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ollamaResponse.data.on("error", async (err) => {
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await saveError({
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error_message: err.message,
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details: {
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stack: err.stack,
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request: requestData,
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cleaned_request: cleanedRequest,
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},
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await logError(err, {
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operation: 'Ollama stream error',
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request: requestData,
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cleaned_request: cleanedRequest
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});
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if (!res.headersSent) {
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res.status(500).json({
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@ -285,23 +485,14 @@ async function handleGenerate(req, res) {
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res.status(ollamaResponse.status).json(ollamaResponse.data);
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}
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} catch (error) {
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const errorDetails = {
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message: error.message,
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request: requestData,
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response: error.response?.data,
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stack: error.stack,
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};
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await saveError({
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error_message: error.message,
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details: errorDetails,
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await logError(error, {
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operation: 'Generate handler',
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request: requestData
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});
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if (!res.headersSent) {
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res.status(error.response?.status || 500).json({
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error: error.response?.status
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? "Ollama API Error"
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: "Internal Server Error",
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error: error.response?.status ? "Ollama API Error" : "Internal Server Error",
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message: error.message,
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details: error.response?.data || undefined,
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});
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