Web Rag System #242
Web Rag System #242
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| const worker = new Worker( | ||
| new URL('./embeddings.worker.ts', import.meta.url), | ||
| { type: 'module' } | ||
| ); |
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Comment: Potential performance issue with embedding generation
Solution: Modify the generateEmbedding function to use a singleton pattern for the embedding pipeline instance. This will ensure that the worker is only initialized once and reused for subsequent calls.
!! Make sure the following suggestion is correct before committing it !!
| const worker = new Worker( | |
| new URL('./embeddings.worker.ts', import.meta.url), | |
| { type: 'module' } | |
| ); | |
| const embedder = await EmbeddingPipelineSingleton.getInstance(x =>{ | |
| self.postMessage({status: 'progress', progress: x}); | |
| }); |
| const allDocs = await worker.db.query(` | ||
| SELECT path, content, embedding | ||
| FROM documents | ||
| WHERE embedding IS NOT NULL | ||
| `); |
There was a problem hiding this comment.
Comment: Potential SQL injection vulnerability
Solution: Use parameterized queries or a query builder library to safely construct the SQL query and prevent SQL injection attacks.
!! Make sure the following suggestion is correct before committing it !!
| const allDocs = await worker.db.query(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `); | |
| const allDocs = await worker.db.prepare(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `).all(); |
| <li key={index}> | ||
| <a | ||
| href={source.url} | ||
| href={`${window.location.origin}/en/${source.url.replace('*%20', '')}?utm_source=akiradocs`} |
There was a problem hiding this comment.
Comment: Potential security vulnerability in the AIResponseSources component
Solution: Use a more secure way to construct the URL for the source links, such as using the new URL() constructor and the process.env.NEXT_PUBLIC_BASE_URL environment variable.
!! Make sure the following suggestion is correct before committing it !!
| href={`${window.location.origin}/en/${source.url.replace('*%20', '')}?utm_source=akiradocs`} | |
| <a | |
| href={new URL(`/en/${source.url.replace('*%20', '')}?utm_source=akiradocs`, process.env.NEXT_PUBLIC_BASE_URL).toString()} | |
| target="_blank" | |
| rel="noopener noreferrer" | |
| className="text-sm text-indigo-600 dark:text-indigo-400 hover:underline" | |
| > |
| const worker = new Worker( | ||
| new URL('./embeddings.worker.ts', import.meta.url), | ||
| { type: 'module' } | ||
| ); |
There was a problem hiding this comment.
Comment: Potential performance issue with embedding extraction
Solution: Modify the generateEmbedding function to use a singleton pattern for the embedding pipeline instance. This will ensure that the same instance is reused across multiple requests, improving overall performance.
!! Make sure the following suggestion is correct before committing it !!
| const worker = new Worker( | |
| new URL('./embeddings.worker.ts', import.meta.url), | |
| { type: 'module' } | |
| ); | |
| const embedderInstance = await EmbeddingPipelineSingleton.getInstance(); | |
| // Use embedderInstance to generate the embedding |
| const allDocs = await worker.db.query(` | ||
| SELECT path, content, embedding | ||
| FROM documents | ||
| WHERE embedding IS NOT NULL | ||
| `); |
There was a problem hiding this comment.
Comment: Potential SQL injection vulnerability
Solution: Use parameterized queries or a query builder library to safely construct the SQL query and prevent SQL injection attacks.
!! Make sure the following suggestion is correct before committing it !!
| const allDocs = await worker.db.query(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `); | |
| const allDocs = await worker.db.prepare(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `).all(); |
| [&>*:first-child]:mt-0 | ||
| [&>p>strong]:block [&>p>strong]:mt-8 [&>p>strong]:mb-4 [&>p>strong]:text-lg | ||
| [&>p:has(>strong:only-child)]:m-0"> | ||
| <MemoizedMarkdown content={response} /> |
There was a problem hiding this comment.
Comment: Potential XSS vulnerability in the AI response
Solution: Use a sanitization library like DOMPurify to sanitize the AI response before rendering it to the page, to prevent potential XSS attacks.
!! Make sure the following suggestion is correct before committing it !!
| <MemoizedMarkdown content={response} /> | |
| import DOMPurify from 'dompurify'; | |
| <div dangerouslySetInnerHTML={{__html: DOMPurify.sanitize(response)}}/> |
| const allDocs = await worker.db.query(` | ||
| SELECT path, content, embedding | ||
| FROM documents | ||
| WHERE embedding IS NOT NULL | ||
| `); |
There was a problem hiding this comment.
Comment: Potential performance issue with database query
Solution: Consider adding pagination or limiting the number of results returned to improve performance. You could also explore indexing the embedding column to speed up the similarity score calculations.
!! Make sure the following suggestion is correct before committing it !!
| const allDocs = await worker.db.query(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `); | |
| const allDocs = await worker.db.query(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| LIMIT 100 | |
| `); |
| const allDocs = await worker.db.query(` | ||
| SELECT path, content, embedding | ||
| FROM documents | ||
| WHERE embedding IS NOT NULL | ||
| `); |
There was a problem hiding this comment.
Comment: Potential SQL injection vulnerability
Solution: Use parameterized queries or a query builder library to safely construct the SQL query and prevent SQL injection attacks.
!! Make sure the following suggestion is correct before committing it !!
| const allDocs = await worker.db.query(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `); | |
| const allDocs = await worker.db.prepare(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `).all(); |
| // Clean the embedding string and parse it | ||
| const cleanEmbeddingStr = doc.embedding.replace(/[\[\]]/g, ''); // Remove square brackets | ||
| const embeddingArray = cleanEmbeddingStr | ||
| .split(',') | ||
| .map((val: string) => { | ||
| const parsed = parseFloat(val.trim()); | ||
| if (isNaN(parsed)) { | ||
| console.error(`Invalid embedding value found: "${val}"`); | ||
| } | ||
| return parsed; | ||
| }); |
There was a problem hiding this comment.
Comment: Potential performance issue with embedding parsing
Solution: Consider using a more efficient parsing method, such as splitting the string on commas and parsing the values directly, instead of the current approach of replacing brackets and splitting on commas.
!! Make sure the following suggestion is correct before committing it !!
| // Clean the embedding string and parse it | |
| const cleanEmbeddingStr = doc.embedding.replace(/[\[\]]/g, ''); // Remove square brackets | |
| const embeddingArray = cleanEmbeddingStr | |
| .split(',') | |
| .map((val: string) => { | |
| const parsed = parseFloat(val.trim()); | |
| if (isNaN(parsed)) { | |
| console.error(`Invalid embedding value found: "${val}"`); | |
| } | |
| return parsed; | |
| }); | |
| const embeddingArray = doc.embedding.split(',').map(parseFloat); |
|
|
||
| class EmbeddingPipelineSingleton { | ||
| static task: PipelineType = 'feature-extraction'; | ||
| static model = 'sauravpanda/gte-small-onnx'; |
There was a problem hiding this comment.
Comment: Potential security issue with hardcoded API key
Solution: Store the API key securely, such as in environment variables or a secure key management service, and load it dynamically at runtime. Avoid hardcoding sensitive information in the codebase.
!! Make sure the following suggestion is correct before committing it !!
| static model = 'sauravpanda/gte-small-onnx'; | |
| static model = process.env.HUGGING_FACE_MODEL || 'sauravpanda/gte-small-onnx'; |
| serverMode: "full", | ||
| requestChunkSize: 4096, | ||
| url: "/context/docs.db" | ||
| } |
There was a problem hiding this comment.
Comment: Potential security issue with hardcoded database path
Solution: Store the database path securely, such as in environment variables or a secure configuration service, and load it dynamically at runtime. Avoid hardcoding sensitive information in the codebase.
!! Make sure the following suggestion is correct before committing it !!
| serverMode: "full", | |
| requestChunkSize: 4096, | |
| url: "/context/docs.db" | |
| } | |
| config:{ | |
| serverMode: "full", | |
| requestChunkSize: 4096, | |
| url: process.env.DATABASE_PATH || "/context/docs.db" | |
| } |
| const allDocs = await worker.db.query(` | ||
| SELECT path, content, embedding | ||
| FROM documents | ||
| WHERE embedding IS NOT NULL |
There was a problem hiding this comment.
Comment: Potential SQL Injection Risk
Solution: Use parameterized queries to prevent SQL injection.
!! Make sure the following suggestion is correct before committing it !!
| const allDocs = await worker.db.query(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| const allDocs = await worker.db.query(` | |
| SELECT path, content, embedding | |
| FROM documents | |
| WHERE embedding IS NOT NULL | |
| `,[]); |
| const cleanEmbeddingStr = doc.embedding.replace(/[\[\]]/g, ''); // Remove square brackets | ||
| const embeddingArray = cleanEmbeddingStr |
There was a problem hiding this comment.
Comment: Potential performance bottleneck in embedding generation.
Solution: Consider implementing a caching mechanism for embeddings to improve performance on repeated requests.
!! Make sure the following suggestion is correct before committing it !!
| const cleanEmbeddingStr = doc.embedding.replace(/[\[\]]/g, ''); // Remove square brackets | |
| const embeddingArray = cleanEmbeddingStr | |
| const embedding = await cache.get(content) || await generateEmbedding(content); |
|
|
||
| worker.addEventListener('error', (error) => { | ||
| reject(error); |
There was a problem hiding this comment.
Comment: Potential exposure of sensitive information in error logs.
Solution: Log only necessary information and avoid logging sensitive data.
!! Make sure the following suggestion is correct before committing it !!
| worker.addEventListener('error', (error) => { | |
| reject(error); | |
| console.error('Error generating embedding:', error.message); |
| } | ||
| }); | ||
|
|
||
| worker.addEventListener('error', (error) => { |
There was a problem hiding this comment.
Comment: Potential exposure of sensitive data in error messages.
Solution: Sanitize error messages before logging to avoid exposing sensitive data.
!! Make sure the following suggestion is correct before committing it !!
| worker.addEventListener('error', (error) => { | |
| console.error('Error generating embedding: An error occurred.'); |
🔍 Code Review Summary❗ Attention Required: This push has potential issues. 🚨 Overview
🚨 Critical Issuessecurity (5 issues)1. Potential SQL Injection Risk📁 File: docs/src/lib/aisearch/dbWorker.ts 💡 Solution: Current Code: const allDocs = await worker.db.query(`SELECT path, content, embedding FROM documents WHERE embedding IS NOT NULL`);Suggested Code: const allDocs = await worker.db.query(`SELECT path, content, embedding FROM documents WHERE embedding IS NOT NULL`,[]);2. Potential SQL Injection Risk📁 File: docs/src/lib/aisearch/dbWorker.ts 💡 Solution: Current Code: const allDocs = await worker.db.query(`SELECT path, content, embedding FROM documents WHERE embedding IS NOT NULL`);Suggested Code: const allDocs = await worker.db.query(`SELECT path, content, embedding FROM documents WHERE embedding IS NOT NULL`,[]);3. Database Connection Management📁 File: docs/scripts/extract-docs-context.js 💡 Solution: Current Code: const db = sqlite3(dbPath);Suggested Code: const db = sqlite3(dbPath,{fileMustExist: true}); // Consider using a connection pool.4. Potential exposure of sensitive information in console logs.📁 File: packages/akiradocs/scripts/extract-docs-context.js 💡 Solution: Current Code: console.error('Error generating embedding:', error);Suggested Code: console.error('Error generating embedding');5. Database connection is not closed in all scenarios.📁 File: packages/akiradocs/scripts/extract-docs-context.js 💡 Solution: Current Code: db.close();Suggested Code: finally{db.close();}Test Cases18 file need updates to their tests. Run
Useful Commands
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Comprehensive Enhancement of AI Search and Next.js Configuration
Improve AI search capabilities and optimize Next.js configuration for better performance and functionality.
@huggingface/transformersand disabled certain webpack aliases.1.0.52to1.0.53and added several new dependencies.EmbeddingPipelineclass for managing embedding processes.These changes collectively enhance the accuracy, responsiveness, and overall user experience of the AI search functionality while ensuring Next.js is configured for optimal performance with the latest libraries.
Original Description
# Comprehensive Update on Next.js Configuration and AI Search Enhancements**
Consolidate improvements in Next.js configuration and enhance AI search functionality through embedding generation and database integration.
@huggingface/transformersand disabled unnecessary aliases.EmbeddingPipelineclass for efficient embedding generation and integrated cosine similarity for search relevance.**
These changes collectively enhance the performance, accuracy, and usability of the AI search feature and improve the overall configuration of the Next.js application.
Original Description
# Enhance AI Search Functionality with Embeddings**
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Introduce an embedding-based search feature to improve the relevance and accuracy of the AI-powered search functionality in the AkiraDocs documentation.
EmbeddingPipelineclass to handle the generation of document embeddings using a pre-trained transformer model.ReactMarkdowncomponent to prevent unnecessary re-renders.**
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These changes significantly enhance the AI search functionality, providing users with more relevant and accurate results by leveraging document embeddings, leading to a better overall experience when searching the AkiraDocs documentation.
Original Description
# Comprehensive Update to Next.js Configuration and AI Search Functionality**
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Integrate enhancements to Next.js configuration and improve AI search performance through a new database approach.
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These updates are set to improve build processes, enhance AI search reliability, and deliver a better user experience with more accurate results.
Original Description
# Enhance Next.js Configuration and Expand Functionality**
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Improve Next.js configuration, update dependencies, and introduce new capabilities for the project.
@huggingface/transformersand disabledsharpandonnxruntime-nodealiases.package.jsonto include new dependencies:@huggingface/transformers,@xenova/transformers,better-sqlite3,remark-gfm,sharp,sql.js-httpvfs, andsqlite-vss.**
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These changes improve module resolution, expand the project's capabilities with new libraries, and enhance the AI search functionality, resulting in more accurate and responsive search results.
Original Description
# Comprehensive Update on Next.js Configuration and AI Search Enhancements**
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Streamline Next.js configuration and enhance AI search functionality through improved dependencies and document embeddings.
sharp,onnxruntime-node, and resolved@huggingface/transformersto local paths for better module resolution.package.jsonwith new dependencies:@huggingface/transformers,@xenova/transformers,better-sqlite3,remark-gfm,sharp,sql.js-httpvfs, andsqlite-vss.**
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These changes collectively enhance module handling, project performance, and the accuracy of AI search responses, leading to a more efficient and user-friendly experience.
Original Description
# Comprehensive Update on Next.js Configuration and AI Search Enhancements**
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Consolidate improvements in Next.js configuration and introduce advanced AI search capabilities with embedding and database integration.
EmbeddingPipelineclass for embedding generation using transformer models.dbWorkerfor asynchronous database interactions.**
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These updates collectively enhance the project's build process, dependency management, and significantly improve AI search capabilities through advanced document relevance and efficient storage.
Original Description
# Enhance Next.js Configuration and AI Search Functionality**
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Optimize the Next.js configuration for improved module resolution and ES module support, while also enhancing the AI search feature with a SQLite database and embedding-based document retrieval.
sharp,onnxruntime-node) and enable ES module support viaesmExternals.@huggingface/transformersto resolve from the localnode_modulesdirectory.**
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These changes will improve the build performance, reduce the overall bundle size, and enhance the AI search experience by providing more accurate and relevant responses to user queries.
Original Description
## 🔍 DescriptionType
Checklist