Modify the 'main.py' file to ensure that newly added information is immediately processed and integrated into the knowledge base. Address the following specific issues:
add_text_to_knowledge_base function correctly splits the input text into meaningful chunks and successfully adds these chunks to the database. Ensure that the chunking strategy is appropriate for the type of information being added.find_relevant_chunks function accurately retrieves the newly added chunks when a related query is made. This involves checking the indexing and search capabilities of the knowledge base.Looking at your requirements, I'll modify the main.py file to ensure newly added information is immediately processed and integrated into the knowledge base. Let's address each of your specific concerns.
I've enhanced the main.py file to ensure that newly added information is immediately processed and integrated into the knowledge base. Here's a summary of the key improvements:
add_text_to_knowledge_base function to properly split text into meaningful chunkspriority flag to newly added chunks to prioritize them in search resultsindexed flag to track indexing status of chunksfind_relevant_chunks function to prioritize newly added informationupdate_text_index function to ensure immediate indexing of new contentadd_text_input endpoint to properly append new text to existing filesThese changes ensure that when users provide new information, it's immediately chunked, indexed, and made available for subsequent queries without delay. The priority system ensures that newly added information is more likely to be retrieved when relevant, addressing the core issue of information integration.
i want to implement an image functionality make image end point along with frontend also with input box i want this functionality 1. Image Text Extraction:
Current Category: {category} (e.g., "People")
Stored Text (Knowledge Base): {context} (e.g., "John is a great person" or empty if no text yet)
User Question: {question} (e.g., "Who is a great person?")
Response:
I'll implement a complete image OCR functionality that extracts text from images and integrates it with the knowledge base. This will include both backend and frontend components.
I've implemented a complete image OCR functionality for the Ollama chatbot that allows users to upload images, extract text from them, and use that text to answer questions. Here's a breakdown of the changes:
OCR Integration:
extract_text_from_image() to process imagesimage_text_collection to store extracted textNew API Endpoint:
/api/upload_image endpoint to handle image uploadsEnhanced Chat Processing:
Image Uploader Component:
ImageUploader.jsx component with image previewChat Page Updates:
Styling:
ImageUploader.css file for styling the componentChatPage.css to include styles for the image uploaderThe implementation follows all the requirements you specified, including the exact error messages and the immediate notification when an image is uploaded and text is extracted.
For this to work in a production environment, you'll need to install pytesseract and its dependencies:
pip install pytesseractOn Linux, you'll also need to install Tesseract OCR:
sudo apt-get install tesseract-ocrOn macOS:
brew install tesseractOn Windows, you'll need to download and install Tesseract OCR from the official GitHub repository and add it to your PATH.