I want to add some more info on this as some may think that the reference (name of the manual, the date of publishing, the section and paragraph) is part of the information used with RAG and I did not envision it that way and did not give some of the needed details. I did envision using metadata; for me data and metadata have very different meanings and usage.
When I asked ChatGPT about this it gave a reply that was along the lines I envisioned but with a few more specifics like Elasticsearch.
Prompt
How can one use RAG (Retrieval Augmented Generation) with a PDF manual for a product and get the title, publication date, page and paragraph number from the manual as a reference with the noted information?
Completion
Using Retrieval Augmented Generation (RAG) with a PDF manual to extract specific information like the title, publication date, page, and paragraph number involves several steps. RAG, developed by Facebook AI, combines the power of retrieval (fetching relevant documents or data) with language generation, enabling the model to reference and incorporate external knowledge. Here’s a general approach to use RAG with a PDF manual:
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Convert the PDF Manual to a Text Format:
- First, you need to convert the PDF manual into a text format that can be processed. Tools like Adobe Acrobat, pdftotext (part of the Xpdf project), or Python libraries such as PyPDF2 or PDFMiner can be used for this purpose.
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Preprocess the Text:
- Once you have the text, you may need to preprocess it. This includes removing unnecessary formatting, handling special characters, and possibly segmenting the text into sections, pages, and paragraphs with identifiable markers.
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Indexing the Manual:
- For RAG to work effectively, the content of the manual needs to be indexed. This means creating a database or a structured format where each piece of information (like a paragraph or a section) is stored with its corresponding metadata (page number, paragraph number, etc.). Elasticsearch or a similar search engine can be used for this purpose.
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Integrating RAG:
- With your manual indexed, you can now integrate RAG. RAG typically combines a transformer-based language model (like BERT or RoBERTa) for retrieval and a sequence-to-sequence model (like BART or T5) for generation. You’ll need to set up RAG to query your indexed manual. This might involve customizing the retrieval component to work with your search engine.
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Querying the System:
- When you query the RAG system, the retrieval component first finds the most relevant sections of the manual based on your query. This information is then passed to the generation component, which formulates a response.
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Extracting Reference Information:
- To get the title, publication date, page, and paragraph number, you’ll need to ensure that these details are either included in the retrieved segments or that the system can reference back to the indexed data to fetch this metadata. This might require additional logic in your system.
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Post-Processing for References:
- After the response is generated, you may need a post-processing step to format the references correctly, ensuring that the title, publication date, page, and paragraph number are accurately included.
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Fine-Tuning and Evaluation:
- Depending on your specific requirements and the complexity of the manual, you might need to fine-tune the RAG model on similar types of documents or queries. Evaluate the system’s performance and adjust your approach as needed.
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Integration with User Interface:
- Finally, if this system is meant for end-users, integrate it with a user-friendly interface where users can input queries and receive responses with the referenced information.
Remember that the effectiveness of this approach heavily depends on the quality of the indexed data, the capabilities of the retrieval model, and how well the generation model has been trained or fine-tuned for your specific use case.
Note: While I do use ChatGPT to generate information I also do not post information from ChatGPT if I think it is wrong. In other words ChatGPT can be useful but you have to know if the information given is correct or not. While I have not implemented exactly what ChatGPT notes here, I have enough experience with this that I would use this or a deviation of it.