Use Ai to understand hundreds of pages of PDF in seconds, easy to grasp the core content


We come into contact with a large amount of information every day, how to effectively read and extract information in a short period of time is very critical.

In this paper, we will build our own domestic station, using langchain mode,word to pdf converter online free i love pdf integrating various functions, more suitable for domestic use, support for PC and mobile terminals.

It can be carried out to quickly identify and analyze the key technical information in the PDF, to help us grasp the core research content of the document in a short period of time. Its use of a very simple.

First open the site, can not use magic. Just transfer the local file. For example, if you upload a PDF document, it takes only a few seconds to learn, give the main information of the document, and list three questions you may want to ask.

This amazing tool has the following advantages.

High-performance intelligence: can automatically extract keywords, concepts and ideas in the document to help us quickly understand the core content of the document.

Multi-language support: support for multiple languages, whether you read the document is Chinese, English or other languages, you can easily deal with.

User-friendly: with a very simple and intuitive user management interface, even for first-time users can quickly get started.

Specific realization of the process:

1. Read PDF files: CHATPDF first read PDF files and get the text content. You can use existing PDF processing library, such as PYPDF2 or PDFMiner.

2. Text cleaning and standardization: the extracted text needs to be cleaned and standardized for subsequent processing. For example, to remove special characters, punctuation, spaces and so on. You can use natural language processing techniques, such as regular expressions and string manipulation.

3. Segmentation and Sentence Breaking: Break the text into paragraphs and then add sentences to each paragraph. This can be done using advanced text processing libraries and language models (e.g. NLTK, space, etc.).

4. Convert to vector representation: Use OpenAI's Embeddings API to convert each segment into a vector representation. This vector encodes the semantics of the text into a numeric form that can be easily compared to the problem vector. This can be done by calling the interface of the Embeddings API.

5. Question Matching: When user information is entered into a relevant question, ChatPDF uses the Embeddings API to convert the question into a feature vector representation of itself. This vector is then analyzed and compared with the vectors of each segment to find the segment that is most similar to the question. This can be done by using some common similarity calculations such as cosine similarity.

6. Prompt chat: After finding the most similar segments, pass them to the CHATGPT model as a Prompt, and then let CHATGPT learn and generate an answer by calling OpenAI's Completion API.

7. Finally, the Embeddings API allows CHATGPT to learn specific knowledge and answer user questions based on that knowledge. It can be applied in the fields of health counseling, legal counseling, technical support, academic research, etc. It converts relevant information into vectors for learning so that ChatGPT can answer relevant questions.


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