100% Private & Local PDF ChatBot (without langchain)

TL;DR

Learn how to build a PDF chatbot from scratch using PDF Miner and Sentence Transformer libraries.

Transcript

hello everyone and welcome to my YouTube channel today we are going to build a PDF chatbot today we are not going to use open AI or chat apt or any other libraries that office create a lot of code we are going to write the code on our own uh for the pre-processing part and we are going to use only two libraries one of them is PDF Miner and another ... Read More

Key Insights

• 🤳 The PDF chatbot is built without relying on external libraries like OpenAI or ChatGPT, making the code more self-contained.
• 📚 The PDF Miner library is used to extract text from PDF files, while the Sentence Transformer library is used for creating sentence embeddings.
• 😚 The search function utilizes cosine similarity to find the closest matches to a given query.
• 😜 The Cross Encoder model is used to re-rank the search results and improve the relevance of the chatbot's responses.
• 😑 The code can be easily extended to work with different types of documents and perform more advanced pre-processing techniques.
• 👨‍🔬 The chatbot can handle multiple queries and returns the top five search results for each query.
• 👨‍💻 The code can be run locally or using the Docker server for text generation.

Questions & Answers

Q: What libraries are used to build the PDF chatbot?

The PDF chatbot is built using the PDF Miner library for text extraction and Sentence Transformer library for creating sentence embeddings.

Q: How are the extracted paragraphs preprocessed?

The extracted paragraphs are preprocessed by removing white spaces and splitting them into sentences.

Q: How does the search function work?

The search function takes a query and finds the closest matches based on the cosine similarity between the query embedding and paragraph embeddings.

Q: What is the purpose of the Cross Encoder model?

The Cross Encoder model is used to re-rank the search results and improve the accuracy of the chatbot.

Summary & Key Takeaways

• Utilize PDF Miner library to extract text from PDF files and preprocess the text by removing white spaces and splitting it into sentences.

• Use Sentence Transformer library to create sentence embeddings for the extracted paragraphs.

• Implement a search function that takes a query and returns the closest matches based on cosine similarity between the query embedding and paragraph embeddings.

• Re-rank the search results using the Cross Encoder model and display the top five results.