Summary of "How to chat with your PDFs using local Large Language Models [Ollama RAG]"
The video demonstrates how to create a local RAG (Retrieval-Augmented Generation) system using Ollama and Python, allowing users to chat with their PDF documents without connecting to the internet. The benefits of a local RAG system include the ability to work with sensitive documents and maintain privacy. The methodology involves loading PDF files, extracting content, embedding text, querying a Vector database, utilizing a multiquery retriever module, passing questions to a local language model, and retrieving responses.
Methodology
- Load PDF files using unstructured PDF loader from Longchain.
- Extract content from PDF files and split characters using Longchain functions.
- Embed text using an embedding model (such as Nomic) and load them into a Vector database (e.g., ChromaDB).
- Query the Vector database with user questions using a multiquery retriever module from Longchain to generate additional questions for context.
- Pass the questions and context to a local language model (LLM) like Mistol using a RAG prompt with Longchain functions.
- Retrieve and display responses from the LLM based on the given prompt.
Speakers
- The speaker of the video is not clearly identified throughout the subtitles.
