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

1. Load PDF files using unstructured PDF loader from Longchain.
2. Extract content from PDF files and split characters using Longchain functions.
3. Embed text using an embedding model (such as Nomic) and load them into a Vector database (e.g., ChromaDB).
4. Query the Vector database with user questions using a multiquery retriever module from Longchain to generate additional questions for context.
5. Pass the questions and context to a local language model (LLM) like Mistol using a RAG prompt with Longchain functions.
6. 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.