Local AI (with Docs Query Engine) running just on Laptops!!!

Step-by-Step Tutorial: Implementing Local Document Processing and Retrieval

1. Install Required Libraries:

   • Install the following libraries:
     • llama index
     • Transformers Lang chain
     • Sentence Transformers Lang chain
   • Ensure that llama index is present in your local folder.

2. Install and Run OLama:

   • Install OLama, a large language model.
   • Start OLama by running the command: o Lama run Lama 2 .
   • OLama should be running in the background with an endpoint available for use.

3. Define Data Directory:

   • Use the code snippet from llama index import simple directory reader to define the folder where your data is stored.
   • This code reads and assigns all supported files (e.g., PDF, text) in the specified directory as documents.

4. Download Embedding Model:

   • Download the embedding model using from llama index.embeddings import hugging face embedding .
   • This model allows you to create embeddings for querying with the large language model.

5. Download OLama Query Engine Pack:

   • Download the OLama query engine pack from the llama pack.
   • Save the downloaded pack in a specific directory.
   • Use from _pack.Bas import AMA query engine pack to connect OLama with the query engine pack.

6. Data Processing and Retrieval:

   • Run the query engine pack using _pack.run() to start questioning.
   • Ask questions such as invoice number, seller tax ID, client tax ID, etc., to retrieve specific information from the documents.

7. Evaluate Responses:

   • Assess the accuracy and speed of responses based on the questions asked.
   • Note that response times may vary based on machine capabilities and the complexity of queries.

8. Improving Retrieval Accuracy:

   • Experiment with different embedding models to enhance the accuracy of responses.
   • Analyze incorrect responses and consider adjustments to improve retrieval accuracy.

9. Generate Custom Content:

   • Utilize the query engine to create custom content based on the document data.
   • For example, generate a tweet by asking a question like "write a viral tweet based on the document."

10. Final Remarks:

    • The tutorial demonstrates a local AI solution for document processing and retrieval without relying on external APIs or internet connectivity.
    • The provided code and techniques enable you to leverage local resources for data analysis and content generation.

By following these steps, you can set up and utilize a local document processing and retrieval system using the described tools and methodologies. Feel free to refer back to this tutorial for guidance and further exploration of the capabilities offered by this local AI solution.