Local LLM

Exploring Local LLMs with Context Augmentation from External Data

Objective:

This lab provides students with hands-on experience in deploying and enhancing the capabilities of local Large Language Models (LLMs). Students will set up a local environment, load pre-trained LLMs, and extend their functionality by incorporating external context from a PDF file. Additionally, they will explore the use of semantic embeddings and vector databases for efficient context retrieval. By comparing query results with and without external context, students will understand the practical applications and limitations of LLMs in real-world scenarios.

Lab Overview

1. Introduction to Local LLMs:

   • Understand the steps of deploying local LLMs compared to cloud-based solutions.

   • Familiarize yourself with model types such as Meta-Llama-3-8B-Instruct-Q4_K_S.gguf and/or Phi-3-mini-4k-instruct.Q6_K.gguf.

2. Local Environment Setup:

   • Install and configure the tools needed to deploy a local LLM environment .

3. Working with Semantic Embeddings and Vector Databases:

   • Generate semantic embeddings for the PDF content.

   • Use a vector database like FAISS to store and retrieve relevant context for queries.

4. Augmenting LLM Responses with External Context:

   • Extend the LLM's responses by feeding additional context from the PDF.

   • Compare the accuracy and relevance of query responses with and without context augmentation.

5. Optional Exercise:

   • Ingest data from a JSON file and query the LLM to evaluate its ability to reason and respond based on structured data.

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Lab Workflow

1. Environment Setup:

   • Install the required dependencies (e.g., transformers, sentence-transformers, faiss-cpu).

   • Download pre-trained LLMs (Meta-Llama-3-8B-Instruct-Q4_K_S.gguf or Phi-3-mini-4k-instruct-Q6_K.gguf) and configure them for local use.

2. Load and Test the LLM:

   • Load the model into memory and test its base capabilities with simple queries.

   • Observe the LLM’s performance without additional context.

3. Prepare the PDF for Context Retrieval:

   • Extract the text content from the PDF using tools like PyPDF2.

   • Generate semantic embeddings for the extracted text using a library like sentence-transformers.

4. Set Up FAISS Vector Database:

   • Create a FAISS index to store the semantic embeddings.

   • Implement a query pipeline to retrieve the most relevant context from the PDF based on the user’s query.

5. Query the LLM with Augmented Context:

   • Pass retrieved PDF-based context to the LLM along with the query.

   • Compare query responses:

     • Without context augmentation.

     • With PDF-based context.

6. Optional: Ingest JSON Data:

   • Load a JSON file into memory and preprocess the data for querying.

   • Evaluate the LLM’s ability to respond accurately to queries about the JSON data.

7. Reflection and Reporting:

   • Reflect on the challenges and benefits of deploying and extending local LLMs.

   • Prepare a report comparing query results, highlighting the importance of context augmentation for LLM performance.

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Learning Objectives

1. Understand Local LLM Deployment

• Learn how to set up a local environment for deploying Large Language Models.

• Explore the differences between deploying models locally versus using cloud-based solutions.

2. Generate and Use Semantic Embeddings

• Understand the concept of semantic embeddings and their role in context retrieval.

• Use a vector database like FAISS to store and retrieve context efficiently.

3. Extend LLMs with Contextual Data

• Learn how to augment LLM responses by integrating additional context from external sources (e.g., PDFs).

• Compare the accuracy and relevance of query results with and without external context.

4. Explore Real-World Use Cases

• Understand how local LLMs can be extended for real-world applications such as document analysis and knowledge retrieval.

• Evaluate the limitations and capabilities of LLMs in handling structured data like JSON.

5. Develop Problem-Solving Skills

• Gain practical experience in configuring and extending AI tools.

• Reflect on the challenges of integrating external data sources into LLM workflows.

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Outcome

By the end of this lab, students will have hands-on experience in deploying and enhancing the capabilities of local LLMs. They will understand how to augment LLMs with external context and apply them to real-world scenarios, gaining skills that are critical for advancing AI applications in various domains.