Retrieval augmented generation with quantized LLM

Retrieval augmented generation (RAG) demos with Mistral, Zephyr, Phi, Gemma, Llama, Aya-Expanse

The demos use quantized models and run on CPU with acceptable inference time. They can run offline without Internet access, thus allowing deployment in an air-gapped environment.

The demos also allow user to

• apply propositionizer to document chunks
• perform reranking upon retrieval
• perform hypothetical document embedding (HyDE)

🔧 Getting Started

You will need to set up your development environment using conda, which you can install directly.

conda env create --name rag python=3.11
conda activate rag
pip install -r requirements.txt

We shall use unstructured to process PDFs. Refer to nstallation Instructions for Local Development.

You would also need to download punkt_tab and averaged_perceptron_tagger_eng from nltk.

import nltk
nltk.download('punkt_tab')
nltk.download('averaged_perceptron_tagger_eng')

Note that we shall only use strategy="fast" in this demo. WIP for extraction of tables from PDFs.

Activate the environment.

conda activate rag

🧠 Use different LLMs

Using a different LLM might lead to poor responses and even fail to output a response. It will require testing, prompt engineering and code refactoring.

Download and save the models in ./models and update config.yaml. The models used in this demo are:

• Embeddings
  • BAAI/bge-small-en-v1.5
  • sentence-transformers/all-mpnet-base-v2
  • sentence-transformers/all-MiniLM-L6-v2
• Rerankers:
  • BAAI/bge-reranker-base: save in models/bge-reranker-base/
  • facebook/tart-full-flan-t5-xl: save in models/tart-full-flan-t5-xl/
• Propositionizer
  • chentong00/propositionizer-wiki-flan-t5-large save in models/propositionizer-wiki-flan-t5-large/
• LLMs
  • bartowski/aya-expanse-8b-GGUF
  • bartowski/Llama-3.2-3B-Instruct-GGUF
  • allenai/OLMoE-1B-7B-0924-Instruct-GGUF
  • bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
  • microsoft/Phi-3-mini-4k-instruct-gguf
  • QuantFactory/Meta-Llama-3-8B-Instruct-GGUF
  • lmstudio-ai/gemma-2b-it-GGUF
  • TheBloke/zephyr-7B-beta-GGUF
  • TheBloke/Mistral-7B-Instruct-v0.2-GGUF
  • TheBloke/Llama-2-7B-Chat-GGUF

The LLMs can be loaded directly in the app, or they can be first deployed with Ollama server.

You can also choose to use models from Groq. Set GROQ_API_KEY in .env.

Add prompt format

Since each model type has its own prompt format, include the format in ./src/prompt_templates.py. For example, the format used in openbuddy models is

"""{system}
User: {user}
Assistant:"""

🤖 Tracing

We shall use Phoenix for LLM tracing. Phoenix is an open-source observability library designed for experimentation, evaluation, and troubleshooting. Before running the app, start a phoenix server

python3 -m phoenix.server.main serve

The traces can be viewed at http://localhost:6006.

💻 App

We use Streamlit as the interface for the demos. There are three demos:

• Conversational Retrieval

streamlit run app_conv.py

• Retrieval QA

streamlit run app_qa.py

• Conversational Retrieval using ReAct

---

NOTE: Using gemini-1.5-flash as LLM

---

Create vectorstore first and update config.yaml

python -m vectorize --filepaths <your-filepath>

Run the app

streamlit run app_react.py

🔍 Usage

To get started, upload a PDF and click on Build VectorDB. Creating vector DB will take a while.