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Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is a technique that enables generative artificial intelligence (Gen AI) models to retrieve and incorporate new information. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to supplement information from its pre-existing training data. This allows LLMs to use domain-specific and/or updated information. Use cases include providing chatbot access to internal company data or generating responses based on authoritative sources.

Here are the integrations: - vLLM + langchain + milvus - vLLM + llamaindex + milvus

vLLM + langchain

Prerequisites

  • Setup vLLM and langchain environment
pip install -U vllm \
            langchain_milvus langchain_openai \
            langchain_community beautifulsoup4 \
            langchain-text-splitters

Deploy

  • Start the vLLM server with the supported embedding model, e.g.
# Start embedding service (port 8000)
vllm serve ssmits/Qwen2-7B-Instruct-embed-base
  • Start the vLLM server with the supported chat completion model, e.g.
# Start chat service (port 8001)
vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001

python retrieval_augmented_generation_with_langchain.py

vLLM + llamaindex

Prerequisites

  • Setup vLLM and llamaindex environment
pip install vllm \
            llama-index llama-index-readers-web \
            llama-index-llms-openai-like    \
            llama-index-embeddings-openai-like \
            llama-index-vector-stores-milvus \

Deploy

  • Start the vLLM server with the supported embedding model, e.g.
# Start embedding service (port 8000)
vllm serve ssmits/Qwen2-7B-Instruct-embed-base
  • Start the vLLM server with the supported chat completion model, e.g.
# Start chat service (port 8001)
vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001

python retrieval_augmented_generation_with_llamaindex.py