vllm/docs/source/deployment/frameworks/retrieval_augmented_generation.md

(deployment-retrieval-augmented-generation)=

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

• Use the script: gh-file:examples/online_serving/retrieval_augmented_generation_with_langchain.py

• Run the script

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

• Use the script: gh-file:examples/online_serving/retrieval_augmented_generation_with_llamaindex.py

• Run the script

python retrieval_augmented_generation_with_llamaindex.py