[doc] Add RAG Integration example

docs/source/deployment/frameworks/index.md

@@ -11,6 +11,7 @@ helm
 lws
 modal
 open-webui
+retrieval_augmented_generation
 skypilot
 streamlit
 triton

docs/source/deployment/frameworks/retrieval_augmented_generation.md

@@ -0,0 +1,84 @@
+(deployment-retrieval-augmented-generation)=
+
+# Retrieval-Augmented Generation
+
+[Retrieval-augmented generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) 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](https://github.com/langchain-ai/langchain) + [milvus](https://github.com/milvus-io/milvus)
+- vLLM + [llamaindex](https://github.com/run-llama/llama_index) + [milvus](https://github.com/milvus-io/milvus)
+
+## vLLM + langchain
+
+### Prerequisites
+
+- Setup vLLM and langchain environment
+
+```console
+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.
+
+```console
+# 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.
+
+```console
+# 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
+python retrieval_augmented_generation_with_langchain.py
+```
+
+## vLLM + llamaindex
+
+### Prerequisites
+
+- Setup vLLM and llamaindex environment
+
+```console
+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.
+
+```console
+# 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.
+
+```console
+# 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
+python retrieval_augmented_generation_with_llamaindex.py
+```

examples/online_serving/retrieval_augmented_generation_with_langchain.py

@@ -0,0 +1,249 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Retrieval Augmented Generation (RAG) Implementation with Langchain
+==================================================================
+
+This script demonstrates a RAG implementation using LangChain, Milvus
+and vLLM. RAG enhances LLM responses by retrieving relevant context
+from a document collection.
+
+Features:
+- Web content loading and chunking
+- Vector storage with Milvus
+- Embedding generation with vLLM
+- Question answering with context
+
+Prerequisites:
+1. Install dependencies:
+    pip install -U vllm \
+                 langchain_milvus langchain_openai \
+                 langchain_community beautifulsoup4 \
+                 langchain-text-splitters
+
+2. Start services:
+    # Start embedding service (port 8000)
+    vllm serve ssmits/Qwen2-7B-Instruct-embed-base
+
+    # Start chat service (port 8001)
+    vllm serve qwen/Qwen1.5-0.5B-Chat --port 8001
+
+Usage:
+    python retrieval_augmented_generation_with_langchain.py
+
+Notes:
+    - Ensure both vLLM services are running before executing
+    - Default ports: 8000 (embedding), 8001 (chat)
+    - First run may take time to download models
+"""
+
+import argparse
+from argparse import Namespace
+from typing import Any
+
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_core.documents import Document
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_milvus import Milvus
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+
+def load_and_split_documents(config: dict[str, Any]):
+    """
+    Load and split documents from web URL
+    """
+    try:
+        loader = WebBaseLoader(web_paths=(config["url"], ))
+        docs = loader.load()
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=config["chunk_size"],
+            chunk_overlap=config["chunk_overlap"],
+        )
+        return text_splitter.split_documents(docs)
+    except Exception as e:
+        print(f"Error loading document from {config['url']}: {str(e)}")
+        raise
+
+
+def init_vectorstore(config: dict[str, Any], documents: list[Document]):
+    """
+    Initialize vector store with documents
+    """
+    return Milvus.from_documents(
+        documents=documents,
+        embedding=OpenAIEmbeddings(
+            model=config["embedding_model"],
+            openai_api_key=config["vllm_api_key"],
+            openai_api_base=config["vllm_embedding_endpoint"],
+        ),
+        connection_args={"uri": config["uri"]},
+        drop_old=True,
+    )
+
+
+def init_llm(config: dict[str, Any]):
+    """
+    Initialize llm
+    """
+    return ChatOpenAI(
+        model=config["chat_model"],
+        openai_api_key=config["vllm_api_key"],
+        openai_api_base=config["vllm_chat_endpoint"],
+    )
+
+
+def get_qa_prompt():
+    """
+    Get question answering prompt template
+    """
+    template = """You are an assistant for question-answering tasks.
+Use the following pieces of retrieved context to answer the question.
+If you don't know the answer, just say that you don't know.
+Use three sentences maximum and keep the answer concise.
+Question: {question}
+Context: {context}
+Answer:
+"""
+    return PromptTemplate.from_template(template)
+
+
+def format_docs(docs: list[Document]):
+    """
+    Format documents for prompt
+    """
+    return "\n\n".join(doc.page_content for doc in docs)
+
+
+def create_qa_chain(retriever: Any, llm: ChatOpenAI, prompt: PromptTemplate):
+    """
+    Set up question answering chain
+    """
+    return ({
+        "context": retriever | format_docs,
+        "question": RunnablePassthrough(),
+    }
+            | prompt
+            | llm
+            | StrOutputParser())
+
+
+def get_parser() -> argparse.ArgumentParser:
+    """
+    Parse command line arguments
+    """
+    parser = argparse.ArgumentParser(description='RAG with vLLM and langchain')
+
+    # Add command line arguments
+    parser.add_argument('--vllm-api-key',
+                        default="EMPTY",
+                        help='API key for vLLM compatible services')
+    parser.add_argument('--vllm-embedding-endpoint',
+                        default="http://localhost:8000/v1",
+                        help='Base URL for embedding service')
+    parser.add_argument('--vllm-chat-endpoint',
+                        default="http://localhost:8001/v1",
+                        help='Base URL for chat service')
+    parser.add_argument('--uri',
+                        default="./milvus.db",
+                        help='URI for Milvus database')
+    parser.add_argument(
+        '--url',
+        default=("https://docs.vllm.ai/en/latest/getting_started/"
+                 "quickstart.html"),
+        help='URL of the document to process')
+    parser.add_argument('--embedding-model',
+                        default="ssmits/Qwen2-7B-Instruct-embed-base",
+                        help='Model name for embeddings')
+    parser.add_argument('--chat-model',
+                        default="qwen/Qwen1.5-0.5B-Chat",
+                        help='Model name for chat')
+    parser.add_argument('-i',
+                        '--interactive',
+                        action='store_true',
+                        help='Enable interactive Q&A mode')
+    parser.add_argument('-k',
+                        '--top-k',
+                        type=int,
+                        default=3,
+                        help='Number of top results to retrieve')
+    parser.add_argument('-c',
+                        '--chunk-size',
+                        type=int,
+                        default=1000,
+                        help='Chunk size for document splitting')
+    parser.add_argument('-o',
+                        '--chunk-overlap',
+                        type=int,
+                        default=200,
+                        help='Chunk overlap for document splitting')
+
+    return parser
+
+
+def init_config(args: Namespace):
+    """
+    Initialize configuration settings from command line arguments
+    """
+
+    return {
+        "vllm_api_key": args.vllm_api_key,
+        "vllm_embedding_endpoint": args.vllm_embedding_endpoint,
+        "vllm_chat_endpoint": args.vllm_chat_endpoint,
+        "uri": args.uri,
+        "embedding_model": args.embedding_model,
+        "chat_model": args.chat_model,
+        "url": args.url,
+        "chunk_size": args.chunk_size,
+        "chunk_overlap": args.chunk_overlap,
+        "top_k": args.top_k
+    }
+
+
+def main():
+    # Parse command line arguments
+    args = get_parser().parse_args()
+
+    # Initialize configuration
+    config = init_config(args)
+
+    # Load and split documents
+    documents = load_and_split_documents(config)
+
+    # Initialize vector store and retriever
+    vectorstore = init_vectorstore(config, documents)
+    retriever = vectorstore.as_retriever(search_kwargs={"k": config["top_k"]})
+
+    # Initialize llm and prompt
+    llm = init_llm(config)
+    prompt = get_qa_prompt()
+
+    # Set up QA chain
+    qa_chain = create_qa_chain(retriever, llm, prompt)
+
+    # Interactive mode
+    if args.interactive:
+        print("\nWelcome to Interactive Q&A System!")
+        print("Enter 'q' or 'quit' to exit.")
+
+        while True:
+            question = input("\nPlease enter your question: ")
+            if question.lower() in ['q', 'quit']:
+                print("\nThank you for using! Goodbye!")
+                break
+
+            output = qa_chain.invoke(question)
+            print(output)
+    else:
+        # Default single question mode
+        question = ("How to install vLLM?")
+        output = qa_chain.invoke(question)
+        print("-" * 50)
+        print(output)
+        print("-" * 50)
+
+
+if __name__ == "__main__":
+    main()