RAG • Chapter 6

Integrating LLMs (OpenAI, HuggingFace)

RAG engineering module on Integrating LLMs (OpenAI, HuggingFace).

6 note blocks4 exam topics

🎯 Exam Focus Areas

Evaluate chunking and embedding strategies.Understand Vector DB indexing architectures like HNSW.Analyze RAG prompts for injection vulnerabilities.Calculate and utilize RAGAS evaluation metrics.

Once relevant context is retrieved, it must be passed to a Large Language Model. You can utilize proprietary APIs like OpenAI or deploy open-weight models via HuggingFace.

Advanced System Mechanics

Frameworks like LangChain and LlamaIndex provide standardized interfaces to swap LLMs seamlessly. When using an API, the prompt, context, and user query are combined into a system and user message format. When running locally with HuggingFace, one must consider GPU VRAM limits, often utilizing quantization (like 4-bit AWQ or GGUF) to fit models like Llama-3 into memory.

1Understand the vector space implications of this concept.
2Identify potential hallucination risks.
3Optimize for low latency and high relevance.
4Ensure robust system prompts.

Implementation Blueprint

import os
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Ensure OPENAI_API_KEY is in environment
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant. Use the following context to answer: {context}"),
    ("user", "{question}")
])

chain = prompt | llm
response = chain.invoke({"context": "RAG improves accuracy.", "question": "What does RAG do?"})
print(response.content)

📝 Quick Revision Points

1Review the differences between similarity metrics.
2Practice the LangChain/LlamaIndex code snippets.
3Understand the HyDE architecture deeply.
4Memorize the security guardrail implementations.

← PreviousSemantic Search & Distance Metrics Next →Prompt Engineering for RAG

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import os from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate # Ensure OPENAI_API_KEY is in environment llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant. Use the following context to answer: {context}"), ("user", "{question}") ]) chain = prompt | llm response = chain.invoke({"context": "RAG improves accuracy.", "question": "What does RAG do?"}) print(response.content)