RAG Part 3: Advanced Implementation

In the final part of our series, we’ll look at how to implement a robust RAG system using modern tools and best practices.

The Stack

• LLM: GPT-4o or Claude 3.5 Sonnet
• Framework: LangChain or LlamaIndex
• Vector DB: Pinecone, Weaviate, or Qdrant
• Embeddings: OpenAI text-embedding-3-small

Implementation Code Snippet

Here’s a simplified example using LangChain:

from langchain.vectorstores import Pinecone
from langchain.embeddings import OpenAIEmbeddings
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA

# 1. Initialize Embeddings & Vector DB
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone.from_existing_index("my-index", embeddings)

# 2. Initialize LLM
llm = ChatOpenAI(model_name="gpt-4o", temperature=0)

# 3. Create Retrieval Chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5})
)

# 4. Ask a Question
query = "How does RAG handle hallucinations?"
response = qa_chain.run(query)
print(response)

Evaluation (RAGas)

How do you know your RAG is working? Use RAGas (Retrieval Augmented Generation Assessment) metrics:

• Faithfulness: Is the answer derived from the context?
• Answer Relevance: Does the answer address the query?
• Context Precision: Is the retrieved context relevant?

Conclusion

RAG is a powerful architecture that bridges the gap between LLMs and your data. By understanding the basics, optimizing your pipeline, and implementing robust evaluation, you can build AI applications that users trust.