Implementing RAG: Practical Steps, Code Snippets, and Tools

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Implementing a RAG system involves setting up the retrieval and generation components, then integrating them into a pipeline.

Prerequisites:

  • Python 3.8+
  • Transformers library
  • FAISS (for vector search)
  • A document corpus (can be custom or dataset)

Step 1: Prepare the Document Corpus

# Example: embedding documents
from transformers import AutoTokenizer, AutoModel
import torch
from sklearn.preprocessing import normalize

documents = ["Document 1 text", "Document 2 text", "Document 3 text"]

tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')

def embed(texts):
    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
    with torch.no_grad():
        embeddings = model(**inputs).last_hidden_state[:,0,:]
    return normalize(embeddings.numpy())

embeddings = embed(documents)

Step 2: Create a FAISS index:

import faiss
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)

Step 3: Retrieval Function:

def retrieve(query, top_k=3):
    query_embedding = embed([query])
    distances, indices = index.search(query_embedding, top_k)
    return [documents[i] for i in indices[0]]

Step 4: Generate Response using a Pre-trained Generator (e.g., T5):

from transformers import T5ForConditionalGeneration, T5Tokenizer

generator_tokenizer = T5Tokenizer.from_pretrained('t5-small')
generator_model = T5ForConditionalGeneration.from_pretrained('t5-small')

def generate_answer(query, retrieved_docs):
    input_text = f"question: {query} context: { ' '.join(retrieved_docs) }"
    inputs = generator_tokenizer.encode(input_text, return_tensors='pt', max_length=512, truncation=True)
    output_ids = generator_model.generate(inputs, max_length=150, num_beams=4, early_stopping=True)
    return generator_tokenizer.decode(output_ids[0], skip_special_tokens=True)

Step 5: Full Pipeline Example:

query = "What are the latest advances in renewable energy?"
retrieved_docs = retrieve(query)
answer = generate_answer(query, retrieved_docs)
print(answer)

This pipeline demonstrates how to set up a RAG system end-to-end using open-source tools. For production, consider optimizing indexing, retrieval speed, and response coherence.