Retrieval-Augmented Generation (RAG) is an AI architecture that combines retrieval and generative models to create a more efficient and context-aware system. It combines traditional information retrieval (IR) techniques with large language models (LLMs) to respond to user queries in a robust and contextually relevant manner. By augmenting generation with retrieval, RAG applications can answer complex questions more accurately by leveraging external knowledge sources.
In this article, we will cover how to build an advanced RAG application, focusing on setting up a query routing mechanism to intelligently route user queries to the correct retrieval or generative component. We’ll dive into coding examples to provide a clearer understanding and conclude with insights into the importance of RAG and query routing.
Understanding RAG Applications
RAG integrates two main components:
- Retriever: This component searches for relevant information from a knowledge base, documents, or other external sources.
- Generator: This part of the system uses language generation models (e.g., GPT) to generate a human-like response, using the retrieved information to enhance contextual understanding.
Benefits of RAG Systems
RAG systems can:
- Answer open-domain questions more effectively than standalone language models.
- Increase the factual accuracy of the generated content.
- Access external knowledge databases, ensuring responses are more up-to-date.
Despite these advantages, an advanced RAG application needs to route queries correctly. Some queries might be better handled by retrieval, while others benefit from generation.
Building the RAG System
To begin with, the primary goal is to integrate a retriever and generator while implementing a query router to decide which approach to use. Below is a step-by-step guide to building the system with query routing:
Setting up the Environment
You’ll need the following Python libraries to set up a basic RAG system:
- Hugging Face Transformers for the language model.
- faiss or elasticsearch for building the retriever.
- scikit-learn for implementing the query routing mechanism.
First, install the dependencies:
pip install transformers faiss-cpu elasticsearch scikit-learn
Loading the Pretrained Models
Next, load a language model for generation and set up the retriever.
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Load pretrained models for generation
model_name = “facebook/bart-large”
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
For the retrieval component, let’s assume we are using FAISS:
import faiss
import numpy as npindex = faiss.IndexFlatL2(768) # 768-dimension vectors for BERT-based embeddings
Indexing Knowledge Sources for Retrieval
You’ll need to index your documents or knowledge base to use the retriever. Convert the text into vector embeddings for efficient search.
from transformers import AutoModel
# Load a model to create embeddings for your documents
embedding_model = AutoModel.from_pretrained(“sentence-transformers/all-mpnet-base-v2”)
tokenizer_embed = AutoTokenizer.from_pretrained(“sentence-transformers/all-mpnet-base-v2”)
# Convert your documents to embeddings
def embed_documents(documents):
inputs = tokenizer_embed(documents, return_tensors=“pt”, padding=True, truncation=True)
with torch.no_grad():
embeddings = embedding_model(**inputs).last_hidden_state.mean(dim=1).numpy()
return embeddings
# Sample documents to index
documents = [“Document 1 content”, “Document 2 content”, “Document 3 content”]
doc_embeddings = embed_documents(documents)
index.add(np.array(doc_embeddings)) # Index the document embeddings
Query Processing and Search
Process a user query by first embedding it and searching the FAISS index for relevant information:
def retrieve(query, top_k=5):
query_embedding = embed_documents([query])
D, I = index.search(query_embedding, top_k) # I gives the indices of top_k results
return [documents[i] for i in I[0]]query = “What is the content of Document 1?”
retrieved_docs = retrieve(query)
Generating a Response
For generation, pass the retrieved documents into the language model to generate a final response:
def generate_response(context):
inputs = tokenizer(context, return_tensors="pt", truncation=True)
output = model.generate(**inputs, max_length=150)
return tokenizer.decode(output[0], skip_special_tokens=True)context = ” “.join(retrieved_docs)
response = generate_response(context)
print(response)
Implementing Query Routing
Query routing is the mechanism that determines whether to use the retriever or the generator (or a mix of both). A simple query router could be based on query classification. For instance, questions that are highly factual may benefit more from retrieval, while subjective or open-ended questions should rely on generation.
Classifying Queries
We can classify queries based on keywords, patterns, or machine learning classifiers. For simplicity, let’s use keyword matching:
def is_factual_query(query):
factual_keywords = ["who", "what", "when", "where", "how many", "list", "define"]
return any(keyword in query.lower() for keyword in factual_keywords)if is_factual_query(query):
# Route to retriever
retrieved_docs = retrieve(query)
response = ” “.join(retrieved_docs)
else:
# Route to generator
response = generate_response(query)
Training a Classifier for Query Routing
For a more sophisticated query routing, you can train a classifier that predicts whether to use retrieval or generation based on historical query data.
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVCqueries = [“What is the capital of France?”, “Can you write a poem?”, “Define photosynthesis.”]
labels = [1, 0, 1]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(queries)
clf = SVC(kernel=‘linear’)
clf.fit(X, labels)
new_query = “What is the population of Japan?”
X_new = vectorizer.transform([new_query])
route_to_retriever = clf.predict(X_new)[0] == 1
Combining Retrieval and Generation for Complex Queries
Sometimes, the query might require both retrieval and generation. In these cases, you can pass the retrieved results to the generator to refine the response.
def handle_complex_query(query):
retrieved_docs = retrieve(query)
if retrieved_docs:
context = " ".join(retrieved_docs)
return generate_response(context)
else:
return generate_response(query)Advanced Query Routing Mechanisms
To improve your query routing mechanism, you could explore deep learning approaches like transformers or recurrent neural networks to classify queries. Additionally, routing decisions can factor in the confidence scores of the retriever or generator, allowing the system to make more dynamic routing choices.
For example, if the retriever returns very few or low-confidence results, you may default to generating a response directly from the LLM.
Confidence-based Query Routing
def route_based_on_confidence(query):
retrieved_docs = retrieve(query)
if len(retrieved_docs) < 3: # If few documents retrieved, fallback to generation
return generate_response(query)
else:
return generate_response(" ".join(retrieved_docs))Conclusion
Building an advanced RAG application involves more than just connecting a retriever and generator. A well-thought-out query routing mechanism can drastically improve the performance and accuracy of the system by determining when to retrieve information and when to generate it. This hybrid approach allows for richer, more contextually relevant responses while maintaining the flexibility of large language models.
In the age of information overload, RAG systems represent a promising architecture for creating intelligent systems that combine retrieval and generation in innovative ways. By incorporating robust query routing mechanisms, you can build applications that deliver superior results, whether users seek factual data or creative, open-ended answers.