RAG Architecture: Building Retrieval-Augmented Generation from Scratch

Building intelligent applications that understand and respond to complex queries requires more than just a pre-trained language model. We’ve found Retrieval-Augmented Generation (RAG) to be a game-changer, allowing us to provide more accurate and contextually relevant results for users on MisuJob.

Demystifying RAG Architecture: From Concept to Implementation

At its core, RAG is a framework that combines the strengths of retrieval-based and generative models. Instead of solely relying on the internal knowledge of a large language model (LLM), RAG first retrieves relevant information from an external knowledge source and then uses that information to generate a more informed response. This approach significantly improves the accuracy, relevance, and trustworthiness of the generated content. We use RAG extensively to power our AI-powered job matching features, enabling users to find the perfect role based on their unique skills and experience. Since MisuJob processes 1M+ job listings and aggregates from multiple sources, RAG helps us sift through vast amounts of data to pinpoint the most pertinent opportunities for each individual.

The RAG Pipeline: A Step-by-Step Breakdown

The RAG pipeline typically consists of the following key components:

1. Query Encoder: Transforms the user’s query into a vector representation (embedding).
2. Retrieval: Uses the query embedding to search for relevant documents or passages in a knowledge base.
3. Augmentation: Combines the retrieved information with the original query.
4. Generation: Feeds the augmented query to a language model to generate a response.

Let’s dive deeper into each of these components and explore how we’ve implemented them at MisuJob.

Query Encoding: Transforming Text into Vectors

The first step in the RAG pipeline is to encode the user’s query into a vector representation. This allows us to perform semantic search and find documents that are relevant to the query, even if they don’t contain the exact same keywords. We’ve experimented with several embedding models, including sentence transformers and OpenAI’s embeddings, and have found that sentence transformers offer a good balance between accuracy and performance for our use case.

Here’s an example of how you can use a sentence transformer to encode a query in Python:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-mpnet-base-v2')
query = "Software Engineer roles in Berlin with experience in Python and AWS"
query_embedding = model.encode(query)

print(query_embedding.shape) # Output: (768,)

This code snippet demonstrates how to load a pre-trained sentence transformer model and use it to encode a query into a 768-dimensional vector. This vector representation captures the semantic meaning of the query and can be used for similarity search.

Retrieval: Finding Relevant Information

Once we have the query embedding, we need to use it to search for relevant documents in our knowledge base. At MisuJob, our knowledge base consists of structured job descriptions, company information, and skills data. We’ve explored several options for storing and indexing these embeddings, including FAISS, Annoy, and vector databases like Pinecone and Weaviate. We initially chose FAISS for its speed and scalability, and its integration into our existing infrastructure.

Here’s an example of how you can use FAISS to perform similarity search:

import faiss
import numpy as np

# Assuming you have a matrix of document embeddings called 'document_embeddings'
# with shape (num_documents, embedding_dimension)
embedding_dimension = 768 # Example: SentenceTransformer 'all-mpnet-base-v2'
num_documents = 1000

# Create a FAISS index
index = faiss.IndexFlatL2(embedding_dimension) # L2 distance for similarity

# Add the document embeddings to the index
document_embeddings = np.random.rand(num_documents, embedding_dimension).astype('float32') # Example data
index.add(document_embeddings)

# Search the index for the most similar documents to the query embedding
k = 5 # Number of nearest neighbors to retrieve
distances, indices = index.search(query_embedding.reshape(1, -1).astype('float32'), k)

print("Distances:", distances)
print("Indices:", indices)

This code snippet demonstrates how to create a FAISS index, add document embeddings to the index, and search the index for the most similar documents to the query embedding. The distances array contains the distances between the query embedding and the retrieved document embeddings, and the indices array contains the indices of the retrieved documents in the knowledge base.

To improve the retrieval accuracy, we’ve implemented several techniques, including:

• Keyword filtering: We filter the documents based on keywords extracted from the query to narrow down the search space.
• Metadata filtering: We filter the documents based on metadata such as job title, location, and industry to further refine the results.
• Re-ranking: We use a cross-encoder model to re-rank the retrieved documents based on their relevance to the query.

Augmentation: Combining Query and Retrieved Information

Once we have the relevant documents, we need to combine them with the original query to create an augmented query. This augmented query is then fed to the language model to generate a response. We’ve experimented with several augmentation strategies, including:

• Concatenation: We simply concatenate the query and the retrieved documents.
• Prompt engineering: We use a carefully crafted prompt that instructs the language model to use the retrieved documents to answer the query.
• Question answering: We use a question answering model to extract the relevant information from the retrieved documents and then use that information to answer the query.

For example, if the user’s query is “What are the required skills for a Data Scientist role in Amsterdam?” and the retrieved document contains a job description for a Data Scientist role in Amsterdam, the augmented query might look like this:

“Answer the following question using the information provided in the context. Question: What are the required skills for a Data Scientist role in Amsterdam? Context: [Job description for a Data Scientist role in Amsterdam]”

Generation: Generating a Response

The final step in the RAG pipeline is to feed the augmented query to a language model to generate a response. We currently use a variety of open-source and proprietary LLMs, evaluating based on performance, cost, and specific task requirements.

Here’s an example of how you can use the Hugging Face Transformers library to generate a response:

from transformers import pipeline

# Choose a model suitable for question answering or text generation
model_name = "google/flan-t5-base"  # Example, can be replaced

qa_pipeline = pipeline("question-answering", model=model_name, tokenizer=model_name)

context = "The Data Scientist role in Amsterdam requires proficiency in Python, SQL, machine learning, and data visualization."
question = "What are the required skills for a Data Scientist role in Amsterdam?"

result = qa_pipeline(question=question, context=context)

print(result['answer']) # Output: Python, SQL, machine learning, and data visualization.

This code snippet demonstrates how to use the Hugging Face Transformers library to generate a response based on the augmented query. The qa_pipeline is a question answering pipeline that takes a question and a context as input and returns the answer.

Performance Optimization: Making RAG Scalable

RAG can be computationally expensive, especially when dealing with large knowledge bases and complex queries. To make RAG scalable, we’ve implemented several performance optimization techniques, including:

• Caching: We cache the results of the retrieval and generation steps to avoid redundant computations.
• Asynchronous processing: We use asynchronous processing to handle multiple queries concurrently.
• Hardware acceleration: We use GPUs to accelerate the embedding and generation steps.

Let’s look at a real-world example. Before implementing caching, our average response time for a complex query was around 500ms. After implementing caching, we reduced the average response time to 150ms. This significantly improved the user experience and allowed us to handle a higher volume of traffic.

Salary Insights Enabled by RAG

RAG architecture is not just for text generation; it’s excellent at data synthesis and summarization. At MisuJob, we use RAG to provide users with salary insights based on location, experience, and skills. By retrieving and processing salary data from various sources, we can generate personalized salary ranges for specific roles and locations.

Here’s a table illustrating the average salary range for Software Engineers in various European cities (data is based on aggregated and anonymized data from MisuJob’s platform):

We generate this kind of table by first using the LLM to structure the data into a table format, and then using RAG to verify and augment the table with relevant salary information. This provides users with accurate and up-to-date salary insights, helping them make informed career decisions.

The Evolution of Our RAG Implementation

Our RAG implementation is constantly evolving as we learn more about the needs of our users and the capabilities of the underlying technologies. We are currently exploring several new directions, including:

• Multi-hop retrieval: This involves retrieving information from multiple sources and chaining them together to answer complex queries.
• Knowledge graph integration: This involves integrating our knowledge base with a knowledge graph to improve the accuracy and efficiency of the retrieval process.
• Personalized RAG: This involves tailoring the RAG pipeline to the individual user based on their skills, experience, and preferences.

For example, we’re experimenting with multi-hop retrieval to answer questions like “What are the career paths for someone with a background in Data Science and experience in the Finance industry?”. This requires retrieving information from multiple sources, including job descriptions, career guides, and industry reports.

Key Takeaways

Implementing RAG architecture is a complex but rewarding endeavor. Here are some key takeaways from our experience at MisuJob:

• Choose the right embedding model: The choice of embedding model can have a significant impact on the accuracy and performance of the RAG pipeline. Experiment with different models and choose the one that best suits your use case.
• Optimize the retrieval process: The retrieval process is a critical bottleneck in the RAG pipeline. Implement techniques such as keyword filtering, metadata filtering, and re-ranking to improve the retrieval accuracy and efficiency.
• Experiment with different augmentation strategies: The augmentation strategy can have a significant impact on the quality of the generated responses. Experiment with different strategies and choose the one that works best for your use case.
• Monitor and optimize performance: RAG can be computationally expensive. Monitor the performance of your RAG pipeline and implement performance optimization techniques such as caching, asynchronous processing, and hardware acceleration.

By following these guidelines, you can build a powerful RAG system that delivers accurate, relevant, and trustworthy results for your users. We’re excited to continue pushing the boundaries of RAG at MisuJob to provide the best possible job search experience for professionals across Europe.