LLMs like GPT-3, GPT-4, and their open-source counterpart often struggle with up-to-date information retrieval and can sometimes generate hallucinations or incorrect information.
Retrieval-Augmented Generation (RAG) is a technique that combines the power of LLMs with external knowledge retrieval. RAG allows us to ground LLM responses in factual, up-to-date information, significantly improving the accuracy and reliability of AI-generated content.
In this blog post, we’ll explore how to build LLM agents for RAG from scratch, diving deep into the architecture, implementation details, and advanced techniques. We’ll cover everything from the basics of RAG to creating sophisticated agents capable of complex reasoning and task execution.
Before we dive into building our LLM agent, let’s understand what RAG is and why it’s important.
RAG, or Retrieval-Augmented Generation, is a hybrid approach that combines information retrieval with text generation. In a RAG system:
- A query is used to retrieve relevant documents from a knowledge base.
- These documents are then fed into a language model along with the original query.
- The model generates a response based on both the query and the retrieved information.
This approach has several advantages:
- Improved accuracy: By grounding responses in retrieved information, RAG reduces hallucinations and improves factual accuracy.
- Up-to-date information: The knowledge base can be regularly updated, allowing the system to access current information.
- Transparency: The system can provide sources for its information, increasing trust and allowing for fact-checking.
Understanding LLM Agents
When you face a problem with no simple answer, you often need to follow several steps, think carefully, and remember what you’ve already tried. LLM agents are designed for exactly these kinds of situations in language model applications. They combine thorough data analysis, strategic planning, data retrieval, and the ability to learn from past actions to solve complex issues.
What are LLM Agents?
LLM agents are advanced AI systems designed for creating complex text that requires sequential reasoning. They can think ahead, remember past conversations, and use different tools to adjust their responses based on the situation and style needed.
Consider a question in the legal field such as: “What are the potential legal outcomes of a specific type of contract breach in California?” A basic LLM with a retrieval augmented generation (RAG) system can fetch the necessary information from legal databases.
For a more detailed scenario: “In light of new data privacy laws, what are the common legal challenges companies face, and how have courts addressed these issues?” This question digs deeper than just looking up facts. It’s about understanding new rules, their impact on different companies, and the court responses. An LLM agent would break this task into subtasks, such as retrieving the latest laws, analyzing historical cases, summarizing legal documents, and forecasting trends based on patterns.
Components of LLM Agents
LLM agents generally consist of four components:
- Agent/Brain: The core language model that processes and understands language.
- Planning: The capability to reason, break down tasks, and develop specific plans.
- Memory: Maintains records of past interactions and learns from them.
- Tool Use: Integrates various resources to perform tasks.
Agent/Brain
At the core of an LLM agent is a language model that processes and understands language based on vast amounts of data it’s been trained on. You start by giving it a specific prompt, guiding the agent on how to respond, what tools to use, and the goals to aim for. You can customize the agent with a persona suited for particular tasks or interactions, enhancing its performance.
Memory
The memory component helps LLM agents handle complex tasks by maintaining a record of past actions. There are two main types of memory:
- Short-term Memory: Acts like a notepad, keeping track of ongoing discussions.
- Long-term Memory: Functions like a diary, storing information from past interactions to learn patterns and make better decisions.
By blending these types of memory, the agent can offer more tailored responses and remember user preferences over time, creating a more connected and relevant interaction.
Planning
Planning enables LLM agents to reason, decompose tasks into manageable parts, and adapt plans as tasks evolve. Planning involves two main stages:
- Plan Formulation: Breaking down a task into smaller sub-tasks.
- Plan Reflection: Reviewing and assessing the plan’s effectiveness, incorporating feedback to refine strategies.
Methods like the Chain of Thought (CoT) and Tree of Thought (ToT) help in this decomposition process, allowing agents to explore different paths to solve a problem.
To delve deeper into the world of AI agents, including their current capabilities and potential, consider reading “Auto-GPT & GPT-Engineer: An In-Depth Guide to Today’s Leading AI Agents”
Setting Up the Environment
To build our RAG agent, we’ll need to set up our development environment. We’ll be using Python and several key libraries:
- LangChain: For orchestrating our LLM and retrieval components
- Chroma: As our vector store for document embeddings
- OpenAI’s GPT models: As our base LLM (you can substitute this with an open-source model if preferred)
- FastAPI: For creating a simple API to interact with our agent
Let’s start by setting up our environment:
# Create a new virtual environment python -m venv rag_agent_env source rag_agent_env/bin/activate # On Windows, use `rag_agent_env\Scripts\activate` # Install required packages pip install langchain chromadb openai fastapi uvicorn Now, let's create a new Python file called rag_agent.py and import the necessary libraries: [code language="PYTHON"] from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.text_splitter import CharacterTextSplitter from langchain.llms import OpenAI from langchain.chains import RetrievalQA from langchain.document_loaders import TextLoader import os # Set your OpenAI API key os.environ["OPENAI_API_KEY"] = "your-api-key-here"
Building a Simple RAG System
Now that we have our environment set up, let’s build a basic RAG system. We’ll start by creating a knowledge base from a set of documents, then use this to answer queries.
Step 1: Prepare the Documents
First, we need to load and prepare our documents. For this example, let’s assume we have a text file called knowledge_base.txt with some information about AI and machine learning.
# Load the document
loader = TextLoader("knowledge_base.txt")
documents = loader.load()
# Split the documents into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
# Create embeddings
embeddings = OpenAIEmbeddings()
# Create a vector store
vectorstore = Chroma.from_documents(texts, embeddings)
Step 2: Create a Retrieval-based QA Chain
Now that we have our vector store, we can create a retrieval-based QA chain:
# Create a retrieval-based QA chain qa = RetrievalQA.from_chain_type( llm=OpenAI(), chain_type="stuff", retriever=vectorstore.as_retriever() )
Step 3: Query the System
We can now query our RAG system:
query = "What are the main applications of machine learning?" result = qa.run(query) print(result) This basic RAG system demonstrates the core concept: we retrieve relevant information from our knowledge base and use it to inform the LLM's response. Creating an LLM Agent While our simple RAG system is useful, it's quite limited. Let's enhance it by creating an LLM agent that can perform more complex tasks and reason about the information it retrieves. An LLM agent is an AI system that can use tools and make decisions about which actions to take. We'll create an agent that can not only answer questions but also perform web searches and basic calculations. First, let's define some tools for our agent: [code language="PYTHON"] from langchain.agents import Tool from langchain.tools import DuckDuckGoSearchRun from langchain.tools import BaseTool from langchain.agents import initialize_agent from langchain.agents import AgentType # Define a calculator tool class CalculatorTool(BaseTool): name = "Calculator" description = "Useful for when you need to answer questions about math" def _run(self, query: str) -> str: try: return str(eval(query)) except: return "I couldn't calculate that. Please make sure your input is a valid mathematical expression." # Create tool instances search = DuckDuckGoSearchRun() calculator = CalculatorTool() # Define the tools tools = [ Tool( name="Search", func=search.run, description="Useful for when you need to answer questions about current events" ), Tool( name="RAG-QA", func=qa.run, description="Useful for when you need to answer questions about AI and machine learning" ), Tool( name="Calculator", func=calculator._run, description="Useful for when you need to perform mathematical calculations" ) ] # Initialize the agent agent = initialize_agent( tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True )
Now we have an agent that can use our RAG system, perform web searches, and do calculations. Let’s test it:
result = agent.run(“What’s the difference between supervised and unsupervised learning? Also, what’s 15% of 80?”)
print(result)
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This agent demonstrates a key advantage of LLM agents: they can combine multiple tools and reasoning steps to answer complex queries.
Enhancing the Agent with Advanced RAG Techniques
While our current RAG system works well, there are several advanced techniques we can use to enhance its performance:
a) Semantic Search with Dense Passage Retrieval (DPR)
Instead of using simple embedding-based retrieval, we can implement DPR for more accurate semantic search:
from transformers import DPRQuestionEncoder, DPRContextEncoder
question_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base")
context_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
# Function to encode passages
def encode_passages(passages):
return context_encoder(passages, max_length=512, return_tensors="pt").pooler_output
# Function to encode query
def encode_query(query):
return question_encoder(query, max_length=512, return_tensors="pt").pooler_output
b) Query Expansion
We can use query expansion to improve retrieval performance:
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained(“t5-small”)
tokenizer = T5Tokenizer.from_pretrained(“t5-small”)
def expand_query(query):
input_text = f”expand query: {query}”
input_ids = tokenizer.encode(input_text, return_tensors=”pt”)
outputs = model.generate(input_ids, max_length=50, num_return_sequences=3)
expanded_queries = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
return expanded_queries
# Use this in your retrieval process
c) Iterative Refinement
We can implement an iterative refinement process where the agent can ask follow-up questions to clarify or expand on its initial retrieval:
def iterative_retrieval(initial_query, max_iterations=3):
query = initial_query
for _ in range(max_iterations):
result = qa.run(query)
clarification = agent.run(f”Based on this result: ‘{result}’, what follow-up question should I ask to get more specific information?”)
if clarification.lower().strip() == “none”:
break
query = clarification
return result
# Use this in your agent’s process
Implementing a Multi-Agent System
To handle more complex tasks, we can implement a multi-agent system where different agents specialize in different areas. Here’s a simple example:
class SpecialistAgent:
def __init__(self, name, tools):
self.name = name
self.agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
def run(self, query):
return self.agent.run(query)
# Create specialist agents
research_agent = SpecialistAgent(“Research”, [Tool(name=”RAG-QA”, func=qa.run, description=”For AI and ML questions”)])
math_agent = SpecialistAgent(“Math”, [Tool(name=”Calculator”, func=calculator._run, description=”For calculations”)])
general_agent = SpecialistAgent(“General”, [Tool(name=”Search”, func=search.run, description=”For general queries”)])
class Coordinator:
def __init__(self, agents):
self.agents = agents
def run(self, query):
# Determine which agent to use
if “calculate” in query.lower() or any(op in query for op in [‘+’, ‘-‘, ‘*’, ‘/’]):
return self.agents[‘Math’].run(query)
elif any(term in query.lower() for term in [‘ai’, ‘machine learning’, ‘deep learning’]):
return self.agents[‘Research’].run(query)
else:
return self.agents[‘General’].run(query)
coordinator = Coordinator({
‘Research’: research_agent,
‘Math’: math_agent,
‘General’: general_agent
})
# Test the multi-agent system
result = coordinator.run(“What’s the difference between CNN and RNN? Also, calculate 25% of 120.”)
print(result)
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This multi-agent system allows for specialization and can handle a wider range of queries more effectively.
Evaluating and Optimizing RAG Agents
To ensure our RAG agent is performing well, we need to implement evaluation metrics and optimization techniques:
a) Relevance Evaluation
We can use metrics like BLEU, ROUGE, or BERTScore to evaluate the relevance of retrieved documents:
from bert_score import score def evaluate_relevance(query, retrieved_doc, generated_answer): P, R, F1 = score([generated_answer], [retrieved_doc], lang="en") return F1.mean().item()
b) Answer Quality Evaluation
We can use human evaluation or automated metrics to assess answer quality:
from nltk.translate.bleu_score import sentence_bleu def evaluate_answer_quality(reference_answer, generated_answer): return sentence_bleu([reference_answer.split()], generated_answer.split()) # Use this to evaluate your agent's responses c) Latency Optimization To optimize latency, we can implement caching and parallel processing: import functools from concurrent.futures import ThreadPoolExecutor @functools.lru_cache(maxsize=1000) def cached_retrieval(query): return vectorstore.similarity_search(query) def parallel_retrieval(queries): with ThreadPoolExecutor() as executor: results = list(executor.map(cached_retrieval, queries)) return results # Use these in your retrieval process
Future Directions and Challenges
As we look to the future of RAG agents, several exciting directions and challenges emerge:
a) Multi-modal RAG: Extending RAG to incorporate image, audio, and video data.
b) Federated RAG: Implementing RAG across distributed, privacy-preserving knowledge bases.
c) Continual Learning: Developing methods for RAG agents to update their knowledge bases and models over time.
d) Ethical Considerations: Addressing bias, fairness, and transparency in RAG systems.
e) Scalability: Optimizing RAG for large-scale, real-time applications.
Conclusion
Building LLM agents for RAG from scratch is a complex but rewarding process. We’ve covered the basics of RAG, implemented a simple system, created an LLM agent, enhanced it with advanced techniques, explored multi-agent systems, and discussed evaluation and optimization strategies.
