LLMs like GPT-3, GPT-4, and their open-source counterpart often struggle with up-to-date information retrieval and might sometimes generate hallucinations or misinformation.
Retrieval-Augmented Generation (RAG) is a way that mixes the ability 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.
On this blog post, we’ll explore methods to construct LLM agents for RAG from scratch, diving deep into the architecture, implementation details, and advanced techniques. We’ll cover every little thing from the fundamentals of RAG to creating sophisticated agents able to complex reasoning and task execution.
Before we dive into constructing our LLM agent, let’s understand what RAG is and why it is vital.
RAG, or Retrieval-Augmented Generation, is a hybrid approach that mixes information retrieval with text generation. In a RAG system:
- A question is used to retrieve relevant documents from a knowledge base.
- These documents are then fed right into a language model together with the unique query.
- The model generates a response based on each the query and the retrieved information.
RAG
This approach has several benefits:
- Improved accuracy: By grounding responses in retrieved information, RAG reduces hallucinations and improves factual accuracy.
- Up-to-date information: The knowledge base could be frequently 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
Whenever you face an issue with no easy answer, you frequently must follow several steps, think twice, and remember what you’ve already tried. LLM agents are designed for exactly these sorts of situations in language model applications. They mix thorough data evaluation, strategic planning, data retrieval, and the flexibility to learn from past actions to resolve complex issues.
What are LLM Agents?
LLM agents are advanced AI systems designed for creating complex text that requires sequential reasoning. They will think ahead, remember past conversations, and use different tools to regulate their responses based on the situation and magnificence needed.
Consider a matter within the legal field corresponding to: “What are the potential legal outcomes of a selected form of contract breach in California?” A basic LLM with a retrieval augmented generation (RAG) system can fetch the vital information from legal databases.
For a more detailed scenario: “In light of recent data privacy laws, what are the common legal challenges firms face, and the way have courts addressed these issues?” This query digs deeper than simply looking up facts. It’s about understanding latest rules, their impact on different firms, and the court responses. An LLM agent would break this task into subtasks, corresponding to retrieving the newest laws, analyzing historical cases, summarizing legal documents, and forecasting trends based on patterns.
Components of LLM Agents
LLM agents generally consist of 4 components:
- Agent/Brain: The core language model that processes and understands language.
- Planning: The aptitude 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
On the core of an LLM agent is a language model that processes and understands language based on vast amounts of information it’s been trained on. You begin by giving it a selected prompt, guiding the agent on methods to respond, what tools to make use of, and the goals to aim for. You possibly can customize the agent with a persona suited to 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 fundamental varieties 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 higher decisions.
By mixing a lot of these memory, the agent can offer more tailored responses and remember user preferences over time, making 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 fundamental 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 just like the Chain of Thought (CoT) and Tree of Thought (ToT) assist in this decomposition process, allowing agents to explore different paths to resolve an issue.
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 construct our RAG agent, we’ll need to establish our development environment. We’ll be using Python and a number of other 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 possibly can substitute this with an open-source model if preferred)
- FastAPI: For creating a straightforward API to interact with our agent
Let’s start by organising our surroundings:
# Create a brand new virtual environment python -m venv rag_agent_env source rag_agent_env/bin/activate # On Windows, use `rag_agent_envScriptsactivate` # Install required packages pip install langchain chromadb openai fastapi uvicorn Now, let's create a brand new Python file called rag_agent.py and import the vital 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"
Constructing a Easy RAG System
Now that now we have our surroundings arrange, let’s construct a basic RAG system. We’ll start by making a knowledge base from a set of documents, then use this to reply queries.
Step 1: Prepare the Documents
First, we want to load and prepare our documents. For this instance, let’s assume now we have a text file called knowledge_base.txt with some details 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 now we have our vector store, we are able to 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 are able to now query our RAG system:
query = "What are the fundamental 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 tell the LLM's response. Creating an LLM Agent While our easy RAG system is beneficial, it's quite limited. Let's enhance it by creating an LLM agent that may perform more complex tasks and reason concerning the information it retrieves. An LLM agent is an AI system that may use tools and make decisions about which actions to take. We'll create an agent that cannot only answer questions but in addition 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 should answer questions on math" def _run(self, query: str) -> str: try: return str(eval(query)) except: return "I could not calculate that. Please make certain your input is a sound 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 now we have an agent that may use our RAG system, perform web searches, and do calculations. Let’s test it:
result = agent.run(“What is 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’ll mix multiple tools and reasoning steps to reply complex queries.
Enhancing the Agent with Advanced RAG Techniques
While our current RAG system works well, there are several advanced techniques we are able to use to boost its performance:
a) Semantic Search with Dense Passage Retrieval (DPR)
As an alternative of using easy embedding-based retrieval, we are able to 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 are able to use query expansion to enhance 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 are able to implement an iterative refinement process where the agent can ask follow-up inquiries to make clear 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 query 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 are able to implement a multi-agent system where different agents concentrate on different areas. Here’s a straightforward 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 make use of
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 is 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 might handle a wider range of queries more effectively.
Evaluating and Optimizing RAG Agents
To make sure our RAG agent is performing well, we want to implement evaluation metrics and optimization techniques:
a) Relevance Evaluation
We are able to use metrics like BLEU, ROUGE, or BERTScore to guage the relevance of retrieved documents:
from bert_score import rating def evaluate_relevance(query, retrieved_doc, generated_answer): P, R, F1 = rating([generated_answer], [retrieved_doc], lang="en") return F1.mean().item()
b) Answer Quality Evaluation
We are able to use human evaluation or automated metrics to evaluate 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 guage your agent's responses c) Latency Optimization To optimize latency, we are able to 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 glance to the longer term of RAG agents, several exciting directions and challenges emerge:
a) Multi-modal RAG: Extending RAG to include 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
Constructing LLM agents for RAG from scratch is a fancy but rewarding process. We have covered the fundamentals of RAG, implemented a straightforward system, created an LLM agent, enhanced it with advanced techniques, explored multi-agent systems, and discussed evaluation and optimization strategies.
