Introduction
Artificial Intelligence has entered a brand new period. Gone are the times when fashions would merely output data based mostly on predefined guidelines. The cutting-edge method in AI right this moment revolves round RAG (Retrieval-Augmented Generation) methods, and extra particularly, the usage of brokers to intelligently retrieve, analyze, and confirm data. That is the way forward for clever knowledge retrieval — the place machine studying fashions not solely reply questions however accomplish that with unprecedented accuracy and depth.
On this weblog, we’ll dive into how one can construct your individual agent-powered RAG system utilizing CrewAI and LangChain, two of probably the most highly effective instruments which might be revolutionizing the way in which we work together with AI. However earlier than we dive into the code, let’s get acquainted with these game-changing applied sciences.
Studying Outcomes
- Study the basics of RAG and its position in enhancing AI accuracy via real-time knowledge retrieval.
- Explored the performance of CrewAI and the way its specialised brokers contribute to environment friendly activity automation in AI methods.
- Understood how LangChain permits activity chaining, creating logical workflows that improve AI-driven processes.
- Found how one can construct an agentic RAG system utilizing instruments like LLaMA 3, Groq API, CrewAI, and LangChain for dependable and clever data retrieval.
This text was printed as part of the Data Science Blogathon.
What’s Retrieval-Augmented Technology?
RAG represents a hybrid method in trendy AI. Not like conventional fashions that solely depend on pre-existing data baked into their coaching, RAG methods pull real-time data from exterior knowledge sources (like databases, paperwork, or the online) to enhance their responses.
In easy phrases, a RAG system doesn’t simply guess or depend on what it “is aware of”—it actively retrieves related, up-to-date data after which generates a coherent response based mostly on it. This ensures that the AI’s solutions usually are not solely correct but additionally grounded in actual, verifiable information.
Why RAG Issues?
- Dynamic Data: RAG permits the AI to fetch present, real-time knowledge from exterior sources, making it extra responsive and up-to-date.
- Improved Accuracy: By retrieving and referencing exterior paperwork, RAG reduces the probability of the mannequin producing hallucinated or inaccurate solutions.
- Enhanced Comprehension: The retrieval of related background data improves the AI’s skill to offer detailed, knowledgeable responses.
Now that you simply perceive what RAG is, think about supercharging it with brokers—AI entities that deal with particular duties like retrieving knowledge, evaluating its relevance, or verifying its accuracy. That is the place CrewAI and LangChain come into play, making the method much more streamlined and highly effective.
What’s CrewAI?
Consider CrewAI as an clever supervisor that orchestrates a workforce of brokers. Every agent focuses on a selected activity, whether or not it’s retrieving data, grading its relevance, or filtering out errors. The magic occurs when these brokers collaborate—working collectively to course of complicated queries and ship exact, correct solutions.
Why CrewAI is Revolutionary?
- Agentic Intelligence: CrewAI breaks down duties into specialised sub-tasks, assigning every to a singular AI agent.
- Collaborative AI: These brokers work together, passing data and duties between one another to make sure that the ultimate result’s strong and reliable.
- Customizable and Scalable: CrewAI is very modular, permitting you to construct methods that may adapt to a variety of duties—whether or not it’s answering easy questions or performing in-depth analysis.
What’s LangChain?
Whereas CrewAI brings the intelligence of brokers, LangChain lets you construct workflows that chain collectively complicated AI duties. It ensures that brokers carry out their duties in the fitting order, creating seamless, extremely orchestrated AI processes.
Why LangChain is Important?
LLM Orchestration: LangChain works with all kinds of huge language fashions (LLMs), from OpenAI to Hugging Face, enabling complicated pure language processing.
- Information Flexibility: You may join LangChain to various knowledge sources, from PDFs to databases and net searches, making certain the AI has entry to probably the most related data.
- Scalability: With LangChain, you may construct pipelines the place every activity leads into the subsequent—good for classy AI operations like multi-step query answering or analysis.
CrewAI + LangChain: The Dream Crew for RAG
By combining CrewAI’s agent-based framework with LangChain’s activity orchestration, you may create a sturdy Agentic RAG system. On this system, every agent performs a job—whether or not it’s fetching related paperwork, verifying the standard of retrieved data, or grading solutions for accuracy. This layered method ensures that responses usually are not solely correct however are grounded in probably the most related and up to date data accessible.
Let’s transfer ahead and construct an Agent-Powered RAG System that solutions complicated questions utilizing a pipeline of AI brokers.
Constructing Your Personal Agentic RAG System
We’ll now begin constructing our personal agentic RAG System step-by-step under:
Earlier than diving into the code, let’s set up the required libraries:
!pip set up crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29 sentence-transformers langchain-groq --quiet
!pip set up langchain_huggingface --quiet
!pip set up --upgrade crewai langchain langchain_community
Step1: Setting Up the Atmosphere
We begin by importing the required libraries:
from langchain_openai import ChatOpenAI
import os
from crewai_tools import PDFSearchTool
from langchain_community.instruments.tavily_search import TavilySearchResults
from crewai_tools import software
from crewai import Crew
from crewai import Process
from crewai import Agent
On this step, we imported:
- ChatOpenAI: The interface for interacting with giant language fashions like LLaMA.
- PDFSearchTool: A software to look and retrieve data from PDFs.
- TavilySearchResults: For retrieving web-based search outcomes.
- Crew, Process, Agent: Core parts of CrewAI that enable us to orchestrate brokers and duties.
Step2: Including GROQ API key
To entry the Groq API, you usually have to authenticate by producing an API key. You may generate this key by logging into the Groq Console. Right here’s a basic define of the method:
- Log in to the Groq Console utilizing your credentials.
- Navigate to API Keys: Go to the part the place you may handle your API keys.
- Generate a New Key: Choose the choice to create or generate a brand new API key.
- Save the API Key: As soon as generated, be certain that to repeat and securely retailer the important thing, as will probably be required for authenticating API requests.
This API key might be utilized in your HTTP headers for API requests to authenticate and work together with the Groq system.
All the time confer with the official Groq documentation for particular particulars or further steps associated to accessing the API.
import os
os.environ['GROQ_API_KEY'] = 'Add Your Groq API Key'
Step3: Setting Up the LLM
llm = ChatOpenAI(
openai_api_base="https://api.groq.com/openai/v1",
openai_api_key=os.environ['GROQ_API_KEY'],
model_name="llama3-8b-8192",
temperature=0.1,
max_tokens=1000,
)
Right here, we outline the language mannequin that might be utilized by the system:
- LLaMA3-8b-8192: A big language mannequin with 8 billion parameters, making it highly effective sufficient to deal with complicated queries.
- Temperature: Set to 0.1 to make sure the mannequin’s outputs are extremely deterministic and exact.
- Max tokens: Restricted to 1000 tokens, making certain responses stay concise and related.
Step3: Retrieving Information from a PDF
To exhibit how RAG works, we obtain a PDF and search via it:
import requests
pdf_url="https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"
response = requests.get(pdf_url)
with open('attenstion_is_all_you_need.pdf', 'wb') as file:
file.write(response.content material)
This downloads the well-known “Consideration is All You Want” paper and saves it domestically. We’ll use this PDF within the following step for looking.
Step4: Making a RAG Instrument to Cross PDF
On this part, we create a RAG software that searches a PDF utilizing a language mannequin and an embedder for semantic understanding.
- PDF Integration: The PDFSearchTool masses the PDF (attention_is_all_you_need.pdf) for querying, permitting the system to extract data from the doc.
- LLM Configuration: We use LLaMA3-8b (by way of Groq’s API) because the language mannequin to course of consumer queries and supply detailed solutions based mostly on the PDF content material.
- Embedder Setup: Huggingface’s BAAI/bge-small-en-v1.5 mannequin is used for embedding, enabling the software to match queries with probably the most related sections of the PDF.
Lastly, the rag_tool.run() operate is executed with a question like “How did the self-attention mechanism evolve in large language models?” to retrieve data.
rag_tool = PDFSearchTool(pdf="/content material/attenstion_is_all_you_need.pdf",
config=dict(
llm=dict(
supplier="groq", # or google, openai, anthropic, llama2, ...
config=dict(
mannequin="llama3-8b-8192",
# temperature=0.5,
# top_p=1,
# stream=true,
),
),
embedder=dict(
supplier="huggingface", # or openai, ollama, ...
config=dict(
mannequin="BAAI/bge-small-en-v1.5",
#task_type="retrieval_document",
# title="Embeddings",
),
),
)
)
rag_tool.run("How did self-attention mechanism evolve in giant language fashions?")
Step5: Including Internet Search with Tavily
Setup Your Tavily API Key so as additionally to allow net search performance:
import os
# Set the Tavily API key
os.environ['TAVILY_API_KEY'] = "Add Your Tavily API Key"
web_search_tool = TavilySearchResults(ok=3)
web_search_tool.run("What's self-attention mechanism in giant language fashions?")
This software permits us to carry out an online search, retrieving as much as 3 outcomes.
Step6: Defining a Router Instrument
@software
def router_tool(query):
"""Router Perform"""
if 'self-attention' in query:
return 'vectorstore'
else:
return 'web_search'
The router software directs queries to both a vectorstore (for extremely technical questions) or an online search. It checks the content material of the question and makes the suitable resolution.
Step7: Creating the Brokers
We outline a sequence of brokers to deal with totally different elements of the query-answering pipeline:
Router Agent:
Routes inquiries to the fitting retrieval software (PDF or net search).
Router_Agent = Agent(
position="Router",
aim="Route consumer query to a vectorstore or net search",
backstory=(
"You're an skilled at routing a consumer query to a vectorstore or net search."
"Use the vectorstore for questions on idea associated to Retrieval-Augmented Technology."
"You do not want to be stringent with the key phrases within the query associated to those matters. In any other case, use web-search."
),
verbose=True,
allow_delegation=False,
llm=llm,
)
Retriever Agent:
Retrieves the knowledge from the chosen supply (PDF or net search).
Retriever_Agent = Agent(
position="Retriever",
aim="Use the knowledge retrieved from the vectorstore to reply the query",
backstory=(
"You're an assistant for question-answering duties."
"Use the knowledge current within the retrieved context to reply the query."
"It's a must to present a transparent concise reply."
),
verbose=True,
allow_delegation=False,
llm=llm,
)
Grader Agent:
Ensures the retrieved data is related.
Grader_agent = Agent(
position="Reply Grader",
aim="Filter out inaccurate retrievals",
backstory=(
"You're a grader assessing relevance of a retrieved doc to a consumer query."
"If the doc incorporates key phrases associated to the consumer query, grade it as related."
"It doesn't have to be a stringent check.It's a must to make it possible for the reply is related to the query."
),
verbose=True,
allow_delegation=False,
llm=llm,
)
Hallucination Grader:
Filters out hallucinations(incorrect solutions).
hallucination_grader = Agent(
position="Hallucination Grader",
aim="Filter out hallucination",
backstory=(
"You're a hallucination grader assessing whether or not a solution is grounded in / supported by a set of information."
"Be sure to meticulously evaluation the reply and examine if the response supplied is in alignmnet with the query requested"
),
verbose=True,
allow_delegation=False,
llm=llm,
)
Reply Grader:
Grades the ultimate reply and ensures it’s helpful.
answer_grader = Agent(
position="Reply Grader",
aim="Filter out hallucination from the reply.",
backstory=(
"You're a grader assessing whether or not a solution is helpful to resolve a query."
"Be sure to meticulously evaluation the reply and examine if it is sensible for the query requested"
"If the reply is related generate a transparent and concise response."
"If the reply gnerated shouldn't be related then carry out a websearch utilizing 'web_search_tool'"
),
verbose=True,
allow_delegation=False,
llm=llm,
)
Step8: Defining Duties
Every activity is outlined to assign a particular position to the brokers:
Router Process:
Determines whether or not the question ought to go to the PDF search or net search.
router_task = Process(
description=("Analyse the key phrases within the query {query}"
"Based mostly on the key phrases resolve whether or not it's eligible for a vectorstore search or an online search."
"Return a single phrase 'vectorstore' whether it is eligible for vectorstore search."
"Return a single phrase 'websearch' whether it is eligible for net search."
"Don't present some other premable or explaination."
),
expected_output=("Give a binary alternative 'websearch' or 'vectorstore' based mostly on the query"
"Don't present some other premable or explaination."),
agent=Router_Agent,
instruments=[router_tool],
)
Retriever Process:
Retrieves the required data.
retriever_task = Process(
description=("Based mostly on the response from the router activity extract data for the query {query} with the assistance of the respective software."
"Use the web_serach_tool to retrieve data from the online in case the router activity output is 'websearch'."
"Use the rag_tool to retrieve data from the vectorstore in case the router activity output is 'vectorstore'."
),
expected_output=("You must analyse the output of the 'router_task'"
"If the response is 'websearch' then use the web_search_tool to retrieve data from the online."
"If the response is 'vectorstore' then use the rag_tool to retrieve data from the vectorstore."
"Return a claer and consise textual content as response."),
agent=Retriever_Agent,
context=[router_task],
#instruments=[retriever_tool],
)
Grader Process:
Grades the retrieved data.
grader_task = Process(
description=("Based mostly on the response from the retriever activity for the quetion {query} consider whether or not the retrieved content material is related to the query."
),
expected_output=("Binary rating 'sure' or 'no' rating to point whether or not the doc is related to the query"
"It's essential to reply 'sure' if the response from the 'retriever_task' is in alignment with the query requested."
"It's essential to reply 'no' if the response from the 'retriever_task' shouldn't be in alignment with the query requested."
"Don't present any preamble or explanations aside from 'sure' or 'no'."),
agent=Grader_agent,
context=[retriever_task],
)
Hallucination Process:
Ensures the reply is grounded in information.
hallucination_task = Process(
description=("Based mostly on the response from the grader activity for the quetion {query} consider whether or not the reply is grounded in / supported by a set of information."),
expected_output=("Binary rating 'sure' or 'no' rating to point whether or not the reply is sync with the query requested"
"Reply 'sure' if the reply is in helpful and incorporates truth in regards to the query requested."
"Reply 'no' if the reply shouldn't be helpful and doesn't incorporates truth in regards to the query requested."
"Don't present any preamble or explanations aside from 'sure' or 'no'."),
agent=hallucination_grader,
context=[grader_task],
)
Reply Process:
Offers the ultimate reply or performs an online search if wanted.
answer_task = Process(
description=("Based mostly on the response from the hallucination activity for the quetion {query} consider whether or not the reply is helpful to resolve the query."
"If the reply is 'sure' return a transparent and concise reply."
"If the reply is 'no' then carry out a 'websearch' and return the response"),
expected_output=("Return a transparent and concise response if the response from 'hallucination_task' is 'sure'."
"Carry out an online search utilizing 'web_search_tool' and return ta clear and concise response provided that the response from 'hallucination_task' is 'no'."
"In any other case reply as 'Sorry! unable to discover a legitimate response'."),
context=[hallucination_task],
agent=answer_grader,
#instruments=[answer_grader_tool],
)
Step9: Constructing the Crew
We group the brokers and duties right into a Crew that can handle the general pipeline:
rag_crew = Crew(
brokers=[Router_Agent, Retriever_Agent, Grader_agent, hallucination_grader, answer_grader],
duties=[router_task, retriever_task, grader_task, hallucination_task, answer_task],
verbose=True,
)
Step10: Operating the Pipeline
Lastly, we ask a query and kick off the RAG system:
inputs ={"query":"How does self-attention mechanism assist giant language fashions?"}
outcome = rag_crew.kickoff(inputs=inputs)
print(outcome)
This pipeline processes the query via the brokers, retrieves the related data, filters out hallucinations, and gives a concise and related reply.
Conclusion
The mixture of RAG, CrewAI, and LangChain is a glimpse into the way forward for AI. By leveraging agentic intelligence and activity chaining, we will construct methods which might be smarter, sooner, and extra correct. These methods don’t simply generate data—they actively retrieve, confirm, and filter it to make sure the best high quality of responses.
With instruments like CrewAI and LangChain at your disposal, the probabilities for constructing clever, agent-driven AI methods are countless. Whether or not you’re working in AI analysis, automated buyer assist, or some other data-intensive subject, Agentic RAG methods are the important thing to unlocking new ranges of effectivity and accuracy.
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Key Takeaways
- RAG methods mix pure language era with real-time knowledge retrieval, making certain AI can pull correct, up-to-date data from exterior sources for extra dependable responses.
- CrewAI employs a workforce of specialised AI brokers, every accountable for totally different duties like knowledge retrieval, analysis, and verification, leading to a extremely environment friendly, agentic system.
- LangChain permits the creation of multi-step workflows that join numerous duties, permitting AI methods to course of data extra successfully via logical sequencing and orchestration of huge language fashions (LLMs).
- By combining CrewAI’s agentic framework with LangChain’s activity chaining, you may construct clever AI methods that retrieve and confirm data in actual time, considerably enhancing the accuracy and reliability of responses.
- The weblog walked via the method of making your individual Agentic RAG system utilizing superior instruments like LLaMA 3, Groq API, CrewAI, and LangChain, making it clear how these applied sciences work collectively to automate and improve AI-driven options.
Often Requested Questions
A. CrewAI orchestrates a number of AI brokers, every specializing in duties like retrieving data, verifying relevance, and making certain accuracy.
A. LangChain creates workflows that chain AI duties collectively, making certain every step of information processing and retrieval occurs in the fitting order.
A. Brokers deal with particular duties like retrieving knowledge, verifying its accuracy, and grading responses, making the system extra dependable and exact.
A. The Groq API gives entry to highly effective language fashions like LLaMA 3, enabling high-performance AI for complicated duties.
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