Building a Customized Knowledge Base with RAG, Llama 3, FAISS, and Langchain
In today’s digital age, efficiently managing and utilizing information is more important than ever. A private knowledge base can be a powerful tool to boost productivity by keeping your data organized and readily accessible. This article will walk you through the process of building a Retrieval-Augmented Generation (RAG) application using Llama3 and Langchain, two cutting-edge technologies designed to streamline information retrieval and generation.
I’ll start by explaining the fundamentals of RAG and explore why integrating Llama3 and Langchain can greatly enhance your knowledge base. You’ll get a clear, step-by-step guide, complete with code snippets, to help you set up and customize your private knowledge base. Whether you’re a developer or just interested in improving your data management, this guide will provide you with the tools and knowledge you need to create an efficient, personalized information system.
What is RAG?
Retrieval-augmented generation(RAG) is an advanced method that combines retrieval-based techniques with generative models to provide more accurate and contextually relevant responses. RAG enhances the performance of language models by integrating a retrieval mechanism that fetches relevant documents from a knowledge base, which the model then uses to generate informed answers.
RAG stands for Retrieval-Augmented Generation. It’s a technique that combines the strengths of Large Language Models (LLMs) with external knowledge sources.
How RAG Works
- Retrieval: When a query is presented, the RAG system first retrieves relevant information from an external knowledge base. This could be a database, a document repository, or any other structured or unstructured data source.
- Augmentation: The retrieved information is then combined with the original query and fed to the LLM.
- Generation: The LLM generates a response based on both the original query and the additional context provided by the retrieved information.
Benefits of RAG
- Improved Accuracy: By grounding the LLM in factual information, RAG reduces the likelihood of hallucinations or generating incorrect information.
- Up-to-date Information: RAG can access and incorporate the latest data, ensuring that the generated responses are relevant and timely.
- Domain Specificity: RAG can be tailored to specific domains by providing relevant knowledge bases, and enhancing performance in those areas.
Implementation
Let’s break down each step in detail to understand what the code is doing and how the components fit together to build a Retrieval-Augmented Generation (RAG) chain using LangChain, FAISS, and a language model (LLM) from Ollama.
Prerequisites
Before diving into building your private knowledge base using RAG with llama3 and Langchain, ensure you have the following prerequisites:
- Install the Ollama and Llama3 Models
- How to Install Python?
- Install Langchain and FAISS for Coordinating LLM and Vector Database
pip install langchain langchain-openai faiss-cpuImporting Necessary Modules
from langchain_community.document_loaders import TextLoader # Text loader
from langchain.text_splitter import CharacterTextSplitter # Text splitter
from langchain_community.embeddings import OllamaEmbeddings # Ollama embeddings
from langchain.prompts import ChatPromptTemplate # Chat prompt template
from langchain_community.chat_models import ChatOllama # ChatOllma chat model
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser # Output parser
from langchain_community.vectorstores import FAISS # Vector database
import requestsDownloading and Saving the PDF File
This block downloads a PDF file from the given URL and saves it locally.
url = "https://arxiv.org/pdf/2005.11401"
res = requests.get(url)
with open("2005.11401.pdf", "wb") as f:
f.write(res.content)Loading the Document
Here, the TextLoader is used to read the PDF file and load its content as text documents. The encoding='latin-1' ensures that the text is correctly decoded.
loader = TextLoader('2005.11401.pdf', encoding='latin-1')
documents = loader.load()Splitting the Document into Chunks
The document is split into smaller chunks of text. Each chunk is 1000 characters long with an overlap of 100 characters between consecutive chunks. This helps manage the context window limitations of LLMs.
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
chunks = text_splitter.split_documents(documents)Creating Embeddings and Storing in Vector Database
This step involves:
- Generating embeddings for each text chunk using the
OllamaEmbeddingsmodel. - Storing these embeddings in a FAISS vector database for efficient retrieval.
vectorstore = FAISS.from_documents(
documents=chunks,
embedding=OllamaEmbeddings(model="llama3")
)Creating a Retriever
The retriever is created from the vector store. It will use semantic similarity to retrieve relevant chunks of text based on user queries.
retriever = vectorstore.as_retriever()Defining the LLM Prompt Template
This template defines how the retrieved context and the user query should be formatted before being sent to the LLM. It instructs the assistant to use the context to answer the question and to admit if the answer is not known.
template = """You are an assistant for specific knowledge query tasks.
Use the following pieces of retrieved context to answer the question.
If you don't know the answer, just say that you don't know.
Question: {question}
Context: {context}
Answer:
"""
prompt = ChatPromptTemplate.from_template(template)Combining Components into a RAG Chain
This chain combines:
- The retriever to get relevant context.
- The prompt template to format the query and context.
- The
ChatOllamamodel to generate a response. - The
StrOutputParserto parse the output.
The temperature=0.2 setting makes the LLM's responses more deterministic (lower temperature).
llm = ChatOllama(model="llama3", temperature=0.2)rag_chain = (
{"context": retriever, "question": RunnablePassthrough()} # context window
| prompt
| llm
| StrOutputParser()
)Querying and Getting Feedback
Finally, a query is made to the RAG chain. The system retrieves relevant context from the vector database, formats it with the query, and generates an answer using the LLM. The result is printed out.
query = "What did this paper mainly talk about?"
print(rag_chain.invoke(query))Conclusion
Building a private knowledge base using RAG with Llama3 and Langchain offers a powerful tool for managing and accessing information. By following the steps outlined in this guide, you can set up your system to enhance productivity and ensure you have accurate and relevant information at your fingertips.
