Building an Efficient Knowledge Question-Answering System with Langchain
Knowledge Question-Answering Systems (KQA) are one of the core technologies in the field of Natural Language Processing (NLP). They help users to quickly and accurately retrieve the information they need from a vast amount of data. KQAs have become essential tools for individuals and businesses to acquire, filter, and process information. They play a significant role in various domains like online customer service, smart assistants, data analytics, and decision support.
Langchain not only offers the essential modules to build a basic Q&A system but also supports more complex and advanced questioning scenarios. For example, it can handle structured data and code, allowing Q&A operations on databases or code repositories. This significantly expands the scope of KQA, making it adaptable to more complex real-world needs. This article will introduce how to build a basic KQA system with Langchain through a simple hands-on example.
Next, we will go through a hands-on example to guide you through building a KQA system with Langchain.
1. Document Loading and Preprocessing
The first step in building a KQA system is to load and preprocess documents. Langchain offers a
WebBaseLoader module to easily load documents:
from langchain.document_loaders import WebBaseLoader
# Load documents
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
documents = loader.load()
After loading, we need to preprocess the documents for further operations. The
RecursiveCharacterTextSplitter module can help us split the document into smaller chunks for easier handling:
from langchain.text_splitter import RecursiveCharacterTextSplitter
# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
2. Text Embedding
Text embedding is the process of converting text into vectors, a fundamental step in NLP. Langchain offers the
OpenAIEmbeddings module for quick text embedding:
from langchain.embeddings import OpenAIEmbeddings
# Create embeddings
embeddings = OpenAIEmbeddings()
3. Build Vector Store
A vector store is where document embeddings are stored. The
Chroma module can help us easily create and manage a vector store:
from langchain.vectorstores import Chroma
# Build vector store
docsearch = Chroma.from_documents(texts, embeddings)
4. Building the Retrieval QA Chain
The Retrieval QA Chain is the heart of the KQA system. It is responsible for handling user queries and retrieving relevant documents from the vector store:
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
# Build Retrieval QA Chain
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())
5. Query Execution and Result Retrieval
Finally, we can execute user queries and retrieve answers from the system:
# Execute query
query = 'What is Task Decomposition?'
answer = qa.run(query)
Through the Langchain library, we've quickly built a basic KQA system. Langchain also offers a wealth of modules and features that enable developers to customize the Q&A system based on project requirements. For instance, we can use different document loaders, text splitters, and vector stores to accommodate different types and scales of data. Moreover, Langchain supports various retrieval and answering modes, such as Retrieval-augmented Generation (RAG), allowing us to build more advanced and complex KQAs.
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