One of the standout features of LangChain is its Retrieval-Augmented Generation (RAG) capability. This allows developers to build applications that can retrieve information from various data sources and then generate contextually relevant responses based on that information. This feature is particularly useful for creating conversational agents that need to provide accurate and up-to-date information.
For instance, by combining document retrieval with language generation, applications can answer questions about a specific topic while drawing from a large corpus of text. Below is a simple example that showcases how to implement RAG with LangChain:
from langchain import OpenAI
from langchain.chains import RetrievalQA
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import TextLoader
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
# Step 1: Load your documents.
documents = TextLoader('path_to_your_file.txt').load()
# Step 2: Split your documents into chunks.
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs = text_splitter.split_documents(documents)
# Step 3: Create an embeddings and vector store.
embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_documents(docs, embeddings)
# Step 4: Setup the RetrievalQA chain.
qa_chain = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=vector_store.as_retriever()
)
# Step 5: Run the QA chain with a query.
response = qa_chain({"query": "What is the main point of the document?"})
print(response['result'])
This code snippet illustrates how to load a text document, split it into manageable chunks, create embeddings for those chunks, and then query the data using a language model. The result is a powerful setup that enhances your application's ability to provide accurate responses rooted in real data.